Data analysis

Data analysis is one of the least developed and most difficult aspects of qualitative case study research since there are few standardized procedures available to analyse case study data (Eisenhardt, 1989; Yin, 1994, 2003). However, a number of analytical methods and techniques are suggested in the literature (Dey, 1993; Eisenhardt, 1989, 1999; Miles & Huberman, 1994; Patton, 2002; Strauss & Corbin, 1998; Yin, 1989, 1994, 2003). These strategies and techniques can be applied in within-case analysis and, in a multiple-case design, in cross-case analysis (Yin, 2003). Furthermore, the analysis may take place at several stages in the research

process and at several levels11 (Dey, 1993; Miles & Huberman, 1994; Strauss & Corbin, 1998; Yin, 1989, 1994, 2003).

In this study a modified version of the qualitative data analysis procedure suggested by Dey12 (1993) was used, with additional insights from the other qualitative research authors (Eisenhardt, 1989, 1999; Miles & Huberman, 1994; Patton, 2002; Strauss & Corbin, 1998; Yin, 1989, 1994, 2003). Dey has advocated three iterative processes in the analysis of qualitative data – describing, classifying, and connecting. According to this framework, these three processes are circular, that is, one informs the other and the whole process repeats until the entire analysis is completed (Figure 3.3).

The data analysis process in this study was commenced from the very beginning of the research fieldwork in Bangladesh, a technique recommended by a number of authors (Miles & Huberman, 1994; Silverman, 2005). After each day’s fieldwork, the investigator used to listen to the audio-tapes, take personal notes and read through the field diaries, make meaning from the interviews and observations, and attempt to identify important theoretical concepts. The formal process of analysis began only after the investigator had returned to New Zealand following the completion of the research fieldwork in Bangladesh. In the following sections the data analysis process used in this study is described.

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For example, the lowest level may involve presenting the data without interpretation and abstraction. A higher level may involve the development of descriptive narratives, which are rich and believable. The highest level involves building theories based on high levels of interpretation and abstractions (see Strauss & Corbin, 1998).

12 _{The investigator undertook training in qualitative research methodology at Massey University. The training involved} practical exercise with Dey’s (1993) analytical procedure.

Describing Connecting

Classifying

Qualitative Analysis

3.4.4.1 Describing

Describing is the most preliminary process of data analysis (Dey, 1993). This involves a thorough and comprehensive description of the phenomenon – a “thick description” (Denzin, 1978; Geerz, 1973), including the context within which the phenomenon occurred (Dey, 1993; Patton, 2002). To describe the data of this study, at first, one set of transcripts from each stakeholder group (e.g. the DAE, the FLE-NGO, and the rural people) involved with the FLE organisation were summarised. According to the conceptual framework (Chapter 2), these summaries were arranged under three major headings: (i) contextual factors, (iii) project implementation performance, and (ii) organisational design and strategies. Within these three headings, important concepts and instances from the data were further organised under subheadings. For example, under the heading of “contextual factors”, there were two subheadings – institutional context and material resources. Important points were identified and arranged under these sub-headings. In the description process, tables and diagrams were used to elaborate the data. The summaries also facilitated the identification of important categories and their relationships. As suggested by Dey, several versions of the same transcripts were prepared and iterated. The number of iterations, however, gradually declined over time and stopped when the investigator was satisfied that no new categories were emerging from the data.

3.4.4.2 Classifying

According to Dey (1993), classification is the process of segregating the data into well-defined categories, subcategories, and supracategories. The process is also called coding (Dey, 1993) or open coding (Strauss & Corbin, 1998). In the data analysis process of this study, coding the data was the most difficult, extremely complex and time-consuming part. This was mainly because of a large volume of unstructured textual data. To code this bulk of the data, a computer software programme called NVivo (Richards, 1999) was used. The NVivo facilitates rapid coding of the data, compiling them into nodes, and development of conceptual models.

