Data Analysis Process 163 

Quantitative research employing a questionnaire uses closed questions. This is not only to provide a structure for a subsequent interview in qualitative research mode, but also to provide choices for participants to make according to the preset response categories, such as a range from ‘strongly agree’ to ‘strongly disagree’ with corresponding scores attached. Hence, the collected data are numerical in nature and can be conveniently collated for subsequent data analysis. On the other hand, qualitative research such as by interview allows the exploration of values, meanings, beliefs, thoughts, experiences and feelings that are characteristics of the phenomenon under investigation (Halcomb & Andrew, 2005). In this paradigm, data analysis relies very much on verbatim transcription of the interview data as the initial data management. However, in view of the costs incurred in verbatim transcription in respect of time, effort and money, as well as the potential for human errors of various kinds, there are arguments against such a practice. In this regard, the costs have to be weighed against the potential benefits of making a verbatim transcription as part of interview data management. To address these issues, Halcomb and Davidson’s (2006) analysis of the case for and the case against verbatim transcription are worth studying by any researcher for the data management of their qualitative research. The two writers also suggested using an alternative process for managing interview data in addition to using a conventional verbatim transcription technique provided that the underlying philosophy of the methodology of a specific investigation has been adequately matched with this strategy.

Quantitative Analysis. After obtaining data from the questionnaires completed by the participants, I entered all the data into an electronic file to work with the specialised software SPSS (version 19) for data analysis. Given the nature of the data collected, the information obtained from the scores allowed running the appropriate tests for data processing. As this is a longitudinal study, data collected at UNE and UML would be

merged together to form two big groups at two time-points, T1 and T2. Descriptive statistics, such as frequency counts, means and standard deviations, were employed in the data analysis process and T-tests and correlations were used for subsequent data analysis. It may be argued that the use of a non-probability sample such as the convenience sampling in this study does not comply with the fundamental assumption of statistical tests (Cohen et al., 2011; Bryman, 2012). Nonetheless, it has also been recognised that educational research can rarely allow costly randomisation (Mertens, 1998). In this regard, it is a common practice for researchers to employ statistical tests to deal with non- probability samples. Furthermore, in this study, key factors have been considered in sampling in the hope that fairly representative samples could provide data on major variables exhibiting near-normal distribution. A T-test is often preferred in practice as it is a more powerful technique given that equal differences between scores within each scale can be assumed. The level of significance for this study was set at p<0.01. Correlation tests were run to examine the relationships between all the PP variables, the relationships between participants’ PP variables and their perceived engagements of LLA, and the relationships between participants’ PP variables and their perceived LP. Cronbach’s alpha is used for measuring internal consistency in order to see how a set of items is closely related as a group for each variable. In this current study, I made use of the Cronbach’s alpha coefficient of each PP variable to verify that its questionnaire items were good enough to support the PP variables concerned.

Qualitative Analysis. Interviews have been regarded as a common method for a qualitative approach to data collection in a range of disciplines such as sociology and health care as participants and researchers can have the advantage of an interactive dialogue (Burnard, 1994; Fasick, 2001; Fielding, 1994; Teddlie & Tashakkori, 2003; Wellard & McKenna, 2001). Regarding the management of interview data, many researchers have reported that they transcribed audio-recorded interviews into written text

for subsequent analysis, but detail about the data management and the actual process of transcription are very often not sufficiently described (Poland, 1995; Wellard & McKenna, 2001). As well as reproducing spoken words from an audio-recorded interview into written text, various researchers have argued the need to incorporate non- verbal cues such as silences, body language and emotional signs into the transcribed text (MacLean et al., 2004; Wellard & McKenna, 2001). Conventionally, the transcription of spoken words from an audio-recorded interview into written text refers to verbatim transcription in which the written text contains word-for-word replication of the audio- recorded words (Poland, 1995). That being the case, Poland (1995) posited that accuracy of transcription is at issue given the “inter-subjectivity of human communication, and transcription as an interpretative activity” (ibid. p. 292). A transcriber plays a pivotal role in the process of transcription (MacLean et al., 2004). As transcription is part of the management of the data analysis process, it should be clearly described in the methodology of a project (Wellard & McKenna, 2001) so that the underlying philosophy of the methodology of a specific investigation can appear to have been well supported.

