Hostname: page-component-cd9895bd7-8ctnn Total loading time: 0 Render date: 2024-12-22T15:23:20.694Z Has data issue: false hasContentIssue false
  • English
  • Français

The Impact of Party Cues on Manual Coding of Political Texts*

Published online by Cambridge University Press:  29 September 2017

Laurenz Ennser-Jedenastik  and
Thomas M. Meyer
Get access Rights & Permissions [Opens in a new window]

Abstract

Do coders of political texts incorporate prior beliefs about parties’ issue stances into their coding decisions? We report results from a coding experiment in which ten coders were each given 200 statements on immigration that were extracted from election manifestos. Party labels in these statements were randomly assigned (including a control category without party cues). Coders were more likely to code a statement as pro-immigration if it was attributed to the Greens and less likely choose the anti-immigration category if it was attributed to the populist radical right. No effect was found for mainstream parties of the center-left and center-right. The results also suggest that coders resort to party cues as heuristics when faced with ambiguous policy statements.

Type
Research Notes
Information
Political Science Research and Methods , Volume 6 , Issue 3 , July 2018 , pp. 625 - 633
Copyright
© The European Political Science Association 2017 

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

*

Laurenz Ennser-Jedenastik, Assistant Professor ([email protected]) and Thomas M. Meyer, Assistant Professor ([email protected]), Department of Government, University of Vienna, Rooseveltplatz 3/1, 1090 Vienna. To view supplementary material for this article, please visit https://doi.org/10.1017/psrm.2017.29

