Sampling: Types, Methods and Techniques

Table of Contents

What is Sampling?

Let us first understand the difference between population and sample.
- A population is an entire group of people that we want to draw conclusions about. It is a group which can answer all research questions.
- On the other hand, sample is a specific group of people/individuals from whom we will collect the data. It is a small part of the population taken to do the research.
Sampling is a process of taking/deciding samples for something.
Sampling is the process of selecting a sufficient number from the population so that by studying the sample, and understanding the properties or the characteristics of the sample subjects, a researcher will be able to generalize the properties or characteristics to the population elements.
Sampling is a method used in numerical analysis in which a prearranged quantity of interpretations is taken from a larger populace.
It is a technique of choosing a characteristic portion of a population for defining constraints of a population as a whole.

Why Sampling is Needed?

To make estimate regarding the population
To test the hypothesis
Sampling helps to save the resources
It produces precise results by saving time and cost.

Different Types of Sampling Methods

Sampling methods are broadly divided into two types:

1) Probability Sampling

2) Non-probability sampling

1) Probability Sampling

Probability sampling is a sampling where each member of the population has an equal chance of being selected.
Probability sampling is a sampling method that chooses random members of a population by setting a small number of selection standards.
It utilizes some form of random numbers.
Random selection is usually done using tools like random number generators.
The selection process in probability sampling is fair and unbiased.
These selection considerations permit every participant to have equal chances to be a part of several samples.
There are different types of probability sampling.

a) Simple Random Sampling

Simple random sampling is a sampling method where each element in the population has equal and known chance of being selected.
Here, every element/sample is selected independently of every other element.
Simple Random Sampling method is a reliable method of gaining facts from every possible member of a randomly chosen population, simply by chance and each has the exact same probability of being selected to be a part of a sample.
This sampling method is used when the population should be finite and randomly distributed. Moreover, it requires a specific sampling frame to provide unique designation to every population member.
Samples are selected in this method either by using lottery method or random number table.
Limitation: chance of underrepresentation is possible.

b) Systematic Random Sampling

Systematic random sampling is used when the population is randomly distributed.
With a systematic sampling method, members of a sample are picked at systematic intervals of a population. Therefore, it requires a sample frame where numbers are assigned to each members of the sample.
It involves the selection of a preliminary point for the sample and sample size that can be repetitive at systematic intervals.
Here, we need to find the starting point of the sample through the lottery method. Then, select sample at the calculated interval.
The sample is chosen by selecting a random starting point (through lottery method) and then picking every i-th element in calculated interval (succession) from the sampling frame.
Limitation: While applying systematic random sampling, the distributed population should not be cyclical in distribution

c) Stratified Random Sampling

Stratified Random Sampling is a method where the population can be separated into minor groups, which do not intersect but exemplify the whole population together.
Stratified sampling is used where the population distribution is ‘homogenous within’ and ‘heterogenous between’ two groups.
The strata should be mutually exclusive and collectively exhaustive in that every population element should be assigned to one and only one stratum and no population elements should be omitted.
Few members should be selected from each strata and no strata should be left behind. Elements are selected from each stratum by a random procedure, usually Simple Random Sampling.
Lottery method should be done to select samples from each strata.
A major objective of stratified sampling is to increase precision without increasing cost.

d) Cluster Sampling

Cluster sampling is a method where the researchers separate the total population into units or clusters that signify a population.
Cluster sampling is used where the population distribution is ‘heterogenous within’ and ‘homogenous between’ two groups.
Cluster sampling requires only few clusters to be selected and some clusters may also be missed.
The target population is first divided into mutually exclusive and collectively exhaustive subpopulations, or clusters. Any of which can be considered a representative sample.
Then a random sample of clusters is selected, based on a probability sampling technique such as Simple Random Sampling.
For each selected cluster, either all the elements are included in the sample (one-stage) or a sample of elements is drawn probabilistically (two-stage).
Clusters are known and contained within a sample based on considerations such as age, location, sex, etc. that marks it tremendously easy for a survey initiator to develop effective implication from the feedback.

e) Multi-stage sampling

Multistage sampling involves more than one layer of sampling.
Multistage sampling involves, combining various probability techniques in the most efficient and effective manner possible.
The process of estimation is carried out stage by stage, using the most appropriate methods of estimation at each stage.

