Hostname: page-component-78c5997874-xbtfd Total loading time: 0 Render date: 2024-11-10T14:49:58.415Z Has data issue: false hasContentIssue false

Experimental Measurement of Misperception in Political Beliefs

Published online by Cambridge University Press:  10 March 2021

Taylor N. Carlson
Affiliation:
Department of Political Science, Washington University in Saint Louis, One Brookings Drive, St. Louis, MO63130, USA
Seth J. Hill*
Affiliation:
Department of Political Science, University of California, San Diego, 9500 Gilman Drive #0521, La Jolla, CA92093-0521, USA
*
*Corresponding author. Email: sjhill@ucsd.edu

Abstract

Recent research suggests widespread misperception about the political views of others. Measuring perceptions often relies on instruments that do not separate uncertainty from inaccuracy. We present new experimental measures of second-order political beliefs. To carefully measure political (mis)perceptions, we have subjects report beliefs as probabilities. To encourage accuracy, we provide micro-incentives for each response. To measure learning, we provide information sequentially about the perception of interest. We illustrate our method by applying it to perceptions of vote choice in the 2016 presidential election. Subjects made inferences about randomly selected American National Election Study (ANES) respondents. Before and after receiving information about the other, subjects reported a probabilistic belief about the other’s vote. We find that perceptions are less biased than in previous work on second-order beliefs. Accuracy increased most with the delivery of party identification and report of a most important problem. We also find evidence of modest egocentric and different-trait bias.

Type
Research Article
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of The Experimental Research Section of the American Political Science Association

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

The experiments presented here are approved by the UCSD Human Research Protections Program. We thank David Broockman, Dan Butler, Jamie Druckman, Anthony Fowler, James Fowler, Federica Izzo, and Shiro Kuriwaki for their helpful discussion. The data, code, and any additional materials required to replicate all analyses in this article are available at the Journal of Experimental Political Science Dataverse within the Harvard Dataverse Network, at: doi:10.7910/DVN/OJ3HJE (Hill and Carlson, 2021). The authors declare no conflicts of interest. Support for this research was provided by the University of California San Diego Academic Senate.

References

Ahler, Douglas J. and Sood, Gaurav. 2018. The Parties in Our Heads: Misperceptions about Party Composition and Their Consequences. Journal of Politics 80(3): 964–81.CrossRefGoogle Scholar
Ahn, T. K., Huckfeldt, Robert, and Ryan, John Barry. 2014. Experts, Activists, and Democratic Politics: Are Electorates Self-Educating? Cambridge: Cambridge University Press.Google Scholar
Broockman, David E. and Skovron, Christopher. 2018. Bias in Perceptions of Public Opinion among Political Elites. American Political Science Review 112(3): 542–63.CrossRefGoogle Scholar
Carlson, Taylor N. Settle, Jaime E.. n.d. What Goes Without Saying: Navigating Political Discussion in America. Working Manuscript.Google Scholar
Coppock, Alexander and McClellan, Oliver A.. 2019. Validating the Demographic, Political, Psychological, and Experimental Results Obtained from a New Source of Online Survey Respondents. Research & Politics 6(1). https://journals.sagepub.com/doi/full/10.1177/2053168018822174 CrossRefGoogle Scholar
Enamorado, Ted, Fifield, Benjamin, and Imai, Kosuke. 2018. Users Guide and Codebook for the ANES 2016 Time Series Voter Validation Supplemental Data. Technical report American National Election Studies.Google Scholar
Eveland, William P., Song, Hyunjin, Hutchens, Myiah J., and Levitan, Lindsey Clark. 2019. Not Being Accurate Is Not Quite the Same as Being Inaccurate: Variations in Reported (in) Accuracy of Perceptions of Political Views of Network Members Due to Uncertainty. Communication Methods and Measures 13(4): 305311.CrossRefGoogle Scholar
Feddersen, Timothy J. and Pesendorfer, Wolfgang. 1996. The Swing Voter’s Curse. American Economic Review 86(3): 408–24.Google Scholar
Hertel-Fernandez, Alexander, Mildenberger, Matto, and Stokes, Leah C.. 2019. Legislative Staff and Representation in Congress. American Political Science Review 113(1): 118.CrossRefGoogle Scholar
Hill, Seth J. 2017. Learning Together Slowly: Bayesian Learning About Political Facts. Journal of Politics 79(4): 1403–18.CrossRefGoogle Scholar
Hill, Seth J. and Carlson, Taylor N.. 2021. Replication Data for: Experimental Measurement of Misperception in Political Beliefs. Harvard Dataverse. doi: 10.7910/DVN/OJ3HJE Google Scholar
Hill, Seth J. and Huber, Gregory A.. 2019. On The Meaning of Survey Reports of Roll Call ‘Votes’. American Journal of Political Science 63(3): 611–25.CrossRefGoogle Scholar
Huckfeldt, Robert and Sprague, John. 1995. Citizens, Politics, and Social Communication: Information and Influence in an Election Campaign. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Levendusky, Matthew S. and Malhotra, Neil. 2015. (Mis) Perceptions of Partisan Polarization in the American Public. Public Opinion Quarterly 80(S1): 378–91.CrossRefGoogle Scholar
Lumley, Thomas. 2004. Analysis of Complex Survey Samples. Journal of Statistical Software 9(1): 119.CrossRefGoogle Scholar
Mildenberger, Matto and Tingley, Dustin. 2017. Beliefs about Climate Beliefs: The Importance of Second-Order Opinions for Climate Politics. British Journal of Political Science 49(4): 1279–307.CrossRefGoogle Scholar
Prior, Markus and Lupia, Arthur. 2008. Money, Time, and Political Knowledge: Distinguishing Quick Recall and Political Learning Skills. American Journal of Political Science 52(1): 169–83.CrossRefGoogle Scholar
R Core Team. 2020. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.Google Scholar
Supplementary material: Link

Carlson and Hill Dataset

Link
Supplementary material: PDF

Carlson and Hill supplementary material

Carlson and Hill supplementary material

Download Carlson and Hill supplementary material(PDF)
PDF 874.9 KB