Abstract: School Connectedness and Peer Bullying in the U.S.: Testing Direction of Causality Using a Cross-Lagged Panel Design and Latent Constructs (Society for Social Work and Research 26th Annual Conference - Social Work Science for Racial, Social, and Political Justice)

School Connectedness and Peer Bullying in the U.S.: Testing Direction of Causality Using a Cross-Lagged Panel Design and Latent Constructs

Friday, January 14, 2022
Liberty Ballroom K, ML 4 (Marriott Marquis Washington, DC)
* noted as presenting author
Jordan Goodwin, LSW, MDiv, Graduate Research Assistant, Rutgers University, New Brunswick, NJ
Andrew Peterson, PhD, Professor, Rutgers University, NJ
Background: Although studies have explored the relationship between school connectedness and peer bullying, few have tested causal hypotheses with latent variables due to cross-sectional designs and small samples. Given the dearth of evidence on the directionality of these constructs, it remains undetermined whether school connectedness is a protective factor or the negative outcome of exposure to peer bullying. This study fills the gap by testing the strength of the temporal relationship between school connectedness and bullying among a large national sample of youth at two time points while addressing measurement and specification concerns.

Methods: This study used data from the Fragile Families and Child Wellbeing Study, a longitudinal birth cohort study of 4,898 births in large U.S. cities between 1998 and 2000. The study sample (n=2,894) was drawn from Waves 5 and 6, when the child was roughly 9 and 15 years respectively. Approximately half of the sample was female, half identified as Black, 31% were born to mothers without a high school degree, one-quarter was born to married parents, most were born to mothers who were U.S. citizens (86%), and 34% were living below the poverty line at birth.

Cross-lagged panel analysis was used to compared four latent variable models: (1) baseline with autoregressive paths; (2) autoregressive effects and school connectedness predicting later bullying; (3) autoregressive effects and bullying predicting later school connectedness; and, (4) cross-lagged with autoregressive effects and both school connectedness and bullying predicting each other at a later time point. Maximum likelihood estimation was used to test the models.

Results: Results for Model 1 indicated strong autoregressive effects for both school connectedness and peer bullying, demonstrating robust temporal stability. Model 3 (bullying predicting future school connectedness) was found to provide a significantly better fit to the data than the baseline model. Model 3 included statistically significant factor loadings for connectedness items and bullying items (ranging from .31 to .73) for each measure relative to its hypothesized latent factor, and the autoregressive paths for the variables were relatively strong, indicating that the measurement models were stable over time. Importantly, the cross-lagged effect of bullying at Time 1 on connectedness at Time 2 was statistically significant. However, results comparing the fully cross-lagged model did not indicate a better fit to the data compared to Model 3. Taken together, these data suggest that Model 3 was more parsimonious and fit the data better than competing models.

Implications: These results suggest that peer bullying at Year 9 predicts school connectedness at Year 15; however, there is no evidence that school connectedness at Year 9 predicts peer bullying at Year 15. These results suggest that early intervention to address bullying at a younger age can impact later levels of school connectedness, however more research is needed in this area to explore possible confounds in this relationship. This study contributes to the knowledge base on the effects of peer bullying and suggest that poor school connectedness is a likely outcome in the absence of timely bullying interventions.