Utilising qualitative data for social network analysis in disaster research: opportunities, challenges, and an illustration

被引:1
作者
Benedict, Bailey C. [1 ,5 ]
Lee, Seungyoon [2 ]
Jarvis, Caitlyn M. [3 ]
Siebeneck, Laura K. [4 ]
Wolfe, Rachel [4 ]
机构
[1] Calif State Univ San Bernardino, Dept Management, San Bernardino, CA USA
[2] Purdue Univ, Brian Lamb Sch Commun, W Lafayette, IN USA
[3] Northeastern Univ, Dept Commun Studies, Boston, MA USA
[4] Univ North Texas, Dept Emergency Management & Disaster Sci, Denton, TX USA
[5] Calif State Univ San Bernardino, Dept Management, 5500 Univ Pkwy, San Bernardino, CA 92407 USA
基金
美国国家科学基金会;
关键词
disasters; qualitative data; quantitative analyses; research methods; social network analysis; HAZARD MITIGATION PLANS; HURRICANE KATRINA; COMMUNITY; RECOVERY; TWITTER; COMMUNICATION; RESPONDERS; RESILIENCY; TWEETS; HEALTH;
D O I
10.1111/disa.12605
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
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页数:29
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