In the coding process, the transcribed data were converted into rich-text word files (.rtf) and imported into the Nvivo system. Each file was given a code name according to the identity/origin of the transcript, stakeholder, and area/location (document attributes). Following this, each document was meticulously browsed, read line-by-line and relevant text units were coded using the Nvivo coder, and compiled under nodes13. The nodes were given names and definitions. At first, the coded texts were stored under “free nodes”, which were then arranged into “tree nodes” or hierarchically structured nodes. This structuring was done on the basis of logic.

In the classification process, most coding categories were drawn from the existing literature (Chapter 2). However, a limited number of categories were developed inductively by the investigator during the analysis, and a few others were taken directly from the conceptual structure of the people studied (Miles & Huberman, 1994). Most of these new categories were related to the “cultural features of the communities” involved with the case study organisational system. A form of comparative analysis (Dey, 1993; Glaser & Strauss, 1967; Miles & Huberman, 1984; Strauss & Corbin, 1998) was also used in the classification process in which a unit of text was compared across categories to examine if it was similar to them or different from them (Dey, 1993). Appropriate subcategories were developed if there were theoretically important distinctions between the data within a category (Strauss & Corbin, 1998). Moreover, the categories were structured into supra- categories if this provided a useful theoretical concept (Dey, 1993; Strauss & Corbin, 1998). The structure of a category hierarchy was decided based on logic (Dey, 1993). The entire classification process was dialectical and involved several iterations. Thus, as the analysis progressed, the categories were renamed, redefined, repositioned in the structure hierarchy, merged, and/or segregated into subcategories (Dey, 1993).

3.4.4.3 Connecting

The final step in the analysis process was connecting (Dey, 1993) or axial coding (Glaser & Strauss, 1967; Strauss & Corbin, 1998). While classification involves

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fragmenting the data into categories, axial coding enables pulling the data back together again in new ways, making connections between categories and sub- categories (Strauss & Corbin, 1998). The connection process results in the development of conceptual frameworks or theories, which can be presented diagrammatically.

During the connection process, relationships between categories and sub- categories were identified and defined. Such relationships were identified through linking words or conjunctions (Dey, 1993) such as “and then”, “because”, “therefore”, “as a result”, “and after that”, “as a consequence”, “that’s why”, and so on. The connection process resulted in the identification of important causal relationships between concepts, for example, the influence of a particular “contextual condition” on the “choice of the DAE” or on the “ability of the FLE-NGO” to support or not to support the FLE system post-project. Like the two other processes described earlier, the connection process was also carried out in an iterative way using tables and diagrams (Miles & Huberman, 1994).

3.4.4.4 Subsequent analysis

After the analysis of a couple of key transcripts and documents through the process of describing, classifying and connecting, the entire process was re-iterated. During this phase, a second summary was prepared by using the previous summary, category hierarchy, and the same sets of data. This summary was much more elaborate than the previous one and was structured according to the category hierarchy developed through the first iteration. The data, categories and their inter- relationships were re-analysed. During this process, frequent consultations with the investigator’s supervisors were made. The supervisors examined the outcome of the first iteration and passed on their critical comments. Accordingly, the categories, their definitions and arrangements were readjusted to avoid investigator bias. This process continued until a certain level of satisfaction was achieved. Then, the process was applied in analysing the rest of the data. These successive analyses required much less time compared to the first and second iterations since they mainly involved matching the data with the existing category hierarchies.

3.4.4.5 Comparison with the existing literature

After the general model explaining the non-sustainability of the FLE system had been developed through the analytical process, the model was compared with the existing literature (Eisenhardt, 1989, 1999). Such comparison was very crucial since this study was based on a single-case design (Eisenhardt, 1989, 1999; Yin, 1989, 1994, 2003). Thus, generalisability of the findings was sought to the existing theoretical propositions (Yin, 1989, 1994, 2003). The comparison was intended to identify the areas in which the findings were similar to or different from those found in other empirical studies, the areas in which the results provided empirical support or refuted the prescriptive literature. If there were differences between the results and those in the literature, explanations for such differences were sought. Furthermore, an attempt was made to identify the areas in the literature where this study provided new or additional insights as well as the factors which were mentioned in earlier studies but were not found in this study.