Using Bayesian Meta-Analysis to Explore the Components of Early Literacy Interventions

The What Works Clearinghouse (WWC) released a report that applies two methodological approaches new to the WWC that together aim to improve researchers' understanding of how early literacy interventions may work to improve outcomes for students in grades K-3. First, this report pilots a new taxonomy developed by early literacy experts and intervention developers as part of a larger effort to develop standard nomenclature for the components of literacy interventions. Then, the WWC uses Bayesian meta-analysis—a statistical method to systematically summarize evidence across multiple studies—to estimate the associations between intervention components and intervention impacts.

Twenty-nine studies of 25 early literacy interventions that were previously reviewed by the WWC and met the WWC's rigorous research standards were included in the analysis. This method found that the components examined in this synthesis appear to have a limited role in explaining variation in intervention impacts on alphabetics outcomes, including phonics, phonemic awareness, phonological awareness, and letter identification. This method also identified positive associations between intervention impacts on alphabetics outcomes and components related to using student assessment data to drive decisions, including about how to group students for instruction, and components related to non-academic student supports, including efforts to teach social-emotional learning strategies and outreach to parents and families. This report is exploratory because this synthesis cannot conclude that specific components caused improved alphabetics outcomes.