There is growing emphasis on the use of positive behavior supports rather than exclusionary discipline strategies to promote a positive classroom environment. Yet, there has been limited research examining the association between these two different approaches to classroom management and students' perceptions of school climate. Data from 1902 students within 93 classrooms that were nested within 37 elementary schools were examined using multilevel structural equation modeling procedures to investigate the association between two different classroom management strategies (i.e., exclusionary discipline strategies and the use of positive behavior supports) and student ratings of school climate (i.e., fairness, order and discipline, student-teacher relationship, and academic motivation). The analyses indicated that greater use of exclusionary discipline strategies was associated with lower order and discipline scores, whereas greater use of classroom-based positive behavior supports was associated with higher scores on order and discipline, fairness, and student-teacher relationship. These findings suggest that pre-service training and professional development activities should promote teachers' use of positive behavior support strategies and encourage reduced reliance on exclusionary discipline strategies in order to enhance the school climate and conditions for learning.
"For instance, multilevel measurement modeling with categorical indicators represents the basis of multilevel item response theory (IRT; Fox, 2010; Fox & Glas, 2001; Muthén & Asparouhov, 2012b). There are a number of applications of multilevel SEM with categorical outcomes in the research literature using both frequentist (e.g., Gottfredson et al., 2009; Little, 2013; Mitchell & Bradshaw, 2013) and Bayesian (e.g., Diya, Li, Heede, Sermeus, & Lesaffre, 2013; Goldstein, Bonnet, & Rocher, 2007) approaches, yet simulation research has not been conducted to determine whether the sample size recommendations that have been put forth for continuous outcomes also hold for categorical outcomes. Moreover, a Bayesian approach is uniquely beneficial in the context of categorical data because it can be used to estimate categorical variable models that cannot be analyzed with currently available frequentist approaches (see, e.g., Ansari & Jedidi, 2000; Dunson, 2000; Muthén & Asparouhov, 2012b; Steele & Goldstein, 2006). "
[Show abstract][Hide abstract] ABSTRACT: Multilevel Structural equation models are most often estimated from a frequentist framework via maximum likelihood. However, as shown in this article, frequentist results are not always accurate. Alternatively, one can apply a Bayesian approach using Markov chain Monte Carlo estimation methods. This simulation study compared estimation quality using Bayesian and frequentist approaches in the context of a multilevel latent covariate model. Continuous and dichotomous variables were examined because it is not yet known how different types of outcomes—most notably categorical—affect parameter recovery in this modeling context. Within the Bayesian estimation framework, the impact of diffuse, weakly informative, and informative prior distributions were compared. Findings indicated that Bayesian estimation may be used to overcome convergence problems and improve parameter estimate bias. Results highlight the differences in estimation quality between dichotomous and continuous variable models and the importance of prior distribution choice for cluster-level random effects.
[Show abstract][Hide abstract] ABSTRACT: Although it is widely recognized that variation in implementation fidelity influences the impact of preventive interventions, little is known about how specific contextual factors may affect the implementation of social and behavioral interventions in classrooms. Theoretical research highlights the importance of multiple contextual influences on implementation, including factors at the classroom and school level (Domitrovich et al., Advances in School Mental Health Promotion, 1, 6-28, 2008). The current study used multi-level modeling to empirically examine the influence of teacher, classroom, and school characteristics on the implementation of classroom-based positive behavior support strategies over the course of 4 years. Data were collected in the context of a 37-school randomized controlled trial examining the effectiveness of school-wide Positive Behavioral Interventions and Supports. Multi-level results identified several school-level contextual factors (e.g., school size, behavioral disruptions) and teacher-level factors (perceptions of school organizational health and grade level taught) associated with variability in the implementation of classroom-based positive behavior supports. Implications for prevention research and practice are discussed.
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