LESSON 65 - RESEARCH METHODOLOGY || SECTION 3.5: SAMPLE SIZE & SAMPLING TECHNIQUES

Sample size and sampling procedures sit at the heart of a research proposal because they determine who will be studied—and how confidently results can be generalized to the target population. The core message is straightforward: researchers must first determine the sample size, then select the appropriate sampling technique. Trying to choose sampling procedures before knowing the number of participants leads to a weak, incomplete section 3.5.

Sampling is framed as a strategic decision to study a subset rather than every member of a population, based on the idea that credible results can be produced without collecting data from the entire population. The lesson distinguishes key terms. A study population (linked to the accessible target population) is the group from which data can realistically be collected. A sample is a subset of that population made up of selected elements—individuals who share the characteristics the researcher wants to study. Those selected individuals are called subjects, respondents, or participants. The sample size, abbreviated as n, is the number of subjects used in the study.

A crucial operational concept is the sampling frame: a complete list of all elements from which the sample will be drawn. Without a sampling frame, sampling cannot be carried out. The lesson illustrates this relationship by describing how the accessible target population feeds into the sampling frame, and the sampling frame then produces the sample. For example, if the accessible target population is health workers in level 4 county hospitals, the sampling frame would be the list of all those health workers, from which the researcher selects the required number.

Once sample size is set, the next step is choosing the sampling technique. That choice is not random or based on convenience; it is guided by the research paradigm and research design. Quantitative studies aligned with positivism typically use probability (random) sampling designs, while qualitative studies aligned with interpretivism or constructivism typically use non-probability sampling designs. The lesson lists five probability techniques—simple random, systematic, stratified, cluster, and multi-stage—and four non-probability techniques—convenience, purposive (referred to as “for passive quarter” in the transcript), and snowball (with “noble” appearing as a mispronunciation/variant for another non-probability option). For mixed-methods research, sampling approaches may combine probability and non-probability methods.

Finally, after selecting the technique, researchers must describe exactly how the sample will be drawn from the sampling frame. Using a hypothetical example—target population of 500, sample size of 287, and simple random sampling—the researcher should explain how the 287 subjects will be separated from the remaining 213. The lesson ends by reiterating the required order: determine sample size first, then specify sampling techniques and procedures. The next session is set to move from sampling to research instruments and data collection tools in section 3.6.