It is widely considered that one of the main parts of any study is the data analysis because it helps to inspect the gathered data and to arrive at suitable conclusions according to such data. Data analysis procedures consist of examining, categorising, tabulating, testing or otherwise recombining both qualitative and quantitative evidence to address the initial propositions of a study (Yin, 2014). The qualitative data highlights all non- numeric data or data that has not been measured however quantitative data highlights all numeric data (Saunders et al., 2016). Yin (2014) emphasises that to reduce potential analytical difficulties, a general strategy for data analysis should be developed. Such strategy will lead the researcher when selecting an appropriate data analysis tool, ensuring that the evidence is addressed properly, thus generating convincing and sound analytical conclusions while discarding any alternative interpretations. Moreover, despite the existence of a various method of data analysis, no specific data analysis has been found to accommodate case study (Petty, Thomson, & Stew, 2012; Yin, 2014). In this study, NVivo and MS Excel software was used to analyse the data and present it in the appropriate form. Using such software will establish continuity and increase both methodological rigour and transparency (Saunders et al., 2016). This step was conducted after transcribing the collected data from non-written evidence (interviews) to written accounts.
3.9.1
Analysis of Questionnaire Surveys
MS Excel software was used to analyse the data gathered through a questionnaire. After the researcher arrived back in the UK at the beginning of September 2015, the data entry process started immediately. The results from the questionnaire have been entered and analysed. By using the Likert scale in its 5 points’ grading for the degree of importance and for the degree of implementation as explained in Section 3.8.1.1, quantitative data was collected and such primary data was entered into MS Excel spreadsheet. To minimise and avoid any errors, the data set was further proofread.
To handle the missing data “no opinion N/O”, Saunders et al. (2016) argue that a special code for missing data is often reserved by statistical analysis software. For example, 999 is used to distinct the missing data from other answer codes (Wilson, 2013). Therefore, subsequent analyses can exclude such missing data when necessary (Kulatunga, 2008; Saunders et al., 2016; Wilson, 2013).
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According to Saunders et al. (2016), the three most used ways of measuring the central tendency in business research are: mode, median, and mean (average). The median is the mid-point of the data whereas the mode is the most frequently occurring value. The most frequently used measure of central tendency is the mean value since it encapsulates all the values in the sample and hence it was used in this study. Accordingly, to measure the importance and the degree of implementation of the element of good practice disaster response management, the researcher calculated the mean value i.e. the average value of the data sets.
3.9.1.1 Presentation of Questionnaire Survey Analysis
The questionnaire survey findings were presented by using radar charts. The gap between the importance and implementation was presented, as shown in Figure 3.7. The extent of such gaps can be given a good indicator to the weaknesses and the strengths of the current practices related to disaster response management.
Figure 3.7 Radar Chart for Every Data Set to indicate Both Importance and Implementation’s Value
3.9.2
Analysis of Semi-Structured Interviews
Because of the use of semi-structured interviews, the free flowing text was obtained as qualitative data. To analyse such data within this study, content analysis has been utilised. This section will explain the use of it. In qualitative research, content analysis is widely used. Significant desired raw information such as implicit or explicit data are extracted from texts or images by using this method. Before making interpretation and valid inferences, such
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information will be organised into a systematic concept (Busch et al., 1994-2012; Krippendorff, 2012; Kulatunga, Amaratunga, & Haigh, 2007; Smith, 2000). The content analysis tool is also able to quantify qualitative data (Kulatunga et al., 2007; Vaismoradi, Turunen, & Bondas, 2013). Based on Kulatunga et al. (2007), in qualitative research, four approaches to content analysis has been presented. Firstly, word count, by counting the frequency of identified words, the importance of the words can be showed by using the assumptions of the most frequent word. Secondly, the conceptual content analysis focuses on identifying the occurrence and presence of an identified concept and/or themes is examined in text or sets of text (Busch et al., 1994- 2012). The predetermination of concept or themes could be through the literature review or appear from the information itself. Thirdly, the relational analysis considers the relation between concepts inside the text is analysed by this approach (Busch et al., 1994-2012). Fourthly, referential content analysis focus on the underlying meaning of the text is examined and text interpretation is based on the researcher judgement.
In this study, the researcher sought to explore the interviewee’s thoughts about the current disaster response practices. Therefore, the attitudes and opinions concerning the current practices related to disaster response management in Iraq, weaknesses and strengths of the current disaster response practices were examined. The interviewee’s recommendations to enhance the current disaster response practices were also explored. Mere word counting, within this scenario, would not guide the researcher to accomplish the ultimate goals by extracting major concepts from the study. In addition to that, it is not anticipated to develop relationships among the concepts, within the range of the content analysis, nor planned to analyse the complexity of the language. Consequently, by considering the requirements of this study, as this study aim to evaluate and make recommendations to disaster response management in Iraq, the documents related to disaster response management and the experience of the interviewees were investigated by using conceptual content analysis approach. Such approach was selected because it presents the opportunity to inspect the interviewees’ answers in multiple methods so as to discover data which are important to the study. Accordingly, to provide insight on the disaster response management in Iraq, the conceptual content analysis has been utilised in this study.
3.9.2.1 Presentation of Semi-Structured Interview Analysed Data
As conceptual content analysis was identified as the most suitable method to achieve the aim and the objectives of this research, a computer software package called NVivo was used to aid such type of analysis. Since NVivo is a software package that supports qualitative research. It
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has an ability to collect and organise qualitative data, such as an interview. The researcher chose to use NVivo 10 because such software provides great management for the interview transcription and supporting the execution of content analysis. Further, the numbers of interviews were too large to handle manually. The most importantly, in addition to it is easy to understand and simple, it permits a rigorous and comprehensive data analysis process.
The researcher followed a procedure in coding to conduct the analysis using NVivo software. Firstly, the raw data from the interviews are transcribed into text format using Microsoft Word software. After that, this data is imported to NVivo. The preliminary themes and codes using the literature review and manual analysis on the transcripts were then initially established prior to developing themes in NVivo. In NVivo, the theme is known as a node. The structure of such nodes is based on the key theme of the semi-structured interview that reflects the objectives of the study. Finally, the researcher conducted further analysis to the coded texts after developing the nodes. These multiple stages approach that were used to code the interviews transcription are sometimes named open coding, axial coding, and selective coding process (Hayat & Amaratunga, 2014). The researcher in the open coding stage coded and named the information from the interviews depending on the key ideas of the emerging information. At the axial coding stage, the researcher categorised and gathered the nodes into relevant themes (see Figure 3.8).
Figure 3.8 Example of Data Presentation Using Nvivo 10
After finishing the axial coding process and in order to add more elaboration and discussions, the transcription codes were exported to Microsoft word software. The researcher after that
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extracted the information from the transcription depending on its suitability and relevance to the themes. The researcher used direct quotations of the interviewees when presenting the arguments. Thus, this procedure is named selective coding. Accordingly, a series of matrices of themes has been presented after classifying the analysis of data. Throughout this process, in- depth or detailed descriptions were used to give the reader a better understanding of the underlying conditions behind the phenomenon and the activities that had taken place. The credibility of the study was presented through these in-depth descriptions.
Having designated all the elements in the methodological framework, from research philosophy to date collection techniques and analysis, the research methodology adopted by this study is summarised in the following onion model of Saunders et al. (2016) (see Figure 3.9).
Figure 3.9 Methodology for this Research Adopted from Saunders’ Research Onion
Subjectivism Interpretevism Value laden Abductive Interviews Documents Archival records Questionnaire Case study Mixed-method Cross-sectional
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