What Crowdsourcing Can Offer to Cross-Cultural Psychological Science

Dados Bibliográficos

ID7738575
AutoresKelly Cuccolo (0000-0002-0358-7113, University of North Dakota, Grand Forks, USA, autor correspondente), Megan Suzanne Irgens (University of Arizona), Martha S Zlokovich (0000-0002-6362-8175, Psi Chi, the International Honor Society in Psychology, Chattanooga, TN, USA), Jon Grahe (0000-0001-6970-0863, Pacific Lutheran University, Tacoma, WA, USA), John E Edlund (0000-0003-3868-1844, Rochester Institute of Technology)
Ano2021
Volume55
Fascículo1
Páginas3-28
Data de publicação2021-02-01
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoCross-Cultural Research (JOURNAL)
Identificadores do periódicoISSN: 1069-3971 • E-ISSN: 1552-3578
EditoraSAGE Publications (PUBLISHER • US)
DOI10.1177/1069397120950628
OpenAlexW3082530374
IdiomaEN
Citações recebidas7
Referências citadas66

Although the benefits of crowdsourcing research models have been outlined elsewhere, very little attention has been paid to the application of these models to cross-cultural behavioral research. In this manuscript, we delineate two types of crowdsourcing initiatives—researcher crowdsourced and participant crowdsourced. Researcher crowdsourced refers to initiatives where researchers are gathered to work toward a shared goal. Participant crowdsourced refers to those which allow a researcher to gather a large number of participants within a short time frame. We explore the utility of each type of initiative while providing readers with a framework that can be used when deciding whether researcher or participant crowdsourcing initiatives would be most fruitful for their work. Perceived strengths of a researcher crowdsourced initiative with a cross-cultural focus is based on contributor data from Psi Chi’s Network for International Collaborative Exchange (NICE) and is integrated into this framework. Claims are made for the utility of both researcher and participant crowdsourcing as a way to increase generalizability and reliability, decrease time burdens, democratize research, educate individuals on open science, and provide mentorship. These claims are supported with data from NICE contributors

Crowdsourcing · Data science · Generalizability theory · Knowledge management · Medical education · Mentorship · Nice · World Wide Web · Advanced Text Analysis Techniques · Applied Psychology · Climate Change Communication and Perception · Computer Science · Cultural Differences and Values · Medicine · Psychology