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Building on prior work regarding the potential for peer contagion or deviance training in group delivered interventions (Dishion & Dodge, 2005, 2006; Dodge, Dishion, & Lansford, 2006), we leveraged data from a randomized trial, testing the integration of two preventive interventions (Promoting Alternative THinking Strategies and PAX Good Behavior Game), to explore the extent to which classroom contextual factors served as either a barrier to or a motivator for teachers to implement the evidence-based PAX Good Behavior Game with high frequency or dosage. We included students' baseline levels of behavior, measured with regard to both positive (i.e., engagement and social emotional skills) and negative (i.e., hyperactive and aggressive-disruptive) behaviors. Data were collected from 204 teachers in 18 urban elementary schools. A series of multilevel structural equation models were fit to the data. The analyses indicated that classrooms with higher classroom levels of aggressive behavior, on average, at baseline had teachers with lower implementation dosage (i.e., played fewer games) across the school year. In addition, teachers who reported higher baseline levels of emotional exhaustion, regardless of student behavior, also reported lower implementation dosage. Taken together, the results indicated that negative, but not positive, contextual factors at baseline were related to lower implementation dosage; this, in turn, suggests that negative contextual factors may serve as a barrier, rather than a motivator, of teachers' implementation dosage of classroom-based preventive interventions. [ABSTRACT FROM AUTHOR]

Population health scientists increasingly study how contextual-level attributes affect individual health. A major challenge in this domain relates to measurement, i.e., how best to measure and create variables that capture characteristics of individuals and their embedded contexts. This paper presents an illustration of multilevel factor analysis (MLFA), an analytic method that enables researchers to model contextual effects using individual-level data without using derived variables. MLFA uses the shared variance in sets of observed items among individuals within the same context to estimate a measurement model for latent constructs; it does this by decomposing the total sample variance-covariance matrix into within-group (e.g., individual-level) and between-group (e.g., contextual-level) matrices and simultaneously modeling distinct latent factor structures at each level. We illustrate the MLFA method using items capturing collective efficacy, which were self-reported by 2,599 adults in 65 census tracts from the Los Angeles Family and Neighborhood Survey (LAFANS). MLFA identified two latent factors at the individual level and one factor at the neighborhood level. Indicators of collective efficacy performed differently at each level. The ability of MLFA to identify different latent factor structures at each level underscores the utility of this analytic tool to model and identify attributes of contexts relevant to health. [ABSTRACT FROM AUTHOR]

The focus of this study was to prospectively investigate the effect of aggressive behavior and of classroom behavioral context, as measured in the fall of 1st grade, on the timing of 1st school removal across Grades 1-7 in a sample of predominately urban minority youths from Baltimore, Maryland. Using a multilevel discrete-time survival framework, we found that demographic characteristics of the students as well as early individual- and classroom-level of aggression contribute to the onset of school removal. Although early individual aggression was positively associated with the risk of school removal, initially higher levels of classroom aggression corresponded to lower risk of school removal. [ABSTRACT FROM AUTHOR]

Purpose: Characterizing the determinants of child health and development over time, and identifying the mechanisms by which these determinants operate, is a research priority. The growth of precision medicine has increased awareness and refinement of conceptual frameworks, data management systems, and analytic methods for multilevel data. This article reviews key methodological challenges in cohort studies designed to investigate multilevel influences on child health and strategies to address them.Methods: We review and summarize methodological challenges that could undermine prospective studies of the multilevel determinants of child health and ways to address them, borrowing approaches from the social and behavioral sciences.Results: Nested data, variation in intervals of data collection and assessment, missing data, construct measurement across development and reporters, and unobserved population heterogeneity pose challenges in prospective multilevel cohort studies with children. We discuss innovations in missing data, innovations in person-oriented analyses, and innovations in multilevel modeling to address these challenges.Conclusions: Study design and analytic approaches that facilitate the integration across multiple levels, and that account for changes in people and the multiple, dynamic, nested systems in which they participate over time, are crucial to fully realize the promise of precision medicine for children and adolescents. [ABSTRACT FROM AUTHOR]