Qualitative data analysis process

The qualitative data provided a much richer and more in-depth understanding and exploration of meanings than could be derived from the numerical data collected at the quantitative phase. As mentioned before, the challenge is not so much generating data but rather generating useful, valuable data, relevant to the question(s) being asked (Richards, 2005).

As indicated in the previous section, in order to conduct a rigorous qualitative data analysis which ultimately will be convincing and persuasive to policymakers and other researchers, both constant comparative method in grounded theory and thematic analysis, which are methodologically similar analytic frameworks, were used in this study to address the research questions. As both approaches bear similar concepts and coding process, and both aim to identify emerging patterns or themes from the data mass, they are often used in data analysis interchangeably. As mentioned in the Methods Chapter, “data analysis is a systematic search for meaning” (Hatch, 2002, p. 148) and it is through data analysis that the researcher aims to elicit the descriptions and theories, which are grounded in reality. Thus, through systematic data analyses, themes that emerged from the generated data, and the relationships identified among these themes, provided the researcher with an explicit understanding of the participants’ perceptions in relation to the issues addressed in this study, and helped to achieve the five research objectives.

However, during the process, constant comparative method was also used in the initial coding.

The stages of data analysis employed in this study were as follows: firstly, the audio recorded interviews were transcribed and the interview transcripts were combined with the participants’ answers to the open-ended question in the questionnaire. Secondly, the transcripts were imported into NVivo. Thirdly, the researcher read through the responses given by both the teaching staff and the students thoroughly in order to identify emerging themes within the texts. At this stage, with the aid of several functions in NVivo – such as word frequency search, text search, and querying abilities – the researcher was able to collect and display key words and phrases and similarly-coded data for examination (Saldana, 2009), ultimately accomplishing a thorough iterative analytic process. Finally, the researcher wrote up the report based on the results.

5.2.1.1 Thematic analysis

Thematic analysis is one of the most commonly used methods of qualitative data analysis. Essentially, thematic analysis is a categorising strategy. Researchers use thematic analysis as a way of getting close to their data and developing some deeper appreciation of the content. Thematic analysis enables the use of a wide variety of types of information in a systematic manner that increases the accuracy or sensitivity of the research and increases the researcher’s understanding about people, events and situations (Boyatzis, 1998). It is the most useful method in capturing the complexities of meaning within a textual data set. Braun and Clarke (2006) argue that “thematic analysis is a method for identifying, analysing and reporting patterns (themes) within data” (p. 79), and for providing a “detailed and nuanced account” (p. 83) of the identified themes.

Thematic analysis is a process of encoding qualitative information (Boyatzis, 1998). According to Boyatzis, encoding can be theory driven, prior research driven, or data driven. For this study, data driven coding was used with the aim of reflecting the meanings contained within the raw data as closely as possible (Boyatzis, 1998; Braun & Clarke, 2006). Thematic analysis requires involvement and interpretation by the researcher. Researchers review their data, make notes

and start to sort them into categories. Thematic analysis helps researchers move their analysis from a broad reading of the data towards discovering patterns and themes.

In thematic analysis, “a theme captures something important about the data in relation to the research question and represents some level of patterned responses or meaning within the data set” (Braun & Clarke, 2006, p. 82). Themes are likely to be discovered through engagement with the literature, prior experience of the researcher, and the nature of the research question (O'Leary, 2004). In relation to the process of engagement with the transcripts and texts that make up a

researcher’s raw data, O’Leary (2004) asserts that this textual engagement can happen at a number of levels: “qualitative data can be explored from the words that are used, the concepts that are discussed, the linguistic devices that are called upon, and the no-verbal cues noted by the researcher.” (O'Leary, 2004, p. 196) Moreover, in thematic analysis, the task of the researcher is to identify a set of themes or categories that adequately reflect the collected textual data. As with all qualitative analysis, it is important that the researcher is very familiar with the data if the analysis is to be insightful. Thus data familiarisation is a key to

thematic analysis as it is to other qualitative methods. The following is Braun and Clarke’s (2006, p. 87) step-by-step guide to the six phases of conducting thematic analysis:

• Becoming familiar with the data;

• Generating initial codes;

• Searching for themes;

• Reviewing themes;

• Defining and naming themes; and

• Producing the report.

