Emergency management training: Handling rich qualitative and quantitative data

Issue title: ICNC-FSKD 2015

Guest editors: Zheng Xiao and Kenli Li

Article type: Research Article

Authors: Ekker, Knut^*

Affiliations: Information Technology, NORD University, Steinkjer, Norway

Correspondence: [*] Corresponding author. Knut Ekker, Information Technology, NORD University, Steinkjer, Norway. Tel.: +4795217005; E-mail: [email protected].

Abstract: An emergency management tool developed for training police, fire and ambulance teams in Norway and Sweden provided the data for this paper. The teams communicated with representatives of the local power companies and county and municipality officials in responding to various emergency scenarios in a web-based training tool. The project generated rich textual data of the content of the communications as well as a range of quantitative data on who communicated with whom, how often and with what type of information. The author analyzed the qualitative data using the NVivo software package and the quantitative analysis used the R statistics package and the social network analysis (SNA) module. The textual analysis shows distinct patterns of concepts and terms used by the various emergency response agencies. The quantitative analysis illustrates the flow of communication among the participants of the emergency management training (EMT). Visual representation of both the qualitative and quantitative data from the project provides a thorough insight into processes of communication among emergency response personnel in role-playing training sessions. The data visualization enhances the debriefing session following emergency response training. The research group at Mid Sweden University and NORD University recently received funding for a three-year continuation of the project. The new project will emphasize the demand side (community stakeholders) in addition to the supply side (the emergency personnel).

Keywords: Emergency management, crisis communication, training software, qualitative data, content analysis, quantitative data, R statistics, social network analysis, SNA

DOI: 10.3233/JIFS-169023

Journal: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 2, pp. 939-948, 2016