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Introduction To Types Of Sampling
The terms related to statistics for referring to misleading terms.
Sampling is a statistical procedure that is concerned with the selection of the individual observation. In sampling, we assume that samples are drawn from the population and sample means and population means are equal. A population can be defined as a whole that includes all items
Sampling
Following Some terminologies are relevant to sampling:
- Sample: selected part from population.
- Sample size:the number of people in the selected sample.
- Sampling frame: It means the list of people included in the sample.
- Sampling Technique: It refer to select the member of sample
Types of Sampling:
There are two types
- Probability Sampling
- Non-Probability Sampling
Probability Sampling
Probability sampling is a type of sampling where each member of the population has a known probability of being selected in the sample. When the population is highly homogenous, each member has a known chance of being selected in a sample.
For Example:
If you want to pick up some apples from the apple garden, the selected part will have the same characteristics. In this example, each member has a known chance of being selected in the sample.
Types of Probability Sampling:
- Simple Random Sampling
- Stratified Random Sampling
- Systematic Sampling
- Cluster Sampling
- Multi-Stage Sampling
Simple Random Sampling
In simple random sampling,the members are selected randomly and purely by chance.As every member has an equal chance of being selected in the sample, random selection of members does not affect the quality of the sample.it like lottery selection.
Stratified Random Sampling
In Stratified Random Sampling, first the population is divide into sub-groups(i.e. Known as strata) and then each member from each sub-group are selected randomly.This method adopts the population when not highly homogeneous.Hence, the first population is divided into homogenous subgroups on the basis of similarities.Then member of each sub-group divided into randomly.The purpose of these method is less homogeneous of the population.
Systematic Sample
In systematic sampling,members occurring after a fixed interval is selected.The member occurring after a fixed interval is known as Kth element.
For Example:if a select member occurs after every 10th member,the Kth element becomes the 10th element.
Sample = {10,20,30,40,50,60,70,80,90,100}
Select the member in a systematic manner called as systematic sampling.kth element depends upon size of population.
For Example:If you want to select a sample from 20 members from the population of a total 1000 members.We will divide the total population over desired sample 1000/50 = 20. It means that every 50th element member from the population to make the sample of 20 members.
Cluster Sampling
Various segments of population are treated as clusters and members from each cluster selected randomly.it is similar to the stratified sampling but big difference is, in stratified sampling the population into homogeneous subgroups on the basis of similar characteristics i.e.age, sex, religion, etc.and in cluster sample does not divide the population into sub-groups but randomly selected from already existing.Each cluster may be more or less homogenous.
Multi Stage Sampling
Multi-stage sampling is a complex form of clustering sample.Each cluster divides into different smaller clusters and members are selected from each smaller cluster randomly,called as Multi-stage sampling.
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Non-Probability Sampling
Non-Probability sampling is a type of sampling where each member of the population does not have a known probability of being selected in the sample.In this type of sampling,each member of population does not get an equal chance of being selected in the sample.
For Example: study impacts of domestic violence on children.Will arrange an interview for all children,but interview only those children who are subjected to domestic violence.
- Purposive Sampling
- Convenience Sampling
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Purposive Sampling
It is a type of sampling where the members for a sample are selected according to purpose of the study.
For Example,study of drug abuse on health.Every member of the society is not best responded for this study.Only people who are addicted to drugs can be best respondents.
Convenience Sampling
It is sampling where the members of the sample are selected on the basis of their convenient accessibility.
For Example,researchers may visit the college or university and get questioniories about volunteers, same study about volunteers in the market.
Random Sampling
In data collection, every individual observation has equal probability to be selected into a sample. In random sampling, there should be no pattern when drawing a sample.
Conclusion
We have learned about sampling and its types.Sampling which is based on probability and non-probability mode.In this article covered all concepts related to sampling.
