Procedure for Analysing the Data Collected from the Focus Group Discussions

This section will detail how the researcher analysed focus group discussions using content analysis. Content analysis involved identifying, coding and categorising the primary patterns. Field notes were taken. The first step was to make comments on the margin of the papers used. Data was organised into topics and files. Chunks of data were given a name and a label. Since the study involved an assistant, both of us were involved in analysis and coding of data.

Data collection and analysis occurred the same day before leaving the villages in order to promote awareness of the emerging themes and identify areas which needed further exploration or interview. The advantage of manual data analysis was that we were capable of the intellectual and conceptualising processes required to transform data into meaningful category findings (Saunders, Lewis & Thornhill, and 2009:26). Transcription was used to arrive at meaningful categories.

Transcription involved listening, categorising and summarising data. Categorizing data involved developing and attaching these categories to meaningful chunks of data. An example of meaningful chunks of data was training of water committees. Categories were initially derived from hand-pump maintenance factors generated from literature. Codes or labels were used for grouping the categories. Grouping involved identifying key words in the contexts. Those key words with similar meaning were grouped together into piles of similar meaning. Summarizing data involved identifying key points emerging and compressing (condensing) long statements to brief statements in which the main sense of what was said could be rephrased in a few words. The researcher also looked for missing words related to maintenance issues as identified in literature. Missing words indicated that the respondents may not have been familiar with the themes or if it did not happen at all

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in the process of their hand-pump maintenance. After this process the researcher then returned the transcribed description to the respondent end-users for validation of findings. The presentation of data followed thematic coding of the data analysis ; for the purpose of theme emphasis, clarity and easy understanding, some exact words stated by respondents have been included.

3.11.3.1 Advantages of Thematic Coding Approach of Data Analysis in Comparison to Other Qualitative Forms/Approaches

Three main approaches to qualitative data analysis include quasi-statistical approaches, thematic coding and grounded theory (Saunders et al, 2009). Quasi- statically approaches involve content analysis, which uses words or phrase frequencies and inter-connections as key methods of determining the relative importance of terms and concepts (Robinson, 2011). This study adopted some form of Quasi-statically approaches in analysing Individual Case Interviews using Likert-type scales. Likert-type scales allowed identifying the mode-the most frequent factor attributing to hand–pumping maintenance. The advantage of analysing ICI using Likert's scales was that it allowed identifying the mode-that is the most frequent maintenance factor respondents identified.

The other form could have been grounded theory as this can also be applicable to qualitative studies. In a grounded theory data analysis, codes arise from the interaction with the data. These codes are based on the researcher's interpretation of the meanings or patterns in the texts; used to develop a theory grounded in the texts (Saunders et al, 2009). In this approach, different types of coding acquire different specialised terminology. This form was not ideal in analysing CI or focus groups because the CI and focus group data did not aim to form a theory but to confirm the Inclusive Sustainable Development and Stakeholder Management Models identified in the literature review, chapter 2.

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This study used thematic coding to analyse focus groups, documents and observational data. Robinson (2011) states that a thematic coding approach is a generic approach not associated with any particular theory. The data analysis is based on categorisation, which is subject to the re-analysis, allowing reliability checks and replication studies. Data is coded to represent something of interest and labelled. Codes with the same theme are grouped under one heading. Codes and themes emerging in the data are determined inductively by reviewing the data and/or concepts of the research question or previous research, including the theoretical framework. Such themes serve as a background for further analysis and data interpretation. Themes are summarised in matrices; Network maps flow charts or diagrams. Thematic coding approach can be used in descriptive data analysis or exploratory form or based on a certain theoretical framework, as done in this study. This study also used manual data analysis as opposed to computerised methods.

3.11.3.2 Advantages of Manual Data Coding Versus NVivo Analysis

Qualitative data, particularly the focus groups can be analysed using computer processor or manually. This study chose manual data analysis over other forms of qualitative data analysis, which are computerised. The advantage of Computer packages is that they can help to store large quantities of data, organise and keep in user friendly files (Robinson, 2011). Data codes can also be easily accessed using ‘copy’ and ‘paste’ functions. Data can as well be connected against categorising simplifying and reduction (Robinson, 2011). One of the common soft wares is the NVivo. The disadvantages of using NVivo are that:

 Efficiency in their use may take time

 Categories may be hardly changed one established

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 Dependency on technical coding functions may give less emphasis during interpretation

Manual data analysis has been the traditional way of analysing qualitative data. Though it is complex to deal with large volumes, however, this was not applicable as CI data had converged into categories through the iterative process. The manual analysis was used in this study because it allowed the researcher to engage with the data (Strauss & Corbin, 1990) hence opted over the computer generated analysis. Methodological rigour was maintained in different sections of the qualitative data.