POLI 277: MEASUREMENT THEORY


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



Spring Quarter AY2007-2008
Department of Political Science
University of California, San Diego
La Jolla, CA 92093-0521

Classroom: SSB 104
Time: 3:00PM - 5:50PM Wednesday

Instructor: Keith T. Poole

Office: SSB 368
E-Mail: ktpoole@uga.edu
WebSite: Voteview Home Page or UCSD Voteview Home Page

The following texts will be used in this course:


Requirements

This course is intended as an extension of POLS 6382 Advanced Multivariate Statistics. The emphasis will be on 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 learn Epsilon (EMACS), a screen editor, and the open-source statistical package -- R. In the second half of the course we will also use the open-source Bayesian statistical package WINBUGS. 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.


Below are links to my Advanced Multivariate Statistics course web page for the fall of 2001 and and spring of 2003 at the University of Houston and my Prob-Stat II (required MBA course) web pages from AY1997-98 at Carnegie-Mellon University. These should be helpful if you need to refresh your memory about multiple regression.

POLS 6382 Advanced Multivariate Statistics (UH, fall 2001)

Probability and Statistics II Main Page (CMU Spring 1998)

Useful Links -- EPSILON

EPSILON HomePage -- Lugaru Software Ltd.

Useful Epsilon Commands and Examples


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 -- Multidimensional Scaling

How to Use KYST, A Very Flexible Program to do Multidimensional Scaling and Unfolding


2001 Homeworks

First Homework Assignment
Second Homework Assignment
Third Homework Assignment
Fourth Homework Assignment
Fifth Homework Assignment
Sixth Homework Assignment
Seventh Homework Assignment
Eighth Homework Assignment
Ninth Homework Assignment
Tenth Homework Assignment
Eleventh Homework Assignment

2003 Homeworks

First Homework Assignment
Second Homework Assignment
Third Homework Assignment
Fourth Homework Assignment
Fifth Homework Assignment
Sixth Homework Assignment
Seventh Homework Assignment
Eighth Homework Assignment
Ninth Homework Assignment
Tenth Homework Assignment
Eleventh Homework Assignment
Twelveth Homework Assignment
Thirteenth Homework Assignment

2004 Homeworks for UCSD Short Course

First Homework Assignment
Second Homework Assignment
Third Homework Assignment

2006 Homeworks

First Homework Assignment
Second Homework Assignment
Third Homework Assignment
Fourth Homework Assignment
Fifth Homework Assignment
Sixth Homework Assignment
Seventh Homework Assignment
Eighth Homework Assignment

2007 Homeworks


First Homework Assignment
Second Homework Assignment
Third Homework Assignment
Fourth Homework Assignment
Fifth Homework Assignment
Sixth Homework Assignment
Seventh Homework Assignment
Eighth Homework Assignment


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

  2. Assignment:

  3. Classical Scaling of Similarities Data

    Assignment:

  4. Non-Metric Multidimensional Scaling

    Assignment:

  5. Unfolding Analysis: Non-Parametric Methods [Optimal Classification (OC)]

    Assignment:

  6. Unfolding Analysis: Parametric Methods

    1. Analysis of Interval Level Data -- Interest Group Ratings and Thermometer Scores

      Assignment:

    2. Analysis of Perceptual Data -- Seven Point Scales

      Assignment:

    3. Analysis of Roll Call Data (Discrete Data)

      Assignment:

    4.