Clyde H. Coombs
Born: 22 July 1912
Died: 4 February 1988

Spring Semester AY2014-2015
Department of Political Science
School of Public and International Affairs
University of Georgia
Athens, GA 30602

Classroom: Baldwin 301
Time: 3:35-6:35 Mondays

Instructor: Keith T. Poole

Office: Baldwin 304D
E-Mail: ktpoole@uga.edu
WebSite: Voteview Home Page or Office Hours: 2:00 - 4:00PM Thursdays or By Appointment

The following texts will be used in this course:


This course is concerned with dimensional analysis, that is, the measurement of latent dimensions in data matrices. A working knowledge of OLS multiple regression analysis and STATA is required for this course. Students will be required to use two statistical packages -- R and WINBUGS/JAGS. We will also use a variety of "canned" programs that perform various kinds of dimensional analyses.

Grades will be determined by regularly assigned class problems.

Useful Links -- R

PCH Symbols in R

Octal References for Math Symbols that can be used in PlotMath in R

Miscellaneous Useful R Programs

Useful Links -- EPSILON

EPSILON HomePage -- Lugaru Software Ltd.

Useful Epsilon Commands

Epsilon Keyboard Macro Examples

Epsilon Text File Macro Examples

Useful Links -- Old Homeworks

Old Homeworks: 2001 - 2011

Useful Links -- How to Install GNU C/C++ and FORTRAN Compilers for WINDOWS and MAC Machines

How to Install GNU Compilers

Useful Links -- How to Install JAGS on the MAC

How to Install JAGS

Useful Links -- JAGS for WINDOWS 64 bit

Sourcefore JAGS 3.4 -- Runs on 64 bit WINDOWS and 64 bit R

Problem Sets (2015)

Homework 1: Due 20 January 2015 (NOTE THAT THIS IS A TUESDAY!)
Homework 2: Due 26 January 2015
Homework 3: Due 2 February 2015
Homework 4: Due 9 February 2015
Homework 5: Due 16 February 2015
Homework 6: Due 23 February 2015
Homework 7: Due 2 March 2015
Homework 8: Due 16 March 2015
Homework 9: Due 30 March 2015
Homework 10: Due 6 April 2015
Homework 11: Due 13 April 2015
Homework 12: Due 20 April 2015
Homework 13: Due 27 April 2015

Course Outline
  1. Clyde Coombs' Theory of Data: Similarities and Preferential Choice


  2. Analyzing Issue Scales


  3. Classical Scaling of Similarities Data


  4. Non-Metric Multidimensional Scaling


  5. Bayesian Multidimensional Scaling


  6. Unfolding Analysis of Rating Scale Data -- Interest Group Ratings and Thermometer Scores


  7. Unfolding Analysis of Binary Choice Data

    1. Parametric Methods


    2. Non-Parametric Methods [Optimal Classification (OC)]