Hostname: page-component-76fb5796d-9pm4c Total loading time: 0 Render date: 2024-04-25T20:53:27.532Z Has data issue: false hasContentIssue false
  • English
  • Français

Measurement Uncertainty in Spatial Models: A Bayesian Dynamic Measurement Model

Published online by Cambridge University Press:  09 November 2018

Sebastian Juhl Open the ORCID record for Sebastian Juhl [Opens in a new window]
Sebastian Juhl*
Affiliation:
Universität Mannheim, Sonderforschungsbereich 884, B6, 30-32, Room 337, Mannheim, Baden-Württemberg, Germany, 68159. Email: sebastian.juhl@gess.uni-mannheim.de
*
*Email: sebastian.juhl@gess.uni-mannheim.de
Get access Rights & Permissions [Opens in a new window]

Abstract

According to spatial models of political competition, parties strategically adjust their ideological positions to movements made by rival parties. Spatial econometric techniques have been proposed to empirically model such interdependencies and to closely convert theoretical expectations into statistical models. Yet, these models often ignore that the parties’ ideological positions are latent variables and, as such, accompanied by a quantifiable amount of uncertainty. As a result, the implausible assumption of perfectly measured covariates impedes a proper evaluation of theoretical propositions. In order to bridge this gap between theory and empirics, the present work combines a spatial econometric model and a Bayesian dynamic item response model. The proposed model accurately accounts for measurement uncertainty and simultaneously estimates the parties’ ideological positions and their spatial interdependencies. To verify the model’s utility, I apply it to recorded votes from the sixteen German state legislatures in the period from 1988 to 2016. While exhibiting a notable degree of ideological mobility, the results indicate only moderate spatial dependencies among parties of the same party family. More importantly, the analysis illustrates how measurement uncertainty can lead to substantively different results which stresses the importance of appropriately incorporating theoretical expectations into statistical models.

Keywords

dynamic ideal point estimationBayesian statisticsspatial econometricsmeasurement uncertaintymultiparty competition
Type
Articles
Information
Political Analysis , Volume 27 , Issue 3 , July 2019 , pp. 302 - 319
Copyright
Copyright © The Author(s) 2018. Published by Cambridge University Press on behalf of the Society for Political Methodology. 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

Author’s note: An earlier version of this work has been presented at the EPSA conference 2017 in Milan, Italy and the 2018 annual conference of the PSA Political Methodology Group in Essex, UK. I would like to thank Thomas Bräuninger, Heike Klüver, Roni Lehrer, the journal’s editor Jeff Gill, and four anonymous reviewers for valuable comments. I also thank Lea Manger and Oksana Basik for research assistance. Supplementary materials for this article are available on the Political Analysis website and replication materials are retrievable from the Political Analysis Dataverse (Juhl 2018). This work was supported by the German Research Foundation (DFG) via the SFB 884 on “The Political Economy of Reforms” (Project C2) and the University of Mannheim’s Graduate School of Economic and Social Sciences (GESS).

