Who Will Be a Bystander? An Exploratory Study of First-Person Perception Effects on Campus Bystander Behavioral Intentions

  • Laura M. Mercer KollarEmail author
  • Lulu Peng
  • Katie A. Ports
  • Lijiang Shen
Original Article


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.


Bystander intervention Third-person effect First-person effect Intention to intervene 


Compliance with Ethical Standards


The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention


  1. Banyard, V. L. (2008). Measurement and correlates of prosocial bystander behavior: The case of interpersonal violence. Violence and Victims, 23(1), 83–97.CrossRefGoogle Scholar
  2. 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. Scholar
  3. 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. Scholar
  4. 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. Scholar
  5. 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. Google Scholar
  6. 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:
  7. Bonito, J. A., Ruppel, E. K., & Keyton, J. (2012). Reliability estimates for multilevel designs in group research. Small Group Research, 43, 443–467. Scholar
  8. 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.Google Scholar
  9. 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: healthyyouth/data/yrbs.
  10. Coker, A. L. & Bush, H. M. (2016). Survey items from multi college bystander efficacy evaluation. Unpublished survey items.Google Scholar
  11. 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. Scholar
  12. 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. Scholar
  13. 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.
  14. Darley, J. M., & Latane, B. (1968). Bystander intervention in emergencies: Diffusion of responsibility. Journal of Personality and Social Psychology, 8, 377–383.CrossRefGoogle Scholar
  15. Davison, P. W. (1983). The third-person effect in communication. Public Opinion Quarterly, 47, 1–15. Scholar
  16. 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. Scholar
  17. Diamantopoulos, A., & Winklhofer, H. M. (2001). Index construction with formative indicators: An alternative to scale development. Journal of Marketing Research, 38, 269–277.CrossRefGoogle Scholar
  18. Dillard, J. P., & Shen, L. (Eds.). (2013). The SAGE handbook of persuasion: Developments in theory and practice. Los Angeles, CA: Sage.Google Scholar
  19. 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.CrossRefGoogle Scholar
  20. Eisend, M. (2017). The third-person effect in advertising: A meta-analysis. Journal of Advertising, 46, 377–394. Scholar
  21. 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. Scholar
  22. 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. Scholar
  23. 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. Scholar
  24. 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:
  25. Gunther, A. C., & Storey, J. D. (2003). The influence of presumed influence. Journal of Communication, 53(2), 199–215. Scholar
  26. Henriksen, L., & Flora, J. A. (1999). Third-person perception and children: Perceived impact of pro- and anti-smoking ads. Communication Research, 26, 643–665. Scholar
  27. Hox, J. (2002). Multilevlel anlaysis: Techniques and applications. Mahwah, NJ: LEA.CrossRefGoogle Scholar
  28. 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. Scholar
  29. Katz, J., & Moore, J. (2013). Bystander education training for campus sexual assault prevention: An initial meta-analysis. Violence and Victims, 28(6), 1054–1067. Scholar
  30. 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.Google Scholar
  31. 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. Scholar
  32. 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. Scholar
  33. 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. Scholar
  34. 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. Scholar
  35. 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: Retrieved on June 16, 2017.
  36. 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. Scholar
  37. 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. Scholar
  38. No More. (2017). Bystander Scenarios. Retrieved from
  39. 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. Scholar
  40. Nezlek, J. B. (2017). A practical guide to understanding reliability in studies of within-person variability. Journal of Research in Personality, 69, 149–155. Scholar
  41. 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:
  42. 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.Google Scholar
  43. 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.Google Scholar
  44. Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Newbury Park, CA: Sage.Google Scholar
  45. 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.
  46. 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. Scholar
  47. 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. Scholar
  48. 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: Published 2017.
  49. 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:
  50. 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.Google Scholar
  51. Sun, Y., Pan, Z., & Shen, L. (2008). Understanding the third-person perception: Evidence from a meta-analysis. Journal of Communication, 58, 280–300. Scholar
  52. 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. Scholar
  53. 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.Google Scholar

Copyright information

© This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply  2019

Authors and Affiliations

  1. 1.Centers for Disease Control and Prevention, National Center for Injury Prevention and Control, Division of Violence PreventionAtlantaUSA
  2. 2.College of the Liberal Arts, Department of Communication Arts and SciencesPennsylvania State UniversityState CollegeUSA

Personalised recommendations