Spring 2023 Courses
Introduction to R
Instructors: Mark Adkins, MA (madkins@yorku.ca), Sophie Coelho (scoelho@yorku.ca), Arjunvir Ghumman, MSc (arjun10@yorku.ca)
Course Description: R is an independent open source statistical software package that is of value for its wide-ranging pre-programmed statistical procedures and capacity for programming tailored statistical analyses. Also, R is invaluable for generating informative high-quality graphics.
This course is a step-by-step hands-on introduction to R. No familiarity with R is assumed. Participants will learn how to: 1) install R on their computers; 2) enter, import, and manipulate data; and 3) carry out basic programming, graphical operations, and procedures in R. Much of this course will be structured around a collection of packages called the tidyverse (www.tidyverse.org), which contains all of the tools necessary to learn R quickly and easily. Upon completion of this course, participants will be comfortable with, and able to do, basic data manipulation and cleaning work in R. Additionally, they will be familiar with resources for follow-up help and learning about R.
Dates/Times: May 5, 12, 19, & 26 (Friday's, 10:30 am - 1:30 pm)
Format: Hybrid (In-person and online; HNE 230, Health, Nursing and Environmental Studies Building)
Cost: $120
Eligible for a Digital Credential/Badge: Yes
Registration: Click here!
A Hands-On Introduction to Data Visualization in R
Instructors: Arjunvir Ghumman, MA (arjun10@yorku.ca)
Course Description: Data visualization allows us to tell stories by curating data into a form that highlights useful trends and outliers. In today's data-driven world, the ability to effectively communicate insights and trends through visual representations has become a highly sought-after skill. Whether you are a data analyst, researcher, or simply someone who wants to improve your data visualization skills, this course will provide you with the knowledge and tools necessary to create stunning, informative visuals by introducing you to the grammar of graphics. 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 GGplot2 which will provide them with the necessary skills to visualize the data in a way that tells their story.
Dates/Times: May 8, 15, & 22 (Monday's, 1:00 pm - 3:00 pm)
Format: Hybrid (In-person and online; HNE 230, Health, Nursing and Environmental Studies Building)
Cost: $60
Eligible for a Digital Credential/Badge: Yes
Registration: Click here!
Latent Profile Analysis
Instructor: Matthew McLarnon, PhD (mmclarnon@mtroyal.ca)
Course Description: Latent profile analysis (LPA) reflects a series of statistical models that can be used to investigate the presence and nature of unobserved heterogeneous, qualitatively distinct subgroups. LPA is nested within the broader family of structural equation models (referred to as mixture models) and is increasingly being used in numerous areas of research. This course will introduce LPA, focusing on applications in the social, educational, health, and management sciences. This course will provide participants with the theoretical and conceptual background and applied analytical skills needed to specify an appropriate analytical model, interpret the results, and thoroughly address research questions using LPA that involve predictors and outcomes of membership in the estimated subgroups. This course offers lectures that feature worked examples, numerous hands-on activities and practice sessions, as well as ample opportunities to discuss participants’ own LPA-based research ideas.
This course will use the R and Mplus software packages and will supply participants with the syntax and knowledge to thoroughly conduct advanced LPA-based research into a wide range of empirical domains. (for those individuals who do not have an Mplus license, the free demo version is available at http://statmodel.com/demo.shtml and will be sufficient for any Mplus-based activities completed.)
Dates/Times: May 15 & 16 (Monday & Tuesday, 10:00 am - 4:30 pm)
Format: Online
Cost: $130
Eligible for a Digital Credential/Badge: Yes
Registration: Click here!
Introduction to Shiny
Instructors: Arjunvir Ghumman, MA (arjun10@yorku.ca), Naomi Martinez Gutierrez, MA (naomimg@yorku.ca), Eric Tu, MA (erictu@yorku.ca)
Course Description: Shiny is a free and open source R package for developing web applications. Shiny can turn your analyses into interactive web applications without requiring HTML, CSS, or JavaScript knowledge by building dashboards and hosting apps to display your results in interactive ways. Shiny is a flexible package that can also allow you to develop apps for conducting different analyses, create dashboards for teaching/courses, or create apps related to finances or your interests (i.e., sports, technology, movies)-- the possibilities are endless! For an example of Shiny apps, see this page: https://shiny.rstudio.com/gallery/
In this course, we will use R to show you how to create your own Shiny app. This will include (a) an introduction to the basic components of a Shiny app, (b) how to use Shiny base functions, and (c) how to launch a Shiny application onto an online server for public sharing
Dates/Times: May 30, June 6, & 13 (Tuesday's, 1:00 pm - 4:00 pm)
Format: Hybrid (In-person and online; HNE 230, Health, Nursing and Environmental Studies Building)
Cost: $90
Eligible for a Digital Credential/Badge: Yes
Registration: Click here!
