HDFS617C

015517

This course provides an introduction to Multilevel Modeling (MLM) and its implementation using the statistical computing platform 'R'. MLM is used for analyzing nested data, such as longitudinal data (multiple observations nested within individuals) or data arising from couples, families, or groups (individuals nested within larger social units). We will cover both traditional Null Hypothesis Significance Testing (NHST) and the more contemporary approach of Bayesian inference. Topics to be covered include models for longitudinal data, models for dyads and groups, model building and comparison, and the interpretation and reporting of MLM results from both NHST and Bayesian perspectives. The course will combine recorded content, reading, group discussion, and applied data analytic assignments so that students will (a) gain an understanding of the conceptual basis for MLM and its appropriate uses, (b) acquire the ability to formulate and evaluate MLMs in a way that addresses specific research questions, and (c) become proficient in using R for the analysis of models.

GRD - Regular Grades A, B, C, D, E

Graduate

GIDP - STATD (Statistics and Data Science)

HDFS 537A, HDFS 537B.