TERMINOLOGIES

Population or Universe: It refers to the group of people, items or units

under investigation and includes every individual. The entire aggregation of

items from which samples can be drawn is known as population. “N”

represents the size of the population.

Sample: A collection consisting of a part or subset of the objects or

individuals of population which is selected for the purpose, representing the

population

Sampling: It is the process of selecting a sample from the population. For

this population is divided into a number of parts called Sampling Units.

Large sample: The sample size n is greater than 30 (n≥30) it is known

as large sample.

Small Sample: The sample size n is smaller than 30 (n<30) it is known as

small sample.

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Need of Sampling

Large population can be conveniently covered.

Time, money and energy is saved.

Helpful when units of area are homogenous.

Used when percent accuracy is not acquired.

Used when the data is unlimited.

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Advantages of Sampling

Economical: Reduce the cost compare to entire population.

Increased speed: Collection of data, analysis and Interpretation of data

etc. take less time than the population.

Accuracy: Due to limited area of coverage, completeness and accuracy

is possible.

Rapport: Better rapport is established with the respondents, which

helps in validity and reliability of the results.

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Disadvantages of Sampling

Biasedness: Chances of biased selection leading to incorrect

conclusion.

Selection of true representative sample: Sometimes it is difficult to

select the right representative sample

Need for specialized knowledge: The researcher needs knowledge,

training and experience in sampling technique, statistical analysis and

calculation of probable error

Impossibility of sampling: Sometimes population is too small or too

heterogeneous to select a representative sample.

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Characteristics of Good sample

A true representative of the population.

Free from error due to bias.

Adequate in size for being reliable.

Units of sample should be independent and relevant.

Units of sample should be complete precise and up to date.

Free from random sampling error.

Avoiding substituting the original sample for convenience.

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Sampling and Sampling methods

Sampling definition: It is the process of selecting a sample from the

population. For this population is divided into a number of parts

called Sampling Units.

For example, if a drug manufacturer would like to research the

adverse side effects of a drug on the country’s population, it is almost

impossible to conduct a research study that involves everyone. In this

case, the researcher decides a sample of people from

each demographic(group) and then researches them, giving him/her

indicative feedback on the drug’s behavior.

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Probability Sampling

Probability sampling is a sampling technique in which researchers

choose samples from a larger population using a method based on the

theory of probability. This sampling method considers every member of

the population and forms samples based on a fixed process.

For example, in a population of 1000 members, every member will

have a 1/1000 chance of being selected to be a part of a sample.

Probability sampling eliminates bias in the population and gives all

members a fair chance to be included in the sample.

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1.Simple Random Sampling

Definition: Simple random sampling is defined as a sampling technique where

every item in the population has an even chance and likelihood of being selected in

the sample. Here the selection of items entirely depends on luck or probability, and

therefore this sampling technique is also sometimes known as a method of chances.

The main attribute of this sampling method is that every sample has the same

probability of being chosen.

The sample size in this sampling method should ideally be more than a few hundred

so that simple random sampling can be applied appropriately. They say that this

method is theoretically simple to understand but difficult to implement practically.

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Researchers follow these methods to select a simple random sample:

They prepare a list of all the population members initially, and then each member is

marked with a specific number ( for example, there are nth members, then they will

be numbered from 1 to N).

From this population, researchers choose random samples using two ways: Method

of lottery, Random number tables.

• Method of lottery: Using the lottery method is one of the oldest ways and is a

mechanical example of random sampling. In this method, the researcher gives each

member of the population a number. Researchers draw numbers from the box

randomly to choose samples.

•Use of random numbers: The use of random numbers is an alternative method that

also involves numbering the population. Researcher also uses random number

generator software. Researchers prefer a random number generator software, as no

human interference is necessary to generate samples.

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Steps to conduct simple random sampling

Follow these steps to extract a simple random sample of 100 employees out of

500.

1. Make a list of all the employees working in the organization. (as mentioned

there are 500 employees in the organization, the record must contain 500

names).

