Analyzing Data with R: How Much do my Findings Depend on my Analytical Decisions?
Instructor: Michael Truong, MA (firstname.lastname@example.org)
Course Description: This course is an introduction to the use of the multiverse and targets R packages to address two major challenges faced by scientists. The first challenge is that after collecting the data, there are so many possibilities in how one should clean their data that one might reasonably be nervous about how dependent their eventual findings depend on earlier analytical decisions. For example, does keeping an extreme outlier affect my results? The second challenge relates to automatically cleaning and re-analyzing new data. With an on-going data collection process and changes in how the data are prepared for analysis, is there a way to automate the complete re-analysis of the data? For example, I've just collected 50 more participants, is there a way for R to automatically re-run all the data cleaning and analysis code? This workshop will illustrate each of these challenges and outline the latest R solutions for addressing them. Attendees are encouraged to bring their previous projects, so that they may be retrofitted with these solutions. Retrofitting either a previous project or adjusting a workshop-provided project with these solutions will count towards digital credentialing. Familiarity with R is assumed, but not necessary to appreciate the discussed challenges and current solutions.
Date: Wednesday, November 22, 2023 (11:30 am - 2:30 pm)
Eligible for a Digital Credential/Badge: Yes
Registration: Click Here to Register
Crash Course in Factor Analysis
Instructor: David Flora, PhD (email@example.com)
Course Description: The general purpose of exploratory factor analysis (EFA) is to develop a model which represents the pattern of correlations among a potentially large number of empirically observed variables in terms of a small number of unobserved, or latent, variables, which are referred to as factors. In modern research, EFA is most commonly used to investigate the underlying dimensional structure of a set of items in a test or questionnaire: What are the systematic factors that influence the item responses and how strongly are these factors related to the items? This course will provide a broad overview of the statistical foundation of EFA, including estimation methods, how to determine the optimal number of factors (i.e., model comparison), and factor rotation. The course will also address how these methods are adapted to ordinal, categorial variables such as those elicited from Likert-type questionnaire responses. Several example analyses will be presented using R software.
Dates: November 3 & 10 (Fridays) - 10 am - 1 pm
Format: In-person (159 Behavioural Science Building, York University)
Eligible for a Digital Credential/Badge: No
Registration: Coming Soon
Data Management in R
Instructors: Arjunvir Singh, MA (firstname.lastname@example.org); Gabriel Crone, BA (email@example.com)
Course Description: This course is designed to equip you with essential skills in handling data effectively, covering various critical stages of data management. Throughout this program, you will gain proficiency in the following key areas:
• Importing Data in R: We will start by teaching you how to import data into the R programming environment, setting the foundation for your data management journey.
• Understanding Data Formats: You will delve into the fundamental data formats, including scalars, vectors, data frames, and lists. This knowledge forms the bedrock of data manipulation.
• Data Cleaning with Tidyverse: Learn the art of data cleaning using the powerful tidyverse package, ensuring that your data for further exploration.
• String Manipulation with Regex: Explore the intricacies of string manipulation using Regular Expressions (Regex) in R, a crucial skill for data transformation.
In addition to these core topics, and if time permits, this course will also provide you with a brief introduction to data visualization, data sharing techniques, and the utilization of various data storage options, including SQL databases.
By the end of this course, you will have the knowledge and practical skills to confidently manage and manipulate data for your analytical needs.
Dates: October 18 & 25, November 1 (11:30 - 2:30)
Eligible for a Digital Credential/Badge: TBA
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.