Please note the following courses offered by the Institute for Social Research’s Statistical Consulting Service this winter.
1. A GENTLE INTRODUCTION TO R: R is an independent open source statistical software package that is of value for its wide-ranging pre-programmed statistical procedures and capacity for programming tailored statistical analyses. Also, 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. Takes place on Mondays, Jan. 31, Feb. 7 and 14, 2011 from 9am-Noon in Room 159, Hebb Lab, Behavioural Sciences Building (BSB). Instructor: Professor Rob Cribbie (Psychology).
2. AN INTRODUCTION TO SAS FOR WINDOWS: This course provides a basic introduction to the Statistical Analysis System and its underlying logic. It includes an explanation of the Display Manager System and the SAS Data step for reading, transforming and storing data. Programming techniques to modify data and enhance SAS output are included and statistical procedures for general linear models will also be presented. The course is designed for participants with some introductory level statistical knowledge, but no previous experience in using SAS. Takes place on Wednesdays, Feb. 2, 9, 16 and Mar. 2, 2011 from 1-4:30pm in the Steacie Instructional Lab, Room 021, Steacie Science Library. Instructor: Hugh McCague, PhD (Environmental Studies).
3. AN INTRODUCTION TO SPSS FOR WINDOWS: This course presents the basics of the Statistical Package for the Social Sciences. Various ways of formatting data will be explained as well as data modifications, transformations, and the use of SPSS system files. Analysis of Variance and Least Squares Regression will also be presented as well as graphical methods for analyzing data. 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. Takes place on Fridays, Feb. 4, 11, 18 and Mar. 4, 2011 from 9am-12:30pm in the Steacie Instructional Lab, Room 021, Steacie Science Library. Instructor: Lisa Fiksenbaum, MA (Psychology).
4. LONGITUDINAL AND HIERARCHICAL DATA ANALYSIS WITH MIXED MODELS IN R: 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. 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: 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; contextual versus compositional effects, splines, and model building and diagnostics. This short course assumes familiarity with linear regression as presented, for example, in John Fox, Applied Regression Analysis and Generalized Linear Models, Second Edition (Sage, 2008). Familiarity with the basics of R will also be an asset and participants are encouraged to install R and work through introductory tutorials to prepare for the course. Takes place on Thursdays, March 3, 10, 17 and 24, 2011 from 6-9pm in Room 306, Accolade West Building (ACW). Instructor: Professor Georges Monette (Mathematics and Statistics).
5. VISUALIZING CATEGORICAL DATA WITH SAS AND R: This short course provides a brief introduction to statistical methods for analyzing discrete data and frequency data, together with some of the graphical methods which are useful for understanding patterns of association among categorical variables. Some of the topics include: methods for discrete frequency distributions; association plots for two-way tables; correspondence analysis; mosaic displays and friends; effects plots for log-linear models and logistic regression; diagnostic plots for model assumptions; and models for repeated measures. These methods are illustrated using SAS software based on Friendly (2000), Visualizing Categorical Data, and also with R software, using the vcd package. See http://www.math.yorku.ca/SCS/Courses/VCD/ for further course information. This course is designed for people with a basic statistical background and some interest in data visualization. A good working knowledge of the principles and practice of multiple regression, as well as elementary statistical inference, is assumed. Previous experience with either SAS or R is helpful, though not essential. Takes place on Tuesdays, March 8, 15, 22 and 29, 2011 from 2:30-6:30pm in Room 159, Hebb Lab, Behavioural Sciences Building (BSB). Instructor: Professor Michael Friendly (Psychology).
Because material in all courses 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.
For additional information on these short courses and online registration, please click on Short Courses at: http://www.isr.yorku.ca
Please contact Anita Valencia at the Institute for Social Research for additional registration information.