- Introduction to SAS for Windows
- Introduction to SPSS for Windows
- Confirmatory Factor Analysis
- Statistical Issues in Pay Equity
- Model Based Approaches to Cluster Analysis
- Data Visualization Workshop: ViSta, The Visual Statistics System

- Instructor:
- Peggy Ng
- Dates:
- Windows Pre-session: February 1 Basics: Feb 8, 15 Intermediate Topics: Mar 1, 8
- Time:
- WEDNESDAYS, 10:00 a.m. - 1:00 p.m.
- Location:
- Room T107 Steacie Science Library
- Enrolment Limit:
- 30

The course consists of three parts, which may be taken individually or as a whole:

**I Windows Pre-session:** The pre-session is intended to make the
SAS sessions accessible to those without previous experience with
Windows on personal computers. Only the bare essentials of
Windows will be covered; those familiar with Windows need not
attend.

**II Basic Introduction**: 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
Proc (procedure) steps.

**III Intermediate Topics**: Sessions Three and Four will concentrate
on SAS programming techniques to modify data and enhance SAS
output. As well, more statistical procedures will be introduced.

- Instructor:
- Mirka Ondrack
- Dates:
- Windows Pre-session: January 31 Basics: Feb 7, 14 Intermediate Topics: Feb 28, Mar 7, 14
- Time:
- TUESDAYS, 1:00 a.m. - 4:30 p.m.
- Location:
- Room T107 Steacie Science Library
- Enrolment Limit:
- 30

This course consists of three parts, which may be taken individually or as a whole:

**I Windows Pre-session**: The pre-session is intended to make the
SPSS sessions accessible to those without previous experience
with Windows on personal computers. Only the bare essentials
of Microsoft Windows will be covered.

**II Basic Introduction:** Session One is an elementary
introduction to statistical computer programs, computing
concepts, and the essentials of SPSS. At the end of the first
session, participants should be able to run very simple
programs, including some basic descriptive statistical
procedures. Session Two will cover first-session topics in greater
detail, concentrating on data definition facilities and various
ways of formatting data.

**III Intermediate Topics**: Sessions Three and Four will
introduce data modification, transformations, and functions.
Session Five will cover the use of SPSS system files.

- Instructor:
- Roman Konarski
- Dates:
- THURSDAYS, 10:30-12:30, March 2, 9, 16, 23
- Location:
- Room 103, ASB

This course provides an introduction to the theory, methods, and empirical applications of CFA within the "LISREL" framework.

The course will cover the specification of: 1) classical test theory models; 2) the multitrait-multimethod model; 3) the second-order factor model; 4) longitudinal factor analysis; and 5) multi-sample analysis including the estimation of latent means. The course will also address estimation problems (improper solutions), and the assessment of model fit.

The course will be of interest to those who are currently using EFA and find that their research problems are more appropriately analyzed with CFA, and to those who are interested in the general structural-equation ("LISREL") model.

Familiarity with elementary matrix algebra will be useful, though not essential, for understanding LISREL syntax.

- Instructor:
- Professor Georges Monette
- Dates:
- THURSDAYS, 1:30-3:30, March 2, 9, 16, 23 Location: ASB 102.

In a "policy-capture" approach to creating a policy for a large firm, factor analysis is sometimes used to help define job factors. Multiple regression is used to determine the weights to attach to each job factor in computing the value of each job.

While these statistical tools can provide powerful insights they can also, when used without sufficient understanding, produce results worse than those that might have been obtained without statistical methods.

This course will explore some of the policy pitfalls in the use of statistics for pay equity. The thesis of the course is that statistics has a powerful but circumscribed role in pay equity. It is important to appreciate the limitations of statistical techniques so that decisions with important consequences not be entrusted to a statistical procedure that is inappropriate for the task expected of it.

Some of the topics we will consider are:

- the estimation of the gender gap,
- identifying the gender of job factors,
- the use of transformations,
- the role of diagnostics, and
- the use of modern methods of regression.

- Instructor:
- Barry Smith
- Dates:
- TUESDAYS, March 28, April 4
- Time:
- 1:30 - 4:30pm
- Location:
- T107 Steacie
- Enrolment Limit:
- 30

Researchers in a variety of fields use clustering techniques to search for structure in (often) large multidimensional data sets. The goal is to uncover homogeneous or similar subgroups where similarity is often measured by distance between observations within groups. A principal drawback of traditional approaches to clustering lies in testing hypotheses about the number of clusters present in the data. This course will review some of this literature and point to how problems arise. It will also illustrate a new regression-based approach to clustering where it is possible to test hypotheses about the number of clusters.

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