Fall 2008 Short Courses
|
|||||||||||||||||||||||||||||||||||||||||||||
|
Courses
|
|
Instructor:
|
Lisa Fiksenbaum, MA |
---|---|
Dates:
|
October 7, 14, 21 and 28, 2008 (Tuesdays) |
Time:
|
1-4:30pm |
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.
Click here to download SPSS course materials
Instructor:
|
Hugh McCague, PhD |
---|---|
Dates:
|
October 8, 15, 22 and 29, 2008 (Wednesdays) |
Time:
|
9:30am-1pm |
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.
Click here for the SAS course assignments and related materials
An Introduction to
Structural Equation Modeling
Instructor:Professor David Flora Dates:October 16, 23 and 30, 2008 (Thursdays) Times:9-11:30am Location:Hebb Lab, Room 159,
Behavioural Sciences Building (BSB) Enrolment Limit:20 Structural equation modeling (SEM) is a very general framework for specifying and evaluating linear, parametric statistical models that allow any number of independent and dependent variables as well as the incorporation of hypothetical “latent” variables. Specific types of SEM include path analysis, confirmatory factor analysis, and growth curve models, among others.
This course will provide a general introduction to the methods of SEM, including a discussion of developing models, evaluating the fit of models to data, evaluating the significance of model parameters, and performing model modification. The course will include instruction in the use of software for SEM. It is expected that course participants have prior experience with multiple regression analysis.
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.
An Introduction to R
Instructor:Professor Robert Cribbie Dates:November 6, 13 and 20, 2008 (Thursdays) 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 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.
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 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.
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, 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 http://www.isr.yorku.ca/scs to make appointments online with SCS consultants.