1,276
Views
18
CrossRef citations to date
0
Altmetric
Original Articles

Teachers’ Perceived Likelihood of Intervening in Bullying Situations: Individual Characteristics and Institutional Environments

&
Pages 258-269 | Received 23 Aug 2016, Accepted 20 Mar 2017, Published online: 01 Jun 2017
 

ABSTRACT

Complex issues such as bullying have brought to light the importance of expanding school prevention efforts to include interventions focused on multiple levels of practice. Utilizing data gathered from middle-school teachers across the state of Michigan, this study examines how both individual and organizational characteristics influence teacher interventions in bullying situations. The study found that teachers’ beliefs about the perceived seriousness of the bullying situation, teachers’ level of sympathy/empathy toward the students being bullied, and teachers’ ages consistently contributed to their reported likelihood of interventions in bullying situations. Surprisingly, the majority of organizational-level characteristics were not significant predictors of teachers’ reported likelihood of intervention. The findings align with many of the seminal theories of bystander intervention and suggest that school professionals should focus on programs and policies that educate teachers on both the serious consequences of bullying and on factors that promote empathy toward bullied students.

Competing interests

The authors report no competing interests.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 53.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 291.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.