Sampling
Definition:
Sampling is the process of selecting a group of people, objects, or items from a larger population. The purpose of sampling is to gather information about the population that can be used to draw conclusions about that population. The sample size and selection criteria will vary depending on the researcher’s research. Sampling is an essential part of the research process as it allows for more accurate results to be obtained.
Sampling Techniques
There are two techniques used in Sampling for data collection:
- Probability Sampling
- Non-probability Sampling
Probability Sampling
Probability sampling is a method of selecting a sample from a population in which each member of the population has a known and equal chance of being selected. This type of sampling is used when the researcher wants to ensure that the sample is representative of the population as a whole.
Types of Probability Sampling are:
- Simple random sampling
- Cluster sampling
- Systematic sampling
- Stratified random sampling
Non-probability Sampling
Non-probability sampling is a type of sampling method where samples are collected in a non-random manner. This means that the chances of any particular individual being selected for the sample are not known.
Types of Non-probability Sampling are:
- Convenience sampling
- Judgmental or purposive sampling
- Snowball sampling
- Quota sampling
When to use Sampling
One of the most important decisions in any research project is deciding when to use sampling. There are many factors that need to be considered when making this decision, such as the type of data being collected, the purpose of the research, and the resources available. Here are some guidelines to help you decide when to use sampling in your research project.
If you are collecting primary data, sampling is usually necessary. This is because it is not practical or possible to collect data from every individual in a population. For example, if you want to study the opinions of all American adults on a certain issue, it would be impossible to interview every single person. Therefore, you would need to use a sample of the population.
However, there are some situations where sampling is not necessary. For example, if you want to study the number of people who visit a certain park each week, there is no need to sample the population because every person in that population can be studied. If you are collecting secondary data, sampling may not be necessary but it often is useful.
Example of Sampling
Example:
If a researcher wants to study a specific group of people, such as teenagers or senior citizens, they may use a convenience sample. This involves selecting participants who are easily accessible and willing to take part in the research.
Importance of Sampling in Research
As anyone who has ever conducted research knows, sampling is an essential part of the process. Without a representative sample, it is impossible to draw conclusions about a population as a whole. This is why researchers put so much effort into designing their studies to ensure that they obtain a valid and reliable sample.
It allows researchers to make more accurate inferences about the population as a whole. Additionally, it makes it possible to generalize findings from the study to other populations.
Advantages of Sampling
There are many advantages of sampling when conducting research.
- Sampling allows researchers to study a smaller group of individuals in order to draw conclusions about a larger population.
- This is often more efficient and cost-effective than studying the entire population.
- Sampling can help ensure that the research is representative of the population by allowing for random selection.
- This allows for greater accuracy when making generalizations about the population.
- Sampling can help control for bias by ensuring that all members of the population have an equal chance of being selected for the study.
Disadvantages of Sampling
There are a few disadvantages of sampling that should be considered before using this method.
- Samples may not be representative of the population.
- This means that results from the sample may not be accurate when applied to the population as a whole.
- Sampling can be time-consuming and expensive.
- Collecting and analyzing data from a large number of people can take a lot of time and money.
- There is always the possibility of error when taking samples.
- This could lead to incorrect conclusions about the population being studied.
