9.3.5 Sampling

Describes Different Approaches to Sampling: Simple Random, Stratified Random, Systematic and Cluster

Sampling is a critical component of research, and different methods are used depending on the type of study, research questions and the population under investigation. Four common approaches to sampling include simple random, stratified random, systematic and cluster sampling.

Simple random sampling:

Simple random sampling is a method of selecting a sample from a population in which each member of the population has an equal chance of being included in the sample. This method ensures that the sample is representative of the population and helps to minimize sampling bias. Simple random sampling is most effective when the population is relatively homogeneous and when there is little variation between individuals. To perform simple random sampling, researchers can use various techniques, such as random number generators, lottery methods, or computer algorithms to randomly select individuals from the population.

Stratified random sampling:

Stratified random sampling is a method of selecting a sample from a population by first dividing the population into distinct, non-overlapping subgroups or strata based on specific characteristics or attributes. Within each stratum, a simple random sample is then selected. This method is particularly useful when the population is heterogeneous, and the researchers want to ensure representation from each subgroup in the sample. Stratified random sampling helps to increase the precision of the study results by reducing sampling error and ensuring that important subgroups are adequately represented in the sample.

Systematic sampling:

Systematic sampling is a method of selecting a sample from a population by choosing every nth individual in the population list after a random starting point. This method is straightforward to implement and is often used when a complete list of the population is available. Systematic sampling can provide a representative sample if the order of the population list is unrelated to the characteristics of interest. However, it is important to note that systematic sampling may introduce bias if the population list exhibits periodic patterns that are related to the study variable.

Cluster sampling:

Cluster sampling is a method of selecting a sample from a population by first dividing the population into distinct, non-overlapping groups or clusters, usually based on geographical or organizational boundaries. A random sample of clusters is then selected, and all individuals within the chosen clusters are included in the sample. Cluster sampling is particularly useful when the population is spread across a large geographical area or when obtaining a complete list of the population is difficult. This method can save time and resources by reducing the need for extensive travel or data collection. However, cluster sampling may result in higher sampling error compared to simple random sampling, as individuals within a cluster may be more similar to each other than to individuals in other clusters.

Sampling approach	Description	Advantages	Disadvantages
Simple random sampling	A random selection of individuals from a population.	Unbiased; all members of the population have an equal chance of being selected.	May not be representative of the population if the sample size is too small.
Stratified random sampling	Dividing the population into strata and randomly selecting participants from each stratum.	Ensures representation from all subgroups of the population; reduces sampling error.	Requires prior knowledge of the population to create strata.
Systematic sampling	Selecting every nth individual from a population after a random starting point.	Easy to use; requires less effort compared to simple random sampling.	Can introduce bias if the sampling interval coincides with a pattern in the population.
Cluster sampling	Randomly selecting groups or clusters from a population and then sampling individuals within each cluster.	Cost-effective; suitable for geographically dispersed populations.	Clusters may not be representative of the population; may introduce additional sampling error.

The choice of sampling method depends on the research questions, the population under investigation, the available resources and the desired level of precision. Researchers must ensure that their sampling approach is unbiased and representative of the population of interest to ensure the validity and generalizability of their findings.

References:

1. Babbie, E. R. (2016). The basics of social research. Cengage Learning.
2. Creswell, J. W. (2014). Research design: qualitative, quantitative, and mixed methods approaches. Sage publications.
3. Polit, D. F., & Beck, C. T. (2017). Nursing research: Generating and assessing evidence for nursing practice. Wolters Kluwer Health/Lippincott Williams & Wilkins.