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How Dropping Subjects Who Failed Manipulation Checks Can Bias Your Results: An Illustrative Case

Published online by Cambridge University Press:  24 November 2022

Simon Varaine Open the ORCID record for Simon Varaine [Opens in a new window]
Simon Varaine*
Affiliation:
PACTE, Université Grenoble Alpes, CNRS, Sciences Po Grenoble (School of Political Studies), 38000 Grenoble, France
*
Email: [email protected]
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Abstract

Manipulations checks are postexperimental measures widely used to verify that subjects understood the treatment. Some researchers drop subjects who failed manipulation checks in order to limit the analyses to attentive subjects. This short report offers a novel illustration on how this practice may bias experimental results: in the present case, through confirming a hypothesis that is likely false. In a survey experiment, subjects were primed with a fictional news story depicting an economic decline versus prosperity. Subjects were then asked whether the news story depicted an economic decline or prosperity. Results indicate that responses to this manipulation check captured subjects’ preexisting beliefs about the economic situation. As a consequence, dropping subjects who failed the manipulation check mixes the effects of preexisting and induced beliefs, increasing the risk of false positive findings. Researchers should avoid dropping subjects based on posttreatment measures and rely on pretreatment measures of attentiveness.

Keywords

manipulation checksrandomized experimentssurvey experimentscausal inferenceType I error
Type
Short Report
Information
Journal of Experimental Political Science , Volume 10 , Issue 2 , Summer 2023 , pp. 299 - 305
Open Practices
Open data Open materials
Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of American Political Science Association

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Footnotes

This article has earned badges for transparent research practices: Open Data and Open Materials. For details see the Data Availability Statement.

References

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https://doi.org/10.7910/DVN/7DXBGG
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