Abstract
The purpose was to explore the underlying mechanisms that drive relationships between knowledge, attitudes and intervening bystander behavior to improve bystander violence prevention program effectiveness. Perceptual effects theory was used to understand third-person and first-person perceptions (TPP and FPP) as related to bystander intervention programs and to what extent perceptual gaps influence one’s intention to intervene. A web-based survey was conducted with 379 undergraduate students recruited from a large, Northeastern University. The survey covered demographics, previous bystander training, self-efficacy to engage in bystander behavior, social desirability of bystander intervention training programs, and perceived effects on self and others. Participants indicated how they would act in six hypothetical dating violence/bullying and sexual violence scenarios, and how they thought an average student on campus would act. Perceived ambiguity and risk for each of the scenarios were also measured. Descriptive statistics, paired-sample t-tests, and multilevel model analyses were conducted. Results showed that a robust first-person perception effect existed (i.e., the student perceived themselves being more influenced by bystander interventions/messages than their peers). The magnitude of FPP was increased by sex (significantly larger gap among female students) and previous training. Results show promise to further tailor and refine bystander interventions and provide directions to improve program effectiveness. Despite study limitations, the results indicate the first-person effect warrants further consideration for programming and messaging. Tailoring bystander training or repeated exposure may increase bystander behaviors. More research is needed to fully uncover TPP/FPP effects, predictors, and impacts on bystander intervention programs.
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Notes
Coordination and rectification are behavioral responses aimed at the media environment at large. Coordination reactions refer to adaptive behaviors based on perceptions of how others’ possible actions might affect one’s own chance of achieving certain goals. For example, voters are more motivated to come out and vote when they perceive the opponent candidate has a better chance to win due to events that occurred during a campaign. Rectification actions might be taken when individuals perceive others’ reactions would result in less-than-optimal states or inflict harm; therefore, individuals are motivated to take actions to fix/pre-empt the problems or deficiencies. For example, individuals might volunteer to promote media literacy or support censorship in light of false or incomplete information. While not the focus of this research, coordination and rectification may be relevant to bystander intervention programs in that these reactions could be used to advocate and promote training programs and enhance awareness of violence on campus.
References
Banyard, V. L. (2008). Measurement and correlates of prosocial bystander behavior: The case of interpersonal violence. Violence and Victims, 23(1), 83–97.
Banyard, V. L., Moynihan, M. M., & Plante, E. G. (2007). Sexual violence prevention through bystander education: An experimental evaluation. Journal of Community Psychology, 35(4), 463–481. https://doi.org/10.1002/jcop.20159.
Banyard, V. L., Moynihan, M. M., Cares, A. C., & Warner, R. (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.
Basile, K. C., & Smith, S. G. (2011). Sexual violence victimization of women: Prevalence, characteristics, and the role of public health and prevention. American Journal of Lifestyle Medicine, 5, 407–417. https://doi.org/10.1177/1559827611409512.
Basile, K. C., Smith, S. G., Breiding, M. J., Black, M. C., & Mahendra, R. (2014). Sexual violence surveillance: Uniform definitions and recommended data elements. Version 2.0.
Basile, K. C., DeGue, S., Jones, K., Freire, K. E., Dills, J., Smith, S. G., & Raiford, J. L. (2016). STOP SV: A technical package to prevent sexual violence. Atlanta, GA: National Center for Injury Prevention and Control, Centers for Disease Control and Prevention. Retrieved from: https://www.cdc.gov/violenceprevention/pdf/sv-prevention-technical-package.pdf.
Bonito, J. A., Ruppel, E. K., & Keyton, J. (2012). Reliability estimates for multilevel designs in group research. Small Group Research, 43, 443–467. https://doi.org/10.1177/1046496412437614.
Breiding, M., Basile, K. C., Smith, S. G., Black, M. C., & Mahendra, R. R. (2015). Intimate partner violence surveillance: uniform definitions and recommended data elements, Version 2.0. Atlanta, GA: National Center for Injury Prevention and Control, Centers for Disease Control and Prevention.
