- Sampling is a process of selection of a limited number of items from a larger whole or universe of items.
- Sampling provides a representative set of data which stands for whole of the population.
PROBABILITY SAMPLING
- Every element in the sample population has equal chance of being selected.
- Lottery or simple random sampling – Sampling units are mixed and highest degree of randomness is ensured by this true probability sampling.
- Systematic random sampling – Sample is selected on the basis of some predefined criteria. Eg: every 10th member of a population.
- Stratified sampling – Leads to enhanced representation as no stratum of population is excluded. This type of Sampling is used when the researcher wants to highlight specific groups within the population.
- Cluster sampling – Clusters are either spatial or temporal. It can be multistage, like, state level sampling followed by district level sampling which is again followed by block level sampling.
NON-PROBABILITY SAMPLING
- Sometimes randomness or probability is not possible in sampling process.
- It is due to unavailability of data or other constraints.
- Convenience sampling or Accidental sampling – Sample is drawn from the population that is closest at hand.
- Purposive sampling – I is the type of sampling in which a purpose is already there in the mind of the researcher and sample characteristics are predefined. Eg: black male population.
CRITICISM
- Sampling process is prone to errors like faulty design and researcher bias.
- Chances for under coverage and non-response.
- Only a small population is represented.
- Other less faulty research methods are available.