5.2.1.2 Grounded theory

The second qualitative analytic method used in this study was constant

comparative method in grounded theory. According to Glaser and Strauss’s (1967) principles of grounded theory, theory emerges from the data in qualitative

research. Grounded theory is a method that aims to generate theory from data reflecting the lived experiences of research participants. Grounded theory is a set of inductive and iterative techniques designed to identify categories within text that are then linked into formal theoretical models (Glaser & Strauss, 1967; Strauss & Corbin, 1998b). Charmaz (2006) later described grounded theory as a set of methods that “consist of systematic, yet flexible guidelines for collecting and analysing qualitative data to construct theories ‘grounded’ in the data themselves” (p. 2). Grounded theory is mainly used to identify key themes and subthemes from interview data, in the process of which central themes become evident.

This study used grounded theory in order to investigate teaching staff and students’ perceptions and experiences of intercultural awareness in foreign language

teaching and learning in the University of Tasmania. For this study, in adopting a constructivist grounded theory approach to data analysis, as described by

Charmaz (2006), the constructivist researcher acknowledges that the theory that results is an interpretation, dependent on the researcher’s approach and

perspective (Charmaz, 2006). The purpose of grounded theory is “theory

construction, rather than description or application of existing theories” (Charmaz & Bryman, 2011, p. 292).

One of the most commonly used qualitative data analysis techniques is constant comparative analysis, developed by Glaser and Strauss (1967). The application of constant comparative method is a fundamental feature of grounded theory. The idea behind these contrasts and comparisons is to try to bring out what is distinctive about the text and its content (Gibbs, 2007). This method involves comparing like with like, to look for emerging patterns and themes as comparison explores differences and similarities across incidents within the data set (Goulding, 1999). The constant comparative analytic procedure is broken into units of

analysis, for example, a line of text or a paragraph or more. Then, the interpreted meanings among units come to be represented as categories. As the number of categories increase, the data are constantly compared, leading to more abstract categories until a central or core category is conceptualised. When it reaches the point that the categories are declared “saturated” (Glaser & Strauss, 1967) the

researcher can bring the study to an end. In terms of this study, in order to identify the participants’ main concerns, the researcher incorporated the constant

comparative method to code and analyse the transcript data. Comparing incident with incident, code with code and, later, subtheme with subtheme, resulted in the emergence of major themes and the development of preliminary concepts.

5.2.1.3 Coding process

Burns (2000) states that the first stage in analysing the interview data is coding, that is classifying material into themes, issues, topics, concepts and propositions. “Coding gives the researcher a condensed, abstract view with scope and

dimension that encompasses otherwise seemingly disparate phenomena” (Bryman & Charmaz, 2007, p. 266). Coding is the essential step of a thematic analysis. Additionally, coding is central to grounded theory, enabling the researcher to build rather than test theory (Strauss & Corbin, 1998b). It is an analytic process through which concepts are identified and their properties and dimensions are discovered in the data (Strauss & Corbin, 1998b). Similarly, Gibbs (2007) argues that coding is a fundamental analytic process for many types of qualitative research: “It consists of identifying one or more passages of text that exemplify some thematic idea and linking them with a code” (p. 54). Moreover, Richards (2005) succinctly states that “coding is not merely to label all the parts of documents about a topic, but rather to bring them together so they can be reviewed, and [the researcher’s] thinking about the topic developed” (p. 86). Moreover, Charmaz (2006) asserts that “coding is the pivotal link between collecting data and developing an emergent theory to explain these data.” (p. 46) Generally speaking, coding involves the breaking down, analysis, comparison, and categorisation of data. As mentioned earlier, the data management tool NVivo was used in this study because it is well suited to this type of analysis. It allows the researcher to manage text data that may be unstructured, assisting with the processes of indexing, searching, and theorising and also helps the researcher to examine features and relationships in texts (Creswell, 2005; Gibbs, 2002). As Burns (2000) points out in relation to NVivo software, “from the coding it will search for links among the codes and build a hierarchical network of code patterns, categories and relationships in the original data” (p. 437). It is a tool that enables a

researcher to demonstrate the integrity, robustness and, therefore, trustworthiness of an investigation (Smyth, 2006). In this study, several methods were used to identify major themes emerging from the data and to search for connections with the aid of NVivo. These themes were then refined during the analysis of the data, with data being linked by recognising substantive rather than formal relations between things (Dey, 1998). This type of approach was made to gain a deeper understanding of the dimensions of individual learners’ experiences in real life contexts from their own words (Glaser & Strauss, 1967; Morse & Richards, 2002; Strauss & Corbin, 1998a).

