Please note: This course will be taught in hybrid mode. Hybrid delivery of courses will include synchronous live sessions during which on campus and online students will be taught simultaneously

Royce Carroll is a Professor in Politics Science at the University of Houston. His research has focused on representation, political polarization, legislative politics, and analysis of survey and voting data, attitudes, preferences and ideology. He is co-author of the scaling method textbook Analyzing Spatial Models of Choice and Judgment (2nd Ed. 2020), as well as a number of articles on related methods and applications.

Course Content
This course focuses on the tools for discovering, understanding, and visualizing the latent structures that drive human behaviour and judgment, with an emphasis on survey data and observed behaviour. In the social sciences, critical concepts—such as political ideology and other traits—are rarely directly observable. This course focuses on the methods used to extract these “difficult-to-measure” latent quantities from relational and behavioural data. The course is especially suited to students with projects using survey data and other forms of relational data used in political science, sociology, economics, business/marketing, and psychology.

The course introduces students to measurement theory and methods of scaling techniques, covering Multidimensional Scaling, Item Response Theory, and Ideal Point Estimation. Integrating foundations from political science, psychology, and economics, we move from measurement theory to advanced methods for scaling and generating usable measures from data. Students will learn to derive from raw data (survey responses, roll-call votes, and text) measures representing the underlying space and where actors and items are positioned relative to one another.

Key themes of the course include:

  • How can we estimate preferences, traits, and other difficult-to-measure concepts and make them appropriate as measures for subsequent analysis?
  • How can we improve comparability of latent concepts across survey respondents, or across separate surveys?
  • How can we reduce complex behavioural data to its basic underlying dimensions?

The course will demonstrate how to interpret, measure, and visualize latent dimensions of data via a variety of scaling methods using the open-source programming language R. We will discuss a range of applications for social science studies of relational and perception data derived from elite behaviour and surveys, especially for identifying latent preferences of political, economic, and social actors. The course concludes with discussions of recent advances in the field, including applications for text analysis and practical advice for social science research.

The course first covers how to analyse data from scales found in surveys (such as Likert-type scales), focusing on surveys that ask respondents to place themselves and/or stimuli on issue or attribute scales. This begins with approaches to scaling to generate bias-adjusted and latent spatial data, such as Aldrich-McKelvey scaling and ‘Basic Space’ scaling with Anchoring Vignettes for addressing “Differential Item Functioning.” The course next examines estimating latent traits from ordinal scales, similarities data, and rating scale data using multidimensional scaling methods for ordinal and continuous data, such as the SMACOF optimization method.

We then provide an overview of Item Response Theory and ideal point estimation, starting with binary choice data (as often used to study ‘roll call voting’ in parliaments and courts). This includes W-NOMINATE and the Optimal Classification unfolding method, as well as various Bayesian analysis techniques for binary and ordinal choice data. An extensive range of Bayesian techniques is discussed, including Ordinal and Dynamic IRT, and Bayesian forms of Multidimensional Scaling and Aldrich-McKelvey Scaling. The final section discusses recent methods for scaling various data types, including text data and the latest computational innovations for large-scale datasets. Consumers of research based on these methods will also benefit from a deeper understanding of this methodology, its potential, and its limitations.

Course Objectives
Students will learn to use various computational methods to generate measures of ideology and preferences and understand the latent dimensional properties of social science data, including surveys and legislative data. Through a solid understanding of measurement theory and the relationships between Item Response Theory, Ideal Point Estimation, and other scaling methods, students will be able to identify appropriate methods for a given dataset, explain their advantages and limitations, and demonstrate proficiency in relevant software packages in R. Consequently, students will be equipped to both understand and produce research based on these fundamental techniques, effectively analyzing results and presenting them visually.

Course Prerequisites
The course is designed to be accessible to social science graduate students of all backgrounds familiar with quantitative methods. However, students familiar with the R programming environment will find it easier to adapt to course content and assignments, so it is recommended to become familiar with the basic structure of the R syntax and the Rstudio software prior to enrolling. The Introduction to R offered at ESS is recommended for this purpose for those with no background. In addition, the course assumes some basic familiarity with general statistics (OLS and MLE).

Reading
Armstrong, D. A., Bakker, R., Carroll, R., Hare, C., Poole, K. T., & Rosenthal, H. (2020). Analyzing Spatial Models of Choice and Judgment (2nd ed). : CRC Press ISBN 9780367612542 – (this text will be provided by ESS).

Background Knowledge Required
Statistics
OLS – elementary
Maximum Likelihood – elementary

Software
R – elementary

Introduction:

Overview of Scaling Methods, IRT, and Ideal Point Estimation

Core reading: Ch 1 of Armstrong et al. Discussion of scaling methods in general, the theory behind them, and some basics.

Scaling methods to interpret items, identify complexity and Differential Item Functioning

Core reading: Ch 2 of Armstrong et al.

Indicative 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. 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.

Introduction to Scaling Multiple Items with Ordinal Data

Core Reading: Ch 2 of Armstrong et al. Indicative supplementary reading: Hare, C., & Poole, K. T. (2014). “The Polarization of Contemporary American Politics.” Polity, 46(3), pp. 411-429.

Multi-dimensional scaling for Similarities data, Agreement scores and Rating scale data

Core reading: Ch 3 and 4 of Armstrong et al. Indicative supplementary reading: Bornschier, S., 2010. The new cultural divide and the two-dimensional political space in Western Europe. West European Politics, 33(3), pp.419-444

Binary data and NOMINATE Ideal Point Estimation and Non-parametric Unfolding

Core reading: Ch 5 of Armstrong et al. Indicative supplementary reading:

Hix, Simon, Abdul Noury, and Gérard Roland. “Voting patterns and alliance formation in the European Parliament.” 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.

Item Response Theory and applications to binary choice data

Core reading: Ch 5 of Armstrong et al. Indicative supplementary reading: Clinton, J., Jackman, S. and Rivers, D., 2004. The statistical analysis of roll call data. American Political Science Review, 98(2), pp.355-370.

Additional IRT models: Ordinal data and dynamic IRT

Core reading: Ch 6 of Armstrong et al Indicative supplementary reading: Martin, A. D., & Quinn, K. M. (2002). “Dynamic Ideal Point Estimation via Markov Chain Monte Carlo for the U.S. Supreme Court, 1953–1999.”

Scaling for Text Data and other ‘big data’ sources

Core reading: Ch 6 of Armstrong et al and 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. Indicative 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. 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 Catalinac, Amy. (2016). “From Pork to Policy: The Rise of Programmatic Campaigning in Japanese Elections.”