학술논문
Network Models to Organize a Dispersed Literature: The Case of Misunderstanding Analysis of Covariance
Document Type
article
Author
Koen Derks; Julian Burger; Johnny van Doorn; Jolanda J. Kossakowski; Dora Matzke; Ludovica Atticciati; Julia Beitner; Viket Benzesin; Anne L. de Bruijn; Tara R. H. Cohen; Elisa P. A. Cordesius; Marit van Dekken; Nora Delvendahl; Simone Dobbelaar; Eva R. Groenendijk; Merel E. Hermans; Anu P. Hiekkaranta; Ria H. A. Hoekstra; Agnes M. Hoffmann; Sally A. M. Hogenboom; Sercan Kahveci; Irina J. Karaban; Sofieke Kevenaar; Jurriaan L. te Koppele; Anne-wil Kramer; Emese Kroon; Šimon Kucharský; Ricardo Lieuw-On; Gaby Lunansky; Timo P. Matzen; Annemarie Meijer; Annika Nieper; Laura de Nooij; Leonie Poelstra; Wikke J. van der Putten; Alexandra Sarafoglou; Jessica V. Schaaf; Sara A. J. van de Schraaf; Steven van Schuppen; Manon H. M. Schutte; Mitja Seibold; Scarlett K. Slagter; Aishah C. Snoek; Selina Stracke; Zenab Tamimy; Bram Timmers; Han Tran; Elizabeth S. Uduwa-Vidanalage; Laura Vergeer; Linos Vossoughi; Dilan E. Yücel; Eric-Jan Wagenmakers
Source
Journal of European Psychology Students, Vol 9, Iss 1, Pp 48-57 (2018)
Subject
Language
English
ISSN
2222-6931
Abstract
We outline a network method to synthesize a literature overview from search results obtained by multiple team members. Several network statistics are used to create a single representativeness ranking. We illustrate the method with the dispersed literature on a common misinterpretation of analysis of covariance (ANCOVA). The network method yields a top ten list of the most relevant articles that students and researchers can take as a point of departure for a more detailed study on this topic. The proposed methodology is implemented in Shiny, an open-source R package.