Surveys that combine one or more of simple random sampling,

Sampling:
In statistical analysis, the sampling is defined as selection of subjects from the population so as to estimate the whole population. In other words, it is a technique of selecting suitable subjects so that they form a representative of the given population. Due to this process, the cost is lowered and moreover the data collection is quicker than accessing the whole population.
Stages in sampling process:
- Define population of interest
- Identify sampling frame, list of units to be measured
- Identify the sampling method to select the units from sampling frame
- Establish the sample size
- Investigate the sampling plan
- Collect the data
Also, a sample should be selected in such a way that it is a representative of the population. It is also important to know how the sample is selected. The sample selected to be a representative sample; the units in the sample should be randomly selected. There are various sampling methods to select the sample from the given population. Some of them are listed below:
- Simple random sampling
- Stratified sampling
-Cluster sampling
- Systematic sampling
Simple random sampling:
Simple random sampling is defined as a method of sampling in which, a sample of size n is drawn from a population of size N by using a random method, such as a random number table or software such as EXCEL, MINITAB, etc. to ensure that each of the N population units has the same probability of being selected in each draw. The simple random sampling gives a sample that is representative of the population.
Systematic sampling:
Systematic sampling is defined as a method of sampling in which, at first, an entity is chosen from the first k entities in the list, followed by choosing every kth entity thereafter, till the desired sample size is obtained. It is a random sampling method.
Cluster sampling:
Cluster sampling is defined as a method of sampling in which, at first, the entire population is divided into heterogeneous subgroups, especially by geographic areas called cluster, then desired number of clusters are selected from all the clusters, usually by simple random sampling. Each cluster is expected to be representative of the population.
Stratified sampling:
We use this method when we are concerned about differences in subgroups within a population. Simple random samples are collected for each of the sub-groups.
Surveys that combine one or more of simple random sampling, systematic random sampling, cluster sampling, and stratified sampling. Then it is called multistage sampling.