How Community and Peer Perceptions Promote College Students’ Pro-Social Bystander Actions to Prevent Sexual Violence

Abstract

The prevalence of sexual violence crimes on U.S. college campuses is prompting institutions of higher education to increasingly invest in centers to support survivors and programs to prevent the violence before it happens. Understanding bystanders to sexual violence and what may motivate them to step in and help is a promising prevention strategy. The purpose of this study was to understand how potential active bystanders’ (first-year college students) perceptions of community (including a sense of one’s influence in the community and positive peer norms for helping) and individual beliefs about self (including sense of responsibility and self-efficacy) affect their self-reports of performing bystander behavior to address sexual violence risks. Participants were 948 students at two different universities (one a rural, primarily residential campus and the other an urban, mostly commuter campus) in the northeastern United States. Regression and path analysis quantitative results suggest that individual-level characteristics may mediate some of the impact that community-level norms and perceptions have on bystander outcomes, explaining some of the mixed findings in previous research. Prevention strategies should work to change community norms and perceptions of mattering and perceptions of community influence in addition to the more traditional focus on individual-level violence specific attitudes.

Keywords

bystander sexual assault prevention peer norms community

Introduction

In response to wide-spread and growing concern over the consistently high levels of sexual violence (SV) on campus (Fisher, Cullen, & Turner, 2000; Krebs et al., 2016; Krebs, Lindquist, Warner, Fisher, & Martin, 2009), colleges and universities have increasingly been deploying prevention strategies which seek to provide community members with specific roles and behaviors they can adopt to prevent SV, typically through pro-social bystander intervention training (Banyard, Moynihan, & Plante, 2007; Banyard & Potter, 2018; Coker et al., 2011; Katz & Moore, 2013; Potter, 2012). By targeting social networks on campus, bystander programs are likely to be effective in both changing norms away from condoning violence and potentially decreasing more immediate violence in potentially risky situations. Designing effective strategies to mobilize pro-social bystanders requires nuanced research to understand the full range of predictors of these helping behaviors. To date, most research on predictors has focused on individual attitudes about the self—such as confidence and perception of barriers to action. There has been less attention paid to possible influences and correlates at other levels of the social ecological model, including perceptions of self in relationship (e.g., peer norms) and community climate (Casey & Lindhorst, 2009; McMahon, 2015). In the current study, we address this gap by examining individual attitudes about the self as mediators between perceptions of campus communities and peers and pro-social bystander actions.

The Promise of Pro-Social Bystanders

Bystanders are witnesses to crimes who have the potential to intervene. They were first studied as passive individuals in large groups who are less likely to respond when they see a crime and others are present because of a diffusion of responsibility across actors (Latané & Darley, 1970). More recently, they have been researched more as active participants in a situation: “capable guardians” and agents of “informal social control” that make perpetrators less likely to act (Brown, Banyard, & Moynihan, 2014; Cohen & Felson, 1979; Schwartz, DeKeseredy, Tait, & Alvi, 2001). In situations of interpersonal violence, active pro-social bystanders show “moral courage” that goes beyond everyday helping (Osswald, Greitemeyer, Fischer, & Frey, 2010) to stop a perpetrator from violating an important social norm, and are diffusers of innovations to cultivate new, anti-violence norms (Rogers, 2003). The opportunity to intervene to prevent SV exists on a continuum from proactive behaviors (i.e., confronting problematic language, attending events which support awareness of the problem, etc.) that can take place at any time, to reactive behaviors, which could refer to either reacting in a situation to an imminent risk of violence or responding to an individual who has already been assaulted and is in need of support (Banyard, 2015; Frye et al., 2012; McMahon & Banyard, 2012). Research on pro-social bystanders and SV to date has focused mainly on correlates of more reactive behaviors in situations (either where risk for physical violence is escalating or in response to verbal harassment) and helping survivors after an incident (McMahon & Banyard, 2012). These are very important settings for pro-social bystander action. Also important, however, are behaviors that can be done at any time and are not in response to witnessing a crime and that help to set new social norms that are intolerant of SV.

Models of Pro-Social Bystander Helping

The origins of research into understanding why bystanders help began with Darley and Latané’s (1968) work on group size inhibition of helping, an effect more commonly referred to as “diffusion of responsibility.” Their research into barriers and facilitators of individual helping led to the articulation of the broader situational model of bystander intervention: (a) notice the event, (b) identify the situation as intervention-appropriate, (c) take intervention responsibility, (d) decide how to help, and (e) act to intervene (Latané & Darley, 1970). The situational model has been used as a starting point to understanding a variety of aspects of bystanders’ decisions to help (for instance, see Bennett, Banyard, & Garnhart, 2014; Pugh et al., 2016) and helps us understand how aspects of each individual may influence their actions. Burn (2009) adapted these five steps to identify barriers to helping in the context of SV that may exist at each of the steps, including ambiguity in deciding the situation is risky, uncertainty in assuming personal responsibility to help, and knowing how to effectively intervene to help. Adding to these more individual- and immediate contextually centered models, we additionally consider perceptions of community and peers that go beyond perceptions of internal self-competencies.

Individual and Relationship Perception Predictors

In the past two decades, pro-social bystander research into individual factors has blossomed, with much of that research focusing specifically on college campus populations. As with Latané and Darley’s (1970) formative work, these new studies have identified variables within the individual (their sense of confidence to help and perceived barriers) as well as within the relational context (nature of the relationship between the bystander and victims or perpetrators, peer norms that support helping, social norms that affect whether a bystander sees a victim as worthy of help or to blame for their situation) (see Banyard, 2011, 2015, for reviews; Bennett et al., 2014; Brown et al., 2014; Burn, 2009).

Pro-social bystander self-efficacy

Individuals’ beliefs about their own abilities to be effective pro-social bystanders may influence whether or not they help in a given situation, and it is a featured component in recent studies of pro-social bystander action (Krauss et al., 2021). In the literature, this characteristic is referred to by a variety of labels, including pro-social bystander confidence, pro-social bystander efficacy, or pro-social bystander self-efficacy (Banyard, 2015), and seems relevant to Burn’s (2009) discussion of the barrier of failing to intervene because of a perceived skills deficit. The findings are mixed. Banyard and Moynihan (2011) found that college students who reported greater confidence in their ability to intervene successfully also reported more actual SV-specific helping behaviors. However, an earlier study by Banyard (2008) found that while pro-social bystander self-efficacy was positively related to helping intentions, it was not related to actual helping behaviors. More recently, Diamond-Welch, Hetzel-Riggin, and Hemingway (2016) examined a range of different personal characteristics and their correlates to pro-social bystander outcomes, finding that older non-White and non-male participants reported both greater pro-social bystander self-efficacy and more pro-social bystander helping; however, self-efficacy itself did not relate to helping behaviors once characteristics of gender, age, and race were entered in the model. It appears that pro-social bystander self-efficacy has a nuanced relationship with helping intervention, particularly in SV-specific behaviors, but more research is needed to determine if and how pro-social bystander self-efficacy contributes uniquely to helping behaviors.