2. Assign a sequential number to each employee (1,2,3…n). This is your

sampling frame (the list from which you draw your simple random sample).

3. Figure out what your sample size is going to be. (In this case, the samples size is 100).

4. Use a random number generator For example, if your sample size is 100

and your population is 500, generate 100 random numbers between 1 and

500.

Advantages of simple random sampling

It is a fair method of sampling, and if applied appropriately, it helps to

reduce any bias involved compared to any other sampling method involved.

The person conducting the research doesn’t need to have prior knowledge of

the data he/ she is collecting. One can ask a question to gather the researcher

need not be a subject expert.

Don’t need any technical knowledge. You only require essential listening

and recording skills.

Since the population size is vast in this type of sampling method, there is no

restriction on the sample size that the researcher needs to create. From a

larger population, you can get a small sample quite quickly.

The data collected through this sampling method is well informed; more the

samples better is the quality of the data.

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2.Cluster Sampling

Cluster sampling is defined as a sampling method where the researcher

creates multiple clusters of people from a population where they are

indicative of homogeneous characteristics and have an equal chance of

being a part of the sample.

Example: Consider a scenario where an organization is looking to

survey the performance of smartphones across Germany. They can

divide the entire country’s population into cities (clusters) and select

further towns with the highest population and also filter those using

mobile devices.

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Types of cluster sampling

1. Single-stage cluster sampling: As the name suggests, sampling is done just once. An

example of single-stage cluster sampling – An NGO wants to create a sample of girls

across five neighboring towns to provide education. Using single-stage sampling, the

NGO randomly selects towns (clusters) to form a sample and extend help to the girls

deprived of education in those towns.

2. Two-stage cluster sampling: Here, instead of selecting all the elements of a cluster, only

a handful of members are chosen from each group by implementing systematic or simple

random sampling. An example of two-stage cluster sampling – A business owner wants to

explore the performance of his/her plants that are spread across various parts of the U.S.

The owner creates clusters of the plants. He/she then selects random samples from these

clusters to conduct research.

3. Multiple stage cluster sampling: Multiple-stage cluster sampling takes a step or a few

steps further than two-stage sampling. For conducting effective research across multiple

geographies, one needs to form complicated clusters that can be achieved only using the

multiple-stage sampling technique. An example of Multiple stage sampling by clusters –

An organization intends to survey to analyze the performance of smartphones across

India. They can divide the entire country’s population into states (clusters) and select

cities with the highest population, then select village with highest population among cities

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and also filter those using mobile devices.

Steps to conduct cluster sampling

1. Sample: Decide the target audience and also the sample size.

2. Determine groups: Determine the number of groups by including

the same average members in each group. Make sure each of these

groups are distinct from one another.

3. Select clusters: Choose clusters by applying a random selection.

4. Create sub-types: It is bifurcated into two-stage and multi-stage

subtypes based on the number of steps followed by researchers to

form clusters.

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Advantages of Cluster sampling

Consumes less time and cost

Convenient access: Researchers can choose large samples with this

sampling technique, and that’ll increase accessibility to various

clusters.

Data accuracy: Since there can be large samples in each cluster, loss

of accuracy in information per individual can be compensated.

Ease of implementation: Cluster sampling facilitates information

from various areas and groups. Researchers can quickly implement it

in practical situations compared to other probability sampling

methods.

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3.Systematic Sampling

Systematic sampling is defined as a probability sampling method

where the researcher chooses elements from a target population by

selecting a random starting point and selects sample members after a

fixed ‘sampling interval.’

Example: For a sample of 50 students from the population of 400

students, the sampling is 400/50 = 8 i.e. select one student out of every

eight students in the population. The starting points for the selection is

chosen at random.

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Steps to conduct Systematic sampling

1. Develop a defined structural audience to start working on the sampling aspect.

2. As a researcher, figure out the ideal size of the sample, i.e., how many people from the

entire population to choose to be a part of the sample.

