Phase two: Semi-structured interviews data analysis

The data obtained from the semi-structured interviews was analysed by means of thematic analysis. According to Braun et al. (2006: 4), thematic analysis is the first qualitative method of analysis that researchers should learn and needs to be considered a “method in its own right.” Thematic analysis is defined as, “a realist method, which reports experiences, meanings and the reality of participants, or it can be a constructionist method,51 which examines the ways in which events, realities, meanings, experiences…are the effects of a range of discourses

operating within society… [the method can] reflect reality, and unpick or unravel the surface of ‘reality’” (Braun et al., 2006: 9). The researchers state that thematic analysis is widely used but there is no agreement about how to define it and the process of doing it thus making it a “poorly branded method” (Braun et al., 2006: 6). Even though much of the analysis normally conducted by qualitative researchers is thematic, it is often called something else such as discourse or content analysis or it may not be identified as a particular method with the researchers in question merely stating that the data was subjected to qualitative analysis for commonly recurring themes.

Thematic analysis is not just a collection of extracts with little or no analytic narrative nor is it a selection of extracts with analytic comment that is a mere paraphrasing of the content (Braun et al., 2006: 25). The extracts in thematic analysis need to illustrate the analytic points that the researcher made about the data and should be used to support an analysis that goes beyond their specific content, to make sense of the data and to tell the reader what the data means. Thematic analysis, therefore, involves searching across a data set such as a number of

interviews to find repeated patterns of meaning or themes. A theme is a patterned response or meaning within the data set and the importance of the theme lies in whether it captures

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According to Burr (1995), in a constructionist perspective, meaning and experience are socially produced and reproduced, rather than inhering in individuals. Thematic analysis conducted within a constructionist framework cannot and does not seek to focus on motivation or individual psychologies but instead attempts to theorise the socio-cultural contexts and structural conditions that enable the individual accounts that are provided.

something important in relation to the research questions (Braun et al., 2006: 10). In this research study, the researcher provided a detailed account of a group of themes that emerged from the data during the analysis rather than providing a thematic description of the entire data set.

The researcher followed six phases of data analysis as suggested by Braun et al. (2006: 18-23) and loosely based on the work of Attride-Stirling (2001: 385-405):

Phase one consisted of immersing oneself in the data in order to become familiar with the content. This immersion involved repeated reading of the data in an active way by making notes and searching for meaning and patterns. This supports Ryan and Bernard (2003: 11) who state that the researcher should “get a feel for the text by handling the data multiple times.” Attride-Stirling (2001: 390) refers to this step as “devising a coding framework” which may be based on identifying specific topics or words or recurrent issues in the text. In the case of interview data, the data needed to be transcribed into written form in order to conduct a thematic analysis. Brown (2001) maintains that interview transcripts thus constitute the written record. Green, Franquiz and Dixon (1997: 172) state that “A transcript is a text that re-presents an event; it is not the event itself…what is re-presented is data constructed by a researcher for a particular purpose, not just talk written down.” With regard to the interview transcriptions, the researcher decided to:

Transcribe the entire interview session rather than summarise the key points of the interview;

Supplement the verbal transcriptions with written notes in which non-verbal data such as pauses, facial expressions and hand gestures were transcribed. This supports Braun et al. (2006) who maintain that a rigorous and thorough transcript consists of a verbatim account of verbal and non-verbal utterances such as coughing, facial expressions or the use of gestures; and

Transcribe the data herself rather than use a transcription service (Hesse-Biber et al. 2011: 302). This supports Bird (2005: 233) who states that transcribing her own research “developed and honed her awareness of transcription as a key element in data

analysis.”

Hesse-Biber et al. (2011: 303) states that a positivist might dispense with some of these questions, opting to view the transcription process as a simple translation from oral to written language, something that can be done by listening to the interview and typing what one hears. What is transcribed is thus regarded as ‘the truth’ and each transcription is considered to contain a one-to-one correspondence between what was said orally and the printed word. A constructivist-interpretative viewpoint, however, would not see the transcription process as so

transparent as it stresses the importance of the researcher’s viewpoint and experiences during the interview process and the researcher’s influence on the transcription process itself. The researcher, therefore, felt it was important to transcribe the interviews personally.

