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Individual Characteristics and the Multiple Contexts of Adolescent Bullying: An Ecological Perspective

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Abstract

This paper uses an ecological perspective to explore the risk factors associated with bullying behaviors among a representative sample of adolescents aged 11–14 \( {\text{(}}n = 9816,\;\overline X = {\text{12}}{\text{.88}}, s = {\text{.9814)}}. \) Data derived from the Health Behavior in School Children: WHO Cross-National Survey were used to model the relationship between bullying and media effects, peer and family support systems, self-efficacy, and school environment. Overall, the results of this study suggest that bullying increases among children who watch television frequently, lack teacher support, have themselves been bullied, attend schools with unfavorable environments, have emotional support from their peers, and have teachers and parents who do not place high expectations on their school performance. In addition, we found an inverse relationship between being Asian or African American, feeling left out of school activities and bullying. Our results lend support to the contention that bullying arises out of deficits in social climate, but that social support systems mediate bullying behavior irrespective of the student’s racial/ethnic characteristics, parental income levels or media influences. Because the number of friends and the ability to talk to these friends increases the likelihood of bullying, we suggest that bullying is not simply an individual response to a particular environment but is a peer-group behavior. We conclude that limiting television viewing hours, improving student’s abilities to access family support systems and improving school atmospheres are potentially useful interventions to limit bullying behavior.

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Notes

  1. When proportionality is unrealistic, the partial proportional ordinal logistic model appropriately allows for the possibility that some of the estimated coefficients are the same for all values of j while others are not. As Lall et al. (2002) contend, “strikingly” different results can be obtained when using alternative ordinal models. An additional benefit is that this model is much more parsimonious than other models frequently employed when the proportional odds assumption is deemed invalid. Accordingly, the following was fit to the data

    $$ {\begin{gathered} {\text{log}}\left[ {\frac{{{\text{Pr}}(Y \le y_j |X_{\text{1}} \cdots X_p )}} {{{\text{Pr}}(Y > y_j |X_{\text{1}} \cdots X_p )}}} \right] = \alpha _j + \left( {\left[ {\beta _{\text{1}} X_{\text{1}} + \gamma _{j1} T_{\text{1}} } \right] + \left[ {\beta _{\text{2}} X_{\text{2}} + \gamma _{j2} T_{\text{2}} } \right] + \left[ {\beta _q X_q + \gamma _{jq} T_q } \right] + \cdots + \left[ {\beta _p X_p }\right]} \right),j = {\text{1, 2}},...,M - {\text{1}} \hfill \\ \end{gathered} } $$

    where X 1, X 2,...,X p are the set of covariates, q of which are known to have proportional odds and p−q do not. In the parameterization of the partial proportional odds model used in this paper, each X has a constant component associated with it (see Table 4). In addition, each X can have M−2 gamma coefficients (entitled “Increment at cut-off points” in Table 4) where M = 5 (the number of categories in Y) (See Lall et al. 2002 for further explanation). The gamma coefficients represent deviations from the proportionality assumption, in other words if the gammas for a variable are all 0 then the variable meets the proportional odds assumption (Williams 2005).

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Acknowledgements

We would like to thank the United States Department of Justice—Office for Juvenile Justice and Delinquency Prevention and the Michigan Department of Human Services Grant #: 071B2001414 for funding this research.

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Authors and Affiliations

  1. 4400 Massachusetts Ave., NW Washington, DC, 20008, USA

    Gia Elise Barboza

  2. Michigan State University, 8 Human Ecology Bldg., East Lansing, MI, 48823, USA

    Lawrence B. Schiamberg

  3. Michigan State University, 317c Agriculture Hall, East Lansing, MI, 48823, USA

    James Oehmke

  4. Michigan State University, 600 Fee Hall, East Lansing, MI, 48823, USA

    Steven J. Korzeniewski

  5. Michigan State University, 278 Communication Arts and Sciences Bldg., East Lansing, MI, 48823, USA

    Lori A. Post

  6. Michigan State University, 378 Communication Arts and Sciences Bldg., East Lansing, MI, 48823, USA

    Cedrick G. Heraux

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  1. Gia Elise Barboza
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Barboza, G.E., Schiamberg, L.B., Oehmke, J. et al. Individual Characteristics and the Multiple Contexts of Adolescent Bullying: An Ecological Perspective. J Youth Adolescence 38, 101–121 (2009). https://doi.org/10.1007/s10964-008-9271-1

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