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Very few studies of peer victimization have been conducted in low-resource countries, where cultural and contextual differences are likely to influence the dynamics of these experiences in ways that may reduce the generalizability of findings of the larger body of literature. Most studies in these settings are also subject to multiple design limitations that restrict our ability to understand the dynamics of peer victimization experiences. Person-centered approaches such as latent class analysis are an improvement on more traditional modeling approaches as they allow exploration of patterns of victimization experiences. The goal of the current study was to examine associations between patterns of peer victimization in adolescence and both concurrent and longitudinal psychosocial adjustment. Data were included for 3536 youth (49.6% female) in Ethiopia, India, Peru, and Vietnam to examine associations between adolescent peer victimization and indicators of poor psychosocial adjustment. Previously derived latent classes of peer victimization based on youth self-report of past-year exposure to nine forms of peer victimization at age 15 were used to predict self-reported emotional difficulties, self-rated health, and subjective wellbeing at ages 15 and 19 while controlling for sex. The findings show that at age 15, victimization was associated with higher emotional difficulties in all settings, lower subjective wellbeing in all except Peru, and lower self-rated health in Vietnam. At follow-up, all associations had attenuated and were largely non-significant. Sensitivity analyses confirmed the robustness of these results. These findings illustrate the multifinality of outcomes of peer victimization, suggesting social and developmental influences for potential pathways of resilience that hold promise for informing interventions and supports in both low and high resource settings.
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Aberra, M. (2013). The Case of Selected Schools in Addis Ababa. Addis Ababa University. http://etd.aau.edu.et/handle/123456789/7492.
Asparouhov, T., & Muthen, B. (2014). Auxiliary variables in mixture modeling: Using the BCH method in Mplus to estimate a distal outcome model and an arbitrary second model. Mplus Web Notes: No. 21.
Baldry, A. C. (2004). The impact of direct and indirect bullying on the mental and physical health of Italian youngsters. Aggressive Behavior, 30(5), 343–355. CrossRef
Bandeen-Roche, K., Miglioretti, D. L., Zeger, S. L., & Rathouz, P. J. (1997). Latent variable regression for multiple discrete outcomes. Journal of the American Statistical Association, 92(440), 1375–1386. CrossRef
Bellmore, A. D., Witkow, M. R., Graham, S., & Juvonen, J. (2004). Beyond the individual: the impact of ethnic context and classroom behavioral norms on victims’ adjustment. Developmental Psychology, 40(6), 1159–1172. https://doi.org/10.1037/0012-16184.108.40.2069. PubMedCrossRef
Brown, D. W., Riley, L., Butchart, A., & Kann, L. (2008). Bullying among youth from eight African countries and associations with adverse health behaviors. Pediatric Health, 2(3), 289–299. CrossRef
Currie, C., Zanotti, C., Morgan, A., Currie, D., de Looze, M., Roberts, C., Samdal, O., Smith, O. R., Barnekow, V. (2012). Social determinants of health and well-being among young people. Health Behaviour in School-aged Children (HBSC) study: International report from the 2009/2010 survey. Health Policy for children and adolescents, No 6. Copenhagen: WHO Regional Office for Europe.
Espelage, D. L., & De La Rue, L. (2012). School bullying: its nature and ecology. International Journal of Adolescent Medicine and Health, 24(1), 3–10. CrossRef
Gallup. (n.d.). Understanding How Gallup Uses the Cantril Scale. http://www.gallup.com/poll/122453/understanding-gallup-uses-cantril-scale.aspx Accessed 26 Feb 2016.
Kshirsagar, V. Y., Agarwal, R., & Bavdekar, S. B. (2007). Bullying in schools: prevalence and short-term impact. Indian Pediatrics, 44(1), 25–28. PubMed
Lanza, S. T., & Cooper, B. R. (2016). Latent class analysis for developmental research. Child Development Perspectives, 10(1), 59–64. CrossRef
Lister, C., Merrill, R. M., Vance, D., West, J. H., Hall, P. C., & Crookston, B. T. (2015b). Predictors of peer victimization among Peruvian adolescents in the young lives cohort. International Journal of Adolescent Medicine and Health, 27(1), 85–91. PubMed
Manrique Millones, D. L., Ghesquière, P., & Van Leeuwen, K. (2013). Evaluation of a parental behavior scale in a peruvian context. Journal of Child and Family Studies, 23, 885–894.