Sense of responsibility

Whereas self-efficacy is a skill perception attitude that could map onto several steps of the situational model (Latané & Darley, 1968) even though not explicitly named in that model, an individual’s perception of their responsibility to help with community issues like SV is clearly articulated as a step in Latané and Darley’s situational model and Burn’s (2009) adaptation. Several studies have found that participants who endorse greater contemplation of SV as a problem, and feel greater personal responsibility for preventing SV, also report more pro-social helping behaviors (Banyard, Eckstein, & Moynihan, 2009; Banyard & Moynihan, 2011). Others’ research found that individuals who reported a lower sense of responsibility to intervene were less likely to have intervened across a range of SV-specific situations (Bennett et al., 2014; Burn, 2009). While research consistently implicates an individual’s own sense of responsibility to help address SV as a predictor of pro-social bystander helping, the interactions of sense of responsibility with situationally relevant relationship- and community-level perceptions require more nuanced investigation.

Self in Relationship Perceptions

Recent research on SV pro-social bystander intervention highlights that relationship factors may also influence decisions about whether and how to take action. The situational model (Burn, 2009; Latané & Darley, 1968) describes self in relationship perception processes that can affect whether a pro-social bystander perceived the need for action and feels a sense of responsibility to step in and considers these in terms of variables related to diffusion of responsibility (Latané & Nida, 1981). Individuals surrounded by other bystanders who are not taking action can be influenced to assume the group consensus is that no action is needed or be worried that if they did step in, others would negatively judge them (Burn, 2009). Two other lines of research also inform this question: the relationship of the pro-social bystander to the individuals who are in the risky situation, and the pro-social bystander’s perceptions of peer helping norms.

Helping friends versus helping strangers

A pro-social bystander’s relationship to the individuals involved in a risky or dangerous situation may be a powerful contextual predictor of helping or non-helping. Burn (2009) found that participants were more likely to intervene if they knew either the victim or the perpetrator. Greater willingness to help and greater actual helping behaviors are more often directed toward friends than toward strangers (Bennett et al., 2014; Katz, Pazienza, Olin, & Rich, 2015; Nicksa, 2011). Correlates of helping varied by whether the potential victim receiving help was a friend or stranger: participants who indicated a lower degree of responsibility to help also reported lower rates of helping strangers (Katz et al., 2015). Rates of helping friends was explained by a greater sense of responsibility (Katz et al., 2015). Conflicting with that finding, Nicksa (2011) found that sense of responsibility to help among bystanders was unrelated to helping behavior. Given strong evidence that the context of helping a friend versus helping a stranger is an important predictor of behavior, it is necessary to investigate how this relationship context interacts with other individual- and community-level factors.

Peer Norm Perceptions

Although a person’s perceptions of norms about behavior (what is expected or done by other people) may at first consideration appear to be an individual-level factor (because it is an individual’s attitude), we instead operationalize it to be a relational influence, as McMahon (2015) does. Social norms operate in every facet of life. A consistent finding, whether the topic is alcohol use, sexual promiscuity, helping others, or smoking habits, is that the perceived social norm is as much, and sometimes more, influential than the actual standard for a behavior or attitude in a community. Individuals seek social acceptance and thus are likely to try to bring their own behavior in line with how they think others act or should act. Rimal and Lapinski (2015) distinguished these as perceived norms (norms reported by individuals) and collective norms (the true norms which exist in a collection of individuals). Our investigation concerns incorporating perceived norms into a predictive model of pro-social bystander helping, specifically concerning perceived peer helping attitudes and perceived influence to change the community. To the individual, these are aspects of their social context rather than aspects of themselves. These self in relationship perceptions may act to encourage bystander action (norms that encourage pro-social actions and caring about other students) or to inhibit bystander action (norms that act as barriers to intervention, as discussed above). They are also a conceptual connection between individual-level variables in the ecological model and factors outside in their context.

Peer helping norms

General research on helping norms in other domains has found a strong link between normative helping behavior in the situation and actual helping in a situation—for instance, Hart and Miethe (2008) found this relationship for various types of violent crimes including robbery and physical assault. In general, researchers tend to use items that ask either about peer support for using coercion in relationships (a damaging norm) or how much their peers support helping in given situations (a positive norm). Some studies even combine these two types of norms to give a general measure of violence-related norms. As a result, no consistent method for assessing of defining peer norms has emerged from the literature yet which may explain the variety of findings as described below.

The findings on peer norms’ influence on actual helping behaviors in SV contexts specifically are mixed (Austin, Dardis, Wilson, Gidycz, & Berkowitz, 2016; Banyard & Moynihan, 2011; Brown et al., 2014; Hoxmeier, Acock, & Flay, 2020; Hoxmeier, Flay, & Acock, 2018). Banyard and Moynihan (2011) found that perceptions of higher peer norms supporting coercion were, surprisingly, related to greater actual helping behaviors. Brown and colleagues (2014) found that while perceptions of peer norms did predict willingness to intervene as a bystander, actual helping behaviors were not predicted. Hoxmeier and colleagues (2018) found that higher peer helping norms were only related to more actual intervening for seven of the 12 risky situations they asked participants about. In a second study, they found that higher peer norms supporting helping was related to fewer missed opportunities to intervene (Hoxmeier et al., 2020). Murphy Austin and colleagues (2016) found that while perceptions of peer helping norms somewhat predicted actual helping behavior, the individual’s own beliefs and attitudes more robustly predicted helping than peer norms. Peer helping norms are clearly influencing some aspects of helping behavior; however, the inconsistent nature of the relationship warrants a closer consideration.

