DATA ANALYSIS: THEMATIC ANALYSIS

Thematic analysis was used to analyse data in exploring the ways in which social presence manifested itself amongst first-year online undergraduate students in a fully asynchronous web-based course. The method was chosen since it is a flexible technique that can be used to analyse data obtained through various types of qualitative methods (Creswell, 2013). Thematic analysis is a method of identifying, describing, analysing and reporting themes within the data set in rich details (Braun & Clarke, 2006). Fereday and Muir-Cochrane (2006) add that thematic analysis is a form of pattern recognition within the data, where emerging themes become the categories for analysis. Boyatzis (1998) defines a theme as a pattern in the information that at minimum describes and organises the possible observations and at maximum interprets aspects of the phenomenon. Thematic analysis has the potential not only to organise the data into themes through which the social world of the participants are said to be represented (Aguinaldo, 2012), but it also allows for a rich description of the data set related to a detailed description of each particular theme (Braun & Clarke, 2006). This was the aim of the current study, to explore and provide rich description of first-year undergraduate students‟ view of social presence in a fully asynchronous web-based course. Boyatzis (1998) cautions that thematic analysis is a way of seeing, and often what is seen through thematic analysis does not appear to other people even if they are observing the same information, events or situation.

In a qualitative inquiry the researcher is the primary instrument for data collection and data analysis (Patton, 1999). Data analysis ultimately rests with the thinking and choices of the researcher, and qualitative studies in general reflect the researcher‟s subjectivity (Bloomberg & Volpe, 2008). As such, it is possible for different researchers to arrive at different interpretations of findings for similar studies. In addition, the fact that I was facilitating the module under study may influence the data analysis. To become aware of the impact of my subjectivity, Lincoln and Guba‟s (1985) measures of trustworthiness were implemented as explained in the subsequent section, in order to enhance the credibility of the study findings. Following data collection from 18 semi-structured telephone interviews, the recorded audio interviews were transcribed verbatim by the researcher. The interview transcripts were then uploaded into the Atlas.ti 7 programme, and a comprehensive process of data coding and identification of themes was carried out. The Atlas.ti 7 programme was chosen based on its flexibility to organise text codes into themes (Friese, 2013). I provide a summary of a systematic, step-by-step thematic analysis process in the sections below as explained by Braun and Clarke (2006) and Fereday and Muir-Cochrane (2006). Although presented as a linear step-by-step procedure, the research analysis was an iterative, reflexive, ongoing process. A reflexivity journal was used to reflect the evolution of my thinking and documented my rationale for all choices and decisions made throughout the research process (Bloomberg & Volpe, 2008).

Phase 1: Familiarising with data

This phase began with a process of immersion in the data, where I familiarised myself with the data (Braun & Clarke, 2013). In practice, this involved transcribing the recorded interview audio verbatim, reading and re-reading interview transcripts and checking the transcripts back against the original audio-recordings for accuracy control. I was also paying attention to patterns that emerged within the data and made use of a reflexivity journal to take notes of aspects that were interesting during the interviews and throughout the data analysing process. As this study adopts a social constructivism stance which emphasises that the researcher and participants are co-researchers, I sent the interview transcripts to participants for accuracy checks to ensure that the transcripts are an endorsed reflection of their reality.

Phase 2: Generating initial codes

The coding process involves an ongoing segmentation and labelling of text to form descriptions and broad themes in the data (Creswell, 2005). Braun and Clarke (2006)

emphasise that data coding can be done in two ways, namely inductive or data-driven and deductive or theory-driven. Inductive analysis is a process of coding the data without trying to fit it into a pre-existing coding frame, or theoretical framework (Braun & Clarke, 2006). Deductive analysis is driven by the researcher‟s theoretical or analytic interest in the area, and is thus more explicitly analyst-driven (Braun & Clarke, 2006). Theory-driven and data-driven coding approaches were both followed. I first referred to the CoI framework developed by Garrison, Anderson, and Archer (2000), which guided the current study. Garrison et al. (2000) identified three categories of social presence, namely emotional expression, open communication, and group cohesion. Rourke et al. (2007) extended Garrison et al.‟s (2000) work further by conducting content analysis of computer transcripts of graduate students at the University of Alberta, Canada. The process culminated in the amended social presence coding scheme with categories and specific indicators of social presence (refer to Table 5 below). I utilised Rourke et al.‟s (2007) social presence coding scheme to identify the types of social presence categories and indicators reflected by participants in the current inquiry. A data-driven coding approach was simultaneously followed; consequently the initial social presence coding scheme was amended as new themes emerged (see Appendix F for a complete coding scheme).

Table 5: Social presence coding scheme by Rourke et al. (2007)

Category Indicators Definition of Indicators

Affective Responses Expression of emotions Conventional expressions of emotions, or unconventional expressions of emotion, includes repetitious punctuation,

conspicuous capitalization, use of emoticons, teasing, cajoling, irony, understatements, sarcasm

Self-disclosure Presents details of life outside of class, or expresses vulnerability

Interactive Responses Continuing a thread Using reply feature of software, rather than starting a new thread

Quoting from other messages

Using software features to quote others‟ entire message or cutting and pasting sections of others‟ messages

Category Indicators Definition of Indicators

Asking questions Students ask questions of other students or the moderator

Recognition, complimenting,

expressing appreciation

Complimenting others or contents of others‟ messages

Expressing agreement Expressing agreement with others or content of others‟ messages

Cohesive Vocative communication Addressing or referring to participants by name

Addresses or refers to the group using inclusive pronouns

Addresses the group as we, us, our, group

Phatic/salutation Communication that serves a purely social function, greetings, closures (Source: Rourke, L., Anderson, T., Garrison, D. R., & Archer, W. (2007). Assessing social presence in asynchronous text-based computer conferencing. International Journal of E- Learning & Distance Education, 14(2), 50-71.)

Phase 3: Summarising data and identifying initial themes

In this phase, I summarised the transcripts by outlining the key points made by participants, noting individual comments in response to the interview questions (Fereday & Muir- Cochrane, 2006). On completion I started to re-focus the analysis at the broader level of themes; this involved sorting the different codes into potential themes, and collating all the relevant interview extracts within the identified themes, and then I ended with a thematic map with a list of candidate themes.

Phase 4: Reviewing themes

This phase involved two levels of reviewing and refining my list of candidate themes. Firstly, I read all the collated extracts for each provisional theme, paying attention to whether they appear to form a coherent pattern. Secondly, I re-read the entire data set, paying attention to whether themes relate to the data from a broader scope, whether the thematic map accurately reflect the meanings evident in the data set as a whole and how the themes support the data

and the overarching theoretical perspective. When I was satisfied with my thematic map, I then moved on to defining and naming themes which emerged outside of the CoI framework.

Phase 5: Defining and naming themes

In this phase I drew on the CoI framework and the literature while defining the essence of what each theme implied. For each identified theme, I wrote a detailed analysis as well as identifying the story (including interview excerpts) that each theme tells in relation to my research questions.

Phase 6: Corroborating the findings

In this phase, triangulation of sources was further carried out. This means comparing and cross-checking the consistency and inconsistency of the current study findings with other researchers (Patton, 1999). Based on my analysis and corroboration with the literature findings, interpretations and conclusions were drawn, and recommendations were presented for both educational practice and further research, as presented in Chapters 5 (Findings) and 6 (Conclusion). In the next section, I explain how Lincoln and Guba‟s (1985) model of trustworthiness was implemented in order to enhance the credibility of the study findings.