Tischer, D. orcid.org/0000-0003-3180-7862 (2022) Collecting network data from documents to reach non-participatory populations. Social Networks, 69. pp. 113-122. ISSN 0378-8733
Abstract
Collecting social network data is challenging, not least because conventional approaches rely on human participation. However, there are instances where access to research subjects is restricted or non-existent, especially in the high-stakes commercial world. This paper outlines the collection of network data from a relatively obscure financial document – the offering circular. I consider the implications of dealing with a non-participatory population and data that is not produced for social network research. In exploring the process of translating data into social network data I highlight the importance of retaining context and qualitative descriptions in the data. I also consider how different coding strategies impact the network. Finally, I discuss the triangulation, data anonymity and potential ethical-legal implications of collecting data from documents.
Metadata
| Item Type: | Article | 
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| Authors/Creators: | 
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| Copyright, Publisher and Additional Information: | © 2020 Elsevier B.V. This is an author produced version of a paper subsequently published in Social Networks. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). | 
| Keywords: | Data Collection; Social Network Analysis; Documents; Tie Strength; Careers; Biographies; Ethics | 
| Dates: | 
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| Institution: | The University of Sheffield | 
| Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Management School (Sheffield) | 
| Depositing User: | Symplectic Sheffield | 
| Date Deposited: | 03 Mar 2022 09:11 | 
| Last Modified: | 24 Mar 2022 01:38 | 
| Status: | Published | 
| Publisher: | Elsevier BV | 
| Refereed: | Yes | 
| Identification Number: | 10.1016/j.socnet.2020.09.004 | 
| Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:184248 | 

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