As stated above, verbatim transcription should be combined with recording of participants’ non-verbal behaviour which has been regarded as a foundation for the reliability, validity and honesty of qualitative data collection (MacLean et al., 2004; Seale & Silverman, 1997; Wengraf, 2001). Rarely do researchers choose the use of ‘selective’ transcription, and discussions about how this is done are limited (Gilbert, 2008). Nor have any researchers succeeded incredibly demonstrating that the creation of a verbatim transcription of an audio-recorded interview is superior to other methods of interview data management (Britten, 1995). Halcomb and Davidson (2006, p. 40) stated that “in research underpinned by theoretical frameworks such as phenomenology, grounded theory … closeness between researchers and the text is critical to the research design and philosophical tenets of the methodology”. They therefore suggested that “ … a verbatim record of the interview is clearly beneficial in facilitating data analysis by bringing

researchers closer to their data” (ibid. p. 40). In the circumstance of a generic type of mixed-method research, however, the ‘relationships and closeness’ between researchers and their data are not regarded as critical. Halcomb and Davidson (2006) argued that the case for verbatim transcription is that it could be used to provide an avenue for audit purposes by supervisors or independent assessors.

However, in view of the significant potential for errors in verbatim transcription (MacLean et al., 2004; Poland, 1995), cross-checking should be applied to the original audio-recording rather than relying on a potentially error-filled verbatim transcript (Fasick, 2001; Poland, 1995). Even when transcription was carried out by professional transcribers, a study has reported that around 60% of the passages contained significant transcriber errors (Poland, 1995). It might be argued that researchers could carry out the task by themselves having regard to their first-hand knowledge of the interview and the process involved, such as verbal and non-verbal transactions with the participants. So the claimed benefits must be weighed against the need to possess the advanced clerical skills required for properly undertaking an accurate transcription (Halcomb & Davidson, 2006).

Clearly there are significant costs related to verbatim transcription in terms of time, physical resources and human resources. Britten (1995) reported that it requires six to seven hours of transcription for each hour of audio-recorded interview. Many researchers accept that technical dilemmas are associated with the time-consuming process of verbatim transcription (Fasick, 2001; Wellard & McKenna, 2001). Human errors of various kinds are not uncommon, such as the misinterpretation of contents, classes and cultural differences, not to mention language errors of various kinds (Easton

et al., 2000; Gilbert, 2008; MacLean et al., 2004). Such additional factors would add

substantive time and human costs to the research process (Wellard & McKenna, 2001). To address these issues, the use of written field notes has been reported to be superior to the exclusive use of verbatim transcription based on audio-recording (Fasick, 2001; Wengraf, 2001). Other researchers have suggested keeping a reflexive journal in order to

carry out a sound reflective process which could in turn enhance researchers’ capacity to support their reflexive attitude (Tufford & Newman, 2012). Also, the challenges inherent in verbatim transcription and subsequent coding reduce the value of the practice of data collection (Fasick, 2001). Wengraf (2001) suggested the significance of memoing and on-site note taking to facilitate the reflection of researchers’ perceptions and interpretations in the course of listening to the audio-recording of an interview.

Given that the interpretations and generation of meanings from interview data are the major aims of transcription, the genuine need for verbatim transcription in all qualitative research projects is definitely questionable (Halcomb & Davidson, 2006). In this context, there are definite merits in audio-recording interview data (Fasick, 2001):

• It could be used for a subsequent review of an interviewer’s performance;

• It assists interviewers to fill in gaps in their field notes and check the connection between the notes and the actual exchanges;

• It allows interviewers to have self-reflection as to whether the meanings generated by participants are sufficiently represented and thus reduce interviewer bias; • It acts as a piece of evidence to certify the actual conduct of interviews and that

the interview data are truly and accurately represented by a researcher as reported; • It avoids the likelihood of having to contact participants if there is a need to verify

data authenticity;

• It could be referred to for clarification of intended meaning should there be any ambiguity of meanings or areas of inconsistencies arising;

• It allows researchers to look into fine detail of the conversation such as voice and tone of participants to assist in the finer analysis of interview data; and

• It provides researchers with illustrative examples for a report write-up or for publication.

