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Principal Investigator: Greg Welch
Award Date: Sep 30, 2015
End Date: Feb 28, 2019
The overall goal of the project is to identify thresholds of early childhood education quality in predicting social-emotional, cognitive, and language outcomes in multiple secondary data sets that can inform national and state policies that promote optimal child development through early childhood education and child care settings.
The project addresses primary research topics of current relevance identified by the Administration for Children and Families (ACF): ongoing child care quality improvements; Temporary Assistance for Needy Families qualifiers and other special populations in child care; and subsidy policies, practices, and procedures.
The project’s three objectives are to:
- Compare different analytic strategies for identifying thresholds of quality.
- Replicate the analytic strategy with multiple national and state data sets to determine if thresholds are similar or different across data sets and to examine convergent findings across data sets.
- Examine subgroup differences to determine whether minimum levels of quality necessary to promote positive development differ based on family resources (e.g., family income, parent education); child characteristics (child sex, age); child minority status and cultural background (child language, race, ethnicity); or program context (geographic setting/program auspice).
The project will use the following data sets:
- Early Head Start Research and Evaluation Project (EHSREP)
- Quality Interventions in Early Care and Education (QUINCE)
- Early Head Start Family and Child Experiences Survey (Baby FACES)
- Early Childhood Longitudinal Study-Birth Cohort (ECLS-B)
- Study of Statewide Early Education Programs (SWEEP)
- Educare Learning Network (Educare)
- Nebraska Student and Staff Record System in preschool and infant toddler databases (NSSRS)
The research team’s previous ACF-funded research examined “thresholds” or active ranges of quality that influenced children’s development using Generalized Additive Modeling analysis paired with linear spline modeling. These data sets represent multiple age ranges, have common as well as unique developmental outcome variables, and have common and unique measures of quality. The combination of data sets will permit rigorous testing and comparison of thresholds across samples, measures, age ranges and demographic profiles.
Results from this investigation will provide a much-needed empirical basis for the high-stakes cut points now in use in Quality Rating and Improvement Systems and tiered reimbursement systems. Identifying converging results across multiple data sets will increase confidence in the validity of thresholds identified.
The evaluation research team includes, from left, Iheoma Iruka, Helen Raikes, Julia Torquati and Greg Welch.