References

Benoit, Kenneth, Conway, Drew, Lauderdale, Benjamin E., Laver, Michael, and Mikhaylov, Slava. 2016. ‘Crowd-Sourced Text Analysis: Reproducible and Agile Production of Political Data’. American Political Science Review 110(2):278–295.Google Scholar
Bolsen, Toby, Druckman, James N., and Cook, Fay L.. 2014. ‘The Influence of Partisan Motivated Reasoning on Public Opinion’. Political Behavior 36:235–262.Google Scholar
Bräuninger, Thomas, and Giger, Nathalie. 2016. Strategic Ambiguity of Party Positions in Multiparty Competition. Political Science Research and Methods (FirstView).Google 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.Google Scholar
Cohen, Geoffrey L. 2003. ‘Party Over Policy: The Dominating Impact of Group Influence on Political Beliefs’. Journal of Personality and Social Psychology 85:808–822.Google Scholar
Dancey, Logan, and Sheagley, Geoff. 2013. ‘Heuristics Behaving Badly: Party Cues and Voter Knowledge’. American Journal of Political Science 57:312–325.Google Scholar
Däubler, Thomas, Benoit, Kenneth, Mikhaylov, Slava, and Laver, Michael. 2012. ‘Natural Sentences as Valid Units for Coded Political Texts’. British Journal of Political Science 42:937–951.Google Scholar
Dolezal, Martin, Ennser-Jedenastik, Laurenz, Müller, Wolfgang C., and Winkler, Anna K.. 2016. ‘Analyzing Manifestos in their Electoral Context a New Approach Applied to Austria, 2002–2008’. Political Science Research and Methods 4(3):641–650.Google Scholar
Druckman, James N., Peterson, Erik, and Slothuus, Rune. 2013. ‘How Elite Partisan Polarization Affects Public Opinion Formation’. American Political Science Review 107:57–79.Google Scholar
Eagly, Alice H., and Chaiken, Shelly. 1993. The Psychology of Attitudes. Orlando, FL: Harcourt Brace Jovanovich College Publishers.Google Scholar
Esuli, Andrea, and Sebastiani, Fabrizio. 2010. ‘Machines That Learn How to Code Open-Ended Survey Data’. International Journal of Market Research 52(6):775–800.Google Scholar
Fortunato, David, and Stevenson, Randolph T.. 2016. Heuristics in Context. Political Science Research and Methods (FirstView).Google Scholar
Goren, Paul, Federico, Christopher M., and Kittilson, Miki C.. 2009. ‘Source Cues, Partisan Identities, and Political Value Expression’. American Journal of Political Science 53(4):805–820.Google Scholar
Grimmer, Justin, and Stewart, Brendan M.. 2013. ‘Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts’. Political Analysis 21(3):267–297.Google Scholar
Kam, Cindy D. 2005. ‘Who Toes the Party Line? Cues, Values, and Individual Differences’. Political Behavior 27:163–182.Google Scholar
Klüver, Heike, and Sagarzazu, Iñaki. 2016. ‘Setting the Agenda or Responding to Voters? Political Parties, Voters and Issue Attention’. West European Politics 39:380–398.Google Scholar
Koch, Jeffrey W. 2001. ‘When Parties and Candidates Collide: Citizen Perception of House Candidates’ Positions on Abortion’. Public Opinion Quarterly 65:1–21.Google Scholar
Laver, Michael, Benoit, Kenneth, and Garry, John. 2003. ‘Extracting Policy Positions From Political Texts Using Words as Data’. American Political Science Review 97:311–331.Google Scholar
Laver, Michael, and Garry, John. 2000. ‘Estimating Policy Positions from Political Texts’. American Journal of Political Science 44:619–634.Google Scholar
Lowe, Will, and Benoit, Kenneth. 2013. ‘Validating Estimates of Latent Traits from Textual Data Using Human Judgment as a Benchmark’. Political Analysis 21(3):298–313.Google Scholar
Mikhaylov, Slava, Laver, Michael, and Benoit, Kenneth. 2012. ‘Coder Reliability and Misclassification in the Human Coding of Party Manifestos’. Political Analysis 20:78–91.Google Scholar
Nardulli, Peter F., Althaus, Scott L., and Hayes, Matthew. 2015. ‘A Progressive Supervised-Learning Approach to Generating Rich Civil Strife Data’. Sociological Methodology 45(1):148–183.Google Scholar
Nicholson, Stephen P. 2012. ‘Polarizing Cues’. American Journal of Political Science 56:52–66.Google Scholar
Petersen, Michael B., Skov, Martin, Serritzlew, Søren, and Ramsøy, Thomas. 2013. ‘Motivated Reasoning and Political Parties: Evidence for Increased Processing in the Face of Party Cues’. Political Behavior 35:831–854.Google Scholar
Pfarr, Christian, Schmid, Andreas, and Schneider, Udo. 2011. ‘Estimating Ordered Categorical Variables Using Panel Data: A Generalized Ordered Probit Model With an Autofit Procedure’. Journal of Economics and Econometrics 54:7–23.Google Scholar
Proksch, Sven-Oliver, and Slapin, Jonathan B.. 2010. ‘Position Taking in European Parliament Speeches’. British Journal of Political Science 40:587–611.Google Scholar
Ross, Janell. 2016. ‘Michelle Obama Speech: How the First Lady Took Down Donald Trump Without Mentioning His Name’. Independent, 26 July 2016. Available at http://www.independent.co.uk/news/world/americas/michelle-obama-speech-donald-trump-dnc-2016-democrat-take-down-republican-a7156231.html, accessed 14 September 2017.Google Scholar
Rovny, Jan. 2012. ‘Who Emphasizes and Who Blurs? Party Strategies in Multidimensional Competition’. European Union Politics 13(2):269–292.Google Scholar
Rovny, Jan. 2013. ‘Where Do Radical Right Parties Stand? Position Blurring in Multidimensional Competition’. European Political Science Review 5(1):1–26.Google Scholar
Sagarzazu, Iñaki, and Klüver, Heike. 2017. ‘Coalition Governments and Party Competition: Political Communication Strategies of Coalition Parties’. Political Science Research and Methods 5(2):333–349.Google Scholar
Schwarz, Daniel, Traber, Denise, and Benoit, Kenneth. 2017. ‘Estimating Intra-Party Preferences: Comparing Speeches to Votes’. Political Science Research and Methods 5(2):379–396.Google Scholar
Slapin, Jonathan B., and Proksch, Sven-Oliver. 2008. ‘A Scaling Model for Estimating Time-Series Party Positions from Texts’. American Journal of Political Science 52:705–722.Google Scholar
Supplementary material: File

Ennser-Jedenastik and Meyer supplementary material

Ennser-Jedenastik and Meyer supplementary material 1

Download Ennser-Jedenastik and Meyer supplementary material(File)
File 326.9 KB
Supplementary material: Link

Ennser-Jedenastik and Meyer Dataset

Dataset

http://dx.doi.org/10.7910/DVN/S2VOLH
Link