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 are required for all Short Courses

SCS SHORT COURSES, January-March 2018

Introduction to R

Instructor: Joo Ann Lee, MA

Course Dates: Fridays – January 26, February 2, 9 and 16, 2018
Course Time: 9:00am-12:30pm
Course Location: Steacie Instructional Lab, Room 021, Steacie Science Library

Enrolment Limit: 35

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, but participants will need a basic working knowledge of statistics. Participants will learn how to: 1) install R on their computers; 2) enter, import, and manipulate data; and 3) carry out basic mathematical, statistical and graphical operations and procedures in R. 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.

Please note: in the case of the university being unexpectedly closed during the Short Course, a make-up class will be scheduled for February 23, during Reading Week.

The Steacie Instructional Lab [Steacie 021] is accessed by entering Steacie Library and then proceeding to the basement of that Library.

Please note that food and drink are not allowed in Steacie Library and the Steacie Instructional Lab. The only exceptions are capped bottles of water (not juice/pop) and spill proof mugs (not cups of coffee).  Washrooms are available nearby outside the library.

Because this material is presented sequentially and builds upon the basics presented at the beginning of each class, course participants need to arrive on time and attend the entire session.

About the Instructors
Joo Ann Lee is a doctoral candidate in Quantitative Methods in the Psychology Department at York University, and a TA in SCS. A finalist in a big data competition sponsored by SAS, she is proficient in using various statistical software to manage, analyze and visualize large amounts of data.

Introduction to SPSS

Instructor: Alyssa Counsell, PhD

Course Dates: Wednesdays – February 7, 14, 28 and March 7, 2018
Course Time: 1:00-4:30 pm
Course Location: Steacie Instructional Lab, Room 021, Steacie Science Library

Enrolment Limit: 35

Course description:  This course aims to acquaint participants with IBM SPSS Statistics, a popular and respected program for analyzing data that is used across a range of disciplines. The curriculum has been revised to not only introduce the basic functions and features of the software (including data entry and manipulation), but also to demonstrate how to conduct a range of statistical analyses. Hands-on exercises will supplement the lecture material.

The curriculum for this course is designed to be an applied introduction to a statistical program; as such, familiarity with basic statistical procedures (e.g., t-tests, ANOVA, regression) is assumed. Further, participants are encouraged to bring a USB flash drive to store their work.

The Steacie Instructional Lab [Steacie 021] is accessed by entering Steacie Library and then proceeding to the basement of that Library.

Please note that food and drink are not allowed in Steacie Library and the Steacie Instructional Lab. The only exceptions are capped bottles of water (not juice/pop) and spill proof mugs (not cups of coffee). Washrooms are available nearby outside the library.

Because these materials are presented sequentially and build upon the basics presented at the beginning of each day, course participants need to arrive on time and attend the entire sessions.

Download the SPSS course data in a zip file.

About the Instructor
Alyssa Counsell received her PhD from York University’s Quantitative Methods program in Psychology. Her research interests include equivalence testing, robust statistics, measurement invariance, structural equation modeling, and pedagogical methods for improving statistical knowledge in applied psychological research. Alyssa worked as a consultant in SCS for several years, where she taught short courses on both SPSS and R.

Software Carpentry (CANCELLED)

Instructors: Stephen Childs, Office of Institutional Planning and Analysis, York University and Greg Wilson, originator of Software Carpentry

Sponsors: York University Libraries, the Office of Institutional Planning and Analysis (OIPA), the Statistical Consulting Service (SCS) and Compute Canada

Course Dates: Wednesday February 21 and Thursday February 22, 2018
Course Time: 9am – 4:30pm
Course Location: Schulich School of Business, Bronfman Library Room S236

Enrolment Limit: 20

Course Description: Software Carpentry aims to help researchers get their work done in less time and with less pain by teaching them basic research computing skills. This hands-on workshop will cover basic concepts and tools, including program design, version control, data management and task automation. Participants will be encouraged to help one another and to apply what they have learned to their own research problems. For more information on what we teach and why, please see our paper Best Practices for Scientific Computing. The course is aimed at graduate students and other researchers. You do not need to have any previous knowledge of the tools that will be presented at the workshop.

