IPS804 Psychometric Methods in Political Science

Syllabus | Syllabus 21S | Syllabus IPS605

Armstrong II, David A., Ryan Bakker, Royce Carroll, Christopher Hare, Keith T. Poole, and Howard Rosenthal. 2020/2014. Analyzing Spatial Models of Choice and Judgment with R. CRC Press. (Library 3F出納台指參) github | Essex scaling | Keith Poole | Dave A Armstrong II

Mair, P., 2018. Modern Psychometrics with R. Springer. NSYSU ebook | github | smacof

Week 1: Course Introduction

slide

It had to be U - the SVD song

Curini, L. and Franzese, R. eds., 2020. The SAGE handbook of research methods in political science and international relations. Sage Part III Conceptualization and Measurement NSYSU ebook

Hare, C. and Poole, K. T. (2018). Psychometric Methods in Political Science. In The Wiley Handbook of Psychometric Testing (eds P. Irwing, T. Booth and D. J. Hughes). here

Review NSYSU research ethics

Week 2-3: Foundation of Psychometric Methods in Political Science

Experimental Survey And Political Economic Workshop

Armstrong II et al., 2020. Ch 1

Coombs, Clyde H.. 1964. A Theory of Data. New York: John Wiley and Sons, Inc.

Converse, P.E., 1964. The nature of belief systems in mass publics. In Apter, D.E. ed., Ideology and discontent. Free Pr. here | Critical Review 18(1-3), Is Democratic Competence Possible?

Stokes, D.E., 1963. Spatial models of party competition. American political science review, 57(2), pp.368-377. here

Weisberg, H F. 1974. Dimensionland: An Excursion into Spaces. American Journal of Political Science: 743–76. here

Week 4: Analyzing Issue Scales (Aldrich-McKelvey Scaling)

slide | code for mapping Taiwan

Armstrong II et al., 2020. Ch 2.1

Aldrich, J.H. and McKelvey, R.D., 1977. A Method of Scaling with Applications to the 1968 and 1972 Presidential Elections. American Political Science Review, 71(1), pp.111-130. here | code | data

Week 5: Analyzing Issue Scales (Basic Space Scaling)

slide | review codebook

Armstrong II et al., 2020. Ch 2.2

King, G. and Wand, J., 2007. Comparing incomparable survey responses: Evaluating and selecting anchoring vignettes. Political Analysis, 15(1), pp.46-66. Resource

Kubo, H., Matsumoto, T. and Yamamoto, K., 2022. Party switching and policy disagreement: scaling analysis of experts' judgment. Japanese Journal of Political Science, 23(3), pp.254-269. here | replication

Week 6: Analyzing Similarities and Dissimilarities Data (MDS)

slide | code for mapping Taiwan 2

nations data | mds code

Armstrong II et al., 2020. Ch 3 | Mair 2018, Ch 9.2

Weisberg, H.F. and Rusk, J.G., 1970. Dimensions of candidate evaluation. American Political Science Review, 64(4), pp.1167-1185. (non-metric MDS) here | data | code

Rabinowitz, George B. 1975. An Introduction to Nonmetric Multidimensional Scaling. American Journal of Political Science: 343–90. here

Week 7: Multivariate Data Analysis (PCA and FA)

A view on power structure in Asia | UNGA voting data journalism | UNGA IRT data | toy code

Principal Component Analysis: Mair, 2018, Ch 6.1 toy code for pca | Factor Analysis: Mair, 2018, Ch 2.2

Magyar, Zsuzsanna B. 2022. What Makes Party Systems Different? A Principal Component Analysis of 17 Advanced Democracies 1970–2013. Political Analysis 30, no. 2: 250–68. https://doi.org/10.1017/pan.2021.21. (PCA) code 1 | code 2 | data

Week 8: Mid-term week

Week 9: Unfolding Analysis of Rating Scale Data

Armstrong II et al., 2020. Ch 4 | Mair 2018, Ch 9.4

slide | Interest group ratings data | TEDS data | TEDS codebook | R code

Week 10: TPSA

Week 11: Unfolding Analysis of Binary Choice Data (OC)