Perceived Influences in the Community

Examining an individual’s relationship and perception of their own community and feelings toward their context within that community can take various forms. Measures for assessing community context include perceived ability to influence the community, sense of community cohesion (Casey & Lindhorst, 2009), and the degree to which a member of the community feels they can change, contribute to, or have control over situations in their community (Ansari, 2013). Researchers have found reduced rates of general violence in communities where people have greater social cohesion and work collaboratively to solve problems (see Banyard, Edwards, & Siebold, 2017, for a review; Edwards, Mattingly, Dixon, & Banyard, 2014; Frye et al., 2012) and sense of mattering has been linked to lower victimization and perpetration rates (Edwards & Neal, 2017). Although their sample was young adults in rural communities, Edwards et al. (2014) found that individuals with greater connection and cohesion to their community were more likely to engage in positive pro-social bystander intervention in the context of intimate partner violence. Pinchevsky and Wright (2012) theorized that collective efficacy, strong social ties, and the feeling that an individual matters and is connected to others, and that community members can work together to solve problems, make it easier for members to look out for one another and to act on one another’s behalf. A sense of collective influence may make one more likely to see oneself as an effective community actor who can take action to help another. At present, there is little research examining community-perception predictors of SV in college campus settings (DeGue et al., 2012). Banyard (2008) found that greater sense of community was related to actual helping, and Bennett et al. (2014) also found a link between sense of community and willingness to help friends. Beyond a general feeling of belonging, however, we seek to examine a specific aspect of community cohesion—a perception that one’s actions matter and can influence the community to which a person belongs. Educational institutions are predominantly made up of a transient population (the students), who may feel that it is not their responsibility or within their power to affect change on campus. We need a better understanding of how individuals on college campuses perceive their contributions to their community, particularly in the context of SV prevention.

The Current Study

Few studies have heeded the call of Casey and Lindhorst (2009) to include perceptions of peer norms and community-related attitudes. Using Latané and Darley’s situational model, and individual perceptions of self as a central variable, the current study examined how individual self-perceptions of efficacy and responsibility mediate relationships between perceptions of community and peers and pro-social bystander actions. We hypothesized that greater peer norms supporting helping and greater perceived community influence would be related to higher levels of three forms of pro-social bystander action (proactive behaviors, reactive actions to help friends, and reactive behaviors to help strangers). We further hypothesized that these relationships would be mediated by increased self-confidence as an active pro-social bystander and greater sense of responsibility to help.

Method

Participants

For the current study, we recruited 1,236 first-year students between the ages of 18 and 24 from two different college campuses, Campus 1 (n = 711) and Campus 2 (n = 535), as part of a larger longitudinal experimental evaluation of an in-person prevention program and social marketing campaign (Cares et al., 2015; Moynihan et al., 2015). Both were midsized, public New England campuses; however, Campus 1 was largely rural and highly residential while Campus 2 had a mix of commuter and residential students and greater racial and ethnic diversity. Participants were screened such that none was trained as an advocate for a rape crisis center or domestic violence agency. From the 1,236 participants recruited for the study, 948 took the pretest (76.7% of the recruited sample), 550 participants from Campus 1 and 398 participants from Campus 2. The sample was evenly distributed between men (51.5%, n = 489; n = 299, 54.4% for Campus 1 and n = 190, 48% for Campus 2) and women (47.8%, n = 454; n = 250, 45.5% from Campus 1, and n = 204, 51.5% for Campus 2) (as were the samples from each of the campuses), with 3 (0.3%) participants that identified as transgender (n = 1, 0.2% at Campus 1 and n = 2, 0.5% for Campus 2). Overall, 85% of the sample identified as White; 73.2% (n = 699) reported father’s education of at least some college; 20% (n = 189) reported their fathers had graduate school or professional degrees; and 89.6% reported living on campus. Given that there was a limited age range to qualify for the study and the requirement that students be in their first semester ever at college, the mean age of participants was 18.2 years (SD = 0.49). Surveys were administered either online or in-person; participants reviewed an informed consent form prior to beginning. The survey took approximately 40 minutes to complete. Participants were paid US$10 for completing the survey. All procedures in this study were approved by the Institutional Review Boards for the Protection of Human Subjects at each campus.

Measures

Sense of responsibility

To measure individual’s sense of responsibility to intervene as a bystander, we used a nine-item subscale from the larger 33-item Readiness to Help Scale (Banyard, Moynihan, Cares, & Warner, 2014), which is based on Prochaska and DiClemente’s Transtheoretical Model (Banyard et al., 2009; Grimley, Prochaska, Velicer, Blais, & DiClemente, 1994). In the current study, we used the Responsibility subscale which has shown adequate reliability and validity. Examples of items in these subscales include “Sometimes I think I should learn more about SV.” Participants responded on a 5-point scale (1 = not at all true and 5 = very much true) to explicitly indicate how much each of the statements was true to them across nine items. Cronbach’s alpha was .91. Subscale scores were created by taking the mean across items (M = 2.78, SD = 0.83, range = 1-5).

Bystander self-efficacy

This scale (Banyard, 2008), used previously to evaluate intervention self-efficacy, includes 18 statements. Participants rate their confidence levels to perform various bystander behaviors on a scale from 0 (can’t do) to 100 (very certain). An example is “Express discomfort/concern if someone makes a joke about a woman’s body or about gays/lesbians or someone of a different race.” Scale scores are a mean across all 18 items. Cronbach’s alpha for this sample was .93. Previous research with different samples of participants has established the validity of this measure (Banyard, 2008). For the current sample, M = 72.60, SD = 17.64, range = 0 to 100.

Perceived community influence

This scale (Proescholdbell, Roosa, & Nemeroff, 2006) measured the extent to which individuals believed their opinions and actions can impact their community. It includes three items from the larger 24-item Sense of Community scale designed to measure several psychological aspects of sense of community. For each item, participants were asked to rate how much the question described their thoughts on a 5-point scale (1 = none, 2 = a little, 3 = some, 4 = a fair amount, 5 = a great deal). For example, “How much do you feel you can influence what the campus is like?” The subscale score for Community Influence was created by taking the mean across the three items (M = 2.79, SD = 0.82, range = 1-5, Cronbach’s alpha = .78).

Peer helping norms

This scale (Banyard et al., 2014) measured an individual’s perception of the degree to which their friends are engaging in a variety of bystander helping behaviors. The wording was consistent with measurement of descriptive norms in that it asked what participants thought friends would do (as opposed to injunctive norms which assess perceptions of what others think everyone should do). It included 23 items; for each item, participants were asked to rate how likely their friends were to do each of the behaviors on a 5-point scale (1 = not at all likely, 5 = extremely likely). For example, “Do something to help a very intoxicated person who is being brought upstairs to a bedroom by a group of people at a party?” The average of the items was used to indicate perceptions of peer helping norms, with higher values indicating more peer support for helping and lower values indicating less peer support for helping (M = 3.40, SD = 0.80, range = 1-5, Cronbach’s alpha = .96).

