This sample is also referred to as a random sample or a probability sample. This is one of the basic kinds of random sampling. Formally, simple random sampling is defined as the technique of drawing samples in such a way that every possible sample of a given size has an equal chance of being selected from the population. In this context, it is termed the equal probability of selection method (EPSEM), ensuring that each member has an equal chance of being selected.
Let us elaborate on the equal probability of selection method (EPSEM).
Equal Probability of Selection Method (EPSEM):
One of the ways to explain the process of drawing a simple random sample is to imagine the “hat model”. The procedure involves the following steps: First, the researcher has to purchase a big top hat. Secondly, prepare a piece of paper for every person in the population and place them in that big hat, ensuring that the slips are of standard size in terms of shape, size, and weight.
Consider the population of 1500 individuals. In this scenario, the researcher will prepare 1500 papers to align with the population size. If the aim is to draw a sample of 125 people, the next step involves thoroughly shaking and mixing the papers within the hat. After that, the researcher has to select one paper from that hat. The researcher repeats the process of shaking and mixing the paper slips in the hat before selecting another paper slip. Upon choosing the 125 paper slips, the researcher obtains a random sample of 125 individuals from the population.
There are reasons to consider this method as simple.
This method is imagined as “simple” due to its straightforwardness and implementation of a random process for the selection of the sample. Simple random sampling aims to generate a manageable and balanced set of individuals that accurately represents the broader population. To achieve this, specific steps guide the selection of a simple random sample.
Steps to select simple random sample
The process of selecting a simple random sample involves four steps.
1. Defining the population
The initial step in simple random sampling involves making a decision about the population of the study. It is vital for the researcher to have access to every member of the selected population. This enables the researcher to collect data from every selected member of the population. For instance, the research process might focus on the population of a specific district or province within a country.
2. Selection of the sample size:
The next step in simple random sampling is making a decision regarding the size of the sample for research. The study objectives determine the sample size, providing statistical certainty, but they are costly and require more hard work.
One can adopt various methods to make decisions about the size of the population, but a straightforward approach involves using a formula for the desired confidence interval and level of confidence. This formula requires input parameters such as the estimated population size and the standard deviation of whatever the researchers aim to measure in their population.
Typically, researchers use a 0.95 confidence interval and a 0.05 level of significance. They can then employ a sample size calculator to estimate the required sample size.
3. Random selection of the sample:
To randomly select the sample, researchers can use the lottery method, computer program, or random number method.
In the random number method, assign a number to each individual, and the researcher can choose a subset of the population by using a random sample generator or a random number table.
4. Collection of data from the sample:
In the final stage, the researcher collects the required information from the randomly selected sample.
Ensuring the validity of the findings requires the selected participants to actively participate in the research study. The inability of participants to participate in the study can lead to biased findings. There are different reasons for using simple random sampling.
Reasons of using simple random sampling
Researchers use this method in situations where they have limited knowledge about the population of participants. Furthermore, researchers ensure active communication with each participant to guarantee an accurate determination of the true population size.
Advantages of simple random sampling
- As compared to other sampling techniques, there is a minimum sampling bias.
- With the availability of a large sample frame, it is easier to form the sample group.
- Due to the availability of sample representation, one can generalize the results back to the same population.
- In this method, there is no need for advanced technical knowledge.
Disadvantages of simple random sampling
- The availability of all members of the population is necessary for drawing samples through this, which can make this method more costly and time-consuming.
- In the same way, selection of a large sample is one of these disadvantages at a practical level.
- In situations where there is involvement in a face-to-face interview, it can become difficult to cover a large geographical area due to cost and time considerations.
- In situations where the sample does not represent the whole population, sampling errors can occur. In such a situation, the researcher has to restart the process of sampling.