Hostname: page-component-7c8c6479df-ws8qp Total loading time: 0 Render date: 2024-03-19T08:18:07.463Z Has data issue: false hasContentIssue false

Coder Reliability and Misclassification in the Human Coding of Party Manifestos

Published online by Cambridge University Press:  04 January 2017

Slava Mikhaylov*
Affiliation:
Department of Political Science, University College London
Michael Laver
Affiliation:
Department of Politics, New York University. michael.laver@nyu.edu
Kenneth R. Benoit
Affiliation:
Methodology Institute, London School of Economics and Political Science. kbenoit@lse.ac.uk
*
e-mail: v.mikhaylov@ucl.ac.uk (corresponding author)
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

The Comparative Manifesto Project (CMP) provides the only time series of estimated party policy positions in political science and has been extensively used in a wide variety of applications. Recent work (e.g., Benoit, Laver, and Mikhaylov 2009; Klingemann et al. 2006) focuses on nonsystematic sources of error in these estimates that arise from the text generation process. Our concern here, by contrast, is with error that arises during the text coding process since nearly all manifestos are coded only once by a single coder. First, we discuss reliability and misclassification in the context of hand-coded content analysis methods. Second, we report results of a coding experiment that used trained human coders to code sample manifestos provided by the CMP, allowing us to estimate the reliability of both coders and coding categories. Third, we compare our test codings to the published CMP “gold standard” codings of the test documents to assess accuracy and produce empirical estimates of a misclassification matrix for each coding category. Finally, we demonstrate the effect of coding misclassification on the CMP's most widely used index, its left-right scale. Our findings indicate that misclassification is a serious and systemic problem with the current CMP data set and coding process, suggesting the CMP scheme should be significantly simplified to address reliability issues.

Type
Research Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open-Access article, distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
Copyright © The Author 2011. Published by Oxford University Press on behalf of the Society for Political Methodology

Footnotes

Authors' note: Previously presented at the 66th MPSA Annual National Conference, Palmer House Hilton Hotel and Towers, April 3–6, 2008. Our heartfelt thanks goes out to all the volunteer test coders who completed the online coder tests used in the research for this paper. We also thank Andrea Volkens for cooperation and assistance with details of the coding process, and Jouni Kuha, Michael McDonald, Michael Peress, Sven-Oliver Proksch, Jonathan Slapin for useful comments. For replication data and code, see. Supplementary materials for this article are available on the Political Analysis Web site.