Bystander Behavior Scale–Revised

This scale (Banyard, 2008; Banyard et al., 2014; McMahon, Postmus, & Koenick, 2011) included 49 items, each representing a different bystander behavior. Participants were asked to indicate whether, in the past 2 months, they actually performed the behavior, with some questions specific toward a friend and/or toward a stranger. Participants could indicate “yes” (1) or “no” (0) for each item. Previous research with different samples established the validity of versions of this measure (Banyard, 2008; McMahon et al., 2014). Three subscales were created. The Proactive subscale included 11 items that described behaviors preparing to take action or learning more about violence prevention when risk was not necessarily present, and therefore could refer to helping anyone. While the items were grouped under bystander behaviors directed at friends as a reference group, the behaviors could be directed at anyone including strangers. An example was “I thought through the pros and cons of different ways I might help if I saw an instance of sexual abuse or intimate partner abuse.” The mean across all 11 items became the total subscore used. Cronbach’s alpha for this sample was .92, M = 0.36, SD = 0.35, range = 0 to 1. The Friend Reactive subscale included 38 items that described behaviors reacting directly to risky situations that a friend is in. An example is “I told a friend if I thought their drink may have been spiked with a drug.” The mean across all 38 items became the total subscore used. Cronbach’s alpha for this sample was .98, M = 0.44, SD = 0.36, range = 0 to 1. The Stranger Reactive subscale included 38 items that described behaviors reacting to risky situations or intervening in a situation directly in which a stranger was involved. An example was “I told a stranger if I thought their drink may have been spiked with a drug.” The mean across all 38 items became the total subscore used. Cronbach’s alpha for this sample was .97, M = 0.16, SD = 0.24, range = 0 to 1.

Analysis Plan

Initial bivariate relationships between study variables were calculated using Pearson correlations in SPSS 23.0. Path analysis models were computed using MPlus 7.0 software to investigate whether higher levels of perceived community influence and peer helping were related to greater bystander efficacy and sense of responsibility which in turn were related to greater self-reports of bystander action. The same initial model was specified for all bystander behavior outcomes.¹ This model was a fully mediated model, in that it tested indirect paths between community perceptions (peer helping and community influence) and pro-social bystander actions through intrapersonal attitudes of efficacy and sense of responsibility for preventing SV. Regression paths were specified from peer helping and community influence to the indicators of perceived efficacy and responsibility, and from the indicators of perceived efficacy and responsibility to the mean level of proactive bystander actions. Efficacy and responsibility were specified to be correlated with each other. Following guidelines by Chen (2007), we used the following key indicators of model fit: first, the root mean square error of approximation (RMSEA) was used as an absolute index of goodness of fit. The comparative fit index (CFI) was used as an incremental fit index and the Tucker–Lewis index (TLI) was also used. We used the cutoff of under .06 for the RMSEA as an indicator of good fit (with less than .08 as adequate). For CFI and TLI, the cutoff was greater than or equal to .95.

Results

Preliminary Analysis

Table 1 includes intercorrelations for all study variables. The three types of bystander helping behaviors were correlated at between .55 and .79 with each other. Perceptions of peer helping norms and community influence were both correlated with most types of pro-social bystander behaviors, except for perceived community influence and friend reactive behaviors, which was not significant. Both sense of responsibility and bystander self-efficacy were correlated with most types of bystander behaviors (r =.09-.22), except for bystander self-efficacy and friend reactive behaviors, which was not significant. In addition, both sense of responsibility and bystander self-efficacy were correlated with perceptions of peer helping norms (r = .33, .46) and community influence (r = .19, .19), indicating that an exploration of a mediational relationship was warranted. Due to missing data on some indicators and outcomes for 46 cases, the N for analyses was 900 except where specified. List-wise deletion of missing data was used. Regression paths were specified from perceived peer helping norms and community influence to the indicators of bystander self-efficacy and sense of responsibility, and from the indicators of bystander self-efficacy and sense of responsibility to the mean level of proactive bystander actions. Bystander self-efficacy and sense of responsibility were specified to be correlated with each other. Figure 1 shows the overall model used for all three types of bystander behavior outcomes.

Table 1.

Intercorrelations Among Study Variables.

Variable		I	II	III	IV	V	VI	VII
I	Stranger reactive	—
II	Friend reactive	.628**	—
III	Friend proactive	.554**	.790**	—
IV	Perceptions of peer helping	.224**	.076*	.118**	—
V	Perceived influence in community	.097**	0.044	.090**	.145**	—
VI	Sense of responsibility	.175**	.122**	.217**	.330**	.188**	—
VII	Bystander self-efficacy	.126**	0.030	.086**	.464**	.189**	.280**	—

p < .05. **p < .01.

Figure 1.

Path analysis with indirect effects for pro-social action.

Proactive Prevention Actions

To investigate whether perceptions of greater peer helping norms and greater community influence heightened proactive bystander actions to prevent SV by increasing a bystander’s self-efficacy and sense of responsibility to help, a path analysis with mediated effects was specified. Figure 1 describes the model and provides the fully standardized coefficients for each path. The model fit the data well: χ²(2) = 3.413, p = ns; CFI = .997; TLI = .984; RMSEA = .028, 90% confidence interval (CI) = [.00, .077]. As perceptions of peer helping norms and community influence increase, so do bystander self-efficacy and sense of responsibility for preventing SV. Greater levels of sense of responsibility (but not bystander self-efficacy) related to engaging in greater proactive bystander actions. Both heightened perceptions of peer helping norms and perceptions of greater community influence translated into greater sense of responsibility, which in turn translated in to greater mean levels of proactive helping actions. The indirect effect was significant for perceived peer helping norms, β_ind = .07, p < .001; 95% CI = [.04, .09], and for perceived community influence, β_ind = .03, p < .001; 95% CI = [.01, .04]. In contrast, no indirect effect from either perceived peer helping norms or community influence to proactive helping actions through bystander self-efficacy was noted, mainly because bystander self-efficacy did not explain significant variance in proactive helping actions: for peer helping norms—β_ind = .01, p = .42; 95% CI = [–.02, .04]—or for community influence—β_ind = .00, p = .42; 95% CI = [–.01, .01].

