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Modern Meta-Analytic Methods in Prevention Science: Introduction to the Special Issue
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Modern Meta-Analytic Methods in Prevention Science: Introduction to the Special Issue
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Modern Meta-Analytic Methods in Prevention Science: Introduction to the Special Issue
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Modern Meta-Analytic Methods in Prevention Science: Introduction to the Special Issue
Modern Meta-Analytic Methods in Prevention Science: Introduction to the Special Issue
Journal Article

Modern Meta-Analytic Methods in Prevention Science: Introduction to the Special Issue

2022
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Overview
Meta-analyses that statistically synthesize evidence from multiple research studies can play an important role in advancing evidence-informed prevention science. When done in the context of a well-conducted systematic review, meta-analysis is a powerful tool for synthesizing evidence and exploring research questions that are difficult to address in individual studies, such as the association of individual study limitations on intervention effect estimates, replicability of empirical findings, and variation of effect estimates across populations and settings. Alongside the rapid growth in the number of published reviews and meta-analyses, there has been a parallel growth in the development of meta-analytic techniques to handle the increasingly complex types of questions and types of evidence relevant to prevention science. Despite this rapid evolution of meta-analytic techniques and approaches, there is still a lag between the development of new techniques and their uptake by researchers in the field. This paper serves as a brief introduction to this special issue of Prevention Science, entitled “Modern Meta-Analytic Methods in Prevention Science,” which highlights recent developments in meta-analytic methods and demonstrates their application to prevention research. This special issue makes an important contribution to the field by ensuring these methodological advances are widely accessible to prevention science researchers, thereby improving their uptake and utilization, and ultimately improving the utility and rigor of research syntheses for informing evidence-based decision making in prevention.