Bayesian Dynamic Borrowing: A Method for Utilizing Historical Data in Education Research

In education research, long-standing, large-scale educational assessments, such as the National Assessment of Educational Progress (NAEP ), the Programme for International Student Assessment (PISA), and the Early Childhood Longitudinal Program (ECLS), provide historical data that can be used to inform policy-relevant research. However, statistical analyses of current large-scale assessment data may not make use of this historical information, and it may be difficult to do so when applying conventional statistical methods. This project developed and adapted the method of Bayesian dynamic borrowing (Viele et al., 2014) as a means of systematically incorporating prior historical data arising from large-scale education assessments into current analyses. Bayesian dynamic borrowing allows a researcher to account for the fact that not all historical data, even from the same survey program, are of equal quality. As such, prior information can be systematically adjusted to reflect the analyst’s degree-of-confidence in the importance and/or quality of sources of prior data.