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In this exploratory study, data analysis proceeded from qualitative data analysis to quantitative data analysis. Complementary rather than contradictory, both qualitative data analysis and quantitative data analysis work together to triangulate and confirm each other, thus answering the research questions raised, and enhancing the validity and reliability of the present study.

4.7.1 Qualitative Data Analysis

For qualitative data analysis, Interpretative Phenomenological Analysis (IPA) was employed to explore the health and well-being of Chinese international students. Smith, Flower and Larkin (2009) elucidated that IPA pays respectful attention to a person’s direct experiences by encouraging participants to tell their own story in their own words. IPA involves the detailed examination of the “lifeworld” of the participants, coupled with their experiences of a particular phenomenon. According to Todres, Galvin and Dahberg (2007), lifeworld allows to better understand human experiences in relation to temporality, spatiality, intersubjectivity, embodiment, and mood. Lifeworld may “act as a benchmark for understanding health and illness” (Todres, Galvin, & Dahberg, 2007, p.60) as it provides “the holistic context for understanding quality of life” (ibid. 2007, p.59).

Firmly anchored to key phenomenological understandings of live experiences, IPA researchers are urged to “adopt a sensitive and responsive approach to data analysis”

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(Larkin, Watts, & Clifton, 2006, p.108). The experiences of participants are viewed as context-dependent and contingent upon social, historical and cultural perspectives (Eatough, Smith, & Shaw, 2008; Smith, Flower, & Larkin, 2009). Personal stories are not only individually situated, but also living in a social and cultural context with active social interactions (Eatough, Smith, & Shaw, 2008). Motivated by humanisation, IPA researchers put a high value on narrative truth. Grounded in qualitative experiences of people, “a perspective can be opened up and pursued through intense curiosity about the descriptions of others’ experiences” (Todres, et al. 2007, p.59).

As such, IPA is an approach to qualitative research concerned with exploring and understanding the lived experience of a specified phenomenon (Smith, 2004). IPA acknowledges that the researcher’s role of interpretation and engagement and through a careful and explicit interpretative work, it becomes possible to gain an access to an individual’s cognitive inner world. Different from discourse analysis (DA) which emphasizes the role of language and the related interactions (Smith, Jarman, & Osborn, 1999), IPA allows an exploration of idiographic subjective experience and more specifically, social cognitions (Biggerstaff & Thompson, 2008). The key is to make sense of these experiences, and attach meanings to them (Smith, 2015).

With respect to data analysis, IPA revolves around thorough familiarity with the text through reading and repeatedly re-reading (Smith, Jarman, & Osborn, 1999). Notes and reflections from the researcher were noted in the reading and re-reading process on the transcripts. Though the transcriptions of interviews were read by the researcher several times, each time has a different aim. For example, the first round of reading aimed at having an overall picture of their stories without labelling the

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content. The second round of reading of the transcription was conducted while the researcher attempted to label and highlights the key component. After further reading in the third round, the researcher assigned labels on the recurring main themes and started coding. The following seven steps were used to code and analyse the qualitative data collected.

Step 1: Listen to the recordings repeatedly and achieve a better understanding of the interviewees’ intentions (Hammersley, 2010; Qu & Dumay, 2011; Tessier, 2012). While reading the text, the researcher attempts to suspend presuppositions and judgments so as to focus on what is actually presented in the transcript data (Biggerstaff & Thompson 2008).

Step 2: Transcribe the interviews verbatim and code the transcripts with identifiers (Smith, 2004) such as ‘academic stress’, ‘friends support’, ‘dietary change’, ‘weather adaptation’, ‘language deficiency’ and ‘university service’. Initial notes were marked on the transcript. Then put a transcript aside and continue to code another one. If the identifier is similar to (or the same as) others, the researcher grouped them together.

Step 3: Develop broader descriptive categories such as sociocultural adaptation, psychological adaptation, and social support. Sort the categories identified into central categories (Willis, Jost, & Nilakanta 2007). The researcher reflected on her past and current experiences so as to keep the meaning of her own personal experiences separate from those revealed by the participants (Fischer, 2009).

Step 4: Code, sort and scrutinise data and reduce the number of categories by topical headings (Braun & Clarke, 2006). At this stage, the researcher needs a suspense of

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critical judgment and a temporary refusal of critical engagement which would bring in the researcher’s own assumptions and experiences (Tufford & Newman, 2012).

Step 5: Select a topical heading to represent the main theme and develop a systematic analysis of the data, exploring meanings embedded within the personal interviews (Richards, 2003).

Step 6: Describe, interpret and theorise the data. Ask expert-researchers to inspect them and then double-check the similarities and differences (Guest & Bunce, & Johnson, 2006; Willis, Jost, & Nilakanta, 2007).

Step 7: Check for consistency in the findings, probing the possible reasons for health and well-being service/choice, and identifying any particular features/patterns in the qualitative data analysis and reporting.

