![]() Having strategically analysed and coded the transcripts to a point of saturation I had 184 codes and 727 quotations. I used code comments to describe the code and my thoughts, I even attached images to remind me of the context (illustrated in Figure 3). Quotation comments helped to remind me why those words and statements were important and relevant to my research. I made use of comments during the coding process. Unlike theory-driven codes, the data-driven codes came from the stories told by the text itself. As I read through the transcripts relevant and interesting words and segments of data were coded with existing codes (deductive coding) and new codes (inductive coding) that originated from concepts that were identified from the data. Guided by (1) my research questions, (2) the literature review and (3) the multi-theory framework, I analysed the data. It was therefore very necessary to read through the transcripts intensively to understand the story from the respondents’ point of view and interpret their responses. The theory aspect of the research was only part of the investigation, the other being the practical aspect of social media use by the intermediaries. From the pop-up window which appears, search for your documentĪfter importing the code list, I uploaded my interview and focus group transcripts into ATLAS.ti and the reading process began.From the two options that appear select Import from Excel (that is if your document with the codes is saved as an Excel document).With ATLAS.ti Windows open, click on the Import & Export tab.Importing a code list/ codebook into ATLAS.ti The next step was importing the code list (Excel document) into ATLAS.ti as the initial set of codes. Table 1 illustrates this step as it was applied to one of the theories used, Community Development Theory which had 5 core concepts. For each research question, I identified labels (codes) that could be applied to data (participants’ responses) that would be collected based on the specific research question. ![]() This description informed the development of relevant research questions for the investigation. Each theory used was broken down to its core concepts – with each concept being interpreted and described. Since the objectives of my study were multidisciplinary it was necessary to develop a multi-theory framework that drew theories from the domains of development, communication, and technology to guide the study. The deductive coding process was made up of several key steps. In essence, this is the integration of data-driven codes and theory-driven codes in the data analysis process. To suit the data analysis needs of my study I adopted a hybrid coding technique which made use of inductive and deductive coding approaches. Moreover, I was able to utilise the code grouping feature to filter my codes into themes and sub-themes, all of which I visualised using networks. As a multi-purpose qualitative data analysis software program, ATLAS.ti was a useful tool that organised and centralised the process. My data analysis, therefore, required intensive deep dives into a considerable amount of raw narrative data collected through in-depth interviews and focus group discussions, and the use of mixed deductive and inductive coding techniques. They can be generated inductively from the raw information, or deductively from theory and prior research (Boyatzis, 1998). The themes are patterns found in the information that describe, organise and interpret aspects of the phenomenon. ![]() This analysis technique entails coding data and identifying themes. For my research design, I adopted an interpretive qualitative research approach that was guided by thematic data analysis techniques. My doctoral study focused on the use of social media by digital inclusion intermediaries to communicate for development in under-resourced communities. The techniques can be tailored to suit specific research needs and objectives. There are various data analysis techniques available to qualitative researchers including thematic analysis, grounded theory, and phenomenological analysis. Rich qualitative data can be a treasure-trove of valuable information for the curious mind with the right analysis tools.
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