Informace o publikaci

Crowdsourcing Multiverse Analyses to Explore the Impact of Different Data-Processing and Analysis Decisions: A Tutorial

Autoři	HEYMAN Tom PRONIZIUS Ekaterina LEWIS Savannah C. ACAR Oguz A. ADAMKOVIC Matus AMBROSINI Ettore ANTFOLK Jan BARZYKOWSKI Krystian BASKIN Ernest BATRES Carlota BOUCHER Leanne BOUDESSEUL Jordane BRANDSTATTER Eduard COLLINS W. Matthew FILIPOVIC DURDEVIC Dusica EGAN Ciara ERA Vanessa FERREIRA Paulo FINI Chiara GARRIDO-VASQUEZ Patricia GODBERSEN Hendrik GOMEZ Pablo GRATON Aurelien GURKAN Necdet HE Zhiran JOHNSON Dave C. KACMAR Pavol KOCH Chris KOWAL Marta KRATOCHVÍL Tomáš MARELLI Marco MARMOLEJO-RAMOS Fernando MARTINEZ Martin MATTIASSI Alan MAXWELL Nicholas P. MONTEFINESE Maria MORVINSKI Coby NETA Maital NIELSEN Yngwie A. OCKLENBURG Sebastian ONIC Jas PAPADATOU-PASTOU Marietta PARKER Adam J. PARUZEL-CZACHURA Mariola PAVLOV Yuri G. PEREA Manuel PFUHL Gerit ROEMBKE Tanja C. ROER Jan P. ROETTGER Timo B. RUIZ-FERNANDEZ Susana SCHMIDT Kathleen SIEW Cynthia S. Q. TAMNES Christian K. TAYLOR Jack E. THERIAULT Remi ULLOA Jose L. VADILLO Miguel A. VARNUM Michael E. W. VASILEV Martin R. VERHEYEN Steven VIVIANI Giada WALLOT Sebastian YAMADA Yuki ZHENG Yueyuan BUCHANAN Erin M.
Rok publikování	2025
Druh	Článek v odborném periodiku
Časopis / Zdroj	PSYCHOLOGICAL METHODS
Fakulta / Pracoviště MU	Fakulta sociálních studií
Citace
Doi	https://doi.org/10.1037/met0000770
Klíčová slova	multiverse analysis; generalizability; tutorial; data-analytic flexibility; consensus
Popis	When processing and analyzing empirical data, researchers regularly face choices that may appear arbitrary (e.g., how to define and handle outliers). If one chooses to exclusively focus on a particular option and conduct a single analysis, its outcome might be of limited utility. That is, one remains agnostic regarding the generalizability of the results, because plausible alternative paths remain unexplored. A multiverse analysis offers a solution to this issue by exploring the various choices pertaining to data-processing and/or model building, and examining their impact on the conclusion of a study. However, even though multiverse analyses are arguably less susceptible to biases compared to the typical single-pathway approach, it is still possible to selectively add or omit pathways. To address this issue, we outline a novel, more principled approach to conducting multiverse analyses through crowdsourcing. The approach is detailed in a step-by-step tutorial to facilitate its implementation. We also provide a worked-out illustration featuring the Semantic Priming Across Many Languages project, thereby demonstrating its feasibility and its ability to increase objectivity and transparency.