Data coding was used as “a means to generate concepts” and “taken as part of the process of analysis” (Gough & Scott, 2000, p.339). Qualitative data was broken down into discrete parts as the researcher goes through the data. At this stage, rich amount of qualitative data was segmented into analysable units. Through coding and categorising each interview transcript, the qualitative data from the semi-structured interviews were transformed into an appropriate form for qualitative data analyses.

While reading the text, the researcher attempts to suspend presuppositions and judgments in order to focus on what is actually presented in the transcript data. This involves the practice of “bracketing” (Hamill & Sinclair 2010), which refers to the

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‘laying aside’ of the researchers’ reflecting on their past and current experiences so as to keep the meaning of their own personal experiences separate from those revealed by the participants (Fischer 2009). As a Chinese international student, the researcher had the past seven years studying as a BA, MSc and Doctoral candidate in the UK universities, and this experience enables the researcher to interpret the data as an ‘insider’. However, the researcher is also aware of the individual differences in terms of experiences and interpretation of this experience, to ensure the data analysis is based on the participants’ words rather than from the researcher. What this involves is the suspension of critical judgment and a temporary refusal of critical engagement which would bring in the researcher’s own assumptions and experiences (Joshi, Pandey, & Han, 2009). As IPA acknowledges a role for interpretation, the concept of bracketing is somewhat controversial and in any event gives way to a more interpretative process as analysis proceeds.

Data reduction was achieved gradually through constantly comparing the meanings in the labels, themes and sub-themes. Each interview was transcribed by the researcher verbally to ensure the transcription fully captured the meaning conveyed in the interviews. Transcription was basically conducted immediately after each interview, usually within one week. All interviews were conducted mainly in the Mandarin Chinese language, and interviews were transcribed in simplified Chinese (For more details see Chapter 5).

4.7.2 Quantitative data analysis

For quantitative data analysis, the statistical package for the social science (IBM Corp, 2012), Version 22 was used to analyse the data from the questionnaires concerning the

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health and well-being of Chinese international students. Cronbach’s alpha tests, multiple regression tests and hierarchical regressions, coupled with Pearson’s product moment correlation tests were carried out. Furthermore, issues of mediations (such as sociocultural adaptation mediating the association between health status and well- being) and moderations (such as academic stress moderating the relationship between social support and well-being) were investigated. For more specifics, please see chapter 6.

Reliability and validity

Reliability was viewed as “a prerequisite for measurement validity” (Schutt & Chambliss, 2006, p.121). In the present study, reliability was emphasised because it entailed ‘the consistency and replicability over time, over instruction, and over groups of respondents’ (Cohen, Cohen, West, & Aiken, 2013, p.117). DeVellis (1991) proposed a guideline used to find out the satisfactory level of reliability, when there are more than 100 samples, the level of reliability can be deduced based on the following: 1) above 0.90 is considered as strongly reliable; 2) between 0.80 and 0.90 is considered as highly acceptable; 3) between 0.70 and 0.80 is considered as acceptable; 4) between 0.65 and 0.70 is considered as minimally acceptable; 5) between 0.60 and 0.65 is considered as undesirable; and 6) below 0.60 is considered as unacceptable.

In the present study, the Cronbach’s Alpha formula utilizing the SPSS package was used as a measure of the internal reliability coefficient for the research questionnaire. To determine the internal consistency of various measuring instruments in the questionnaire, the Cronbach’s Alpha was run, showing that the reliability of the

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overall questionnaire was .91, which was considered as strongly reliable. The Alpha value for measuring items related to well-being (dependent variable) was .77 (Section 2 of the questionnaire). For the self-developed questionnaire on cultural health belief, the Alpha value was .73. With regard to the psychological adaptation questionnaire, the Alpha value was .67. Regarding sociocultural adaptation questionnaire, the Alpha value was .73. For academic stress and social support, the Alpha value was .78 and .76, respectively.

Validity is viewed as an important aspect of research standards especially for quantitative studies (Bryman & Bell, 2007) which includes content validity and construct validity (Snape & Spencer, 2003). As it determines if the data collection tool or method is measuring what it meant to measure (Mack, Woodsong, MacQueen Guest, & Namey, 2005). However, the validity of mixed research is still in the stage of infancy, as it combines strengths and weakness of quantitative and qualitative research, assessing the validity of findings is “particularly complex” (Onwuegbuzie & Johnson, 2006, p.48). Although the framework for assessing legitimation in mixed research is incomplete yet, certain strategies haven been adopted in the current study to ensure the precision in which the findings accurately reflect the data. First of all, the researcher is aware of the multiple realities, potential bias from samples and the researcher’s interpretation has been addressed. Participant’s excerpts were presented in Chapter 5 with sample coding available in the Appendix VI, to ensure the participants’ views are clearly and accurately presented, thus truth value are ensured. Consistency and applicability of the data are further reached by the triangulation.

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