After decades of descriptive research that demonstrates associations between contextual factors and child and youth development, developmental and educational sciences are increasingly focused on identifying malleable contextual features that are responsive to intervention efforts that improve learners' outcomes. Researchers will need to think carefully about the measurement of dependent and independent variables for generating research evidence demonstrating a longitudinal analysis of change. Understanding how to best derive causal inference from both experimental and non-experimental research is critical for translation of longitudinal evidence to policy or practice. The primary aim of the course is to consider how collection of longitudinal data influences how researchers engage in every facet of research design from sampling to analyses. There are three major objectives that guide the development of course materials and assignments: Students will gain experience designing correlational, experimental and longitudinal studies that provide strong causal inference; this design experience will ideally be applied to students' predissertation/thesis or dissertation work. Students will also learn critical evaluation and application skills, including how to recognize and critique research designs in multiple disciplines - i.e., learn to critique the quality of both studies of change and studies of contexts that support change. Students will gain experience with scientific writing through scaffolded assignments.
Credits: 3.0