Royce Carroll is a Reader in Comparative Politics at the University of Essex. His research focuses on democratic institutions and the role of representation in the policy-making process, particularly legislative politics and the politics of coalitions within and between political parties. His recent research focuses on political parties, the distribution of legislative power and on the spatial analysis of political choices in the measurement of preferences and ideology. He has previously taught at Rice University. Recent relevant publications include.
• Armstrong, D. A., Bakker, R., Carroll, R., Hare, C., Poole, K. T., Rosenthal, H., & others. (2014). Analyzing spatial models of choice and judgment with R. CRC Press. ISBN 9781466517158
• “Recovering a Basic Space from Issue Scales in R” with Keith T. Poole, Jeffrey B. Lewis, James Lo and Howard Rosenthal. 2016 Journal of Statistical Software, 69(1)
• “Using Bayesian Aldrich-McKelvey Scaling to Study Citizens’ Ideological Preferences and Perceptions,” with David Armstrong, Ryan Bakker, Chris Hare, and Keith T. Poole. 2015. American Journal of Political Science, 59(3)
• “The Structure of Utility in Spatial Models of Voting,” with Keith T. Poole, Howard Rosenthal, Jeffrey B. Lewis and James Lo, 2013. American Journal of Political Science, 57(4)

Course Content: This course focuses on methods to find latent patterns in data and is especially suited to students using survey data and other forms of relational data. The course introduces students to measurement theory and methods of ideal point estimation, item response theory and related scaling techniques. The first part of the course will provide an overview of the foundations of these techniques and introduce students to the most common methods used in political science. The course will demonstrate how to interpret latent dimensions of data via a variety of ideal point estimation and scaling methods using the open-source programming language R. The remainder of the course will discuss a range of work applying these methods to studies of relational and perception data derived from elite behavior and surveys. The course concludes with discussions of some practical limitations, challenges, and recent advances in the field.

The course first covers how to analyse data from scales, focusing on surveys that ask respon- dents to place themselves and / or stimuli on issue or attribute scales. The course focuses on the Aldrich-McKelvey scaling and ’basic space’ methods. The course next examines similarities and dissimilarities data, where entries represent the level of similarity or dissimilarity between objects (as in a correlation matrix). Here we covers multidimensional scaling (MDS), focusing on the SMACOF (Scaling by Majorizing the Complicated Function) optimization method implemented in R as well as Bayesian applications to metric MDS. Next, the course covers unfolding analysis of rating scale data such as feeling thermometers in which respondents place a politician or group on favourability scale. Finally, the course covers the unfolding of binary choice data such as legislative roll call voting. We will discuss Poole and Rosenthal’s W-NOMINATE and Poole’s Optimal Classification unfolding method, as well as Bayesian analysis of binary and ordinal choice data using Item Response Theory (IRT) implemented by Jackman’s pscl and Martin and Quinn’s MCMCpack.

This course will enable students to derive latent spatial preference information and/or dimensional structure from various types of choice and judgement data (often used by political scientists in studies of legislators and voters, but applicable to a wide range of social science applications). Consumers of research based on these methods will also benefit from a deeper understanding of this type of methodology, its potential and its limitations.

Text(s): Analyzing spatial models of choice and judgment with R., 1st Edition
Author(s): Armstrong, D. A., Bakker, R., Carroll, R., Hare, C., Poole, K. T., & Rosenthal, H.;

CRC Press. ISBN-13: 9781466517158

Course Objectives: Students will learn to use various methods to generate relational measures of ideology and preferences and understand the latent dimensional properties of social science data. As these techniques are fundamental parts of numerous recent works in political science in particular, students will be able to better understand and produce this type of research.

Course Prerequisites: The course is designed to be accessible to social science graduate students of all backgrounds. However, students familiar with the R programming environment will find it much easier to adapt to course content and assignments, so it is recommended to complete special preparation in this area. In addition, basic familiarity with general statistics will be assumed.

Background knowledge required
Statistics
OLS = e
Maximum Likelihood = e

Computer Background
R = m

e = elementary, m = moderate, s = strong

Intro/R
Core reading
Ch 1,2 of Armstrong et al.

Single items scales pt 1
Core reading
Ch 3 of Armstrong et al.

Supplementary reading
Zakharova, M. and Warwick, P.V., 2014. The Sources of Valence Judgments: The Role of Policy Distance and the Structure of the Left–Right Spectrum. Comparative Political Studies, 47(14), pp.2000-2025.

Issue scales pt 2
Core Reading
Ch 3 of Armstrong et al.

Supplementary reading
Bakker, R., Jolly, S., Polk, J. and Poole, K., 2014. The European common space: Extending the use of anchoring vignettes. The Journal of Politics, 76(4), pp.1089-1101.

Similarities data (and agreement scores)
Core reading
Ch 4 of Armstrong et al.

Supplementary reading
Gross, D.A. and Sigelman, L., 1984. Comparing party systems: A multidimensional approach. Comparative politics, 16(4), pp.463-479.

Bornschier, S., 2010. The new cultural divide and the two-dimensional political space in Western Europe. West European Politics, 33(3), pp.419-444

Rating scale data
Core reading
Ch 5 of Armstrong et al.

Binary data (W-NOMINATE)
Core reading
Ch 6 of Armstrong et al.

Supplementary reading
Hix, Simon, Abdul Noury, and Gérard Roland. “Voting patterns and alliance formation in the European Parliament.” Philosophical Transactions of the Royal Society of London B: Biological Sciences 364.1518 (2009): 821-831.

Binary data 2 (IRT approaches)
Core reading
Ch 6 of Armstrong et al.

Clinton, J., Jackman, S. and Rivers, D., 2004. The statistical analysis of roll call data. American Political Science Review, 98(2), pp.355-370.


Binary data 3 Non-parametric (OC)

Core reading
Ch 6 of Armstrong et al.

Supplementary reading
Rosenthal, Howard, and Erik Voeten. “Analyzing roll calls with perfect spatial voting: France 1946–1958.” American Journal of Political Science 48.3 (2004): 620-632.

Additional topics: Dynamic and Ordinal IRT
Core reading
Ch 7 of Armstrong et al

Additional topics: EM IRT
Core reading
Imai, K., Lo, J. and Olmsted, J., 2016. Fast estimation of ideal points with massive data. American Political Science Review, 110(4), pp.631-656.

Supplementary reading
Barberá, Pablo. “Birds of the same feather tweet together: Bayesian ideal point estimation using Twitter data.” Political Analysis 23.1 (2014): 76-91.

Martin, Andrew D., and Kevin M. Quinn. “Dynamic ideal point estimation via Markov chain Monte Carlo for the US Supreme Court, 1953–1999.” Political Analysis 10.2 (2002): 134-153.

Slapin, Jonathan B., and Sven‐Oliver Proksch. “A scaling model for estimating time‐series party positions from texts.” American Journal of Political Science 52.3 (2008): 705-722.