Meta-analysts often ask a yes-or-no question: Is there an intervention effect or not? This traditional, all-or-nothing thinking stands in contrast with current best practice in meta-analysis, which calls for a heterogeneity-attuned approach (i.e., focused on the extent to which effects vary across procedures, participant groups, or contexts). This heterogeneity-attuned approach allows researchers to understand where effects are weaker or stronger and reveals mechanisms. The current article builds on a rare opportunity to compare two recent meta-analyses that examined the same literature (growth mindset interventions) but used different methods and reached different conclusions. One meta-analysis used a traditional approach (Macnamara & Burgoyne, 2023), which aggregated effect sizes for each study before combining them and examined moderators one-by-one by splitting the data into small subgroups. The second meta-analysis (Burnette et al., 2023) modeled the variation of effects within studies-across subgroups and outcomes-and applied modern, multilevel metaregression methods. The former concluded that growth mindset effects are biased, but the latter yielded nuanced conclusions consistent with theoretical predictions. We explain why the practices followed by the latter meta-analysis were more in line with best practices for analyzing large and heterogeneous literatures. Further, an exploratory re-analysis of the data showed that applying the modern, heterogeneity-attuned methods from Burnette et al. (2023) to the data set employed by Macnamara and Burgoyne (2023) confirmed Burnette et al.'s conclusions; namely, that there was a meaningful, significant effect of growth mindset in focal (at-risk) groups. This article concludes that heterogeneity-attuned meta-analysis is important both for advancing theory and for avoiding the boom-or-bust cycle that plagues too much of psychological science.

In prevention science and related fields, large meta-analyses are common, and these analyses often involve dependent effect size estimates. Robust variance estimation (RVE) methods provide a way to include all dependent effect sizes in a single meta-regression model, even when the exact form of the dependence is unknown. RVE uses a working model of the dependence structure, but the two currently available working models are limited to each describing a single type of dependence. Drawing on flexible tools from multilevel and multivariate meta-analysis, this paper describes an expanded range of working models, along with accompanying estimation methods, which offer potential benefits in terms of better capturing the types of data structures that occur in practice and, under some circumstances, improving the efficiency of meta-regression estimates. We describe how the methods can be implemented using existing software (the metafor and clubSandwich packages for R), illustrate the proposed approach in a meta-analysis of randomized trials on the effects of brief alcohol interventions for adolescents and young adults, and report findings from a simulation study evaluating the performance of the new methods.

Systematic reviews and meta-analyses are important techniques because they synthesize results from multiple primary studies on a similar topic. To influence policy, practice, and research, however, synthesis researchers must translate the results for various audiences. Ideally, the translation drives future research agendas, informs policymaking, or assists in practical decision-making. An Evidence Gap Map (EGM), a graphical or tabular visualization of systematic review and meta-analysis results, is one ideal translation technique because it provides a structured framework to assess contexts for which primary evidence is available or to determine whether the effectiveness of an intervention or a program differs across populations, conditions, and settings. To bolster the field and promote the use of EGMs, we provide an overview of what constitutes an informative EGM, detail multiple examples of EGMs using extant meta-analytic results, and present a free R Shiny application we created to easily generate EGMs from typical meta-analytic datasets. We conclude by reviewing education-based systematic reviews that included an EGM to describe the current state of the field. [This paper will be published in "Journal of Research on Educational Effectiveness."]