Abstract
Participant crowdsourcing platforms (e.g., MTurk, Prolific) offer numerous advantages to addiction science, permitting access to hard-to-reach populations and enhancing the feasibility of complex experimental, longitudinal, and intervention studies. Yet these are met with equal concerns about participant nonnaivety, motivation, and careless responding, which if not considered can greatly compromise data quality. In this article, we discuss an alternative crowdsourcing avenue that overcomes these issues whilst presenting its own unique advantages-crowdsourcing researchers through big team science. First, we review several contemporary efforts within psychology (e.g., ManyLabs, Psychological Science Accelerator) and the benefits these would yield if they were more widely implemented in addiction science. We then outline our own consortium-based approach to empirical dissertations: a grassroots initiative that trains students in reproducible big team addiction science. In doing so, we discuss potential challenges and their remedies, as well as providing resources to help addiction researchers develop these initiatives. Through researcher crowdsourcing, together we can answer fundamental scientific questions about substance use and addiction, build a literature that is representative of a diverse population of researchers and participants, and ultimately achieve our goal of promoting better global health. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Original language | English |
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Pages (from-to) | 444–451 |
Journal | Experimental and Clinical Psychopharmacology |
Volume | 30 |
Issue number | 4 |
Early online date | 13 Jan 2022 |
DOIs | |
Publication status | Published - 31 Aug 2022 |
Bibliographical note
Funding Information:This article is not associated with any funding sources and the authors declare no conflicts of interest, including financial, personal, or other. We confirm that all authors contributed in a significant way to the manuscript and have read and approved the final version.
Funding
This article is not associated with any funding sources and the authors declare no conflicts of interest, including financial, personal, or other. We confirm that all authors contributed in a significant way to the manuscript and have read and approved the final version.