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Multi-modes for Detecting Experimental Measurement Error

Published online by Cambridge University Press:  14 October 2019

Raymond Duch Open the ORCID record for Raymond Duch [Opens in a new window] ,
Denise Laroze Open the ORCID record for Denise Laroze [Opens in a new window] ,
Thomas Robinson Open the ORCID record for Thomas Robinson [Opens in a new window]  and
Pablo Beramendi
Raymond Duch*
Affiliation:
Nuffield College, University of Oxford, Oxford, UK. Email: [email protected]
Denise Laroze
Affiliation:
Centre for Experimental Social Sciences and Departamento de Administración, Universidad de Santiago de Chile, Santiago, Chile. Email: [email protected]
Thomas Robinson
Affiliation:
Department of Politics and International Relations, University of Oxford, Oxford, UK. Email: [email protected]
Pablo Beramendi
Affiliation:
Department of Political Science, Duke University, Durham, NC 27708, USA. Email: [email protected]
*
*Email: [email protected]
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Abstract

Experiments should be designed to facilitate the detection of experimental measurement error. To this end, we advocate the implementation of identical experimental protocols employing diverse experimental modes. We suggest iterative nonparametric estimation techniques for assessing the magnitude of heterogeneous treatment effects across these modes. And we propose two diagnostic strategies—measurement metrics embedded in experiments, and measurement experiments—that help assess whether any observed heterogeneity reflects experimental measurement error. To illustrate our argument, first we conduct and analyze results from four identical interactive experiments: in the lab; online with subjects from the CESS lab subject pool; online with an online subject pool; and online with MTurk workers. Second, we implement a measurement experiment in India with CESS Online subjects and MTurk workers.

Keywords

experimental designmeasurement errorBayesian estimationcausalitycounterfactual analysisheterogeneous effects
Type
Articles
Information
Political Analysis , Volume 28 , Issue 2 , April 2020 , pp. 263 - 283
Copyright
Copyright © The Author(s) 2019. Published by Cambridge University Press on behalf of the Society for Political Methodology.

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Footnotes

Authors’ note: We would like to acknowledge the contributions of the Nuffield College Centre for Experimental Social Sciences postdocs who were instrumental in helping design and implement the experiments reported on in the manuscript—these include, John Jensenius III, Aki Matsuo, Sonke Ehret, Mauricio Lopez, Hector Solaz, Wojtek Przepiorka, David Klinowski, Sonja Vogt, and Amma Parin. We have also benefited from the very helpful comments from colleagues including Vera Troeger, Thomas Pluemper, Dominik Duell, Luke Keele, and Mats Ahrenshop. And thanks to the Political Analysis reviewers, editor and editorial team who were extremely helpful. Of course we assume responsibility for all of the shortcomings of the design and analysis. All replication materials are available from the Political Analysis Dataverse, doi.org/10.7910/DVN/F0GMX1 (Duch et al.2019).

Contributing Editor: Jeff Gill

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