Table of Contents
Importance of Random sampling in the society
A chef fetching a spoon from an entire pot of soup to taste the contents of the ingredients is a common example of sampling we encounter in our everyday life processes.
If a sample is to be used by whatever method it is chosen it is important that the individual selected are representative of the whole population. Therefore, discuss random sampling clearly stating the features that characterize the process?
The chef will taste the spoon of soup and conclude the taste of the entire soup in the pot because the spoon of soup is just a subset (part) of the whole soup. In sampling, we can say that the spoon of soup is the sample and the whole pot of soup is the population.
The Importance of random sampling in the society cannot be over-emphasized, bearing in mind that it must be representative of the entire population.
We can now say, a sample is an item, Organism, or group of items, organisms that are taken from a larger population for the purpose of measurement.
A population is then, a group of items or organisms with one or more similar qualities. The essence of sampling is to measure the quality (variable) and generalize the findings to the population as a whole.
Today, we will be discussing random sampling with its features and methods in relation to the limitations of the sample being the representation of the population as a whole. The term researcher may be used to represent the person carrying out the sampling work.
Random Sampling
Description
Random sampling also referred to as probability sampling is a technique of sampling that is based entirely on chance. Every member of the population has an equal probability of being selected for sampling.
In probability sampling, you start with a complete sample frame, and members are selected for sampling by chance. The counterpart of random sampling is probability sampling where samples are selected by using standard processes and the members don’t have equal chances of being selected.
If properly conducted and all the bias are excluded, sampling should select individuals who are true representatives of the population as a whole. This sampling technique is less biased as compared to the other technique as individuals are selected randomly rather than by choice of preference.
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General Features of Random Sampling
The importance of random sampling in the society as well as its characteristics include
- The method of random sampling to be used depends on the population size. E.g taking a survey of a country can be done by methods of cluster or stratified random sampling but not by methods of systematic or simple sampling as they are used for a comparatively smaller population.
- Like all other sampling techniques, the selected samples represent the population as a whole.
- Individuals are selected randomly from a broader sampling frame. A randomly chosen subset will be selected from a larger population for sampling using different methods. For example, A researcher may assign different numbers to the individuals in the people and then select randomly without standard patterns as in simple random sampling.
- The sample size depends solely on the population size. A larger population size requires a larger sample and vice versa. If 10 people are selected in Benue for a survey on who to vote for in the upcoming elections, then 20 people or more should be selected in Kanuas the population is far more than that of Benue.
- There is an equal chance of selection. Every individual in random sampling has an opportunity of being selected.
- To begin random sampling, the total population size must be known. This helps the researcher to decide his sample size to avoid under or selecting of the sample size.
- It is more time-consuming and more expensive compared to non-probability sampling. A lot of time and resources are used in either gathering the individuals or making a list of the individuals for sampling.
- This technique of sampling does not require technical knowledge.
METHODS OF RANDOM SAMPLING
The four methods of random sampling include;
Simple random sampling
This method of sampling is based entirely on chance. Every member of the population has an equal chance of being selected as the selection is done randomly. This may include techniques such as assigning numbers to every individual and choosing the numbers randomly, till the sample size is achieved.
A researcher decides to select 20 students from the 200 students in biological sciences to ask them about their intercontinental travels. Matriculation numbers were selected randomly until 20 students were selected.
Advantages of simple Random sampling
This allows the sampling error to be calculated thus it reduces bias.
The person collecting the sample doesn’t need to have prior knowledge about the data he/she is collecting.
This is the most straightforward method of random sampling
It does not require any technical knowledge as samples are selected randomly.
Disadvantages of simple random sampling
The selected individuals may not have the characteristic of interest. From the example above, not all the students may have traveled to other countries.
It may be difficult to define a complete sampling frame and inconvenient to contact them. Taking the survey of black men in the world will be so difficult since they are scattered around the world.
A non-response sample may be selected. Imagine that one of the students selected is deaf; it will be difficult to obtain information from the students.
Systematic Random sampling
This method of probability uses fixed numbers at intervals to select a sample. After determining the right sample size the researcher wants to use, the researcher assigns a regular interval number they will use to select which numbers of the target population will be included in the sample. For instance, The HOD of chemistry decides to select 200 students from a population of 5000 students to award scholarships indiscriminately.
He will divide the population size by the sample size I.e 5000/200 = 25th members will be selected.
Assuming the Matriculation numbers of the students is from 1 to 5000. Then the students selected will be matric numbers 25, 50, 75,100…………………5000.
Advantages of systematic random sampling
It is often more convenient than simple random sampling
It is easy to understand and administer
The whole population is sampled evenly.
Disadvantages of systematic random sampling
It may lead to bias if the sample frame (matric numbers) is arranged in an underlying pattern. Let’s say the matric numbers was alternated between two tribes (Tiv and Idoma), choosing odd or even interval may lead to the selection of only a particular tribe.
The size of the population must be known before the sampling is done.
Stratified Random Sampling
In this method, the population is first divided into subgroups known as strata. Every individual in strata share a common characteristic ( i.e they are homogenous) e.g. Tribe.
And there is a general characteristic shared by the whole subgroups which is the characteristic we sample. Individuals are then selected randomly from each stratum until the desired sample size is achieved.
The selection of the individuals depends on the size of the strata e.g 10 individuals are selected from a stratum of 100 then 20 should be selected from that of 200. For example, A journalist decides to carry out a survey to know the people’s choice in the 2023 presidential election in Nigeria.
He then grouped the 36 states as strata and individuals were selected randomly from the strata for the survey. The selection in these strata (states) will not be the same as some states are way too populated than others.
Advantages of Stratified Random Sampling
It is advantageous over simple random sampling as it does ensure the representation of every group.
It improves the accuracy and representatives of results, therefore, reducing bias.
Disadvantages of Stratified Random Sampling
It requires knowledge about the subgroups, their common characteristic, and the entire sample frame
It can only be carried out when researchers form subgroups that are relatively homogeneous to the entire population.
Cluster Random Sampling
In cluster sampling, the population is divided into clusters. Then one or more clusters are selected randomly. In single-stage cluster sampling, all the individuals in the cluster(s) are then selected.
In a two-stage cluster sampling, members are selected randomly from the cluster(s) for sampling. It is usually used when sampling a large geographical region. For example, while selecting students to award scholarships in the federal universities in Nigeria.
The 56 federal universities in the country were grouped as clusters. Randomly five schools (clusters) were selected.
In a single-stage cluster sampling, all the students in the 5 universities will be selected. If it is a two staged cluster sampling, members will be chosen randomly across the 5 schools till the desired number is obtained.
Advantages of cluster sampling
It is more efficient than simple random, in cases where the study place is a wide geographical region
It reduces the cost and time of study.
Disadvantages of cluster sampling
The sample cannot be estimated to be the representation of the whole population as not all the clusters may be selected for research
It is a complex method . This sampling is not easy as researchers need to determine how to divide up a larger population efficiently.
Limitations of random sampling
Though random sampling may be a preferred technique for Sampling by many researchers, there are instances where a sample acquired by random sampling may not be a representation of the sample as a whole.
It cannot be performed on a population of an unknown size. If the size of the population is not known sampling error may arise.
The difficulty arises when a non-respondent (e.g a deaf person) is selected for sampling.
Hard-to-reach groups are usually not a target of random sampling.
Sampling on a very large population such as the whole world may be difficult and if at all carried out, a lot of sample errors may be made.
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