A set of data or elements drawn from a larger population and analyzed to estimate the characteristics of that population is called sample. And the process of selecting a sample from a population is called sampling.
Procedure by which some members of a given population are selected as representatives of the entire population
TYPES OF SAMPLING
There are two types of sampling
- Probability sampling
- Non-probability sampling
- Probability Sampling:
A sampling technique in which each member of the population has an equal chance of being chosen is called probability sampling.
There are four types of probability sampling
- Simple random sampling
- Systemic sampling
- Stratified sampling
- Cluster sampling
- Simple Random Sampling
A probability sampling technique in which, each person in the population has an equal chance of being chosen for the sample and every collection of persons of the same size has an equal chance of becoming the actual sample.
- Systematic Sampling
A sample constructed by selecting every kth element in the sampling frame.
Number the units in the population from 1 to N decide on the n (sample size) that you want or need k = N/n = the interval size randomly select an integer between 1 to k then take every kth unit.
- Stratified Random Sampling.
Is obtained by separating the population elements into non overlapping groups, called strata, and then selecting a simple random sample from each stratum.
- Cluster Sampling.
A simple random sample in which each sampling unit is a collection or cluster, or elements. For example, an investigator wishing to study students might first sample groups or clusters of students such as classes and then select the final sample of students from among clusters. Also called area sampling.
- Non-Probability Sampling
Non-probability sampling is a sampling technique where the samples are gathered in a process that does not give all the individuals in the population equal chances of being selected.
It decreases a sample’s representativeness of a population.
Type of Non-probability sampling
Following are the common types of non-probability sampling:
- Convenience sampling
- Quota Sampling
- Purposive/ judgmental sampling
- Network/ snowball Sampling
- Convenience Sampling:
The members of the population are chosen based on their relative ease of access. Suchsamples are biased because researchers may unconsciously approach some kinds of respondents and avoid others
- Quota Sampling
It is the non-probability version of stratified sampling. Like stratified sampling, the researcher first identifies the stratums and their proportions as they are represented in the population. Then convenience or judgment sampling is used to select the required number of subjects from each stratum. This differs from stratified sampling, where the stratums are filled by random sampling.
- Purposive Sampling.
It is a common non-probability method. The researcher uses his or her own judgment about which respondents to choose, and picks those who best meets the purposes of the study.
- Snowball Sampling
It is a special non-probability method used when the desired sample characteristic is rare. It may be extremely difficult or cost prohibitive to locate respondents in these situations. Snowball sampling relies on referrals from initial subjects to generate additional subjects. While this technique can dramatically lower search costs, it comes at the expense of introducing bias because the technique itself reduces the likelihood that the sample will represent a good cross section from the population.