Abstract
Background
School violence and bullying are a pandemic issue. The academic literature underlined the need to investigate social-contextual risk factors. The United Nations called for more comprehensive and disaggregated data to inform prevention strategies.
Objective
The present study comprises a set of secondary analyses on Italian data from the International Civic and Citizenship Study 2016. We adopted an innovative ‘bottom-up’ approach to identify the level of disaggregation for national data. The researchers focused on community, social, and economic risk indicators at school-level, and investigated whether it was possible to aggregate schools in different classes, depending on their risk profile.
Participants and settings
Analyses were implemented on a nationally representative sample of 170 Principals of lower secondary schools, 2,527 teachers and 3,766 students at grade 8.
Methods and analyses
A Latent Class Analyses was conducted on risk indicators and four classes of risk were identified: No Risk, Community Risk, Socio-economic Risk, Multi-Risk (entropy = .786). No significant differences were found across classes in relation to urban/rural location, school size, and geographical macro-partition. On the contrary, significant differences emerged when considering teachers’ perception of bullying, social problem, and students’ behavior at school. Furthermore significant differences were found for the quality of relationship with teachers as reported by students.
Conclusions
Results a) suggested a potential gradient of increasing risk moving across the classes; b) provided a contribution to address the gap in the investigation of contextual factors and bullying; c) offered a new lens to tailor interventions to prevent school violence and bullying.
School violence and bullying are a pandemic issue. The academic literature underlined the need to investigate social-contextual risk factors. The United Nations called for more comprehensive and disaggregated data to inform prevention strategies.
Objective
The present study comprises a set of secondary analyses on Italian data from the International Civic and Citizenship Study 2016. We adopted an innovative ‘bottom-up’ approach to identify the level of disaggregation for national data. The researchers focused on community, social, and economic risk indicators at school-level, and investigated whether it was possible to aggregate schools in different classes, depending on their risk profile.
Participants and settings
Analyses were implemented on a nationally representative sample of 170 Principals of lower secondary schools, 2,527 teachers and 3,766 students at grade 8.
Methods and analyses
A Latent Class Analyses was conducted on risk indicators and four classes of risk were identified: No Risk, Community Risk, Socio-economic Risk, Multi-Risk (entropy = .786). No significant differences were found across classes in relation to urban/rural location, school size, and geographical macro-partition. On the contrary, significant differences emerged when considering teachers’ perception of bullying, social problem, and students’ behavior at school. Furthermore significant differences were found for the quality of relationship with teachers as reported by students.
Conclusions
Results a) suggested a potential gradient of increasing risk moving across the classes; b) provided a contribution to address the gap in the investigation of contextual factors and bullying; c) offered a new lens to tailor interventions to prevent school violence and bullying.
| Original language | English |
|---|---|
| Article number | 104746 |
| Journal | Child Abuse and Neglect |
| Volume | 109 |
| Early online date | 28 Sept 2020 |
| DOIs | |
| Publication status | Published - Nov 2020 |
Keywords
- Bullying
- International Association for the Evaluation of Educational Achievement (IEA)
- International Civic and Citizenship Study (ICCS)
- Latent class analysis
- Risk factors
- School violence
ASJC Scopus subject areas
- Pediatrics, Perinatology, and Child Health
- Developmental and Educational Psychology
- Psychiatry and Mental health
