Fall 2025
Full details/registration for Fall 2025 QMWS courses will be available soon!
Introduction to Bayesian Analysis in JASP
Instructor: Katherine Newman, PhD candidate (kmnewman@yorku.ca)
Description: Master the free, point-and-click statistical software that has become a standard in psychological research. This workshop provides a practical introduction to JASP, an intuitive platform designed to run advanced analyses without programming. We will use JASP to demystify and apply Bayesian statistics, a powerful framework that moves beyond simple p-values to quantify evidence for your hypotheses directly. Through hands-on exercises with a provided dataset, you will become proficient using JASP’s environment while learning to execute its Bayesian modules for correlations, t-tests, and ANOVA. The session will focus on interpreting JASP’s unique outputs (e.g., Bayes Factor and robustness plots) and understanding how they better inform your findings by quantifying evidence, rather than relying on significance thresholds. To illustrate the real-world impact of this framework, the workshop will conclude with a research spotlight on a cutting-edge application: how Bayesian modeling is used in neuroscience to create personalized maps of brain function. This approach, which would not be possible with traditional statistics, is a critical step towards the future of personalized medicine, where interventions can be tailored to a person’s unique neurological function. This workshop is essential for students at all levels who seek to strengthen their analytical skill set, enhance their research for theses and dissertations, and align with contemporary methodological standards. Leave with the practical skills to implement these sophisticated analyses in JASP right away. After this workshop, you will be able to: 1) Confidently navigate and use JASP for statistical analysis; 2) Perform and interpret Bayesian correlations, t-tests, and ANOVA in JASP; 3) Understand and report Bayes Factors for your hypotheses; 4) Compare Bayesian and frequentist results side-by-side within a single software platform; 5) Articulate the value of Bayesian methods for complex, hierarchical problems in research. Familiarity with basic statistical procedures (e.g., t-tests, p-values) is required. No coding or prior Bayesian experience is needed. Please install the free JASP software (jasp-stats.org) for your operating system before attending.
Dates/Times: TBD
Format: Online (Zoom)
Cost: $TBD
Eligible for a Digital Credential: No
Registration: Coming soon!
Longitudinal Mixed/Hierarchical Models
Instructor: Georges Monette
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Introduction to Python
Instructor: Amir Zarie
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Introduction to R
Instructor: Hannah Tran
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Introduction to jamovi
Instructor: Maria Orlando, MA (morlando@yorku.ca)
Description: This workshop is an introduction to jamovi (https://www.jamovi.org/), a free and open-sourced software for statistical analysis. Jamovi is user-friendly and features a point-and-click interface, making it ideal for those who are new to statistical software. In this two-session workshop, you will learn how to use jamovi to describe, analyze, and interpret data. Session 1 will cover using jamovi for descriptive statistics to summarize data. Session 2 will explore using Jamovi for inferential statistics to interpret data. Each session includes hands-on activities using real datasets, giving you the opportunity to practice your skills and apply them to your own data beyond the workshop. The focus of this workshop is using Jamovi, so a general understanding of statistical concepts is recommended but not required.
Dates/Times: TBA
Format: Online (Zoom)
Cost: $30
Eligible for a Digital Credential: Yes
Registration: Coming Soon ...
Intermediate R
Instructor: Gabriel Crone
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Some courses offered through the QMWS permit students to receive a Digital Credential/Badge for completing the course. There is no cost to you in order to receive the Digital Credential/Badge. For more details please see Digital Credentials.
To see a list of past QMWS workshops, visit our Archives Page