Saldana (2009) argues that “qualitative inquiry demands meticulous attention to language and deep reflection on the emergent patterns and meanings of human experience” (p. 10). Therefore, he describes coding as a cyclical act. Rarely is the first cycle of coding data perfectly attempted. The second cycle and possibly the third and fourth “of recoding further manages, filters, highlights, and focuses the salient features of the qualitative data record for generating categories, themes, and concepts, grasping meanings and building theory” (Saldana, 2009, p. 8). There is a diverse repertoire of coding methods generally applied in qualitative data analysis. This study adopted thematic analysis procedures and three cycles of coding were undertaken: initial coding, focused coding and theoretical coding (Charmaz, 2006; Saldana, 2009, 2011).

The first coding cycle was initial or open coding, performed in the initial stage, in order to discover, name and categorise phenomena (Strauss & Corbin, 1998a). In other words, it is used to identify key words or phrases which connect the

informant’s account to the experience under investigation (Goulding, 1999). It is an opportunity for a researcher to reflect deeply on the contents and nuances of the data and to begin taking ownership of them (Saldana, 2009). Charmaz (2006) advises that detailed, line-by-line initial coding is suitable for interview transcripts. Therefore, with computer-aided qualitative data analysis software NVivo (Version 10.0), the researcher read the transcripts word by word, line by line, incident to incident, and then dragged and dropped the relevant meaning units into the same coding group. This method also “permits the researcher to shift quickly back and forth between multiple analytic tasks, such as coding, analytic memo writing, and

exploring patterns in progress” (Saldana, 2009, p. 26). At this stage, constant comparison was applied as open codes need to be constantly compared in order to generate a conceptual code (Goulding, 1999). The researcher remained open to the raw data without having developed any pre-conceived codes (Glesne & Peshkin, 1992).

Following the first cycle of coding, the second cycle of coding, focused coding was undertaken. According to Charmaz (2006, p. 57), “focused coding means using the most significant and/or frequent earlier codes to sift through large amounts of data.” The researcher searches for the most frequent or significant initial codes to develop the most salient categories in the data corpus and requires decisions about which initial codes make the most analytic sense (Charmaz, 2006; Saldana, 2009). The goal of this method is to develop categories, without

distracting attention to their properties and dimensions, and to build and clarify a category by examining all the data and their variations (Charmaz, 2006; Saldana, 2009).

The final cycle is theoretical coding, which involves a process of integrating and refining the categories into theories. All categories and subcategories now become systematically linked with the central/core category, the one that appears to have the greatest explanatory relevance for the phenomenon (Saldana, 2009; Strauss & Corbin, 1998b). Theoretical coding integrates and synthesizes the categories derived from coding and analysis to create a theory (Saldana, 2009). This coding cycle eventually progresses analysis to a conceptual level. At this cycle, categories developed from initial and focused coding can be applicable to all cases in the study (Saldana, 2009). It is crucial to keep in mind that “the purpose and outcome of data analysis is to reveal to others through fresh insights what [has been] observed and discovered about the human condition” (Saldana, 2011, p. 89). Figure 5.1 demonstrates how the qualitative data is analysed in a systematic way.

Figure 5.1 Qualitative data analytic process

Both thematic analysis and constant comparative method present challenges for the researcher. For example, both methods demand the researcher’s time and energy (Boyatzis, 1998). In addition, the researcher is the main tool for analysis (Denzin & Lincoln, 2000), meaning that the software used in the process of analysis does not actually code the data; that task remains the responsibility of the researcher (Saldana, 2009). For this study, data were coded, themes were

identified, theories were built and analysis undertaken by the researcher, and then discussed with research supervisors. The next two sections discuss analysis in relation to sections of the research instruments: open-ended questionnaire section and semi-structured interview questions.