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Data analysis is described as the process of bringing order, structure and interpretation to a mass of collected data (Rossman & Rallis, 2003, p. 207). In qualitative research, data analysis begins with and continues alongside data collection (Merriam, 2009). Strategies for analyzing data may vary according to the research focus, purpose and data collection strategies (Miles & Huberman, 1994) but are primarily a process of searching for general statements about relationships and underlying themes and trying to make sense of the information gathered (Patton, 2002). In this study, all qualitative data sources including documents, interviews, field notes, comments on the observation sheets and open- ended responses in the survey were analyzed using thematic content analysis which looked for themes across the data set based on content. According to Boyatzis (1998), themes may be initially generated inductively from the raw information, or generated deductively from earlier work such as theories, models and literature reviews. The qualitative data analysis process generally followed what Rossman and Rallis (2003) described as typical analytic procedures consisting of seven phases: (i) organizing the data, (ii) immersion in the data, (iii) generating categories and themes, (iv) coding the data, (v) offering interpretations through analytic memos, (vi) searching for alternative understandings, and (vii) writing the report. However, the researcher decided to combine some steps in the process of analyzing data.

(i) Preparing and being immersed in the data

The first step in organizing the data involved data transcription and data translation. Since both English and Vietnamese were used during the qualitative data collection phase, necessary data were translated from Vietnamese to English and represented in the thesis. The translated texts were sent to be certified by a professional translation body. Besides transcribing and translating the interviews, the researcher read the field notes, listened to the observation recordings, and reviewed the documents to get a general impression of the data. Notes were then written in the margins of the data sources to identify the researcher’s preliminary thoughts about the data. Once data from each interview and observation were collected, they were organized prior to each subsequent interview and observation. This process continued until all interviews and observations were conducted.

After organizing the data, the researcher spent time reading over each transcript, interview notes, documents, observation sheets and trainee teachers’ open responses to familiarize herself with the data sources. At this stage, some fillers, stutters, probe cues and repetitions that did not seem to add anything different to the data were edited or removed. For example, words or phrases like “yes”, “mmm”, “well”, “then”, “you know” were deleted. At this stage, the researcher further noted themes and patterns that seemed to be emerging from the data.

(ii) Coding the data to generate categories and themes

Once all data sources were collected and prepared, they were coded by “making notations next to bits of data that strike you as particularly relevant for answering your research questions” (Merriam, 2009, p. 178). The purpose in the formal coding process was to begin to construct categories. Subsequently, the researcher reread each data source and coded, or labelled, highlighted and grouped each bit of information as making a particular point relevant to the study. Coding of those qualitative data directed the researcher to pay attention to certain variables in the survey analysis.

Categories and themes were then developed based on the codes to “make sense of the data” (Merriam, 2009). The term “category” refers to a broader heading under which several codes may be grouped and “theme” means a major topic (in this case major topic within the area of teacher education) under which a set of categories may be grouped. The development of categories and themes was done by creating file folders, each labelled with a category or a theme, which is visualized as “buckets or baskets into which segments are placed” (Marshall & Rossman, 2006, p. 159) based on the literature, or emergent from the data. This process also involved generating frequency counts of the dominant themes and comparing them to the study purpose and existing literature. For example, the concept “readiness to teach” is defined by various pre-defined qualities such as “personal attributes” and “professional practices”. Each sub-concept is itself defined by further pre-defined features. For example, “personal attributes” is defined by attributes such as “motivation”, “interest”, “confidence”

and “willingness” while “professional practices” is characterized by “knowledge” and “skills” (see Figure 3.1).

Figure 3.1. An example of identifying a concept

To answer the main research question, the researcher then placed all of the themes and categories into one of three data groups for each participant: (i) teaching practicum experiences, (ii) concerns about the effectiveness of the practicum, and (iii) recommendations for improving its effectiveness.

(iii) Presenting the findings

At this stage, the researcher reviewed the coding and all categories and themes through careful reading. This process entailed paraphrasing some citations, further moving of some sections, demoting some categories into codes and promoting other codes into categories. It also involved trying to ensure that each theme was distinct enough to stand on its own while also working on the coherence within the themes because “data within themes should cohere together meaningfully while there should be clear and identifiable distinctions between themes” (Braun & Clarke, 2006, p. 91). The data were then reported thematically under the three research questions to capture similar patterns across all the participants. Thematic analyses of the qualitative data were also triangulated with quantitative data to discuss the findings and also to establish data trustworthiness.