Mark Assink

Applying a multilevel approach to meta-analysis is a strong method for dealing with dependency of effect sizes. However, this method is relatively unknown among researchers and, to date, has not been widely used in meta-analytic research. Therefore, the purpose of this tutorial was to show how a three-level random effects model can be applied to meta-analytic models in R using the rma.mv function of the metafor package. This application is illustrated by taking the reader through a step-by-step guide to the multilevel analyses comprising the steps of (1) organizing a data file; (2) setting up the R environment; (3) calculating an overall effect; (4) examining heterogeneity of within-study variance and between-study variance; (5) performing categorical and continuous moderator analyses; and ( By example, the authors demonstrate how the multilevel approach can be applied to meta-analytically examining the association between mental health disorders of juveniles and juvenile offender recidivism. In our opinion, the rma.mv function of the metafor package provides an easy and flexible way of applying a multi-level structure to meta-analytic models in R. Further, the multilevel meta-analytic models can be easily extended so that the potential moderating influence of variables can be examined.

School absenteeism and dropout are associated with many different life-course problems. To reduce the risk for these problems it is important to gain insight into risk factors for both school absenteeism and permanent school dropout. Until now, no quantitative overview of these risk factors and their effects was available. Therefore, this study was aimed at synthesizing the available evidence on risk factors for school absenteeism and dropout. In total, 75 studies were included that reported on 781 potential risk factors for school absenteeism and 635 potential risk factors for dropout. The risk factors were classified into 44 risk domains for school absenteeism and 42 risk domains for dropout. The results of a series of three-level meta-analyses yielded a significant mean effect for 28 school absenteeism risk domains and 23 dropout risk domains. For school absenteeism, 12 risk domains were found with large effects, including having a negative attitude towards school, substance abuse, externalizing and internalizing problems of the juvenile, and a low parent-school involvement. For dropout, the risk domains having a history of grade retention, having a low IQ or experiencing learning difficulties, and a low academic achievement showed large effects. The findings of the current study contribute to the fundamental knowledge of the etiology of school absenteeism and dropout which in turn contributes to a better understanding of the problematic development of adolescents. Further, more insight into the strength of effects of risk factors on school absenteeism and dropout is important for the development and improvement of both assessment, prevention and intervention strategies.