Sampling Techniques

Random Sampling Techniques

• Random sampling: Everyone in the population the researcher is studying has an equal chance of being selected. This type of sampling is beneficial because it avoids bias and is generally representative of the larger group. However, it can be difficult to implement if the population is very large or spread out.

Opportunity Sampling Techniques

• Opportunity sampling: The researcher uses whoever is available and willing to participate. This technique is cost-effective and easy to execute. However, it may result in a sample that is not representative of the population, skewing results.

Stratified Sampling Techniques

• Stratified sampling: The population is divided into subgroups (strata) based on a specific feature, and participants are selected from each strata in a manner proportional to the population. This sampling technique ensures participants chosen are representative of key groups within the population. It can, however, be time-consuming and difficult to categorise the population into the right strata.

Quota Sampling Techniques

• Quota sampling: Similar to stratified sampling, but participants are recruited until a certain number (quota) for each subgroup is reached. This method is less random, but can be faster and ensure all groups are represented.

Systematic Sampling Techniques

• Systematic sampling: Every nth person from a list or a location is selected. This method is easy to apply when dealing with large populations, but it can introduce bias if the list or location has a pattern.

Snowball Sampling Techniques

• Snowball sampling: Current study participants recruit future participants from among their acquaintances. This technique is useful for reaching populations that may be inaccessible or hard to find, but can result in bias due to the interconnected nature of the individuals sampled.