Question

Sampling is the process of selecting a representative subset of observations from a population to determine characteristics (i.e. the population parameters) of the random variable under study. Probability sampling includes all selection methods where the observations to be included in a sample have been selected on a purely random basis from the population. Briefly explain FIVE (5) types of probability sampling.

Answer 1

The main types of probability sampling methods are simple randomsampling, stratified sampling, clustersampling, multistage sampling, and systematic random sampling. The key benefit of probability samplingmethods is that they guarantee that the sample chosen is representative of the population.

Simple random sampling. Simple random sampling refers to any sampling method that has the following properties.

• The population consists of N objects.
• The sample consists of n objects.
• If all possible samples of n objects are equally likely to occur, the sampling method is called simple random sampling
• Stratified sampling.:-
• With stratified sampling, the population is divided into groups, based on some characteristic. Then, within each group, a probability sample (often a simple random sample) is selected. In stratified sampling, the groups are called strata.
• Cluster sampling.:-
• With cluster sampling every member of the population is assigned to one, and only one, group. Each group is called a cluster. A sample of clusters is chosen, using a probability method (often simple random sampling). Only individuals within sampled clusters are surveyed.

  Note the difference between cluster sampling and stratified sampling. With stratified sampling, the sample includes elements from each stratum. With cluster sampling, in contrast, the sample includes elements only from sampled clusters.

• Multistage sampling:-
• With multistage sampling, we select a sample by using combinations of different sampling methods.

  For example, in Stage 1, we might use cluster sampling to choose clusters from a population. Then, in Stage 2, we might use simple random sampling to select a subset of elements from each chosen cluster for the final sample.

• Systematic random sampling:-
• With systematic random sampling, we create a list of every member of the population. From the list, we randomly select the first sample element from the first k elements on the population list. Thereafter, we select every kthelement on the list.

  This method is different from simple random sampling since every possible sample of nelements is not equally likely.