The correct answer is (a) Stratified Random Sampling.
Stratified random sampling is a sampling technique where the population is divided into smaller groups called strata, and then a random sample is selected from each stratum. This sampling technique can be divided into proportionate and disproportionate stratified random sampling.
In proportionate stratified random sampling, the number of samples selected from each stratum is proportional to the size of the stratum in the population. This sampling technique is used when the researcher wants to ensure that all strata are represented in the sample in proportion to their size in the population.
In disproportionate stratified random sampling, the number of samples selected from each stratum is not proportional to the size of the stratum in the population. This sampling technique is used when the researcher wants to ensure that certain strata are represented more heavily in the sample than their size in the population.
Simple random sampling is a sampling technique where each member of the population has an equal chance of being selected for the sample. This sampling technique is easy to understand and implement, but it can be difficult to achieve if the population is large or if the sampling frame is not accurate.
Cluster sampling is a sampling technique where the population is divided into smaller groups called clusters, and then a random sample of clusters is selected. The members of each selected cluster are then included in the sample. This sampling technique is less expensive and time-consuming than simple random sampling, but it can be less accurate.
Multistage sampling is a sampling technique where the population is divided into smaller and smaller groups at each stage of the sampling process. This sampling technique is often used when the population is large or when the sampling frame is not accurate.
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