Winter 2023 Courses
* Sign up using the code EARLYBIRD to get 50% off! Code is valid up until a week before the workshop begins. *
Data Scraping and Text Analysis
Instructor: Arjun Singh, MSc (arjun10@yorku.ca) and Eric Tu, MA (erictu@yorku.ca)
Course Description: Are you interested in what people are talking about online? What about sports game/player statistics or book movie/TV show/game reviews? The internet has a wide array of data that can be scraped and processed to show different trends. How often is a certain word used on specific subreddits? What is the average points per game for all 6-foot players? This workshop will provide you with the tools to scrape the data and analyze it to answer questions like these.
In this course, we will use R and begin by going over different data formats, strings, and factors. We will then discuss how to scrape data from any website (e.g., IMDB, NBA), as well as looping over functions to scrape for keywords on certain websites (i.e., Reddit, Twitter).
We will discuss cleaning the data using regular expressions (Regex), and show you how to look at word counts, n-grams, use dictionaries to look at the valence of words, and how to use natural language processing (i.e., Latent Dirichlet Allocation) to model words into different topics. We will also demonstrate how to make different visualizations of the data including bar graphs, correlations, and word clouds.
Dates/Times: Jan 25, Feb 1, and Feb 8 (Wednesday's, 12:00 pm - 2:00 pm)
Format: Hybrid (Room TBA)
Cost: $60
Eligible for a Digital Credential/Badge: Yes
Registration: Click here!
Introduction to R
Instructor: Miranda Too, MA (mtoo@yorku.ca)
Course Description: R is a free statistical software for conducting statistical analyses and visualizations and is widely used in the field of Psychology. This workshop will introduce the basics of R to get users familiar with:
- Install and navigate R (e.g., understanding the panes, using packages)
- Enter, import, and manage data (e.g., recoding variables, computing composites)
- Conduct basic analyses (e.g., t-test, ANOVA, correlation, regression)
- Create visualizations of their data (e.g., graphs, plots, charts)
- Generate human readable, shareable, and reproducible code
This course is intended for anybody who has no or little prior experience with R and wishes to develop a basic understanding of the programming language. No prior programming experience is necessary.
Note: The use of your own laptop is necessary for this course.
Dates/Times: Jan 27, Feb 3, Feb 10, and Feb 17 (Friday's, 2:00 pm - 5:00 pm)
Format: In Person (Room TBA)
Cost: $120
Eligible for a Digital Credential/Badge: Yes
Registration: Click here!
Data Analytics and Machine Learning Using R
Instructor: Xin Gao, PhD (xingao@yorku.ca)
Course Description: The aim of this workshop is to provide an application-oriented training on data analytics in an industrial or business setting. The course will cover a wide selection of data analytic techniques to equip students with appropriate computing skills and required statistical methodologies to conduct machine learning and data mining. The lectures will cover various methodologies and algorithms; as well as teach students to use data analytics related software (R or others) to solve real-life problems.
Dates/Times: Feb 10, Feb 17, and Feb 24 (Friday's, 10:00 am - 1:00 pm)
Format: Online
Cost: $90
Eligible for a Digital Credential/Badge: No
Registration: Click here!
Introduction to SPSS for Descriptive and Inferential Statistics
Instructor: Natalie Sisson, MA (n.sisson@mail.utoronto.ca)
Course Description: In this workshop, you will learn how to clean and prepare data (e.g., recode variables, create composites), and conduct descriptive and inferential analyses using SPSS, including: means and sums, t-tests, one-way ANOVAs, correlations, and multiple regressions.
This course is intended for anybody who has no or little prior experience with SPSS and wishes to develop a basic understanding of the program and syntax. No prior experience with SPSS is necessary. We will cover some review of the conceptual knowledge behind conducting each analysis, but the focus will be on how to conduct and interpret analyses in SPSS.
Note: The use of your own laptop is necessary for this course. It is also necessary to have an SPSS subscription for this course. YorkU students may access SPSS here: https://www.yorku.ca/uit/faculty-staff-services/myapps/
Dates/Times: Mar 1 and Mar 8 (Wednesday's, 3:00 pm - 5:00 pm)
Format: Online
Cost: $40
Eligible for a Digital Credential/Badge: Yes
Registration: Click here!
Understanding the Linear Regression: Performing, Interpreting and Writing Up Basic, Moderated, and Mediated Effects
Instructor: Kristina Schrage, PhD (schragekm@gmail.com)
Course Description: This workshop focuses on performing and understanding linear regressions for the purpose of writing up results in an academic paper. In this workshop, you will learn when to use, how to perform, interpret, visualize (graph), and write-up the results of:
1) A basic linear regression
2) Moderation analyses (2-way interactions)
3) Mediation analyses
This course is intended for people who have little to no experience conducting and presenting statistical results, or a basic understanding of linear regression but wish to build on these concepts by performing and interpreting interactions and mediation analyses. This course is taught in SPSS, so participants should have basic introductory experience with the program. Syntax for performing these analyses in R are available upon request.
Note: The use of your own laptop is necessary for this course. It is also necessary to have an SPSS subscription for this course. YorkU students may access SPSS here: https://www.yorku.ca/uit/faculty-staff-services/myapps/
Dates/Times: Mar 9, Mar 16, Mar 23 (Thursday's, 1:00 pm - 3:00 pm)
Format: Online
Cost: $60
Eligible for a Digital Credential/Badge: Yes
Registration: Click here!
Conducting Power Analysis in Structural Equation Modeling
Instructor: Y. Andre Wang, PhD (yilinandre.wang@utoronto.ca)
Course Description: Structural equation modeling (SEM) is a popular analytic technique in psychology, but planning for studies that rely on SEM for data analysis (“SEM studies”) can be challenging. This workshop will introduce attendees to power analysis for SEM studies. In the first hour of the workshop, attendees will learn the distinction between power to detect model misspecification (“Can I tell a good model from a bad one?”) and power to detect a target effect (“Can I find a true effect?”), and they will connect these power considerations to their research goals. In the second hour of the workshop, attendees will walk through a hands-on tutorial on using pwrSEM (Wang & Rhemtulla, 2021), a free, open-source web application to conduct both kinds of power analysis. The workshop will also offer practical guidance on what population values to use in power analysis for SEM studies and dealing with the uncertainty that is inherent in power analysis. Familiarity with SEM is recommended but not required. No statistical software or programming background is required. Recommended for anyone who works with variations of SEM (e.g., confirmatory factor analysis, path analysis, mediation, latent growth curve modeling).
Dates/Times: Mar 20 (Monday, 2:30 pm - 5:30 pm)
Format: Online
Cost: $30
Eligible for a Digital Credential/Badge: Yes
Registration: Click here!
* Sign up using the code EARLYBIRD to get 50% off! Code is valid up until a week before the workshop begins. *
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.
