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Big Corporate Network Data: Problems, Diagnostics, and Fixes

Big Corporate Network Data: Problems, Diagnostics, and Fixes

Heemskerk, Eelke M., Young, Kevin, Takes, Frank, Cronin, Bruce ORCID: 0000-0002-3776-8924, Garcia-Bernardo, Javier, Popov, Vladimir, Winecoff, William, Folke Henriksen, Lasse and Laurin-Lamothe, Audrey (2016) Big Corporate Network Data: Problems, Diagnostics, and Fixes. [Working Paper]

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Network data on connections among corporate actors and entities – whether through investment flows, co-ownership ties, or elite social networks – is increasingly available to researchers interested in probing many important questions related to the study of modern capitalism. We discuss the promise and perils of using Big Corporate Network Data (BCND) given the analytical challenges associated with the nature of the subject matter, variable data quality, and other problems associated with currently available data at this scale. We propose a standard process for how researchers can deal with BCND problems. While acknowledging that different research questions require different approaches to data quality, we offer a schematic platform that researchers can follow to make informed and intelligent decisions about BCND issues and address these issues through a specific work-flow procedure. Within each step in this procedure, we provide a set of best practices for how to identify, resolve, and minimize BCND problems that arise.

Item Type: Working Paper
Uncontrolled Keywords: Corporate Networks, Big Data, Network Analysis, Data Quality, Diagnostics
Subjects: H Social Sciences > H Social Sciences (General)
H Social Sciences > HA Statistics
H Social Sciences > HF Commerce
H Social Sciences > HM Sociology
Faculty / Department / Research Group: Faculty of Business
Faculty of Business > Networks and Urban Systems Centre (NUSC) > Centre for Business Network Analysis (CBNA)
Faculty of Business > Department of International Business & Economics
Last Modified: 06 Jun 2017 10:58
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None

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