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3.15.4. Cleaning the data
All material identifying the participants and the organisation should be omitted. The data concerning the case organisation will be replaced by particular coding, for example Company 1 or C 1. It is important to replace the original case organisation with coding. The cleaning of data would be reviewed by a peer at a later stage of data analysis to analyse the constancy of coding (Boeije 2010). According to Clapper (2014) there are two types of data analysis used in qualitative data collection; content and thematic analysis. Content analysis and thematic analysis can be one and the same depending on the number of steps used and the type of qualitative research design.
Content analysis: may be more related to initial analysis and coding process where similar codes are analysed (Clapper, 2014). Given (2008) stated that content analysis is conducted by detecting themes and patterns within data. Conversely, qualitative content analysis focusses on representing truth by discovering meaning from word-based data (Silverman, 2011).
Thematic analysis: takes place after the coding process. Similar codes are aggregated to form major concepts or themes (Clapper, 2014). According to Hilal and Albri (2013) qualitative data analysis includes identifying the relationship between categories and themes of data in order to increase the understanding of the phenomenon being examined. In this study, thematic analysis will be adopted. Thematic analysis involves sorting, classifying and categorising each section of data and arranging the themes that reflect the key concerns of respondents (Maiga, 2017). According to Boejie (2010) thematic analysis allows the researcher methodically to detect any variance that appears in the empirical data. Therefore, the aim is to disclose imperative processes, concepts, and expert experiences between case organisations. Thematic analysis was adopted for this study. It assisted the researcher in establishing themes that contributed to answering this study‟s research objectives. Due to time constraints the researcher could not acquaint herself with the use of NViVo. However not using NViVo did not affect the quality of the analysis or the dissertation as a whole. The researcher was able to do the thematic analysis successfully without the aid of NViVo.
3.16. DATA QUALITY CONTROL
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researcher biasness. Reliability focuses on establishing whether or not the outcomes of a study are repeatable. It also relates to whether or not the methods that were developed for concepts are unswerving (Bryman & Bell, 2007). Threats that can hinder reliability of study results are: respondent or researcher errors and respondent or researcher biasness (Saunders et al., 2009). Similarly Wilson (2010) stated that reliability issues are closely linked with the researcher having a subjectivity approach to the study which could manipulate the research findings and compromises the truthfulness of the entire study.
In quantitative research, reliability is the constancy, steadiness and repeatability of results.
Results are considered reliable if constancy is attained in undistinguishable situations (Twycross & Shields, 2004). In this study, reliability was achieved by conducting a pilot study which involved pre-testing the questions on two Project Managers (from other the EPWP projects), who were not part of this study‟s sample. This pilot study was included in the data analysis chapter as a pilot study. The questions for the interviews were objective with the motive of getting the most accurate and just research findings.
3.16.2. Validity
Leedy & Ormrod (2010:52) stated that “the validity of a research instrument is if the research instrument measures what it is supposed to measure accordingly”. Similarly, McNiff (2014) stated that validity denotes the accurateness of an assessment instrument; for example, whether it measures what it is supposed to measure or not. It is the extent to which the research findings are accurate and truthful. According to Pallant (2011), validity involves getting the research instrument to measure the concepts being studied properly. This is regarded as an essential requirement for research. According to Forza (2002) if a study does not assess reliability and validity, it will be challenging to identify and quantify errors on theoretical relationships being analysed. Validity in research has two categories, content and construct validity (Forza, 2002).
Content validity: Content validity measures the extent to which the measuring tools adequately cover the objectives of the study (Cooper & Schindler, 2003; Sekaran & Bougie, 2016). To ensure content validity in this study, the researcher conducted a thorough literature review in order to gain an in-depth understanding of the subject matter. Hence, the research instrument was developed based on the information gathered from the literature.
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Construct validity: An instrument has construct validity if it measures the constructs that it is intended to measure. In other words, the instrument should measure the variable which it is intended to measure (Welman et al., 2007). In this regard, the in-depth interview guide was subjected to pre-testing (pilot study) in order to evaluate whether or not it encompassed most of the variables that are required to address the research objectives. The piloting of the in- depth interview guide is discussed in the following section.
According to Mohajan (2018) there are four ways to warrant validity of a research;
Time frame for the study needs to be appropriate;
The methodology chosen has to be tailored for the research, considering the characteristics of the study;
The sample method chosen for the study has to be the most suitable; and
The respondents must be truthful in answering questions and should not be pressured in any way (Mohajan, 2018).
Applying reliability and validity to this study has enhanced the quality of the content.
3.16.3. Piloting of the in-depth interview questions
According to Leedy & Ormrod (2010) the data collection instrument needs to be pretested on a few people to test if there are any flaws or if the data collection instrument is well understood by the participants. After pretesting it, it is often necessary to make amendments and to refine the questions. According to Saunders, Lewis, & Thornhill (2003) the pretesting process is completed prior to the data collection process. The pilot study assists in identifying any weaknesses in the research instrument whilst validating it (Sekaran & Bougie, 2016). An in-depth interview guide was created for this study. A pilot test was administered to two individuals who are working for the EPWP, but who were not part of this study sample. This was done to observe the design and viability of the planned research instrument. The outcome of the pilot test was used to analyse the quality of the questions in addressing the research questions (Henn et al., 2009). According to Henn et al., (2009) participants who have participated in the pilot study will be included in the data analysis; this is done to evade testing defects, which could influence the validity of the study.
3.16.4. Trustworthiness
According to Gunawan (2015) using qualitative research with detailed transcripts and audio recordings are some of the ways to ensure thoroughness and trustworthiness. The researcher
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is employed by the Department of Economic Development, Tourism and Environmental Affairs under the Invasive Alien Species Programme (IASP) which is the organisation this study is investigating. For quality content, it is imperative that the researcher applies utmost trustworthiness. The researcher warranted trustworthiness by ensuring that all information gathered was recorded and analysed without any personal influences. There were no forced or artificial findings. Conformability was applied as the findings were grounded on data presented to the researcher. The researcher took personal notes which recorded the impressions and resolutions taken during the data collection process.
3.16.5. Credibility
Thomas (2010) defined credibility in qualitative research as the degree to which the data analysis is authentic and trustworthy. Credibility is similar to validity in a sense that the findings of the research match reality. It‟s been said that qualitative research has the prospect of representing multiple realities (Thomas, 2010). It is up to the reader to analyse the credibility of the study based on the readers understanding of the study. Some researchers have stated that there is no single reality, instead the reader creates their own reality (Thomas, 2010) According to Thomas & Magilvy (2011), to achieve credibility, the researcher must check for the authenticity of the data. Credibility can be ensured by quoting the participants responses verbatim (Thomas & Magilvy, 2011). Therefore, the researcher analysed the transcribed text after the interview process and assessed the similarity within the information that was collected. Furthermore, the researcher assessed the themes throughout the study that identified the challenges affecting the implementation of Project Management practices in the EPWP.