Dissertation
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Sampling MethodsSampling is the process of choosing some of the participants who could take part in a study or give answers (Moser & Korstjens, 2018). Sampling techniques are divided into two categories: probability and nonprobability. When probability sampling methods are used, each member or unit of the target population has the same chance of being chosen as part of the sample (Baltes & Ralph, 2022). The study employed simple random sampling technique to sample lecturers and final year engineering students. Several factors justify the study's use of simple random sampling to select the lecturers and final year engineering students. Firstly, this sampling method guarantees a representative sample of the population. By providing every member of the population an equal chance of being chosen, the potential for biases that may result from other sampling methods is reduced. This serves to ensure that both lecturers and final-year engineering students are represented fairly and impartially in the study. Moreover, simple random sampling permits the generalizability of the study's results. By using this technique to select a sample of lecturers and students, the researchers can assume that the selected participants are representative of the larger population of lecturers and final-year engineering students. Consequently, any conclusions and insights derived from the study can be applied to the entire population with greater assurance (Campbell et al., 2020). Non-probability sampling, also known as judgmental, non-random, or qualitative sampling, involves the selection of units or people based on the judgment of the researcher (Thapa, 2020). The study employed a purposive sampling technique to select the participants. This sampling approach was chosen due to its ability to specifically target and include individuals who met certain criteria relevant to the research objectives. By using purposive sampling, the study was able to focus on individuals with specific expertise and qualifications that were crucial to the research. Mechanical engineering managers from the automobile industry with ten or more years of working experience were selected to provide insights into the industry's perspective. Similarly, CTVET managers responsible for regulating, promoting, and administering TVET for transformation and innovation for sustainable development were included to gain valuable insights from the education sector. The use of purposive sampling ensured that the chosen participants possessed the necessary knowledge, experience, and perspectives to effectively address the research objectives. By focusing on mechanical engineering managers in the automotive industry and CTVET managers, the study was able to collect data that was directly pertinent to its research objectives. When researching specific populations or individuals who meet specific criteria, purposeful sampling is typically more efficient and applicable. In this instance, selecting managers with considerable experience in mechanical engineering and CTVET managers accountable for TVET transformation and innovation enabled the study to efficiently collect targeted information from key stakeholders in their respective disciplines. While purposive sampling may introduce some degree of selection bias, its use in this study was justified by the need to collect in-depth perspectives from individuals with particular qualifications and expertise. The targeted selection of participants with the desired characteristics ensured that the research focused on the pertinent perspectives and generated useful findings in the context of mechanical engineering management in the automobile industry and CTVET management for transformation and innovation (Baltes & Ralph, 2022). Download 2.1 Mb. Share with your friends:
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