Give 4 differences between stratified and cluster sampling
1. Definition and Purpose: Stratified sampling involves dividing the population into homogeneous subgroups or strata based on certain characteristics, and then selecting a random sample from each stratum. This technique is used when the population contains distinct subgroups that may vary significantly in terms of the variable of interest. On the other hand, cluster sampling involves dividing the population into groups or clusters, and then selecting a random sample of clusters. This technique is used when it is more practical or cost-effective to sample entire groups rather than individual elements.
2. Selection Method: In stratified sampling, after dividing the population into strata, simple random sampling or other probability sampling methods are used to select individuals from each stratum. Whereas, in cluster sampling, simple random sampling or other probability sampling methods are used to select clusters, and then all individuals within the selected clusters are included in the sample.
3. Representativeness: Stratified sampling ensures that each subgroup in the population is represented in the sample, which increases the representativeness of the sample and allows for comparing results between different subgroups. Cluster sampling may result in a less representative sample since only a subset of clusters is selected, and it assumes that the selected clusters are representative of the entire population.
4. Precision and Efficiency: Stratified sampling generally provides more precise estimates and requires a smaller sample size compared to cluster sampling. This is because stratified sampling provides more information about each subgroup separately, allowing for more accurate estimation. Cluster sampling, while being less precise, can be more efficient in terms of data collection and cost, particularly when the clusters are geographically dispersed or when there are logistical constraints in accessing individual elements in the population.