Unscripted E2: Moving From Multiple Regression to the Multilevel Model

Dan and Patrick provide a brief review of the traditional multiple regression model and highlight the important assumption that the residuals are independent. This assumption implies that no two residuals are related to one another, a condition that is almost always violated when data are hierarchically nested (e.g., children are nested within schools) or consist of repeated measures (e.g., time is nested within individual). Ignoring residual dependence can bias test statistics and unnecessarily limit the types of research questions that can be empirically assessed. However, the multilevel (or mixed effects) model explicitly allows for nested data structures and the inclusion of predictors at each level of the data (e.g., child-level and school-level predictors). In this episode, Dan and Patrick discuss the basics of the multilevel model, the advantages this approach provides, and potential directions for learning more about this analytic framework.
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[@seungjukim8202]

Absolutely LOVE this video! Y’all are amazing instructors, truly a gift

[@centerstat]

thanks so much for your comment -- we're glad you enjoy our silliness -- Patrick

[@alejandroleguizamo7722]

I took your SEM course a couple of years ago. It was really life changing. Nobody can make stats exciting like you two guys can. Plus, nothing like laughing out loud often while watching a stats video/training.

[@centerstat]

Hello Alejandro -- thanks so much for your nice words. I now have a new inscription for my tombstone -- "Nobody can make stats exciting like you". Either that, or "He was just trying to be funny". I hope you stick with us for future episodes -- who knows where the heck we go from here -- Patrick