Data Included Within the Qualitative Analysis

Participants from the same localities where the survey took place (n=24) were invited to attend the focus group session, which considered the following research focus:

“An exploration of participants’ perceptions of their roles as water consumers, consumer dissatisfaction and its root causes including solutions of managing the same, and consumer views on how satisfaction impact sustainability”.

Each session lasted for forty five minutes and was conducted by two researchers.

The following sections describe the steps used in Thematic Analysis used for qualitative guide questions 1-8 (Appendix 2). FG discussions were voice recorded, and the audio recordings were then transcribed by the researcher in order to provide the written data necessary for the Thematic Analysis. The analysis was recursive in nature, whereby “movement is back and forth as needed, throughout the phases” (Braun and Clarke, 2006; 86) in order to refine the analysis process.

Familiarisation with the Data Set

The researcher must first gain familiarity with their data set by immersing themselves within the data (Braun and Clarke, 2006; 87). This was achieved by the researcher listening to the audio recordings, before transcribing them.

As this analysis sought to provide a rich description of the entire data set across the four focus group discussions, some depth and complexity may be lost in favour of an analysis of the overall data set (Braun and Clarke, 2006; Boyatzis, 1998). Braun and Clarke (ibid) argue that this is often the case when exploring an under-researched area, such as participants’ perceptions of the impact of satisfaction and dissatisfaction on rural water sustainability. After transcription, the entire data set was read several times and interesting patterns noted.

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Generating Initial Codes

The process of generating initial codes followed. Codes represent “the most basic segment, or element, of the raw data or information that can be assessed in a meaningful way regarding the phenomenon” (Boyatzis, 1998; 63). Provisional codes were developed through repeated reading of the data set, and extracts of note were numbered on the basis of the code they potentially represented; all data should be coded in some respect at this stage (Braun and Clarke, 2006; 89). These extracts were then transferred to post-it notes. Where necessary, additional information was included within these notes, to ensure that the extract made sense out of context.

Identifying Themes within the Data Set

A theme may be defined as: “something important about the data in relation to the research question, and represents some level of patterned response or meaning within the data set” (Braun and Clarke, 2006; 82).

Following coding of the data set, the researcher then began identifying salient or common themes within the data (Attride-Stirling, 2001; 392. Other codes encompassed a high volume of data across a number of participants and were therefore considered to be a theme in their own right. At this stage seven themes were identified within the data as follows:

1. Roles of the consumer

a. Critical roles of the consumer 2. Pillars of sustainable rural water supply 3. Determinants of consumer satisfaction 4. Root causes of consumer dissatisfaction 5. Managing root causes.

6. Effects of consumer satisfaction on consumer roles 7. Effects of consumer dissatisfaction on consumer roles

The researcher then illustrated these themes within a graphic and noted the codes contributing to each theme. As part of the recursive nature of the familiarisation process, it was then necessary to revisit the data set to ascertain whether the extracts and initial codes were representative of the proposed themes.

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Reviewing the Themes

This stage required the researcher to revisit the initial themes and reconsider whether these themes remained representative of the ‘overall picture’ of the data, or whether some themes required some refinement. At this stage themes may be combined or further subdivided depending upon the apparent trends within the data.

The researcher then sought to establish overarching themes, designed to encapsulate collections of themes within the data.

Naming and Operationalising Themes

This phase seeks to operationalise each theme and provide each theme with a final label. The aim here is to articulate the ‘essence of what each theme is about’ (Braun and Clarke, 2006; 92) and to ensure that each theme is mutually exclusive.

Consideration should also be given to the ‘keyness’ of each theme i.e. the extent to which that theme contributes to the overall story of the data set and the initial research questions. The prevalence of the theme (i.e. the number of times the theme is apparent within the data) is not a sole determinant of the ‘keyness’ of a theme and it could be argued that establishing ‘keyness’ on the basis of frequency may be misleading, given that some individual participants reiterated similar points within the entire data set, thereby increasing the prevalence of that theme. The researcher considers the prevalence of a theme in terms of the number of participants making reference to that theme, as opposed to solely acknowledging the number of times a theme is apparent within the overall data set.

4.3.2 Analysis Strategy

The current research qualitative phase followed a hybrid process of inductive and deductive thematic analysis which was consistent with the wider pragmatic approach of the current research. Flick (2006) define hybridization as the pragmatic use of methodological principles and avoidance of restricted subscription to a specific methodological discourse thus acknowledging that real world research is never purely inductive nor purely deductive (Bernard and Ryan, 2010). The advantage of the hybrid approach to thematic analysis was that it allows to draw upon a “priori” issues (those informed by the original research aims and introduced into the topic via the topic guide), emergent issues raised by the focus group discussion (FGD) participants themselves, and analytic themes arising from occurrence or patterning of particular views and experiences (Ritchie and Spencer, 2002).

Analysis was performed manually for two reasons; firstly, the qualitative phase of the current research had a character of a study within a mixed method project, which made manual

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coding manageable, and secondly, manual coding offers the analyst the best possible sense of the data and more control over the analytic process (Saldana, 2009, p.22). Before proceeding with the analysis the audio recorded interviews were transcribed verbatim (Seale and Silverman, 1997). Although transcription is a preparatory step of the analytic process, it has been often perceived as the very first step for the identification of themes within a data set (Bernard and Ryan, 2010)