ANALYZING THE DATA

Analyzing (qualitative) data is the “process of bringing order, structure and interpretation to the mass of collected data. It is a messy, ambiguous, time-consuming, creative, and fascinating process. It does not proceed in a linear fashion; it is not neat. Qualitative data analysis is a search for general statements about relationships among categories of data; it builds grounded theory. It is the search among data to identify content for ethnographies and participants’ truths” (Marshall and Rossman 1999, 150). The section which follows presents the data analysis steps conducted for this study.

3.2.1 The method used: grounded theory

Numerous qualitative studies and researchers over the years have relied on the grounded theory method developed by Strauss and Corbin three decades ago (Flick 2002, 177). Although the method has been refined over time, the key principle remains the same: the theory is developed from the data (Kvale 1996, 206-207). In practice, however, the process combines induction and deduction (see Rossman and Rallis 1998, 10). The grounded theory method “uses a data analysis procedure called theoretical coding to develop hypotheses based on what the research participants say. Grounded theory derives its name from the fact that theoretical coding allows you to ground your hypotheses in what your research participants say” (Auerbach and Silverstein 2003, 7 emphasis original).

Though the method followed in this dissertation relies on grounded theory, it follows more closely the approach outlined by Auerbach and Silverstein in Qualitative Data: An Introduction to Coding and Analysis (2003) regarding how to handle a ‘sea of information’ when

analyzing data. Though as the authors point out the process is hardly linear, in reality the approach main advantage, but also drawback, is that it fragments the process of data analysis into seven discrete steps that one must follow to arrive at the grounded theory (Auerbach and Silverstein 2003, 43). To palliate the approach’s deficiency, the researcher supplemented the data analysis process with more ‘traditional’ grounded theory approaches such as the ones by Rubin and Rubin (1995, 226-256 for data analysis chapter) and Rossman and Rallis (1998, 164-189) to guarantee that coding was properly conducted.

Next, after the transcription phase, ‘the raw text’ which constituted the first step, the remaining steps of the data analysis included the following (Auerbach and Silverstein 2003, 43):

1. An explicit statement of the research concerns and theoretical framework (broader than the research questions)

2. Selecting the relevant text from the raw text (transcripts)

3. Recording Repeating Ideas (the ideas that keep being repeated in the relevant text) 4. Organizing Repeating Ideas into Themes

5. Theoretical constructs (more general concepts) 6. Creating the theoretical narrative

These staircase coding steps involve moving from a lower to a higher level of abstraction and could be used for generating as well as testing hypotheses although grounded theory primary aim is to generate hypotheses (Auerbach and Silverstein 2003, 3-9; and 13-21). The following seven steps involve first, making the text manageable (steps 1, 7, and 6); second, hearing what was said (steps 5 and 4); and third, developing theory (steps 3 and 2).

Finally, the order of analysis goes from the bottom to the top (bottom-up coding) (Auerbach and Silverstein 2003, 104), but before starting coding, the researcher has to openly

state his research concerns, in the case of this dissertation the main research question, so that he can direct the process and does not feel overwhelm with the data at hand (Auerbach and Silverstein 2003, 44).

3.2.2 The coding process

To code the data, the researcher started with interview transcripts, and then applied the codes discovered from those transcripts to the field notes as well as the documentary and archival evidences. The aim was to triangulate the data and ensure that the coding was consistent throughout all three sets of data as well as corroborate or confront the coding categories (see Bowen 2009, 28; 35; 37), in this case Repeating Ideas (RI), Themes, and Theoretical Constructs (TC). Over all, during the coding process, the researcher discovered about fifty (50) RI, which when recoded were reduced to twenty two (22). From these 22 RI, it was determined that five (5) key themes could be found in the data (see Appendix B). Finally, the researcher ended up with five theoretical constructs that were later merged during the writing stage into two key constructs: the Big Man and governance problems.

All these coding categories, especially the TC, were tentative until the writing phase when they were recoded, reorganized as well as renamed because the researcher realized that what he had conceptualized as separate categories before were in fact not. In practice, the process was simultaneous; that is to say that the researcher did not code the data separately or looked for a sole construct, rather he used the constant comparison method (see Flick 2002, 213) to include the data into a given category.

On the whole, the process was similar to theoretical coding of the grounded theory method, for it involves moving to a higher level of abstraction, from RI to Themes to TC as in

open, axial and selective coding (see Flick 2002, 176-190; see also Montgomery and Bailey 2007, 68-69). The researcher concluded data coding when the process had reached theoretical saturation, that is when the coding process provided no further knowledge (see Flick 2002, 183).

3.2.3 An iterative process

Figure 3.1. Data analysis model

Source: adapted from (Auerbach and Silverstein 2003, 35)

Contrary to the image of the discrete steps described in the preceding section, and as Figure 3.1 indicate, coding the data was an iterative process, moving back and forth from raw data to the theoretical constructs and constantly comparing the categories (RI, themes, TC). The aim was to guarantee that the coding was properly conducted and that the ideas and concepts developed fit into the categories they had been assigned to.

The coding and analysis model which the researcher followed is best illustrated by Figure 3.1 above which shows the adapted version of the Auerbach and Silverstein’s model. The model

Relevant Ideas (RI)

Coding and recoding back to RI, Themes and Theoretical constructs

Theoretical narrative and interpretation

Themes and theoretical constructs (TC)

Raw data

Relevant text

Constant comparison throughout

here reflects the process utilized for this dissertation; a process not involving discrete steps as the figure would suggest, but rather an iterative, non-definitive model, constantly moving back and forth between the data analysis and the writing processes.

Finally, the model used above is not sequential but rather circular with the constant comparison box in the middle to signify the iterative nature of the process throughout, as well as the fact that coding and categories generating was constantly compared to ensure the rigor of the process.

3.3 SUMMARY

This chapter has described the research methods as well as the data analysis process followed in this study. The chapter began with a brief discussion about the rationale for the selection of a qualitative approach, operationalized the independent and dependent variables as well as presented the three primary methods used in the study documents and archival records; direct observations; and in-depth semi-structured interviewing. The chapter concluded with a description of the data analysis process which involved building a grounded theory from the data.

4.0 THE INSTITUTIONAL FRAMEWORK OF FOREST MANAGEMENT