Data Analysis: Thematic Analysis Approach

Data analysis is one of the most important steps in qualitative research. It is the systematic search for meaning from the qualitative data in order to communicate with others what the researcher has gained. Brotherton (2008) and Hatch (2002) noted that the analysis in qualitative research (inductive approach) allows the researcher to understand patterns, connections, themes, relationship in order to interpret its significance and produce meaningful explanation or generate theory.

Data analysis process needs to be part of the overarching plans, and should start when the data is being collected during the fieldwork data until the formal data analysis (Coffey and Atkinson, 1996; Robson, 2011; Yin, 2014). Coffey and Atkinson (1996, p.2 cited in Maxwell, 2013, p. 105) indicated that “we should never collect data without substantial analysis going on simultaneously.” Other considerations that the researcher has to be prepared for are noted by Kvale and Brinkmann (cited in 2009 in Robson, 2011, pp. 300-301); and are:

 How shall I conduct my interviews so that the results and meanings can be analysed in a coherent and creative way?

 How do I go about finding out what the interview tells me about what I want to know?

 How can the interviews assist in extending my knowledge of the phenomena I am investigating?

For this reason the researcher started her analysis throughout her data collection, and revised the analysis again after all data collection was gathered.

It has been argued that data analysis within the context of the realism paradigm does not entail recording large amounts of transcript data, as is the case in constructivist or

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critical theorist type studies (Guba and Lincoln, 1994). While constructivists and radical theorists often use analytical methods that track and count every word and phrase from an interview, sometimes using computer programs to do this (Guba and Lincoln, 1994), Realism research on the other hand, seeks only contingencies, structures, mechanisms, connections or relationships that help explain phenomena (Sobh and Perry, 2006), and which are meaningful as explanations to those interviewees involved. The investigation and explanation of phenomena in realism research is achieved via note taking and analysis, and subsequent triangulation via other methods. In realism-based research methodologies, only those perceptions relevant to the reality being investigated are felt worthy to be examined and recorded (Sobh and Perry, 2006). This means that finding categories or codes for data can happen during the data gathering process, and is achieved with the participation of the interviewee. These categories are then subject to further analysis and refinement during subsequent stages of research.

Huberman and Mile (1994 in Berg & Lune, 2012, p. 54-55) noted that in qualitative research data analysis is achieved through:

 Data reduction — focusing, simplifying, and transforming raw data into more manageable and understandable forms.

 Data display — presenting data as an organised and compressed assembly of information that permits conclusions to be analytically drawn.

Yin (2014, pp 136-142) suggested the following three guidelines for data analysis:

 Rely on theoretical propositions — theoretical orientation guided the analysis,

 Working with data form ground up,

 Examining plausible rival explanations.

Examples of analysis technique in qualitative research include the grounded theory, narrative analysis content analysis, pattern matching, and thematic analysis (Berg & Lune, 2012; Brotherton, 2008; Creswell, 2014; Yin, 2009). Content analysis is a careful, detailed, systematic examination and interpretation of the contents of a study in order to identify patterns, themes, categories, bias and meaning (Berg & Lune, 2012; Morse & Field, 1996). It is the most commonly used data analysis technique in qualitative research. It aims to derive meaning and make inferences from textual or visual data

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(Brotherton, 2008). The sources of data in content analysis can be all sorts of recorded communication (transcripts of interviews, discourses, protocols of observations, video tapes, and documents) (Creswell, 2014).

Despite the popularity of content analysis, it involves establishing categories and then counting the number of instances in which that category is used in a text or image. It determines the frequency of the occurrence of particular categories; and therefore content analysis mainly relies on counting attributes in data.

Meanwhile, thematic analysis goes beyond content analysis. This has invariably been described as a technique for identifying, analysing and reporting patterns (themes) within data (Braun & Clarke, 2006; Robson, 2011). According to Joffe (2012) thematic analysis is:

“The method for identifying and analysing patterns of meaning in a data set and illustrates theme which are important in description of the phenomenon under study. The result of thematic analysis is the highlight the most salient constellations of meaning present in the data set.” (p.9)

The analysis process which is used when undertaking thematic analysis is presented in Table 3.16 below.

Analysis processes Description

Familiarising yourself with your data

Transcribing data (if necessary), reading and re-reading the data, noting down initial ideas.

Generating initial codes

Coding interesting features of the data in a systematic fashion across the entire data set, collating data relevant to each code. Searching for themes Collating codes into potential themes, gathering all data

relevant to each potential theme.

Reviewing themes Checking if the themes work in relation to the coded extracts (Level 1) and the entire data set (Level 2), generating a thematic ‘map’ of the analysis.

Defining and naming Themes

Ongoing analysis to refine the specifics of each theme, and the overall story the analysis tells, generating clear definitions and names for each theme.

Producing the report The final opportunity for analysis. Selection of vivid, compelling extract examples, final analysis of selected extracts, relating back of the analysis to the research question and literature, producing a scholarly report of the analysis. Table 3.16: Thematic analysis process

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This thesis will adopt thematic data analysis process. The full process of qualitative data analysis and the model which is used for data analysis in this research is presented in Figure 3.3 below.

(Source: Adapted from Braun & Clarke, 2006, pp. 86-93; Creswell, 2014, p. 197; Robson, 2011)

Writing Report

Interpreting the meaning of themes/Descriptions

Interrelating Themes/ Description

Revising Themes

Searching for themes

Generating initial codes by hand (theoretical driven coding)

Familiarising with the raw data (read and reread) transcripts, fieldnotes, image, etc.)

Validating the accuracy of the information

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The process of data analysis was guided by the flow-diagram in Figure 3.3; and the accompanying details of each stage are given below:

 Firstly, tape recorded interviews were transcribed. Also, the researcher was writing notes during interviews; as well as notes from observations and other documents.

 These were returned back to the participants in order to recheck the correction of the transcripts.

 Since the transcripts, notes and most other documents were in Thai, the researcher translated them into English.

 To get a better understanding of all raw data that had been collected the researcher read through all the documents again.

 Coding from the relevant quotes was undertaken, and the quotes/ideas were then categorised into themes.

 The different themes were then interpreted – according to whether they addressed questions on the definition and identification of talent; or whether they addressed managing talent in hotel industry. The themes and categories that emerged from the data analysis are discussed in the findings and discussion chapters that follow.

The substantial amount of data collected through semi-structured interviews, observation, and documentation was analysed manually (See Appendix 10, example of thematic analysis – Importance of talent in the Thai hospitality). Rather than manually analysing the data, the researcher can opt to use computer-assisted qualitative data analysis software such as the QDA package (Robson, 2011). He noted that the software package benefit the researher as follows:

 They provide an organised single location storage system for all stored material

 They give quick and easy acces to coded material without using cut and paste techniques

 They can handle large amouts of data very quickly

 They can anlalyse differences, similarities and relationship between coded elements (p. 472).

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However, using some software may have some drawbacks. For example:

 There may be difficulties in changing, or reluctance to change, categorised of

information once they have been established.

 Particular programs tend to impose specific approaches to data analysis

Despite some benefits of using the computer software, the researcher opted to analyse the data manually because this allows her to get close to the data. The researcher can cut, paste, highlight snippets of data and move it around.