Skip to main content

Advertisement

SpringerLink
Log in

Violent Physical Bullying Victimization at School: Has There Been a Recent Increase in Exposure or Intensity? An Age-Period-Cohort Analysis in the United States, 1991 to 2012

  • Published:
Child Indicators Research Aims and scope Submit manuscript

Abstract

Using data from an annual nationally representative survey of U.S. 8th, 10th, and 12th graders from 1991 to 2012, this paper applies a new two-step method to study trends in self-reports of victimization during the last year from four forms of violent bullying at school (threatened without injury, threatened with a weapon, injury without a weapon, injury with a weapon). First, we develop a statistical algorithm for estimating, for each school year, the exposure probability (likelihood or risk of being victimized) and intensity rate (rate of victimization among those exposed to the risk of being victimized) parameters of zero-inflated Poisson models of truncated and combined self-reported victimization frequency data for the four forms of violent bullying. Estimates of both the exposure to, and intensity of, the self-reported frequencies for each the four forms for each of the grades show increases into the middle part of the 2000–2010 decade with slight declines in the years 2008–2012. Exceptions are found for intensity rates of threats without injury and threats with a weapon among 12th graders. Second, age-period-cohort analysis was applied to the estimated exposure and intensity parameters of violent bullying victimization. This analysis reveals: (1) that both the exposure probabilities and intensity rates decrease from the 8th (typically 13–14 year olds) to the 10th (typically 15–16 year olds) to the 12grades (typically 17–18 years old); (2) that the school years 2006 to 2012 were associated with decreases in time period exposure probabilities and increases in intensity rates - fewer students victimized per school year but those who are victimized are victimized more frequently; and (3) that birth cohorts born since the late-1980s had decreases in intensity rates, but their exposure probabilities increased until the most recent (1995–1996) cohorts for which the exposure probabilities have stabilized or declined.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Notes

  1. Presumably, this selection effect also applies to bullies. Nevertheless, an age decline in bullying victimization is expected no matter whether such selection effect applies to both victims and bullies.

  2. The advantage of ZIP models over Poisson models can be suggested by comparing observed frequencies with predicted frequencies using different estimation strategies (see Appendix).

  3. The R source code is available upon request.

  4. Numerical results for all school years are available from the authors on request.

  5. In conventional applications of APC multiple classification/accounting models, the cell entries are estimated population occurrence/exposure rates or proportions, which, in statistical terminology are random variables. In the present analyses, the estimated P and λ parameters are functions of the annual grouped and truncated frequency counts of the forms of violent victimization in the MTF data. In statistical terminology, the frequency counts are random variables, and functions of random variables also are random variables. Thus, by arraying the estimated P and λ parameters in grade/age by time period tables, the objects of the APC analysis in the present study are random variables, as is the case in classical APC accounting model applications.

  6. Estimable functions are linear functions of the effect coefficients of an underidentified linear model that are invariant to whatever solution to the normal equations assumed for estimation of the coefficients.

  7. The numerical estimates for both sets of APC analyses are available from the authors on request.

  8. Corresponding tables for the other three MTF bullying questions are available from the authors on request.

References

  • Ahmad, Y., & Smith, P. K. (1990). Behavioural measures: bullying in schools. Newsletter of Association f or Child Psychology and Psychiatry, 12, 26–27.

    Google Scholar 

  • Arora, T., & Thompson, D. A. (1987). Defining bullying for a secondary school. Education and Child Psychology, 4, 110–120.

  • Batsche, G. M., & Knoff, H. M. (1994). Bullies and their victims: understanding a pervasive problem in the schools. School Psychology Review, 23, 165–175.

    Google Scholar 

  • Berger, K. S. (2007). Update on bullying at school: science forgotten? Developmental Review, 27, 90–126.

    Article  Google Scholar 

  • Bosworth, K., Espelage, D. L., & Simon, T. R. (1999). Factors associated with bullying behavior in middle school students. The Journal of Early Adolescence, 19, 341–362.

    Article  Google Scholar 

  • Brooke, J. 1998. “Witnesses trace brutal killing of gay student.” In New York Times, vol. November 21. New York.

