Application of the Social Network Analysis to Better Comprehend Relationships in Qualitative Health Data

Lam, LT; Lam, MK; Benrimoj, CS; Cardens, VG

Application of the Social Network Analysis to Better Comprehend Relationships in Qualitative Health Data

Lam, LT

Lam, MK

Benrimoj, CS

Cardens, VG

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Publication Type:: Journal Article
Citation:: Studies in health technology and informatics, 2018, 252 pp. 86 - 91
Issue Date:: 2018-01-01

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Field	Value	Language
dc.contributor.author	Lam, LT https://orcid.org/0000-0001-6183-6854	en_US
dc.contributor.author	Lam, MK https://orcid.org/0000-0001-9451-8203	en_US
dc.contributor.author	Benrimoj, CS https://orcid.org/0000-0001-9768-7838	en_US
dc.contributor.author	Cardens, VG	en_US
dc.date.issued	2018-01-01	en_US
dc.identifier.citation	Studies in health technology and informatics, 2018, 252 pp. 86 - 91	en_US
dc.identifier.issn	0926-9630	en_US
dc.identifier.uri	http://hdl.handle.net/10453/130549
dc.description.abstract	OBJECTIVE: To explore social network analysis (SNA) as an additional approach to elucidate quantifiable insight from qualitative health-related textual data. METHODS: Key concepts gained from thematic analyses of a set of qualitative health data obtained from an implementation study was analysed using the Excel Add-on module NodeXL. RESULTS: Our results show that SNA provided useful visualisation and quantifiable information of the relationship between key concepts obtained from the thematic analysis. DISCUSSION: SNA is a useful technique for exploring and analysing qualitative data, particularly when the research interest is in complex relationships that may exist among a large number of qualitative variables. In addition to providing a way to visualise the relationship between concepts, SNA provides metric measures that can be further analysed quantitatively. CONCLUSION: The SNA approach allows researchers to explore deeper relationships that may exist among various variables and enable researchers to derive potentially a fuller and more complete appreciation and comprehension of health-related data.	en_US
dc.relation.ispartof	Studies in health technology and informatics	en_US
dc.subject.classification	Medical Informatics	en_US
dc.subject.mesh	Research	en_US
dc.subject.mesh	Qualitative Research	en_US
dc.subject.mesh	Social Support	en_US
dc.subject.mesh	Delivery of Health Care	en_US
dc.subject.mesh	Statistics as Topic	en_US
dc.title	Application of the Social Network Analysis to Better Comprehend Relationships in Qualitative Health Data	en_US
dc.type	Journal Article
utslib.citation.volume	252	en_US
utslib.for	0807 Library and Information Studies	en_US
utslib.for	1117 Public Health and Health Services	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Health
pubs.organisational-group	/University of Technology Sydney/Faculty of Health/IMPACCT
pubs.organisational-group	/University of Technology Sydney/Graduate School of Health
pubs.organisational-group	/University of Technology Sydney/Strength - CHSP - Health Services and Practice
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	252	en_US

Abstract:

OBJECTIVE: To explore social network analysis (SNA) as an additional approach to elucidate quantifiable insight from qualitative health-related textual data. METHODS: Key concepts gained from thematic analyses of a set of qualitative health data obtained from an implementation study was analysed using the Excel Add-on module NodeXL. RESULTS: Our results show that SNA provided useful visualisation and quantifiable information of the relationship between key concepts obtained from the thematic analysis. DISCUSSION: SNA is a useful technique for exploring and analysing qualitative data, particularly when the research interest is in complex relationships that may exist among a large number of qualitative variables. In addition to providing a way to visualise the relationship between concepts, SNA provides metric measures that can be further analysed quantitatively. CONCLUSION: The SNA approach allows researchers to explore deeper relationships that may exist among various variables and enable researchers to derive potentially a fuller and more complete appreciation and comprehension of health-related data.

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http://hdl.handle.net/10453/130549