SCS Short Courses, Winter 2003

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SCS Courses

Data Analysis Using SAS
Introduction to SPSS for Windows
Introduction to Structural Equation Modeling
Introduction to Geographic Information Systems (GIS)
Seminar on Bayesian Stochastic Simulation: MCMC and Related Methods

Data Analysis Using SAS for Windows

(with Baifang Xing)
Instructor: Ernest Kwan, MA
Dates: Thursdays: February 6, 13, 27, March 6, 2003
Time:9:30 a.m. - 12:30 p.m.
Location:Room T128 (PC lab) Steacie Science Library
Enrolment Limit: 25

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, transforming, and storing data; and a demonstration of how statistical analyses may be performed in SAS Insight.

Sessions Three and Four will concentrate on SAS programming techniques to modify data and enhance SAS output. More statistical procedures will be introduced for general linear models.

Introduction to SPSS for Windows

(with Oren Amitay)
Instructor: Mirka Ondrack, MSc
Dates: Wednesdays: February 5, 12, 26, March 5, 2003
Time: 10:00 a.m. - 1:30 p.m.
Location: Room CS139 Scott Library (Central Square) on February 5, 12, and March 5, 2003
Room T128 Steacie Science Library on February 26
Enrolment Limit: 20

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.

Introduction to Structural Equation Modeling

(with Dan Denis)
Instructor: Professor Robert Cribbie
Dates: Wednesdays, January 22, 29, February 5 and 12, 2003
Time: 1:30 - 4:00 p.m.
Location: BSB 061-- 1:30-3:00, BSB 159 (Hebb Computer Lab)-- 3:00-4:00
Enrolment Limit: 20 30
This course will provide a general introduction to the methods of structural equation modeling (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 primary objectives of this class will be to provide:
  1. the ability to recognize situations where these techniques may be useful in research;
  2. an appreciation for the roles of sound theory in making these techniques useful;
  3. an understanding of the limitations of these methods; and
  4. the ability to use available software for analyzing data.

Introduction to Geographic Information Systems (GIS)

Instructor: Lu Wang (MSc, PhD candidate in Geography)
Dates: Tuesday, February 4, 11, 25 and March 4, 2003
Time: 9:00 - 12:00 noon
Location: Room N302 (GIS lab), Ross building
Enrolment Limit: 25
Geographic Information System (GIS) is a computer tool for visualizing, processing and analyzing data with a spatial dimension such as population distribution and facility location. This course provides hands-on experience with ArcView 3.2 and participants will learn how to construct, plot and edit maps, perform queries and basic spatial analyses. Examples will be drawn from Canadian census and other social science data. The class will be convened in the GIS computer lab.

The four three-hour sessions in this short course will cover:

  1. introducing basic GIS concepts and getting started with ArcView
  2. mapping census data
  3. working with attribute table and spatial queries
  4. geocoding street addresses

Seminar on Bayesian Stochastic Simulation: Markov Chain Monte Carlo and Related Methods

Instructor: Professor Jeff Gill, University of Florida
Dates: January 17, 2003
Time: 9:00 - 12:00 noon, 1:00 - 4:00 p.m.
Location: Room 3009, Vari Hall
Enrolment Limit: 40
Historically, a major historical problem with the Bayesian approach is that sometimes realistic models lead to posterior calculations that are difficult or impossible to perform analytically. Suppose that instead of performing difficult analytical calculations, one could produce a large number of simulations from the posterior and describe statistics of interest empirically. This is what Markov chain Monte Carlo methods provide for the applied Bayesian researcher. This seminar will review MCMC from the ground up: basics and theory through applications and diagnostics. Contents:
  1. Summary of Bayesian Inference
  2. Troublesome Posteriors and Other Annoyances
  3. Markov Chain Theory
  4. Metropolis-Hastings and Related Algorithms
  5. The Gibbs Sampler
  6. Applications with Real Data
  7. Using WinBUGS and R for MCMC
  8. Convergence and Additional Worries
  9. Advanced Tools: Simulated Annealing, Coupling From the Past
Course notes may be found at:
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