Data analysis

In the next step, the data collected by the four ‘C’ model were prepared and put into data analysis and a writing up process for a comparison of multiple case studies. Yin (2004) argues case study methods are suitable when the research intends to address ‘a descriptive question (what happened?) or an explanatory question (how or why did something happen?)’ (p.2). The strength of the case study method is well-accepted as having the ability to examine a case in indepth within its ‘real-life’ context (Yin 2004, p.1). However, a single case study is often prone to criticism for its perceived failure to meet standards of methodological rigor, researcher objectivity and external validity

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(Evers and Staa 2012). In this study, every village which participated in its respective research zone is observed within the four ‘C’ framework not only as a stand-alone case but also as a comparative study with other villages from the same zone. In the next layer of analysis, the key distinct features of the four ‘Cs’ in all three zones are observed with a ‘cross-case comparison’ analysis.

This study enhances the external validity or generalizability of its findings by using various research elements. From the selection process of the villages to cross-case analysis in multiple case comparisons, it attempts to prove generalizability. Here, generalizability is meant only for the theoretical application of ‘human security in the context of disaster vulnerability and resilience’ in replicated cases rather than generalizability as a case that represents situations in the whole cyclone-affected area. Although the picture of the whole affected region cannot be characterized here, the findings for the three areas provide strength for the entire study and indicate that they are a replication of each other.

Organizing the data

After transcription from hand notes, video and audio files, field data was collected village by village. All the information coming out from the individual interviews, and focus group discussion were organized in one master file for each village. It is a long process as it involved checking the consistency of written notes and audio-visual records. At the end, 45 master files resulted from three respective research zones. Before comparing one zone to another, the internal validation was made for each village by counter checking the rich discussion of the focus group with the data collected from individual participants. When a separate master file for each village was developed by gathering information as much as possible. Together with field notes which reflected the manner, tone and highlights of the participants, a full transcription of each village was combined in one master sheet. All quantifiable data coming out from the background interviews of each village were organized in excel files as one master sheet that visualized comparability of the village’s vulnerability and resilience factors. All these data are not measured by the purely quantitative approach, they are complementary to the rich narratives coming out from the focus group discussion. It helped the researcher to verify and confirm the findings from focus group discussion.

89 Coding

Coding is part of a process that raises raw data to a conceptual level (Juliet and Anselm 1998). Miles and Huberman (1994) refer to the role of coding in data analysis as follows:

To review a set of fieldnotes, transcribed or synthesized and to dissect them meaningfully while keeping the relations between the parts intact, is the stuff of analysis. This part of analysis involves how you differentiate and combine the data you have retrieved and the reflections you make about this information. (p. 56)

While transcribing the data from audio and hand-written formats to a computerized word sheet in the Burmese language, the thematic coding process started. Before jumping into the coding process, a set of pre-identified themes were developed within the frame of the four Cs models. In a thematic coding process, as explained by Ayres (2008), the researcher can begin with a list of anticipated themes that are already found in the raw material. This process helps to reduce the data from raw material that came out from semi-structured interviews. Thematic coding is useful for a study in which the conceptual frame is explicitly reflected in the data collection. In this stage, codes are now directly relevant to the conceptual model of the study, the literature the researcher studied and professional experience (Ayres 2008).

During the coding process, the researcher also added some more items to the existing list of themes when new topics appeared from reading and analyzing raw data. Additional list consists of short descriptions of issues that became apparent during the focus group discussions and village interviews. Once data from each village was categorized into different themes, and a tentative set of coding categories for each respective theme was developed, I need to lend a hand of an independent second rater. A code book that had phrases and words with their own definition and their own address (number) was also developed to makes the code fits the data rather than putting the data in the box of specific code. Based on the codebook, we parallelly tried to test the same three villages from each research zone. After reviewing our independent finding, we could construct an agreement for a final version of codebooks that would apply to the rest of the villages. In the final discussion, we still had to adjust the existing

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codes and defined new ones. As the transcription work of all 45 villages was very rich, the process of data analysis one year from March 2014 to February 2015.

