Institute for Social Research

Winter 2013 SCS Short Courses

Courses

An Introduction to SAS for Windows

An Applied Introduction to SPSS

Longitudinal and Hierarchical Data Analysis with Mixed Models in R

Introduction to Item Response Theory

Pre-registration and payment of fees is required for all Short Courses.

Please follow these links for details on:

Course Fees
Registration
Certificate of Completion
Statistical Consulting Service

[Click here for Previous Courses]

---Sorry, this course is full --- An Introduction to SAS for Windows
Instructor:	Ryan Barnhart, MA
Dates:	Feb. 6, 13, 27 and Mar. 6, 2013 (Wednesdays)
Time:	1:00pm - 4:30pm
Location:	Steacie Instructional Lab, Room 021, Steacie Science Library
Enrolment Limit:	35
This short course provides an introduction to the Statistical Analysis System (SAS) syntax commands and procedures. We will cover the basics of: reading, transforming, sorting, merging and saving data files in some common formats; selecting cases, and modifying and computing variables; performing some basic statistical procedures and tests such as descriptive statistics, correlations, contingency tables, Chi-square tests, t-tests, ANOVA and linear regression; creating bar charts and scatter plots; composing simple macros for tailored procedures; and saving output results and work in some common formats. This course is designed for participants with some introductory level statistical knowledge, but no previous experience in using SAS. Please note that while this course will focus on the implementation of introductory statistics in SAS, it is not intended as a review of basic statistics. This short course will get you well underway in using SAS. Please note that the Steacie Instructional Lab [Steacie 021] is accessed by entering Steacie Library and then proceeding to the basement of that Library. Because this material is presented sequentially and builds upon the basics presented at the beginning of each class, course participants need to arrive on time and attend the entire session. Please note that food and drink are not allowed in Steacie Library and the Steacie Instructional Lab. The only exceptions are capped bottles of water (not juice/pop) and spill proof mugs (not cups of coffee). Washrooms are available nearby outside the library.

--- Sorry, this course is full --- An Applied Introduction to SPSS
Instructor:	Matthew Sigal, MA
Dates:	Feb. 8, 15, Mar. 1 and 8, 2013 (Fridays)
Time:	9:00am – 12:30pm
Location:	Steacie Instructional Lab, Room 021, Steacie Science Library
Enrolment Limit:	35
This course aims to acquaint participants with the Statistical Package for the Social Sciences (SPSS). The curriculum has been recently revised to not only introduce the basic functions and features of the software (including data entry and manipulation), but also demonstrate how to conduct a range of statistical analyses. Hands-on exercises will supplement the lecture material. This course is designed as an applied introduction to a statistical program; as such, familiarity with basic statistical procedures is assumed (e.g. t-tests, ANOVA, regression). Please note that the Steacie Instructional Lab [Steacie 021] is accessed by entering Steacie Library and then proceeding to the basement of that Library. Because this material is presented sequentially and builds upon the basics presented at the beginning of each class, course participants need to arrive on time and attend the entire session. Please note that food and drink are not allowed in Steacie Library and the Steacie Instructional Lab. The only exceptions are capped bottles of water (not juice/pop) and spill proof mugs (not cups of coffee). Washrooms are available nearby outside the library. Click here to download the SPSS course data in a zip file.

Longitudinal and Hierarchical Data Analysis with Mixed Models in R
Instructor:	Professor Georges Monette
Dates:	Feb. 28, Mar. 7, 14, 21 and 28, 2013 (Thursdays)
Time:	7-10pm
Location:	Room 1004, Technology Enhanced Learning (TEL) Building
Enrolment Limit:	30
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 emphasizes the visualization of the basic concepts in longitudinal and hierarchical data analysis to help with an understanding of the strengths and limitations of these methods. Topics include: 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. Extensive examples will be given in the R programming environment. For further information and class notes, please see: http://scs.math.yorku.ca/index.php/Mixed_Models_with_R Because this material is presented sequentially and builds upon the basics presented at the beginning of each class, course participants need to arrive on time and attend the entire session.

