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.

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.

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Structural Equation Modeling:
A Second Course

Instructor:	Professor Robert Cribbie
Dates:	February 20, 27, March 6, 2006
Time:	11:30 a.m. - 2:30 p.m.
Location:	Room 159 (Hebb Lab) Behavioural Sciences Building
Enrolment Limit:	20

This course will expand on topics in structural equation modeling (SEM) that are often not fully developed in introductory SEM courses. Topics will include missing data issues, equivalent models, mediation and moderation, and growth curve models. The course will involve both lectures and hands-on exercises (using the AMOS software package).

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Repeated Measures and Beyond:
Longitudinal Data Analysis with Mixed Models

Instructor:	Professor Georges Monette
Dates:	February 24, March 3, 10, 17 and 24, 2006
Time:	9:30 a.m. - 12:30 p.m.
Location:	Room 104 Accolade West Building
Enrolment Limit:	25

This course uses classical repeated measures, univariate and multivariate, as a point of departure for studying methods for the analysis of longitudinal and hierarchical data using mixed models. Mixed models allow the analysis of repeated measures data when the data are unbalanced and classical models do not work, e.g. subjects are observed at different times or time-varying covariates are included in the model. The ability to analyze a wider range of data comes at a price. Not only do data analysts need to learn new techniques, they also need to become aware of concepts that are not as salient in the analysis of "balanced" data. The course will emphasize the visualization of the basic concepts to help participants develop a strong understanding of the strengths and limitations of these methods.

The proposed list of topics includes: classical univariate and multivariate repeated measures models, extensions to 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; statistical control with observational data; borrowing strength, shrinkage and bias in random effects models; contextual versus compositional effects; model building and diagnostics; consequences of measurement error and approaches to adjustment; modelling correlation; approaches for dealing with missing data and dropouts; logistic regression for binary responses; non-linear models for binary and categorical responses.

Participants should have some familiarity with multiple regression. Examples will use the R statistical language and SAS. The fifth session will be a discussion of research problems brought by participants to the class.

• For York students, staff, and faculty, the fee is $40 per course.

• Full-time students at other post-secondary institutions may enroll for a fee of $60 per course.

• For external participants, the fees per course are:
  • Exploratory Factor Analysis ...................................$240
  • Introduction to SAS for Windows.............................$240
  • Introduction to SPSS for Windows...........................$240
  • Structural Equation Modeling: A Second Course....$180
  • Repeated Measures and Beyond:
    Longitudinal Data Analysis with Mixed Models......$240

• All participants, Certificate of Completion ......................$5.00 each

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

To register in person (weekdays, from 9:00am to 12:00pm or
2:00pm to 4:00pm), please see:

Anita Valencia
Room 5075
Technology Enhanced Learning (TEL) Building

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

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

Additional information regarding registration, please telephone 416-736-5061, weekdays, from 9:00am to 12:00pm or 2:00pm to 4:00pm

• Further information on SCS courses, contact:
  

Professor Robert Cribbie
Statistical Consulting Service
Institute for Social Research (ISR)
York University
Telephone: 416-736-5115, Ext. 88615
Fax: 416-736-5749
email: cribbie@yorku.ca

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

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