The purpose of this course is to examine current issues in learning sciences and education and introduce students to the analysis of nested data structures (e.g., students within classrooms). Longitudinal or repeated measures data can also be thought of as clustered data with measurement occasions nested within subjects. This course will focus on understanding the hierarchical (generalized) linear models and their assumptions, as well as practical aspects of developing models to address research questions and interpreting the findings. This course emphasizes practical, hands-on development, analysis and interpretation of hierarchical linear models. Readings will be drawn from book chapters on multilevel modeling and journal articles that utilize national longitudinal data sets to answer questions about student learning. The lab portion of this course will provide students with opportunities to learn and apply hierarchical linear modeling, mediation, and moderation to longitudinal data using two computer programs (HLM and SPSS). Students who received credit for SS 590: Applied Multi-Level Modeling in 2018 or 2015 cannot also take PSY 507 for credit.
PSY 507: Applied Multi-Level Modeling
Department