CHAPTER 4 RESEARCH PHILOSOPHY, METHODOLOGY AND APPROACHES
4.7 Data collection instruments, procedures, and data analysis
Research methodology and research methods primarily perform two functions. First, they act as controlling mechanism on how data is collected, prepared, and extracted.
Secondly, they dictate how analysis and reporting is undertaken (Hussey & Hussey, 1997). Qualitative and quantitative research are the two distinct research methodologies, each with its own worldview and application. There is then mixed- method research that takes features of both qualitative and quantitative, and usually is applicable in instances where neither the two distinct research methodologies are appropriate to address the research phenomenon under study.
Within quantitative research methodologies realm, Matthews and Ross (2010:143) describe the quantitative research methodology as an inquiry into a social or human
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problem that is driven by deductive mindset of testing the existing theory. These research methodologies align with a positivistic epistemological approach and are largely concerned with gathering and working with structured dataset (Trochim, 2006).
With qualitative research studies where often, a researcher may only have a general idea of what they attempt to study, the results are subjective. Meanwhile in quantitative research methodologies, the researcher can quantify the variables and thus makes it easier for objectivity (Matthews & Ross, 2010:142).
Following the inductive nature of the study as outlined above and alignment to the interpretivist paradigm as the chosen research philosophy, the qualitative research mono-methodology and related research methods were employed in this study. The application of qualitative research methodology was deemed appropriate for the study as the aim was to employ the associated research methods such as interviews, transcription, and purposive sampling to address the research problem. Although qualitative research is associated with high degree of inability to generalize the findings to the population, they boost advantage of ability to obtain rich data from few participants, which was the case with the current study (Creswell, 2012; McDaniel
& Gates, 2001). The following subsections explain in detail these research methods and how they were employed.
4.7.1 Data collection instruments and procedures
In this section, data collection instruments and procedures are expounded. According to Malhotra (2017:44), data collection is a system of gathering a piece of raw information on the study field that concerns a particular number of events or human beings aimed at addressing the study’s research questions. In this study, qualitative research method, specifically semi-structured interviews, were employed for the collection of data. The data collection procedure began with the careful selection of participants representing relevant stakeholders, who include metro police officers from the CTMM. Informed consent was obtained, and an interview guide with open-ended questions was developed to elicit comprehensive information about the police department's role in asset protection.
Pilot testing was conducted to refine the guide, and interviews took place in private settings, recorded with participant consent, and supplemented with detailed field
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notes. Subsequently, thematic analysis was employed to identify pattern s and themes within the interview data.
4.7.2 Semi-structured interviews
There are various approaches to data collection and a variety of instruments can be used for this purpose. In-depth and unstructured interview structured, or semi- structured interviews can be conducted as a way of gathering data from the research participants (Gray, 2017:41). According to Hennink et al. (2017:212), an in-depth interview refers to one-on-one conversations between the researcher (interviewer) and the research participant (the source). In the current study, semi- structured interviews were employed, and this allowed the researcher flexibility in the way the data was collected. Both structured and unstructured questions as shown in Appendix D were asked.
On the other hand, the benefit of using a semi-structured interview lies in its ability to offer the researcher flexibility and freedom to not only probe pre -determined questions, but to also ask to follow up questions depending on the interview direction. The study used semi-structured interviews to gather data. This system was chosen because it provides a comprehensive, adaptable, and semantically deep comprehension of the TMPD's crime prevention role and its impact on safeguarding assets within the municipality. Semi-structured interviews enable this study to engage with a varied range of participants and obtain perspectives needed to conduct an in-depth investigation of the subject under study. This procedure led to me being able to collect well-detailed and rich information. There were two participants who were not available for personal one-on-one interviews, and as a result, virtual interviews through Microsoft Teams were thus arranged.
4.7.3 Interview schedule
This study made use of the interview schedule, sometimes referred to as an interview guide and it had both open-ended and structured questions. The guide was used to steer the direction of the interview sessions. Each interview session lasted between 45 to 60 minutes, with small breaks of 10-15 minutes to allow the research participants an opportunity to stretch the body and gather some strength.
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4.7.4 Data cleaning and analysis procedure
Once the interviews were completed, the recorded sessions were stored on the researcher’s personal laptop and only the researcher and the data analysts had access to the recordings. Before the analysis, data which would be in the form of texts from the conducted interviews first had to be transcribed.
4.7.5 Data transcription
The process of transcription would be both manual and automated through computer- aided artificial intelligence tool on Microsoft Teams transcription service. Given the errors in language processing model on the Teams platform, especially with regards to African, and by extension South African accent, both manual and computer aided transcription methods had to be employed. The recordings were later transcribed verbatim on the Microsoft word processor in preparation for further analysis. The collected and transcribed data was cleaned for repetitions and unclear or inaudible text. The following steps were thus performed as part of transcription process:
Step 1 - Audio was uploaded on to Atlas.ti software.
Step 2 – Populate and create a Word file from uploaded audio in the previous step.
Step 3 – Import a Word File into Atlas.ti.
Step 4 – Transcription process starts until the last audio is listened to.
4.7.6 Data coding
When dealing with qualitative data, one of the first primary steps prior to analysis is coding. This process follows transcription stage, and it involves organizing collected data and assigning labels with the aim of identifying themes and relationships that are emerging from that data (Smit, 2002). Labels are assigned to sentences, words, paragraphs, numbers, phrases, or text that represent significant or recurring themes in every response. It is from this premise that thematic analysis, which involves analysing words and sentences the structure is built on and was adopted.
4.7.7 Data analysis steps
The research followed six main steps to thematic analysis proposed by Braun and Clarke (2018:88). This procedure is illustrated in Figure 4.5 and applied steps involved:
Familiarising myself with data;
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Generating initial code;
Searching for themes;
Reviewing potential themes;
Defining and naming the emerging themes; and
Producing the report
Figure 4.5: Six phases of thematic analysis Source: Braun and Clarke (2018)
4.7.8 Thematic and relational analyses
Two approaches to data analysis were employed, namely conceptual and relational analyses. The former is known as a flat coding frame and determines the existence and frequency of concepts in the text being analysed. This technique or procedure identified with thematic analysis method. Relational analysis is associated with hierarchical coding frame and develops conceptual analysis further by examining the relationships among concepts in a text. These two data analysis procedures aid in enhancing rich information emerging from the data in order to identify presence of relationship between and priority of themes.
On the other hand, coding data into themes was done after a preliminary analysis of responses was conducted, which involved a process of sense-making. The developed codes were used to identify the emergent themes from the data and then identify the relationship between those themes. The data was analysed through version 23 of the Atlas.ti data analysis software. Figure 5.4 is an excerpt (output) from the Atlas.ti software and illustrates the identified themes through conceptual and relational analysis. The identified or emergent themes are illustrated in the interview guide that
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was used to interview participants (see Appendix D). The identified themes were guided by both primary and secondary RQs.