Winter 2009 Short Courses


The 2009 Winter Courses have concluded.

Courses will be offered again in Spring 2009.

Please check this website for course information.



Courses


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

Please follow these links for details on:
REGISTRATION
Course Fees
Certificate of Completion
Statistical Consulting Service

SPSS is cancelled due to University timetable difficulties.

This short course will be held again
on Thursdays in May 2009: 7, 14, 21, and 28.

Please note that registrations cannot be transferred:
you will have to register again later.

We apologize for any inconvenience.



Introduction to SPSS for Windows

Lab entrance is through Steacie Science Library (ground floor).

Instructor: 
Lisa Fiksenbaum, MA
Dates: 
February 4, 11, 25 and March 4, 2009
(Wednesdays)
Time: 
9:30am-1pm
Location: 
Steacie Instructional Lab,
Room 021, Steacie Science Library
Enrolment Limit: 
35

This course presents the basics of the Statistical Package for the Social Sciences (SPSS). Session One will introduce the computing concepts of SPSS, the different facilities for reading data into an SPSS spreadsheet, and saving SPSS data files for future use. At the end of the first session, participants should be able to run simple programs, including some statistical procedures.

Sessions Two and Three will cover basic data modifications, transformations and other functions, including the uses of SPSS system files. More statistical procedures will also be introduced, with an emphasis on the use of graphical methods for examining univariate and bivariate relationships. Session Four will cover Analysis of Variance and Least Squares Regression. As with previous sessions, graphical techniques will be demonstrated. Participants will benefit if they have a basic level of statistical knowledge up to general linear models, but the course is designed as an introduction to data analysis using the SPSS program and not as a statistics course.

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



SAS is cancelled due to University timetable difficulties.

This short course will be held again
on Tuesdays in May 2009: 5, 12, 19, and 26.

Please note that registrations cannot be transferred:
you will have to register again later.

We apologize for any inconvenience.



Introduction to SAS for Windows

Lab entrance is through Steacie Science Library (ground floor).

Instructor: 
Hugh McCague, PhD
Dates: 
February 5, 12, 26 and March 5, 2009
(Thursdays)
Time: 
11am-2:30pm
Location: 
Steacie Instructional Lab,
Room 021, Steacie Science Library
Enrolment Limit: 
35

This short course provides a basic introduction to the Statistical Analysis System (SAS). Sessions One and Two provide: an overview of SAS and its underlying logic; an explanation of the use of the Display Manager System to run a SAS job; an introduction to the SAS Data step for reading, importing, transforming and storing numeric and character data; and, a demonstration of how output can be changed with different options. In addition, some basic procedures in SAS will be introduced.

Sessions Three and Four will concentrate on SAS programming techniques to modify data, create charts and plots, and transform temporary datasets to permanent datasets. A demonstration of how to use SAS/INSIGHT and SAS/ANALYST will be presented, as well as a basic description of the general linear model. The course is designed for participants with some introductory level statistical knowledge, but no previous experience in using SAS.

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



Professor Monette's Short Course is now rescheduled.

Please note that the room has also been changed.

We apologize for any inconvenience.



Longitudinal and Hierarchical Data Analysis
with Mixed Models in R

Instructor:
Professor Georges Monette
Dates:
February 26, March 5, 12, 19 and 26, 2009
(Thursdays)
Times:
6-9pm
Location:
Room 305, Accolade West Building (ACW)
Enrolment Limit:
25

Mixed models provide a flexible approach for the analysis of data in which each subject is observed more than once, or in which subjects are clustered in groups like classes. Mixed models are easily extended to allow non-linear models and response variables that are not normally distributed.

This course will emphasize the visualization of the basic concepts in longitudinal and hierarchical data analysis to help participants develop a strong understanding of the strengths and limitations of these methods. The proposed list of topics includes: mixed models; clustered data; longitudinal data; extensions of mixed models; the structure of the linear mixed model: fixed effects, random effects, variance and covariance components; how mixed models are used to fit longitudinal data; contextual versus compositional effects; and model building and diagnostics.

This short course assumes familiarity with linear regression as presented, for example, in John Fox, Applied Regression Analysis and Generalized Linear Models, Second Edition (Sage, 2008). Familiarity with the basics of R will also be an asset and participants are encouraged to install R and work through introductory tutorials to prepare for the course. Extensive examples will be given in the R programming environment
(http://cran.r-project.org/) with the nlme package for mixed models.

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





Visualizing Categorical Data with SAS and R

Instructor:
Professor Michael Friendly
Dates:
February 18, 25, March 4 and 11, 2009
(Wednesdays)
Times:
2-5pm
Location:
Room 159, Hebb Lab,
Behavioural Sciences Building (BSB)
Enrolment Limit:
20

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. These methods can be helpful for both data exploration and for communicating results to others. Some of the methods 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 in the lectures using SAS software based on Friendly (2000), Visualizing Categorical Data, and also with R software, using the vcd package that is based on that book. This course is designed for people who need to analyze and understand categorical data, with a basic statistical background and some interest in data visualization. A good working knowledge of the principles and practice of multiple regression, as well as elementary statistical inference, is assumed. Some previous experience with either SAS or R is helpful, though not essential.

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



The enrolment limit for this course has been reached as space in the computer lab is limited.

Thank you for your interest.



An Introduction to R

Instructor: 
Professor Robert Cribbie
Dates: 
March 16, 23 and 30, 2009
(Mondays)
Time: 
9-11:30am
Location: 
Hebb Lab, Room 159,
Behavioural Sciences Building (BSB)
Enrolment Limit: 
20

R is an independent open source (i.e., free) statistical software package that is of great value for its wide-ranging pre-programmed statistical procedures and capacity for programming tailored statistical analyses. In addition, R is invaluable for generating informative high-quality graphics.

This short course is a gentle 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) do 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.

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




Course Fees

  • For York students, staff, and faculty, the fee is $40 per course.
  • Full-time students at other post-secondary institutions may enrol for a fee of $60 per course.
  • For external participants, the fees per course are:
    • Introduction to SPSS for Windows........................$240
    • Introduction to SAS for Windows..........................$240
    • Longitudinal and Hierarchical Data Analysis
      with Mixed Models in R
       ........................................$240
    • Visualizing Categorical Data with SAS and R.......$240
    • An Introduction to R..............................................$180

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

  • To register in person (weekdays, from 9am-Noon or 2-4pm),
    please see:

Anita Valencia
Room 5075
Technology Enhanced Learning Building (TEL)

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

  • For further information, please telephone 416-736-5061, weekdays, from 9am-Noon or 2-4pm
  • Directions to York University (Keele Campus), information on parking and building locations click here.

For those coming to York from off campus, please allow plenty of time to find parking and the building and room where your course is taking place.




Certificate of Completion

  • Available on request, full attendance is required.

  • A $5.00 administrative fee applies, for each certificate requested.




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 drawn from York University's Departments of Sociology, Psychology, Geography, and Mathematics and Statistics, in conjunction with full-time professional staff at ISR. The faculty and staff have extensive experience with all 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, bootstrapping 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 http://www.isr.yorku.ca/scs to make appointments online with SCS consultants.


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