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 2018 Fall Seminar Courses

SCS SHORT COURSES, September-December 2018

“p-values had a Good Run”: A Primer on the ‘New Statistics’

Instructor: Professor Rob Cribbie
Course Dates: Mondays, September 17, 24 and October 1, 2018
Course Time: 12:30-3:30pm
Course Location: 159 Behavioural Sciences Building (Hebb Computer Lab)
Enrolment Limit: 20
Enrolment Minimum: At least five registrants are required in order to hold the course.

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 16 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

Instructor: Professor Michael Friendly
Course Dates: Tuesdays, September 25, October 9, 16 and 23, 2018 
Course Time: 2:30-5:30pm
Course Location: 159 Behavioural Sciences Building (Hebb Computer Lab)
Enrolment Limit: 20
Enrolment Minimum: At least five registrants are required in order to hold the course.

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 Professor of Psychology at York. In addition to research interests in Psychology, Professor he 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), and an Associate Editor of the Journal of Computational and Graphical Statistics and Statistical Science. Recent work includes a new book, The Origin of Graphical Species, to be published by Harvard University Press(2018).

Introduction to R & the Tidyverse

Instructor: Mark Adkins, MA
Course Dates: Fridays, October 5, 19, 26 and November 2 (November 9, make-up if needed), 2018
Course Time: 9:00am-12:30pm
Course Location: Steacie Instructional Lab, Room 021, Steacie Science Library
Enrolment Limit: 35
Enrolment Minimum: At least five registrants are required in order to hold the course.

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. Much of this course will be structured around a collection of packages called the tidyverse (www.tidyverse.org), which contains all of the tools necessary to learn R more easily and quickly. 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.

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 Instructor:
Mark Adkins is a doctoral candidate in Quantitative Methods in the Psychology Department at York University and a TA in SCS. He has years of experience as a statistical/programming tutor and has the ability to help students master complicated material regardless of their stage in the learning process.

Mixed Models for Nested and Longitudinal Data including Bayesian Analysis using STAN

Instructor: Professor Georges Monette
Dates: Mondays, October 15, 22, 29, November 5, 12, 19, 26 and December 3, 2018
Time:  2-5pm
Location: Classroom, Research Data Centre (RDC), Statistics Canada, York Lanes 283B (enter first through 283 not 282)
Enrolment Limit: 20
Enrolment Minimum: At least five registrants are required in order to hold the course.

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 in which observations are clustered in groups.

Throughout the course, we will focus on applying methods to analyses that answer real questions with real data. We will work with extensive examples and have many opportunities for hands-on practice with software.

We 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. These methods allow you to work with data that has complex structure.

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 such as models with missing data and discrete or non-normal responses. We will exploit 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/ . In this course, extensive examples will be given in the R programming environment.

The first session will consist of an overview of R and its major functions for regression analysis.

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.

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.

Advanced Research Design Seminar

Instructor: Professor Bryn Greer-Wootten
Dates: Tuesdays, October 23, November 6, 20 and December 4, 2018
Times: 6:00pm-9:00pm
Location: Room 5082, Victor Phillip Dahdaleh Building (Formerly TEL Building)
Enrolment Limit: 10

Course Description:  Research design in the social, environmental and behavioural sciences today must consider the choices to be made between quantitative, qualitative and mixed (i.e., both quantitative and qualitative) methods. This short course is designed as a seminar to examine such choices. An introductory presentation distinguishes between these approaches from philosophical perspectives. Subsequent sessions discuss (i) the primary issues, based on assigned readings, (ii) critical reviews of participant-chosen research articles, and (iii) group critique of individual research proposals. Sufficient time between meetings is allowed for the work required for these activities.

Enrolment is limited to 10 in order to maximize the seminar setting. This short course is open to everyone, but the participant likely to gain most from the experience is a PhD candidate post-comprehensives or a junior faculty person.

It may be necessary to select participants based on their applications: please be sure to enter your reasons for applying for this Short Course in the online Registration Form in the box marked “Additional Information”.

Applicants will be notified of acceptance one week prior to the first seminar meeting, i.e., by October 15, 2018.

About the Instructor:
Bryn Greer-Wootten is Professor Emeritus in Environmental Studies and Geography at York University. In 2002 he joined the staff of the Statistical Consulting Service, where he is currently an Associate Coordinator, and in 2004 was appointed an Associate Director of ISR. He has taught and carried out quantitative and qualitative research, with a particular interest in survey research, especially for environmental and social policy, for over fifty years.

ISR Fall Seminar Series, October-November 2018

The Survey Research Process and Data Analysis

Sorry, this course has been cancelled.

Instructors: Professor Bryn Greer-Wootten and Mirka Ondrack, MSc
Dates: Tuesday October 30 – Thursday November 1, 2018
Times: 9:30am-Noon; 1:00pm-3:30pm
Location: Lab 2114, Victor Phillip Dahdaleh Building (Formerly TEL Bldg)
Enrolment Limit: 25

Survey research is one of the many methodological possibilities that can be considered in the decision processes for carrying out social research, for any phenomena of interest: hence, it must be contextualized. Commonly, it is located within the philosophical criteria employed in social research, i.e., taking into account the ontological, epistemological and methodological aspects of any research project. These general principles are discussed on the first day of this three-day Workshop, with examples drawn from many social science areas using quantitative, qualitative and mixed methods research designs. The afternoon group activity is based on a questionnaire design exercise for an ISR survey on ‘Canadians and Their Pasts’ (CTP), involving the reconstruction of the conceptual framework used in this study.

