Methods of Sampling

Methods of Sampling

It would normally be impractical to study a whole population, for example when doing a questionnaire survey. Sampling is a method that allows researchers to infer information about a population based on results from a subset of the population, without having to investigate every individual. Reducing the number of individuals in a study reduces the cost and workload, and may make it easier to obtain high quality information, but this has to be balanced against having a large enough sample size with enough power to detect a true association. 

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. For example, if the electoral roll for a town was used to identify participants, some people, such as the homeless, would not be registered and therefore excluded from the study by default.

There are several different sampling techniques available, you can find it below; 

Above image shows the methods of Sampling, Lets discuss one by one. 

There are majorly two types of Sampling

1. Probability Sampling
2. Non-Probability Sampling

What is Probability Sampling?

Probability sampling is a sampling technique where in the samples are gathered in a process that gives all the individuals in the population equal chances of being selected this is known as an "equal probability of selection" (EPS) design.

• EPS designs are also referred to as 'self-weighting' because all sampled units are given the same weight.
• A researcher must identify specific sampling elements (e.g. persons) to include in the sample

For example: 

A company HR wants to conduct a survey among the employees regarding company facilities and he selects employee who have specifically 2 years of company experience.

There are different types of Probability Sampling methods, those are:

1. Simple Random Sampling
2. Stratified Sampling
3. Cluster Sampling
4. Systematic Sampling

Lets discuss one by one 

1. Simple Random Sampling:

A simple random sample is a subset of a statistical population in which each member of the subset has an equal probability of being chosen.

• It is applicable when population is small, homogeneous & readily available.
• All subsets of the frame are given an equal probability. Each element of the frame thus has an equal probability of selection.
• It provides for greatest number of possible samples. This is done by assigning a number to each unit in the sampling frame.
• A table of random number or lottery system is used to determine which units are to be selected.
• The key to random selection is that there is no bias involved in the selection of the sample.
• Any variation between the sample characteristics and the population characteristics is only a matter of chance.

Example: 

A simple random sample would be the names of 25 employees being chosen out of a hat from a company of 250 employees. 

In this case, the population is all 250 employees, and the sample is random because each employee has an equal chance of being chosen. Random sampling is used in science to conduct randomized control tests or for blinded experiments.

• Researchers can create a simple random sample using a couple of methods. With a lottery method, each member of the population is assigned a number, after which numbers are selected at random.
• The example in which the names of 25 employees out of 250 are chosen out of a hat is an example of the lottery method at work.
• Each of the 250 employees would be assigned a number between 1 and 250, after which 25 of those numbers would be chosen at random.
• Because individuals who make up the subset of the larger group are chosen at random, each individual in the large population set has the same probability of being selected.
• This creates, in most cases, a balanced subset that carries the greatest potential for representing the larger group as a whole, free from any bias.

2. Stratified Sampling:

Stratified sampling is a type of sampling method in which the total population is divided into smaller groups or strata to complete the sampling process.

Steps for Stratified Sampling technique:

• Partition the Population into groups(Strata)
• Obtain a simple random sample from each group(Stratum)
• Collect Data on each sampling unit that was randomly sampled from each group(Stratum)

Example:

For marketing Analysis scenario, The ABC company wants to find the different age group men and woman for launching some age group specific products in a given geography.

3. Cluster Sampling: 

Cluster sampling is a sampling technique used when "natural" but relatively homogeneous groupings are evident in a statistical population.

• Cluster Sampling is an example of 'two-stage sampling'

                         1. A sample of areas is chosen;

                         2. Sample of respondents within those areas is selected.

• Population divided into clusters of homogeneous units, usually based on geographical contiguity.
• Sampling units are groups rather than individuals.
• A sample of such clusters is then selected.
• All units from the selected clusters are studied.
• It is often used in marketing research.
• In this technique, the total population is divided into these groups (or clusters) and a simple random sample of the groups is selected.