Centers for Disease Control and Prevention (2016). Youth online: High school YRBS. Atlanta, GA: National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention. Retrieved from: http://www.cdc.gov/ healthyyouth/data/yrbs.
Coker, A. L. & Bush, H. M. (2016). Survey items from multi college bystander efficacy evaluation. Unpublished survey items.
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. https://doi.org/10.1177/1077801211410264.
Coker, A. L., Bush, H. M., Fisher, B. S., Swan, S. C., Williams, C. M., Clear, E. R., & DeGue, S. (2016). Multi-college bystander intervention evaluation for violence prevention. American Journal of Preventive Medicine, 50(3), 295–302. https://doi.org/10.1016/j.amepre.2015.08.034.
Coker, A. L., Bush, H. M., Cook-Craig, P. G., DeGue, S. A., Clear, E. R., Brancato, C. J., et al. (2017). RCT testing bystander effectiveness to reduce violence. American Journal of Preventive Medicine, 52(5), 566–578. https://doi.org/10.1016/j.amepre.2017.01.020.
Darley, J. M., & Latane, B. (1968). Bystander intervention in emergencies: Diffusion of responsibility. Journal of Personality and Social Psychology, 8, 377–383.
Davison, P. W. (1983). The third-person effect in communication. Public Opinion Quarterly, 47, 1–15. https://doi.org/10.1086/268763.
DeGue, S., 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. Aggression and Violent Behavior, 19(4), 346–362. https://doi.org/10.1016/j.avb.2014.05.004.
Diamantopoulos, A., & Winklhofer, H. M. (2001). Index construction with formative indicators: An alternative to scale development. Journal of Marketing Research, 38, 269–277.
Dillard, J. P., & Shen, L. (Eds.). (2013). The SAGE handbook of persuasion: Developments in theory and practice. Los Angeles, CA: Sage.
Dunlap, W. P., Cortina, J. M., Vaslow, J. B., & Burke, M. J. (1996). Meta-analysis of experiments with matched groups or repeated measures design. Psychological Methods, 1, 170–177.
Eisend, M. (2017). The third-person effect in advertising: A meta-analysis. Journal of Advertising, 46, 377–394. https://doi.org/10.1080/00913367.2017.1292481.
Espelage, D. L., Basile, K. C., & Hamburger, M. E. (2012). Bullying perpetration and subsequent sexual violence perpetration among middle school students. Journal of Adolescent Health, 50(1), 60–65. https://doi.org/10.1016/j.jadohealth.2011.07.015.
Fass, D. F., Benson, R. I., & Leggett, D. G. (2008). Assessing prevalence and awareness of violent behaviors in the intimate partner relationships of college students using internet sampling. Journal of College Student Psychotherapy, 22, 66–75. https://doi.org/10.1080/87568220801952248.
Garcia, S. M., Weaver, K., Moskowitz, G. B., & Darley, J. M. (2002). Crowded minds: The implict bystander effect. Journal of Personality and Social Psychology, 83, 843–853. https://doi.org/10.1037//0022-3524.83.3.843.
Gladden RM, Vivolo-Kantor AM, Hamburger ME, Lumpkin CD. (2013). Bullying Surveillance Among Youths: Uniform Definitions for Public Health and Recommended Data Elements, Version 1.0. Atlanta, GA; National Center for Injury Prevention and Control, Centers for Disease Control and Prevention and U.S. Department of Education. Retrieved from: http://www.cdc.gov/violenceprevention/pdf/bullying-definitions-final-a.pdf
Gunther, A. C., & Storey, J. D. (2003). The influence of presumed influence. Journal of Communication, 53(2), 199–215. https://doi.org/10.1111/j.1460-2466.2003.tb02586.x.
Henriksen, L., & Flora, J. A. (1999). Third-person perception and children: Perceived impact of pro- and anti-smoking ads. Communication Research, 26, 643–665. https://doi.org/10.1177/009365099026006001.
Hox, J. (2002). Multilevlel anlaysis: Techniques and applications. Mahwah, NJ: LEA.
