This course builds on the concepts that were covered in RSM3066H (Quantitative Methods in the Applied Behavioural Sciences) to expose students to more advanced statistical modeling techniques. The primary goals of the course are: 1) to provide students with an expanded toolkit of analytic approaches, 2) to facilitate statistical literacy and the ability to learn new techniques in the future, and 3) to provide a supportive group environment for discussing ongoing data issues. The statistical topics covered by this course will begin where the basic course left off, including working with Generalized Linear Models, Bayesian Statistics, Advanced Longitudinal Analyses, and Big Data. The course also provides a more detailed exploration of SEM and HLM, including more complex designs within these two approaches. The course also features "Data Workshop" days. These days allow discussion of real-world data challenges that students are facing in their ongoing research programs. Students will present their research questions, designs, and analytic strategies so that we can have group discussions about the underlying statistical issues. The goal here is to provide direct statistical support for student research projects.