  • Browning, M., Crawford, I., & Knoef, M. (2012). The age-period cohort problem: set identification and point identification. CWP02/12. Department of Economics, UCL: Cemmap working paper.

  • Cairns, R. B., & Cairns, B. D. (1986). In D. Olweus, J. Block, & M. Radke-Yarrow (Eds.), The developmental-interactional view of social behavior: Four issues of adolescent aggression. New York: Academic.

    Google Scholar 

  • Carlson, E. (2009). 20th-century U.S. generations. Washington, DC: Population Reference Bureau.

    Google Scholar 

  • Cohen, A. C., Jr. (1954). Estimation of the poisson parameter from truncated samples and from consored samples. Journal of the American Statistical Association, 49, 158–168.

    Google Scholar 

  • Espelage, D. L., & Swearer, S. M. (2003). Research on school bullying and victimization: what have we learned and where do we go from here? School Psychology Review, 32, 365–383.

    Google Scholar 

  • Fekkes, M., Frans, I. M., Pijpers, A. M., Fredriks, T. V., & Verloove-Vanhorick, S. P. (2006). Do bullied children get ill, or do ill children get bullied? A prospective cohort study on the relationship between bullying and health-related symptoms. Pediatrics, 117, 1568–1574.

    Article  Google Scholar 

  • Finkelhor, D. (2013). Trends in bullying and peer victimization. Crimes against children research center. Durham: University of New Hampshire.

    Google Scholar 

  • Fox, J. (2008). Applied regression analysis and generalized linear models. Los Angeles: Sage Publications, Inc.

    Google Scholar 

  • Fraire, M., Prino, L. E., & Sclavo, E. (2008). An analysis of italian newspaper articles on the bullying phenomenon. International Journal of Psychology and Psychological Therapy, 8, 237–245.

    Google Scholar 

  • French, J. R. P., & Bertram, R. (1959). In D. Cartwright (Ed.), The bases of social power. Ann Arbor: University of Michigan Press.

    Google Scholar 

  • Fu, W. (2000). Ridge estimator in singulah oesiun with application to age-period-cohort analysis of disease rates. Communications in Statistics-Theory and Methods, 29, 263–278.

    Article  Google Scholar 

  • Fu, W. (2008). A smoothing cohort model in age–period–cohort analysis with applications to homicide arrest rates and lung cancer mortality rates. Sociological Methods & Research, 36, 327–361.

    Article  Google Scholar 

  • Fu, W., & Hall, P. (2006). Asymptotic properties of estimators in age-period-cohort analysis. Statistics & Probability Letters, 76, 1925–1929.

    Article  Google Scholar 

  • Fu, W., Land, K. C., & Yang, Y. (2011). On the intrinsic estimator and constrained estimators in age-period-cohort models. Sociological Methods & Research, 40, 453–466.

    Article  Google Scholar 

  • Fu, Q., Land, K. C., & Lamb, V. L. (2013). Bullying victimization, socioeconomic status and behavioral characteristics of 12th graders in the United States, 1989 to 2009: repetitive trends and persistent risk differentials. Child Indicators Research, 6, 1–21.

    Article  Google Scholar 

  • Gelman, A., & Hill, J. (2007). Data analysis using regression and multilevel/hierarchical models. New York: Cambridge University Press.

    Google Scholar 

  • Gladden, R. M., Vivolo-Kantor, A. M., Hamburger, M. E., & Lumpkin, C. D. (2014). Bullying surveillance among youths: Uniform definitions for public health and recommended data elements, version 1.0. Atlanta: National Center for Injury Prevention and Control, Centers for Disease Control and Prevention and US Department of Education.

    Google Scholar 

  • Glenn, N. D. (1976). Cohort analysts’ futile quest: statistical attempts to separate age, period and cohort effects. American Sociological Review, 41, 900–904.

    Article  Google Scholar 

  • Greenbaum, S., Turner, B., & Stephens, R. D. (1989). Set straight on bullies. Malibu: National School Safety Center.

    Google Scholar 

  • Harris, K. M., Duncan, G. J., & Boisjoly, J. (2002). Evaluating the role of “nothing to lose” attitudes on risky behavior in adolescence. Social Forces, 80, 1005–1039.

