Explain different sampling techniques?

Q. Explain different sampling techniques?

Ans -
Probability Sampling
    Probability sampling is where the probability of each person or thing being part of the sample is known.

1. In simple random sampling, every member of the population has an equal chance of being chosen. The drawback is that the sample may not be genuinely representative. Small but important sub-sections of the population may not be included.
2. Researchers therefore developed an alternative method called stratified random sampling. This method divides the population into smaller homogeneous groups, called strata, and then takes a random sample from each stratum.
3. Proportional stratified random sampling takes the same proportion from each stratum, but again suffers from the disadvantage that rare groups will be badly represented. Non-proportional stratified sampling therefore takes a larger sample from the smaller strata, to ensure that there is a large enough sample from each stratum.
4. Systematic random sampling relies on having a list of the population, which should ideally be randomly ordered. The researcher then takes every nth name from the list.
5. Cluster sampling is designed to address problems of a widespread geographical population. Random sampling from a large population is likely to lead to high costs of access. This can be overcome by dividing the population into clusters, selecting only two or three clusters, and sampling from within those.

Non-Probability Sampling

Using non-probability sampling methods, it is not possible to say what is the probability of any particular member of the population being sampled. Although this does not make the sample ‘bad’, researchers using such samples cannot be as confident in drawing conclusions about the whole population.

1. Convenience sampling selects a sample on the basis of how easy it is to access. Such samples are extremely easy to organise, but there is no way to guarantee whether they are representative.
2. Quota sampling divides the population into categories, and then selects from within categories until a sample of the chosen size is obtained within that category. Some market research is this type, which is why researchers often ask for your age: they are checking whether you will help them meet their quotas for particular age groups.
3. Purposive sampling is where the researcher only approaches people who meet certain criteria, and then checks whether they meet other criteria. Again, market researchers out and about with clipboards often use this approach: for example, if they are looking to examine the shopping habits of men aged between 20 and 40, they would only approach men, and then ask their age.
4. Snowball sampling is where the researcher starts with one person who meets their criteria, and then uses that person to identify others. This works well when your sample has very specific criteria: for example, if you want to talk to workers with a particular set of responsibilities, you might approach one person with that set, and ask them to introduce you to others.