Courses & short courses

This page contains brief descriptions and links to courses and short courses I teach that have online materials.

Psychology 6135: Psychology of Data Visualization

A one-semester graduate course in data visualization for Psychology and other students. This course examines a variety of issues related to data visualization from a largely psychological perspective, but also touches upon other related communities of research and practice related to this topic:
  • history of data visualization,
  • computer science and statistical software,
  • visual design,
  • human factors.
It also considers visualization methods for a wide range of types of data from the points of view of both the viewer and designer/producer of graphic displays.
Course web site

Psychology 6136: Categorical Data Analysis

A one-semester graduate course in categorical data analysis for Psychology and other students. This course is designed as a broad, applied introduction to the statistical analysis of categorical (or discrete) data, such as counts, proportions, nominal variables, ordinal variables, discrete variables with few values, continuous variables grouped into a small number of categories, etc.

Throughout, there is a strong emphasis on associated graphical methods for visualizing categorical data, checking model assumptions, etc.

It uses my book, Discrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data and the accompanying web site.

Course web site

Psychology 6140: Multivariate Data Analysis

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)

Course web page

Visualizing Categorical Data with SAS and R

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


Categorical Data Analysis with Graphics

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.

Course web page

Exploratory and Graphical Methods of Data Analysis

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.

Data Screening

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

Course web page

Exploratory and Confirmatory Factor Analysis

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)

Course web page

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