The Institute’s Statistical Consulting Service (SCS) offers short courses on various aspects of statistics and statistical computing, including regular introductions to commonly used statistical packages, three times a year (Fall, Winter and Spring). Recent course offerings have addressed factor analysis, structural equation modeling, graphical methods for categorical data, linear regression and mixed models.

The Statistical Consulting Service maintains a regular schedule of office hours during the academic year. The Service primarily serves the York University community; for others, consultation is available on a fee-for-service basis. Please go to the Institute’s SCS website to make appointments online with SCS consultants.

Pre-registration and payment of fees is required for all courses.

SCS SHORT COURSES, January-March 2021

Introduction to Programming in R

This course is full, registration is for wait list only.

Instructor: Ronda Lo
Dates: Mondays, January 25, February 1, 8 and 22, 2021
Time: 1:30pm-4:30pm
Location: This course will be held remotely via Zoom; meeting access TBA
Enrolment Limit: 10
Enrolment Minimum: At least five (5) registrants are required in order to hold the course.

Course Description:  R is a free, open-source statistical software package that is valued for its wide-ranging pre-programmed statistical procedures and its transparent, reproducible workflow options. R is also known for easily generating high-quality graphics.

This course is a step-by-step hands-on introduction to R. No familiarity with R is assumed, but participants will need a basic working knowledge of statistics. Participants will learn how to: 1) install R on their computers; 2) import, clean and restructure data; 3) carry out basic descriptive and inferential statistics; and 4) visualize data in R. Instruction in this course will be hands-on, and materials will be given out for personal use. Upon completion of this course, participants will be comfortable with, and able to do, basic statistical work in R. Additionally, they will be familiar with resources for follow-up help and learning about R.

This course is highly recommended for those at all stages, including undergraduate students looking to complete independent research projects or Honours theses, graduate students, or post-docs/faculty members looking to transition into R.

NOTE: It is strongly recommended that participants have their own laptops to install the R package: this will simplify installation initially and participants will then have the program to keep at home!

About the Instructor:  Ronda Lo is a 4th year Psychology PhD candidate at York University and is completing the Quantitative Methods Diploma. She has been using R for over five years in her own research. She is familiar with different types of data to investigate the influence of culture on visual attention (reaction time and accuracy data), biology (genetic data), values and beliefs (survey and qualitative data), and social behaviours (behavioural data).

An Intermediate R course on Linear Models

Instructor: Adam Yuan Zhong
Dates: Tuesdays, February 23, and March 2, 9, and 16, 2021
Times: 2:00 – 5:00 pm
Location: This course will be held remotely via Zoom; meeting access TBA
Enrolment Limit: 10
Enrolment Minimum: At least five (5) registrants are required in order to hold the course.

Course Description:  Linear models are widely used to capture the relationships between different variables through statistical inference. This course is aimed to offer training in the application of the most commonly used tools in linear models, and the topics will cover linear regression, the logistic regression model, and mixed-effect models. For the various models, the lectures will provide a wide selection of case studies. From this course, participants will learn how to use programming techniques effectively using R software and to understand the fundamentals of linear models for real world data applications. The course will teach participants how to use R for linear models, but some prior knowledge of R would be beneficial for the course.

About the Instructor: Adam Yuan Zhong is a PhD student in Statistics at York University. His research interests centre around computational statistics, multivariate analysis, and high dimensional data analysis.

R Graphics for Data Analysis

Instructor: Professor Phil Chalmers
Dates: Wednesdays, February 24 and March 3, 2021
Time: 1:00-4:00pm
Location: This course will be held remotely via Zoom; meeting access TBA
Enrolment Limit: 10
Enrolment Minimum: At least five (5) registrants are required in order to hold the course.

Course description: One of R’s most prominent features is its flexibility in generating graphical output using either the default packages that ship with the software (e.g., the graphics and stats packages) or by user-contributed package extensions. One popular and powerful package extension found in R’s graphic generating ecosystem is the ggplot2 package. This R package provides high-level and flexible plotting utilities for presenting empirical data, using a well organized and intuitive framework for translating aesthetic presentation mapping rules into meaningful visual representations of data.

