Data Management in R
Instructor: Arjun Singh, MSc (arjun10@yorku.ca)
Course Description: This short course is designed to cover various aspects of data management. This course will cover the basics needed for importing, cleaning, and sharing data.
NOTE: This course is not intended for those with no prior exposure to programming.
What will you learn:
- Importing data in R.
- Understanding data formats (scalar, vectors, data frames and lists).
- Cleaning the imported data using tidyverse.
- Brief introduction to string manipulation using Regex and string R.
- Use of iterative functioning to reduce duplication.
Finally, this course will be followed by another course in the Winter term titled "Communicating Data using R Markdown with an introduction to HTML and CSS".
Dates/Times: Oct 7, Oct 14, and Oct 21 (Friday's, 1:30 pm - 3:30 pm)
Format: Online
Cost:$30
Eligible for a Digital Credential/Badge: Yes
Registration: Click Here to Register
Introduction to R
Instructor: Eric Tu, MA (erictu@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: Oct 18, Oct 25, Nov 1, and Nov 8 (Tuesday's, 1:00 pm - 4:00 pm)
Format: In Person (FC 105)
Cost: $60
Eligible for a Digital Credential/Badge: Yes
Registration: Click Here to Register
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: Nov 16 and Nov 23 (Wednesday's, 3:00 pm - 5:00 pm)
Format: Online
Cost: $20
Eligible for a Digital Credential/Badge: Yes
Registration: Click Here to Register
Introduction to SAS [POSTPONED]
Instructor: Octavia Wong, MSc (owong3@yorku.ca)
Course Description: Introduction to programming in SAS
Course description: This short course is an introduction to the Statistical Analysis System (SAS) syntax commands and procedures. We will be covering:
- Reading, transforming, merging, and saving data files in some common formats
- Selecting cases, and modifying and computing variables
- Performing some basic statistical procedures and tests, including descriptive statistics, correlations, contingency tables, Chi-square tests, t-tests, ANOVA, regression, general linear models, and multilevel models
- Creating various figures, including bar charts, box plots, histograms, normal QQ plots, scatterplots, density curves, and line graphs
- Saving output results and work in some common formats
- Creating simple macros (if time allows)
Note: This course is not intended as an introduction or review of basic 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 but no previous experience in using SAS.
Dates/Times: Oct 4, Oct 11, Oct 18, and Oct 25 (Tuesday's, 1:00 pm - 3:00 pm)
Format: Online
Cost: $40
Eligible for a Digital Credential/Badge: No
Registration: Click Here to Register
Visualizing Linear Models: An R Bag of Tricks [POSTPONED]
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: Oct 26, Nov 2, Nov 9 (Wednesday's, 1:00 pm - 4:00 pm)
Format: In Person (Hebb Lab BSB 159)
Cost: $45
Eligible for a Digital Credential/Badge: Yes
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.
