Quantitative Methods Workshop Series (QMWS)

Winter 2023 

 

Introduction to R
Instructors: Sarah Campbell, BSc (scampb@yorku.ca) and Kaiwen Zhou, MSc (kevnook@yorku.ca)
Course Description:  R is a free and open-source statistical programming language that is widely used in academia for data analysis and producing insightful graphics with ease. In this course, you will learn the following: 1) Install R and set up a working directory; 2) Enter, import, and manipulate simple data (e.g., Load packages, import different file types); 3) Create basic graphs (e.g., Bar graph, histogram, scatterplot); 4) Carry out basic analyses (e.g., t-test, correlation, regression); 5) Gain familiarity and experience using highly common R syntax, such as creating or formatting a data column based on a condition; 6) Integrate R knowledge and use R to perform data formatting and analysis through a walkthrough of an empirical research project example; 7) A brief intro to R Markdown - Incorporate your R code, results, interpretations, and notes in one document. The course is intended for anyone who has little to no experience with R. No prior programming experience is necessary.
Date/Time: Mar 8th, Mar 15th, Mar 22nd, Mar 29th (Fridays, 11 am – 2 pm)
Format: Online
Cost: $60
Eligible for a Digital Credential/Badge: Yes
Registration: Click Here to Register

 

Introduction to LaTeX and Overleaf
Instructor:
Cathy Zhang, PhD (xijuan@yorku.ca)
Course Description: 
LaTeX is a powerful software system that produces professional-looking documents. If you want to write neat mathematical equations, embed programming code, and/or easily format your paper in the APA style, you need to learn LaTeX. Overleaf is an online platform that allows you to run LaTeX and produce professional-looking documents in PDF format. In this tutorial, I will teach you how to write assignments, APA-style papers, and presentation slides using the LaTeX software system on Overleaf.
Dates/Times: Wednesday, February 21, 2024, 2:00 pm - 4:30 pm
Format: Online (Zoom)
Cost: $15
Eligible for a Digital Credential: Yes
Registration: Click Here to Register

 

Introduction to Path Analyses in R: Conducting, Visualizing, Interpreting, and Writing Up Simple and Multiple Linear Regression, Mediation, and Moderation Results
Instructor: Veerpal Bambrah, MA (bambrahv@yorku.ca)
Course Description: This workshop will focus on conducting simple linear regressions, multiple linear regressions, mediation analyses, and moderation analyses in R, with an additional focus on how to write up these results in a research paper. In this workshop, participants will learn when to conduct, as well as how to conduct, interpret, visualize, and write up the results of:

  1. Simple/multiple linear regression, including regression diagnostics
  2. Mediation analyses
  3. Moderation analyses

This course is intended for participants who possess little-to-no experience with conducting statistical analyses and presenting statistical results, or who possess an elementary understanding of linear regression but would like to build on this understanding by conducting and interpreting mediation and moderation analyses. This course will be taught in R, so participants should have introductory experience with this software.
Note: The use of your own laptop or computer will be necessary for this course. It is also necessary to have R and RStudio installed on your laptop or computer.
Dates/Times: Wednesday March 6th, Wednesday March 13th, Wednesday March 20th, 2024 (2:30pm to 5:30pm); 3 hours/session
Format: Online (via Zoom)
Cost: $45
Eligible for a Digital Credential/Badge: Yes
Register: Click Here to Register

 

Introduction to jamovi
Instructors:
Gabriel Crone (gc2001@my.yorku.ca), BSc; Victoria Celio (celio12@my.yorku.ca)
Course Description: jamovi is open-source and beginner-friendly statistical software. While other software can be expensive or difficult to use, jamovi is neither: It is both easy to learn and free to install (https://www.jamovi.org/download.html). In addition, it is capable of performing entire data analyses, generating elegant plots, and producing scripts and code that are ideal for collaboration. If you are new to data analysis, statistics, or statistical software, or are just curious to learn about jamovi, then this workshop is for you! In this two-day workshop, you will learn to use jamovi through live demos, interactive activities, and practice questions. The workshop uses real data, so is more applicable to data analysis in a substantive, real-world context. The course starts by introducing students to the basics of jamovi (e.g., familiarity with environment, modifying variables, creating filters), then proceeds to show students how to perform effective descriptive (Day 1) and inferential (Day 2) statistical analyses. By the end of the workshop, we hope that attendees leave with a set of transferable data analysis skills, a further appreciation for data analysis, and a curiosity to learn more!
Dates/Times: January 29, 2024; February 5, 2024. Each workshop will take place from 2:30 PM - 5:30 PM (3 hours/session)
Format: Online (via Zoom)
Cost: $30
Eligible for a Digital Credential/Badge: Yes
Registration: Click Here to Register

 

Visualizing Linear Models: An R Bag of Tricks
Instructor:
Michael Friendly, PhD (friendly@yorku.ca)
Course Description: 
OK, so you ran your ANOVA, multiple regression (MRA), or multivariate counterparts (MANOVA, MMRA), but now you need to visualize the results to both understand them and communicate.  Who you gonna run to? – R of course. This course covers data visualization methods designed to convert models and tables into insightful graphs.  It starts with a review of graphical methods for univariate linear models---data plots, model (effect) plots and diagnostic plots. The second session gives a brief introduction to multivariate linear models. I use data ellipses (or ellipsoids) as visual summaries of 2D (or 3+ D) of multivariate relations.  The Hypothesis-Error (HE) framework provides a set of tools for visualizing effects of predictors in multivariate linear models. The third session gives some examples of these methods for MANOVA and MMRA designs. Finally, some model diagnostic plots for detecting multivariate outliers and lack of homogeneity of (co)variances will be described.

Participants should have a background in statistics including a course in linear models (ANOVA, multiple regression). In addition, they should have some familiarity with using R and R Studio, such as the QMWS course, An Introduction to R or equivalent. A web page for the course gives access to lecture notes, exercises and resources: https://friendly.github.io/VisMLM-course/
Dates/Times: TBA
Format:
TBA
Cost: 
$TBA
Eligible for a Digital Credential/Badge:
TBA

 

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