Fitting Models to Data Not Data to Models Workshop Series: Interaction Terms and Hierarchical Linear Models/Linear Mixed Models
February 28 at 2:00 pm - 3:00 pmFree
This workshop will introduce interaction terms in linear models along with random and fixed effects, including random and fixed intercepts and slopes, in the context of Hierarchical Linear Models (also known as Linear Mixed Models).
By the end of this session, participants should be able to fit (Hierarchical) Linear Models (HLMs) with interaction terms and interpret the output of the
summary() function for Hierarchical Linear Models. Additionally, participants will be able to identify the limitations of (H)LMs.
This workshop qualifies for the Scholarly Research, Writing, and Publishing Credential offered through the College of Graduate Studies.
Fitting Models to Data Not Data to Models Workshop Series
You might have heard someone say that “all models are wrong, but some are useful”. The best way to ensure models are useful is to choose a model that is appropriate to your data and research questions, rather than forcing your data to fit your model’s assumptions (e.g., normality, independence, constant variance).
This series introduces early-career researchers to statistical models that extend beyond linear models (i.e., ANOVAs) so that they may learn how to fit models to their data rather than fitting their data to models. All workshops will use
R and RStudio, so some experience with
R or other programming languages is encouraged but not required. See the R Fundamentals for Data Analysis for an introduction to
R and RStudio. Attendees who do not have experience with
R are encouraged to review this content or take the introductory workshop concurrently if it’s being offered.
You can either register for all workshops, or register for individual workshops that are most applicable to you.