Information
Probability sampling is a method employed in quantitative research designs that aims to ensure that each member of the population has an equal chance of being included in the sample (Mwansa et al., 2022). This method's primary objective is to ensure that researchers draw valid conclusions from their findings and that their results represent the entire population. Researchers employ four primary sampling techniques to achieve this, which we will discuss below.
Simple random sampling involves using completely random techniques or tools, such as random number generators, to give each individual in the population an equal chance of being selected.
Systematic sampling is like simple sampling, but specific individuals are chosen regularly. However, it is essential to ensure that the list does not contain hidden patterns that could skew the sample (Mwansa et al., 2022).
When sampling the population, stratified sampling involves dividing it into different subpopulations that are significantly different from one another. Each subgroup is well-represented in the sample, and researchers must divide the population into subgroups based on related characteristics, such as gender, age group, income class, or function. Then, they randomly or systematically select samples from each subgroup.
Cluster sampling involves dividing a population into subgroups with characteristics comparable to those of the sample as a whole and randomly selecting entire subgroups. This method is proper when dealing with large and dispersed populations, but it is more likely to introduce sampling errors as there can be significant differences between clusters.
On the other hand, non-probability sampling is a subjective approach to selecting units from a population, making it a fast, easy, and inexpensive way of obtaining data. However, it assumes that the sample is representative of the population, which can be a risky assumption. Additionally, elements are chosen arbitrarily, making it impossible to estimate the probability of any element being included in the sample or identify possible bias.
Convenience sampling, also known as random sampling, utilises individuals who are most easily accessible as study participants.
Snowball sampling, also known as chain sampling or network sampling, asks early sample members to find and refer additional people who meet eligibility requirements.
Quota sampling involves the researcher determining the necessary number of participants from each population stratum and identifying population strata.
Lastly, Purposive sampling, also known as judgmental sampling, is based on the idea that the researcher's understanding of the population can select individuals for the sample.
