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Placebo statements in list experiments: Evidence from a face-to-face survey in Singapore

Published online by Cambridge University Press:  15 May 2020

Guillem Riambau  and
Kai Ostwald
Guillem Riambau*
Affiliation:
Institute of Political Economy Research Group (IPERG)—Universitat de Barcelona, Spain
Kai Ostwald
Affiliation:
School of Public Policy & Global Affairs and Department of Political Science, University of British Columbia, Canada
*
*Corresponding author. Email: [email protected]
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Abstract

List experiments are a widely used survey technique for estimating the prevalence of socially sensitive attitudes or behaviors. Their design, however, makes them vulnerable to bias: because treatment group respondents see a greater number of items (J + 1) than control group respondents (J), the treatment group mean may be mechanically inflated due simply to the greater number of items. The few previous studies that directly examine this do not arrive at definitive conclusions. We find clear evidence of inflation in an original dataset, though only among respondents with low educational attainment. Furthermore, we use available data from previous studies and find similar heterogeneous patterns. The evidence of heterogeneous effects has implications for the interpretation of previous research using list experiments, especially in developing world contexts. We recommend a simple solution: using a necessarily false placebo statement for the control group equalizes list lengths, thereby protecting against mechanical inflation without imposing costs or altering interpretations.

Keywords

List experimentitem count techniquesurvey designplacebo statementsocial desirability bias
Type
Research Note
Information
Political Science Research and Methods , Volume 9 , Issue 1 , January 2021 , pp. 172 - 179
Copyright
Copyright © The European Political Science Association 2020

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Footnotes

Research carried out under NUS-IRB S-18-343 from the National University of Singapore. We would like to thank Edmund Malesky, Steven Oliver, Tom Pepinsky, and Risa Toha, for insightful comments and suggestions. All errors are ours. Supplementary materials can be found at http://guillemriambau.com/

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Supplementary material: PDF

Riambau and Ostwald supplementary material

Riambau and Ostwald supplementary material

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Riambau and Ostwald Dataset

Dataset

https://doi.org/10.7910/DVN/S6B7SE
Link