institute for social research

York University  

Over 40 years of excellence in conducting applied and academic social research
York University
4700 Keele Street
Toronto, ON Canada
M3J 1P3

Telephone: 416-736-5061
Toll-free: 1-888-847-0148
Fax: 416-736-5749
E-mail: isrnews@yorku.ca

Winter 2014 Short Courses

Courses
An Introduction to Meta-analysis and Systematic Reviews
Introduction to Data Analysis with R
An Applied Introduction to SPSS
An Introduction to SAS for Windows

Introduction to Linear Multilevel Modeling

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

Please follow these links for details on:

Course Fees
Registration
Certificate of Completion
Statistical Consulting Service

[Click here for Previous Courses]

Sorry, this course is full. Register for waiting list only.
An Introduction to Meta-analysis and Systematic Reviews
Instructor:
Professor Michael Rotondi
Dates:
Fridays - Jan. 24, 31 and Feb. 7, 2014
Time:
9:30am - 12:30pm
Location:

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

Enrolment Limit:
20

This course provides an introduction to the role of meta-analysis and systematic reviews in evidence-based medicine. Statistical and practical issues relating to the design and interpretation of meta-analyses and systematic reviews are emphasized. Selected topics include:

  • What is a meta-analysis?
  • Defining the research question
  • Searching for and selecting studies for inclusion
  • Statistical methods and interpretations
  • Fixed and random effects models
  • Performing meta-analyses in R

Advanced topics including meta-regression and incorporation of other study designs (prospective cohorts and cluster randomized trials) will also be discussed. This course is designed for participants with introductory knowledge of statistics and statistical computing, but no prior exposure to meta-analysis is expected. Statistical computing is performed in the R environment, thus some prior knowledge of R is an asset.

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.

Sorry, this course is full. Register for waiting list only.
Introduction to Data Analysis with R
Instructor:
Matthew Sigal, MA
Dates:
Mondays - Feb. 3, 10, 24 and Mar 3, 2014
Time:
1:00pm - 4:00pm
Location:

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

Enrolment Limit:
20

R is a free open-source programming language tailored for data analysis. It has numerous benefits, from wide-ranging pre-programmed statistical procedures and its capacity for programming tailored statistical analyses, to functions for generating informative high-quality graphics. This short course is a step-by-step hands-on introduction to R. No familiarity with R is assumed, but participants will require a basic working knowledge of statistics. Participants will learn how to install R on their computers; enter, import, and manipulate data; and carry out basic mathematical, statistical and graphical operations and procedures. Upon completion of this course, participants will be comfortable with, and able to do, basic statistical work in R. Additionally, they will become familiar with resources to seek follow-up help for learning more about R.

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.

An Applied Introduction to SPSS
Instructor:
Carrie Smith, MA
Dates:
Wednesdays - Feb. 5, 12, 26 and Mar. 5, 2014
Time:
1:00pm - 4:30pm
Location:

Steacie Instructional Lab, Room 021
Steacie Science Library

Enrolment Limit:
35

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 recently 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.

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

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.

An Introduction to SAS for Windows
Instructor:
Ryan Barnhart, MA
Dates:
Fridays - Feb. 7, 14, 28 and Mar. 7, 2014
Time:
9:00am - 12:30pm
Location:

Steacie Instructional Lab, Room 021
Steacie Science Library

Enrolment Limit:
35

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.

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.

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.

Introduction to Linear Multilevel Modeling
Instructors: Professor Jolynn Pek
Dates: Fridays - Feb. 28, Mar. 7 and 14, 2014
Time: 9:30am - 12:30pm
Locations:

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

Enrolment Limit: 20

Data structures are often hierarchical in that cases are clustered into groups (e.g., students in classrooms or repeated measures of individuals). Multilevel models are designed to model such nested data structures, where the usual assumption of independence of observations in classical techniques is violated, and within - and between - group effects may be separately estimated.

