Quantitative Methods Workshop Series (QMWS)

Spring 2022 Courses


Introduction to Bayesian Analysis

Instructors: Ji Yeh Choi, PhD (jychoi@yorku.ca); Robert Cribbie, PhD (cribbie@yorku.ca); Xijuan (Cathy) Zhang, PhD (xijuan@yorku.ca)
Course Description: Bayesian analysis has steadily been increasing in popularity among social/behavioral science researchers due to its many theoretical and practical advantages over traditional (frequentist) analyses.

This workshop will provide a gentle introduction to the elements of Bayesian analysis, including an introduction to probability from a Bayesian perspective (e.g., Bayes theorem), priors/posteriors, model fitting and diagnostics, Bayes factors, etc. We will apply Bayesian analysis to two independent groups designs, paired groups designs, correlation, simple/multiple regression, structural equation modeling, etc.

The workshop will be hands on and participants will learn to conduct (and interpret) analyses within R.

Dates/TimesMay 31, June 1, June 2 (9:30 am - 12:00 pm)
Format: Online
Cost: $35
Eligible for a Digital Credential/Badge: Yes
Registration: Click Here to Register



Introduction to R

Instructor: Udi Alter, MA (udialter@yorku.ca)
Course DescriptionR is one of the most popular programming languages for data science and statistical operations. R is a free, open-source software that is widely used in psychological research for statistical analysis and visualizing data (e.g., graphs, plots, charts, etc.).

What will I learn in this short course? This short course will introduce you to R and its integrated development environment (IDE), RStudio. This is a hands-on, interactive, and inclusive course. By the end of the final session, you will be comfortable performing basic data science and statistical operations in R. Specifically, (a) entering, importing, and manipulating data, (b) using add-on packages, (c) running core mathematical and statistical analyses (e.g., t-test, ANOVA, correlation, regression), (d) graphing/plotting data, (e) generating shareable, reproducible code in accordance with open science practices, and (f) be familiar with additional resources for maintaining and/or advancing your R skills.

Who is this short course meant for? This course is intended for anybody who has no, or little prior experience with R/RStudio, or users who wish to improve their grounding in the basics. We welcome attendees of any career/education stage (e.g., undergraduate/graduate students, faculty, researchers/assistants, alumni, etc.) and of any discipline (e.g., cognitive science, psychology, kinesiology, etc.).

Are there any prerequisites? None. No prior programming experience is necessary. Although statistical expertise is not required, basic knowledge will be assumed (e.g., mean, standard deviation, t-tests, correlation, regression, simple plots, etc.).

Dates/Times: Wed, May 18, 25, June 1, 8,  13:00 – 16:00 (1:00 pm - 4:00 pm).
Format: Online
Eligible for a Digital Credential/Badge
: Yes
Registration: Click Here to Register



Intermediate Applications with SAS

Instructor: Octavia Wong, MSc (owong3@yorku.ca)
Course Description: This short course will focus on intermediate Statistical Analysis System (SAS) application. We will be covering:

  • Longitudinal data analysis
  • Multi-level modeling
  • Automating repetitive tasks through PROC SQL and macros, including importing data, printing and saving results, performing data manipulations, creating figures, and performing data analyses

Note: This course is not intended as an introduction or review of statistics. Rather, it focuses on the implementation of these statistics in SAS. As such, this course is designed for participants with some introductory level statistical knowledge and some basic previous experience in using SAS.

Dates/Times: June 1, 8, 15, and 22 (Wednesdays), 10:30 am - 12:30 pm
Cost: $40
Eligible for a Digital Credential/Badge: No
Registration:Click Here to Register



A Hands-On Introduction to Data Visualization in R

Instructor: Arjunvir Ghumman, MA (arjun10@yorku.ca)
Course Description: Data visualization allows us to tell stories by curating data into a form that highlights the trends and outliers. A good visualization tells us a story, removing the noise from data and highlighting useful information. This course provides a hands-on introduction to visualizing data using R. From visualizing a single variable to visualizing multiple variables or even creating novel visualizations, the goal of this course is to introduce users to the grammar of graphics in GGplot which will provide them with the necessary skills to visualize the data in a way that tells their story.

Why R? R is a free software environment for statistical computing and graphics. It is the most widely used language for data science, together with python. Additionally, R is also a great tool for data visualization. The native tools available in R are enhanced courtesy of external packages such as ggplot2.

Dates/Times: May 16, 18, 20, 1:00 pm - 3:00 pm
Cost: $30
Eligible for a Digital Credential/Badge: Yes
Registration: Click Here to Register



Introduction to Monte Carlo Simulation Experiments

Instructor: R. Philip Chalmers, PhD (chalmrp@yorku.ca)
Course Description: Monte Carlo Simulation Experiments  offer a powerful and flexible way to investigate the quality of statistical models, the efficiency/consistency of estimation criteria (e.g., maximum-likelihood), the power of statistical tests, the robustness of inference to model violations, and more. This workshop will provide an introduction to the theory and software elements required to design Monte Carlo simulation experiments.

The workshop will be hands-on, where participants will learn how to design and execute Monte Carlo simulation experiments using R.

Dates/Times: June 7, 14 (1:00 pm - 4:00 pm)
Cost: $30
Eligible for a Digital Credential/Badge:
Registration: Click Here to Register


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.

Archive of Past Quantitative Methods Workshop Series