Whenever we are conducting a scientific research, we need a specimen to get to know facts. Sampling is an essential part of every kind of research.

1. RESEARCH METHODOLOGY
POPULATION, SAMPLE FRAME, SAMPLE AND SAMPLING
Whenever we are conducting a scientific research, we need a specimen to get to know facts.
Sampling is an essential part of every kind of research.
Sample is a representative of a study population. A below illustration can describe it very well:
Two types of target population:
A target population could be of two types.
I. Heterogeneous:Inthistype of population,all sortof individuals have variant characteristics
and choosing a sample from such a population could have differing inference with the
collected data.
II. Homogeneous: This could be an ideal population for sample collection because
characteristics of each individual can coincide with that of another. This makes a relative
inference.

2. Two categories of sampling:
Sampling could be classified into two categories in accord to their nature.
I. PROBABILITY/REPRESENTATIVE/UNBIASED SAMPLING: Most of the sample collection is
done throughprobability where everyelementgetsan equal chance of being selected. This
method yields less sample error.
II. NON-PROBABILITY/NON-REPRESENTATIVE/BIASED SAMPLING: Some sampling techniques
require a non-probability method. In this way each element does not get a chance to be
selected. This method could be a source of yielding major sample error.
Four points to considerwhile selectingsample type:
1. The focus of the study: Some studies do not require generalization of sample with that of
entire population.Hence,aninference isnotessentialtobe developed.These studiesmostly
determine a relationship between two variables. They could be regarded as pilot studies.
2. Error acceptability: Usually in pilot studies, sample error cannot be a problem as the
research is on a preliminary level.
3. Time limitation: Dependingondeadline forconductingaresearch,itissometimesapplicable
to utilize non-probability sampling.
4. Least cost more value: Sample should provide greater value/data in return of less
investment.Non-probabilitysamplingcanbe takenin countif it’s less expensive for sample
selection

3. FIVE TYPES OF NON-PROBABILITY/NON-REPRESENTATIVE/BIASED SAMPLES
Each and every element cannot be picked under this technique. A specific proportion is always
reserved for specific group or population subset.
1. Quota sample
In this type of non-representative sampling, each
element is not considered to become a part of
research. A percent of population is already reserved
for subset collection. For instance, there are 50
vacancies open in a corporate firm. Among these 4 are
reserved for foreigners, 8 are reserved for Christians,
12 are reserved for Muslims, and rest are open to the
nationality holders. Here a quota is defined and hence
not everybody from the applicants is getting an equal
chance to apply.
2. Judgmental (Purposive) sample
This choice of sampling is based upon a researcher’s
self-intuition and reasoning. The following attributes
are to be looked while considering sample in this
category:
 Extremity
 Intensity
 Maximum variation
 Similarity/Homo
For instance, a researcher choses rural area as a field of
study. Why? Because he wishes to study the radio
listening habits of audiences. He is pre-determined that
radio is still popular among people who are not literate
enough to read a newspaper.
3. Volunteer sample
This is a technique by which individuals themselves feel honored to be a part of a specific research study.

4. 4. Convenient sample
This is to select a randomly accessible subject for
studying. Usually researchers criticize this sampling
technique because there’s no external validity. Not to
mention, such sampled elements are never flawless.
However, these accessiblesamples can bebeneficial for
pretesting questionnaires or for conducting pilot
studies. For instance a researcher, who intends to study
people’s preference of latest trends, tries to speak to
them on public places especially at shopping malls.
5. Snowball Sampling
This is a technique through which a
researcher finds sample through
another sample. For instance, when a
researcher is determining social media
usage habits of people, one individual
might refer him to a friend for obtaining
data. No doubt such individuals take
high interest in being a part of a specific
study.
FIVE TYPES OF PROBABILITY/REPRESENTATIVE/UN-BIASED SAMPLES
Each and every element can be picked underthis technique.Itis much cheaper,easierand suitable to
a researcher.
1. Simple Random Sampling(SRS)
As the name signifies, it is a simple selection of
sample from anywhere and anytime. For instance,
selecting first few people you meet at a public place.

5. 2. StratifiedSampling
This should be collected from homogeneous
population. We classify sample on the basis of its
attributes, properties or characteristics. They are
group together through strata and then are
reselected as stratified samples.
3. ClusterSampling
This type of sampling is done when the population
is too largein a wide-spread area. In this technique,
the samples are chosen in divided parts and then
some parts of those divisions are used as clustered
samples. For instance, if a researcher aims to study
newspaper reading habits of Pakistani nation, he
collects the samples from each province i.e. Punjab,
Sindh, KPK, Gilgit-Baltistan and Baluchistan.
However, in his final study, he considers samples
selected from Punjab and Sindh.
4. Systematic Sampling
It is a systematic way to select and give a chance to
every element of the population. For instance, a
researcher needs to study how much satisfied are
students of Mass Media Department with their
academics and instructors. So for this, he would
select every nth student of each semester.
5. Multistage Sampling(in whichsome of the methodsabove are combinedin stages)
Multistagesamplingrefers to samplingplans wherethe samplingis carried outin stages usingsmaller and
smaller sampling units ateach stage. Multistagesamplingcan be a complex form of cluster samplingbecause
it is a type of samplingwhich involves dividingthepopulation into groups (or clusters).Then, one or more
clusters arechosen at random and everyone within the chosen cluster is sampled.

6. Whatis meant by samplesize?
Sample size isreferredtoashowmuch elementsof astudy population can be kept in consideration
while conductingaresearch. The considerationcanbe a proportionality of the following attributes:
1) Time Constraint
2) Financial Constraint
3) StudyObjective
4) Sample Error
5) PriorStudies
Sample size isleastimportantincomparisontosample accuracy.Selectionof awrong sample is
indeedwastage of time andresourceswithazerogain.
Whatis meant by sampleerror?
It isa disjunctionof aspecimen withparticularelements of a study population due to which results
do not generalize on a relative scale with target population.
Preparedby: FizaZiaUl Hannan
References:RogerD.Wimmerand JosephR.DominickinChapter4 of Mass MediaResearch,an
introduction