63 especially where they expected a particular answer. However, these occurred mostly as follow-up questions and not as main questions.
64 depending on the length of the recording. The interviews were predominantly conducted in the local language (Dholuo). As a native speaker of the local Dholuo language, the principal investigator (PI) personally transcribed the audio-records of the data and concurrently translated them into English. The audio recordings were imported for transcription using Express Scribe Transcription Software Pro version 5.51 (NCH Software, 2013). This software was used to make the process of managing the audio files and the transcription more efficient.
Each interview audio recording was uniquely labeled with codes indicating the site and method of data collection, the identity of the research assistant, and the details of the respondent—which were so devised to ensure anonymity of the participants. Care was taken to ensure that no unauthorized persons got access to the audio recordings in keeping with the ethical guidelines.
Transcribing the audio recordings personally by the PI provided valuable insights that assured the quality of the transcripts and their fidelity to the original interviews. Personal transcription also helped save on time since outsourcing the transcription would have still required that the transcripts be proof-checked against the audio-recordings for accuracy. Another crucial benefit of self-transcription is that it allowed the PI to develop an intimate connection and familiarity with the data. This familiarity provided insights into themes and categories that functioned as codes and suggested initial thoughts on the analytical process. Self-transcription continuously triggered novel thoughts about the data that were not evident during the design of the study and development of research instruments thus providing alternative interpretative perspectives (Gibbs, 2007).
As the transcription proceeded, completed transcripts were imported into MAXQDA Plus Student License Version 11, a qualitative data analysis software. The data were stored as PI assigned codes on the basis of which the data were subsequently retrieved for analysis. The qualitative data analysis software MAXQDA makes the process of data storage and retrieval efficient and easy and does so in a short period of time compared to manual methods (Gibbs, 2007).
2.8.3 Data Coding
The process of analysis involved a series of actions. After being collected, qualitative data was transformed from audio to textual form to provide a reliable basis for data coding and analysis. As has been mentioned, transcription was personally undertaken by the PI. The PI is a native speaker of the language of the research respondents and in which the data was collected. The next step was coding. Coding is the process of identifying research themes
65 from interview and focus group discussions (FGDs) transcripts. According to Gibbs (2007:
38) coding
“…involves identifying and recording one or more passages of text or other data items such as the parts of pictures that, in some sense, exemplify the same theoretical or descriptive idea. Usually, several passages are identified and they are then linked with a name for that idea – the code.”
Coding involved identifying the basic themes from the responses of the interviewees that corresponded to the various research questions. This was followed by the identification of the intermediate and major themes, which were deduced from the basic themes. Intermediate and major themes represent a higher level of coding and allow for patterns to be identified within the data. The patterns identified provided the basis for making generalizations about the data.
The generalizations provided answers to research questions and generated explanations and understandings of the experiences of living with HIV (Gibbs, 2007).
Transcription and coding proceeded simultaneously. After the completion of transcription, the coding system was reviewed and refined. Even as transcription and coding were underway, analytical and interpretative insights that emerged from this close interaction with the data were promptly noted down in the research diary. These insights provided initial hunches and hypotheses for analysis and interpretation.
This study used both concept-driven and data-driven coding strategies. According to Gibbs (2007), concept-driven coding is where a PI builds codes based on “…research literature, previous studies, topics in the interview schedule, hunches…about what is going on, and so on” (Gibbs, 2007:44). This coding strategy corresponds to the deductive approach to qualitative analysis and was the one adopted in this study. The code book was however treated as tentative and considered amenable to amendments as the process of coding and analysis progressed because new ideas kept emerging from the text (Barbour, 2008; Gibbs, 2007). The second approach, data-driven coding, corresponds to the inductive approach and begins from the data themselves and does not depend on prior notions or concepts gleaned from literature or theory. The researcher starts “…by reading the texts and trying to tease out what is happening” (Gibbs, 2007:45). These two methods are however not necessarily mutually exclusive.
66 This study entailed a mix of both strategies (concept-driven and data-driven coding) whereby concepts or themes identified prior to data collection were used while sufficient flexibility was allowed to incorporate new ideas or concepts that emerged from the data as transcription and analysis continued (Barbour, 2008; Gibbs, 2007). The PI identified and interrogated his preconceived notions and ideas that could be a source of bias in the identification of codes, definition of analytical categories and analysis and interpretation data (Barbour, 2008; Gibbs, 2007).
In the concept-driven approach, three different coding levels can be identified. Coding levels mainly refer to the function a code is performing as well as the level of abstraction at which it is operating. The levels of abstraction denote the distance between the analytical codes and the responses and words/concepts used by interviewees. The three levels, beginning with the most basic are descriptive codes, analytic codes and pattern codes (Lewins and Silver, 2007).
Descriptive Codes
A descriptive code represents the lowest level of coding and relates to concepts, ideas, actions or themes as they appear in the data. They are the initial themes that are derived directly from the responses of the research participants and are used to facilitate the grouping of segments of text in terms of the ideas they are basically about. These codes are closely related to the aims of the study and could be based on the guiding theoretical framework, literature review, research questions or on the interview items (Gibbs, 2007). At this level of coding, the researcher defined grouping labels that served to bring together responses from various respondents that corresponded to the ideas represented by that label. More analytical work was done to these text segments at the next stage of coding. Descriptive codes are to be seen as tentative and are identified by reading though the transcripts to identify the chunks of text that fit to these pre-defined codes and applying the code to them (Lewins and Silver, 2007).
Interpretive or Analytical Codes
Once the data have been descriptively coded, the next stage involves closely reading and re-reading the coded segments and grouping those that have common characteristics or that are related with regard to a certain domain of interest. This process often entails breaking down the existing codes and developing a more analytical and detailed coding system. This process may also involve merging and/or splitting of codes based no how best they capture the emerging patterns in the data. At this level, categorization and classification occurs whereby similarities and differences between coded segments form the basis for developing higher level of codes and for grouping the chunks of text from the respondents. Analytic coding
67 involves a constant process of comparing and contrasting the coded texts segments and those found to be similar are then lumped together (Barbour, 2008; Gibbs, 2007; Lewins and Silver, 2007).
Pattern Codes
Pattern codes are the next level of abstraction as the distance between researcher generated categories and the raw data increases. At the pattern code level, inferences are drawn and plausible interpretations made based on patterns or regularities observed from the data. This stage involves the exploration of the patterns with which codes defined at both the descriptive and analytical stages appear in the data, both within and across cases. It also involves deciphering the patterns that emerge from various sub-sets of data. In this study, the patterns were assessed based on how they were differentiated among other considerations: the health facility from which the PLHIV received care; their socio-demographic characteristics such as gender, age, marital status, education levels; the length of time a PLHIV had known their status; and the specific circumstances that surrounded their diagnosis. Cases or instances that appeared exceptional or non-typical were identified and the extent of their difference examined and possible explanations for these differences given (Barbour, 2008; Lewins and Silver, 2007).