Contributing Editor: Jeff Gill

References

Adams, James. 2001. Party competition and responsible party government: a theory of spatial competition based upon insights from behavioral voting research . Ann Arbor: University of Michigan Press.Google Scholar
Adams, James, and Somer-Topcu, Zeynep. 2009. Policy adjustment by parties in response to rival parties’ policy shifts: spatial theory and the dynamics of party competition in twenty-five post-war democracies. British Journal of Political Science 39(4):825–846.Google Scholar
Bafumi, Joseph, Gelman, Andrew, Park, David K., and Kaplan, Noah. 2005. Practical issues in implementing and understanding Bayesian ideal point estimation. Political Analysis 13(2):171–187.Google Scholar
Bailey, Michael A. 2007. Comparable preference estimates across time and institutions for the court, congress, and presidency. American Journal of Political Science 51(3):433–448.Google Scholar
Bakker, Ryan. 2009. Re-measuring left–right: a comparison of SEM and Bayesian measurement models for extracting left–right party placements. Electoral Studies 28(3):413–421, Special symposium: measurement methods for better longitudinal modelling.Google Scholar
Beck, Nathaniel, Gleditsch, Kristian Skrede, and Beardsley, Kyle. 2006. Space is more than geography: using spatial econometrics in the study of political economy. International Studies Quarterly 50(1):27–44.Google Scholar
Benoit, Kenneth, and Laver, Michael. 2007. Estimating party policy positions: comparing expert surveys and hand-coded content analysis. Electoral Studies 26(1):90–107.Google Scholar
Benoit, Kenneth, Laver, Michael, and Mikhaylov, Slava. 2009. Treating words as data with error: uncertainty in text statements of policy positions. American Journal of Political Science 53(2):495–513.Google Scholar
Blackwell, Matthew, Honaker, James, and King, Gary. 2017. A unified approach to measurement error and missing data: overview and applications. Sociological Methods and Research 46(3):303–341.Google Scholar
Böhmelt, Tobias, Ezrow, Lawrence, Lehrer, Roni, and Ward, Hugh. 2016. Party policy diffusion. American Political Science Review 110(2):397–410.Google Scholar
Bräuninger, Thomas, Müller, Jochen, and Stecker, Christian. 2016. Modeling preferences using roll call votes in parliamentary systems. Political Analysis 24(2):189–210.Google Scholar
Bräuninger, Thomas, and Debus, Marc. 2012. Parteienwettbewerb in Deutschen Bundesländern . Wiesbaden: VS Verlag.Google Scholar
Busch, Kathrin Barbara. 2016. Estimating parties’ left–right positions: determinants of voters’ perceptions’ proximity to party ideology. Electoral Studies 41:159–178.Google Scholar
Clinton, Joshua, Jackman, Simon, and Rivers, Douglas. 2004. The statistical analysis of roll call data. American Political Science Review 98(2):355–370.Google Scholar
Crabtree, C. D., and Fariss, Christopher J.. 2015. Uncovering patterns among latent variables: human rights and de facto judicial independence. Research and Politics 2(3):1–9.Google Scholar
Dalton, Russell J., and McAllister, Ian. 2015. Random walk or planned excursion? Continuity and change in the left–right positions of political parties. Comparative Political Studies 48(6):759–787.Google Scholar
Davis, Otto A., Hinich, Melvin J., and Ordeshook, Peter C.. 1970. An expository development of a mathematical model of the electoral process. American Political Science Review 64(2):426–448.Google Scholar
Debus, Marc, and Müller, Jochen. 2013. The programmatic development of CDU and CSU since reunification: incentives and constraints for changing policy positions in the german multi-level system. German Politics 22(1–2):151–171.Google Scholar
Denwood, Matthew J. 2016. runjags: An R package providing interface utilities, model templates, parallel computing methods and additional distributions for MCMC models in JAGS. Journal of Statistical Software 71(9):1–25.Google Scholar
Downs, Anthony. 1957. An economic theory of democracy . New York, USA: Harper and Row.Google Scholar
Fariss, Christopher J. 2014. Respect for human rights has improved over time: modeling the changing standard of accountability. American Political Science Review 108(2):297–318.Google Scholar
Franzese, R. J., and Hays, Jude C.. 2007. Spatial econometric models of cross-sectional interdependence in political science panel and time-series-cross-section data. Political Analysis 15(2):140–164.Google Scholar
Franzese, R. J., and Hays, Jude C.. 2008. Interdependence in comparative politics. Comparative Political Studies 41(4–5):742–780.Google Scholar
Fuller, Wayne A. 1987. Measurement error models . New York, USA: Wiley.Google Scholar
Grimmer, Justin. 2011. An introduction to Bayesian inference via variational approximations. Political Analysis 19(1):32–47.Google Scholar
Hays, Jude C., Kachi, Aya, and Franzese, Robert J.. 2010. A spatial model incorporating dynamic, endogenous network interdependence: a political science application. Statistical Methodology 7(3):406–428.Google Scholar
Herron, Michael C. 2004. Studying dynamics in legislator ideal points: scale matters. Political Analysis 12(2):182–190.Google Scholar
Hix, Simon, and Noury, Abdul. 2016. Government-opposition or left–right? The institutional determinants of voting in legislatures. Political Science Research and Methods 4(2):249–273.Google Scholar
Hug, Simon. 2013. Parliamentary voting. In Party governance and party democracy , ed. Müller, Wolfgang C. and Narud, Hanne M.. New York, USA: Springer, pp. 137–157.Google Scholar
