Date(s) - 09/24/2020 - 09/26/2020
This seminar from September 24 to 26, 2020 provides an intensive introduction to multilevel models, a class of regression models for data that have a hierarchical (or nested) structure. Common examples of such data structures are students nested within schools or classrooms, patients nested within hospitals, or survey respondents nested within countries.
Using regression techniques that ignore this hierarchical structure (such as ordinary least squares) can lead to incorrect results because such methods assume that all observations are independent. Perhaps more important, using inappropriate techniques (like pooling or aggregating) prevents researchers from asking substantively interesting questions about how processes work at different levels.
Starting September 24, Statistical Horizons are offering this seminar as a 3-day synchronous*, remote workshop for the first time. Each day will consist of a 4-hour, live morning lecture held via the free video-conferencing software Zoom. Participants are encouraged to join the lecture live, but will have the opportunity to view the recorded session later in the day if they are unable to attend at the scheduled time.
Each lecture session will conclude with a hands-on exercise reviewing the content covered, to be completed on your own. An additional session will be held Thursday and Friday afternoons as an “office hour”, where participants can review the exercise results with the instructor and ask any questions.
*We understand that scheduling is difficult during this unpredictable time. If you prefer, you may take all or part of the course asynchronously. The video recordings will be made available within 24 hours of each session, meaning that you will get all of the class content and discussions even if you cannot participate synchronously.
The fee of $795 includes all course materials.
PayPal and all major credit cards are accepted.
For more information and to register, visit https://statisticalhorizons.com/seminars/public-seminars/multilevel-and-mixed-models.