Action Toward Friends in Reactive Situations

To investigate whether perceptions of greater peer helping norms and greater community influence heightened reactive bystander actions toward friends by increasing a bystander’s self-efficacy and sense of responsibility to help, a path analysis with mediated effects was specified. Figure 2 depicts the model and fully standardized coefficients. The model fit the data well: χ²(2) = 2.03, p = ns; CFI = 1.00; TLI = 1.00; RMSEA = .004, 90% CI = [.00, .07]. Perceptions of greater peer helping norms and greater community influence were related to greater bystander self-efficacy and sense of responsibility for preventing SV. Increased levels of sense of responsibility (but not bystander self-efficacy) were related to greater reactive bystander actions toward friends. Both heightened perceptions of peer helping norms and perceptions of greater community influence translated into greater sense of responsibility, which in turn translated in to greater mean levels of actions to help friends. The indirect effect was significant for perceptions of peer helping norms, β_ind = .04, p = .001; 95% CI = [.02, .06], and community influence, β_ind = .02, p < .001; 95% CI = [.00, .03]. In contrast, no indirect effect from either peer helping norms or community influence on reactive friend helping actions was noted through bystander self-efficacy, mainly because heightened bystander self-efficacy did not explain significant variance in actions to help friends: for peer helping norms—β_ind = −.00, p = .87; 95% CI = [–.03, .03]—or for community influence—β_ind = −.00, p =.87; 95% CI = [–.01, .01].

Figure 2.

Path analysis for indirect effects for helping friends in reactive situations.

Action Toward Strangers in Reactive Situations

To investigate whether perceptions of greater peer helping norms and greater community influence heightened reactive bystander actions to help strangers to prevent SV by increasing a bystander’s self-efficacy and sense of responsibility to help, a path analysis with mediated effects was specified. This model did not demonstrate good fit although all individual paths in the model were significant, χ²(2) = 24.81, p < .001; CFI = .95; TLI = .76; RMSEA = .11, 90% CI = [.08, .15]. Modification indices suggested the addition of a direct path from perceptions of peer helping norms to reactive actions to help strangers. Figure 3 depicts the final revised model and fully standardized coefficients. The model fit the data well: χ²(1) = 2.316, p = ns; CFI = .997; TLI = .972; RMSEA = .04, 90% CI = [.00, .11]. Perceptions of greater peer helping norms and greater community influence were related to greater bystander self-efficacy and sense of responsibility for preventing SV. Perceptions of greater peer helping norms was related to higher mean actions reacting to risk to strangers. Greater levels of sense of responsibility (but not bystander self-efficacy) explained significant variance in reactive bystander actions toward strangers. Both heightened perceptions of peer helping norms and perceptions of greater community influence translated into greater sense of responsibility, which in turn translated in to greater mean levels of actions. The indirect effect was significant for peer helping norms, β_ind = .03, p = .002; 95% CI = [.01, .06], and for community influence, β_ind = .02, p = .01; 95% CI = [.00, .03]. In contrast, no indirect effect of bystander self-efficacy from either peer helping norms or community influence on reactive stranger helping actions was noted, mainly because heightened bystander self-efficacy did not explain significant variance in actions to help strangers: peer helping norms, β_ind = .01, p = .59; 95% CI = [–.02, .04], and community influence, β_ind = .00, p = .59; 95% CI = [–.01, .01].

Figure 3.

Path analysis for indirect and direct effects for helping strangers in reactive situations.

Discussion

This study contributes to growing literature examining motivating and facilitating factors for bystander helping action to prevent SV by expanding upon the situational model of pro-social bystander behavior (Burn, 2009; Latané & Darley, 1968). Measuring perceptions of community influence and peer helping norms, we integrated insights from the social ecological model and the newer action coils model of pro-social bystander action related to interpersonal violence (Banyard, 2015). The study extends previous work that has modeled intra-individual-level correlates (particularly attitudes related to helping such as sense of responsibility to help, awareness of the problem, and confidence in one’s ability to take action) by including perceptions of community- and relational-level factors (peer helping norms, and models of reactively helping friends and strangers separately). Perceptions of peer helping norms and of community influence were linked to higher levels of sense of responsibility for helping (but not bystander self-efficacy) which in turn was related to greater mean pro-social bystander action across all three relationship contexts of helping (helping friends and strangers in risky situations and proactive actions when risk for imminent violence was not present).

The findings fit with results from other research focusing on more specific dimensions of social processes in the community including collective efficacy perceptions (Edwards et al., 2014), mattering (Edwards & Neal, 2017), and sense of trust in campus authorities as boosters for pro-social bystander action across forms of violence (Sulkowski, 2011). These constructs go beyond a general sense of belonging and suggest that a critical ingredient for action is a sense of influence and engagement at the community level and the perception that peers support helping. Previous research has been mixed in finding relationships between feeling a sense of community and pro-social bystander action (Banyard, 2008; Nicksa, 2011). Perhaps this is because a more general sense of community measure was used, rather than parsing out discrete aspects of community belonging as done here. In the current study, we found the specific aspect of sense of community—perceived community influence—to be significant. Researchers have also focused more often on peer norms about use of coercion in relationships, which predicts perpetration but has had mixed relationships with pro-social bystander action. The current study found support peer norms about pro-social bystander action as a clearer correlate of pro-social bystander behavior.

Interestingly, pro-social bystander self-efficacy was not a significant mediator in any of the models in spite of the fact that it has been found to correlate with active bystanders’ intent to help and it is featured as an important component of the situational model (Latané & Darley, 1970) of pro-social bystander intervention. It may be that bystander self-efficacy exerts influence in a different way. For example, a review by Wood (2000) on attitude change points out that a higher sense of efficacy is often associated with more systematic processing of new messages that challenge personal beliefs. Thus, efficacy may not be a factor that directly influences active bystanders’ behavior but rather exerts its effects as a mechanism for attitude change.