Several researchers have supported an assertion that the use of analysis techniques such as thematic or content analysis which seek to identify common ideas from interview

data actually does not require verbatim transcripts. This is because verbatim transcription is only one of the methods for capturing interview data (Seale & Silverman, 1997; Silverman, 1993; Van Teijlingen & Ireland, 2003). Halcomb and Davidson (2006) proposed an alternative method of data management for those investigations which do not need a specific closeness between researchers and the interview data. They suggested that a reflexive, repetitive process of data management can be practised, as presented in the following steps:

Step 1. Audio-recording of an interview and concurrent note taking – this is to

note down in broad terms the researcher’s impression of interactions with participants, which will allow the researcher to go into greater detail afterwards.

Step 2. Reflective journaling immediately after interview – while the memory

remains fresh, this is to allow researchers to review their field notes so as to enrich their initial impression of the interactions with participants including their major ideas or concepts raised.

Step 3. Listening to the audio-recording and amending field notes and observation

notes, as necessary – this is to let researchers check against their field notes following step 2 and amend them accordingly.

Step 4. Preliminary content analysis – this process is intended to allow researchers

to elicit common themes from the interview data. This could be done manually or through the use of various software packages such as NVivo.

Step 5.Secondary content analysis – this is to let a second researcher (for example,

a researcher’s supervisor) review what the researcher has done in terms of audio-recording review and field notes. Subsequently, the development of themes from the interview data could be validated.

Step 6. Thematic review – this final stage is to enable researchers to review what

established themes. Relistening to the audio-recording serves the purpose of identifying good examples for better illustration of the meaning of themes from the participants’ perceptions.

Clearly there are advantages in using this six-step data management technique for this current study. According to Braun and Clarke (2006), these advantages are that the process is much less time-consuming and much less labour-intensive, easy to learn and flexible to apply. It allows the summarisation of large amount of data, makes it convenient to highlight similarities and differences across data, and helps the researcher to identify consistencies and inconsistencies across data. There are, however, some disadvantages, as noted by Braun and Clarke (2006). These could be issues arising from the quality of the conduct of analyses or the formulation of research questions, the data being too broad leading to difficulties in focusing on the right aspects of the data; difficulties in retaining a sense of continuity, and contradiction between individual items of data, unlike the narrative approach.

In this current study, I had taken into consideration all of the rationales and arguments set out above regarding interview data management and the underlying philosophy of the methodology of this study. I decided that I would perform audio- recording of the interviews, and concurrent onsite note taking, reflexive journaling, observations and all the other steps of interview data management described in the six steps above. As Cantonese is my first language, I was confident that I would handle well those interviews which were conducted in Cantonese. Regarding those participants who spoke in Mandarin, I could seek help from my Mainland Chinese peer who comes from Guangdong and both his parents are Mandarin and Cantonese speaking. Hence, with the help of my Guangdong peer, I was able to obtain accurately translated expressions as given by those Mandarin-speaking participants following the audio-recording of the interviews. Based on the field notes which I had jotted down, all the essential Cantonese and Mandarin verbal exchanges were translated into English and then my Guangdong

peer offered help again in double-checking all of the points made by re-listening to the audio-recordings of the interviews together with me for a second time. In this way I was able to make all the amendments necessary. Clarifications of intended meanings were therefore achieved and areas of inconsistency were considerably minimised.

For step 4 in the six-step interview data management process, I used NVivo (Gibbs, 2002) to help me to carry out a preliminary contents analysis of the data with a view to generating themes and sub-themes from the interview data. Using NVivo helped me to examine possible relationships between themes and sub-themes and enabled me to index segments of text to particular themes, linking interview notes to coding and performing complex search-and-retrieve operations. It should be emphasised at this juncture that NVivo could not make any kind of judgement for me. Rather, this software enabled me to work efficiently and effectively with large amount of written text and the subsequent complex coding in the process of interview data analysis. With the help of NVivo and based on my understanding and interpretations in the interviews, various comments made by the participants were highlighted and put into the five major categories, with one question per category (see Appendix IV). In addition, after I had gone through the six-step data management process described above, the compiled interview data were further divided into nine sub-categories with their corresponding items identified in each sub-category. In this way, the results could be precisely and concisely presented.