Differentiation and integration of data into only respective themes was challenging in a comparative study. For instance, when the ‘Conditions’ segment had a thematic code with a label ‘medium to receive emergency warning’, multiple answers could emerge to a simple open-ended question such as, ‘how did the participants of a focus group discussion receive a cyclone warning?’ Although the purpose of this question is to find out the pre-existing vulnerability of the community in the days before Cyclone Nargis, the respondents often brought their experiences not only from the pre-impact hours but also from the impact hours and associated events.

In a quantitative interview, this kind of question may go together with a tick box by which the respondent can choose one of the answers as an information source they could access to get warnings. The strength of a qualitative study is that it can provide more variables to one question than one can imagine. For example, if the respondent could only tick pre-identified types of answers in the questionnaire sheet, it was not possible for the researcher to know of cases such as Zee Thaung in Zone I and Ayeyar in Zone II as the warning came from the nearby army unit, which was an unusual factor in comparison with others. Here the challenge is how to handle these diverse materials although they fall under the same thematic code. As Ayres (2008) argues, in this stage, ‘coding facilitates the development of themes, and the development of themes facilitates coding’ (p.868).

Again, some of the paragraphs from transcribed materials cannot be decontextualized with labels as they are attached to more than one ‘C’. When the respondents discussed the ‘Conditions’, the ‘Characteristics’ became inseparable in data analysis as well as in writing up. For example, the absence of primary school education was related to pre-Nargis vulnerability of showing a lack of shelter during Cyclone Nargis as well as to the weak potential of a human system to provide community resilience in the post- Nargis ‘Chronology’. That is why a topic code is also needed for issues that overarch the concepts, ideas, categories and phenomena.

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Data analysis with constant comparative method

Throughout the coding, the constant comparative method (Glaser and Strauss 1967) was applied to avoid missing some key points without coding.

Constant comparison among the three research zones was used to find out similarities and contrasts. Comparison matrices were also built to underline concrete similarities and differences. As hypothesis of the pattern had been developing based on initial findings since the researcher started the field interviews and group discussion, these patterns are clearer throughout the transcription process into a word document. They are also tested by building multiple matrices manually by applying to the following units of comparisons. The relationships between patterns are noted and discussed in respective chapters.

1. Comparison within a single interview or a single focus group discussion.

2. Comparison of one village to another within the same research zone (10 villages under Zone I and Zone III and 25 villages from Zone II)

3. Comparison of one village tract to another (comparison among Kone Gyi, Dee Du Kone and Oke Twin village tracts in Zone I; Aye Yar, Ka Don Ka Ni and Kyein Chaung Gyi village tracts in Zone II; Taw Kha Yan (East), Taw Kha Yan (West), Thone Gwa, Kanyin Gone and Toe in Zone III)

4. Comparison of one township to another within the same research zone (Labutta township and Ngapudaw township in Zone I; Bogale township and Dedaye township in Zone III,)

5. Comparison of several aspects of resilience and vulnerability among Zone I, II and III

6. Comparison of one administrative ‘Region’ to another (Ayeyarwaddy and Yangon Regions)

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7. Comparison of one moment to another (Days before Cyclone Nargis, 24 hours before and after Cyclone Nargis, the first week after Cyclone Nargis, Current situation of 2013 and 2015)

8. Comparison of one disaster experience to another (comparison between 2004 Indian ocean tsunami and Cyclone Nargis)

In short, data analysis involved findings agreement or contrast between theoretical propositions and the empirical evidence from these three research zones are identified through pattern-matching. Expected patterns always specify the value of one or more dependent and independent variables. In the first round of data analysis, differences or similarities are retrieved from multiple cases in the same zone and later comparisons move outside of the same zone and cross-case analysis (Khan and VanWynsberghe 2008) is used to examine the commonalities and differences among the four ‘Cs’ of the three zones. Through cross-case analysis, it was possible to find explanatory factors that contributed to the outcome of Cyclone Nargis in the four ‘Cs’ for every region.