References

Agresti, A. 1996. An introduction to categorical data analysis. New York: Wiley.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: 495513.CrossRefGoogle Scholar
Bross, I. 1954. Misclassification in 2 × 2 tables. Biometrics 10: 488–95.CrossRefGoogle Scholar
Budge, Ian, Klingemann, Hans-Dieter, Volkens, Andrea, Bara, Judith, and Tanenbaum, Eric. 2001. Mapping policy preferences: Estimates for parties, electors, and governments 1945-1998. Oxford: Oxford University Press.CrossRefGoogle Scholar
Cohen, J. 1960. A coefficient of agreement for nominal scales. Educational and Psychological Measurement 20(1): 37.CrossRefGoogle Scholar
Fleiss, J. L. 1971. Measuring nominal scale agreement among many raters. Psychological Bulletin 76: 378–82.CrossRefGoogle Scholar
Fleiss, Joseph L., Levin, B., and Paik, M. C. 2003. Statistical methods for rates and proportions. 3rd ed. New York: John Wiley.CrossRefGoogle Scholar
Hayes, A. F., and Krippendorff, K. 2007. Answering the call for a standard reliability measure for coding Data. Communication Methods and Measures 1: 77.CrossRefGoogle Scholar
Heise, D. R. 1969. Separating reliability and stability in test-retest correlation. American Sociological Review 34: 93101.CrossRefGoogle Scholar
Hopkins, Daniel, and King, Gary. 2010. A method of automated nonparametric content analysis for social science. American Journal of Political Science 54: 229–47.CrossRefGoogle Scholar
King, G., and Lu, Y. 2008. Verbal autopsy methods with multiple causes of death. Statistical Science 23(1): 7891.CrossRefGoogle Scholar
Klingemann, Hans-Dieter, Volkens, Andrea, Bara, Judith, Budge, Ian, and McDonald, Michael. 2006. Mapping policy preferences II: Estimates for parties, electors, and governments in eastern Europe, European Union and OECD 1990-2003. Oxford: Oxford University Press.CrossRefGoogle Scholar
Krippendorff, Klaus. 2004. Content analysis: An introduction to its methodology. 2nd ed. Thousand Oaks, CA: Sage.Google Scholar
Kuha, Jouni, and Skinner, Chris. 1997. Categorical data analysis and misclassification. In Survey measurement and process quality, eds. Lyberg, Lars E., Biemer, Paul, Collins, Martin, De Leeuw, Edith D., Dippo, Cathryn, Schwarz, Norbert, and Trewin, Dennis. New York: John Wiley & Sons.Google Scholar
Kuha, Juni, Skinner, C., and Palmgren, J. 2000. Misclassification error. In Encyclopedia of epidemiologic methods, eds. Gail, M. and Benichou, J., 578–85. New York: Wiley.Google Scholar
Landis, J. R., and Koch, G. G. 1977. The measurement of observer agreement for categorical data. Biometrics 33: 159–74.CrossRefGoogle ScholarPubMed
Laver, Michael, Benoit, Kenneth, and Garry, John. 2003. Estimating the policy positions of political actors using words as data. American Political Science Review 97: 311–31.CrossRefGoogle Scholar
McDonald, Michael, and Mendes, Silvia. 2001. Checking the party policy estimates: Convergent validity. In Mapping policy preferences: Estimates for parties, electors, and governments 1945-1998, eds. Budge, Ian, Klingemann, Hans-Dieter, Volkens, Andrea, Bara, Judith, and Tanenbaum, Eric. Oxford: Oxford University Press.Google Scholar
Mikhaylov, Slava, Laver, Michael, and Benoit, Kenneth. 2011. Replication data for: Coder reliability and misclassification in the human coding of party manifestos. http://hdl.handle.net/1902.1/16863UNF:5:/DiFWifTzUKbX0eH64QF9g==IQSS Dataverse Network [Distributor] V1 [Version].Google Scholar
Roberts, Chris. 2008. Modelling patterns of agreement for nominal scales. Statistics in Medicine 27: 810–30.CrossRefGoogle ScholarPubMed
Rogan, W. J., and Gladen, B. 1978. Estimating prevalence from the results of a screening test. American Journal of Epidemiology 107: 71–6.CrossRefGoogle ScholarPubMed
Slapin, J. B., and Proksch, S.-O. 2008. A scaling model for estimating time-series party positions from texts. American Journal of Political Science 52: 705–22.CrossRefGoogle Scholar
Volkens, Andrea. 2001a. Manifesto research since 1979: From reliability to validity. In Estimating the policy positions of political actors, ed. Laver, Michael, 3349. London: Routledge.Google Scholar
Volkens, Andrea. 2001b. Quantifying the election programmes: Coding procedures and controls. In Mapping policy preferences: parties, electors and gGovernments: 1945-1998: Estimates for parties, electors and governments 1945-1998, eds. Budge, Ian, Klingemann, Hans-Dieter, Volkens, Andrea, Bara, Judith, Tannenbaum, Eric, Fording, Richard, Hearl, Derek, Min Kim, Hee, McDonald, Michael, and Mendes, Silvia. Oxford: Oxford University Press.Google Scholar
Volkens, Andrea. 2007. Strengths and weaknesses of approaches to measuring policy positions of parties. Electoral Studies 26: 108120.CrossRefGoogle Scholar
Wüst, Andreas M., and Volkens, Andrea. 2003. Euromanifesto Coding Instructions. Mannheim, Germany: Mannheimer Zentrum für Europäische Sozialforschung.Google Scholar
Supplementary material: File

Mikhaylov et al. supplementary material

Supplementary Material 1

Download Mikhaylov et al. supplementary material(File)
File 12.3 KB
Supplementary material: PDF

Mikhaylov et al. supplementary material

Supplementary Material 2

Download Mikhaylov et al. supplementary material(PDF)
PDF 61.4 KB