Área De Recursos

“Linda: I have learned the most from analysing data.

Mike (supervisor): I agree totally. When you buried yourself in the data, you were particularly honest handling the data and not ignoring complexities. Some other people, in order to get things done, just ignore it. But if you allow the complexities to emerge and face them, then you are doing it better, you are researching better.” (Transcript of

supervision on 12th _{May 2009)}

Both raw quantitative and qualitative data need to be analyzed to make sense of the meaning they are conveying (Basit 2003). Quantitative data offers a broad picture and precision, whereas qualitative data presents details and depth (Basit 2010; Basit 2003). Due to the fact that this study has collected both quantitative and qualitative data, various analysis techniques have been applied.

SPSS software has been used to analyze the quantitative data. The questionnaire was carefully formulated taking data analysis requirements into consideration. Most of the questions were closed questions. Although there were some open questions, the required answers were only words or phrases. Hence the quantitative data was straightforward to analyze.

Before the qualitative data could be analyzed, the interviews had to be transcribed (one individual- and one group-interview transcript samples are provided as Appendix 6 and Appendix 7). I transcribed the interviews in Chinese, the language used when conducting the interviews. As mentioned before, all interviews were audio-recorded with permission from the interviewees. Although Mishler (1986:365) argues that audio-recording ‘filters out important contextual factors, neglecting the visual and non-verbal aspects of the interview communication’ which ‘frequently gives more information than the verbal communication’ (Cohen et al 2007:36-7), it was not considered appropriate to use video-recording for the current research. There are several reasons for this decision. First, I self-administered all the interviews. During each interview, I not only audio-recorded the conversation, but also took notes when I noticed anything occurring worth noting (i.e. when the interviewee laughed, smiled or appeared to be confused when watching the clips). Each interviewee has a profile, which includes notes which were taken during the interview and reflexive notes taken after the interview. Second, video-recording is much more intrusive than audio recording. People can easily become uncomfortable when realising their every

movement is being recorded. Hence, I decided not to video-tape the interviews. As discussed before, in the Ethical issues section, as well as in the Validity and Reliability section, research needs to be carried out in an ethical way. Bias should be minimised. The use of video might risk interfering with interviewees too much, because they would be very aware of the camera’s presence, and as a result, the data collected could have less openness or honesty. In addition, if using video makes respondents uncomfortable, it is not an ethical way to conduct research. Plus, it would not preserve the anonymity of the interviewees as easily. Most importantly, I listened to the audio-recording and took further notes right after each interview when my memory was still fresh. I took all the actions possible to ensure the comprehensiveness of the data.

4.7.1 Grounded Theory

“Linda: I’m not going impose any theories on my data. I want to put what I have read before aside and just focus on my data to see what my data tells me.

Mike: Yes, let the data speak for itself.” (Transcript for supervision on 23rd _{January 2008).}

Grounded theory has been applied in the process of data analysis. In particular, qualitative data has been analysed in the spirit of grounded theory. That theory emerges from the data, instead of the data analysis being directed by existing theory, is a significant feature of grounded theory (Glaser and Strauss, 1967). As discussed before, the purpose of the qualitative data is to answer the core Research Questions 3 and 4 which are, in nature, exploratory. It would not be true to the findings to force any predetermined theory onto the data. It was considered most appropriate to let the data speak for itself, and to develop a theory that is grounded in data which is systematically gathered.

Hence, throughout the data analysis process, the four features of grounded theory summarized by Cohen et al (2007:491) have been the guiding principles:

a) Theory is emergent rather than predefined and tested b) Theory emerges from the data rather than vice versa

c) Theory generation is a consequence of, and partner to, systematic data collection and analysis

d) Patterns and theories are implicit in data, waiting to be discovered

4.7.2 Bilingual Coding Techniques

Basit (2010:189) points out that ‘coding is another significant element of the grounded theory approach, the purpose of which is to break down and deconstruct the data to make sense of them and then to reconstruct and synthesize the data to consider the links, similarities and differences’. Miles and Huberman (1994:56) define codes as ‘tags or labels for assigning units of meaning to the descriptive or inferential information complied 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. They can take the form of straightforward category label or a more complex one.

In the process of coding the qualitative data, I used both English and Chinese. I used whichever language that I felt could best describe what was in my mind and deliver my meaning. There are three reasons why this happened. First, the interviews were conducted in Chinese. Transcriptions are in Chinese. Automatically, the words or phrases used for coding would be Chinese. Second, although transcriptions are in Chinese, the majority of the literature is in English and this thesis is written in English. Third, due to the fact that I had spent considerable amount of time in the U.K. where the daily language for communication and study had been English, I sometimes thought in Chinese and sometimes in English. Before I came to the U.K., I always thought in Chinese. This is perhaps due to the improvement of my English proficiency.