Innes, J. M., & Zeitz, H. (1988). The public’s view of the impact of mass media: A test of the ‘third person’ effect. European Journal of Social Psychology, 18, 457–463. https://doi.org/10.1002/ejsp.2420180507.
Katz, J., & Moore, J. (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.
Krebs, C. P., Lindquist, C. H., Warner, T. D., Fisher, B. S., & Martin, S. L. (2007). The campus sexual assault (CSA) study. Washington, DC: National Institute of Justice, U.S. Department of Justice.
Krebs, C. P., 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(6), 639–649. https://doi.org/10.3200/jach.57.6.639-649.
Leone, R. M., Parrott, D. J., & Swartout, K. M. (2017). When is it 'manly' to intervene?: Examining the effects of a misogynistic peer norm on bystander intervention for sexual aggression. Psychology of Violence, 7(2), 286–295. https://doi.org/10.1037/vio0000055.
Marby, A., & Turner, M. M. (2016). Do sexual assult bystander interventions change men’s intentions? Applying the theory of normative social behavior to predict bystander outcomes. Journal of Health Communication, 21, 276–292. https://doi.org/10.1080/10810730.2015.1058437.
McMahon, S., Allen, C. T., Postmus, J. L., McMahon, S. M., Peterson, N. A., & Lowe Hoffman, M. (2014). Measuring bystander attitudes and behavior to prevent sexual violence. Journal of American College Health, 62(1), 58–66. https://doi.org/10.1080/07448481.2013.849258.
Menard, S. (2002). Short- and long-term consequences of adolescent victimization. Youth violence research bulletin. Washington, DC: U.S. Department of Justice, Office of Justice Programs, Office of Juvenile Justice and Delinquency Prevention. Retrieved from: https://www.ncjrs.gov/pdffiles1/ojjdp/191210.pdf. Retrieved on June 16, 2017.
Miller, L. M. (2010). Physical abuse in a college setting: A study of perceptions and participation in abusive dating relationships. Journal of Family Violence, 26, 71–80. https://doi.org/10.1007/s10896-010-9344-2.
Milletich, R. J., Kelley, M. L., Doane, A. N., & Pearson, M. R. (2010). Exposure to interparental violence and childhood physical and emotional abuse as related to physical aggression in undergraduate dating relationships. Journal of Family Violence, 25, 627–637. https://doi.org/10.1007/s10896-010-9319-3.
No More. (2017). Bystander Scenarios. Retrieved from https://nomore.org/learn/bystander-scenarios/
Morris, S. B., & DeShon, R. P. (2002). Combining effect size estimates in meta-analysis with repeated measures and independent-groups designs. Psychological Methods, 7(1), 105–125. https://doi.org/10.1037/1082-989X.7.1.105.
Nezlek, J. B. (2017). A practical guide to understanding reliability in studies of within-person variability. Journal of Research in Personality, 69, 149–155. https://doi.org/10.1016/j.jrp.2016.06.020.
Niolon, P. H., Kearns, M., Dills, J., Rambo, K., Irving, S., Armstead, T. L., & Gilbert, L. (2017). Preventing intimate partner violence across the lifespan: A technical package of programs, policies, and practices. Atlanta, GA: National Center for Injury Prevention and Control, Centers for Disease Control and Prevention. Retrieved from: https://www.cdc.gov/violenceprevention/pdf/ipv-technicalpackages.pdf.
Perloff, R. M. (2009). Mass media, social perception, and the third-person effect. In J. Bryant & M. B. Oliver (Eds.), Media effects: Advances in theory and research (3rd ed., pp. 252–268). New York, NY: Routledge.
Polanin, J. R., Espelage, D. L., & Pigott, T. D. (2012). A meta-analysis of school-based bullying prevention Programs' effects on bystander intervention behavior. School Psychology Review, 41(1), 47–65.
Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Newbury Park, CA: Sage.
