The five common sampling techniques used to select a sample are listed below, along with some good explanations.
With the random sample, every member of the population has a certain chance of being selected.
When the probability of being selected is the same for every member of the population, the random sample is called simple random sample.
A computer is often used to generate phone numbers at random.
The word convenience undoubtedly reminds you of something that is comfortable or requires little effort.
This is what the comfort test is all about. Sampling is done in this way, as little effort is required.
You might want to know how many college students in the state of California enjoy playing basketball.
This is very convenient as you don’t have to do anything.
To make it even more convenient, the researcher can choose schools that are close to his home.
In the systematic sample, we first select a starting point at random and then select every nth member of the population.
n is an integer.
In the systematic sample presented above, the fourth member is selected first.
Every third member is then selected.
For 200 people, your sample looks like this
4th, 7th, 10th, 13th, 16th, 19th, 22nd, 25th and so on.
Cluster sampling divides the population into clusters or groups. Then some of these clusters are selected.
Finally, select all members from these selected clusters.
Suppose a country has 20 cities.
First of all, you can divide the land into 20 clusters. Second, you can choose 5 clusters or cities or groups at random.
Finally, you can select all members from these 5 cities.
In stratified sampling, you first divide the population into at least two non-overlapping subsets or strata.
This could be done by age, gender, nationality, etc.
Then take one sample at a time.
For example, you could have 2 subgroups of men and women.
Note that the ratio for both subgroups is 2 to 3. The proportion must be the same for all subgroups.