Data Analysis

Data were analysed on two levels: within- and cross-case. First, documents and interviews were inductively coded, as described further in this section. Then, within- and cross-case analyses were conducted. The next section provides information about qualitative data analysis, followed by each stage of coding.

4.7.1 Qualitative data analysis

Qualitative data analysis allows the researcher to “improve understanding, expand theory, and advance knowledge” (Neuman, 2011, p. 507). Data analysis in qualitative research involves gathering and organising data, then “reducing the data into themes through a process of coding and condensing the codes, and finally representing the data in figures, tables, or a discussion” (Creswell, 2013, p. 180). In qualitative research, the researcher organises the raw data into conceptual categories to create themes or concepts (Neuman, 2011). Coding data consists of categorising data into meaningful sections and assigning names or a code to the sections (Creswell, 2013; Neuman, 2011).

Miles and Huberman (1994) described codes as

tags or labels for assigning units of meaning to the descriptive or inferential information compiled during a study. Codes usually are attached to “chunks” of varying size – words, phrases, sentences or whole paragraphs, connected or unconnected to a specific setting (p. 56).

Saldaña (2015) noted that the nature of coding suggests that qualitative analysis is “cyclical rather than linear” (Ch. 3, para. 2). The first cycle of coding is rarely perfect and a second, third, fourth, and potentially more cycles are required to further manage, filter, and highlight the essential features of the qualitative data record to create

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categories, themes, and concepts (Miles et al., 2014). Miles et al. (2014) stated that “coding is analysis” (p. 72, emphasis in original).

Strauss (1987) identified three successive stages of coding: open, axial, and selective. Both Saldaña (2009; 2015) and Strauss’ (1987) processes for analysing qualitative data guided the data analysis for this research. Figure 5 below illustrates the cycles of coding that occurred (see Appendix G for detailed cycles of coding) and the next sections provide further detail about each of the cycles and how they were used in this research.

Figure 5: Four cycles of coding for this research Source: Adapted from Saldaña (2013, 2015)

4.7.2 Cycle one coding

In the first cycle of coding, large quantities of raw qualitative data are labelled. Open coding involves meticulously examining field notes, interviews, transcripts, and other documents with the aim to identify concepts that are applicable to the data

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(Strauss, 1987). During cycle one coding, the portion of data to be coded can range from a single word, full sentences, or an entire page of a document (Saldaña, 2009). Within cycle one coding, Saldaña (2009) suggested utilising a combination of the following four approaches. The first is attribute coding (for all data as a management technique); structural coding or holistic coding (for all data as a “grand tour” overview); descriptive coding (for field notes, documents, and artefacts as a detailed inventory of either contents); and, in vivo coding, initial coding, and/or values coding (for interview transcripts as a method of attuning yourself to participant language, perspectives, and worldviews) (p. 48).

When open coding, names, categories, and themes are derived from the data (Neuman, 2011). The themes can develop from the initial research questions, concepts within the relevant literature (e.g. IOR and stakeholder management), and words or phrases used by interviewees (Neuman, 2011). New thoughts or ideas can also emerge from involvement with the data (Neuman, 2011). The documents and interview transcripts were read in detail, and people, key events, terms, and phrases were documented. The first cycle of coding produced 65 open codes (see Appendix G for detailed list of four cycles of coding). As an example of first cycle coding, “challenges that stakeholders experience in trying to leverage the Games” was coded to material that related to challenges with external communication and/or establishing trust/rapport.

4.7.3 Cycle two coding

Cycle two coding is more focused; it reviews cycle one coding and further refines and categorises the data. The portions of data coded can be the exact same units as in the first level, or longer passages of text and even a reorganisation of the initial codes developed thus far. Abbott (2004) compared second cycle coding to “decorating a room; you try it, step back, move a few things, step back again, try serious

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reorganization, and so on” (p. 101). Within cycle two, Saldaña (2015) suggested utilising pattern coding and/or focused coding in order to create categories for the coded data as an initial analytic strategy. According to Hatch (2002), a pattern can be characterised by:similarity (things happen the same way);

difference (they happen in predictably different ways); frequency (they happen often or seldom);

sequence (they happen in a certain order);

correspondence (they happen in relation to other activities or events); and causation (one appears to cause another) (p. 155).