Armstrong II et al., 2020. Ch 5.4 Slide | Slide 2 | R code

Croft, W. and Poole, K.T., 2008. Inferring universals from grammatical variation: Multidimensional scaling for typological analysis. Theoretical linguistics, 34(1), pp.1-37. here

Week 12: Unfolding Analysis of Binary Choice Data (NOMINATE)

slide | R code

Armstrong II et al., 2020. Ch 5.3

McCarty, N., Poole, K.T. and Rosenthal, H., 2013. Political bubbles: Financial crises and the failure of American democracy. Princeton University Press. 政治泡沫 : 金融危机与美国民主制度的挫折诺兰.麦卡蒂(Nolan M. McCarty), 基思.普尔(Keith T. Poole), 霍华德.罗森塔尔(Howard Rosenthal)著 ; 贾拥民译民103[2014] Ch 2

Koo, B.S., Kim, J. and Choi, J.Y., 2019. Testing legislative shirking in a new setting: the case of lame duck sessions in the Korean National Assembly. Japanese Journal of Political Science, 20(1), pp.33-52. here

Week 13: Bayesian IRT Models

Armstrong II et al., 2020. Ch 6 | Mair, 2018, Ch 4 slide | r code

Jeong, G.H., 2018. Measuring foreign policy positions of members of the US Congress. Political Science Research and Methods, 6(1), p.181. here

Pellegrina, L.D., Garoupa, N. and Lin, S.C.P., 2012. Judicial Ideal Points in New Democracies: The Case of Taiwan. NTU Law Review, 7. here

Week 14: Psychometric Properties of Social Network

Mair, 2018 Ch 11

Bond, R. and Messing, S., 2015. Quantifying social media’s political space: Estimating ideology from publicly revealed preferences on Facebook. American Political Science Review, 109(1), pp.62-78. here

Wang, M.H., Chang, A.C.H., Chen, K.T. and Lei, C.L., 2017. Estimating ideological scores of Facebook pages: an empirical study in Taiwan. The Computer Journal, 60(11), pp.1675-1686. here

Week 15: Classification and Scaling with Texts

Mair, 2018, Ch 12 slide | toy R code | Wordfish R code | oc on 10th LY code

Chen, T.C. and Hsu, C.H., 2018. Double-speaking human rights: analyzing human rights conception in Chinese politics (1989–2015). Journal of Contemporary China, 27(112), pp.534-553. here

Proksch, Sven-Oliver, Jonathan B. Slapin, and Michael Thies. 2011. Party System Dynamics in Post-War Japan: A Quantitative Content Analysis of Electoral Pledge, Electoral Studies 30(1), 114-124. here

Week 16: Final week

Week 17-18: Self-study

Public Choice Volume 176, issue 1-2, July 2018, Special Issue: Honoring Keith T. Poole / Edited by Howard Rosenthal and Keith L. Dougherty here

Frontiers of Psychometrics in Political Science

Phil Swatton on Aldrich-McKelvey Scaling desc | preprint

Bølstad, J., Forthcoming. Hierarchical Bayesian Aldrich–McKelvey Scaling. Political Analysis.

Binding, Garret, and Lukas F. Stoetzer. 2023. Non-Separable Preferences in the Statistical Analysis of Roll Call Votes. Political Analysis 31, no. 3 : 352–65. https://doi.org/10.1017/pan.2022.11.

Jerzak, Connor T., Gary King, and Anton Strezhnev. 2023. An Improved Method of Automated Nonparametric Content Analysis for Social Science. Political Analysis 31, no. 1: 42–58. https://doi.org/10.1017/pan.2021.36.

Mair, P., Groenen, P.J. and de Leeuw, J., 2022. More on multidimensional scaling and unfolding in R: smacof version 2. Journal of Statistical Software, 102, pp.1-47.

Balafas, S.E., Krijnen, W.P., Post, W.J. and Wit, E.C., 2020. Mudfold: An R package for nonparametric IRT modelling of unfolding processes. The R Journal, 12(1), pp.49-75.

Goplerud, M., 2019. A Multinomial Framework for Ideal Point Estimation. Political Analysis, 27(1), pp.69-89.