    Article  Google Scholar 

  • Hawley, P. H., Little, T. D., & Pasupathi, M. (2002). Winning friends and influencing peers: strategies of peer influence in late childhood. International Journal of Behavioral Development, 26, 466–474.

    Article  Google Scholar 

  • Johnston, L. D., O’Malley, P. M., & Bachman, J. G. (1993). “Monitoring the future study for goal 6 of the national education goals: A special report for the national education goals panel.”. Ann Arbor: University of Michigan, Institute for Social Research.

    Google Scholar 

  • Kendall, M. G., & Stuart, A. (1976). The advanced theory of statistics: Volume 3: Design and analysis, and time series (Vol. 3). New York: Macmillan.

    Google Scholar 

  • Lambert, D. (1992). Zero-inflated poisson regression, with an application to defects in manufacturing. Technometrics, 34, 1–14.

    Article  Google Scholar 

  • Land, K. C., McCall, P. L., & Williams, J. R. (1992). In J. McCord & R. E. Tremblay (Eds.), Intensive supervision of status offenders: Evidence on continuity of treatment effects for juveniles and a “hawthorne effect” for counselors. New York: The Guilford Press.

    Google Scholar 

  • Long, J. S., & Freese, J. (2006). Regression models for categorical dependent variables using stata. College Station: Stata Press.

    Google Scholar 

  • Mason, K. O., Mason, W. M., Winsborough, H. H., & Kenneth Poole, W. (1973). Some methodological issues in cohort analysis of archival data. American Sociological Review, 38, 242–258.

    Article  Google Scholar 

  • Molcho, M., Craig, W., Due, P., Pickett, W., Harel-Fisch, Y., Overpeck, M., et al. (2009). Cross-national time trends in bullying behaviour 1994–2006: findings from Europe and North America. International Journal of Public Health, 54, 225–234.

    Article  Google Scholar 

  • Nansel, T. R., Overpeck, M., Pilla, R. S., June Ruan, W., Simons-Morton, B., & Scheidt, P. (2001). Bullying behaviors among US youth: prevalence and association with psychosocial adjustment. JAMA, the Journal of the American Medical Association, 285, 2094–2100.

    Article  Google Scholar 

  • O’Brien, R. M. (2011). Constrained estimators and age-period-cohort models. Sociological Methods & Research, 40, 419–452.

    Article  Google Scholar 

  • Olweus, D. 1991. Bully/victim problems among schoolchildren: Basic facts and effects of a school based intervention program, Edited by D. J. Pepler and K. H. Rubin. Hillsdale: Lawrence Erlbaum Associates, Inc.

  • Olweus, D. (1994). Bullying at school: basic facts and effects of a school based intervention program. Journal of Child Psychology and Psychiatry, 35, 1171–1190.

    Article  Google Scholar 

  • PACER’S National Bullying Prevention Center. 2013. “October is national bullying prevention month.”

  • Pellegrini, A. D., & Bartini, M. (2000). A longitudinal study of bullying, victimization, and peer affiliation during the transition from primary school to middle school. American Educational Research Journal, 37, 699–725.

    Article  Google Scholar 

  • Pellegrini, A. D., & Long, J. D. (2002). A longitudinal study of bullying, dominance, and victimization during the transition from primary school through secondary school. British Journal of Developmental Psychology, 20, 259–280.

    Article  Google Scholar 

  • Pepler, D., Jiang, D., Craig, W., & Connolly, J. (2008). Developmental trajectories of bullying and associated factors. Child Development, 79, 325–338.

    Article  Google Scholar 

  • Rigby, K. (2002). New perspectives on bullying. London: Jessica Kingsley Publishers.

    Google Scholar 

  • Rigby, K., & Slee, P. (1999). Suicidal ideation among adolescent school children, involvement in bully—victim problems, and perceived social support. Suicide and Life-Threatening Behavior, 29, 119–130.

