Sampling Techniques

Sampling is a process of drawing a predetermined number of observations from a larger population.It is difficult to collect the data from larger population so random samples are drawn and analysis is done on sample data which represents the population data.

A sample refers to a smaller, manageable version of a larger group. It is a subset containing the characteristics of a larger population.A good maximum sample size is usually around 10% of the population, as long as this does not exceed 1000.

The sampling distributions are of two types:

1)Probability Distribution

With randomization every element gets equal chance to be picked up.

2)Non-Probabilistic Distribution

Does not get equal chance to be selected.

In this article we will focus upon Probability Distribution types:

a)Simple Random Sampling :

Simple Random Sampling

Simple Random Sampling is mainly used when we dont have any prior knowledge about the target variable.

In this type of sampling all the elements have equal chance of being selected.

An example of a simple random sample would be the names of 50 employees being chosen out of a hat from a company of 500 employees.

A simple random sample is meant to be an unbiased representation of a group.

How you do simple random sampling?

1. Define the population.
2. Choose your sample size.
3. List the population.
4. Assign numbers to the units.
5. Find random numbers.
6. Select your sample.

b)Systematic Sampling:

Here the elements for sample are chosen at regular intervals of population.First all the elements are put together in a sequence.Here the selection of elements is systematic and not random except first element.

It is popular with researchers because of its simplicity. Researchers select items from an ordered population using a skip or sampling interval.

For example,Saurabh can give a survey to every fourth customer that comes in to the movie theatre.

How you do systematic sampling?

1. Calculate the sampling interval (the number of households in the population divided by the number of households needed for the sample)
2. Select a random start between 1 and sampling interval.
3. Repeatedly add sampling interval to select subsequent households.

c) Stratified Sampling:

In stratified sampling we divide the elements of population into strata(means small groups) based upon the similarity measure.All the elements are homogenous within one group and heterogenous from other.

How you do stratified sampling?

1. Divide the population into smaller subgroups, or strata, based on the members’ shared attributes and characteristics.
2. Step 2: Take a random sample from each stratum in a number that is proportional to the size of the stratum.

Advantages:

• A stratified sample can provide greater precision than a simple random sample of the same size.
• Because it provides greater precision, a stratified sample often requires a smaller sample, which saves money.

For example, one might divide a sample of adults into subgroups by age, like 18–29, 30–39, 40–49, 50–59, and 60 and above.

The sample size for each strata (layer) is proportional to the size of the layer: Sample size of the strata = size of entire sample / population size * layer size.

d) Cluster Sampling

Here our entire population is divided into different clusters and then clusters are randomly selected.

In one stage,entire cluster is selected randomly for sampling.

In second stage,here we first randomly select the clusters,combine those clusters and then randomly select samples from them.

How you do cluster sampling?

1. Estimate a population parameter.
2. Compute sample variance within each cluster (for two-stage cluster sampling).
3. Compute standard error.
4. Specify a confidence level.
5. Find the critical value (often z-score or a t-score).
6. Compute margin of error.

Cluster sampling is less expensive and more quick.

e) Multi -Stage Sampling

In multi stage sampling the clusters are divided into groups and the groups are divided into subgroups until they cannot be further divided.

Here,we can see the example where States is divided into districts further divided into villages and then households.

How you do multi-stage sampling?

1. Choose a sampling frame, considering the population of interest.
2. Select a sampling frame of relevant separate sub-groups.
3. Repeat the second step if necessary.
4. Using some variation of probability sampling, choose the members of the sample group from the sub-groups.

Advantages: cost and speed. convenience (only need list of clusters and individuals in selected clusters) usually more accurately than cluster for same total size.

In my next article i will cover about Non-probabilistic sampling stay tuned.

Thank you :)