Handling Complex Meta-analytic Data Structures Using Robust Variance Estimates: a Tutorial in R

Abstract

Purpose: Identifying and understanding causal risk factors for crime over the life-course is a key area of inquiry in developmental criminology. Prospective longitudinal studies provide valuable information about the relationships between risk factors and later criminal offending. Meta-analyses that synthesize findings from these studies can summarize the predictive strength of different risk factors for crime, and offer unique opportunities for examining the developmental variability of risk factors. Complex data structures are common in such meta-analyses, whereby primary studies provide multiple (dependent) effect sizes. Methods: This paper describes a recent innovative method for handling complex meta-analytic data structures arising due to dependent effect sizes: robust variance estimation (RVE). We first present a brief overview of the RVE method, describing the underlying models and estimation procedures and their applicability to meta-analyses of research in developmental criminology. We then present a tutorial on implementing these methods in the R statistical environment, using an example meta-analysis on risk factors for adolescent delinquency. Results: The tutorial demonstrates how to estimate mean effect sizes and meta-regression models using the RVE method in R, with particular emphasis on exploring developmental variation in risk factors for crime and delinquency. The tutorial also illustrates hypothesis testing for meta-regression coefficients, including tests for overall model fit and incremental hypothesis tests. Conclusions: The paper concludes by summarizing the benefits of using the RVE method with complex meta-analytic data structures, highlighting how this method can advance research syntheses in the field of developmental criminology.