Project SCORE! Coaches’ Perceptions of an Online Tool to Promote Positive Youth Development in Sport

Research points to the potential of youth sport as an avenue to support the growth of particular assets and outcomes. A recurring theme in this line of research is the need to train coaches to deliberately deliver themes relating to positive youth development (PYD) consistently in youth sport programs. The purpose of the study was to design and deliver a technology-based PYD program. Project SCORE! (www.projectscore.ca) is a series of 10 lessons to help coaches integrate PYD into sport. Four youth sport coaches completed the program in this first phase of this research and were interviewed. The goal of this study was to gain some insights from coaches as they completed the program. Positive comments about the program (i.e. ease of use, success of particular lessons, coach’s personal growth) and challenges regarding teaching positive skills to youth are discussed. These results helped to shape the program and make necessary changes so that it may be used for a larger research study. Other implications and future research directions are discussed.

Estimating Dynamic Treatment Effects from Project STAR (Working Paper 36)

This paper considers the analysis of data from randomized trials which offer a sequence of interventions and suffer from a variety of problems in implementation. In experiments that provide treatment in multiple periods (T>1), subjects have up to 2^{T}-1 counterfactual outcomes to be estimated to determine the full sequence of causal effects from the study. Traditional program evaluation and non-experimental estimators are unable to recover parameters of interest to policy makers in this setting, particularly if there is non-ignorable attrition. We examine these issues in the context of Tennessee's highly influential randomized class size study, Project STAR. We demonstrate how a researcher can estimate the full sequence of dynamic treatment effects using a sequential difference in difference strategy that accounts for attrition due to observables using inverse probability weighting M-estimators. These estimates allow us to recover the structural parameters of the small class effects in the underlying education production function and construct dynamic average treatment effects. We present a complete and different picture of the effectiveness of reduced class size and find that accounting for both attrition due to observables and selection due to unobservable is crucial and necessary with data from Project STAR