October 14, 2026 04:00 PM to 05:30 PM
Interested in learning how to examine the relationship between multiple variables and an outcome of interest? Multivariable regression analysis is a foundational statistical technique that allows researchers to account for potential confounding factors, evaluate associations, and generate more robust evidence from their data. Through live demonstrations and hands-on exercises in R, participants will gain practical experience developing, evaluating, and interpreting multivariable regression models.
This workshop introduces the complete workflow for conducting multivariable analyses in R, from data preparation and model building to diagnostic testing and research reporting. Whether you are conducting quantitative research, analyzing observational data, or looking to strengthen your statistical skills, this session will provide a practical foundation for applying multivariable regression methods in a research context.
This session is ideal for researchers, graduate students, and analysts seeking to strengthen their quantitative research skills. Basic familiarity with R and introductory statistical concepts is recommended.
By the end of this session, participants will be able to:
- Prepare and clean research datasets for multivariable analysis in R.
- Fit and interpret multivariable linear and logistic regression models.
- Assess model assumptions and diagnose common analytical issues.
- Select appropriate variables and develop multivariable models using practical modeling strategies.
Details: Any preparatory work for the session can be found on its information page. This virtual workshop will be recorded and shared on the same page, and discoverable via the Sherman Centre's Online Learning Catalogue.
Certificate Eligibility: This workshop is eligible for the Sherman Centre's certificate program. For more information, visit scds.ca/certificate-program. It is also eligible for the Canadian Certificate for Digital Humanities. To learn more, visit ccdhhn.ca or contact scds@mcmaster.ca.