    Google Scholar 

  • Robers, S., Zhang, J., Truman, J., & Snyder, T. D. (2012). In U. S. D. o. E. National Center for Education Statistics, and Bureau of Justice Statistics, Office of Justice Programs (Ed.), “Indicators of School Crime and Safety: 2011.”. Washington, DC: U.S. Department of Justice.

    Google Scholar 

  • Salmon, G., James, A., & Smith, D. M. (1998). Bullying in schools: self reported anxiety, depression, and self esteem in secondary school children. British Medical Journal, 317, 924–925.

    Article  Google Scholar 

  • Scheithauer, H., Hayer, T., Petermann, F., & Jugert, G. (2006). Physical, verbal, and relational forms of bullying among German students: Age trends, gender differences, and correlates. Aggressive Behavior, 32, 261–275.

    Article  Google Scholar 

  • Schwadel, P. (2011). Age, period, and cohort effects on religious activities and beliefs. Social Science Research, 40, 181–192.

    Article  Google Scholar 

  • Smith, P. K., & Myron-Wilson, R. (1998). Parenting and school bullying. Clinical Child Psychology and Psychiatry, 3, 405–417.

    Article  Google Scholar 

  • Smith, P. K., & Sharp, S. (1994). School bullying: Insights and perspectives. London: Routledge.

    Book  Google Scholar 

  • Smith, P. K., Madsen, K. C., & Moody, J. C. (1999). What causes the age decline in reports of being bullied at school? Towards a developmental analysis of risks of being bullied. Educational Research, 41, 267–285.

    Article  Google Scholar 

  • Sourander, A., Helstelä, L., Helenius, H., & Piha, J. (2000). Persistence of bullying from childhood to adolescence: a longitudinal 8-year follow-up study. Child Abuse & Neglect, 24, 873–881.

    Article  Google Scholar 

  • Srabstein, J. (2008). Deaths linked to bullying and hazing. International Journal of Adolescent Medicine and Health, 20, 235–239.

    Google Scholar 

  • Staff, J., & Kreager, D. A. (2008). Too cool for school? Violence, peer status and high school dropout. Social Forces, 87, 445–471.

    Article  Google Scholar 

  • Vossekuil, B, Fein, R.A, Reddy, M, Borum, R., and Modzeleski, W. 2004. “The final report and findings of the safe school initiative: implications for the prevention of school attacks in the united states.” US Department of Education.

  • Woods, S., & Wolke, D. (2004). Direct and relational bullying among primary school children and academic achievement. Journal of School Psychology, 42, 135–155.

    Article  Google Scholar 

  • Yang, Y., Fu, W., & Land, K. C. (2004). A methodological comparison of age‐period‐cohort models: the intrinsic estimator and conventional generalized linear models. Sociological Methodology, 34, 75–110.

    Article  Google Scholar 

  • Yang, Y., Schulhofer‐Wohl, S., Fu, W., & Land, K. C. (2008). “The Intrinsic estimator for age‐period‐cohort analysis: what it is and how to use it.”. American Journal of Sociology, 113, 1697–1736.

    Article  Google Scholar 

  • Zeileis, A., Kleiber, C., & Jackman, S. (2008). Regression models for count data in R. Journal of Statistical Software, 27, 1–25.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Sociology, The University of British Columbia, Vancouver, BC, V6T 1Z1, Canada

    Qiang Fu

  2. Department of Sociology, Duke University, Durham, NC, 27708, USA

    Kenneth C. Land

  3. Department of Human Sciences, North Carolina Central University, Durham, NC, 27707, USA

    Vicki L. Lamb

Authors
  1. Qiang Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kenneth C. Land
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vicki L. Lamb
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qiang Fu.

Additional information

This research was supported by research grants from the Foundation for Child Development.

Appendix

Appendix

Table 5

Table 5 Observed frequencies and predicted frequencies using different estimation strategies
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fu, Q., Land, K.C. & Lamb, V.L. Violent Physical Bullying Victimization at School: Has There Been a Recent Increase in Exposure or Intensity? An Age-Period-Cohort Analysis in the United States, 1991 to 2012. Child Ind Res 9, 485–513 (2016). https://doi.org/10.1007/s12187-015-9317-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12187-015-9317-3

Keywords

Search

Navigation