Implications

The current study confirms that key components of the situational model of bystander action (Burn, 2009; Latané & Darley, 1968), especially sense of responsibility, is a key proximal correlate of bystanders helping across situations of risk for SV and more proactive prevention behaviors. This type of individual-level variable is often a potentially malleable focus of prevention strategies on campus. Increasingly, researchers are studying perceptions of self-in-relationship variables such as peer norms (Brown et al., 2014; McMahon, 2015). These variables at the next level of the ecological model are also potential levers for change. Social norms have been a component of prevention messages for other campus problems such as alcohol consumption, but less is known about norms related to SV (Fabiano, Perkins, Berkowitz, Linkenbach, & Stark, 2003). The current study suggests that methods like social marketing campaigns or training popular opinion leaders to model peer support for pro-social bystander actions may enhance SV prevention effects. Furthermore, given that college students may identify with a range of sub-communities (single-gender groups, more racially homogeneous groups, groups of students of their own age, etc.) more strongly than with their entire campus community, it may be beneficial to implement pro-social bystander focused prevention strategies in peer groups that have strong social ties (athletic teams, residence halls, student organizations), rather than classrooms. Campuses may also need to implement a series of social marketing messages that are more tailored to different peer audiences rather than just overall campus messaging (Potter & Stapleton, 2011).

Understanding how individuals view their impact on the community is a largely unexamined aspect of predicting pro-social bystander behavior. This is a variable that may be less amenable to change through health promotion efforts or policies. However, the current findings raise the point that campuses may see different rates of pro-social bystander action related more broadly to student voice and influence on campus. Bystander-oriented prevention programs may have more success in contexts where broader positive community social processes and perceptions are also actively cultivated. For example, Hart and Miethe (2008) found that the situational context of an assault is a strong predictor of pro-social bystander helping, particularly the presence of positive social norms for helping. Broader campus programs, which are not specifically branded as violence prevention, could address students’ perceptions of their own community influence. Messages during orientation that include students as “creators” of the campus culture and traditions have shown some promise in increasing student feelings of community ownership and investment, though much more research is needed of this approach (Shutt et al., 2016).

Limitations

There are several limitations to this study. First, as is the case with most research on sexual and gender-based violence and its prevention, our study is reliant on self-reports of attitude and behaviors, and so, results may be skewed due to social desirability. The pro-social bystander behavior measures were limited to an assessment of diversity of types of action as a series of yes/no questions was used. There was no measure of opportunity to act, and so we cannot assume that students who reported “no” are necessarily non-interveners (McMahon et al., 2015; Palmer, 2016). Future studies need to include measures of opportunity to not only properly account for participant’s opportunity to intervene at all but whether frequency of opportunities to intervene may be an important factor to examine. Although accounting for the opportunity to intervene in given situations is important for establishing a clear baseline of intervention for a participant, as a construct in and of itself, opportunity to help has been largely ignored. The best ways to capture this construct are still debated in the field (McMahon, Palmer, Banyard, Murphy, & Gidycz, 2017). Although measures used in this study have been previously validated, future investigations of multi-level socioecological models should distill outcomes to examine certain types of situations (high vs. low risk, sexual vs. intimate partner violence, etc.) separately to unpack possible differences due to the type of violence under consideration. In addition, our sample consisted of students who were in their first semester in college and all of whom were aged 18 to 24. Although the sample was large, it was not representative of all college students. Given that our study focused on students in the first months of their first semester in college, future research is needed to understand whether students are more or less likely to help as their college careers continue. It is also not clear if and how perceived community influence and sense of responsibility may change over a college career. Are older students more likely to feel tied to their campus community and responsible for making it safe and free from violence? Finally, both campuses were located in New England and provided limited racial and ethnic diversity among the participants of the study. The impact of holding a minority identity on pro-social bystander behavior and community perceptions is not frequently examined, but it is an area that needs to be considered by future studies. Although we asked about gender identity, only three students did not choose male or female, a number too small for meaningful analyses. We know that the applicability of findings of research is tied closely to the representativeness of samples used. While the current sample was geographically representative, it fell far short of representing the multiplicity of perspectives of college students more broadly. This is an important limitation, given previous research that suggests that social identity and location variables like race or gender identity may have an impact on pro-social bystander attitudes and behaviors (Banyard, 2015; Brown et al., 2014). Future research with a more demographically diverse sample of college students is needed to extend the findings of the current study. In sum, to address these limitations, the study should be replicated on other, more diverse campuses, studying both first-year students and more advanced students.

Conclusion

The current study does expand our models of pro-social bystander intervention to an understanding of how perceptions of relationship and community context influence individual pro-social bystander attitudes that in turn help explain rates of self-reported actions to prevent SV. It points to the need to consider multiple levels of the ecological model in designing prevention, as programs that naturally focus on proximal individual attitudes may work differently depending on students’ sense of voice, and influence what they perceive their peers would do as pro-social bystanders. Campus administrators and preventionists need to understand what norms and perceptions of community exist among students and build prevention logic models that include them.

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) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by the Centers for Disease Control and Prevention (Grant 5 R01 CE001388-02, Principal Investigator: Banyard). The content is solely the responsibility of the authors and does not necessarily represent the official views of the Centers for Disease Control and Prevention.

ORCID iD

Andrew J. Rizzo

Notes

Author Biographies

Victoria L. Banyard, PhD, is a professor in the Department of Psychology at the University of New Hampshire, with an affiliation in justice studies. She is co-director of Prevention Innovations: Research and Practices for Ending Violence Against Women on Campus. She received her doctorate in clinical psychology with a certificate in women’s studies from the University of Michigan. She has completed postdoctoral research and clinical training at the Family Research Lab at the University of New Hampshire and the Trauma Center in Boston. She conducts research on the long-term consequences of trauma and interpersonal violence, including factors related to resilience and recovery, and on interpersonal violence prevention.

Andrew J. Rizzo, MS, is a researcher and PhD student in social psychology at the University of New Hampshire. He is also co-founder and a principal consultant for Catalytical Consulting LLC and serves as a committee co-chair on the Campus Advocates & Prevention Professionals Association (CAPPA) Leadership Council. He holds a BA in psychology from the University of Pennsylvania and an MS in higher education from Oklahoma State University. His research focuses on sexual violence prevention program evaluation across a variety of populations including rural communities, middle and high school students, and college campuses.

Yamilex Bencosme worked as a McNair Scholar under the mentorship of Victoria L. Banyard in the Department of Psychology at the University of New Hampshire. She is currently working on a degree in Sociology and Women’s Studies with a minor in African American Studies from the University of New Hampshire. Her research interests include bystanders and interpersonal violence.

Alison C. Cares, PhD, is an associate professor of sociology and criminology at Assumption College in Worcester, Massachusetts, and a Visiting Scholar at Wellesley Centers for Women, where she works in collaboration with the Justice and Gender Based Violence Research Initiative. Her current research focuses on campus victimization, particularly sexual violence. Presently, she is a co-principal investigator on a project investigating faculty use of and attitudes toward trigger warnings and another project funded by the U.S. Department of Justice, National Institute of Justice on investigation and adjudication of campus sexual assault.

