Data analysis

In most qualitative research projects, data analysis is an ongoing process that, in many ways, overlaps with data collection (Carson et al., 2001; Marshall & Rossman, 1999). Because qualitative research generates a large amount of data very quickly, researchers are confronted with hundreds, if not thousands, of pages of interview transcripts, field notes and other written documents (Miles & Huberman, 1994). The absence of a well- formulated method for analysing qualitative data adds to the analysis stage being considered a fairly daunting task (Miles & Huberman, 1994). In overcoming these potential issues, this study used Computer Aided Qualitative Data Analysis Software (CAQDAS), and preliminary codes that were drawn from the literature, informant interviews, or the interview schedule. Preliminary data analysis began when the researcher commenced her informant interviews in stage one, and did not end until the write up of the research project was complete. During the initial phase of stage two, the researcher began simultaneously analysing and interpreting the perspectives of those she was interviewing. After conducting the first fifteen wine producer interviews, the

interview schedule was modified in order for it to be more reflective of the major themes and issues the researcher had encountered during these initial stages. In effect, raw data were being initially coded and interpreted as further data were collected (Carson et al., 2001).

Silverman (2005) is one of many qualitative researchers who advocate this iterative approach, particularly in relation to doctoral research. A number of other scholars support the practice of overlapping the data collection and analysis stages. For example, Lofland, Snow, Anderson and Lofland (2006) explain that fieldwork involves a complex

136 Marshall and Rossman (1999) add that through checking and testing ideas at the same time, researchers can exercise control over emerging themes and the direction of their research. On a more practical level, the sheer volume of raw data collected via more than thirty in-depth interviews suggests it is not wise to delay analysis until data collection has been fully completed (Shaw, 1999). Once interviews were completed, the researcher re- read every transcript in order to identify any potential spelling mistakes and to ensure the use of acronyms or jargon was consistent. These Word documents, along with any reflective notes taken during or after the interviews or at industry events, were then prepared for the more formal stage of data analysis.

With the assistance of CAQDAS, the data resulting from stage two of data collection were formally analysed through a process of data reduction, data display, and conclusion drawing and verification (Miles and Huberman, 1994). In this study, QSR NVivo Version 8 was the CAQDAS program used. Its main purpose was to facilitate coding through data reduction, and to keep track of the researcher‟s thoughts and interpretations as the data analysis process evolved (Fielding & Lee, 1998). Gibbs (2002) describes the role of CAQDAS, such as QSR NVivo, as more akin to that of a database. First and foremost, it enables researchers to electronically record their thoughts, ideas and outcomes of

analyses in a much more efficient and rigorous way (Bazeley, 2007). CAQDAS also aids the transparency of the analysis process, through facilitating text searches, data counts, identifying negative cases (Silverman, 2005) and recording the source details (Wickham & Woods, 2005). However, the interpretation of the data, and many of the analytical procedures, should still be performed by the researcher and not just the computer (Bazeley, 2007; Bryman & Bell, 2007).

6.5.1 Data reduction

Arguably the most popular method for analysing qualitative research data is coding. In essence, coding involves organising and sorting raw data into categories based on the research concepts and the researcher‟s interpretations (Coffey & Atkinson, 1996; Gibbs,

137 2002). However, when used properly, the process of coding is quite complicated and time consuming. That is, sophisticated coding involves conceptualising the data, questioning the data, providing provisional answers to the research questions, and opening up the data to a whole other set of analytical possibilities (Coffey & Atkinson, 1996).

Prior to entering the data reduction stage of analysis, Miles and Huberman (1994) advise researchers to begin with a preliminary set of codes that derive from the theoretical framework, research questions, problem areas, and key variables the researcher has an interest in. In this study, preliminary codes related to Research Questions One and Three were drawn from the theory and literature on horizontal networks and inter-organisational relationships. With regard to Research Question Two, which focused on the specific types of collaborative marketing activity, meaningful codes emerged from the data. Although the literature offers some insight into the types of collaborative marketing agri- food and tourism producers use, a key theme to emerge from the data was the value of collaborative marketing events and promotional activity. In addition, separate nodes were created as new forms of collaborative marketing were coded. This allowed a list of the types of collaborative marketing Tasmanian wine producers engaged in to be devised, and comparisons made of the nature and benefits of each type of activity.

