The constant comparative method has four stages of analysis: inductive category coding; refinement of categories; exploration of relationships and patterns across categories; integration of data and writing up the research (Maykut and Morehouse, 1994).
3.8.1 Inductive Category Coding
This involves drawing diagrams of the researcher‘s progress. This is what Lincoln and Guba (1985) refer to as an ‗audit trail‘ which traces the researcher‘s thought development throughout the entire research process. At this point, the researcher had a list of recurring themes or concepts already marked out which were useful to guide her in preliminary analysis of the data. Some ideas overlapped, and these were combined. The first stage then was to select one theme or concept which recurred and this formed the first provisional category code. However, as these were derived from the researcher‘s initial contact with the data, these were provisional categories that could be changed or modified subject to further rigorous scrutiny.
In the second stage of this phase, the researcher reviewed her unitising files to see if there was any overlap between the categories of unitising meaning and the list of provisional categories as formulated above. Then, ‗like was put with like‘ or the evidence for each category was ‗cut and pasted‘ under this category. This involved evidence from all three sources: documentary, field-notes, and the interview transcripts.
Often the provisional categories differed from those already formulated from the unitising meaning section. If so, then these categories were added, and the data sourced
for further evidence that related, either positively or negatively, to the category in question. This process was continued for all categories that had arisen in some form or other, until all the units of meaning were categorised and all the provisional categories were used or merged. Categories were renamed as necessary, as provisional labels given may prove insufficiently comprehensive or specific as the case may be.
Another difficulty at this point was the fact that data often fell under two categories. At this stage, it is advised to place them initially under both categories. Secondly, extraneous pieces of information may fit under certain categories, for example, unrecorded but noted pieces of conversation, or articles from a newspaper or magazine that are relevant but not directly related. These were initially added under categories for further consideration later. Finally, some pieces of data fell outside any immediate category but appeared nonetheless to be imported. These were placed on ‗temporary hold‘ under the category of ‗miscellaneous‘. The number of categories had to be expanded in order to reconstruct the data in a more meaningful way. At this point, the researcher accumulated numerous categories, and so moved on to the next stage; refinement of categories.
3.8.2 Refinement of Categories
This stage involves writing rules for inclusion which help to narrow the scope of inclusion of categories. Lincoln and Guba (1985) believe that this can be done by means of writing propositional statements. Propositional statements are statements of fact, grounded in the data from which they emerge (Taylor and Bogdan, 1984). This statement contains the essential meaning of the category. This is the first stage in understanding the phenomenon under research, and the first step towards one‘s outcomes (Maykut and Morehouse, 1994). The data that is placed under such
propositional statements may be either positive or negative. So, some responses may not agree with the propositional statement, for example, a respondent might have said, ‗everybody can not be a teacher therefore the teacher education programme has not particular affect on them‘. However, Taylor and Bogdan (1984) recommend that such statements should be derived from propositional statements, which are in turn derived from a substantial accumulation of positive instances.
Indeed, Maykut and Morehouse (1994) go some way in explaining this further:
A rule for inclusion is developed for a category when several data cards have been clustered under it based on the look/feel-alike criterion. The rule for inclusion is inductively derived from the properties or characteristics of the initial set of data cards clustered together under it. The rule for inclusion is stated as a proposition that summarises the meaning contained in the data cards. Data cards that on closer examination do not fit the resulting rule are categorized elsewhere. Remaining data are now included in or excluded from a category based on its rule for inclusion, not the look/feel-alike criterion. Data cards are coded to their rule-based categories. Data analysis continues until all data cards have been categorized into a substantive category or the miscellaneous pile. (P.142)
However, the researcher preferred to refine the above process further by placing all the positive responses, first, then followed by the negative responses, so as to get an overall balanced view of the category in hand. The next part of this process involved coding data cards to their categories. Coding data cards is a further refining of the categories formulated. This involves ear-marking the data to that particular category. This is done by means of coding each category (for example education policy). This category is labelled ‗Education Policy‘ and is coded EP. This data piece of information is coded in all the documentary evidences and fieldwork as such. The transcripts were organised around this method, but by cutting and pasting and creating a separate file called ‗Education Policy‘ to cater for all related responses.
Once all the data had been categorised, the researcher then reviewed it again for overlap and ambiguity. This required a thorough re-examination of all the material and
some categories or modify others. Finally, the miscellaneous pile was re-examined for the purpose of either formulating a new category which may encompass some of the issues, or else distributing some of the miscellaneous pile into existing categories. This led on to the next stage.
3.8.3 Exploration of Relationships and Pattern across Categories
This stage involves pulling the categories together which both accurately reflect the data gathered as well as synthesising it into a more comprehensively detailed analysis of the data. This begins with a thorough re-examination of the propositional statements which have emerged so far. The next step is to prioritise these propositions according to importance in relation to the focus of the inquiry. In essence, these propositions form the preliminary outcomes of the research they have yet to be interviewed with other categories, formulating themes. Some may be sufficient to stand alone. This will be the case if it explains the phenomenon under investigation on its own without auxiliary information. Some salient propositions may need to be linked with others to become a core outcome of the research. Either way, both these types of data are referred to as
outcome propositions.
Concurrently, the researcher still continued to collect new data as indicated by the design of her research. However, there comes a saturation point (Strauss and Corbin, 2000), which Lincoln and Guba (1985) refer to as the point of ‗redundancy‘. In any case, once all the links are made between categories and within categories and the final part of phase II has been completed, the researcher must move on to the next phase. This phase is the fourth stage of phase I, writing up the research and making the findings public.
3.8.4 Integration of Data and Writing up the Research
This is the stage that now links up with part 4 or Phase I of the research as described above-Bereday‘s Comparative Stage. This stage involves writing up the research which makes sense of the phenomenon under examination. This is the last phase of the analytic process. This also requires a rethinking of the data. This can often lead to new insights and a deeper understanding of the research inquiry. At this point, it is necessary to review a number of facets of the research which are vital for it to have credibility and integrity. The first issue is that of ‗the trustworthiness of the research‘. Qualitative researchers use trustworthiness as a criterion for judging the quality of the inquiry (Lincoln & Guba, 2000). This is achieved in the manner and process by which the research was carried out. It is also achieved in one‘s own evaluation of the work of other researchers in the field. The question may be asked ‗how can we trust the outcomes of this research? In essence, it is an ethical question. The first issue here is that of transparency. The sources of research must be stated. Foundations, who fund and support different kinds of research reports must have their interests stated from the beginning and the researcher needs to note this. The researcher must be conscientious about the categories formulated, and if she has any prejudices, preconceptions or assumptions about the research and its outcomes, these should be recorded from the beginning, and these checked against the data, following analysis, for the possibility of subjectivity in either the analysis or interpretation of the information gathered.
The researcher also gave her work to English peers to check the language and colleagues in the field to critique which always exposes one‘s own ideas as to certain aspects of the research, to point out the weaknesses. The research was also carefully checked for bias. The researcher acknowledged that it is virtually impossible to remove
all sources of prejudice, as one needs to arrive at some conclusion based on the evidence. Nonetheless, every attempt was made to be critical and ‗objective‘ at all times. Objectivity is a concept which must be placed in inverted commas as from the philosophical ontological argument; while total objectivity certainly can not be attained, it is vital that researchers rigorously attempt to minimise the possibility of bias (Pring, Walford and Wilson, 1997/98, cited in Griffin, 2001).