Mary M. Moynihan, PhD, is the Prevention and Evaluation Consultant of Prevention Innovations Research Center and affiliate associate professor of women’s studies and justice studies, at the University of New Hampshire. She received her PhD from the University of Akron in sociology, held a post-doctoral fellowship at the Mary Ingraham Bunting Institute (now Radcliffe Institute) of Harvard University and was a research associate at the Family Research Laboratory. She is a co-creator and evaluator of the Bringing in the Bystander® in-person prevention program and the Know Your Power® social marketing campaign. She has been a member of research teams which have received funding from the U.S. DOJ, NIJ, and CDC.

References

Ansari

(2013). Social capital and collective efficacy: Resource and operating tools of community social control. Journal of Theoretical & Philosophical Criminology, 5, 75-94.

Austin

M. 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, 90-111. doi:10.1177/1077801215597790

Banyard

V. L.

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

Banyard

V. L.

(2011). Who will help prevent sexual violence: Creating an ecological model of bystander intervention. Psychology of Violence, 1, 216-229.

Banyard

V. L.

(2015). Toward the next generation of bystander prevention of sexual and relationship violence: Action coils to engage communities. London, England: Springer.

Banyard

V. L.

Eckstein

R. P.

Moynihan

M. M.

(2009). Sexual violence prevention: The role of stages of change. Journal of Interpersonal Violence, 25, 111-135. doi:10.1177/0886260508329123

Banyard

V. L.

Edwards

K. M.

Siebold

(2017). Involving community in sexual violence prevention: Engaging bystanders. In Sturmey

(Ed.), Handbook of violence and aggression: Part XVII Societal interventions (pp. 3:122:1-12). Malden, MA: John Wiley Sons.

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, 287-301. doi: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, 101-115. doi:10.1037/a0033470

10.

Banyard

V. L.

Moynihan

M. M.

Plante

E. G.

(2007). Sexual violence prevention through bystander education: An experimental evaluation. Journal of Community Psychology, 35, 463-481. doi:10.1002/jcop.20159

11.

Banyard

V. L.

Potter

S. J.

(2018). Envisioning comprehensive sexual assault prevention for college campuses. In White

Tavris

(Eds.), Handbook of psychology of women. Washington, DC: American Psychological Association.

12.

Bennett

Banyard

V. L.

Garnhart

(2014). To act or not to act, that is the question? Barriers and facilitators of bystander intervention. Journal of Interpersonal Violence, 29, 476-496. doi:10.1177/0886260513505210

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, 350-362. doi:10.1177/0361684314526855

14.

Burn

S. M.

(2009). A situational model of sexual assault prevention through bystander intervention. Sex Roles, 60, 779-792. doi:10.1007/s11199-008-9581-5

15.

Cares

A. C.

Banyard

V. L.

Moynihan

M. M.

Williams

L. M.

Potter

S. J.

Stapleton

J. G.

(2015). Changing attitudes about being a bystander to violence: Translating an in-person sexual violence prevention program to a new campus. Violence Against Women, 21, 165-187. doi:10.1177/1077801214564681

16.

Casey

E. A.

Lindhorst

T. P.

(2009). Toward a multi-level, ecological approach to the primary prevention of sexual assault: Prevention in peer and community contexts. Trauma, Violence, & Abuse, 10, 91-114. doi:10.1177/1524838009334129

17.

Chen

F. F.

(2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 14, 464-504. doi:10.1080/10705510701301834

18.

Cohen

Felson

(1979). Social change and crime rate trends: A routine activity approach. American Sociological Review, 44, 588-608. doi:10.2307/2094589

19.

Coker

A.L.

Cook-Craig

P.G.

Williams

C.M.

Fisher

B.S.

Clear

E.R.

Garcia

L.S.

Hegge

L.M.

(2011). Evaluation of Green Dot: An active bystander intervention to reduce sexual violence on college campuses. Violence Against Women, 17(6), 777-796. doi:10.1177/1077801211410264

20.

Darley

J. M.

Latané

(1968). Bystander intervention in emergencies: Diffusion of responsibility. Journal of Personality and Social Psychology, 8(4, Pt. 1), 377-383. doi:10.1037/h0025589

21.

DeGue

Holt

M. K.

Massetti

G. M.

Matjasko

J. L.

Tharp

A. T.

Valle

L. A.

(2012). Looking ahead toward community-level strategies to prevent sexual violence. Journal of Women’s Health, 21(1), 1-3. doi:10.1089/jwh.2011.3263

22.

Diamond-Welch

B. K.

Hetzel-Riggin

M. D.

Hemingway

J. A.

(2016). The willingness of college students to intervene in sexual assault situations: Attitude and behavior differences by gender, race, age, and community of origin. Violence and Gender, 3(1), 49-54. doi:10.1089/vio.2015.0023

23.

Edwards

K. M.

Mattingly

Dixon

Banyard

V. L.

(2014). Community matters: Intimate partner violence among rural young adults. American Journal of Community Psychology, 53, 198-207. doi:10.1007/s10464-014-9633-7

24.

Edwards

K. M.

Neal

A. M.

(2017). School and community characteristics related to dating violence victimization among high school youth. Psychology of Violence, 7, 203-212. doi:10.1037/vio0000065

25.

Fabiano

P. M.

Perkins

H. W.

Berkowitz

Linkenbach

J. W.

Stark

(2003). Engaging men as social justice allies in ending violence against women: Evidence for a social norms approach. Journal of American College Health, 52, 105-112. doi:10.1080/07448480309595732

26.

Fisher

B. S.

Cullen

F. T.

Turner

M. G.

(2000). The sexual victimization of college women (Research Report No. NCJ-182369). Washington, DC: Department of Justice, National Institute of Justice. Retrieved from https://eric.ed.gov/?id=ED449712

27.

Frye

Paul

M. M.

Todd

M.-J.

Lewis

Cupid

Coleman

. . . O’Campo

(2012). Informal social control of intimate partner violence against women: Results from a concept mapping study of urban neighborhoods. Journal of Community Psychology, 40, 828-844. doi:10.1002/jcop.21493

28.

