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Jan 31
1. Simple Random Sampling: In this method, each member of the population has an equal chance of being selected. A random number generator or a randomization table can be used to select the sample.
2. Systematic Sampling: This method involves selecting every nth member from the population after a random starting point is chosen. For example, if the population size is 1000 and a sample size of 100 is desired, every 10th member can be selected.
3. Stratified Sampling: This method involves dividing the population into homogeneous subgroups called strata and then selecting a random sample from each stratum. This ensures representation from each subgroup in the sample.
4. Cluster Sampling: In this method, the population is divided into clusters or groups, and a random sample of clusters is selected. Then, all members within the selected clusters are included in the sample. This method is useful when it is difficult to obtain a complete list of the population.
5. Multi-stage Sampling: This method combines different sampling techniques. It involves selecting clusters or groups in the first stage, then selecting subgroups within the clusters in the second stage, and finally selecting individuals within the subgroups in the final stage. This method is often used in large-scale surveys or studies.
2. Systematic Sampling: This method involves selecting every nth member from the population after a random starting point is chosen. For example, if the population size is 1000 and a sample size of 100 is desired, every 10th member can be selected.
3. Stratified Sampling: This method involves dividing the population into homogeneous subgroups called strata and then selecting a random sample from each stratum. This ensures representation from each subgroup in the sample.
4. Cluster Sampling: In this method, the population is divided into clusters or groups, and a random sample of clusters is selected. Then, all members within the selected clusters are included in the sample. This method is useful when it is difficult to obtain a complete list of the population.
5. Multi-stage Sampling: This method combines different sampling techniques. It involves selecting clusters or groups in the first stage, then selecting subgroups within the clusters in the second stage, and finally selecting individuals within the subgroups in the final stage. This method is often used in large-scale surveys or studies.
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