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Many Labs 3: Evaluating participant pool quality across the academic semester via replication

Authors :
Ebersole, Charles
Atherton, Olivia
Belanger, Aimee
Skulborstad, Hayley
Adams, Reginald
Allen, Jill
Banks, Jonathan
Baranski, Erica
Bernstein, Michael
Bonfiglio, Diane
Boucher, Leanne
Brown, Elizabeth
Budiman, Nancy
Cairo, Athena
Capaldi, Colin
Chartier, Christopher
Cicero, David
Coleman, Jennifer
Conway, Morgan
Davis, William
Devos, Thierry
Dopko, Raelyne
Grahe, Jon
German, Komi
Hicks, Joshua
Hermann, Anthony
Humphrey, Brandon
Johnson, David
Joy-Gaba, Jennifer
Juzeler, Hannah
Klein, Richard
Lucas, Richard
Lustgraaf, Christopher
Menon, Madhavi
Metzger, Mitchell
Moloney, Jaclyn
Morse, Patrick
Nelson, Anthony
Prislin, Radmila
Razza, Timothy
Re, Daniel
Rule, Nicholas
Sacco, Donald
Sauerberger, Kyle
Shultz, Megan
Smith, Jessi
Sobocko, Karin
Steiner, Troy
Sternglanz, R. Weylin
Tskhay, Konstantin
Vaughn, Leigh Ann
van Allen, Zack
Walker, Ryan
Wilson, John
Wirth, James
Wortman, Jessica
Zelenski, John
Nosek, Brian
Publication Year :
2022
Publisher :
Open Science Framework, 2022.

Abstract

Many Labs 3 is a crowdsourced project that will systematically evaluate time-of-semester effects across many participant pools. The project team will create a single experimental protocol assessing 10 known effects within 30 minutes. As many labs as can join will administer the same protocol across the academic semester. The aggregate data will provide high-powered evidence whether time-of-semester moderates the detectability of effects. Effects will be selected based on the lay theories of what is likely to be influenced by time of semester.

Details

Database :
OpenAIRE
Accession number :
edsair.doi...........f37276acadaaeb5e4ebf8d9bcac7272e
Full Text :
https://doi.org/10.17605/osf.io/qgjm5