Method of data analysis

The choice of qualitative analysis method involves several important considerations. These include type of data, the number of cases,

epistemological leanings, underlying theories, and the expected outcome of the analysis in terms of what type of results are needed. For this study, the

elements of the data are:

• Semi-structured interviews, translated or with participants with limited

English language;

• Relatively large number of participants for a qualitative study, with 30-

35 participants in each of two sites;

• Inductive-type method to answer research questions;

• Two case studies (research sites) with three groups of participants in

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• Results to include within case study comparison of groups, and

contrasting between case studies;

• Focus on the perceptions and experiences of the participants, rather

than trying to get at one particular ‘truth’.

A review of various types of qualitative data analysis led me towards thematic coding using template or framework type analysis. Following is an explanation of how this method was chosen, and why others were rejected as being less appropriate.

In the sociological tradition of qualitative research, text is analysed as a proxy for experience, with the research participant’s perceptions, feelings, knowledge and behaviour represented in text that has been generated from our interactions with them, for example in an interview (Tesch, 1990). Within this, there is a distinction between analysis of the actual words, and analysis using themes and codes (Guest, MacQueen and Namey, 2011). An analysis of the actual words used, concentrating on the linguistics, phrasing, pauses and such would be problematic with the data in this study, since English was a second (or third) language for all participants and translators. Therefore, analysis methods such as discourse analysis, or phenomenological analysis (Biggerstaff and Thompson, 2008) were discounted as being inappropriate if strictly

adhered to. However, the focus of interpretive phenomenological analysis in looking at the subjective experiences of the participants, and the

acknowledgement of the interpretive role of the researcher are relevant and useful to the current study, and so a method inclusive of these broad elements was sought.

Another distinction between types of analysis is along epistemological lines, with interpretivism and positivism. Interpretivism is less structured, and seeks to interpret deeper meanings and understand multiple realities, rather than one objective reality. Positivism on the other hand is closer to the scientific method and is more systematic and structured, less subjective and looks towards an objective reality (Guest et al., 2011). The desired analysis method for this study comes somewhere in between these extremes.

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There are elements of interpretivism in that it is the perceptions and experiences of the participants that is the focus, rather than trying to get at an objective ‘truth’. However, the structure and evidence base of a more positivist approach is attractive in adding rigour to the research findings. An approach was sought which effectively adds some structure to a method that allows for inductive reasoning and interpretation.

A general inductive approach that effectively forms the basis for inductive methods upon which different structures are built was outlined by Thomas (2003). The main purpose of this approach is to summarise the text, identify links between this summary and the research objectives, and then develop a theory about the underlying experience or processes. Categories or themes evident in the data are coded, and the codes refined until there are about three to eight summary or main categories. The higher level codes are derived from the research questions, with lower level codes coming from the reading of the data. The research questions and theory related to them are thus involved from the outset, making this approach slightly more structured than grounded theory. To maintain an analytical rather than purely descriptive approach, continual reflection back to the research objectives is required. Some general principles for this are three reflexive questions from Patton (2002): 1) Self-reflexivity - what do I know and how do I know what I know? 2) Reflexivity about those studied - how do those studied know what they know? 3) Reflexivity about the audience - how do those who receive my findings make sense of what I give them?

Using these reflexive questions as a basis, a practical iterative framework for analysis was developed, which involved constantly asking three similar questions: 1) What are the data telling me? - To clarify the lenses through which the data are viewed. 2) What is it I want to know? To connect the identified lenses with the research objectives. 3) What is the dialectical relationship between the - what the data are telling me - and what I want to know? Insights and analytical focus are continually refined through identifying gaps in the understandings (Srivastava and Hopwood, 2009).

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While this iterative framework constitutes a method in of itself, it also provides a useful set of questions to guide analysis, suitable for a number of inductive methods. Another element of my required ingredients is the

comparison between the groups of participants in my study - the villagers, the aid organisations and the governments. Qualitative comparative methods are often suited to smaller data sets than in this study, and often focus on a

comparison of individuals rather than groups, as required in my study (Rihoux, 2006). So-called “fuzzy sets” are used for larger data sets without the

individual case focus, however, this approach tends more towards quantitative analysis than I would like. This type of comparative analysis is looking at causality, which is not a strong focus in the current study.

A different type of analytical comparison was described by Hennie Boeije, labelled as ‘constant comparison’ (Boeije, 2002). This approach is well suited to comparison of groups, and outlines comparative steps: -

1) Comparison within a single interview - of different applications of the

same code, looking for similarities, differences, contradictions. Produces - summary of the transcript, list of provisional codes, conceptual profile using the codes, memos describing the analysis process;

2) Comparison between interviews within the same group - themes as

criteria for systematic comparison of interviews, clusters of interviews with similar codes, combinations of codes, criteria on which some interviews differ from others. Produces - typology based on the criteria, completion of new codes;

3) Comparison of interviews from different groups - becomes part of data

triangulation with comparison of groups to confirm and validate typologies. Looks at view of different groups on the same themes, and themes appearing in one group and not another.

