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The Filter Curve: Uncovering p-Hacking from filtering

  • Many empirical studies filter participants (e.g., for incorrect attention checks or quick re-sponses), especially when using participant pools such as Amazon MTurk. Yet, there is no consensus on whether and how to filter. This might originate from different perspectives on filtering participants: it may be evaluated positively (e.g., as it might be necessary to prevent inattentive participants from biasing results) or negatively (e.g., as it may enable p-hacking). This research aims to bridge these opposites: first, we empirically compare the effects of different filters and filter levels on validity, reliability, power and effects sizes of the results. Second, we introduce the Filter Curve and our R-package “FiltR” as a means to recognize filtering which might be used to p-hack results. We suggest that filtering is not per se bad – although some filters decrease reliability and validity – but that researchers should be trans-parent in how sensitive results are for different filter combinations.

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Author: Florian DostORCiD, Lennard Schmidt, Erik MaierORCiD
URL:http://proceedings.emac-online.org/index.cfm?abstractid=A2021-94598&The%20Filter%20Curve:%20Uncovering%20p-Hacking%20from%20filter
Title of the source (English):Proceedings of the European Marketing Academy (EMAC), 50th Annual Conference
Document Type:Conference publication peer-reviewed
Language:English
Year of publication:2021
Tag:p hacking
First Page:1
Last Page:8
Article number:94598
Faculty/Chair:Fakultät 5 Wirtschaft, Recht und Gesellschaft / FG ABWL, insbesondere Marketing
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