During the data analysis process, Nvivo software was used to assist the organization of texts, codes, sub-categories and categories, instead of the traditional cut and paste method. Nvivo was only used to help organize the data. The software was not used in the analysis.

Although the qualitative data focuses on a smaller number of people than the quantitative data, the data collected was very detailed and rich. Cohen et al (2007:461) state that there is no single or correct way to analyse qualitative data. In addition to the four principles of grounded theory presented in previous section, the principles of fitness for purpose and always keeping research

questions in mind have been guiding me through the data analysis.

Due to the fact that no existing theory was imposed on the data, which is extremely rich and complex, the qualitative data analysis process was very time-consuming. I tried two different approaches. At the beginning, I tried to treat the data as a whole and looked for patterns and categories. This turned out to be inappropriate approach, because the individuality of each participant was largely sacrificed, which ended up losing valuable findings. Hence, it was decided to combine two different approaches, treating each interview data separately, but meanwhile looking at data as a whole to see broad categories.

Several coding techniques have been applied during the analysis process, namely, open, axial, selective and constant comparison, all of which are recognized as key coding methods in grounded theory (Basit 2010). Constant comparison, which was referred to as the core method of analysis by Glaser and Strauss (1967), has been extremely helpful in my case. Strauss and Corbin (1990) state that the function of the constant comparison method is that it helps the research break through assumptions and uncover new dimensions. By using constant comparison, not only

did categories become clear and emerge from the data, but I could also draw on my personal experience and professional knowledge, as well as relevant literature on the subject. In particular, by drawing on personal experience, I came to understand myself better and better, and, meanwhile, also understood data better and better. As Walford (2001:98) points out, “all research is researching yourself”.

During the data-analysis process, I adopted Mason’s (2002) three types of data-reading- approaches, namely literal reading, interpretive reading and reflexive reading. These techniques have been very helpful, especially when I struggled with coming up with categories.

I have also come to realise that there is not always one single meaning of a text. As Cohen et al (2007) point out, texts carry many layers of meaning, requiring researchers to exert a great amount of effort in striving to catch them. Interpretation usually happens simultaneously with data analysis, especially during the coding process. At the initial stage of data analysis, I ended up having more than 300 codes. In the process of categorising and grouping the codes, it became apparent that it was not a straightforward process. Some of the texts can have more than one code. Some of the codes seem to belong to more than one category. There is not always a clear- cut division between categories. Many of them are inter-connected. The final lists of categories under two themes, make sense (RQ3) and effect (RQ4) are presented below. (A list of codes from which the categories were developed is provided in Appendix 8.)

make sense

comprehension compare: identify difference

compare: identify similarities

Re-

contextualization Perceiving as realistic

effect understanding of and attitudes to self/significant indiviudals understanding of and attitudes to groups of Chinese in general understanding of and attitudes to generalized people represented by fictional character(s) reinforcing / challenging philosophy of life

The role that I have been playing during the journey of data analysis has gradually drawn my attention. The research subjects were Chinese students who were still studying in China. I am a Chinese researcher who has been studying and living in the U.K. for several years. Being older than the research subjects, having received higher education, and having more life experience both in China and the U.K., has made me able to have more insight into the clips than my subjects. This allowed me to understand and evaluate the CCSs’ interpretation of the clips and identify any misunderstandings which are culturally specific. However, sometimes it occurred to me that, although I have been looking at the codes that emerged from the data as the other, and trying to distance myself and look at the data as an outsider, the Chinese culture deeply rooted within me caused me to experience a reaction towards a couple of the clips similar to my research subjects. This sometimes made me feel like I was switching places.

4.7.3 Bilingual Data Presentation

Berreman (2004) suggested that it is not possible to literally translate words between cultures, because languages and people are shaped by their cultures. My data analysis experiences agree with Berreman’s view. As mentioned previously, all interviews were conducted in Chinese, and hence the Chinese transcripts. In addition to the bilingual-coding during the data analysis process, I presented selected text extracts both in Chinese and English for my two supervisors in each supervision session. Those sessions turned out to be very useful, because we identified and tackled several translation difficulties thanks to the input from my bi-lingual and bi-cultural supervisor-team. In addition, in the process of writing up data analysis, I gradually realised an issue in regard to translation. Translating from one language to another is like cooking a foreign dish. Sometimes, although all the right ingredients are used, the taste might not turn out to be the same. Hence, I agree with Gonzalez and Lincoln (2006) in terms of the importance of presenting the data in its natural language, and the importance of making data accessible for readers of the language.

Consequently, making the results accessible in the multiple languages, will give readers the option of the original language of the data along with the “presentation” language (p.34).

As the current study is mainly concerned with using films and TV series produced in the U.K. and the U.S.A. in ELT in the Chinese context, it is safe to assume that the readers will be able to speak either English or Chinese or both. Hence, it was decided that the text extracts be presented both in Chinese and English in the data analysis chapters.