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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN" "http://www.w3.org/TR/html4/loose.dtd">
<html>
<head>
<title>
Psychology 6136: Categorical Data Analysis
</title>
<link rel="stylesheet" href="assets/doc.css" type="text/css">
<link rel="stylesheet" href="assets/tabs-menu.css" type="text/css">
<link rel="shortcut icon" href="icons/favicon_io/favicon-32x32.png" />
<!-- Author: Michael Friendly York University Copyright 1999 -->
</head>
<body>
<h1><center>
<img height=75 src="icons/psy6136.png" align="absmiddle" alt="psy6136 icon" hspace=6>
Psychology 6136: Categorical Data Analysis
<img height=75 src="icons/psy6136.png" align="absmiddle" alt="psy6136 icon" hspace=6>
</center>
</h1>
<center>
<ul class="basictab">
<li><a href="#description">Course description</a></li>
<li><a href="schedule.html">Class Schedule</a></li>
<li><a href="about.html#assign">Assignments</a></li>
<li><a href="about.html#evaluation"> Evaluation </a></li>
<li><a href="R/index.html">R Examples</a></li>
</ul>
</center>
<script language="JavaScript">
<!--
document.write("Updated " + document.lastModified);
// -->
</script>
<hr>
<ul>
<li> <strong> Instructor:</strong> <a href="http://datavis.ca">Michael Friendly</a> (my home page)
<ul>
<li> Email: <a href="mailto:friendly@yorku.ca"> <em> friendly AT yorku DOT ca</em></a>
<li> Office: 226 BSB
<li> Phone: x66249
<li> <strong> Office hour:</strong> Wednesday: 11:30-12:30 (other times by appt.)
</ul>
<li><strong> Class meetings: </strong>
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 <strong>Jan. 10</strong>.
</ul>
<h2><a name="description">Course description</a></h2>
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.
<ul>
<li> The course begins with methods designed for cross-classified table of counts, (i.e., contingency tables), using simple chi square-based methods.
<li> It progresses to generalized linear models, for which log-linear models provide a natural extension of simple chi square-based methods.
<li> This framework is then extended to comprise logit and logistic regression models for binary responses and generalizations of these models for polytomous (multicategory) outcomes.
<p>Throughout, there is a strong emphasis on associated graphical methods for visualizing categorical data, checking model assumptions, etc. Lab sessions will familiarize the student with software using R for carrying out these analyses.
</ul>
<h2>Text books and readings</h2>
<table>
<TD><a href="https://www.taylorfrancis.com/books/mono/10.1201/b19022/discrete-data-analysis-michael-friendly-david-meyer"><IMG src="icons/ddar-cover.png" height="120" align="left" border="0" hspace="5" alt="Discrete Data Analysis with R"></a>
<TD><a href="http://books.google.com/books?id=OG9Eqwd0Fh4C"><IMG src="icons/agresti-intro-3rd.jpg", height="120" align="left" border="0" hspace="5"></a>
<TD><a href="http://books.google.com/books?id=hpEzw4T0sPUC"><IMG src="icons/agresti-cover-3rd.jpg" height="120" align="left" border="0" hspace="5"></a>
<TD><a href="https://us.sagepub.com/en-us/nam/applied-regression-analysis-and-generalized-linear-models/book237254"><IMG src="icons/Fox-APR3rd.jpg" height="120" hspace="5" align="left" border="0"></a>
<TD><a href="https://www.amazon.ca/COMPANION-APPLIED-REGR-ESSION/dp/1544336470/"><IMG src="icons/fox-Companion3e.jpg" height="120" hspace="5" align="left" border="0"></a>
<TD><a href="http://r4ds.had.co.nz/"><IMG src="icons/r4ds.png" height="120" hspace="5" align="left" border="0"></a>
</TR>
</table>
<h4>Main texts</h4>
<p>
The main texts for this course are:
<ul>
<li>Friendly, M. and Meyer, D. (2016). <cite><a href="https://www.taylorfrancis.com/books/mono/10.1201/b19022/discrete-data-analysis-michael-friendly-david-meyer">Discrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data </cite></a>.
Chapman & Hall. ISBN 9781498725835.
Web site for book: <a href="http://ddar.datavis.ca">ddar.datavis.ca</a>
<li>Agresti, A. (2019).
<cite><a href="http://books.google.com/books?id=OG9Eqwd0Fh4C">Introduction to Categorical Data Analysis</a></cite>, 3rd ed., NY: Wiley. ISBN ISBN: 978-1-119-40528-3.
</ul>
<h4>Supplementary readings:</h4>
<ul>
<li>Agresti, A. (2013). <a href="https://www.wiley.com/en-us/Categorical+Data+Analysis,+3rd+Edition-p-9780470463635"><cite>Categorical Data Analysis</cite></a>,
3rd ed., NY: Wiley. A much more technical book, that many consider the "bible" for categorical data analysis methods.
<a href="http://www.stat.ufl.edu/~aa/cda/cda.html">Web site for the book</a>.
There is also a manual for
<a href="https://home.comcast.net/~lthompson221/Splusdiscrete2.pdf">R and S-plus users</a>
to accompany this text.</li>
<li>Fox, John. <a href="https://us.sagepub.com/en-us/nam/applied-regression-analysis-and-generalized-linear-models/book237254"><cite>Applied Regression Analysis and Generalized Linear Models</cite></a>, 3rd Ed.
Sage, 2015. An excellent text on linear models; Part IV on Generalized Linear Models provides a clear and comprehensive discussion.
