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What is sampling techniques?

To get started with Sampling, the first question in your mind will be what is Sampling techniques?

What is sampling techniques?
What is Sampling Techniques?

Sampling is a technique that allows us to get information about the population based on the statistics from a subset of population (sample) with having to investigate every individual.

Why do we need Sampling?

Sampling saves our time, it is less cumbersome, more practical and it is very cost efficient. 

Lets take an example: 

We want to know the height of people in Mumbai. Is it practically possible to measure the height of each and every person in Mumbai? The answer is no. Even if you try it, the process will be quite tedious and time consuming.

So what we do. We simply taken random people and measure their height with the concept of sampling.

Lets see what points we should consider in Sampling. 

1. Identify and define the target population.
2. Select sampling frame.
3. Choose sampling method.
4. Determine sample size.
5. Collect the required data.

We shall cover the above steps with a real life example. We all are aware of exit polls. Exit Poll's gives us idea as to which party will win how many seats in the election and is declared once the voting phase is completed. 

So let's see how exit poll results are predicted or estimated. 

Step 1: Identify and define the target population

Our target will be only those people who vote. To vote a person has to be above 18 years of age. So we will consider only those people whose age is above 18.

Step 2: Select sampling frame

Do all the people above 18 years of age have their voter Id made? We will choose the people whose name appears on the voter list of the constituency.

Step 3: Choose sampling method

Generally, probability sampling methods are used because every vote has equal value and any person can be included in the sample irrespective of his caste, community or religion. 

Different samples are taken from different regions all over the country.

Step 4: Determine sample size

The larger the sample size, the more accurate our interference about the population would be.

For the exit polls, agencies try to get as many people as possible of diverse background to be included in the sample as it would help in predicting the number of seats a political party can win.

Step 5: Collect the required data

Once the target population, sampling frame, sampling technique and sample size have been established, the next step is to collect data from the sample.

So this is how exit poll results are predicted. 

Now lets see some Sampling Methods:

There are two types of Sampling Methods:

1) Probability Sampling ( every one has equal chance)

2) Non Probability Sampling ( every one does not has equal chance)

Probability Sampling has four sub-groups in it:

i) Simple Random Sampling 

In this type of sampling technique where every item in the population has an even chance and likelihood of being selected in the sample is called  simple random sampling.

ii) Systematic Sampling 

In Systematic Sampling the elements are chosen from a target population by selection a random starting point and selecting other members after a fixed sampling interval.

For example: It's like if you select 3 then your next selection will be 6, 9, 12, 15 and so on. The selection follows a fixed sampling interval of 3. 

iii) Stratified Sampling 

In stratified sample, the researchers divide the population into separate groups called strata. Then, a probability sample is drawn from each group.

For example: We divide the population on the basis of gender into male and female. Then we use simple random sampling method to draw from each group.

iv) Cluster Sampling

In cluster sampling, the researcher divides the population into separate groups called clusters,  Then a simple random sample of clusters is selected from the population.

An example of cluster sampling is area sampling, or geographical cluster sampling.

Non Probability Sampling has four sub-groups in it:

i) Convenience Sampling 

Convenience Sampling is a non probability sampling method. It is also known as grab sampling, accidental sampling or opportunity sampling. It is a sampling technique where subjects are selected because of their convenient accessibility and proximity to the researcher.

Example: This type of sampling is most useful for pilot testing. Using student volunteers as subject for research.

ii) Quota Sampling

In quota sampling, a population is first segmented into mutually exclusive sub-groups, just as in stratified sampling. Then judgement is made to select the subjects or units from each segment based on a specific proportion.

Example: A researcher might ask for a sample of 100 males and 150 females between the age of 30 and 40. This mean that researcher can put a demand on who they want to sample.

iii) Judgement Sampling

It is a sampling technique where the researcher selects the unit to be sampled based on his own knowledge or his professional judgement.

Example: A TV researcher wants a quick sample of opinion about a political announcement. They stop what seems like a reasonable cross-section of people in the street to get their views.

iv) Snowball Sampling

It is also called as chain sampling, referral sampling, chain-referral sampling. In this type of sampling existing study subjects recruit future subjects from among their acquaintances. This the sample group is said to grow like a rolling snowball.

Example: Snowball sampling is quite useful when members of population are hidden and difficult to locate and these members are closely connected. For instance, samples of homeless people,  users of illegal drugs.

This is a just a brief description of the types of Sampling Techniques. Hope you enjoyed reading it.