"It is very rare that education studies have constant intervention effects through simple mechanisms to independent individuals. It is well-documented that schooling is a complex process because teachers, students, and administrators interact with each other in a diverse set of social contexts (e.g., An, 2018; Frank, 1998; Hong, 2015; Kim, Frank, & Spillane, 2018; Maroulis et al., 2010). As such, considering potential bias due to unobserved or uncontrolled spillover, heterogeneity and alternative mediators is important to making an inference for policy implications. Additionally, since the ultimate goal of education research is to inform decision-makings in the allocation of educational resources regarding curricula, pedagogy, practices or school organizations (e.g., Bulterman-Bos, 2008; Cook, 2002), education research must be accessible to practitioners. Consequently, a sensitivity framework that can account for all potential sources of bias, including spillover, heterogeneity and alternative mediators, is required to allow all stakeholders to conceptualize the quality of evidence independently so that the debate for future policy manipulations can take place in a more transparent, effective and equitable way.Drawn on the work by Frank, Maroulis, Duong, and Kelcey (2013), Chapters 1 and 2 in this dissertation propose a non-parametric case replacement approach to quantify the robustness of inference in multisite randomized control trials and value-added measures for teacher effectiveness, accounting for spillover and heterogeneity. Throughout, the Tennessee class size experiment (Project STAR) is applied to demonstrate the case replacement approach. Chapters 3 and 4 focus on unobserved mediators in a single-mediator model. Specifically, Chapter 3 examines whether and how omitting an alternative mediator can bias causal mediation effect estimates in a crosssectional single-mediator model. Further, a sensitivity analysis approach is proposed to evaluate the robustness of causal mediation inference to missing a potential confounding mediator. Chapter 4 continues the discussion in Chapter 3 and a parameter framework is developed to characterize inconsistency in mediation models. This parameter framework is also applied to a longitudinaldesign for a post-treatment confounder."--Pages ii-iii.
Read
