Bailey C. Benedict, Seungyoon Lee, Caitlyn M. Jarvis, Laura K. Siebeneck, Rachel Wolfe
{"title":"Utilising qualitative data for social network analysis in disaster research: opportunities, challenges, and an illustration","authors":"Bailey C. Benedict, Seungyoon Lee, Caitlyn M. Jarvis, Laura K. Siebeneck, Rachel Wolfe","doi":"10.1111/disa.12605","DOIUrl":null,"url":null,"abstract":"<p>An abundance of unstructured and loosely structured data on disasters exists and can be analysed using network methods. This paper overviews the use of qualitative data in quantitative social network analysis in disaster research. It discusses two types of networks, each with a relevant major topic in disaster research—that is, (i) whole network approaches to emergency management networks and (ii) personal network approaches to the social support of survivors—and four usable forms of qualitative data. This paper explains five opportunities afforded by these approaches, revolving around their flexibility and ability to account for complex network structures. Next, it presents an empirical illustration that extends the authors' previous work examining the sources and the types of support and barrier experienced by households during long-term recovery from Hurricane (Superstorm) Sandy (2012), wherein quantitative social network analysis was applied to two qualitative datasets. The paper discusses three challenges associated with these approaches, related to the samples, coding, and bias.</p>","PeriodicalId":2,"journal":{"name":"ACS Applied Bio Materials","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2023-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/disa.12605","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Bio Materials","FirstCategoryId":"91","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/disa.12605","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, BIOMATERIALS","Score":null,"Total":0}
引用次数: 0
Abstract
An abundance of unstructured and loosely structured data on disasters exists and can be analysed using network methods. This paper overviews the use of qualitative data in quantitative social network analysis in disaster research. It discusses two types of networks, each with a relevant major topic in disaster research—that is, (i) whole network approaches to emergency management networks and (ii) personal network approaches to the social support of survivors—and four usable forms of qualitative data. This paper explains five opportunities afforded by these approaches, revolving around their flexibility and ability to account for complex network structures. Next, it presents an empirical illustration that extends the authors' previous work examining the sources and the types of support and barrier experienced by households during long-term recovery from Hurricane (Superstorm) Sandy (2012), wherein quantitative social network analysis was applied to two qualitative datasets. The paper discusses three challenges associated with these approaches, related to the samples, coding, and bias.