Short Courses

Statistical Consulting Service (SCS) offers short courses on various aspects of statistics and statistical computing, including regular introductions to the SPSS and SAS statistical packages three times a year (Fall, Winter, and Summer). Recent course offerings have addressed factor analysis, structural equation modeling, graphical methods for categorical data, introduction to the R programming language, 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 Short Courses.

*** 2016 Spring Seminar Series on Social Research Methods- Course listing (PDF version) ** On-line registration will soon be available on this website end of March 2016.

List of Winter 2016 Courses offered

1) Introduction to R

Instructor: Professor Rob Cribbie

Dates: Wednesdays, February 3, 10, 24 and March 2, 2016

Time: 1:00pm – 4:00pm

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 that 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).

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

2) An Introduction to SAS for Windows

Instructor: Ryan Barnhart, MA

Dates: Fridays, February 5, 12, 26 and March 4, 2016

Time: 9:00am – 12:30pm

Location: Steacie Instructional Lab, Room 021, Steacie Science Library

Enrolment Limit: 35

Course Description: This short course provides an introduction to the Statistical Analysis System (SAS) syntax commands and procedures. We will cover the basics of:

  •  reading, transforming, sorting, merging and saving data files in some common formats;
  • selecting cases, and modifying and computing variables;
  • performing some basic statistical procedures and tests such as descriptive statistics, correlations, contingency tables, Chi-square tests, t-tests, ANOVA and linear regression;
  • creating bar charts and scatter plots;
  • composing simple macros for tailored procedures; and
  • saving output results and work in some common formats.

This course is designed for participants with some introductory level statistical knowledge, but no previous experience in using SAS. Please note that while this course will focus on the implementation of introductory statistics in SAS, it is not intended as a review of basic statistics. This short course will get you well underway in using SAS.

Please note that 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). 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.

SAS course materials

3) Advanced Topics in Structural Equation Modeling (SEM)

Instructor: Professor David Flora

Dates: Wednesdays, February 24, March 2 and 9, 2016

Time: 2:30pm – 4:30pm

Location: Room 207, Behavioural Sciences Building (BSB)

Enrolment Limit: 18

Course description:  Structural equation modeling (SEM) is a very general framework for specifying and evaluating linear, parametric statistical models that allow any number of independent and dependent variables, as well as the incorporation of “latent” variables. This course will cover advanced topics related to the use of SEM, specifically models with mean structures (e.g., multiple-group models and latent growth curve models), methods for non-normal, categorical, or missing data, and multilevel SEM for nested data structures. The material will include examples worked out using the “lavaan” package in the R statistical computing project. Course participants should be very comfortable with the basic principles of SEM involving model specification, identification, estimation, and interpretation.

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.

4) Visualizing Categorical Data with SAS and R

Instructor: Professor Michael Friendly

Dates:  Wednesdays, March 2, 9, 16 and 23, 2016

Times:  2:30pm – 5:30pm

Location: Room 159 (Hebb Lab), Behavioural Sciences Building (BSB)

Enrolment Limit: 20

Course description: Statistical methods for categorical data, such as log-linear models and logistic regression, represent discrete analogs of the analysis of variance and regression methods for continuous response variables. This short course provides a brief introduction to statistical methods for analyzing discrete data and frequency data, together with some of the graphical methods which are useful for understanding patterns of association among categorical variables. Some of the topics include: methods for discrete frequency distributions; association plots for two-way tables; correspondence analysis; mosaic displays and friends; effects plots for log-linear models and logistic regression; diagnostic plots for model assumptions; and models for repeated measures.

These methods are illustrated using SAS software based on Friendly (2000), Visualizing Categorical Data, and also with R software based on Friendly & Meyer (2016), Discrete Data Analysis with R. For further course information, see http://datavis.ca/courses/VCD/.

This course is designed for people with a basic statistical background and some interest in data visualization. A good working knowledge of the principles and practice of multiple regression is assumed. Some previous experience with either SAS or R is helpful, though not essential.

Please note: This course is taught as a lecture-lab combination in the Psychology Department Hebb Lab, 159 Behavioural Sciences Building. You will need to have an active FAS computer login account for the Hebb Lab (all Psychology undergraduates, graduate students and faculty already have one) to carry out the lab components. A temporary FAS computer account for the Hebb Lab for this course will be provided if you do not have one.

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.

Note: Qualitative research methods courses will not be offered during this Winter 2016 (but later in Spring (May) 2016 – Exact dates: TBD).

Course Fees

All fees include HST.

For external participants, the lab access fee of $33.90 has been included.

For York students (with FAS account), the fees are:
The Survey Research Process and Data Analysis $271.20
Approaches to Qualitative Data $99.44
Conducting Focus Groups for Social Research $99.44
Interpreting Qualitative Data: An Overview $99.44
An NVivo 11 for Windows Workshop $198.88
Accessing Statistics Canada Data and Resources** $56.50
An Introduction to SAS for Windows $198.88
An Introduction to SPSS $198.88
Full-time students at other post-secondary institutions, the fees per course are:
An Introduction to R $192.10
An Introduction to SAS for Windows $192.10
Advanced Topics in Structural Equation Modeling $152.55
Visualizing Categorical Data with SAS and R $192.10
For external participants, the fees per course are:
An Introduction to R $431.66
An Introduction to SAS for Windows $431.66
Advanced Topics in Structural Equation Modeling $332.22
Visualizing Categorical Data with SAS and R $431.66

All participants, Certificate of Completion: $5.65 each

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.

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
Technology Enhanced Learning (TEL) Building

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

Betty Tai
Institute for Social Research
Room 5075
Technology Enhanced Learning Building
York University
4700 Keele Street
Toronto, ON M3J 1P3
Canada

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

 

Certificate of Completion

Available on request, full attendance is required.

A $5.65 administrative fee applies, for each certificate requested.

Instructors

Ryan Barnhart is a PhD candidate in Psychology at York University with the specialization in Quantitative Methods. His research interests and statistical work have focused on longitudinal data analysis using multilevel modeling and generalized linear multilevel modeling. This work has helped Ryan to develop a multi-platform approach to using statistical software, including SAS, STATA, R and SPSS.

Robert Cribbie is a Professor in the Department of Psychology at York University and Joint Coordinator of the Statistical Consulting Service. He received his PhD in Quantitative Psychology from the University of Manitoba. His research interests include multiple comparison procedures, robust ANOVA strategies, and structural equation modeling.

David Flora is the Joint Coordinator of the Statistical Consulting Service and an Associate Professor in the Department of Psychology with the Quantitative Methods Area. His research interests include methodologies for psychometric and longitudinal data.

Michael Friendly received his doctorate in Psychology from Princeton University, specializing in Psychometrics and Cognitive Psychology. He is a Professor of Psychology at York. Professor Friendly has broad experience in data analysis, statistics and computer applications. He is the author of SAS for Statistical Graphics, 1st Edition and Visualizing Categorical Data, both published by the SAS Institute, and an Associate Editor of the Journal of Computational and Graphical Statistics.

Additional Information

Any questions? Contact Information

Additional information regarding registration, contact Institute for Social Research (ISR) by telephone 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