Writing memos and teasing out themes were also used during the data reduction stage. During the coding stage, appropriate categorisation schemes were also developed so that the properties of codes were clearly defined, and the allocation of data to these categories was more accurate and efficient. At the end of the data reduction phase, the coded data were retrieved and a report for each category was produced. Text that was either

mistakenly coded at certain categories, or considered unnecessary detail, was removed to improve clarity. These reports were also used to identify key words, which were later inputted into text searches as a way of confirming the major theme areas. In addition, close inspection of each category report allowed the researcher to confirm or reject data from that category, which resulted in a more refined set of codes.

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6.5.2 Data display

The most analytical part of the coding process lies in establishing and thinking about the linkages between the data categories (Coffey & Atkinson, 1996). As mentioned

previously, once the transcripts were imported into the CAQDAS program, the raw interview data were classified into a set of codes. A process of reallocating these codes into „tree nodes‟ represented the first step in data display (refer to Appendix F), as by doing so, themes and patterns within the coded data were identified, and any relationships across the code categories were explored. This is in line with Coffey and Atkinson (1996), who suggest that the first step in moving from coding to interpretation involves

displaying the data in such a way that they can be read and explored easily. Similarly, Miles and Huberman (1994: 92) argue that data displays should be „focused enough to permit a viewing of a full data set in the same location, and arranged systematically to answer the research questions at hand‟. By displaying the data in a visual format within the QSR NVivo program, in addition to comparing and contrasting the printed code reports, the researcher started to draw preliminary conclusions. Furthermore, the data display stage of analysis allowed the researcher to perform further coding where

necessary. For example a preliminary node titled „horizontal networks‟ evolved into four sub-categories following a second round of coding. The creation of these „child nodes‟ was necessary given that numerous references were made to certain networks, such as the Tamar Valley Wine Route and WIT. Additionally, the data contained comments which were coded as either formal or informal networks, and as discussion of past industry groups that were of a horizontal nature. Coding the data in this way allowed for more straightforward comparisons to be made, and for key themes and ideas to emerge.

6.5.3 Conclusion drawing and verification

The final step in the analysis process was to draw meaning from the data contained in the tree nodes. Essentially this involved searching each tree node for key events and issues, and then identifying the underlying patterns of thought or behaviour (Hill, 1993). Miles

139 and Huberman (1994) suggest a range of different tactics for generating meaning from qualitative data, whilst Dey (1993) suggests playing with and exploring the data categories that have been created. In this thesis, taking note of patterns and themes, making contrasts and comparisons, and clustering the data were the main techniques used at this stage. Transforming the data into meaningful interpretations was performed by extracting key quotes and organising these in accordance with a range of theme areas. An electronic diary and memos were also kept, as a means of noting themes and patterns in the data as they emerged. This approach is advocated by Easterby-Smith et al. (1991) who suggest that diaries which contain a rationale for the research, emergent ideas and findings, and the opinions and attitudes of the researcher, are useful for recalling the process from which conclusions are drawn.

Comparisons of the data relating to each of the industry‟s three horizontal networks were also performed as a way of verifying conclusions and identifying any conflicting findings. Although CAQDAS facilitates some level of comparison within data, theory building is still an activity best reserved for the researcher‟s mind (Silverman, 2005). Thus, in this study, this procedure was performed with pen and paper in hand. Throughout the analysis process the researcher was also „on the lookout‟ for irregularities within the data. Such divergent cases or „negative‟ findings are equally important to the analysis process, as they allow the researcher to establish boundaries for their conclusions (Coffey & Atkinson, 1996). Delamont (2002) even suggests that researchers should seek data contrasts, paradoxes and irregularities, as much as they look for patterns, themes and regularities.

The preliminary conclusions, emerging concepts and themes derived from the analysis of interview data were then compared with the theory and research reviewed in Chapters Two, Three, Four and Five. Glaser (1978) and Glaser and Strauss (1967) highlight the importance of comparing current findings with those from previous studies as part of the overall conclusion drawing and verification process. By doing so, the researcher is able to improve the density and scope of their findings (Glaser, 1978). Here, the researcher compared the outcomes of data analysis with the literature, and noted any consistencies

140 and departures from previous theory. This process also helped to enhance the internal validity of the research findings, as will be discussed below.