Transcribing research data is not a passive act but rather an interactive one which engages the researcher in the process of deep listening, analysis and interpretation. This supports Braun et al. (2006: 17) who state that transcription is “an interpretive act, where meanings are created rather than simply a mechanical one of putting spoken sounds on paper.” Furthermore, Lapadat and Lindsay (1999) maintain that data analysis begins during the transcription process as the act of transcribing facilitates the close attention and interpretive thinking required for data analysis. It provides the researcher with a valuable opportunity to actively engage with the research material from the beginning of data collection. It also ensures that researchers are aware of their impact on the data early in the gathering process and have the opportunity to connect with the data in a grounded manner which enhances the trustworthiness and validity of the data-gathering techniques (Hesse-Biber et al., 2011: 304).

Hesse-Biber et al. (2011: 305; 314) refer to the process described above as the ‘art of the memo’ saying that it is a vital step for researchers “who want to get a closer picture of their data to build theory and to potentially draw out some findings.” Hesse-Biber et al. (2011: 305; 314) describes a similar process to Braun et al. (2006) which involves a close reading of the transcripts and careful thought about the data and the use of descriptive memos such as written notes or visual aids such as mind maps to summarise the data. The researcher needs to identify and include key quotes as well. The analytic memos are, therefore, not only written ideas about the analysis and interpretation of the data but also ideas and impressions about the data and how it fits together. Hesse-Biber et al. (2011: 305; 314) state that memos are

pathways to the meaning of the data, an intermediate step between the researcher, the researcher’s interpretation and the write-up of the data.

Phase two involved generating initial codes from the data. These codes identified interesting features of the data and the data was sorted into meaningful groups. According to Hesse-Biber et al. (2011: 308-9), the coding process starts with the researcher’s engagement with the data and ends with a theory generated from or grounded in the data. The researcher worked systematically through the data set and identified interesting aspects in the data items that formed the basis of repeated patterns (themes) across the data set. The researcher used highlighters to indicate potential patterns as suggested by Attride-Stirling (2001); Braun et al. (2006: 19) and Alhojailan (2012). This supports Hesse-Biber et al. (2011: 315) who maintain that there is no predefined set of coding categories as the analysis is primarily inductive and relies on the researcher’s insights. All actual data extracts were coded and then collated together

within each code. Braun et al. (2006: 19) state that no data set is without contradictions and thematic analysis produces an overall conceptualisation of the data patterns, and relationships between them but it is important that the researcher does not smooth out and ignore tensions and inconsistencies within or across data items. It is thus important that accounts which departed from the dominant story in the analysis were retained (Braun et al., 2006: 20). Hesse- Biber et al. (2011: 315) in private correspondence with David Karp (2004) quotes Karp as saying, “especially at the beginning you will hear people say things that you just hadn’t thought about” and the researcher must ensure that cognisance is taken of “the words of people who do not fit

the pattern.”

Phase three involved searching for themes within the coded data. The researcher used visual representations such as mind maps to sort the codes into themes and investigated the

relationships between codes, between themes and between different levels of themes such as main and sub-themes. Thus at the end of this phase, the researcher had a collection of possible themes and sub themes and extracts of data that had been coded in relation to them.

According to Braun et al. (2006: 20), the researcher should, at this stage, begin to have a sense of the significance of individual themes.

Phase four involved reviewing the various themes so as to refine them in order to achieve coherence and find clear and identifiable distinctions between themes. The researcher approached this phase by reviewing at the level of the coded data extracts and re-reading all the collated extracts for each theme to see whether they formed a pattern. The researcher then reviewed at the level of the entire data set. Braun et al. (2006: 21) maintain that it is vital to consider the validity of the individual themes in relation to the data set and to check whether the themes accurately map and reflect the meanings evident in the data set as a whole.

Therefore, there were two aims in re-reading the data which were to ascertain whether the themes worked in relation to the data set and to code any additional data within themes that had been missed in the earlier coding stages. At the end of phase four, the researcher had a clear idea of the different themes, how they fitted together and the overall story they told about the data (Braun et al., 2006: 21).