The practical analysis of survey research data (using the CTP data) is presented in the next two days. First, we explore the properties of the data, using a matrix representation of the survey data, including levels of measurement for typical survey questions, the distributional properties of variables and simple descriptive statistics. Subsequently, the construction of scales (e.g., for attitude items) and the fundamentals of statistical inference and hypothesis testing in a survey context are developed. The final day continues with the implementation of a survey analysis design, including the analysis of groups (e.g., gender differences using t-tests; age or regional differentials using the analysis of variance), and extended analyses of contingency tables, the most common form of data representation in surveys.

On these analysis days, the morning sessions are used for lectures and demonstrations; afternoon lab sessions involve individual assignments that replicate procedures used in the morning. To benefit from the course, participants should have some background knowledge in basic statistics or the fundamentals of survey research, as well as some prior knowledge of SPSS.

Please note that food and drink are not allowed in the 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, participants need to arrive on time and attend all sessions.

About the Instructors:
Bryn Greer-Wootten is Professor Emeritus in Environmental Studies and Geography at York University. In 2002 he joined the staff of the Statistical Consulting Service, where he is currently an Associate Coordinator, and in 2004 was appointed an Associate Director of ISR. He has taught and carried out quantitative and qualitative research, with a particular interest in survey research, especially for environmental and social policy, for over fifty years.

Mirka Ondrack is Statistical Consultant Emerita at ISR. She received her Master’s degree in Physics from Masaryk University in the Czech Republic. She has held the position of Programmer/Analyst at ISR since 1971. Ms. Ondrack is currently a consultant with the Statistical Consulting Service and also does custom programming and data analysis, consulting

Using Computers in Qualitative Analysis: An NVivo 11 for Windows Workshop

Sorry, this course has been cancelled.

Instructor: Stella Park, MA
Dates: Friday, November 2 and Monday, November 5, 2018
Time: 9:30am-Noon; 1:00pm-3:30pm
Location: Room 2004, Victor Phillip Dahdaleh Building
Enrolment Limit: 20

Course Description: This hands-on Workshop will provide both a basic and advanced introduction to NVivo 11 for Windows. As this Workshop will focus on how to move forward into your analysis, participants are required to have had some prior experience and/or exposure to qualitative research assumptions, theories and methods before attending this Workshop. The overall objective is to provide you with the tools to ensure that the theory and methods guiding your project remain central as you move into NVivo.

On Day One you will create a project and learn how to import and work with a wide range of qualitative data formats (e.g., interview transcripts, focus group transcripts, survey spreadsheets, etc.). On Day Two you will learn how to organize and explore your material, use advanced queries, identify relationships and use charts to show patterns in your information. Time will be provided on both days of the Workshop for participants to work with their own data. The weekend break is designed to allow participants to practise on their own data using the principles learned on the first day.

Please note this course is designed for NVivo 11 for Windows users (and not NVivo for Mac users). NVivo for Mac has different features and cross platform limitations.

Please note that food and drink are not allowed in the 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, participants need to arrive on time, attend all sessions 

About the Instructor
Stella Park
joined ISR in 2014 as a Project Manager. She has over 10 years of experience in conducting both quantitative and qualitative research projects at the local, provincial, and international levels, on a diverse range of topics, including health, education, employment, and the non-profit sector. At ISR, she is currently managing the YouthREX (qualitative) research project, CAMH’s Ontario Student Drug Use and Health Survey, CAMH’s Monitor Survey, and the SSHRC-funded Second-generation Employment project.

Course Fees

All fees include HST.

York students (with FAS account)
  • “p-values had a Good Run”: A Primer on the ‘New Statistics’ … $91.53
  • Data Visualization in R … $122.04
  • Introduction to R & the Tidyverse … $122.04
  • Mixed Models for Nested and Longitudinal Data including Bayesian Analysis using STAN … $183.06
  • Advanced Research Design Seminar … $122.04
  • The Survey Research Process and Data Analysis … $186.45
  • Using Computers in Qualitative Analysis: An NVivo 11 for Windows Workshop … $226.00
York faculty and staff
  • “p-values had a Good Run”: A Primer on the ‘New Statistics’ … $203.40
  • Data Visualization in R … $271.20
  • Introduction to R & the Tidyverse … $271.20
  • Mixed Models for Nested and Longitudinal Data including Bayesian Analysis using STAN … $406.80
  • Advanced Research Design Seminar … $271.20
  • The Survey Research Process and Data Analysis … $406.80
  • Using Computers in Qualitative Analysis: An NVivo 11 for Windows Workshop … $497.20
Full-time students at other post-secondary institutions

For non-York students, a lab access fee of $33.90 (including HST) has been included.

  • “p-values had a Good Run”: A Primer on the ‘New Statistics’ … $193.23
  • Data Visualization in R … $246.34
  • Introduction to R & the Tidyverse … $246.34
  • Mixed Models for Nested and Longitudinal Data including Bayesian Analysis using STAN … $386.46
  • Advanced Research Design Seminar … $246.34
  • The Survey Research Process and Data Analysis … $372.90
  • Using Computers in Qualitative Analysis: An NVivo 11 for Windows Workshop … $440.70
External participants

For external participants, a lab access fee of $33.90 (including HST) has been included.

  • “p-values had a Good Run”: A Primer on the ‘New Statistics’ … $406.80
  • Data Visualization in R … $531.10
  • Introduction to R & the Tidyverse … $531.10
  • Mixed Models for Nested and Longitudinal Data including Bayesian Analysis using STAN … $813.60
  • Advanced Research Design Seminar … $531.10
  • The Survey Research Process and Data Analysis … $779.70
  • Using Computers in Qualitative Analysis: An NVivo 11 for Windows Workshop … $937.90

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

Additional information regarding registration, contact 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.