Method: Data from the Multisite Violence Prevention Project (MVPP) on two cohorts of students (N=5,106) from 37 schools in four communities (MVPP, 2004) are used. Data were collected as part of a randomized trial where schools were randomly assigned to one of four conditions: a universal intervention, a selective intervention, a combined intervention, or no intervention. This study uses data from fall of 6th grade prior to the intervention for each cohort. Students completed measures of aggression, victimization and school climate including norms supporting aggression and nonviolence, student-teacher relationships, and awareness and reporting of peer victimization; their teachers completed measures of student-teacher relationships, staff relationships, and awareness and reporting of peer victimization. We use BMLC models to identify classes of aggression and peer victimization (level 1) and examine the influence of school climate (level 2) on classes, building on models developed by Vermunt (2003) and implemented, among others, in Henry and Muthén (2010)
Results: The priors used for the BMLC will be captured from a study by Bettencourt and Farrell, 2013, which found 4 latent classes: non-victimized aggressors, aggressive-victims, predominantly victimized and well-adjusted. Additional analyses are being conducted to examine the influence of school climate at level 2 on level 1 classes.
Discussion: Use of Bayesian estimation methods are not terribly commonly in prevention science but could prove to be incredibly useful with the increasing complexity of data available to researchers. The Bayesian approach is appealing because of the use of informative priors, reduced computation time, and the ability to handle a small number of clusters.