Difference between Strata and Cluster 

• All strata are represented in the sample; but only a subset of clusters are in the sample.
• With stratified sampling, the best survey results occur when elements within strata are internally homogeneous.
• However, with cluster sampling, the best results occur when elements within clusters are internally heterogeneous.

4. Systematic Sampling: 

Systematic sampling is a type of probability sampling method in which sample members from a larger population are selected according to a random starting point but with a fixed, periodic interval. This interval, called the sampling interval, is calculated by dividing the population size by the desired sample size.

• Systematic sampling relies on arranging the target population according to some ordering scheme and then selecting elements at regular intervals through that ordered list.
• Systematic sampling involves a random start and then proceeds with the selection of every k'th element from then onward.

In this case, k = (population size/sample size).

• It is important that the starting point is not automatically the first in the list, but is instead randomly chosen from within the first to the k'th element in the list.
• Samples are chosen in a systematic, or regular way.
• They are evenly/regularly distributed in a spatial context, for example every two meters along a transect line.
• They can be at equal/regular intervals in a temporal context, for example every half hour or at set times of the day.
• They can be regularly numbered, for example every 10th house or person.
• A simple example would be to select every 10th name from the telephone directory (an 'every 10th' sample, also referred to as 'sampling with a skip of 10’).

Non-Probability Sampling Method

Non-Probability Sampling is a sampling technique where the odds of any member being selected for a sample cannot be calculated. It’s the opposite of Probability Sampling.

• Unequal chance of being included in the sample (non-random)
• Non random or non - probability sampling refers to the sampling process in which, the samples are selected for a specific purpose with a predetermined basis of selection.
• The sample is not a proportion of the population and there is no system in selecting the sample, The selection depends upon the situation.
• No assurance is given that each item has a chance of being included as a sample.
• There is an assumption that there is an even distribution of characteristics within the population, believing that any sample would be representative.
• Non Probability sampling includes.

There are different types of Non-Probability Sampling methods, those are:

1. Quota Sampling
2. Convenience Sampling
3. Judgement Sampling

Lets discuss one by one 

1. Quota Sampling: 

• The defining characteristic of a quota sample is that the researcher deliberately sets the proportions of levels or strata within the sample. This is generally done to insure the inclusion of a particular segment of the population.
• The proportions may or may not differ dramatically from the actual proportion in the population. The researcher sets a quota, independent of population characteristics.

2. Convenience Sampling: 

Convenience sampling is defined as a method adopted by researchers where they collect market research data from a conveniently available pool of respondents. It is the most commonly used sampling technique as it’s incredibly prompt, uncomplicated, and economical.

• Convenience sampling is a sample taken from a group you have easy access to the idea that anything learned from this study will be applicable to the larger population.
• By using a large, convenient size, you are able to more confidently say the sample represents the population, Furthermore, the convenient group you are testing should not be fundamentally different than if you had taken a sample from another area.
• Involves collecting information from members of a population who are conveniently available to provide this information 

Examples : 

1. ‘Pepsi Challenge’ contest with the purpose of determining whether people prefer one product over another, might be set up at a shopping mall visited by many shoppers.
2. Suppose 100 car owners are to be selected. Then we may collect from the RTO's office the list of car owners and then make a selection of 100 from that to form the sample.

3. Judgement Sampling: 

Judgment sampling, also referred to as judgmental sampling or authoritative sampling, is a non-probability sampling technique where the researcher selects units to be sampled based on his own existing knowledge, or his professional judgment.

• Judgment sample is a type of nonrandom sample that is selected based on the opinion of an expert.
• Results obtained from a judgment sample are subject to some degree of bias, due to the frame and population not being identical.
• The frame is a list of all the units, items, people, etc., that define the population to be studied.
• This is used primarily when there is a limited number of people that have expertise in the area being researched.

Example : A TV researcher wants a quick sample of opinions about a political announcement. Taking views of people in the street.

Author: Mohit (Algae Study)