Further Related Topics

Latent Variable Modelling

For a more technical treatment of some of the topics later in the module, I recommend “Latent Variable Models and Factor Analysis: A Unified Approach” Bartholomew, Knott, and Moustaki (2011), which is available online via the UCL library

Latent Dirichlet Analysis

Latent Dirichlet Analysis is a method for class mixture modelling, particularly useful for analysing word count data.

• David Blei. “Introduction to Probabilistic Topic Models”
• Chang et al. “Reading Tea Leaves: How Humans Interpret Topic Models”
• Chris Tufts. “The Little Book of LDA”
• Rui Miguel Forte. “Mastering Predictive Analytics with R” O’Reilly. Ch 10
• Jeffrey Pennington, Richard Socher, Christopher D Manning. “GloVe: Global Vectors for Word Representation”

Correspondence Analysis

Correspondent analysis is, roughly speaking, principle components analysis for count data.

• Bartholomew et al. (2008), Ch 4

Multidimensional Scaling

Multidimensional scaling methods simplify a large number of distance measures into coordinates in a small number of dimensions, almost always 2. They provide a way to take multiple senses of distance and simplify them into something that looks like a map.

• Bartholomew et al. (2008), Ch 3
• Everitt and Hothorn (2011), Ch 4

Confirmatory Factor Analysis and Structural Equation Modelling

Confirmatory factor analysis and structural equation modelling are tools for modelling relationships between observed and latent variables, and are a book-length topic in their own right. In principle, structural equation modelling allow one to integrate the task of measurement with descriptive/causal modelling, although in practice most applied structural equation modelling is quite naive with respect to the difficulty of making compelling causal claims.

• Bartholomew et al. (2008), Ch 11
• Everitt and Hothorn (2011), Ch 7

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Alvarez, Mike, José Antonio Cheibub, Fernando Limongi, and Adam Przeworski. 1996. “Classifying Political Regimes.” Studies in Comparative International Development 31 (2): 3–36.

Bartholomew, David J, M Knott, and Irini Moustaki. 2011. Latent Variable Models and Factor Analysis : A Unified Approach. 3rd ed. / David Bartholomew, Martin Knott, Irini Moustaki. Wiley Series in Probability and Statistics. Hoboken, N.J.: Wiley.

Bartholomew, David J, Fiona Steele, Jane Galbraith, and Irini Moustaki. 2008. Analysis of Multivariate Social Science Data. Chapman; Hall/CRC.

Blalock, Hubert M. 1982. Conceptualization and Measurement in the Social Sciences. 04; H61, B5.

Everitt, Brian, and Torsten Hothorn. 2011. An Introduction to Applied Multivariate Analysis with r. Springer Science & Business Media.

Ferrı́n, Monica, Marta Fraile, and Gema Garcı́a-Albacete. 2017. “The Gender Gap in Political Knowledge: Is It All about Guessing? An Experimental Approach.” International Journal of Public Opinion Research 29 (1): 111–32.

Hanretty, Chris. 2017. “Areal Interpolation and the UK’s Referendum on EU Membership.” Journal of Elections, Public Opinion and Parties 27 (4): 466–83.

James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2013. An Introduction to Statistical Learning. Vol. 112. Springer.

Lizotte, Mary-Kate, and Andrew H Sidman. 2009. “Explaining the Gender Gap in Political Knowledge.” Politics & Gender 5 (2): 127–51.

Mondak, Jeffery J, and Mary R Anderson. 2004. “The Knowledge Gap: A Reexamination of Gender-Based Differences in Political Knowledge.” The Journal of Politics 66 (2): 492–512.

Pemstein, Daniel, Stephen A Meserve, and James Melton. 2010. “Democratic Compromise: A Latent Variable Analysis of Ten Measures of Regime Type.” Political Analysis 18 (4): 426–49.

World Bank. 2018. “The Human Capital Project.” International Bank for Reconstruction and Development / World Bank.

Zucco Jr, Cesar, Mariana Batista, and Timothy J Power. 2019. “Measuring Portfolio Salience Using the Bradley–Terry Model: An Illustration with Data from Brazil.” Research & Politics 6 (1): 2053168019832089.

References

Bartholomew, David J, M Knott, and Irini Moustaki. 2011. Latent Variable Models and Factor Analysis : A Unified Approach. 3rd ed. / David Bartholomew, Martin Knott, Irini Moustaki. Wiley Series in Probability and Statistics. Hoboken, N.J.: Wiley.

Bartholomew, David J, Fiona Steele, Jane Galbraith, and Irini Moustaki. 2008. Analysis of Multivariate Social Science Data. Chapman; Hall/CRC.

Everitt, Brian, and Torsten Hothorn. 2011. An Introduction to Applied Multivariate Analysis with r. Springer Science & Business Media.