In this workshop, we will explore a small set of focused tools that R has to offer for creating and customizing statistical graphics. First, a brief introduction to the base and grid graphics frameworks will be presented, while the remainder of the workshop will utilize the ggplot2 plotting framework. It is recommended that participants have some familiarity with the R software, but an intermediate understanding of the language is sufficient for our purposes.

NOTE: It is strongly recommended that participants have their own laptops with the R package already installed.

 About the Instructor: Phil Chalmers is an Assistant Professor at York University in the Department of Psychology in the Quantitative Methods area, and an Associate Coordinator in the Statistical Consulting Service. He is an author of multiple R packages hosted on CRAN (e.g., mirt, mirtCAT, SimDesign, matlib), and focuses on research pertaining to psychological measurement, latent variable modelling and computational statistics.

Visualizing Linear Models: An R Bag of Tricks

Instructor: Professor Michael Friendly
Dates: Mondays, March 1, 8 and 15, 2021
Time: 2:30-5:30pm
Location: This course will be held remotely via Zoom; meeting access TBA
Enrolment Limit: 20
Enrolment Minimum: At least five (5) registrants are required in order to hold the course.

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. A brief introduction to multivariate linear models uses data ellipses (or ellipsoids) as visual summaries of 2D (or 3+ D) multivariate relations.  The Hypothesis-Error (HE) framework provides a set of tools for visualizing effects of predictors in multivariate linear models. I give some examples of HEplots for MANOVA and MMRA designs. Finally, if time permits, 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 SCS course, An Introduction to R and the Tidyverse, or equivalent.  A web page for the course will give access to lecture notes and resources.

About the Instructor: Michael Friendly is a Joint-Coordinator of the Statistical Consulting, Professor in the Department of Psychology, and a Fellow of the American Statistical Association. In addition to his research interests in psychology, Professor Friendly has broad experience in data analysis, statistics and computer applications. He is the author of several books using SAS (SAS for Statistical Graphics, 1st Edition and Visualizing Categorical Data), and more recently of Discrete Data Analysis with R. See www.datavis.ca/books.  He is also an Associate Editor of the Journal of Computational and Graphical Statistics and Statistical Science. His recent work includes the further development of graphical methods for categorical data and multivariate data analysis.

Course Fees

All fees include HST.

York students
  • An Introduction to R … $122.04
  • An Intermediate R course on Linear Models … $122.04
  • R Graphics for Data Analysis … $61.20
  • Visualizing Linear Models: An R Bag of Tricks … $91.53
York faculty and staff
  • An Introduction to R … $271.20
  • An Intermediate R course on Linear Models … $271.20
  • R Graphics for Data Analysis … $135.60
  • Visualizing Linear Models: An R Bag of Tricks … $203.40
Full-time students at other post-secondary institutions
  • An Introduction to R … $212.44
  • An Intermediate R course on Linear Models … $212.44
  • R Graphics for Data Analysis … $106.22
  • Visualizing Linear Models: An R Bag of Tricks … $159.33
External participants
  • An Introduction to R … $497.20
  • An Intermediate R course on Linear Models … $497.20
  • R Graphics for Data Analysis … $248.60
  • Visualizing Linear Models: An R Bag of Tricks … $372.90

See the registration form for payment method.

Refunds are available upon three business days’ notice prior to the course start date and are subject to an administrative fee.

Please review our policy regarding refunds.

Certificate of Completion

Available on request, full attendance is required.
A $5.65 administrative fee applies, for each certificate requested.

Registration

You can register for courses by completing the on-line registration form.

You will get an e-mail confirmation for your registration.   Credit card payment will be processed by our Administrative Assistant, Betty Tai over the telephone.

Email: btai@yorku.ca or isrcours@yorku.ca

Tel: 416-736-5061 Ext. 33024

Additional Information

Additional information regarding registration, contact Institute for Social Research (ISR) by telephone at 416-736-5061 Ext. 33024 , weekdays, from 10:00am to 4:00pm