Q UALITATIVE DATA ANALYSIS

5.7.1 The interplay of data collection and analysis

Data analysis in qualitative research means to analyze texts in one or another form. Qualitative data collection and data analysis are non-separable integrated processes that guide and inform each other: “The process of analysis should not be seen as a distinct stage of research; rather it is a reflexive activity that should inform data collection, writing, further data collection, and so forth” (Coffey & Atkinson, 1996, p.6). The image of data collection and data analysis in qualitative research can be like a ‘zigzag’

process (Creswell, 1998, p. 57): Out to the research sites to gather data, analyze the data, back to the field to interview more actors, and so on. When the theory is elaborated, in all its complexity, and final conclusions can be made about its scope and validity, the study is then saturated. The relationship between the cyclical operations involved in qualitative data analysis is illustrated in figure 5.3 below.

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Figure 5.3: The interplay of data collection and data analysis

Data Collection

DisplayData

Data Reduction

Conclusions

Source: Miles and Huberman (1994a, p. 429)

According to Miles and Huberman, two interrelated, tactics are important in the conceptual ordering of the empirical material: Data reduction and data display. Data display builds on the coding activities and guides the drawing of conclusions, which again informs the further data display and data collection. Data display is defined as an ongoing compressed assembly of information that permits conclusion drawing and/or action taken in a second part of analysis (Miles & Huberman, 1994a, p. 429). Theory-focused displays may include structured summaries, coding threes or network like diagrams. The data display techniques available in the computer program N6⁵⁹ were used to enable the researcher to keep intuitive overview and maintain a thread in the material. Verbatim transcriptions of the interviews were structured and encoded into the format that the computer program requires. Open coding techniques where then practiced, in order to grasp the immediate interpretation of the meaning of the interviews.

Regarding data reduction, the potential universe of data is reduced in an anticipatory way as the researcher chooses a conceptual framework, research questions, cases and instruments. Data reduction is an integrated part of the data display, which enable conclusive judgments all at an early stage, at least related to choices of further data collection. The iterative model gives a fairly good representation of the data analysis of the study, especially in the

59 The computer program was previously known as NUDIST. Current name is ENVIVO. See the website www.qsrinternational.com

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early part of process. Data reduction was employed in this study in three distinct forms. Firstly, some portion of the interview material was reduced during the analysis, through concentrating on the categories and dimensions that coupled to the conceptual framework. Secondly, the shift between the first and second case study represents itself a radical form of data reduction, since some of the observations became first-order and others second-order.

Data reduction was also, thirdly, an important analytical operation, when secondary text sources were imported to the analysis. The documents were not imported in raw form, since fractions that added information to interview texts were selected for analysis.

5.7.2 Coding procedures

Coding is thus about breaking the data apart in analytically relevant ways in order to lead toward further questions about the data (Coffey & Atkinson, 1996, p. 31). After initial coding takes place data will be resorted according to patterns and themes that emerge from the data (Creswell, 1998, p. 153).

The process of qualitative data analysis normally uses a ‘toolkit’ of three distinctive coding strategies; open coding, axial coding and selective or discriminate coding, which respectively provide different outcomes for the elements. The coding procedures and their outputs are illustrated in figure 5.4.

Figure 5.4: The qualitative analysis ‘toolkit’

Open CodingOpen Coding

Axial CodingAxial Coding

Selective CodingSelective Coding

The Initial Category Dimensions Subcategories

The Logic Diagram The Central Category

Causal Conditions Outcomes

“The Data Analysis Toolkit”

Propositions

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Analysis procedures are activated when the researcher has started the gathering. At the beginning of the research dialogue, the analytical tool is limited to identifying categories in the data material, centered round a so-called central category that appears frequently in the data material. The process is labeled open coding, and the researcher forms the initial empirical categories, based raw data, about the phenomenon under investigation. This means that in the cases investigated, there are indicators pointing to specific concepts that relate directly to the conceptual framework⁶⁰. During the process, the central category will be refined and sharpened, described more abstractly, leading to a category of more theoretical content. Sub-categories of a category can also be identified, and the process of relating categories to their subcategories is termed axial coding (Strauss & Corbin, 1998, p. 123).

In this study, the data analysis started with open coding of the interview material. The computer program N6 was used from the early part of the analysis, in order to systemize the coding of the data, and to provide flexibility to the process and reduce the time consumption. Annotations and memos were extensively used as supplementary tools to pure categorization, in order to map the conceptual content of the actor interviews.

Axial coding is generally described as a follow up procedure to open coding.

The key operation in axial coding is to construct the connections between the category and its sub-category in a hierarchical tree-fashion. It is a way to make decisions concerning the category identified: What is the conceptual category, and what are subunits of it? Axial coding was undertaken in all cases, based on the interview transcripts. Some of them generated important theoretical discussions, for example the axis of the ‘dissonance’

phenomenon found in the data. Along with specifications of the learning process, as modeled in chapter ten, these two entities benefited from axial coding. It is, however, worth underscoring that the axis-trees were also subject to substantial data reduction during the analytical operations.

Although providing some contributions, axial coding was, however, a secondary operation in the present data analysis.

In selective coding, the researcher identifies a ‘story line’ and presents possible propositions. Selective coding played a crucial role in this study, through constructing a story line of the case. The major grip was to build up a system of memos from the single interview to the case. A pure descriptive case memo formed the early start of the construction process, and this entity was based on interview memos and conceptual schemes. For example, an extensive case memo was constructed for each of the sites included in the case study, and these documents were important material for comparative

60 Category is defined as “concept that stands for phenomena” (Strauss & Corbin, 1998, p. 101)

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cross-case analysis of the two sites. The starting point, however, of the case memos was the open coding. Dimensions are conceptual tools for capturing variation in the material along categories. Both open coding and selective coding were proactively used to capture the learning-adaptation process, the learning context, the repertoire and the middle management practices involved. Grounded on the data, analytical categories were constructed

5.7.3 Data management aided by computer software

The use of the computer software program, in this study N6, serves multiple purposes. First of all, a computer system supports the interrelated processes of data reduction, data display and directing further data collection. Besides, when effectively employed, computer aided tools can be at the heart of qualitative data management. Besides contributing to accessible data, documentation of the kinds of analyses carried out is ensured. This enables retention of data and associated analyses after the study is completed, too.

To sum up, using computer software is not a necessity in the analysis of qualitative data, but it provides the researcher with several opportunities for improving data analysis as well as data management, e.g. storage, documentation and retrieval. In this study, the use of the computer program primarily served management purposes though it should not be underestimated that the availability of the coding tools made the analytical process ‘smoother’. Moreover, the use of the computer program ensured that the researcher constructed an audit trail that accesses external inspection to the material.