Probability Sampling Methods Explained
It would normally be impractical to study a whole population. Thus, sampling is a method that allows researchers to infer information about a population based on results from a subset of population, without having to investigate every individual.
If a sample is to be used, by whatever method it is chosen, it is important that the individuals selected are representative of the whole population. This may involve specifically targeting hard to reach groups.
There are several different sampling techniques available and they can be sub divided into two groups: Probability sampling and Non-Probability sampling.
Under Probability Sampling:-
1.Simple Random Sampling
2. Systematic Sampling
3. Stratified Sampling
4. Cluster Sampling
In this article, let us see only the Probability Sampling Method in detail.
it involves random selection, allowing to make statistical inference about the whole group.
a. Simple random sampling-
Each individual is chosen entirely by chance and each member of population has an equal chance / probability of being selected.
E.g. — You want to select a simple random sample of 100 employees of Company X. You assign a number to every employee in the company database from 1 to 1000, and use a random number generator to select 100 numbers.
b. Systematic Sampling-
It is similar to simple random sampling, but is usually slightly easier to conduct. Every member of population is listed with a number, but instead of randomly generating numbers, individuals are chosen at regular intervals.
E.g. — All employees of the company are listed in alphabetical order. From the first 10 numbers, you randomly select a starting point: number 6. From number 6 onwards, every 10th person on the list is selected (6, 16, 26, 36, and so on), and you end up with a sample of 100 people.
c. Stratified Sampling-
This method is appropriate when population has mixed characteristics and you want to ensure that every characteristic is proportionally represented in sample.
Population is divided into subgroups (Strata) based on relevant characteristics. From overall proportions of population, how many people should be sampled from each sub group is calculated. Then random / systematic sampling is done to select a sample from each subgroup.
E.g.- The company has 800 female employees and 200 male employees. You want to ensure that the sample reflects the gender balance of the company, so you sort the population into two strata based on gender. Then you use random sampling on each group, selecting 80 women and 20 men, which gives you a representative sample of 100 people.
d. Cluster Sampling-
Cluster sampling also involves dividing population into sub — groups, but each sub-group should have similar characteristics to whole sample. Instead of sampling individuals from sub groups, you randomly select entire sub groups. This method is good for dealing with large and dispersed populations, but there is more risk of error in sample as there should be substantial difference b/w clusters. It’s difficult to guarantee that sample clusters are really representative of whole population.
E.g.- The company has offices in 10 cities across the country (all with roughly the same number of employees in similar roles). You don’t have the capacity to travel to every office to collect your data, so you use random sampling to select 3 offices — these are your clusters.
In the next article, lets have a detailed study of Non- Probability Sampling Methods.
The link for it is : https://medium.com/@minions.k/non-probability-sampling-methods-explained-afab51fcbdd7
