Written by Sampling is a process in statistical analysis where researchers take a predetermined number of observations from a larger population.
It allows researchers to conduct studies about a large group by using a small portion of the population.
There are two major methods of sampling, namely; probability and non-probability sampling methods.
Probability Sampling Methods
Probability sampling methods involve selecting a sample from a population in a way that each member of the population has a known and non-zero chance of being included in the sample.
These methods rely on the principles of probability theory to provide representative and unbiased samples.
Examples of probability methods are simple random sampling, systematic sampling, stratified sampling, and cluster sampling.
Let us now look at each one in details.
1. Simple random sampling: This is the most basic form of sampling where each member of the population has an equal chance of being selected.
Simple sampling involves randomly selecting individuals from the entire population, where each unit has an equal chance of being selected.
This method is often used when the population is relatively small and easily accessible.
2. Systematic sampling: This involves selecting every kth unit from the population, where k is a constant determined by dividing the population size by the desired sample size.
The first unit is randomly selected, and subsequent units are selected at regular intervals.
For example, if you have a list of 4,000 inhabitants and want to select a sample of 100 people using systematic sampling, you can calculate the interval (e.g., 40) and select every 40th person from the list.
Systematic sampling is used when the population is ordered in some way, such as a list. It provides a simple and efficient way to select a representative sample.
3. Stratified sampling: This involves dividing the population into homogeneous and mutually exclusive groups called strata, and then selecting a random sample from each stratum.
Stratified sampling ensures that each stratum is represented in the sample proportionally to its size in the population.
This method is mostly used when there are important subgroups within the population that need to be represented accurately in the sample.
4. Cluster Sampling: This involves dividing the population into clusters or groups, and then randomly selecting a few clusters to include in the sample.
Within each selected cluster, all units are included in the sample.
This method is often used when it is impractical or costly to sample individuals directly.
Non-probability Sampling Methods
Non-probability sampling methods are techniques used to select samples for research studies based on subjective judgment rather than random selection.
Non-probability sampling methods do not rely on random selection.
They do not provide a known probability of inclusion for each member of the population.
Non-probability sampling methods are often used when it is difficult or impractical to obtain a random sample.
The most common non-probability sampling methods are convenience sampling, snowball sampling, quota sampling and purposive sampling.
1. Convenience sampling: This method involves selecting samples that are readily available and accessible to the researcher.
It is the most common non-probability sampling method due to its speed, cost-effectiveness, and ease of availability of the sample.
2. Snowball sampling: This method is used when the target population is difficult to locate or when the sample size is small.
The researcher starts with a few initial subjects and then asks them to refer other subjects who meet the criteria for the study.
This technique helps in forming a sample that may not be easily available through other methods.
3. Quota sampling: In this method, the researcher select a predetermined number or proportion of units, called a quota.
The selected quota should comprise subgroups with specific characteristics (e.g., individuals, cases, or organizations) and should be selected in a non-random manner.
Quota sampling is useful when the researcher is interested in studying specific subgroups within the population.
4. Purposive Sampling: Also known as judgmental sampling, this method involves selecting samples based on the researcher’s knowledge and judgment.
The researcher chooses individuals who they believe are most suitable for the research study.
However, this method is subjective and can introduce bias into the result.
