TY - GEN
T1 - Methodological Transparency and Big Data
T2 - 14th International Conference on Information in Contemporary Society, iConference 2019
AU - Sanfilippo, Madelyn Rose
AU - McCoy, Chase
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - Big data is increasingly employed in predictive social analyses, yet there are many visible instances of unreliable models or failure, raising questions about methodological validity in data driven approaches. From meta-analysis of methodological institutionalization across three scholarly disciplines, there is evidence that traditional statistical quantitative methods, which are more institutionalized and consistent, are important to develop, structure, and institutionalize data scientific approaches for new and large n quantitative methods, indicating that data driven research approaches may be limited in reliability, validity, generalizability, and interpretability. Results also indicate that interdisciplinary collaborations describe methods in significantly greater detail on projects employing big data, with the effect that institutionalization makes data science approaches more transparent.
AB - Big data is increasingly employed in predictive social analyses, yet there are many visible instances of unreliable models or failure, raising questions about methodological validity in data driven approaches. From meta-analysis of methodological institutionalization across three scholarly disciplines, there is evidence that traditional statistical quantitative methods, which are more institutionalized and consistent, are important to develop, structure, and institutionalize data scientific approaches for new and large n quantitative methods, indicating that data driven research approaches may be limited in reliability, validity, generalizability, and interpretability. Results also indicate that interdisciplinary collaborations describe methods in significantly greater detail on projects employing big data, with the effect that institutionalization makes data science approaches more transparent.
KW - Big data
KW - Critical data studies
KW - Ethics
KW - Meta-analysis
KW - Research design
UR - http://www.scopus.com/inward/record.url?scp=85064039296&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85064039296&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-15742-5_5
DO - 10.1007/978-3-030-15742-5_5
M3 - Conference contribution
AN - SCOPUS:85064039296
SN - 9783030157418
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 50
EP - 62
BT - Information in Contemporary Society - 14th International Conference, iConference 2019, Proceedings
A2 - Taylor, Natalie Greene
A2 - Christian-Lamb, Caitlin
A2 - Martin, Michelle H.
A2 - Nardi, Bonnie
PB - Springer
Y2 - 31 March 2019 through 3 April 2019
ER -