Masiello, M. G. (2014). Public health and bullying prevention. In M. G. Masiello & D. Schroeder(Eds.), A public health approach to bullying prevention. 1st edn. (pp. 1–22). Washington, DC: American Public Health Association. CrossRef
McCutcheon, A. L. (1987). Latent class analysis. Beverly Hills, CA: Sage. CrossRef
Muthén, L. K., & Muthén, B. O. (1998-2017). Mplus User’ s Guide (Seventh Ed). Los Angeles, CA: Muthen & Muthen.
Mynard, H., & Joseph, S. (2000). Development of the multidimensional peer-victimization scale. Aggressive Behavior, 26, 169–178. CrossRef
Organisation for Economic Co-operation and Development. (2013). OECD Guidelines on Measuring Subjective Well-being. Paris: OECD Publishing.
Pells, K., Portela, M. J. O., & Revollo, P. E. (2016). Experiences of peer bullying among adolescents and associated effects on young adult outcomes: longitudinal evidence from Ethiopia, India, Peru and Viet Nam. Discussion Paper UNICEF Innocenti Research Centre, 03 (61).
Roman, M., & Murillo, J. (2011). Latin America: school bullying and academic achievement. CEPAL Review, 104, 37–53.
Rosato, N. S., & Baer, J. C. (2012). Latent class analysis: a method for capturing heterogeneity. Social Work Research, 36(1), 61–69. CrossRef
Ruchkin, V., Schwab-Stone, M., & Vermeiren, R. (2004). Social and health assessment (SAHA): Psychometric development summary. New Haven: Yale University.
Save the Children Denmark. (2008). A study on violence against girls in primary schools and its impacts on girls’ education in Ethiopia. Ethiopia: Addis Ababa.
Singham, T., Viding, E., Schoeler, T., Arseneault, L., Ronald, A., Cecil, C. M., & Pingault, J. B. (2017). Concurrent and longitudinal contribution of exposure to bullying in childhood to mental health: the role of vulnerability and resilience. JAMA Psychiatry, 74(11), 1112–1119. https://doi.org/10.1001/jamapsychiatry.2017.2678. PubMedPubMedCentralCrossRef
StataCorp. (2015). Stata Statistical Software: Release 14. College Station, TX: StataCorp LP.
Ttofi, M. M., Farrington, D. P., Lösel, F., & Loeber, R. (2011). Do the victims of school bullies tend to become depressed later in life? A systematic review and meta‐analysis of longitudinal studies. Journal of Aggression, Conflict and Peace Research, 3(2), 63–73. CrossRef
Weiss, B., Dang, M., Trung, L., Nguyen, M. C., Thuy, N. T. H., & Pollack, A. (2014). A nationally representative epidemiological and risk factor assessment of child mental health in vietnam. International Perspectives in Psychology: Research, Practice, Consultation, 3(3), 139–153.
Woldehanna, T., Gudisa, R., Tafere, Y., & Pankhurst, A. (2011). Understanding changes in the lives of poorchildren: initial findings from Ethiopia. Oxford: Young Lives.
Wolke, D., & Lereya, S. T. (2015). Long-term effects of bullying. Archives of Disease in Childhood, 100(9), 879–85. https://doi.org/10.1136/archdischild-2014-306667. PubMedPubMedCentralCrossRef
World Bank. (2018). Country and Lending Groups. http://data.worldbank.org/about/country-and-lending-groups Accessed 23 July 2018.
Young, L. (2015). Young Lives Theory of Change. Oxford, UK: Young Lives. Available online at https://www.younglives.org.uk/content/young-lives-theory-change.
Young, L. (2017). A guide to Young Lives Research. Oxford, UK: Young Lives.
- It Gets Better: Attenuated Associations Between Latent Classes of Peer Victimization and Longitudinal Psychosocial Outcomes in Four Low-Resource Countries
Amanda J. Nguyen
Catherine P. Bradshaw
- Springer US