2) Non-probability Sampling

Non-probability sampling is a sampling where each member of the population does not have an equal chance of being selected.
The Sampling method is dependent on a researcher’s capability to handpicked participants at random.
Non-probability sampling depends on subjective judgement.
This sampling method is not a stable selection process that makes it challenging for all components of a population to have equal chances to counted in a sample.
Nonprobability sampling is well suited for exploratory research intended to generate new ideas that will be systematically tested later.

There are different types of non-probability sampling. They are:

a) Convenience Sampling

This method is reliant on the simplicity of access to matters such as surveying clienteles at a mall etc.
This sampling is not actually about the researchers convenience but it is mainly about the respondent’s convenience while collecting the sample.
It is generally labeled as convenience sampling, as it is carried out based on its flexibility for a researcher to make interaction with the subjects.
Researchers have nearly no authority over selecting elements of the sample and it is decently done based on proximity and not representativeness.
Limitation: Error occurs in the form of members of the population who are infrequent or nonusers of that location.

b) Purposive/Judgmental Sampling

Purposive/Judgmental sampling is a form of convenience sampling in which the population elements are selected based on the judgment of the researcher.
Here, researchers develop a criteria to select a sample.
In judgmental sampling, the sample is made by the preference of the judge morally allowing for the purpose of study along with the consideration of target subjects.
‘Always set a criteria’ to select samples.
Correspondingly well known as deliberate sampling, the participants are selected exclusively based on research necessities and essentials who do not serve the purpose are not included in the sample.

c) Quota Sampling

In this sampling, the selection of participants in this sampling technique happens based on pre-set guidelines.
In this scenario, as a sample is made based on precise characteristics, the formed sample will have identical characteristics that are established in the total population.
We form a group first and then select fixed members from each group.

d) Snowball Sampling

Snowball sampling is a sampling method that is used in research and education purposes that needs to be supported in order to understand subjects, which are challenging to trace and are hidden.
This sampling method is applied in circumstances where the subject matter is exceedingly sensitive and not openly conversed such as carrying out surveys to gather data on HIV and AIDS.
It is conducted where the members of the population are less known or disliked generally, therefore it is difficult to get access to them easily.
This sampling is also known as referral sampling.

e) Accidental Sampling

A type of nonprobability sampling which involves the sample being drawn from that part of the population which is close to hand
The researcher using such a sample cannot scientifically make generalizations about the total population

Advantages and Limitations of Sampling Types

Sampling Types

Advantages

Limitations

Simple Random Sampling

Denotes the target population and exclude sampling bias or errors

Target population and bias exclusion are difficult to attain based on time, effort, and money.

Stratified Sampling

Sample should be greatly representative of the target population and thus simplify from the obtained results.

Time-consuming

Difficult to perform.

Cluster Sampling

Feasibility

Obliges smaller amount of resources

High sample error

Subjective samples

Systematic Sampling

Easy to implement and understand

Controller and sense of process

Assumption that population size can be dogged

Prerequisite for Natural Degree of Uncertainty

Convenience Sampling

Time saving

Financial friendly

Useful for Pilot Studies

Probability of Sampling Error

No general Outcomes

Judgmental or Purposive Sampling

Provide researcher explanation to make generalities

Provide a wide range of non-probability sampling

techniques

Greatly susceptible to researcher bias.

Difficult to shield the representatives of a sample.

Snowball Sampling

Cheap, simple, and cost-efficient.

Takes less time compared to another sampling.

The researcher has slight control over the sampling technique.

Representativeness of the sample is not definite.

The researcher has no idea of the true distribution of the population and of the sample

Quota Sampling

Quick and easy as it does not need a sampling frame

Use of quota sample that indicates to the stratification of a sample (e.g. male and female students), permits to compare these groups (strata) easily.

Assures some point of representatives of all the strata in the population.