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Is mixed methods social network analysis ethical?
Maina Korir; Jenna Mittelmeier; Bart Rienties;
Maina Korir; Jenna Mittelmeier; Bart Rienties;
Is mixed methods social network analysis ethical?
This book chapter aims to explore the affordances and limitations of Mixed Methods Social Network Analysis (MMSNA) from an ethics perspective. In line with Ifenthaler and Schumacher (2016) we define ethics as “a system of fundamental principles and universal values of right conduct”. There may be substantial ethical considerations when conducting MMSNA research in comparison to more ’standard’ social science approaches: lack of anonymization; potential to identify non-respondents; and identification of “hidden” sub-groups. We will use one practical example to highlight potential ethical issues when conducting MMSNA research. We hope that by raising awareness of the potential ethical issues, researchers, practitioners, and the actual participants will become more mindful of the affordances and limitations of MMSNA research approaches.
Microsoft Academic Graph classification: Identification (information) Ethical issues Perspective (graphical) Engineering ethics Psychology Affordance Social network analysis
ACM Computing Classification System: ComputingMilieux_THECOMPUTINGPROFESSION
Microsoft Academic Graph classification: Identification (information) Ethical issues Perspective (graphical) Engineering ethics Psychology Affordance Social network analysis
ACM Computing Classification System: ComputingMilieux_THECOMPUTINGPROFESSION
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).3 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average downloads 275 citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).3 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average - 275downloads
citations
Citations provided by BIP!
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
Citations provided by BIP!popularityPopularity provided by BIP!
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
Popularity provided by BIP!influenceInfluence provided by BIP!
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
Influence provided by BIP!impulseImpulse provided by BIP!
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
Impulse provided by BIP!downloadsDownloads provided by UsageCounts
Downloads provided by UsageCounts 3
Average
Average
Average
275
This book chapter aims to explore the affordances and limitations of Mixed Methods Social Network Analysis (MMSNA) from an ethics perspective. In line with Ifenthaler and Schumacher (2016) we define ethics as “a system of fundamental principles and universal values of right conduct”. There may be substantial ethical considerations when conducting MMSNA research in comparison to more ’standard’ social science approaches: lack of anonymization; potential to identify non-respondents; and identification of “hidden” sub-groups. We will use one practical example to highlight potential ethical issues when conducting MMSNA research. We hope that by raising awareness of the potential ethical issues, researchers, practitioners, and the actual participants will become more mindful of the affordances and limitations of MMSNA research approaches.