3. Once you decide the sample size, assign a number to every member of the sample.

4. Define the interval of this sample. This will be the standard distance between the

elements.

5. For example, the sample interval should be 10, which is the result of the division of 5000

(N=size of the population) and 500 (n=size of the sample).

Systematic Sampling Formula for interval (i) = N/n = 5000/500 = 10

6. Select the members who fit the criteria which in this case will be 1 in 10 individuals.

7. Randomly choose the starting member (r) of the sample and add the interval to the

random number to keep adding members in the sample. r, r+i, r+2i, etc. will be the

elements of the sample.

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Advantages of Systematic sampling

It’s extremely simple and convenient for the researchers to create, conduct, analyze

samples.

As there’s no need to number each member of a sample, it is better for representing

a population in a faster and simpler manner.

The samples created are based on precision in member selection and free from

favoritism.

In the other methods of probability sampling methods such as cluster sampling

and stratified sampling or non-probability methods such as convenience sampling,

there are chances of the clusters created to be highly biased which is avoided in

systematic sampling as the members are at a fixed distance from one another.

The factor of risk involved in this sampling method is extremely minimal.

In case there are diverse members of a population, this sampling technique can be

beneficial because of the even distribution of members to form a sample.

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4.Stratified random Sampling

Stratified random sampling is a type of probability sampling using which

a research organization can branch off the entire population into multiple non-

overlapping, homogeneous groups (strata) and randomly choose final members

from the various strata for research which reduces cost and improves efficiency.

Members in each of these groups should be distinct so that every member of all

groups get equal opportunity to be selected using simple probability. This sampling

method is also called “random quota sampling”.

Age, socioeconomic divisions, nationality, religion, educational achievements and

other such classifications fall under stratified random sampling.

Let’s consider a situation where a research team is seeking opinions about religion

amongst various age groups. Instead of collecting feedback from 326,044,985 U.S

citizens, random samples of around 10000 can be selected for research. These

10000 citizens can be divided into strata according to age, i.e, groups of 18-29, 30-

39, 40-49, 50-59, and 60 and above. Each stratum will have distinct members and

number of members.

Types of Stratified Random sampling

1. Proportionate Stratified Random Sampling: In this approach, each stratum sample size is

directly proportional to the population size of the entire population of strata. That means each

strata sample has the same sampling fraction.

If you have 4 strata with 500, 1000, 1500, 2000 respective sizes and the research organization selects

½ as sampling fraction. A researcher has to then select 250, 500, 750, 1000 members from the

respective stratum. Irrespective of the sample size of the population, the sampling fraction will

remain uniform across all the strata.

Stratum A B C D

Population Size 500 1000 1500 2000

Sampling Fraction 1/2 1/2 1/2 1/2

Final Sampling

250 500 750 1000

Size Results

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2. Disproportionate Stratified Random Sampling:

Sampling fraction is the primary differentiating factor between the proportionate

and disproportionate stratified random sampling. In disproportionate sampling, each

stratum will have a different sampling fraction.

The success of this sampling method depends on the researcher’s precision at

fraction allocation. If the allotted fractions aren’t accurate, the results may be biased

due to the overrepresented or underrepresented strata.

Stratum A B C D

Population Size 500 1000 1500 2000

Sampling Fraction 1/2 1/3 1/4 1/5

Final Sampling

250 333 375 400

Size Results

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Steps to conduct Stratified Random sampling

1. Define the target audience.

2. Figure out the number of strata to be used.

3. Considering the entire population, each stratum should be unique and should cover each

and every member of the population. Within the stratum, the differences should be

minimum whereas each stratum should be extremely different from one another. Each

element of the population should belong to just one stratum.

4. Assign a random, unique number to each element.

5. Figure out the size of each stratum according to your requirement. The numerical

distribution amongst all the elements in all the strata will determine the type of

sampling to be implemented. It can either be proportional or disproportional stratified

sampling.

6. The researcher can then select random elements from each stratum to form the sample.

Minimum one element must be chosen from each stratum so that there’s representation

from every stratum.