Jackman, Simon. 2001. Multidimensional analysis of roll call data via Bayesian simulation: identification, estimation, inference, and model checking. Political Analysis 9(3):227–241.Google Scholar
Jessee, Stephen A. 2017. ‘Don’t know’ responses, personality, and the measurement of political knowledge. Political Science Research and Methods 5(4):711–731.Google Scholar
Juhl, Sebastian. 2018 Replication data for: Measurement uncertainty in spatial models: A Bayesian dynamic measurement model, doi:10.7910/DVN/ICTG9Z, Harvard Dataverse, V2.Google Scholar
Kollman, Ken, Miller, John H., and Page, Scott E.. 1992. Adaptive parties in spatial elections. The American Political Science Review 86(4):929–937.Google Scholar
König, Thomas, Marbach, Moritz, and Osnabrügge, Moritz. 2013. Estimating party positions across countries and time? A dynamic latent variable model for manifesto data. Political Analysis 21(4):468–491.Google Scholar
Krehbiel, Keith, and Peskowitz, Zachary. 2015. Legislative organization and ideal-point bias. Journal of Theoretical Politics 27(4):673–703.Google Scholar
Laver, Michael. 2005. Policy and the dynamics of political competition. American Political Science Review 99(2):263–281.Google Scholar
Laver, Michael. 2006. Legislatures and parliaments in comparative context. In The Oxford handbook of political economy , ed. Weingast, Barry R. and Wittman, Donald A.. Oxford: Oxford University Press, pp. 121–140.Google Scholar
Laver, Michael, and Sergenti, Ernest. 2012. Party competition: an agent-based model . Princeton: Princeton University Press.Google Scholar
Laver, Michael, Benoit, Kenneth, and Garry, John. 2003. Extracting policy positions from political texts using words as data. American Political Science Review 97(2):311–331.Google Scholar
Loken, Eric, and Gelman, Andrew. 2017. Measurement error and the replication crisis. Science 355(6325):584–585.Google Scholar
de Marchi, Scott, and Page, Scott E.. 2014. Agent-based models. Annual Review of Political Science 17(1):1–20.Google Scholar
Martin, A. D., and Quinn, Kevin M.. 2002. Dynamic ideal point estimation via Markov chain Monte Carlo for the U.S. supreme court, 1953–1999. Political Analysis 10(2):134–152.Google Scholar
Mayhew, David R. 1974. Congress: the electoral connection . New Haven, USA: Yale University Press.Google Scholar
McAvoy, Gregory E. 1998. Measurement models for time series analysis: estimating dynamic linear errors-in-variables models. Political Analysis 7(1):165–186.Google Scholar
Neumayer, Eric, and Plümper, Thomas. 2016. W. Political Science Research and Methods 4(1):175–193.Google Scholar
Park, Jong Hee. 2011. Modeling preference changes via a hidden Markov item response theory model . Chapman and Hall/CRC, pp. 479–491.Google Scholar
Pemstein, Daniel, Meserve, Stephen A., and Melton, James. 2010. Democratic compromise: a latent variable analysis of ten measures of regime type. Political Analysis 18(4):426–449.Google Scholar
Plummer, Martin. 2016. JAGS: a program for analysis of Bayesian graphical models using Gibbs sampling. Version 4.2.0. http://mcmc-jags.sourceforge.net.Google Scholar
Plümper, Thomas, and Neumayer, Eric. 2010. Model specification in the analysis of spatial dependence. European Journal of Political Research 49(3):418–442.Google Scholar
Poole, Keith T., and Rosenthal, Howard. 1985. A spatial model for legislative roll call analysis. American Journal of Political Science 29(2):357–384.Google Scholar
Poole, Keith T., and Rosenthal, Howard. 1991. Patterns of congressional voting. American Journal of Political Science 35(1):228–278.Google Scholar
Poole, Keith T., and Rosenthal, Howard. 2001. D-nominate after 10 years: a comparative update to congress: a political-economic history of roll-call voting. Legislative Studies Quarterly 26(1):5–29.Google Scholar
R Core Team. 2016. R: A language and environment for statistical computing . Vienna, Austria: R Foundation for Statistical Computing, https://www.R-project.org/.Google Scholar
Schnakenberg, Keith E., and Fariss, Christopher J.. 2014. Dynamic patterns of human rights practices. Political Science Research and Methods 2(1):1–31.Google Scholar
Shor, Boris, and McCarty, Nolan. 2011. The ideological mapping of American legislatures. American Political Science Review 105(3):530–551.Google Scholar
Stecker, Christian. 2015. How effects on party unity vary across votes. Party Politics 21(5):791–802.Google Scholar
Theriault, Sean M., Hickey, Patrick, and Blass, Abby. 2011. Roll-call votes. In The Oxford handbook of the American congress , ed. Edwards III, George C., Lee, Frances E., and Schickler, Eric. Oxford: Oxford University Press, pp. 575–597.Google Scholar
Treier, Shawn, and Jackman, Simon. 2008. Democracy as a latent variable. American Journal of Political Science 52(1):201–217.Google Scholar
Williams, Laron K. 2015. It’s all relative: spatial positioning of parties and ideological shifts. European Journal of Political Research 54(1):141–159.Google Scholar
Williams, L. K., and Whitten, Guy D.. 2015. Don’t stand so close to me: spatial contagion effects and party competition. American Journal of Political Science 59(2):309–325.Google Scholar
Williams, Laron K., Seki, Katsunori, and Whitten, Guy D.. 2016. You’ve got some explaining to do the influence of economic conditions and spatial competition on party strategy. Political Science Research and Methods 4(1):47–63.Google Scholar
Supplementary material: File

Juhl supplementary material

Juhl supplementary material

Download Juhl supplementary material(File)
File 175.1 KB