The iterative framework provides a set of questions to aid the analytical process in linking findings with the research aims and questions. The constant comparison method provides logical steps for comparison between and within groups to identify similarities and differences. These elements were used with

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the framework approach, as developed by social policy researchers at the National Centre for Social Research in the United Kingdom (Smith and Firth, 2011). This approach is more structured than general inductive methods, with interconnected states for systematic analysis. It is well suited to cross-sectional descriptive data, where different aspects of the phenomenon under

investigation are captured (Ritchie, Spencer and O'Connor, 2003).

Having the three different perspectives from my three groups may be seen as cross-sectional. My two case study sites of Fiji and Tonga add complexity to this cross-sectional element of the research design. The stages, as described by Ritchie and Lewis are:

⁃ Data management - reading and re-reading the data, identifying initial

themes and categories, developing a coding matrix and assigning data to themes and categories in the coding matrix;

⁃ Descriptive accounts - refining initial themes and categories to

summarise the range and diversity of coded data, identifying associations between themes with a ‘whole picture’ emerging and developing more abstract concepts;

⁃ Explanatory accounts - develop associations or patterns within concepts

and themes, reflecting back to the original data to ensure accurate representation, interpreting and finding meaning in the concepts and themes, and seeking a wider application of concepts and themes (Ritchie et al., 2003).

In this approach, there will only be a small number of concepts at the end, each of which has a small number of themes within it. Each theme may have multiple categories.

My method of analysis for these data employs a framework approach as a structured inductive method of thematic content analysis. Interviews were audio recorded and then transcribed with the assistance of Transcriva software. Codes were applied to the data, with themes emerging from the data, using HyperResearch software. These themes and codes were then be refined, with associations between themes leading to the development of more abstract concepts, and patterns in the concepts and themes then being used to interpret

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the data and develop explanatory accounts. Within this approach, the questions in the iterative framework approach were used to guide and focus the analysis and development of concepts. The constant comparison method was the basis of the logical sequence of analysis within and between groups of participants.

3.8 Methodological issues

There are two methodological issues that may affect the interpretation of the results of this study. The first concerns the participants and fieldwork locations. The two islands chosen for this study are deliberately remote, outer islands, with as many similarities between them as possible from the available options. The results of the study are observations from these remote islands, and contrasts and comparisons between them. Ideally fieldwork would also have been undertaken at a regional or urban centre in each country, to allow for comparisons between the remote and urban settings. This was not possible due to time and resource constraints, which meant that differences between those settings could not be tested in this study. Therefore, where assertions are made in this regard, they remain assertions to be further tested in future research. The second methodological issue concerns the use of interpreters.

Resources did not allow for trained interpreters to be hired for the fieldwork, and time did not allow me to learn both anything more than rudimentary Fijian and Tongan languages. The fieldwork therefore relied upon a combination of interviewees being able to speak English, and local community members being willing to act as interpreters. This had different outcomes in the two countries. In Fiji, two local community members acted as interpreters for interviews. Both displayed fluent knowledge of English, and were willing to repeatedly assist. However, one was the outspoken daughter of the village Chief. When interview questions and conversations steered towards possible criticism of the Chief, cultural norms dictated that it was unlikely to proceed very far in that direction in her presence.

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In Tonga a greater number of people assisted with interpreting, but most for only one or two interviews. There were varying levels of English amongst these volunteers, meaning that overall it was more difficult to have in-depth conversations during the Tongan than the Fijian fieldwork. The turnover of interpreters in Tonga also meant that the translation was inconsistent, and no individual was trained and more at ease with the role through the experience of multiple interviews.

3.9 Conclusion

This chapter provided details of the methods chosen for this study, and the ways in which the method and procedure evolved with the project as

circumstances dictated. The cross-cultural nature of the research was both challenging and rewarding. It added layers of complexity to the task, with identifying and observing culturally appropriate approaches and behaviours, and the practicalities involved in overcoming the language barrier. However, the rich primary data gathered from the experience cannot be gleaned from the safe confines of desktop research.

The fieldwork in Tonga was, in many ways, less successful than in Fiji. There was a less welcoming and inclusive atmosphere, and the need to change focus to the larger and less familiar village part-way through the stay hampered efforts both to foster good relationships and trust in the communities, and to find interviewees. Nevertheless, the fieldwork locations were similar in many important aspects - relative remoteness, population size in one of the villages, and recent experience with cyclones. Despite the challenges encountered in Tonga, a similar number of interviews were conducted in both countries, and the consistency in many of the results suggests good comparability. The opportunity to re-visit the island in Tonga following their most severe cyclone was valuable and provided important follow-up information, and contrast for those whose focus had been on cyclones and responses more than 20 years ago.

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