4 CHAPTER FOUR: RESEARCH METHODOLOGY 4.1 Introduction
4.6 Data analysis
4.6.1 Quantitative research
Creswell (2014) advocates a six-step process to analyse quantitative date. These steps are discussed below:
The first step is to compile an overview of the sample and response rate into an easy to read Table format. The next step was to determine if there is any response bias, wave analysis can be utilised to measure any potential bias. Wave analysis is a method that tracks specific item’s average response changes on a week-by-week basis and that the last wave of respondents can be utilised to indicate the probable response of non-respondents (Halbesleben and Whitman, 2013). This is followed by the third step, descriptive statistics such as measures of central tendency i.e. mean, medium, mode and standard deviation are to be utilised for the survey responses. Thereafter, internal consistency needs to be addressed. The fifth step involved the use of a statistical software package to process the collected data, SPSS was utilised for this research. However, due to the entire population of the research being small and therefore part of the study, the sample is also the population and there will not be a need for inferential statistics. According to Gibbs et al. (2015, pg. 1) “While the use of inferential statistics is a nearly universal practice in the social sciences, there are instances where its application is unnecessary”. Gibbs et al. (2015, pg. 1) further adds “inferential statistics are necessary tools to analyse sample data. Inference to the unknown population parameter is possible …”. The second point of Gibbs et al.
(2015) is largely corroborated by Anvari et al. (2015).. It should be noted that the sample taken from the population at the KSEF falls under qualitative data analysis.
The final step is the presentation of findings in Tables, which is accompanied by a narrative interpreting the results.
108 4.6.2 Qualitative research
According to Creswell (2014), qualitative data analysis can be conceptualised as a six-step process, which is depicted in Figure 4.1 and discussed below. It was observed that there are many steps for qualitative analysis, but they can broadly be categorised as observation, transcribing, undertaking coding and development of themes (Leech and Onwuegbuzie, 2008; Fossey et al., 2002).
Figure 4.1: Qualitative data analysis process Source: Adapted from Creswell (2014).
The first step in qualitative data analysis, according to Creswell (2014), begins with preparing qualitative data, which is purely an administrative step that essentially evolves arranging and formalising collected data into formats and categories that are of academic quality. Thereafter, the data should be thoroughly read. This step will give the researcher a good overview of the data and start to highlight aspects such as consolidated meaning, depth and credibility. The third step involves categorising data into homogenous components and then entails providing a name for each portion of data. It is important to note that no pre-determined codes will be utilised for this research, all codes will be developed as a result of the emergent portions of data. The next step is to then utilise codes as a basis for developing a description of various related aspects. The descriptions are in turn utilised to create themes. While Creswell
Raw data (Transcripts, etc.)
Themes Description Preparing data for analysis
Coding the data Reading through all the data
Interpretation Integration
Data validation
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(2014) states that a small number of themes should be developed, this research will not contain the possible number of themes that will be identified. Creswell (2014) further states that the complexity of the themes do not have to be confined to merely identifying themes but can also progress to more advanced levels, which would include mapping themes. The fifth step involves developing a narrative that further develops the descriptions and themes. It is important to note that as this research is based on a mix method strategy and as a result a separate narrative will contain the quantitative narrative and a third will then discuss the dynamics between the qualitative and quantitative and will lead us to conclude where both sets of data are convergent, divergent or has elements of both. The process is concluded with a final interpretation of the entire data set, with the fundamental finding being highlighted.
This narrative will also identify any gaps in the data collected, which will need to be filled to ensure that a comprehensive outcome is developed.
It should be noted that a qualitative expert was engaged to undertake the coding of the data, utilising the Nvivo 10 Software. The researcher undertook all other data analysis steps. Due to the nature of this research, in trying to bring about an understanding of the eThekwini green economy and how it operates, the most appropriate approach is Inductive Thematic Analysis. In addition, the data analysis process made use of word frequency counts, tag clouds, tree maps and matrix coding.
4.6.3 Quantitative and qualitative research comparison
It is important to keep in mind that this research was based on a concurrent triangulation strategy, one of the mixed methods procedures. As a result, it was critical that both sets of data and interpretation, for qualitative and quantitative data, to be discussed in a simultaneous manner. This discussion will be predominately in the form of a narrative, with Tables and Figures, from both data sets being employed to support the discussion. In essence, this discussion led to the conclusion on whether there is divergence, convergence, or a mixture of the qualitative and quantitative data.
One of the main outcomes from this was in the form of a map that will synthesis the findings to take us closer to meeting the key research objectives. The map will contain the types of agents and their interaction channels. This will be utilised as a basis for the development of the ACE framework. The maps are not presented in this research.
110 4.7 Data validation
This section discusses the manner in which the validation of data was ensured.
According to Heale and Twycross (2015, pg. 66), “Validity is defined as the extent to which a concept is accurately measured …”. This is supported by Mohajan (2018, pg.
1) “Validity concerns what an instrument measures, and how well it does so”.
4.7.1 Quantitative research
According to Golafshani (2003), quantitative validity is to ensure that what is ultimately measured by the survey must match what was initially envisioned, which is supported by Thatcher (2010). As a result, an external statistician, assessed the construct validity for quantitative data of this research.