Rospenda, K. M., Richman, J. A., Wolff, J. M., & Burke, L. A. (2013). Bullying victimization among college students: Negative consequences for alcohol use. Journal of Addictive Diseases, 32(4), 325–342. https://doi.org/10.1080/10550887.10552013.10849971.
Salazar, L. F., Vivolo-Kantor, A., Hardin, J., & Berkowitz, A. (2014). A web-based sexual violence bystander intervention for male college students: Randomized controlled trial. Journal of Medical Internet Research, 16(9), e203. https://doi.org/10.2196/jmir.3426.
Shen, L., Palmer, J., Mercer Kollar, L. M., & Comer, S. (2015). A social comparison explanation for the third-person perception. Communication Research, 42(2), 260–280. https://doi.org/10.1177/0093650212467644.
Smith SG, Chen J, Basile KC, et al. The National Intimate Partner and sexual violence survey (NISVS): 2010–2012 state report. Atlanta, GA: National Center for Injury Prevention and Control, Centers for Disease Control and Prevention. Retrieved from: https://www.cdc.gov/violenceprevention/pdf/NISVS-StateReportBook.pdf. Published 2017.
Spybrook, J., Bloom, H., Gongdon, R., Hill, C., Martinez, A., & Raudenbush, S. (2011). Optimal Design Plus Empirical Evidence: Documentation for the “Optimal Design” software. Retrieved at: http://hlmsoft.net/od/
Sun, Y. (2013). When presumed influence turns real: An indirect route of media influence. In J. P. Dillard & L. Shen (Eds.) The Sage handbook of persuasion: Developments in theory and practice (2nd. Ed.) (pp. 371–387). Los Angeles, CA: Sage.
Sun, Y., Pan, Z., & Shen, L. (2008). Understanding the third-person perception: Evidence from a meta-analysis. Journal of Communication, 58, 280–300. https://doi.org/10.1111/j.1460-2466.2008.00385.x.
Xu, J., & Gonzenbach, W. J. (2008). Does a perceptual discrepancy lead to action? A meta-analysis of the behavioral component of the third-person effect. International Journal of Public Opinion Research, 20, 375–385. https://doi.org/10.1093/ijpor/edn031.
Yzer, M. (2013). Reasoned action theory: Persuasion and belief-based behavior change. In J. P. Dillard & L. Shen (Eds.), The Sage handbook of persuasion: Developments in theory and practice (2nd. ed., pp. 120–136). Los Angeles, CA: Sage.
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Appendix A
Appendix A
Scenario 1: Dating Violence
You and your date are at the movies. As you are moving up the line at the concession area, you see a couple arguing loudly. One of them is grabbing the other firmly by the arm and starts yelling at them.
Scenario 2: Bullying.
You hear a group of guys harassing a girl who is walking to class. The girl looks uncomfortable and upset.
Scenario 3: Dating Violence.
At lunch, you overhear an upper-class student talking about how her boyfriend is always telling her what to do. She says, “I’m really sick of it, but I’m too scared to make him mad, so I just do what he says.”
Scenario 4: Sexual Violence.
After a study group in the library, an older guy you don’t know well invites your friend back to his room to study more. He has a bad reputation, but your friend has a crush on him.
Scenario 5: Sexual Violence.
You are on the campus bus line heading home from a night out and you see a student from one of your classes who looks really drunk. Two guys are trying to get her to get off of the bus and go with them.
Scenario 6: Sexual Violence.
You are at a party. During the past hour you notice one of your male friends has been talking to a young woman. They seem to be having a good time but it is clear that the woman has had too much to drink. At one point your friend walks by you and you hear him say he is just going to get her “one more” and “that should be enough.”
A few minutes later you see him put his arm around the young woman and start to lead her upstairs.
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Mercer Kollar, L.M., Peng, L., Ports, K.A. et al. Who Will Be a Bystander? An Exploratory Study of First-Person Perception Effects on Campus Bystander Behavioral Intentions. J Fam Viol 35, 647–658 (2020). https://doi.org/10.1007/s10896-019-00054-2
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DOI: https://doi.org/10.1007/s10896-019-00054-2