Winter 2008 Short Courses


The 2008 Winter Courses have concluded.
An Introduction to SAS for Windows

Instructor:

Lisa Fiksenbaum 

Dates:

February 7, 21, 28 and March 6, 2008 (Thursdays) 
Time:

14:30pm 
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. 
Instructor:

Professor David Flora 

Dates:

February 20, 27, March 5 and 12, 2008 (Wednesdays) 
Time:

911:30am 
Location:

Room 002, Accolade West Bldg 
Enrolment Limit:

30 
This short course will introduce the structural equation modeling (SEM) approach to analyzing longitudinal data. This type of analysis is often called "latent trajectory",or "latent growth curve" modeling. After introducing the concepts of individual and average trajectories, the course will give a detailed description of the model for linear change. Next, extensions of the model will be described, such as the incorporation of covariates (i.e., predictors of change) and models for non linear change. The use of various software packages (e.g., SAS, LISREL) will also be presented. Some prior exposure to SEM is beneficial but not essential, nor is familiarity with the software. 
Instructors:

Professor Robert Cribbie Professor Georges Monette Professor John Fox Hugh McCague 

Dates:

February 22, 29, March 7, 14, 2008 (Fridays) 
Time:

Noon2:30pm 
Location:

Room 2118, Technology Enhanced Learning (TEL) Bldg 
Enrolment Limit:

30 
R is the most common programming language for statistics. It is an independent open source (i.e., free) statistical software package. R is of great value for its wideranging preprogrammed statistical procedures and capacity for programming tailored statistical analyses. Also, R is invaluable for generating informative highquality 2D graphics and interactive 3D graphics. This short course is a gentle stepbystep handson 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 followup help and learning about R. 
Instructor:

Professor Michael Friendly 

Dates:

February 25, March 3, 10, 2008 (Mondays) 
Time:

14pm 
Location:

Room 3006, Vari Hall 
Enrolment Limit:

30 
This course provides an overview of the theory and practice of exploratory and confirmatory factor analysis. Topics to be covered include: a. the use of principal components analysis as a data reduction technique; b. the basic ideas of factor analysis and the common factor model; c. factor rotation methods; d. the development of confirmatory factor models; and e. applications of CFA models, including testtheory models of "equivalence" of measures, multitraitmultimethod data, and multisample analysis. The level of the course will be largely conceptual and applied. Some familiarity with elementary matrix algebra will be useful, though not essential. Extensive examples, primarily using SAS and LISREL, will be presented. 
Course fees must be paid at the time of registration.
Refunds are available upon three business days' notice Please review our policy regarding refunds here. 

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 fulltime 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, modelbased approaches to cluster analysis, introduction to the R programming language, and visual methods for statistical data analysis. The Statistical Consulting Service staff also assist in teaching these topics by giving presentations in regular university classes. 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 feeforservice basis. Appointments can be made at http://www.isr.yorku.ca/scs with the online Appointment Scheduler. 