This workshop includes a panel of researchers with expertise in applied experimental research in child welfare, advanced statistical analyses, and child welfare policy. The panelists will discuss how the integration of Bayesian methods into phased-based approaches to evidence building holds great promise for policymaking by building on low-cost formative studies to generate replicable results in summative studies for what works, for whom, and under what conditions. Using examples from a low-cost, randomized control trial called Safe Families for Children (SFFC), this workshop provides insights into the Bayesian paradigm regarding its ability to build on or strengthen conclusions about the efficacy of a promising intervention for a vulnerable population: children at risk of entering foster care. The panel will demonstrate the advantages of the Bayesian paradigm and offer perspectives on future directions. After the workshop, participants will have an understanding of the core features, assumptions, methods, and challenges in using Bayesian modeling in intervention research.
Objectives: This workshop will: Provide an overview of evidence-building and information accumulation using a low-cost, randomized control trial Safe Families for Children (SFFC) in child welfare research; Introduce and illustrate Bayesian modeling, which incorporates prior knowledge in the data likelihood function; Compare the frequentist and Bayesian analytic approaches; and Demonstrate the use of Bayesian analytics in an application. By the end of the symposium, participants will be able to describe: The key scientific rationale for Bayesian models in intervention research; The advantages and limitations of Bayesian modeling; and Steps in applying Bayesian models with intervention data.
Methods and Approach: PowerPoint presentation with handouts, lecture interspersed with questions and answers, software demonstration, and discussion.