Intro to LaTeX and R Markdown
Instructor: Xijuan (Cathy) Zhang, PhD (xijuan@yorku.ca)
Course Description: LaTeX and RMarkdown are two powerful softwares that produce 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 and RMarkdown. In this tutorial, I will teach you how to write assignments, APA style papers, and presentation slides using LaTeX and RMarkdown software systems with the most popular editors (i.e., Overleaf editor for LaTeX and RStudio editor for RMarkdown) . Because LaTeX and RMarkdown share many similarities, it is easier to learn both of them at the same time. I will also talk about the similarities and differences between these two software systems, and when it is the best to use one versus the other.
Dates/Times: June 7 & 9 (Wednesday & Friday, 2:00 pm - 4:00 pm)
Format: Online
Cost: $40
Eligible for a Digital Credential/Badge: Yes
Registration: Click here!
Instructor Bios:
Mark Adkins
Mark is a doctoral candidate in the Quantitative Methods program within the Psychology Department at York University. He has years of experience as a statistical/programming tutor and has the ability to help students master complicated material regardless of their stage in the learning process. He has taught the introduction to R course many times, as well as courses on data cleaning with R, and a course on preregistration. You can find out more about his work on his website at https://standard-deviator.com/.
Sophie Coelho
Sophie is a graduate student in the Clinical Psychology program at York University. Her research uses quantitative methods and intensive longitudinal data (e.g., ecological momentary assessment) to understand patterns of substance use among young adults and people living with HIV. She regularly works with data in R and is looking forward to teaching Introduction to R for the first time during the Spring 2023 Quantitative Methods Workshop Series.
Arjunvir Ghumman
Arjun is a PhD student in the Quantitative Methods department at York University. As a passionate coder, I enjoy developing new and innovative ways to teach coding in a beginner-friendly way. I have a strong interest in data visualization, data science, and statistical modelling. One of my main focuses is helping researchers and organizations develop and implement effective data analytic strategies. I have previously taught data visualization, data management and string analysis and theory. I take great satisfaction in seeing others succeed and develop confidence in their abilities to work with data. In my free time, I like to stay active by working out and staying physically fit. Overall, I'm excited to continue sharing my knowledge and passion for data with others, and I'm dedicated to introducing this valuable skill to anyone who wants to learn in a fun and accessible way.
Naomi Martinez-Gutierrez
Naomi is a PhD candidate in the Quantitative Methods program within the Psychology Department at York University. Her research interests include statistical suppression, equivalence testing, teaching statistics and programming in R. She has taught introductory workshops to programming in R and has launched several online Shiny applications using the Shiny package in R.
Matthew McLarnon
Matthew McLarnon is an Associate Professor at the Bissett School of Business at Mount Royal University, in Calgary, Alberta. He is an active contributor to the quantitative methodology literature, with work that presents down-to-earth applications of advanced statistical methods. His research has been published in leading journals like the Journal of Applied Psychology, the Journal of Management, Academy of Management Learning and Education, and Organizational Research Methods. He has taught a semester-long graduate-level class on mixture models, and has delivered intensive two- and three-day workshops on mixture models several times previously.
Eric Tu
Eric is a PhD student in the Social/Personality Psychology program at York University. He has taken and has been a teaching assistant for many statistics courses and plans to complete the Quantitative Methods diploma. He has taught introduction to R and a course on data scraping and text analysis using R. You can find out more about his work at https://erictu.ca.
Xijuan (Cathy) Zhang
Cathy Zhang is an assistant professor in the quantitative methods area at York University. Her major research focus is in the area of structural equation modelling. She has taught statistics at both undergraduate and graduate levels. This spring, she is offering a workshop that teaches how to both LaTeX and RMarkdown software systems to produce professionally-looking academic documents such as APA-style papers, presentation slides, and dissertations. She hopes that by teaching LaTeX and RMarkdown together, students can see the similarities and differences between the two software systems, facilitating their learning in both software systems.
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