Courses & short courses
This page contains brief descriptions and links to courses and short courses I teach that have online materials.
- history of data visualization,
- computer science and statistical software,
- visual design,
- human factors.
Course web page
Course web page
Psychology 6140 provides an integrated, in depth, but applied approach to multivariate data analysis and linear statistical models in behavioural science research. There is a strong emphasis on using graphical methods to understand your data. The statistical topics covered include:
- Regression analysis
- Univariate and multivariate ANOVA and ANCOVA
- Discriminant analysis
- Logistic regression
- Canonical correlation analysis
- Principal components, factor analysis, LISREL models (CFA, SEMs)
- Cluster analysis and Multidimensional Scaling (time & interest permitting)
A 5-day intensive short course, with lectures and workshop sessions
offered occasionally through
the Statistical Consulting Service at York University
and the GESIS Spring Seminar Series, Cologne, Germany, Mar 16-20, 2009.
Topics covered include:
- Introduction & overview
- Two-way and n-way tables
- mosaic displays & loglinear models
- Logit models & logistic regression
- Polytomous response models
An older version of the VCD short course offered through the Statistical Consulting Service. The course used SAS exclusively for examples, but also provided supplemental notes for SPSS users.
Online materials include lecture slides and (even older) PDF and HTML documents of lecture notes.
A short course offered through the Statistical Consulting Service at York University and our Summer Program in Data Analysis (SPIDA) in 2001. The online version contains the text, tables and character-based graphs of the printed version, but does not include any of the many high-resolution graphs.
This workshop covers a variety of practical aspects of data screening, including:
- Entering and checking raw data
- Assessing univariate problems (distribution shape, outliers)
- Assessing bivariate problems (linearity, regression diagnostics)
- Assessing multivariate problems (multivariate normality, detecting multivariate outliers)
- Dealing with missing data
A three-week short course taught occasionally through the Statistical Consulting Service
- Part 1: PCA (Overview; Principal components analyssis; PCA details; biplots)
- Part 2: EFA (Basic ideas of factor analysis; Factor estimation methods; Factor and component rotation)
- Part 3: CFA (Restricted ML factor analysis; ACOVS model; LISREL model: CFA and SEM; Factorial invariance; Power and sample size for EFA and CFA)