</li>
<li>Grolemund & Wickham, <a href="http://r4ds.had.co.nz/"><cite>R for Data Science</cite></a> A good tutorial book on the
tidyverse approach to data analysis in R. Available free online.</li>
<li>
Fox & Weisberg <a href="https://www.amazon.ca/COMPANION-APPLIED-REGR-ESSION/dp/1544336470/"><cite>An R Companion to Applied Regression</cite></a>,
3rd Ed., Sage, 2018. There is also a <a href="http://socserv.socsci.mcmaster.ca/jfox/Books/Companion/">web page for the book</a>,
containing data files, R scripts and a collection of web appendices on other topics.
</li>
</ul>
<h3><a name="assign">Assignments</h3>
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 <a href="assign/assign1.pdf">Assignment 1</a>.
See <a href="http://rmarkdown.rstudio.com/r_notebook_format.html">Compiling Notebooks</a>,
which describes how to compile HTML, PDF, or MS Word notebooks from R scripts for further details.
<p>
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}".
</p>
<ul>
<li><a href="assign/assign1.pdf">Assignment 1 </a>
<!--
[<a href="assign/ans1.R">R solutions </a>]
-->
</li>
<li><a href="assign/assign2.pdf">Assignment 2 </a>
<!--
[<a href="assign/ans2.R">R solutions </a>]
-->
</li>
<li><a href="assign/assign3.pdf">Assignment 3 </a>
<!--
[<a href="assign/ans3.R">R solutions </a>]
-->
</li>
<li><a href="assign/assign4.pdf">Assignment 4 </a></li>
</ul>
<h2 id="evaluation">Evaluation</h2>
<p>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.</p>
<p>Here is a <a href="assign/RMarkdown-template.Rmd">template for a markdown .Rmd file</a> you can use if you are working in RStudio and want to
write in R markdown.</p>
<ol style="list-style-type: decimal">
<li><p><a href="assign/project1.pdf">Project 1 </a>: a selection of data sets for the material up to and including logistic regression. Due date: Nov. 3</p></li>
<li><p><a href="assign/project2.pdf">Project 2</a>: a selection of data sets for the material from logistic regression to the end of the course. Due date: Dec. 15</p></li>
<li>The remaining 20% can be earned either as
<ol style="list-style-type: lower-alpha">
<li>an assignment portfolio, containing a selection of your best work (possibly edited/enhanced) on a selection of assignment questions, or by</li>
<li>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</li>
</ol></li>
</ol>
<h3>Resources</h3>
<h4>Statistical software</h4>
In lectures and lab sessions I will be using <a href="http://www.r-project.org">R</a> software
nearly exclusively, together with the <a href="http://www.rstudio.com/">R Studio</a> user interface for R.
You are well-advised to download and install these to your computer so you can follow along.
<ul>
<li> <a href="http://www.r-project.org">R Project for Statistical Computing</a>.
R is a free software environment for statistical computing and graphics (Windows, MacOS, Linux).
<br>
You can download it from any <a href="cran.r-project.org/mirrors.html">CRAN mirror site</a>.
<br>
See John Fox's notes, <a href="http://socserv.socsci.mcmaster.ca/jfox/Books/Companion-1E/installation.html">Installing R for Windows</a>.
Somewhat out of date, but still useful.
<li>
For this course, use this <a href="R/install-vcd-pkgs.R">R script to install useful add-on packages</a> for categorical data analysis.
<li> <a href="http://www.rstudio.com/">R Studio</a> 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.
<!--
<li>If you insist on SAS or SPSS, see <a href="http://www.yorku.ca/computing/facultystaff/software/grouplicense/index.php">
York group licenses for SAS and SPSS</a>
<br>You may also be able to use the
<a href="http://www.yorku.ca/computing/facultystaff/labs/webacadlabs/">Web Acadlabs service</a>
to access SAS, SPSS from home. See <li><a href="webacadlabs.pdf">Accessing software remotely using Web Acadlabs</a> by Manolo Romero.
-->
</ul>
<li>R software guides
<ul>
<li>Getting started: <a href="http://cran.r-project.org/doc/contrib/Torfs+Brauer-Short-R-Intro.pdf">
<cite>A (very) short introduction to R</cite>,
</a> covers the basics of installing R and R Studio,
the R Studio window layout, and an overview of R commands, data structures and functions. </li>
<li><a href="http://www.maths.lancs.ac.uk/~rowlings/R/Simple/RinOnePage.pdf">R in One Page</a> (well, 2); also: The
<a href="http://cran.r-project.org/doc/contrib/Short-refcard.pdf">R Reference Card</a> (4 pages)
<li><a href="http://cran.r-project.org/doc/manuals/R-intro.pdf">An Introduction to R</a> (Official introductory guide: 100 pages)</li>
<li><a href="http://tutorials.iq.harvard.edu/">R Tutorials</a> A nice collection of tutorials, from introduction, to graphics, to programming by Ista Zahn</li>
<li><a href="http://www.statmethods.net/">Quick R for SAS, SPSS, Stata Users</a> Great site for conversions to R!</li>
<li><a href="http://wiki.stdout.org/rcookbook/">R Cookbook</a>, a collection of recipes for analyzing data from psychology
experiments.</li>
<li><a href="http://www.cookbook-r.com/Graphs/">R Graphics Cookbook</a>, good book on graphics in R, with a useful web site
containing lots of examples, particularly for <tt>ggplot2</tt></li>
<li><a href="http://www.datavis.ca/courses/RGraphics/">An Introduction to R Graphics</a> Notes from my SCS short course
on R Graphics. </li>
</ul>
</ul>
<P>
<hr>
<P>
<EM> © 2014-- Michael Friendly</EM>
<BR>
Canonical URL for this course is: <code>http://www.yorku.ca/friendly/psy6136</code>
</body>
</html>