Fall 2007 Short Courses
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Courses
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Instructor:
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Lisa Fiksenbaum, MA |
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Dates:
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October 3, 10, 17 and 24, 2007 (Wednesdays) |
Time:
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1-4:30pm |
Location:
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Steacie Instructional Lab, Room 021, Steacie Science Library |
Enrolment Limit:
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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:
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Nikolai Slobodianik |
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Dates:
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October 4, 11, 18 and 25, 2007 (Thursdays) |
Time:
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Noon-3pm |
Location:
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Steacie Instructional Lab, Room 021, Steacie Science Library |
Enrolment Limit:
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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.
Longitudinal and Hierarchical Data Analysis
with Mixed Models
Instructor:Professor Georges Monette Dates:Oct. 15, 22, 29, Nov. 5 and 12, 2007 (Mondays) Times:6-9pm Location:006 Accolade East Building (ACE) Enrolment Limit:25 Mixed models provide a flexible approach for the analysis of data in which each subject is observed more than once, or in which subjects are clustered in groups like classes. Mixed models are easily extended to allow non-linear models and response variables that are not normally distributed.
This course will emphasize the visualization of the basic concepts in longitudinal and hierarchical data analysis to help participants develop a strong understanding of the strengths and limitations of these methods. The proposed list of topics includes the following: Mixed models; Clustered data; Longitudinal data; Extensions of 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; Modeling correlation; Missing data patterns; Modeling panel attrition; Logistic regression for binary responses; and Non-linear models for binary and categorical responses.
This short course assumes familiarity with linear regression methods as presented, for example, in John Fox, Applied Regression Analysis, Linear Models, and Related Methods, Sage, 1997.
An Introduction to Meta-Analytic Methods
Instructor:Professor David Flora Dates:Oct. 16, 23 and 30, 2007 (Tuesdays) Time:9am-Noon Location:Room 061, Behavioural Sciences Building (BSB) Enrolment Limit:15 This course will provide a general introduction to methods for quantitative research synthesis, or meta-analysis. We will begin with a brief discussion of problem definition, literature search, and study coding. The remainder of the course will focus on the statistical concepts that are central to meta-analysis: effect size calculation, fixed- vs. random-effects models, study homogeneity, the incorporation of moderating variables, and publication bias.
Multiple Comparison Issues
in Behavioural Science Research
Instructor:Professor Robert Cribbie Dates:Nov. 7, 14 and 21, 2007 (Wednesdays) Time:1-3:30pm Location:Room 061, Behavioural Sciences Building (BSB) Enrolment Limit:20 Behavioural science researchers are frequently confronted with the problem of having to decide how to control for inflated Type I error rates when conducting multiple tests of significance. This course will outline the issues surrounding inflated Type I error rates and discuss available options for controlling these rates in popular applications such as ANOVA and regression designs and structural equation modeling.
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
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 LISREL 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 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 fee-for-service basis. Appointments can be made at http://www.isr.yorku.ca/scs with the on-line Appointment Scheduler.