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 2017 Winter Seminar Courses 

List of Winter 2017 Courses offered

1) Introduction to R

Instructor: Amanda Tian, MSc and Joshua Guilfoyle, MA

Dates: Wednesdays, February 1, 8, 15 and March 1, 2017

Time: 1:00pm – 4:30pm

Locations: 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.

NOTE: This introductory course would be beneficial for participants who intend to register for the Mixed Models Short Course by Professor Monette, which starts March 1st, and/or those who wish to register for Professor Pek’s Short Course on Power Analysis, which starts March 17th.

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 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.

Click here for the current files for the R course.

2) An Introduction to SAS for Windows

Instructor: Ryan Barnhart, MA

Dates: Fridays, February 3, 10, 17 and March.3, 2017

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 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.

SAS course materials

3) Modeling and analysis of longitudinal and nested data

Instructor: Professor Georges Monette

Dates: Wednesdays, March 1, 8, 15, 22 and 29, 2017

Time:  6:00pm – 9:00pm

Location: Classroom, Research Data Centre (RDC), Statistics Canada
York Lanes 283B (enter first through 283 not 282)

Enrolment Limit: 25

Course description:  This course focuses on models and methods suitable for longitudinal data in which each subject is observed on a number of occasions over time. Each subject may be observed a different number of times and times may be spaced differently for different subjects. In contrast with classical repeated measures designs, the methods we consider handle unbalanced data, including time-varying covariates.

These methods are also appropriate for nested data with no time component but observations are clustered in groups.

The course will begin by developing concepts and techniques that are specifically relevant for nested and longitudinal data: random versus fixed effects, variance-covariance components, temporal autoregression, contextual versus compositional effects, splines, missing data and diagnostics, among others.

We will apply these concepts with mixed models using software in the statistical programming environment R (https://cran.r-project.org/), including the ‘nlme’ package for linear and non-linear modelling with continuous responses.

We will then learn Bayesian methods using Markov Chain Monte Carlo techniques that can be used for more complex models and for discrete responses. For this purpose we will focus on the Stan modelling language and program (http://mc-stan.org/).

This short course assumes familiarity with linear regression as presented, for example, in John Fox, Applied Regression Analysis and Generalized Linear Models, Third Edition (Sage, 2015). Familiarity with the basics of R will also be an asset and participants are encouraged to install R and work through an introductory tutorial, such as the one at ( https://cran.r-project.org/doc/manuals/r-release/R-intro.html ) to prepare for the course. Another option is Swirl: a package in R with interactive tutorials for different skill levels. You need to know how to run R and install packages, but there are instructions on the site — http://swirlstats.com/ . If participants have completed the first course in this series (see above) they will be well prepared. In this course, extensive examples will be given in the R programming environment.

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.

You are encouraged to bring your laptop. There will be many opportunities to practise using examples in R. Wireless access will be provided for non-York community participants.

4) An Introduction to R Graphics

Instructor: Professor Michael Friendly

Dates:  Tuesdays, March 7, 14, 21 and 28, 2017

Times:  12:30pm – 3:30pm

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

Enrolment Limit: 20

Course description (updated January 9, 2017):

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). 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.

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.

5) Practical Power Analysis

Instructor: Professor Jolynn Pek

Dates:  Fridays, March 17, 31 and April 7, 2017 (updated, January 23, 2017).

Times:  9:30am – 12:30pm

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

Enrolment Limit: 20

Course description: Power analysis is an important component of experimental design. In the context of limited resources, which should be responsibly expended, power analysis allows researchers to determine a range of sample sizes which would presumably provide adequate power to detect statistical significance of effect sizes.

This Short Course provides an introduction to the basic concepts of power analysis, with a focus on conducting power analysis. First, guidelines are provided in determining an effect size and accompanying error variances. Next, power analysis using G*Power, SAS PROC POWER, and the R pwr package is introduced and illustrated. Finally, Monte Carlo power analysis using R as a flexible approach is described and illustrated.

This course assumes familiarity with basic linear and categorical models. Familiarity with the R environment is also recommended.

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.

 

 

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:

An Introduction to R $90.40
An Introduction to SAS for Windows $90.40
Modeling and analysis of longitudinal and nested data $113.00
Using R for Graphics $90.40
Practical Power Analysis $90.40

For York faculty and staff, the fees are:

An Introduction to R $198.88
An Introduction to SAS for Windows $198.88
Modeling and analysis of longitudinal and nested data $248.60
Using R for Graphics $198.88
Practical Power Analysis $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
Modeling and analysis of longitudinal and nested data $231.65
Using R for Graphics $192.10
Practical Power Analysis $152.55

For external participants, the fees per course are:

An Introduction to R $431.66
An Introduction to SAS for Windows $431.66
Modeling and analysis of longitudinal and nested data $531.10
Using R for Graphics $431.66
Practical Power Analysis $332.22

 

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 after Jan 8, 2017 (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.

 

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.

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 SAS for Statistical Graphics, 1st Edition and Visualizing Categorical Data, both published by SAS Institute, and 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.

Joshua Guilfoyle is a doctoral candidate in Social/Personality program in Psychology with a minor in Quantitative Methods. His research interests include interpersonal transgressions, conflict resolution, and the use of apologies. He is currently a Statistical Consulting Services TA with proficiency in R, including the suite of R-packages collectively known as the “tidyverse”.

Georges Monette is an Associate Professor of Mathematics and Statistics at York and an Associate Coordinator with the Statistical Consulting Service. Most of his research has been in the mathematical foundations of statistical inference. His recent interests are the geometric visualization of statistical concepts and the modeling and analysis of longitudinal data. He has worked in a number of applied areas, including pay equity and the statistical analysis of salary structures.  He received his PhD in Statistics from the University of Toronto.

Jolynn Pek is an Assistant Professor in the Department of Psychology at York University and an Associate Coordinator with the Statistical Consulting Service. She received her PhD in Quantitative Psychology from the University of North Carolina at Chapel Hill. Her research interests involve quantifying different aspects of uncertainty in results obtained from fitting latent variable models (e.g., factor analysis models, structural equation models, structural equation mixture models, multilevel models, and latent growth curve models) to data.

Amanda Tian is a PhD student in the Department of Mathematics and Statistics at York University. Her doctoral research area is discrete log-concave density estimators in multiple dimensional space. She is currently an SCS TA, and would like to help in the following areas: hierarchical and longitudinal mixed models, generalized linear mixed models, R and SPSS.

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