Your browser doesn't support javascript.
loading
Utilising qualitative data for social network analysis in disaster research: opportunities, challenges, and an illustration.
Benedict, Bailey C; Lee, Seungyoon; Jarvis, Caitlyn M; Siebeneck, Laura K; Wolfe, Rachel.
Afiliação
  • Benedict BC; PhD is an Assistant Professor at the Department of Management, California State University - San Bernardino, United States.
  • Lee S; PhD is a Professor at the Brian Lamb School of Communication, Purdue University, United States.
  • Jarvis CM; PhD is an Assistant Teaching Professor at the Department of Communication Studies, Northeastern University, United States.
  • Siebeneck LK; PhD is a Professor at the Department of Emergency Management and Disaster Science, University of North Texas, United States.
  • Wolfe R; MS is a Graduate Student at the Department of Emergency Management and Disaster Science, University of North Texas, United States.
Disasters ; 48(2): e12605, 2024 Apr.
Article em En | MEDLINE | ID: mdl-37471176
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.
Assuntos
Palavras-chave

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Desastres / Tempestades Ciclônicas Tipo de estudo: Qualitative_research Limite: Humans Idioma: En Revista: Disasters Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Desastres / Tempestades Ciclônicas Tipo de estudo: Qualitative_research Limite: Humans Idioma: En Revista: Disasters Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Estados Unidos