Sampling Techniques

Sampling helps a lot in research. It is one of the most important factors which determines the accuracy of your research/survey result. If anything goes wrong with your sample then it will be directly reflected in the final result. There are lot of techniques which help us to gather sample depending upon the need and situation. This blog post tries to explain some of those techniques.

To start with, let’s have a look on some basic terminology:

• Population
• Sample
• Sampling

Population is the collection of the elements which has some or the other characteristic in common. Number of elements in the population is the size of the population.

Sample is the subset of the population. The process of selecting a sample is known as sampling. Number of elements in the sample is the sample size.

A visual representation of the sampling process

There are lot of sampling techniques which are grouped into two categories as:

1. Probability Sampling
2. Non- Probability Sampling

The difference lies between the above two is whether the sample selection is based on randomization or not. With randomization, every element gets equal chance to be picked up and to be part of sample for study.

1. Probability Sampling

This Sampling technique uses randomization to make sure that every element of the population gets an equal chance to be part of the selected sample. It’s alternatively known as random sampling.

• Simple Random Sampling
• Stratified Sampling
• Cluster Sampling
• Systematic Sampling
• Multi stage Sampling

1.1. Simple Random Sampling

Every element has an equal chance of getting selected to be the part sample. It is used when we don’t have any kind of prior information about the target population.

Example: Random selection of 4 students from class of 12 student. Each student has equal chance of getting selected. Here probability of selection is 1/12.

A visual representation of selecting a simple random sample

1.2. Stratified Sampling

This technique divides the elements of the population into small subgroups (strata) based on the similarity in such a way that the elements within the group are homogeneous and heterogeneous among the other subgroups formed. And then the elements are randomly selected from each of these strata. We need to have prior information about the population to create subgroups.

A visual representation of selecting a random sample using the stratified sampling technique

1.3. Cluster Sampling

Our entire population is divided into clusters or sections and then the clusters are randomly selected. All the elements of the cluster are used for sampling. Clusters are identified using details such as age, sex, location etc.

Cluster sampling can be done in following ways:

i) Single Stage Cluster Sampling

Entire cluster is selected randomly for sampling.

ii) Two Stage Cluster Sampling

Here first we randomly select clusters and then from those selected clusters we randomly select elements for sampling.

A visual representation of selecting a random sample using the cluster sampling technique

1.4. Systematic Clustering

Here the selection of elements is systematic and not random except the first element. Elements of a sample are chosen at regular intervals of population. All the elements are put together in a sequence first where each element has the equal chance of being selected.

For a sample of size n, we divide our population of size N into subgroups of k elements.

We select our first element randomly from the first subgroup of k elements.

To select other elements of sample, perform following:

We know number of elements in each group is k i.e N/n

So if our first element is n1 then

Second element is n1+k i.e n2

Third element n2+k i.e n3 and so on..

Taking an example of N=20, n=5

No of elements in each of the subgroups is N/n i.e 20/5 =4= k

Now, randomly select first element from the first subgroup.

If we select n1= 3

n2 = n1+k = 3+4 = 7

n3 = n2+k = 7+4 = 11

A visual representation of selecting a random sample using the systematic sampling technique

1.5. Multi-Stage Sampling

It is the combination of one or more methods described above.

Population is divided into multiple clusters and then these clusters are further divided and grouped into various sub groups (strata) based on similarity. One or more clusters can be randomly selected from each stratum. This process continues until the cluster can’t be divided anymore. For example country can be divided into states, cities, urban and rural and all the areas with similar characteristics can be merged together to form a strata.

A visual representation of selecting a random sample using the Multi-Stage Sampling

2. Non-Probability Sampling

It does not rely on randomization. This technique is more reliant on the researcher’s ability to select elements for a sample. Outcome of sampling might be biased and makes difficult for all the elements of population to be part of the sample equally. This type of sampling is also known as non-random sampling.

• Convenience Sampling
• Purposive Sampling
• Quota Sampling
• Snowball or Referral Sampling

2.1. Convenience Sampling

Here the samples are selected based on the availability. This method is used when the availability of sample is rare and also costly. So based on the convenience samples are selected.

Example: Researchers prefer this during the initial stages of survey research, as it’s quick and easy to deliver results.

2.2. Purposive Sampling

This is based on the intention or the purpose of study. Only those elements will be selected from the population which suits the best for the purpose of our study.

Example: If we want to understand the thought process of the people who are interested in pursuing master’s degree then the selection criteria would be “Are you interested for Masters in..?”

All the people who respond with a “No” will be excluded from our sample.

2.3. Quota Sampling

This type of sampling depends of some pre-set standard. It selects the representative sample from the population. Proportion of characteristics/ trait in sample should be same as population. Elements are selected until an exact proportion of certain types of data are obtained or sufficient data in different categories is collected.

Example: If our population has 45% females and 55% males then our sample should reflect the same percentage of males and females.

2.4. Snowball or Referral Sampling

This technique is used in the situations where the population is completely unknown and rare.

Therefore we will take the help from the first element which we select for the population and ask him to recommend other elements who will fit the description of the sample needed.

So this referral technique goes on, increasing the size of population like a snowball.

A visual representation of selecting a random sample using the Snowball Sampling

Example: It’s used in situations of highly sensitive topics like HIV Aids where people will not openly discuss and participate in surveys to share information about HIV Aids.

Not all the victims will respond to the questions asked so researchers can contact people they know or volunteers to get in touch with the victims and collect information.

Helps in situations where we do not have the access to sufficient people with the characteristics we are seeking. It starts with finding people to study.

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Thanks for reading @

References:

1. Seema Singh

2. Wikipedia