Introduction to Item Response Theory
Instructor:	Professor David Flora
Dates:	Mar. 5 and 12, 2013 (Tuesdays)
Time:	2:30-5:00pm
Location:	Room 163 Behavioural Sciences Building (BSB)
Enrolment Limit:	40
Item Response Theory (IRT) encompasses a set of statistical models and related methods for analyzing the individual items within a test or questionnaire. Through item analysis, IRT can be used for test or scale construction, validation, scoring, bias assessment, or basic theoretical research. IRT has been most commonly applied in educational testing, but it has also become more popular for psychological questionnaires measuring attitudes, psychopathology, personality traits, and so on. These two lectures will provide a basic introduction to IRT. The most commonly used models will be defined and explained (two- and three-parameter models for dichotomously scored items and a graded model for items with ordered response categories, such as Likert-type items), utilizing empirical examples and accompanying software implementation throughout. Additional topics that may be addressed, time permitting, include test scoring, differential item functioning (DIF), computerized adaptive testing (CAT), and multidimensional IRT. Because this material is presented sequentially and builds upon the basics presented at the beginning of each class, course participants need to arrive on time and attend the entire session.

Course Fees

All fees include HST

For York University students, the fees are $45.20 per course.

For York University faculty and staff, the fees are $99.44 per course.

Full-time students at other post-secondary institutions may register for a fee of $90.40 per course.

For external participants, the fees per course are:

An Introduction to SAS for Windows...................	$397.76
An Applied Introduction to SPSS........................	$397.76
Longitudinal and Hierarchical Data Analysis with Mixed Models in R ....................................	$397.76
Introduction to Item Response Theory...............	$198.88

All participants: Certificate of Completion............	$5.65 each

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.

Registration

You can register for courses by completing the
on-line registration form.

To register in person (weekdays, from 10:00am to 12:00pm or
2:00pm to 4:00pm), please see:

Betty Tai
Room 5075
Technology Enhanced Learning (TEL) Building

To register by mail, print a blank registration form,
complete, and send to:

Betty Tai
Institute for Social Research
Room 5075
Technology Enhanced Learning Building
York University
4700 Keele Street
Toronto, ON M3J 1P3
Canada

You may also fax a completed registration form to: 416-736-5749

Certificate of Completion

Available on request, full attendance is required.

A $5.65 administrative fee applies, for each certificate requested.

Additional Information

Additional information regarding registration:
please telephone 416-736-5061, weekdays,
from 9:00am to 12:00pm or 2:00pm to 4:00pm

Directions to York University (Keele Campus), building and parking lot locations click here. For additional information on parking click here.

Instructors

Ryan Barnhart, MA is a PhD candidate in Psychology at York University with specialization in Quantitative Methods. His research interests and statistical work has focused on longitudinal data analysis using multilevel modeling and generalized linear multilevel modeling. This work has helped Mr. Barnhart develop a multi-platform approach to using statistical software, including SAS, STATA, R and SPSS.

Professor David Flora is an Associate Professor in the Department of Psychology at York University and the Co-Coordinator (with Robert Cribbie) of the Statistical Consulting Service. He received his PhD in Quantitative Psychology from the University of North Carolina at Chapel Hill. His research interests include longitudinal data analysis, psychometric analysis, factor analysis, and structural equation modeling.

Professor Georges Monette is an Associate Professor of Mathematics and Statistics at York and an Associate Coordinator with the Statistical Consulting Service. Most of his research has been in the mathematical foundations of statistical inference. His recent interests are the geometric visualization of statistical concepts and the modeling and analysis of longitudinal data. He has worked in a number of applied areas, including pay equity and the statistical analysis of salary structures. He received his PhD in Statistics from the University of Toronto.

Matthew Sigal, MA is a doctoral student in the Quantitative Methods area of Psychology. He is a member of Dr. Michael Friendly's lab and is particularly interested in methods of data visualization, multilevel and structural equation modeling, and alternative modeling strategies within the framework of survival analysis. He has been a Teaching Assistant for both undergraduate and graduate statistics courses, and will be teaching an introductory statistics course this Term (W2013).

Statistical Consulting Service (SCS)

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 Psychology, Mathematics and Statistics, and Geography in conjunction with full-time professional staff at ISR. The faculty and staff have extensive experience with many forms of statistical analysis. Topics for which assistance is available include regression analysis, multivariate analysis, analysis of categorical data, structural equation modeling, factor analysis, multilevel/mixed 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 addressed factor analysis, structural equation modeling, graphical methods for categorical data, introduction to the R programming language, and mixed models.

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. Please go to the Institute's Web site at www.isr.yorku.ca/scs to make appointments online with SCS consultants.

. top