The data sources in this study consisted of two types, quantitative and qualitative data. The quantitative data were gained from closed questions in the questionnaires, while the qualitative data was gained from open-ended questions in the questionnaires, TSG observations, and semi-structured interviews. The quantitative and qualitative data were analysed separately. Below is the discussion about each stage of the data analysis.
3.7.1 Quantitative data analysis
The quantitative data (i.e. responses to scales and multiple choice questions) in the initial questionnaires and the follow-up questionnaires were transferred and analysed using the computer software Statistical Package for Social Sciences (SPSS). Descriptive statistics, such as means and standard deviations, were used to describe the data that had been collected from the participants. An analysis of variance (one-way ANOVA) was used to investigate changes and perceived levels of EFL teachers’ self-efficacy in the TSES subscales, and based on their attendance in TSGs. If one-way ANOVA showed differences of perceived levels and changes in teacher self-efficacy, a post hoc test was then performed to identify the group that was significantly different.
Finally, two-way ANOVA analysis were conducted to investigate the interaction between the level of participation in TSGs and teaching experience, between the level of participation in TSGs and gender, and between the level of participation in TSGs and geographical location.
3.7.2 Qualitative data analysis
Bogdan and Biklen (2003) mention that data analysis in qualitative research involves the process of “systematically searching and arranging interview transcripts, field notes
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and other materials” (p. 147) that the researcher uses to come up with the findings. In
order to investigate teachers’ perceptions of good quality PD, and the relationship
between teachers’ participation in PD (TSGs) and teachers’ self-efficacy, thematic
analysis was used in the present study. Thematic analysis is a search for themes that emerge as being important to the description of the phenomenon (Daly, Kellehear, & Gliksman, 1997). The process involves the identification of themes through “careful reading and re-reading of the data” (Rice & Ezzy, 1999). It is a form of pattern recognition within the data, where emerging themes become the categories for analysis. The method of thematic analysis chosen for this study incorporated both inductive approaches and deductive approaches (Boyatzis, 1998). These approaches complemented the research questions by allowing the themes emerged from elements of theory (the theory-driven approach) while also allowing for themes to emerge directly from the data using inductive coding. The coding process involved recognizing (seeing) important points/aspects and encoding them prior to the process of interpretation (Boyatzis, 1998). Boyatzis states that a “good code” is one that captures the qualitative richness of the phenomenon (p. 1) and organizes the data to identify and develop themes. Boyatzis defines a theme as “a pattern in the information that at minimum describes and organizes the possible observations and at maximum interprets aspects of the phenomenon” (p. 161).
The data from open-ended responses, semi-structured interviews transcriptions, and observation notes were entered into the NVivo 10 data management program, and a comprehensive process of data coding and identification of themes was undertaken. Although presented as a linear, step-by-step procedure, the research analysis was an iterative and reflexive process. This interactivity, applied throughout the process of qualitative inquiry, is described by Tobin and Begley (Tobin & Begley, 2004) as the overarching principle of ‘goodness’. The data collection and analysis stages in this study were undertaken concurrently, and I reread the previous stages of the process before undertaking further analysis to ensure that the developing themes were grounded in the original data. The primary objective for data collection was to represent the subjective viewpoint of the participants who shared their experiences and perceptions
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about PD, TSGs, and teacher self-efficacy. The process of coding is described as a systematic, step-by-step process as follows:
Stage 1: Developing the code manual
The choice of a code manual for this study was important because it served as a data management tool for organizing segments of similar or related text to assist in interpretation (Crabtree & Miller, 1999). For this study, codes were written with reference to Boyatzis (1998, p. 31) and identified by:
a. the code label (i.e., a name);
b. the definition of what the theme concerns (i.e., the characteristic or issue constituting the theme); and
c. a description of how to know when the theme occurs. Stage 2: Summarizing data and identifying initial themes
The process of paraphrasing or summarizing each piece of data enters information “into your unconscious, as well as consciously processing the information” (Boyatzis, 1998, p. 45). This process involves reading, listening to, and summarizing the raw data. I used this technique as a first step when analyzing data from open-ended responses, semi- structured interviews transcriptions, and observation notes. I summarized the data separately by outlining the key points made by participants in response to the questions asked. The summary for each data source reflected the initial processing of the information and provided the opportunity to make sense and takes note of potential themes in the raw data.
Stage 3: Connecting the codes and identifying themes
Connecting codes is the process of discovering themes and patterns in the data (Crabtree & Miller, 1999). The process of connecting the codes and identifying themes across the three sets of data in this study were clustered under headings that were directly related to the topics (i.e. self-efficacy, PD, TSGs, and the impact of PD (TSGs) on teacher self- efficacy). Similarities and differences between separate groups of data were emerging at
79 this stage, indicating areas of consensus in response to the topics and areas of potential conflict. Themes within each data group were also beginning to cluster, with differences identified between the responses of the participants with varying demographics; for example, differences were expressed by the teachers who attended the TSGs very frequently and those who never attended the TSGs, or between very experienced teachers and novice teachers.
Stage 4: Corroborating and legitimating coded themes
The final stage illustrates the process of further clustering the themes that were
previously identified from the coded text. “Corroborating” is a term used to describe the
process of confirming the findings (Crabtree & Miller, 1999, p. 170). At this stage, the previous stages were closely scrutinized to ensure that the clustered themes were representative of the initial data analysis and assigned codes. The interaction of text, codes, and themes in this study involved several iterations before the analysis proceeded to an interpretive phase in which the themes were connected into an explanatory framework consistent with the data. The themes were then further clustered and were assigned succinct phrases to describe the meaning that underpinned the themes.