The goal of this course is to learn how to structure and analyze nested data (e.g., within-participant experiments, longitudinal data).
One core assumption of traditional OLS regression is that residuals are independently, and identically distributed (iid). This assumption is often violated in data where, for example, two data points were generated by the same person (e.g., within-participant experimental designs, longitudinal data collection). There are many cases where data is nested. As such, a class of mixed (sometimes called multilevel, hierarchical, or nested) models have been developed to analyze data when the iid assumption is violated.
The first pillar of this course is data wrangling. Nested data can be structured in a variety of ways. Understanding how the data in your dataframe is structured is important, both for the types of analyses you can conduct on the data, and its interpretation. Students will learn how to evaluate the structure of their data and how to change the data structure to test different hypotheses.
The second pillar of this course is data analysis. We will cover a variety of multilevel models. All of the models covered will involve frequentist estimation (i.e., non-Bayesian) and be linear; though the insights covered in this brief course will be applicable to Bayesian and non-linear models. Most analysis examples will involve within-participant experimental designs, but there will be a few longitudinal cases.
An intermediate proficiency in a programming language (preferably R) is strongly encouraged for this class.
Students should have proficiency in (at least) multiple regression models.
25% class participation, 75% assignments.
Further reading and resources will be posted on Canvas after each lecture.
The timetable for this course can be found here.
ERIM PhD candidates can register for this course via Osiris Student.
External (non-ERIM) participants are welcome to this course. To register, please fill in the registration form and e-mail it to the ERIM Doctoral Office by four weeks prior to the start of the course. For external part