What is Simple Random Sampling?
Simple random sampling is a subset of a sample chosen from a larger population. Each individual is chosen randomly and purely by chance, such that each individual has the same probability of being chosen at any stage during the sampling process.
This process and technique is known as simple random sampling, and should not be confused with systematic random sampling. A simple random sample is a fair sampling technique.
Simple random sampling is a very basic type of sampling method and can easily be a component of a more complex sampling method. The main attribute of this sampling method is that every sample has the same probability of being chosen.
The sample size in this sampling method should ideally be more than a few hundred so that simple random sampling can be applied in an appropriate manner. It is sometimes argued that this method is theoretically simple to understand but difficult to practically implement. Working with large sample size isn’t an easy task and it can sometimes be a challenge finding a realistic sampling frame.
Simple random sampling methods
The following steps are involved in selecting simple random sampling:
1. A list of all the members of the population is prepared initially and then each member is marked with a specific number ( for example, there are nth members then they will be numbered from 1 to N).
2. From this population, random samples are chosen using two ways: random number tables and random number generator software. A random number generator software is preferred more as the sample numbers can be generated randomly without human interference.
There are two approaches that aim to minimize any biases in the process of simple random sampling:
- Method of lottery
Using the method of the lottery is one of the oldest methods and is a mechanical example of random sampling. In this method, each member of the population has to number systematically and in a consequent manner by writing each number on a separate piece of paper. These pieces of paper are mixed and put into a box and then numbers are drawn out of the box in a random manner.
- Use of random numbers
The use of random numbers is an alternative method that also involves numbering the population. The use of a number table similar to the one below can help with this sampling technique.
Simple Random Sampling Formula
Consider a hospital has 1000 staff members and they need to allocate night shift to 100 members. All their names will be put in a bucket to be randomly selected. Since each person has an equal chance of being selected and since we know the population size (N) and sample size (n) the calculation can be as follows:
Simple Random Sampling Example
- An organization has 500 employees. We want to extract a sample of 100 from them.
- List employees from 1 to 500Draw 100 numbers among 500 workers
- The sample will consist of the 100 employees that were selected from the numbers obtained.
Advantages of Simple Random Sampling
1.It is a fair method of sampling and if applied appropriately it helps to reduce any bias involved as compared to any other sampling method involved.
2. Since it involves a large sample frame it is usually easy to pick smaller sample size from the existing larger population.
3. The person who is conducting the research doesn’t need to have a prior knowledge of the data being collected. One can simply ask a question to gather the researcher need not be a subject expert.
4. This sampling method is a very basic method of collecting the data. There is no technical knowledge required and need basic listening and recording skills.
5. Since the population size is large in this type of sampling method there is no restriction on the sample size that needs to be created. From a larger population, you can get a small sample quite easily.
6. The data collected through this sampling method is well informed, more the samples better is the quality of the data.
Disadvantages of Simple Random Sampling
1. It is a costlier method of sampling as it requires a complete list of all potential respondents to be available beforehand.
2. This sampling method is not suitable for studies involving face-to-face interviews as covering large geographical areas have cost and time constraints.
3. A sample size that is too large is also problematic since every member of the population has an equal chance of selection. The larger population means a larger sample frame. It is difficult to manage the large population.
4. The quality of the data depends on the researcher and his/her perspective. If the researcher is experienced then there are fair chances the quality of data collected is of a superior quality. But if the researcher is inexperienced then the data collected may or may not be upto the mark.
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