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Advantages of Stratified Random sampling

Better accuracy in results in comparison to other probability sampling methods such

as cluster sampling, simple random sampling, and systematic sampling or non-

probability methods such as convenience sampling. This accuracy will be

dependent on the distinction of various strata, i.e., results will be highly accurate if

all the strata are extremely different.

Due to statistical accuracy of this method, smaller sample sizes can also retrieve

highly useful results for a researcher.

This sampling technique covers maximum population as the researchers have

complete charge over the strata division.

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Non-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.

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1.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.

Researchers use convenience sampling in situations where additional inputs are not necessary for the

principal research. There are no criteria required to be a part of this sample. Thus, it becomes

incredibly simplified to include elements in this sample. All components of the population are

eligible and dependent on the researcher’s proximity to get involved in the sample.

The researcher chooses members merely based on proximity and doesn’t consider whether they

represent the entire population or not. Using this technique, they can observe habits, opinions, and

viewpoints in the easiest possible manner.

For example, a university student working on a project and wants to understand the average

consumption of soda on campus on a Friday night will most possibly call his/her classmates and

friends and ask how many cans of soda they consume. Or may go to a party nearby and conduct an

easy survey. There is always a chance that the randomly selected population may not accurately

represent the population of interest, thus increasing the chances of bias.

Another example of the 10,000 university students, if we were only interested in achieving a sample

size of say 100 students. we may simply stand at one of the main entrances to campus, where it

would be easy to invite the many students that pass by to take part in the research. So, it is very easy

(Convenient) to select. www.DuloMix.com

2.Quota Sampling

In this method, the sample size is determined first and then quota is fixed for various

categories of population, which is followed while selecting the sample.

In this method the quota has to be determined in advance and intimated to the investigator.

The quota for each segment of the population may be fixed at random or with a specific

basis. Normally such a sampling method does not ensure representativeness of the

population.

For example, a cigarette company wants to find out what age group prefers what brand of

cigarettes in a particular city. He/she applies quotas on the age groups of 21-30, 31-40, 41-

50, and 51+. From this information, the researcher gauges the smoking trend among the

population of the city.

TYPES OF QUOTA SAMPLING:

1.Controlled quota sampling: Controlled quota sampling imposes restrictions on the

researcher’s choice of samples. Here, the researcher is limited to the selection of samples.

2.Uncontrolled quota sampling: Uncontrolled quota sampling does not impose any

restrictions on the researcher’s choice of samples. Here, the researcher chooses sample

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members at will.

3. Purposive (Judgmental) Sampling

In this method, the sample selection is purely based on the judgment of the

investigator or the researcher. This is because, the researcher may lack information

regarding the population from which he has to collect the sample. Population

characteristics or qualities may not be known, but sample has to be selected.

In this method of sampling the choice of sample items depends primarily on the

judgment of the researcher. In other words, the researcher determines and includes

those items in the sample which he thinks are most typical of the universe with

regard to the characteristics of research project.

For example, suppose 100 boys are to be selected from a college with 1000 boys. If

nothing is known about the students in this college, then the investigator may visit

the college and choose the first 100 boys he meets. Or he may select 100 boys all

belonging to III Year. Or he might select 25 boys from Commerce course, 25 from

Science courses, 25 boys from Arts courses and 25 from Fine arts courses. Hence,

when only the sample size is known, the investigator uses his discretion and select

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4.Snowball Sampling

Selecting participants by finding one or two participants and then asking them to

refer you to others.

This sampling method that researchers apply when the subjects are difficult to

trace.

For example, it will be extremely challenging to survey shelterless people or illegal

immigrants. In such cases, using the snowball theory, researchers can track a few

categories to interview and derive results.

Researchers also implement this sampling method in situations where the topic is

highly sensitive and not openly discussed—for example, surveys to gather

information about HIV Aids. Not many victims will readily respond to the questions. Still, researchers can contact people they might know or volunteersa ssociated with the cause to get in touch with the victims and collect information.