Psychology 6136: Categorical Data Analysis

This is the OLD website for psy6136. It is no longer maintained and exists only for historical reasons. Please go to friendly.github.io/psy6136 for the current course.

• 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: Tues, 2:30 am - 5:30 pm, 014A BSB, lab session: 4:30-5:30, Hebb lab, 159 BSB. The first class will be on Jan. 10.

Text books and readings

Main texts

The main texts for this course are:

• Friendly, M. and Meyer, D. (2016). Discrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data . Chapman & Hall. ISBN 9781498725835. Web site for book: ddar.datavis.ca
• Agresti, A. (2019). Introduction to Categorical Data Analysis, 3rd ed., NY: Wiley. ISBN ISBN: 978-1-119-40528-3.

Supplementary readings:

• 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. Web site for the book. There is also a manual for R and S-plus users to accompany this text.
• Fox, John. Applied Regression Analysis and Generalized Linear Models, 3rd Ed. Sage, 2015. An excellent text on linear models; Part IV on Generalized Linear Models provides a clear and comprehensive discussion.
• Grolemund & Wickham, R for Data Science A good tutorial book on the tidyverse approach to data analysis in R. Available free online.
• Fox & Weisberg An R Companion to Applied Regression, 3rd Ed., Sage, 2018. 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

Week	Topic	Readings	R files
1	Overview [slides] [4up] [Working with R Studio] [4up]	DDAR: Ch1, Ch2; Agresti: Ch1	R-into.R []
2	Discrete distributions [slides] [4up]	DDAR: Ch3	R-data.R [] binomial.R []
3	Two-Way Tables: Independence and Association [slides] [4up]	DDAR: Ch4; Agresti: Ch2	berk-4fold.R [] vision-sieve.R []
4	Two-Way Tables: Ordinal Data and Dependent Samples [Tutorial] on two-way tables	DDAR: Ch4; Agresti: Ch2	msdiag-agree.R [] haireye-spineplot.R []
5	Loglinear Models and Mosaic Displays [slides] [4up] [Tutorial] on loglin models; [Mosaic display animation]	DDAR: Ch5; Agresti: 2.7, Ch. 7	berkeley-glm.R [] titanic-loglin.R []
6	Correspondence Analysis [slides] [4up]	DDAR: Ch6	mental-ca.R [] mca-presex3.R []
7	Logistic Regression I [slides] [4up] [Logistic regression tutorial]	DDAR: 7.1-7.3; Agresti: 3.1-3.2; Ch 4	arthritis-logistic.R [] cowles-logistic.R [] Arrests-logistic.R []
8	Logistic Regression II [slides] [4up]	DDAR: 7.3-7.4; Agresti: Ch 4-5	cowles-effect.R [] Arrests-effects.R [] berkeley-diag.R []
9	Multinomial Logistic Regression [slides] [4up]	DDAR: 7.5-7.6; Agresti: Ch 6	arthritis-propodds.R [] wlf-nested.R [] wlf-glogit.R []
10	Log-Linear Models I [slides] [4up]	DDAR: 8.1-8.4; Agresti: Ch 7
11	Log-Linear Models II [slides] [4up]	DDAR: 8.5-8.11; Agresti: Ch 8
12	Generalized Linear Models: Count Data[slides] [4up] [Count data GLMs tutorial]	DDAR: 9.1-9.4; Agresti 3.3-3.5	phdpubs.R [] quine.R []
13	Generalized Linear Models: Further topics	DDAR: 9.5-9.7

Assignments

Please submit your assignments to me by email, as a PDF, Word, or HTML attachment (together with the associated R file), with a Subject: line "PSYC 6136: Assignment XX". To help me keep them straight, it would be most convenient to name them something like "YourName-AssignXX.{pdf,docx,html}".

• Assignment 1
• Assignment 2
• Assignment 3
• Assignment 4

Evaluation

There are three components to your evaluation in the course: two take-home projects (each worth 40%) 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.

Here is a template for a markdown .Rmd file you can use if you are working in RStudio and want to write in R markdown.

1. Project 1 : a selection of data sets for the material up to and including logistic regression. Due date: Nov. 3

2. Project 2: a selection of data sets for the material from logistic regression to the end of the course. Due date: Dec. 15

3. The remaining 20% can be earned either as
   1. an assignment portfolio, containing a selection of your best work (possibly edited/enhanced) on a selection of assignment questions, or by
   2. 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 (~15 min) presentation to the class (sometime in Nov.) to earn bonus marks. Due date: Dec. 29

Resources

Statistical software

• 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.