Braun et al., (2006: 22) state that phase five consists of defining and naming the various themes and then analysing the data within the themes. Thus the themes were organised into a

coherent and internally consistent account with an accompanying narrative. The researcher conducted a detailed analysis for each theme and identified the story that each theme told. In addition, sub-themes were identified which were useful in giving structure to particularly large and complex themes and also for demonstrating the hierarchy of meaning within the data. Attride-Stirling (2001: 393) maintains that at this stage, the researcher needs to return to the

original text but rather than approaching the text in a linear manner, the researcher should read the original text through the lens of the identified themes. The various themes thus become a tool which “anchors the researcher’s interpretation” of the data.

Once the researcher had a set of fully worked out themes, the final analysis and writing-up of the data began which constituted phase six (Braun et al., 2006: 23). The story of the data needed to be written in a way that convinced the reader of the merit and validity of the analysis. In addition, the write-up needed to contain sufficient evidence of the themes extracted from the data, that is, enough data extracts to demonstrate the prevalence of the theme. The researcher chose extracts which demonstrated the essence of the point being made. According to Braun et al. (2006: 23), the extracts thus needed to be embedded within the analytic narrative that illustrates the story being told about the data. Furthermore, the analytic narrative needed to go beyond a mere description of the data and make an argument in relation to the research question.

Thematic analysis has several advantages which include (Braun et al., 2006: 27-8; 37): It is a relatively easy and quick method to learn and do;

It is accessible to novice researchers with little or no experience of qualitative research; The results are generally accessible to the general public;

It can usefully summarise key features of a large body of data and offer a ‘thick’ description of the data set;

It is flexible and allows for a range of analytic options thus the potential range of

statements about the data is broad. Braun et al. (2006: 28) states that thematic analysis is “a flexible approach that can be used across a range of epistemologies and research questions.”

It can highlight similarities and differences across a data set; It can generate unanticipated insights into a data set; and

It allows for social as well as psychological interpretations of data. There are a number of limitations of thematic analysis. These include:

A loss of context due to a focus on the written transcription. Stone (1997: 37) compares this to “reading song lyrics without having heard the song.” The researcher overcame this limitation by keeping a research journal which contained contextual notes. A loss of content as the researcher selects themes and then discards the rest of the information which does not pertain to the selected themes. Stone (1997: 38) refers to this as “systematically throwing away information” while Bird (2005: 237) recalls how as a novice researcher she used only the data that served her purposes, “and the rest of the life experiences I discarded.”

3.9. Limitations

In this research study, the following methodological limitations apply:

This study focused on a specific group of TESOL teachers at a particular point in time. The research study is thus a cross-sectional study. It is static and time-bound. It would have been interesting to conduct the same study longitudinally, that is, to measure burnout levels at the beginning of a year using the MBI-ES and semi-structured interviews and then to measure burnout again at the end of the year. Thus one could see whether after the period of one year, without any interventions to assist those suffering from burnout, if participants experienced higher levels of burnout as measured by the survey and in the semi-structured interviews. One, therefore, could gain rich comparative data if the study were conducted over a period of time;

It was not possible for the MBI-ES survey to be administered anonymously. This was due to the schools being in different locations and the fact that the participants could not be gathered as one group in order for the MBI-ES to be administered. Thus the fact that the MBI-ES was not anonymous and the researcher knew who had filled in each survey may have affected the responses. It is possible that teachers who were experiencing burnout falsified their responses out of concern that the results may be made available to their supervisors, despite reassurances that the results were confidential. For many people, being burnt-out carries a stigma and that, along with the very real need to hold on to one’s job in the TESOL industry, could have affected the final number of participants for the semi-structured interviews. Thus there may have been more than twenty teachers suffering from burnout out of the forty-three teachers who participated in the study; The researcher initially struggled to convince the various language schools to participate in the research study. Most of the schools were concerned that the research study would show them in a negative light. This is the reason that all the schools involved in the study chose to remain anonymous; and

Overall, a small number of participants, forty-three, took part in the research study. The total number of potential participants was eighty-seven. There were a number of reasons why teachers did not want to participate in the study such as a lack of time to commit to participation, concerns about school administrators and management accessing the results or recognising the statements of the participants in the semi- structured interviews and a concern that involvement in the study meant that others would think the participants were actually experiencing burnout.