---------- Sorry, this course is full ----------- An Introduction to Structural Equation Modeling
Instructors:	Constance Mara, MA, and Professor Robert Cribbie
Dates:	January 16, 23 and 30, 2012 (Mondays)
Times:	12:30-3:30pm
Locations:	Room 159 (Hebb Lab), Behavioural Sciences Building (BSB)
Enrolment Limit:	20
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 course will include instruction in the use of software for SEM. It is expected that course participants have previous statistical training up to the level of multiple regression analysis and that they are familiar with SPSS. Please note: This course is taught as a lecture-lab combination in the Psychology Department Hebb Lab, 159 Behavioural Sciences Building. You will need to have an active FAS login account for the Hebb Lab (all Psychology undergraduates, graduate students and faculty already have one) to carry out the lab components. A temporary FAS login account for the Hebb Lab for this course will be provided if you do not have one. 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.

A Gentle Introduction to R
Instructors:	Carrie Smith, MA, and Professor Robert Cribbie
Dates:	January 25, February 1, 8 and 15, 2012 (Wednesdays)
Times:	1-4pm
Locations:	Room 159 (Hebb Lab), Behavioural Sciences Building (BSB)
Enrolment Limit:	20
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) carry out 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. Please note: This course is taught as a lecture-lab combination in the Psychology Department Hebb Lab, 159 Behavioural Sciences Building. You will need to have an active FAS login account for the Hebb Lab (all Psychology undergraduates, graduate students and faculty already have one) to carry out the lab components. A temporary FAS login account for the Hebb Lab for this course will be provided if you do not have one. 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.

An Introduction to SPSS for Windows
Instructor:	Lisa Fiksenbaum, MA
Teaching Assistant:	Danielle Salomonczyk
Date:	February 1, 8, 15 and 29, 2012 (Wednesdays)
Time:	1-4:30pm
Location:	Steacie Instructional Lab, Room 021, Steacie Science Library
Enrolment Limit:	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. 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 self-extracting (.exe) file. Click here to download the SPSS course data in a zip file.

An Introduction to SAS for Windows
Instructors:	Ryan Barnhart, MA and Hugh McCague, PhD
Teaching Assistant:	Matt Friedlander
Date:	February 3, 10, 17 and March 2, 2012 (Fridays)
Time:	9am-12:30pm
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. Click here to download the SAS course data Click here for the SAS course assignments and related materials

Visualizing Categorical Data with SAS and R
Instructor:	Professor Michael Friendly
Teaching Assistant:	Bin Sun
Date:	February 29, March 7, 14, 21 and 28, 2012 (Wednesdays)
Time:	2:30-6:30pm
Location:	Room 159 (Hebb Lab), Behavioural Sciences Building (BSB)
Enrolment Limit:	20
Statistical methods for categorical data, such as log-linear models and logistic regression, represent discrete analogs of the analysis of variance and regression methods for continuous response variables. 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 in the lectures using SAS software based on Friendly (2000), Visualizing Categorical Data, and also with R software, using the vcd package that is based on that book. 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. Some previous experience with either SAS or R is helpful, though not essential. Please note: This course is taught as a lecture-lab combination in the Psychology Department Hebb Lab, 159 Behavioural Sciences Building. You will need to have an active FAS login account for the Hebb Lab (all Psychology undergraduates, graduate students and faculty already have one) to carry out the lab components. A temporary FAS login account for the Hebb Lab for this course will be provided if you do not have one. 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.

An Introduction to Structural Equation Modeling ..	$271.20
A Gentle Introduction to R ...............................	$361.60
An Introduction to SPSS for Windows.................	$361.60
An Introduction to SAS for Windows...................	$361.60
Visualizing Categorical Data with SAS and R .........	$361.60
Longitudinal and Hierarchical Data Analysis with Mixed Models in R.....................................	$361.60
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 9:00am 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 Gordon Barnhart 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 Robert Cribbie is an Associate Professor in the Department of Psychology at York University and Joint Coordinator of SCS (2011-12). He received his PhD in Quantitative Psychology from the University of Manitoba. His research interests include multiple comparison procedures, robust ANOVA strategies, and structural equation modeling.

Lisa Fiksenbaum is a doctoral candidate in Social/Personality Psychology at York University. She also received her BA and MA from York. Her training and experience in statistics includes a Teaching Assistantship for the Honours Thesis course (Psych 4170) and she has been involved in several research projects, both at the University and in the private sector. Her research interests include organizational issues, work-family relationships, stress and coping. Ms. Fiksenbaum is proficient in SPSS, and regularly consults with graduate and undergraduate students for SCS.

Professor Michael Friendly received his doctorate in Psychology from Princeton University, specializing in Psychometrics and Cognitive Psychology. He is a Professor of Psychology at York and Joint Coordinator (2011-2012) with the Statistical Consulting Service. In addition to his research interests in psychology, Professor Friendly has broad experience in data analysis, statistics and computer applications. He is the author of SAS for Statistical Graphics, 1st Edition and Visualizing Categorical Data, both published by SAS Institute, and an Associate Editor of the Journal of Computational and Graphical Statistics and Statistical Science. His recent work includes the further development of graphical methods for categorical data and multivariate data analysis.

Constance Mara is a PhD Candidate in Psychology at York University in the Quantitative Methods area of specialization. Her research interests include equivalence testing, statistical mediation analysis, longitudinal data analysis, and structural equation modeling. She is familiar with several statistical software programs, including SPSS, SAS and R.

Hugh McCague completed a BMath in Statistics at the University of Waterloo, and an MA in Statistics and a PhD in Environmental Studies both at York University. He is a statistician at the Institute for Social Research, and a consultant and instructor for the Statistical Consulting Service. His research and publications concentrate on applications of mathematics and statistics in health and environmental studies.

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.

Carrie Smith is a PhD Candidate in Psychology at York University specializing in Quantitative Methods. She received her MA in Psychology at York, and BASc in Engineering at the University of Toronto. Her research interests include data visualization and developing robust methods of statistical analysis appropriate for behavioural science data. She has been using R for statistical computing in her research and consulting for several years.

Teaching Assistants

Matt Friedlander completed his BA and MA degrees in Statistics at York University and is currently pursuing his PhD degree. His doctoral research is in Statistics in the area of Bayesian model selection and discrete graphical models. Matt is currently a consultant with ISR and is able to help with data analysis using SAS and R.

Danielle Salomonczyk is a doctoral candidate in the Brain, Behaviour and Cognitive Science stream of the Psychology Graduate Program at York University. She received her B.Sc.(Hons) from the University of Toronto and her M.A. from San Diego State University. Her training and experience in statistics includes a Teaching Assistantship for Statistics I (PSYC2021) and the honours thesis course (PSYC4170), assisting the SCS SPSS short course for 2010 and 2011, as well as statistical consulting for York university graduate and undergraduate students and external organizations. Her research interests include motor learning and sensory plasticity in healthy young and older adults and individuals with Parkinson's disease.

Bin Sun is a PhD student in Statistics in the Department of Mathematics and Statistics at York University. She was a part-time biostatistician at Princess Margaret Hospital for two years. Her primary research interest now is Semi-Parametric modeling with missingness. She has experience in longitudinal data analysis, mixed modeling, survival analysis and genetic statisics with R and SAS.

Yawen Xu is a PhD student in the Department of Mathematics and Statistics at York University. Recently, she was enrolled in the Financial Engineering Program in the Schulich School of Business. Her main research interests are regression models, generalized linear models, longitudinal data analysis and numerical methods for Finance. Yawen is proficient in Splus/R, SAS and Maple.

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.

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