Data Analysis Tools and Methods

The two guiding principles in a qualitative study that assumes an interpretive epistemology stipulate that data analysis is an ongoing process that feeds back into the research design right up to the last moment of data gathering, and that “whatever theory or working hypothesis eventually develops must grow naturally from the data analysis rather than standing to the side as an a priori statement that the data will find to be accurate or wanting” (Powell & Connaway, 2004, p.196). Data collection in a qualitative study integrates with data analysis. Hence, qualitative research “requires a cyclical approach in which the collection of data affects the analysis of the data which, in turn, affects the gradual formation of theory which, in turn, affects the further collection of data” (Powell & Connaway, 2004, p.196). The same views are held by Bryman (2004); Creswell (2003); Denzin & Lincoln (2005); Gorman &

Clayton (2005) and Miles & Huberman (1994).

This research used qualitative techniques to code and analyse the data collected, according to the emerging themes and categories. These techniques provided rich and thick descriptions of organisational, social, political, cultural and technological settings and events, to generate contextual understanding of the factors affecting adoption of ICT for communication of research in research institutions in Kenya, unlike quantitative researchers who are deliberately unconcerned with rich

descriptions which they see as interrupting the process of developing generalisations (Denzin & Lincoln, 2005). One of the chief reasons for the emphasis on descriptive detail is that it is often precisely this detail that provides the mapping of context in terms of which behaviour is understood. The propensity for description can also be

interpreted as a manifestation of the naturalism that pervades much qualitative research, because it places a premium on detailed, rich descriptions of social settings (Bryman, 2004).

Specifically, the researcher employed three overlapping processes of qualitative data analysis: data reduction; data display; and conclusion drawing and verification (Huberman & Miles, 1998; Miles & Huberman, 1994) (See figure 4.2 below).

Figure 4.2: Components of data analysis: Interactive Model (Huberman & Miles, 1998, p. 181)

4.5.1 Data reduction

Data reduction is the process whereby one “sharpens, sorts, focuses, discards, and organizes data in such a way that ‘final’ conclusions can be drawn and verified”

(Miles & Huberman, 1994, p.11). It entails reducing the data into categories, summarising, coding and identifying themes, and enables the researcher to simplify, abstract and transform raw data into meaningful units. Miles and Huberman (1994) propose the following tools and strategies that can aid data reduction as employed in this study’s analyses:

• Contact summary sheets: These are documents that contain a number of questions that help the researcher to summarise the main points and capture impressions of any field contact. This enabled the researcher to reflect and focus on the main issues as brought out by that particular contact.

Data

Collection Data display

Conclusions:

Drawing/verifying Data

reduction

• Data coding: This is assigning labels to pieces of data, such as words or phrases, which help organise and later retrieve the data. Coding is the process of organising the data into “chunks” before bringing meaning to those

“chunks” (Rossman & Rallies, 1998, in (Creswell, 2003, p.192). It involves taking text data or pictures, segmenting sentences (or paragraphs) or images into categories, and labeling those categories with a term, often based in the actual language of the participants (an in vivo term) (Creswell, 2003, p. 192).

This enabled comparison between incidents or events (Strauss & Corbin, 1990) and also helped in preventing data overload.

• Memoing: Memos are notes that researchers might write concerning coding or concepts. Corbin & Strauss (1990, p. 198) describe memos as “the written forms of our abstract thinking about data”. They aid in generation of concepts and categories by serving as “reminders about what is meant by the terms being used and provide the building blocks for a certain amount of reflection”

(Bryman, 2004, p.405). They help researchers to crystallise ideas while at the same time keeping track of their thinking on various topics (Bryman, 2004).

Memoing is, therefore, a conceptual process that aids the process of analytical thinking.

To identify common themes, relationships and potential categories of data in this study, the researcher analysed and coded the interview transcripts by using descriptive keywords and pattern coding based on the elements of the research model and research questions, taking care not to exclude emergent codes. She used the processes of bridging, filling in, extending and surfacing to ensure the codes were updated to take care of emerging issues and themes (Bryman, 2004; Lincoln & Guba, 1985).

Coding was done immediately after the data were collected to appraise with emerging themes and concerns and also to ensure interview questions evolved accordingly to reflect the emerging theoretical ideas and issues.

Soon after codes had been assigned to sufficient data, pattern-matching logic to identify emergent patterns was applied. That is, descriptive and interpretive codes (representing identified themes and issues) were grouped within a series of higher level encompassing themes, identified by a set of pattern codes – explanatory or

inferential codes (Miles & Huberman, 1994). The researcher initially used the NVivo version 8, a computer-assisted qualitative data analysis software (CAQDAS), to organise and analyse the qualitative data. NVivo was chosen owing to its ready availability in the researcher’s university library and its use in a wide range of research projects (Bryman, 2004; QSR, 2003). The researcher imported all the transcripts into the programme and succeeded in analysing two transcripts. However, she kept losing data because the new NVivo version was still unstable. Therefore, on the advice of her supervisors, she undertook the analysis manually using MS word and spreadsheet. The supervisors also expressed concern that the software would not allow free exploration of the data collected, an opinion shared by Grbich (2007) and the researcher also concurred with.

4.5.2 Data display

Data display is the organisation of information in such a way that actions can be taken and conclusions drawn. It makes data more accessible for interpretation and drawing of conclusions. Data display techniques used in this study included text and excel spreadsheets highlighting the various themes and categories, relevant quotes, codes, and notes and memos explaining linkages to and divergence from the initial framework and extant literature. Categories were assigned different colours for differentiation and ease of reference when reporting. The tables helped summarise, synthesise and consolidate information from interviews and served as a reporting tool.

4.5.3 Drawing conclusions and verifications

The process of drawing conclusions and verification is an interpretive one, whereby the researcher attempts to draw meaning from the displayed data and validate them. It is about the lessons learnt (Lincoln & Guba, 1985) from a comparison between the findings and the information gleaned from literature and extant theories (Creswell, 2003). The ultimate goal of this research was to develop a contextual model for ICT-enabled research communication by and for scholars and researchers in applied sciences and technology in Kenya. The preliminary model in this research was informed by Rogers’ Diffusion of Innovations theory and Hofstede’s Cultural Dimensions framework and extant literature. Accordingly, the researcher drew conclusions on points of conformity and divergence and used those to inform the

development of a contextual model and interpretations calling for action agendas for reform and change in ICT-enabled communication of research outputs by and for Kenyan researchers, as a means towards greater participation in the global knowledge economy.