Quant #24: Types of Random Sampling

One of the sampling methods is random sampling. In random sampling, every member of the population has a chance to be selected in the sample. There are various types of random sampling. These types include:

1. Simple random sampling: in this sampling method, every member of the population has an equal chance of being selected in the sample. The sampling frame in this method should ideally cover the whole population.
2. Systematic sampling: this sampling method involves sampling members of the population by following a fixed and preset interval. A researcher selects a starting point randomly and selects members of the population after the sampling interval. For example, you can start by choosing a random household, and then decide to skip every two households before you select the next member of the population.
3. Stratified sampling: this sampling method involves dividing the population into subgroups or subpopulations (strata) that may differ in considerable ways. The strata are based on the relevant features or characteristics. For instance, a researcher may decide to stratify the population based on age group, gender, or income level. This method is often used to ensure that every stratum is properly represented in the ultimate sample. Once the strata are identified, data can be collected using either simple or systematic sampling from each stratum.
4. Cluster sampling: in this sampling method, a researcher divides the population into subgroups. Each subgroup should have similar characteristics to the whole sample. Rather than sampling participants from each subgroup, a researcher randomly selects entire subgroups (not individual members). Where practically possible, a researcher then includes every individual member from each sampled cluster. However, this is not always possible as the selected clusters themselves may be large. In this case, an alternative is to also sample individuals from within each cluster, a process called multistage sampling.