To address gaps in the literature, this project proposes to investigate HS teacher turnover using the Educare Learning Network (ELN) cross-site database that spans several years and includes rich data at the child, teacher, classroom, parent, and program-levels. In collaboration with researchers at the Frank Porter Graham Child Development Institute at the University of North Carolina at Chapel Hill and at the ELN, we plan innovative and advanced analytic methods including survival analysis and a combination of Bayesian Network learning algorithms with Structural Equation Modeling to answer several questions related to the complexity of HS teacher turnover and its correlates and outcomes. Research questions include the following:
- What patterns and variations exist in HS teacher turnover over time?
- What characteristics of children, teachers, classrooms, and programs are associated with
- HS teacher turnover?
- How are these factors associated with HS teacher turnover (i.e., moderation and e. mediation effects)
- Is HS teacher turnover associated with child outcomes?
- Do associations between HS teacher turnover and child outcomes vary by teacher and
- child characteristics?
The findings of this study will provide a clearer, more nuanced understanding of HS teacher turnover to inform policy and practice targeted at fostering and maintaining stability within the HS workforce. This stability is essential for HS to meet its goal of preparing America’s most vulnerable young children to succeed in school and in life beyond school. (author abstract)