4.4 Non-Probabilistic Sampling

Non-probabilistic sampling occurs when individuals are selected based on convenience or judgment. This means that not every individual has a known or non-zero chance of being chosen. This can introduce bias and a lack of representativeness. However, it can be useful in cases where we do not need a random or representative sample, or where it is infeasible to take a probabilistic sample due to limitations of accessibility, and where ease of access to individuals is important.

Here, we will consider three types of non-probabilistic sampling, namely Convenience Sampling, Judgment Sampling and Quota Sampling.

In convenience sampling, individuals are selected based on how easy they are to reach.

Example 10: Suppose an animal scientist is attempting to study lions in a nature park. Attempting to take a probabilistic sample would require knowledge of how many lions are in the park, which might not be known, since lions could die or be born without the scientist’s knowledge. She might instead study those lions who come to drink at a water hole that is accessible by Jeep. This would exclude lions who drink at rivers, or at water holes that are not accessible to her vehicle. However, it may not be possible for her to study lions she cannot access. In this case, convenience sampling would be appropriate, although there is no way of knowing the chance that each lion has to be selected, and some lions have zero chance of being selected.

In judgment sampling, individuals are selected based on experts’ decisions of which individuals would be most useful for the study.

Example 11: Suppose a conference organiser is tasked with assembling a panel to discuss banking in South Africa. Rather than taking a probabilistic sample of the CEOs and other top officers of South African banks, he might choose to invite only those whom he personally believes will make the most meaningful contribution to the discussion. Although this is a non-probabilistic way of sampling, it can be more targeted and effective in certain scenarios.

In quota sampling,individuals are selected to fulfill predetermined quotas for specific subgroups. It ensures that certain characteristics (e.g., age, gender, occupation) are proportionally represented. However, selection within those groups is not random.

Example 12: A survey of school learners might ensure that 20 learners from each grade answer the survey by asking a prefect to go into a classroom of each grade, and handing out the survey to the first 20 learners in each class that want to fill it in. This is a quick and convenient way to ensure that each grade is adequately represented. However, since it is non-probabilistic, the study might still exhibit various kinds of sampling bias. We will talk about sampling bias in the next section.