The researcher then also acted as the auditor of the outcomes from the process of SPSS coding. The auditor in this context means that the data coding undertaken by the statistician expert will be critiqued and double checked for accuracy.
4.7.2 Qualitative research
Creswell (2014) list various approaches that can be utilised for the validation of qualitative data.
One of the approaches for validating qualitative research is ‘triangulation’ of various data sources. Due to this research employing a concurrent triangulation strategy, the qualitative data was ultimately triangulated with quantitative data, but it should be noted that the triangulation only took place after both the qualitative and quantitative data had been analysed. Secondary data will also be utilised to triangulate the validity of such. It should be noted that the above technique for validating qualitative data took place much later in the study, i.e. after both qualitative and quantitative data was analysed.
Ensuring the soundness and trustworthiness of qualitative data can be derived by ensuring four criteria are met: credibility, transferability, dependability and confirmability, as developed by Guba (Greene, 2014; Shenton, 2004; Chowdhury, 2015).
Credibility is to qualitative research as to what internal validity is to quantitative research (Morse, 2015; Greene, 2014; Chowdhury, 2015). That essentially means that credibility confirms that the process has the ability to measure what it was intended to
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measure. There is a plethora of tactics for establishing credibility, as initially developed by Guba (Greene, 2014; Shenton, 2004). The most relevant tactics for this research was the utilisation of: familiarisation with the culture of participating organisations, triangulation, comprehensive overview of the context of the research and the development of a repertoire and trust in the interview and focus group sessions that allowed participants to be honest and straightforward. Credibility was further established for this research through the use triangulation of interviews and focus groups, with secondary literature data sources. In addition, the researcher has been involved in related work for over ten years and this can be interpreted as being immersed in the context of the research.
Transferability corresponds to the quantitative concept of external validity (Chowdhury, 2015; Shenton, 2004). This concept would essentially ensure that the findings of the research would remain true for a broad application. However, qualitative research outcomes are by nature and definition only applicable to a specific context and as a result it is extremely tricky to generalise the findings. One of the ways in which this construct can be addressed is the use of a comprehensive description of the content in which the research has been conducted. Transferability for this research was difficult to achieve, however the methodology utilised can be replicated to arrive at findings that will be applicable to other contexts.
Dependability is the equivalent of reliability in quantitative research (Morse, 2015;
Shenton, 2004). This construct also has some challenges for qualitative research, as the ever changing context will have an effect on reliability. One of the ways in which this can be overcome is to provide a precise description of the manner in which the research was undertaken. This research endeavoured to demonstrate dependability by describing, in this chapter, the specific steps that were undertaken during the research.
Confirmability is the incorporation of objectivity into the research (Morse, 2015;
Chowdhury, 2015; Shenton, 2004). This is to essentially ensure that no researcher bias is entered into the research. To this end, triangulation was again utilised and audit trails where the research can be traced in a step-by-step fashion throughout the entire research process. Both triangulation and audit trails were utilised during this research.
Triangulation was undertaken with interviews, focus groups and secondary data sources. An audit trail and record of all steps undertaken have also been electronically documented.
112 4.8 Data reliability
According to Golafshani (2003) and Heale and Twycross (2015), reliability entails ensuring that the data collection instruments are utilised consistently throughout the data collection process. This is supported by Roberts et al. (2006) “Reliability described how far a particular test, procedure or tool, such as a questionnaire, will produce similar results in different circumstances, assuming nothing else has changed”. The techniques developed for assessing reliability for qualitative and quantitative data are not the same and are discussed in the next sub-sections. However it should be noted that measuring reliability for quantitative data is easier than for qualitative data (Zohrabi, 2013).
4.8.1 Quantitative research
Internal consistency is ensuring that a tool is able to collect the same data from different respondents (Tashakkori and Teddlie, 1998; Roberts et al., 2006). As a result, the researcher ensured that the surveys were administered in a standard manner, as indicated in the above sections of this chapter. An independent external quantitative expert was engaged for the data coding verification in SPSS. The researcher then acted as the auditor of the outcomes from the process.
4.8.2 Qualitative research
Qualitative data reliability is concerned with the consistency of application of the research techniques employed in the research (Creswell, 2014). This is supported by Zohrabi (2013, pg. 259) “… the purpose is not to attain the same results, rather to agree that based on the data collection processes the findings and results are consistent and dependable”.
According to Zohrabi (2013), triangulation and establishing an audit trail are two aspects that can increase the dependability of qualitative data. The notion that keeping a precise record of the research process adding to the reliability of the research is confirmed by Roberts et al. (2006). Both triangulation and an audit trail were utilised for this research. Roberts et al. (2006) further adds that software packages, such as Nvivo, also contributes to the reliability aspect.
As a result, qualitative data reliability was ensured in this research by continually checking throughout the research, and at the end of the process, that the coding
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remained consistent. Triangulation of data also played a critical part of the research process. In addition, Nvivo assisted in increasing the reliability of the data.
An independent external qualitative expert was engaged for data coding in Nvivo. The researcher was then better able to audit these codes, as the researcher conducted the interviews and focus groups and transcribed the sessions.