Resumen de requerimientos y criterios para las Plantas de Tratamiento.

The study is an interpretivist mixed methods multi-level embedded case study with a qualitative approach. My task in the study remains to achieve a thick description and originality of the responses (Denscombe, 2003). As Bassey (1999) describes, what data analysis is “… about is an intellectual struggle with an enormous amount of raw data in order to produce a meaningful and trustworthy conclusion which is supported by a concise account of how it was reached” (p.84).

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The main approach in the project (pilot study as well as the main study) to organize data and generate themes drawing out from the main research questions by all the stakeholders responses is through the “constant comparison” (Thomas, 2009: p.198) analysis which looks for „patterns and processes, commonalities and differences‟ and draws out themes accordingly. The six element model introduced by Watling and James (2007) combined with the constant comparison method were followed to analyse data as one of the main models, however, the researcher made some changes according to the requirements of this study (Figure 6, p. 148). For example, instead of testing a theory, I explored the Carpe Vitam LfL concept in term of emerging themes which emerged through constant comparison under design and respondent validity in focus. Interviews were tape recorded (after obtaining the consent of the interviewees) and fieldnotes were taken. The interviews were transcribed. Data was analysed following a straightforward systematic approach question by question. No data reduction was done from the pilot study as it identified the research questions for the main study. Miles and Huberman (1994) describe how coding can „differentiate and combine the data‟ with reflections on the information gathered (p.56). Coding (Fielding, 2002; Saldana, 2008) was used as a basic technique to analyse interviews, documents and semi-structured parts of the questionnaires and patterns were identified in the structured parts of the questionnaires.

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Figure 6: Six elements of qualitative data analysis with constant comparison (adapted from Watling and James, 2007 and Thomas, 2009)

Although „Nvivo‟ or „Code-a-Text‟ was available, I completed the coding manually as reading the script developed understanding and gave me a chance to do a constant comparison with direct interaction in this small scale research project. Anselm (1987) argues:

“Any researcher who wishes to become proficient at doing qualitative analysis must learn to code well and easily. The excellence of the research rests in large part on the excellence of the coding” (Anselm, 1987: p.27). Data Collection Data Sorting and Display Reflection on Data Data Coding Generation of Themes Reprting and writing Constant comparison Constant comparison

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Responses from participants were entered in a matrix (Figure 7) and a process of coding, categorisation and theme identification (Saldana, 2008) was applied (Figure 8, p. 150) to find meanings, similarities and differences of opinion as emerged from the data.

Figure 7: Matrix of Responses

Using the systematic and constant comparison approach, the study uses the coding procedure (Figure 8) recommended by Saldana (2008). During analysis, coding helped to do multidimentional analysis of data across research questions and respondents. First of all, keywords/codes were identified with a similarity and difference/addition approach with reference to the theoretical framework of the study (Merriam, 1998) and based on the frequency of responses (Table 5, p. 151). These codes were applied and reapplied to codify and helped to identify main categories. This process of constant comparison permitted data to be “segregated, grouped, regrouped and relinked in order to consolidate meaning and explanation” (Saldana, 2008 p. 12). Coding enabled organization and grouping of data into categories “because they share some characteristic” (Saldana, 2008) similar, that helped identification of themes. Some categories contained clusters of coded data that “merit further refinement into subcategories” (Saldana, 2008: p.11). These categories were compared with

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each other and consolidated together leading to emerging themes. Based on this procedure, colour coding was done on the data that were put together on a matrix, and categories were identified from where themes emerged (Figure 8, p. 150). Documentary analysis was done according to the codes and themes that emerged from interviews and open-ended part of the questionnaires. For example, if the head was asked about the importance of extracurricular activities and sports in the school routine, the presence of evidence in terms of any achievements, celebrations or organization of events in or out of school in the logbook, newsletter and calendars where the students participated, were given the same code as being used in the interview or questionnaires.

Figure 8: Process of Coding, Categorisation and Themes Identification (adapted from Saldana, 2008)

Based on the number of participants under each code and in the questionnaires under five given scales, terminology of most, majority, some and few was used to present and analyse data with percentage-wise illustrations for each of the terms. The key for the terms is as shown in Table 5 (p. 151). In the result records, Microsoft Word was used to record, represent and

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analyse data. The questionnaires had twenty structured questions. These were represented on a graph using Excel, based on the percentages categories shown in Table 5 with regards to the Likert scale (described in the questionnaire section).

Terminology/category Percentage

Most More than 75%

Majority Between 50-75%

Few Between 30-50%

Very Few Between 10-30%

Occasional Less than 10%

Table 5: Percentages of the Categories of the Commonality in Data

I have also provided a graphical overview of the data collected from the Likert scale questions in the Findings chapter ahead. The patterns were observed to identify and highlight grids. In analysing the questionnaire data, Munn and Drever‟s (1990) advice was to put it into a more manageable form. Conclusions were drawn on all of these responses and emerging themes to illustrate points made in the study. The research aims for patterns and themes across the study so data analysis is done question by question instead of case by case.