During the coding process, I regularly met with my supervisors and discussed my rationale behind the initial 65 codes that emerged during first cycle coding. The 65 codes were then clustered together into sub-categories strategically to identify

relationships among those open codes. These 65 codes were grouped, merged, and tiered to create 34 manageable second cycle codes. For example, first cycle codes “professional development”; “Games-focused research output”; “career development”; and “secondment” were refined into second cycle codes “staff opportunities and impact”.

4.7.4 Cycle three coding

In cycle three coding, axial and thematic coding takes place, whereby the researcher examines previously established codes to develop highly refined themes. Strauss (1987) suggested that axial coding involves intensive analysis around one category at a time: it revolves around the “axis” (p. 32) of one category. Axial coding begins with an established set of initial codes or preliminary concepts (Neuman, 2011).

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The researcher should note additional codes or new ideas; however, the focus is on organising ideas or themes identified through the open coding process and identifying the axis of key concepts in analysis (Neuman, 2011).

The 34 second cycle codes were then further refined to identify relationships among second cycle codes. For instance, “employee morale”; “inclusion/exclusion of staff”; “boys’ club mentality”; “personal agendas and influence”; “hoarding

information”; and “executive support” created the axial code “organisational politics and culture”. The 34 second cycle codes were grouped, merged, and tiered to create a manageable list of seven axial codes (third cycle codes).

4.7.5 Cycle four coding

The fourth cycle of coding involved selective coding, which occurred after the major themes and concepts were identified in open and axial coding. The selective coding process involves “coding systematically and concertedly for the core category” (Strauss, 1987, p. 33). Till the selective coding phase, the majority of data has been collected, and the researcher scans all the data and previous codes and looks selectively for cases that demonstrate themes to be able to make comparisons (Neuman, 2011). During selective coding, the data were revisited to find supporting data, and the researcher should identify the major themes in the data (Neuman, 2011). Selective coding elaborates on the central themes, giving a deeper understanding of the data. All the data were searched for explicit examples to describe the central themes, and the seven axial codes were reduced to three selective themes (See Chapters Five and Six). For example, the axial codes “desired and/or achieved legacies” and “missed

opportunities” were refined to form the major theme “intended and unintended legacies”.

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In order to establish reliability and credibility in the qualitative data analysis process, my research supervisors, who are familiar with the literature, reviewed the themes and their definitions after each cycle of coding. My supervisors likened the coding process to coding a chair, a desk, or a lamp in an office, however, to further refine these codes into “office furniture”. This analogy and subsequent discussion produced some hilarity, but facilitated clarity in my own coding procedure and enabled development of refined themes.

4.7.6 Data coding tool

Using the qualitative data analysis software, NVivo10, facilitated my analysis of the data. NVivo10 assisted to manage the data and ideas, query data, visualise data, and report from the data (Bazeley & Jackson, 2013). NVivo10 allows the researcher to assign written data to themes during open coding, which assists in building evidence for each theme. Once I coded all 60 interviews and 145 documents according to the themes, I carefully reviewed and then further analysed the data for relationships and linkages between the various cycles of coding. During cycle three coding, NVivo10 aided me by facilitating recognition of connections among themes, which helped in identifying emerging themes as well as organising data on how and why processes and relationships interact (Gibbs, 2002). Finally, NVivo10 enabled me to efficiently access exemplary data to aid selective coding.

It is acknowledged that there are some concerns with using computerised qualitative data analysis tools. For instance, Bazeley and Jackson (2013) identified four common issues:

 The concern that computers can distance researchers from their data;  The dominance of code-and-retrieve methods to the exclusion of other

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 The fear that use of a computer will mechanise analysis, making it more akin to quantitative or positivist approaches; and

 The misperceptions that computers support only grounded theory methodology, or worse, create their own approach to analysis.

These concerns were discussed with my supervisors and were overcome by acknowledging these issues, continuously re-reading the documents and transcripts throughout the data analysis process, and engaging in manual coding exercises with interview transcripts.

4.7.7 Reporting of results

Figure 6 below outlines how the results are presented in this thesis. Chapter Five will present the within-case results, and Chapter Six will present the cross-case results and discussion. Chapter Seven will provide a conclusion for the overall research findings.

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Figure 6: An overview of the data collection, analysis, and reporting procedure used in this research