Predicting Bystander Behavior to Prevent Sexual Assault on College Campuses: The Role of Self-Efficacy and Intent

Sarah McMahon, N. Andrew Peterson, Samantha C. Winter, Jane E. Palmer, Judy L. Postmus, Ruth Anne Koenick

Research output: Contribution to journalArticlepeer-review

35 Scopus citations


Bystander intervention has been increasingly applied to prevent sexual violence on college campuses. Its underlying theory assumes unidirectional relationships between variables, predicting that bystander behaviors (i.e., actions taken to intervene in sexual violence situations) will be influenced by bystander intentions (BI; i.e., likelihood to intervene in the future), which in turn will be affected by bystander efficacy (BE; i.e., confidence to intervene). One question for theory is whether a reciprocal relationship exists between BI and BE. We used structural equation modeling (SEM) with longitudinal data to test unidirectional and reciprocal causal relations between BI and BE. Participants (n = 1390) were students at a northeastern US university. Four models were examined using SEM: (1) a baseline model with autoregressive paths; (2) a model with autoregressive effects and BI predicting future BE; (3) a model with autoregressive effects and BE predicting future BI; and, (4) a fully cross-lagged model. Results indicated that reciprocal causality was found to occur between BI and BE. In addition, a final model demonstrated indirect effects of a bystander intervention program on bystander behaviors through both BI and BE at different time points. Implications for theory and practice are described, and directions for future research discussed.

Original languageEnglish (US)
Pages (from-to)46-56
Number of pages11
JournalAmerican Journal of Community Psychology
Issue number1-2
StatePublished - Sep 27 2015

All Science Journal Classification (ASJC) codes

  • Health(social science)
  • Applied Psychology
  • Public Health, Environmental and Occupational Health


  • Bystander intervention
  • Campus sexual assault
  • Cross-lagged model


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