Participants must bring a laptop with a Mac, Linux, or Windows operating system (not a tablet, Chromebook, etc.) that they have administrative privileges on.

More Course Information and Resources

About the Instructors
Stephen Childs is a Senior Institutional Analyst at York University where he administers and analyzes surveys of students and other members of the York University Community. Stephen is a certified Software Carpentry Instructor. Before pursuing a career in Institutional Research, Stephen was a part of the research team at the Education Policy Research Initiative and remains an EPRI Policy Fellow. Stephen’s research interests include education policy, specifically access to PSE, student retention and  labour market outcomes of students. Stephen completed a Master’s Degree in Business Economics at Wilfrid Laurier University. Stephen is one of the organizers of PyData Toronto, which promotes the use of Python and other Open Source tools for data analysis.

Greg Wilson, as noted above, is the originator of Software Carpentry and until recently was responsible for their Instructor training program.

"p-values had a Good Run": A Primer on the 'New Statistics' (CANCELLED, as of March 5, 2018)

Instructor: Rob Cribbie, PhD

Course Dates: Thursdays – March 8, 15 and 22, 2018
Course Time: 8:30 am – 11:30 am
Course Location: 159 Behavioural Sciences Building (Hebb Computer Lab)

Enrolment Limit: 20

Course Description: Null hypothesis significance testing (NHST) has been the dominant and, in many cases, the only data analytic approach adopted by behavioural science researchers for decades. The limitations of NHST are finally being acknowledged within the research community, however, and an abrupt shift in research practices is inevitable. This course provides an introduction to effect sizes, confidence intervals, replication, meta-analysis, and other tools that have been dubbed the “New Statistics”.

About the Instructor
Rob Cribbie is a Professor in the Quantitative Methods area of the Department of Psychology at York University. He received his PhD in Quantitative Methods for Psychology from the University of Manitoba in 2001. He has worked with the Statistical Consulting Service at York University for over 15 years. His research interests centre around robust/alternative statistics, including equivalence testing. For more details see http://cribbie.info.yorku.ca.

Data Visualization in R (CANCELLED, as of March 5, 2018)

Instructor: Michael Friendly

Course Dates: Tuesdays – March 6, 13, 20 and 27, 2018
Course Time: 11:30 am – 2:30 pm
Course Location: 159 Behavioural Sciences Building (Hebb Computer Lab)

Enrolment Limit: 20

Course description: R is arguably the most powerful computing environment for statistical computing and graphics, but the learning curve can be steep at first. This Short Course aims to give participants a high-level overview of graphics in R and sufficient details in the form of examples to get a jump start and use R graphics productively in their work.

It is assumed that participants have at least a basic understanding of R and are comfortable using R scripts in the R console or, preferably, the RStudio interactive development environment.

The first session presents an overview of the roles of graphics in data analysis, and the main R graphics systems (base graphics; grid graphics; ggplot2). It gives a high level view of the kinds of graphics that can be produced with R, and some of the R Studio and R package tools to integrate data analysis, graphics, and reporting within a convenient workflow. Session 2 presents the traditional object-oriented design of statistical analysis and graphing in R and illustrates how these can be enhanced or tweaked for different presentation goals. The third and fourth sessions illustrate more powerful forms of graphics in R, produced, respectively with grid/lattice graphics and with ggplot2.

About the Instructor
Michael Friendly received his doctorate in Psychology from Princeton University, specializing in Psychometrics and Cognitive Psychology. He is a Professor of Psychology at York. 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 Visualizing Categorical Data (http://www.datavis.ca/books/vcd/) and Discrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data (http://ddar.datavis.ca). 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, and the history of data visualization. A new book, The Origin of Graphical Species, will be published by Harvard University Press in 2018.

Regression in Depth: A Geometric and Visual Approach (CANCELLED, as of March 5, 2018)

Instructor: Georges Monette

Course Dates: Wednesdays – March 7, 14, 21 and 28, 2018
Course Time: 6:00-9:00 pm
Course Location: Classroom, York Research Data Centre (RDC), Statistics Canada, York Lanes 283B (enter through York Lanes 283)

Enrolment Limit: 20

Course description: This course is intended for researchers who are already familiar with and who use regression in their work. Although regression is one of the most widely used statistical techniques, it is fraught with paradoxes and fallacies. The aim of this course is to help researchers make sense of regression and to perform regressions that make sense.