This short course provides an introduction to the basic concepts of linear multilevel models. Topics include approaches to analyzing nested data structures, computing and interpreting the intra-class correlation, level 1 predictors, partitioning variance into within- and between-group components, level 2 predictors, cross-level interactions, ML and REML estimation, model assumptions, model diagnostics and modeling longitudinal data structures.

The short course assumes familiarity with multiple linear regression, and will involve both lectures and data examples using SAS. Familiarity with the SAS environment is recommended.

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.

Course Fees (including HST)

York University students: $45.20 per course

York University faculty and staff: $99.44 per course

Other post-secondary full-time students: $90.40 per course

For external participants, the fees per course are:

An Introduction to Meta-analysis
and Systematic Reviews ..................................
$298.32
Introduction to Data Analysis with R .................. $397.76
An Applied Introduction to SPSS ....................... $397.76
An Introduction to SAS for Windows .................. $397.76
Introduction to Linear Multilevel Modeling ............ $298.32

All participants: Certificate of Completion ............ $5.65 each

Course fees must be paid at the time of registration.

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 here.

Registration

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

To register in person (weekdays, from 10:00am to Noon 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 (including HST) applies,
for each certificate requested.

Additional Information

Additional information regarding registration:
please telephone 416-736-5061, weekdays,
from 10:00am to Noon or 2:00pm to 4:00pm

Directions to York University (Keele Campus), building and parking lot locations click here. For additional information on parking click here.

Instructors

Ryan Barnhart is a PhD candidate in Psychology at York University with 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 Mr. Barnhart develop a multi-platform approach to using statistical software, including SAS, STATA, R and SPSS.

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.

Michael Rotondi is an Assistant Professor of Biostatistics and Quantitative Methods in the School of Kinesiology and Health Science, and an Associate Coordinator with the Statistical Consulting Service. His research interests span a variety of biostatistical areas, and include the development of statistical methods to estimate power and sample size for meta-analysis and meta-regression models. He has also performed meta-analyses examining the impact of vitamin A supplementation on neonatal mortality, and the role of physical activity in the prevention and treatment of Alzheimer’s disease.

Matthew Sigal is a doctoral student in the Quantitative Methods area of Psychology. He is a member of Dr. Michael Friendly's lab and is particularly interested in methods of data visualization, multilevel and structural equation modeling, and alternative modeling strategies within the framework of survival analysis. He has been a Teaching Assistant for both undergraduate and graduate statistics courses, and taught an introductory statistics course in W2013.

Carrie Smith is a PhD candidate in Psychology at York University, specializing in Quantitative Methods. She received her MA in Psychology at York, and her BASc in Engineering at the University of Toronto. Her research interests include data visualization and developing robust methods of statistical analysis appropriate for behavioural science data.

Statistical Consulting Service (SCS)

The Institute for Social Research's Statistical Consulting Service provides consultation on a broad range of statistical problems and on the use of computers for statistical analysis. Its services extend beyond the social sciences to other disciplines that make use of statistics. Consultation is available to assist in research design, data collection, data analysis, statistical computing and the presentation of statistical material.

Consultation is provided by a group of faculty and graduate students drawn from York University's Departments of Sociology, Psychology, Geography, Mathematics and Statistics, and the School of Kinesiology and Health Science, in conjunction with full-time professional staff at ISR. The consultants have extensive experience with most forms of statistical analysis. Topics for which assistance is available include regression analysis, multivariate analysis, stochastic processes, probability theory, exploratory data analysis, scaling and cluster analysis, analysis of categorical data, structural equation modeling, survey data and longitudinal data, experimental design, survey sampling, and statistical computing.

Three times a year, the Statistical Consulting Service offers short courses on various aspects of statistics and statistical computing, including regular introductions to the SPSS and SAS statistical packages. Recent course offerings have included regression diagnostics, boot-strapping techniques, an introduction to the AMOS module in SPSS, graphical methods for categorical data, confirmatory factor analysis, model-based approaches to cluster analysis, introduction to the R programming language, and visual methods for statistical data analysis.

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 web site at www.isr.yorku.ca/scs to make appointments online with SCS consultants.

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