"Complementarities between Early Educational Intervention and Later Educational Quality? A Systematic Review of the Sustaining Environments Hypothesis"
Bailey's research foci include mathematical development, individual differences, and longitudinal methods. Watch Bailey discuss his research interests with Dean Richard Arum here (28:00).
Jenkins studies early childhood development, child and family policy, policy analysis and management, and program evaluation. Watch Jenkins talk about her focus on policy issues here (24:00).
Alvarez-Vargas, a second-year doctoral student, is pursuing a specialization in Human Development in Context (HDiC). Her research interests include fadeout of educational interventions, child development, mathematics learning, and increasing the representation of minority groups in STEM fields. She is advised by Bailey.
The sustaining environments hypothesis refers to the popular idea, stemming from theories in developmental, cognitive, and educational psychology, that the long-term success of early educational interventions is contingent on the quality of the subsequent learning environment. Several studies have investigated whether specific kindergarten classroom and other elementary school factors account for patterns of persistence and fadeout of early educational interventions. These analyses focus on the statistical interaction between an early educational intervention – usually whether the child attended preschool – and several measures of the quality of the subsequent educational environment. The key prediction of the sustaining environments hypothesis is a positive interaction between these two variables. To quantify the strength of the evidence for such effects, we meta-analyze existing studies that have attempted to estimate interactions between preschool and later educational quality in the United States. We then attempt to establish the consistency of the direction and a plausible range of estimates of the interaction between preschool attendance and subsequent educational quality by using a specification curve analysis in a large, nationally representative dataset that has been used in several recent studies of the sustaining environments hypothesis. The meta-analysis yields small positive interaction estimates ranging from approximately .00 to .04, depending on the specification. The specification curve analyses yield interaction estimates of approximately 0. Results suggest that the current mix of methods used to test the sustaining environments hypothesis cannot reliably detect realistically sized effects. Our recommendations are to combine large sample sizes with strong causal identification strategies, and to study combinations of interventions that have a strong probability of showing large main effects.
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