Data Analysis

“Not everything that can be counted counts, and not everything that counts can be counted” – Albert Einstein

The purpose of the interviews was to try and uncover processes, behaviours and expectations prevalent in client organisations when engaging consultants and to see if the information could be used to help consultants in their approach towards clients. Analysis is about organising and manipulating the data. Silverman (2005) asserts that traditionally, statistical analysis is seen as the bedrock of research. Qualitative data

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analysis differs from statistical in that no matter how it is analysed, it is skewed by interpretation and judgement, therefore subjective (May, 2001; Robson, 2002). According to Kvale (2009) content and purpose precede method therefore the theoretical conceptions of the investigation provide the basis for deciding how to analyse the content.

Analysing data can take different forms of expression. Discourse analysis, for instance, places emphasis on the use of language and how specific ideas are interrelated to particular concepts (Creswell, 2009). Hermeneutic analysis is the sense-making of written texts for people in a situation rather than exploring the dynamics, content, context and structure of social relations (May, 2001:28). Content analysis is essentially a quantitative method of analysing the content of qualitative data (Myers, 2009). It uses pre-identified categories, each instance of which is

systematically counted or logged, to allow pre-existing theory to be tested. Grounded theory analysis encourages hypothesis generation using theoretical constructs

generated from bottom-up data, with the aim of developing theory through the continuous interplay and overlap between analysis and data collection (Eriksson and Kovalainen, 2008).

Gray (2009) says grounded theory analysis emphasises the emergence of themes that are contrasted against their repetitive emergence onto text. Braun and Clarke (2006) view this as ‘grounded theory lite’ and essentially thematic analysis since it does not commit to theory development. Primarily concerned with the exploration of repetitive themes coded under certain categories already decided prior to the analysis, and others which inductively emerge from the data itself, thematic analysis is similar in principle to grounded theory (Denscombe, 2007). It differs in the way the information is encoded. Codes are developed to label and interpret the identified themes, which simultaneously describe and organise the data (Boyatzis, 1998). Thematic analysis focuses on identifying and describing both implicit and explicit ideas within the data. Moving beyond counting explicit words or phrases and developing themes, it promotes discursive interpretation based on context of themes identified since individual codes can cross-reference multiple themes (Boeije, 2010).

The purpose of the interviews was to seek prevalent client processes, behaviours and expectations, thematic analysis appears the most suitable approach to adopt. To this end, the first step in preparing the data for analysis was to transcribe the audio- recorded interviews. The transcriptions included all the words said, but not the other dimensions of oral interviews such as pauses, sighs, laughter. The initial

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categorisation was the high level themes derived from the literature review (Appendix E). Then a process of open coding, assigning a label to a chunk of information and classifying it into a category, was carried out. Coding directly from the data, breaking each interview down into units of analysis, describing specific ideas or events, provides the basis of the thematic analysis (Denscombe, 2007).

Table 5-2 shows the approach taken to dissect the data into analysable information.

Category Source

Primary Resource Package Individual Interview Secondary resource package Role

Sector Country

Initial coding From the groupings of the questions Descriptive coding Issues, labels, processes described Coordinating mechanisms Unifying features

Analytic coding Themes and concepts Table 5-2: Data Structure for developing inferences from raw data

Using this approach aimed to uncover what matters to clients when choosing

consultants, and what they perceive as important in creating and delivering satisfaction. 5.5 Rigour, Reliability, Validity and Generalisability

Rigour, reliability and validity are perennial concerns for qualitative researchers. Methodological rigour is based on checks to ensure that the outcomes of the research are meeting the criteria of reliability and validity. Reliability to a positivist is concerned with the consistency and repeatability of the results obtained in the study. However the fluid nature of phenomena scrutinised in qualitative research makes such provisions problematic, so reliability pertains to the consistency and trustworthiness of the research findings (Kvale, 2009). Reliability is improved through triangulation, the principle of which is that a better understanding of the phenomenon being investigated is reached if that phenomenon is obtained from more than one data source in more than one context (Denscombe, 2007; Gray, 2009). Validity is concerned with the integrity of the outcomes and credibility of the conclusions obtained in the study, and can only be assessed relative to goal and context.

For interpretive qualitative work, reliability, or construct validity, is talked about in terms of dependability or comparability. This comes from the components of the study, the description of the process, the participants and how the research was designed (Schofield, 2002). Internal validity relates to the credibility of the work, based on the factual accuracy of the account (Maxwell, 2002). It can refer to issues of omission as

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well as commission. External validity, also labelled generalisability, concerns the description of the context and the phenomenon to allow comparisons and is referred to as translatability or transferability (Schofield, 2002).

Generalisability depends on how far the capture of nuances and singular

characteristics of multiple particular environments permits inference from the specific instances to the characteristics of a wider environment (Williams, 2002). “Only by comparing a series of interviews can the significance of any one of them be fully understood” (Gerson and Horowitz, 2002:211). In this instance generalisation was made plausible by the high number of interviews carried out in three different sectors and across multiple countries. The crucial task is to identify the significant features on which comparisons can be made (Denscombe, 2007). Given the nature of the

individual interviews and the inclusion of international organisations, extrapolation of the significant features was feasible. Specific activities to increase the validity, reliability and generalisability of the evidence in this research were the use of theory to structure the list of interview topics (Appendix E), the volume of interviews, company and

participant role variety. This captured the investigated phenomena from different perspectives, provided sector comparisons and the inclusion of an international dimension. All of these provided an easily accessible audit trail.