3 credits
Fall 2025 Lecture
This course gives students a basic grounding in the class of statistical techniques known as multilevel modeling (MLM), also known as hierarchical linear modeling (HLM), mixed models, or random coefficient models. Primary discussions will be on applications of these models to the study of marriages, relationships, families, aging, and child and adult development, but also will touch on biomedical, educational, and economic examples. Nesting in both the contextual and longitudinal data situations are examined. Three-level MLMs are also covered. Data preparation, hypothesis testing and estimation approaches for MLMs are introduced throughout the course. While mathematical basics of statistical methods are covered, emphasis is placed on model development, the conceptual understanding of models, and interpretation of model results. Students are assumed to have taken at least two graduate statistics courses and have a solid understanding of regression analysis. Prerequisites: STAT 50100 and STAT 50200 or HDFS 61300 and HDFS 61700, or other similar sequence with instructor approval.
Learning Outcomes1Acquire a basic-to-intermediate understanding of the logic of multi-level modeling, grasp the underpinning statistics of MLM, and develop a sense as to when (and when not) to use the model.
2Apply MLM to relevant questions in their area of research interest.
3Critically read and evaluate empirical journal articles that use MLM.
4Acquire basic programming skills for conducting MLM.
5Select the appropriate methods of data analysis, given multivariate data and study objectives.
6Employ statistical software to carry out statistical analyses,
7Interpret and explain the results of statistical analyses, and
8Write concise and effective summaries of results using APA style.
Restrictions
made by Purdue students.