Almost all the issues underlying regression are richly interconnected and can be understood and visualized using a few simple geometric ideas.   We explore three ‘spaces’: 1) data space: the familiar space of scatterplots; 2) ‘beta’ space: the space of parameters in which confidence intervals and confidence regions live; and 3) the more abstract ‘variable space’ in which n observations on a variable are represented by a vector of length n.

The concepts that will come to life include: Simpson’s Paradox; sign reversals and causality; variance inflation, collinearity and its consequences; when do principal components provide a reasonable solution; strange and unexpected consequences of measurement error; univariate versus multivariate outliers and corresponding concepts of leverage and influence; mediation and moderation; interpreting partial residual and partial regression leverage plots; multiple comparisons and multiparameter confidence coverage, suppression; the relationships between types of data: experimental versus observational and types of inference: causal or explanatory versus predictive.

Most of the graphics created in the course are programmed in R and you are encouraged to bring a laptop to work with the R scripts. Understanding how the graphics are generated, however, is not directly relevant to the course. The course focuses on the visualization of the concepts and theory of regression. It is complementary to courses on data visualization.

About the Instructor
Georges Monette is an Associate Professor of Mathematics and Statistics at York and has been affiliated with the Statistical Consulting Service for over 30 years.  He enjoys teaching statistical concepts in many settings, including York’s Summer Program in Data Analysis from 2000 to 2012 and the Inter-university Consortium for Political and Social Research (University of Michigan) summer course at York University. He is interested in the geometric representation of statistical concepts and in the modeling and analysis of hierarchical and longitudinal data. He has worked in a number of applied areas, including pay equity, the statistical analysis of salary structures, and patterns of cognitive and motor recovery after traumatic brain injury.

Course Fees

All fees include HST.

York students (with FAS account)
  • Introduction to R … $90.40
  • Introduction to SPSS … $90.40
  • Software Carpentry … $56.50
  • “p-values had a Good Run”: A Primer on the ‘New Statistics’ … $90.40
  • Data Visualization in R … $90.40
  • Regression in Depth: A Geometric and Visual Approach … $90.40
York faculty and staff
  • Introduction to R … $198.88
  • Introduction to SPSS … $198.88
  • Software Carpentry … $56.50
  • “p-values had a Good Run”: A Primer on the ‘New Statistics’ … $198.88
  • Data Visualization in R … $198.88
  • Regression in Depth: A Geometric and Visual Approach … $198.88
Full-time students at other post-secondary institutions

For non-York students, a lab access fee of $30.00+HST = $33.90 has been included.

  • Introduction to R … $192.10
  • Introduction to SPSS … $192.10
  • Software Carpentry … $90.40
  • “p-values had a Good Run”: A Primer on the ‘New Statistics’ … $192.10
  • Data Visualization in R … $192.10
  • Regression in Depth: A Geometric and Visual Approach … $192.10
External participants

For external participants, a lab access fee of $30.00+HST = $33.90 has been included

  • Introduction to R … $431.66
  • Introduction to SPSS … $431.66
  • Software Carpentry … $90.40
  • “p-values had a Good Run”: A Primer on the ‘New Statistics’ … $431.66
  • Data Visualization in R … $431.66
  • Regression in Depth: A Geometric and Visual Approach … $431.66

See the registration form for payment options.

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, which is date-stamped.

You can register in person (weekdays, from 10:00am to 12:00pm or 2:00pm to 4:00pm), please see:

Betty Tai
Room 5075
Victor Phillip Dahdaleh Building (DB)

To register by mail, print a blank registration form, complete, and send to:

Betty Tai
Institute for Social Research
Room 5075
Victor Phillip Dahdaleh Building (DB)

York University
4700 Keele Street
Toronto, ON M3J 1P3
Canada

You may also fax a completed registration form to: 416-736-5749.

Additional Information

For additional information regarding registration, contact the Institute for Social Research (ISR) by telephone at 416-736-5061, weekdays, from 10:00am to 12:00pm or 2:00pm to 4:00pm.

Directions to York University (Keele Campus), building and parking lot locations.

Additional information on parking.