The analysis of data in this study was an on-going process. This was in line with Merriam (2009:171) who said that without ongoing analysis, the data could be unfocused, repetitious, and overwhelming in the sheer volume of material that needs to be processed. Data analysis involved the conversion of notes into write-ups to replace the missing links, and editing, classifying and tabulating data collected to make it more compliant to theme identification and coding. This was in line with Creswell (2013:184), who suggested that coding involves aggregating the text or visual data into small categories of information. The spoken data was transcribed before analyzing and interpreting it thematically. Interpretation is a process that begins with the development of the code, the formation of themes from the codes, and then the organization of themes into larger units of concepts to make sense of the data (Creswell, 2013:187). As Gibson and Brown (2009:128) said, the data obtained was first examined to find out general themes or topics. Second, the variations in the themes were identified. The elements
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that were uniform were grouped together and those that proved to be exclusive were put on their own. The idea here was to find whether there were some disparities and unique features.
In this study, the coding of data was done through classification of patterns of relationship in terms of people’s perceptions on the causes of succession disputes. The study adopted the pattern coding system that was suggested by Bless and Higson-Smith (1995), where data was coded in terms of themes, patterns and categories that appear from the data. Basically, a two- step process was followed in this study, and this consisted of making notes in a notebook, keeping record of important points that needed to be remembered, and dividing or breaking up the data and assigning codes to the pieces of data. The researcher had to read every line of the transcribed data and then sorted the data into important segments. The main issue here was to organize data from various sources into categories to produce a readable narrative. Where there were important patterns or themes emerging from the findings, the researcher had to assign codes to the data. The themes that were recurring more frequently on the causes of succession disputes were considered as the major ones and the rest were taken as minor. For purposes of clarity, the causes of succession disputes were divided into four groups, each with its own code. The study used letters for the coding of data and in this vein, religious causes were named RC, social causes were named SC, political causes were named PC, and economic causes were named EC. Miles and Huberman (1994:56), as cited by Bell (2005:214), stated that a code implies that a “tag or label” is used to give “meaning” to the collected data. In line with this, codes were assigned to chunks of data that vary in size.
After the coding of data, the researcher then went into the second step in the data analysis process; that is, describing the data. Here, relevant features of the research study, which include the opinions and beliefs of the participants, were described (Best and Kahn, 2003:259). The last step taken in this study was the interpretation of the data. According to Best and Kahn (2003:259), data interpretation entails making the results clear, providing responses to the “why” questions, assigning meaning to specific outcomes, and designing an orderly structure. The main purpose here was to give logical answers to the research questions (Best and Kahn, 2003:259). Where the research findings were consistent with available literature on the causes of leadership succession disputes, cross references were done. For the areas where the findings were confirming what is already known about the causes of succession disputes, links were also made. Where the findings were breaking new ground, it was also clearly stated in the interpretation stage. Three basic issues were to be achieved here, which were to see the extent
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to which the research findings correspond, contradict, or deepen similar research. In addition, it was to provide explanations for the similarities and differences between the research findings and previous literature. The main task in the interpretation stage of data analysis was to attach significance, make sense out of findings, consider different meanings, and offer potential explanations and conclusions (Patton, 2002:503). The identified patterns in this study were connected to produce a story on the causes of succession disputes in the ZAFM church. The story was all about what is happening in the ZAFM church as far as succession disputes are concerned, the significance of understanding succession disputes, and then lessons that can be learnt from the causes of leadership succession disputes.
All the three steps outlined above were used to weave the data obtained from the different data collection methods used in this study. Caution was taken by not stating facts but giving ideas and suggesting explanations by using qualifiers like ‘it seems’, ‘it looks like’, ‘unlikely’ and ‘probably’. Therefore, in qualitative research, interpretation involves conceptualizing data beyond the codes and themes to get a deeper meaning (Creswell, 2013:186).
Creswell (2013) suggested that there is a need to create some type of visual image of the data, which should prove very useful in being able to fine tune, clarify, and make sense of the data during the analysis stage. In this study, the researcher was mindful not to record what he wanted to see and he maintained objectivity throughout the analysis of data. However, Bogdan and Biklen (1998:34) said that objectivity is difficult to execute. For them, no matter how much you try, you cannot divorce your research and writing from your experiences that is who you are, what you believe, and what you value. These cannot be divorced from any study because they form the real person the researcher is. However, the researcher tried to be objective and applied epoche to let the phenomena speak for themselves.