Qualitative data analysis is a simple process of making sense of the collected data. With this research, the use of thematic analysis was employed to analyze all responses or the collected data from the purposefully selected sample of 20 participants and others through referrals or snowballing within the community. They shared their experiences and their feelings on how the so-announced restrictions impacted the death of their loved ones, funerals, burial rites and their cultural schemas together with religion during level 5 of national lockdown when they lost, buried and mourned their loved ones at that time. Butina (2015) states, when one seeks to use thematic analysis, one must consolidate the data into segments that can give or provide insight into research questions. Since the study participants are put at the center of the data analysis process, the data is presented thematically, which is pertinent in qualitative research.
A fundamental technique for qualitative analysis is thematic data analysis, which is regarded as a prerequisite for carrying out other types of qualitative analysis. It locates, examines, summarizes, arranges, and reports themes within a data collection, enabling qualitative researchers to construct narratives that show the condition of the research subjects in relation to the investigation (Nowell et al., 2017).
The researcher compares elements looking for patterns/ themes (Butina, 2015). Following this articulation, the researcher developed seven themes with numerous sub-themes from the narratives of the people he had gathered. These themes are addressed in more detail in chapter 5 of this research. These themes were interpreted regarding the theories (Chapter Three) and the reviewed literature (Chapter Two) that served as the foundation for this study. According to Braun and Clark (2006), thematic analysis is intended to be a recursive process as opposed to a linear one. Subsequent steps may lead the researcher to return to prior steps concerning new information or newly emerging themes that require further exploration (Kiger & Varpio, 2020). In this study, six steps were followed in formulating themes through a data analysis process:
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4.12.1. Familiarization with data - Listening and registering all collected data from one- on- one in-depth interviews.
This is the first in thematic analysis’s process where I familiarize myself as the researcher with the entire data set, which entails that I repeated and had active reading of the data. Based on this study context and research design, all the data set was from one-on-one interviews since there was no participative observations and focus groups interview because of COVID-19 restrictions which posed some limitations in data collection process. As for audio recordings, comparing transcripts and original recordings were done more frequently for accuracy checkups.
4.12.2. Generational of initial codes- Code all data arranged into different categories as per main interview questions.
This step assisted in fine-grained, detailed data organization. After completing the familiarization task or step, the researcher started making notes on potential data items of interest, queries, connections between data items, and other early concepts. Codes, not themes, were produced at this stage or phase. The construction of all 7 themes was influenced by the researcher's definition and demarcation of each code to prevent overlap when applied to the complete data set.
4.12.3. Searching for themes
I then examined all the coded and compiled data extracts to identify any prospective themes that might be of greater significance. Themes were generated by the researcher through analysing, combining, comparing, and even visually mapping how codes connect to one another. Since this analysis was inductive, researchers specifically drew themes from the coded data. All themes discovered as a result were more closely related to the original data and representative of the complete data set. Regardless of the volume, quantity, or amount of data under a topic, the researcher made note of all themes of relevance, whether or not they were relevant to the study question. The ones that offered the substantial links between the data items and addressed the focal point of the study questions and problem statement are those themes that were designated as important or core themes.
4.12.4. Reviewing themes and linking them to existing literature reviews.
As the researcher, I examined all the coded data extracts for each theme to see whether they formed any meaningful patterns. To assess whether the themes accurately captured the meanings included in the data set as a whole, each theme's validity was examined. At this point,
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the researcher found that numerous themes lacked sufficient evidence from either the body of prior literature or the data that had been gathered to support them. According to the data set, the majority of those themes collapsed, while some were divided into various sub-themes, and some were developed into themes that are both wide and detailed enough to encompass a group of concepts seen in numerous text segments.
4.12.5. Defining and naming themes
According to Braun and Clarke (2006), during this stage the researcher decides what characteristics of the data each topic captures and decides what interests them and why. The researcher prepared an in-depth analysis for each topic during this stage, noting the tale that each theme told while also taking the overall story of the complete data set taking the study questions into consideration. The researcher organized and restructured all the themes until he was confident that all the data had been presented and exhibited in a relevant way. To make sure that participants' phrases were employed flawlessly without disclosing their identities, the researcher returned and reviewed all the topic names in the analysis' final phases.
4.12.6. Producing the report or Manuscripts
The researcher started this stage of thematic analysis after all the themes had been properly identified and he was prepared to start the final analysis and write the report. Nowel et al.
(2017) made the case that the write-up of a thematic analysis should give a brief, clear, logical, non-repetitive, and engaging presentation of the data within and across themes. To make statements relating to the data set credible and convincing, Thorne (2006) recommended researchers clearly express the logical processes by which discoveries were created in a form that is understandable to a critical reader. King (2004) indicated that direct quotes from participants should be included in the final report, and the researcher made sure to do so. Short quotes were used to clarify certain interpretations and show how prevalent certain topics were, while lengthy paragraph citations were used to capture the sense of the original manuscript.
The study's final discussion portion covered all the issues, including the inconsistent data. The theoretical literature that served as the basis for the investigation was referenced in this analytical discussion. These research results were compared to the larger body of literature to determine how they supported, contradicted, or added to the existing body of knowledge directly related to the subject at hand.
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