Grimley

Prochaska

J. O.

Velicer

W. F.

Blais

L. M.

DiClemente

C. C.

(1994). The transtheoretical model of change. In Brinthaupt

T. M.

Lipka

R. P.

(Eds.), Changing the self: Philosophies, techniques, and experiences (pp. 201-227). Albany: State University of New York Press.

29.

Hart

T. C.

Miethe

T. D.

(2008). Exploring bystander presence and intervention in nonfatal violent victimization: When does helping really help? Violence and Victims, 23, 637-651. doi:10.1891/0886-6708.23.5.637

30.

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.

31.

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.

32.

Katz

Moore

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

33.

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, 2775-2792. doi:10.1177/0886260514554290

34.

Krauss

Jouriles

E. N.

Yule

Grych

J. H.

Sargent

K. S.

Banyard

V. L.

(2021). Adverse consequences to assisting victims of campus violence: Initial investigations among college students. Journal of Interpersonal Violence, 36(3-4), NP1607-NP1624. doi:10.1177/0886260517746944

35.

Krebs

Lindquist

C. H.

Berzofsky

Shook-Sa

Peterson

Planty

Stroop

(2016). Campus climate survey validation study final technical report (Technical Report No. NCJ 249545). Washington, DC: Department of Justice, Bureau of Justice Statistics. Retrieved from http://www.bjs.gov/content/pub/pdf/ccsvsftr.pdf

36.

Krebs

Lindquist

C. H.

Warner

T. D.

Fisher

B. S.

Martin

S. L.

(2009). College women’s experiences with physically forced, alcohol- or other drug-enabled, and drug-facilitated sexual assault before and since entering college. Journal of American College Health, 57, 639-649. doi:10.3200/JACH.57.6.639-649

37.

Latané

Darley

J. M.

(1968). Group inhibition of bystander intervention in emergencies. Journal of Personality and Social Psychology, 10, 215-221.

38.

Latané

Darley

J. M.

(1970). The unresponsive bystander: Why doesn’t he help?. New York, NY: Appleton-Century-Crofts.

39.

Latané

Nida

(1981). Ten years of research on group size and helping. Psychological Bulletin, 89, 308-324.

40.

McMahon

(2015). Call for research on bystander intervention to prevent sexual violence: The role of campus environments. American Journal of Community Psychology, 55, 472-489. doi:10.1007/s10464-015-9724-0

41.

McMahon

Allen

C. T.

Postmus

J. L.

McMahon

S. M.

Peterson

N. A.

Hoffman

M. L.

(2014). Measuring bystander attitudes and behavior to prevent sexual violence. Journal of American College Health, 62, 58-66. doi:10.1080/07448481.2013.849258

42.

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. doi:10.1177/1524838011426015

43.

McMahon

Palmer

J. E.

Banyard

V. L.

Murphy

Gidycz

C. A.

(2017). Measuring bystander behavior in the context of sexual violence prevention: Lessons learned and new directions. Journal of Interpersonal Violence, 32, 2396-2418.

44.

McMahon

Peterson

N. A.

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, 46-56. doi: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, 115-130. doi:10.1353/csd.2011.0002

46.

Moynihan

M. M.

Banyard

V. L.

Cares

A. C.

Potter

S. J.

Williams

L. M.

Stapleton

J. G.

(2015). Encouraging responses in sexual and relationship violence prevention: What program effects remain 1 year later? Journal of Interpersonal Violence, 30, 110-132. doi:10.1177/0886260514532719

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.

Nicksa

S. C.

(2011). College students’ self-predicted reactions to witnessing sexual assault: The impact of gender, community, bystander experience, and relationship to the victim (Doctoral dissertation, Northeastern University). Retrieved from http://hdl.handle.net/2047/d20000896

49.

Osswald

Greitemeyer

Fischer

Frey

(2010). What is moral courage? Definition, explication, and classification of a complex construct. In Pury

C. L. S.

Lopez

S. J.

(Eds.), The psychology of courage: Modern research on an ancient virtue (pp. 149-164). Washington, DC: American Psychological Association. doi:10.1037/12168-008

50.

Palmer

J. E.

(2016). Recognizing the continuum of opportunities for third parties to prevent and respond to sexual assault and dating violence on a college campus. Crime Prevention and Community Safety, 18(1), 1-18. doi:10.1057/cpcs.2015.18

51.

Pinchevsky

G. M.

Wright

E. M.

(2012). The impact of neighborhoods on intimate partner violence and victimization. Trauma, Violence, & Abuse, 13, 112-132. doi:10.1177/1524838012445641

52.

Potter

S. J.

(2012). Using a multimedia social marketing campaign to increase active bystanders on the college campus. Journal of American College Health, 60, 282-295. doi:10.1080/07448481.2011.599350

53.

Potter

S. J.

Stapleton

J. G.

(2011). Bringing in the target audience in bystander social marketing materials for communities: Suggestions for practitioners. Violence Against Women, 17, 797-812. doi:10.1177/1077801211410364

54.

Proescholdbell

R. J.

Roosa

M. W.

Nemeroff

C. J.

(2006). Component measures of psychological sense of community among gay men. Journal of Community Psychology, 34(1), 9-24. doi:10.1002/jcop.20080

55.

Pugh

Ningard

Ven

T. V.

Butler

(2016). Victim Ambiguity: Bystander intervention and sexual assault in the college drinking scene. Deviant Behavior, 37(4), 401-418. https://doi.org/10.1080/01639625.2015.1026777

56.

Rimal

R.N.

Lapinski

M.K.

(2015). A re-explication of social norms, ten years later. Communication Theory, 25(4), 393-409. doi:10.1111/comt.12080

57.

Rogers

E. M.

(2003). Diffusion of innovations (5th ed.). New York, NY: Simon & Schuster.

58.

Schwartz

M. D.

DeKeseredy

W. S.

Tait

Alvi

(2001). Male peer support and a feminist routing activities theory: Understanding sexual assault on the college campus. Justice Quarterly, 18, 623-649. doi:10.1080/07418820100095041

59.

Shutt

Rizzo

A. J.

Roche

Sweeney

M. D.

Yarbrough

Williams

(2016, March) Using collaborative leadership to create a first-year orientation program. Session presented at the 2016 Annual Convention of NASPA, Indianapolis, IN.

60.

Sulkowski

M. L.

(2011). An investigation of students’ willingness to report threats of violence in campus communities. Psychology of Violence, 1, 53-65.

61.

Wood

(2000). Attitude change: Persuasion and social influence. Annual Review of Psychology, 51, 539-570.