Explain the four probability sampling methods described in the chapter and give an example of when cluster sampling might be used.

The chapter describes four probability sampling methods: simple random sampling, systematic random sampling, stratified random sampling, and cluster sampling. In simple random sampling, every member of the population has an equal chance of selection via a random method. Systematic random sampling selects participants in a systematic way, such as every twentieth person from a list, with a randomly chosen starting point. Stratified random sampling divides the population into layers with similar characteristics to ensure representation from each group. Cluster sampling is used when the population is large and widely dispersed, making random selection impractical; for example, a study on attitudes toward dietary modifications in people with type 2 diabetes might randomly select primary care practices or clinics as clusters.

The chapter presents probability sampling as the preferred approach in quantitative research because it uses statistical theory to randomly select participants from a larger population, giving each member a known and equal chance of being chosen. This helps ensure a representative sample and supports generalisability. Simple random sampling involves selecting participants entirely at random, for example by assigning numbers to individuals and using a computer-generated process to pick them. Systematic random sampling selects the sample in a systematic rather than random way; for instance, if a sample of 100 is needed from a population of 2000, the researcher selects every twentieth person on a list, with the starting point chosen at random. The list should not be ordered in a way that creates a trend, such as listing patients by age. Stratified random sampling, also called quota sampling, divides the population into layers or strata of participants with similar characteristics, such as nurses, student nurses, and nurse managers. This supports the selection of representative samples from each subgroup. Cluster sampling is employed when the population is large and widely dispersed, making it impractical to select randomly. For example, in a study focusing on attitudes towards dietary modifications in people with type 2 diabetes, a cluster might be a randomly selected group of primary care practices or clinics. From these clusters, a subset might be identified based on household income, age, or educational attainment, and then a sample would be selected randomly from that subset. This approach is also useful when a complete list of the population is unavailable.