Abstract

Political scientists have long been interested in citizens’ support level for socially sensitive actors such as ethnic minorities, militant groups, and authoritarian regimes. Attempts to use direct questioning in surveys, however, have largely yielded unreliable measures of these attitudes as they are contaminated by social desirability bias and high non-response rates. In this paper, we develop a statistical methodology to analyze endorsement experiments, which recently have been proposed as a possible solution to this measurement problem. The commonly used statistical methods are problematic because they cannot properly combine responses across multiple policy questions, the design feature of a typical endorsement experiment. We overcome this limitation by using item response theory to estimate support levels on the same scale as the ideal points of respondents. We also show how to extend our model to incorporate a hierarchical structure of data in order to recoup the loss of statistical efficiency due to indirect questioning. We illustrate the proposed methodology by applying it to measure political support for Islamist militant groups in Pakistan. Simulation studies suggest that the proposed Bayesian model yields estimates with reasonable levels of bias and statistical power. Finally, we offer several practical suggestions for improving the design and analysis of endorsement experiments.

Replication Archive

https://doi.org/10.7910/DVN/DQ23DN

Software

endorse: R Package for Analyzing Endorsement Experiments — CRAN / GitHub

Related Papers

Kenny, Christopher, Cory McCartan, Tyler Simko, and Kosuke Imai (2024). ``Census officials must constructively engage with independent evaluations.'' Proceedings of the National Academy of Sciences (Letter), Vol. 121, No. 11, e2321196121.

Kenny, Christopher, Cory McCartan, Shiro Kuriwaki, Tyler Simko, and Kosuke Imai (2024). ``Evaluating Bias and Noise Induced by the U.S. Census Bureau's Privacy Protection Methods.'' Science Advances, Vol 10, No. 18 (May), pp. 1-13.

McCartan, Cory, Tyler Simko, and Kosuke Imai (2024). ``Rejoinder: We Can Improve the Usability of the Census Noisy Measurements File.'' Harvard Data Science Review, Vol. 6, No. 2 (Spring).

McCartan, Cory, Tyler Simko, and Kosuke Imai (2023). ``Making Differential Privacy Work for Census Data Users.'' Harvard Data Science Review, Vol. 5, No. 4 (Fall).

McCartan, Cory, Tyler Simko, and Kosuke Imai (2023). ``Researchers need better access to US Census data.'' Science, Vol. 380, No. 6648 pp. 902-903.

Kenny, Christopher T., Shiro Kuriwaki, Cory McCartan, Evan T.R. Rosenman, Tyler Simko, and Kosuke Imai (2021). ``The Use of Differential Privacy for Census Data and its Impact on Redistricting: The Case of the 2020 U.S. Census.'' Science Advances, Vol. 7, No. 7 (October), pp. 1-17.

Chou, Winston, Kosuke Imai, and Bryn Rosenfeld (2020). ``Sensitive Survey Questions with Auxiliary Information.'' Sociological Methods & Research, Vol. 49, No. 2 (May), pp. 418-454.

Blair, Graeme, Winston Chou, and Kosuke Imai (2019). ``List Experiments with Measurement Error.'' Political Analysis, Vol. 27, No. 4 (October), pp. 455-480.

Hirose, Kentaro, Kosuke Imai, and Jason Lyall (2017). ``Can Civilian Attitudes Predict Insurgent Violence?: Ideology and Insurgent Tactical Choice in Civil War.'' Journal of Peace Research, Vol. 51, No. 1 (January), pp. 47-63. Winner of the Nils Petter Gleditsch Article of the Year Award. Story by Princeton's communication office.

Rosenfeld, Bryn, Kosuke Imai, and Jacob Shapiro (2016). ``An Empirical Validation Study of Popular Survey Methodologies for Sensitive Questions.'' American Journal of Political Science, Vol. 60, No. 3 (July), pp. 783-802.

Blair, Graeme, Kosuke Imai, and Yang-Yang Zhou (2015). ``Design and Analysis of the Randomized Response Technique.'' Journal of the American Statistical Association, Vol. 110, No. 511 (September), pp. 1304-1319.

Lyall, Jason, Yuki Shiraito, and Kosuke Imai (2015). ``Coethnic Bias and Wartime Informing.'' Journal of Politics, Vol. 77, No. 3 (July), p. 833-848.

Imai, Kosuke, Bethany Park, and Kenneth Greene (2015). ``Using the Predicted Responses from List Experiments as Explanatory Variables in Regression Models.'' Political Analysis, Vol. 23, No. 2 (Spring), pp. 180-196. Translated in Portuguese and Reprinted in Revista Debates Vol. 9, No 1.

Blair, Graeme, Kosuke Imai, and Jason Lyall (2014). ``Comparing and Combining List and Endorsement Experiments: Evidence from Afghanistan.'' American Journal of Political Science, Vol. 58, No. 4 (October), pp. 1043-1063.

Lyall, Jason, Graeme Blair, and Kosuke Imai (2013). ``Explaining Support for Combatants during Wartime: A Survey Experiment in Afghanistan.'' American Political Science Review, Vol. 107, No. 4 (November), pp. 679-705. Winner of the Pi Sigma Alpha Award.

Blair, Graeme and Kosuke Imai (2012). ``Statistical Analysis of List Experiments.'' Political Analysis, Vol. 20, No. 1 (Winter), pp. 47-77.

Imai, Kosuke (2011). ``Multivariate Regression Analysis for the Item Count Technique.'' Journal of the American Statistical Association, Vol. 106, No. 494 (June), pp. 407-416. (featured article)