Introduction of Simple Random Sampling
What is Simple Random Sampling?
A population subgroup that has been randomly selected is referred to as a simple random sample. With this sampling method, every person in the population has the same probability of getting chosen.
Since it only includes one random sample and requires little prior population knowledge, this method is the easiest of all the sampling design techniques. Any study conducted on this population should have a high level of internal and external validity because randomization is used.
A straightforward random sample, where each participant has an equal chance of being selected, selects a tiny, random piece of the total population that will represent the full data set.
Using techniques like jackpots or randomized draws, researchers can generate a straightforward random sample.
If the sampling does not precisely reflect the population it is intended to represent, there may have been a sampling error.
Simple random samples are created by randomly selecting values from a population and assigning consecutive values to each item.
In contrast to systematic, stratified, or cluster sampling, simple random sampling offers a different sampling strategy.
Example
The names of 25 employees from a company with 250 workers would be drawn at random from a hat as an illustration of a simple random sample. Since each worker has an equal chance of being chosen, the population in this example is all 250 employees, and the sample is therefore random.
Utilizing simple random sampling
To draw conclusions about a population using statistics, simple random sampling is utilized. Because randomization is the greatest way to lessen the impact of any confounding factors, it helps to assure good internal validity.
A simple random sample also has external validity when the sample size is large enough since it accurately captures the traits of the broader population.
However, it can be difficult to put the basic random selection into reality. To apply this strategy, there are some necessary conditions:
- Every individual in the population is on your exhaustive list.
- If they are chosen, you can get in touch with or visit each person in the population.
- You have had the time and the money to gather the information from the required sample size.
The best method for conducting a study is simple random sampling if you have plenty of resources and time available or if you are researching a small group that is simple to sample.
A different kind of probability sampling might be more suited in some circumstances:
Systematic Sampling uses a regular interval to select your sample rather than a completely random one. When you don't have a comprehensive population list, you can still use it.
Stratified sampling is suitable when you wish to guarantee that a sample fairly represents a certain set of attributes. You divide your population into stratum (by, say, gender or race), and then choose at random from each of these divisions.
Cluster Sampling is suitable when it is impossible to sample the entire population. After dividing the samples into clusters that roughly represent the whole population, you randomly select the clusters from which to draw your sample.
How to do a simple random sample
To choose a basic random sample, follow these 4 important steps.
Describe the population
Selecting the population for your study should be your first step.
It's crucial to make sure you obtain access to a single person in the population so you can get information from everyone who is chosen for the sample.
Choose the sample size
The next step is to choose the size of your sample. Larger samples offer greater statistical confidence, but they also expense more and involve a lot more work.
There are a number of possible approaches to choosing the sample size, but one of the simplest is to use a formula with your chosen confidence level, anticipated population size, and standard deviation of whatever you wish to measure in your population.
Choose your sample at random
The lottery or the random number approach can be used to accomplish this.
When utilizing the lottery approach, the sample is chosen at random either by "drawing from a hat" or by using a computer program that simulates the process.
You give each person a number using the random number procedure. You then select a portion of the population at random using a random number generator or random number tables. Microsoft Excel's random number function (RAND) can also be used to produce random numbers.
Gather information from your sample
In the end, you should gather information from your sample.
You must make sure that every person chosen actually participates in your study if you want to be certain that your results are valid. Your results may be skewed if some refrain from participating or leave the study for factors related to the research question.
For instance, if young people are systematically less likely to take part in your study, your findings may not be reliable because of the underrepresentation of this population.
Advantages of Simple Random Sampling:
Lack of Bias
Simple random sampling should totally eradicate any signs of bias. Each member of the large population set has the same likelihood of being chosen since the individuals who make up the subgroup of the larger group are selected at random. Most of the time, this results in a balanced subset with the best chance of accurately representing the broader group as a whole.
Simplicity
Making a simple random sample is significantly simpler than other approaches, as its name suggests. Using this technique doesn't require any specialized knowledge, and the results can be fairly trustworthy. As opposed to other sampling techniques like stratified random. This technique includes breaking up larger groups into scaled-down units known as strata.
Less Expertise Needed
We have already proven that simple random sampling is a fairly easy sampling technique to use. It also takes little to no specific knowledge, which is another benefit that is similar. This implies that in order to carry out their duties properly, the investigator is not required to have any expertise or information about the general public.
Comments
Post a Comment