Psychology 6136: Categorical Data Analysis
- Instructor: Michael Friendly (my home page)
- Email: friendly AT yorku DOT ca
- Office: 226 BSB
- Phone: x66249
- Office hour: Wednesday: 11:30-12:30 (other times by appt.)
- Class meetings:
Tuesday, 11:30 am - 2:30 pm, 203 BSB, lab session: 2:00-3:00, Hebb lab, 159
BSB. The first class will be on Jan. 13.
Text books and readings
The main texts for this course are
- Friendly, M. and Meyer, D. (2015). Visualizing Categorical Data with R. Chapman & Hall. Download
chapters from the VCDR links below.
- Agresti, A. (2007).
Introduction to Categorical Data Analysis, 2nd ed., NY: Wiley.
- Agresti, A. (2013). Categorical Data Analysis,
3rd ed., NY: Wiley. A much more technical book, that many consider the "bible" for categorical data analysis methods.
There is also a manual for
R and S-plus users
to accompany this text.
- Fox, John. Applied Regression Analysis and Generalized Linear Models, 2nd Ed.
Sage, 2008. An excellent text on linear models; Part IV on Generalized Linear Models provides a clear and comprehensive discussion.
Fox & Weisberg An R Companion to Applied Regression,
2nd Ed., Sage, 2011. There is also a web page for the book,
containing data files, R scripts and a collection of web appendices on other topics.
Topic schedule and lecture notes
There will be occasional short assignments posted here and announced in class. These assignments are
ungraded, unless a graded assignment is announced in advance. Details regarding a useful way of
formatting R exercises are described in Assignment 1.
See Compiling Notebooks,
which describes how to compile HTML, PDF, or MS Word notebooks from R scripts for further details.
Please submit your assignments to me by email, as a PDF, Word, or HTML attachment
(together with the associated R file),
a Subject: line "PSYC 6136: Assignment XX".
To help me keep them straight, it would be most convenient to name them something like
three two components to your evaluation in the course: two take-home projects (each worth 40% 50%) that will involve analysis of one or more data sets together with a research report describing the background, your analyses, results and conclusions. For these, you can use any software you like, although R is strongly encouraged.
Project 1 : a selection of data sets for the material up to and including logistic regression. Due date: Mar. 10.
Project 2: a selection of data sets for the material from logistic regression to the end of the course. Due date: May 8.
The remaining 20% can be earned either as
- an assignment portfolio, containing a selection of your best work (possibly edited/enhanced) on a selection of assignment questions, or by
- reading and discussing a journal article related to theory or application of categorical data analysis. For the latter, you can volunteer to give a brief (~20 min) presentation to the class (sometime in Mar.) to earn bonus marks. Due date: Apr 7
In lectures and lab sessions I will be using R software
nearly exclusively, together with the R Studio user interface for R.
You are well-advised to download and install these to your computer so you can follow along.
R software guides
- R Project for Statistical Computing.
R is a free software environment for statistical computing and graphics (Windows, MacOS, Linux).
You can download it from any CRAN mirror site.
See John Fox's notes, Installing R for Windows.
Somewhat out of date, but still useful.
For this course, use this R script to install useful add-on packages for categorical data analysis.
- R Studio is a powerful front-end for R, much more convenient than the standard R GUI.
Among other features, it offers integrated tools for help, plotting, history and the ability to run an R script to obtain a
HTML, PDF or Word document containing your input and output.
- If you insist on SAS or SPSS, see
York group licenses for SAS and SPSS
You may also be able to use the
Web Acadlabs service
to access SAS, SPSS from home. See
- Accessing software remotely using Web Acadlabs by Manolo Romero.
- Getting started:
A (very) short introduction to R,
covers the basics of installing R and R Studio,
the R Studio window layout, and an overview of R commands, data structures and functions.
- R in One Page (well, 2); also: The
R Reference Card (4 pages)
- An Introduction to R (Official introductory guide: 100 pages)
- R Tutorials A nice collection of tutorials, from introduction, to graphics, to programming by Ista Zahn
- Quick R for SAS, SPSS, Stata Users Great site for conversions to R!
- R Cookbook, a collection of recipes for analyzing data from psychology
- R Graphics Cookbook, good book on graphics in R, with a useful web site
containing lots of examples, particularly for ggplot2
© 2014 Michael Friendly
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