Sampling is one of the most important
factors which determine the accuracy of your survey result. If the
population under study is small, then there is a possibility to collect the
data for each item or entity. But this is rarely the situation in survey research
as most of the time the universe of population under study is tool large which necessitates
the need to select a sample for the study. Sampling helps researchers to save
time, energy, and money that require studying the whole population. It helps
them to get inferences applicable to whole population with minimum resources.
Definition: Sampling is the process of selecting a representative group from
the population under study with the intention of finding out a conclusion which
can be applied to total population from which they were chosen. In short, it is the process of obtaining information about an
entire population by examining only a portion of it. The method through which a sample is selected is known as ‘Sampling
methods’. Let’s start with the understanding of basic terms in this context:
Population: The entire group of people or
items that the researcher wants to study. It is denoted by ‘N’ and alternatively known as 'Universe' also.
Sample: Samples are drawn from
populations. The portion of the population, the researcher select for the
study. It is denoted by ‘n’.
Sampling: The process of selecting a
group of individuals from a population in order to study them and characterize
the population as a whole.
Need for Sampling: The need for sampling is felt due to the
following reasons:
i) Economy: Sampling saves time, efforts and money
ii) Timeliness: It helps to produce results relatively faster than
a census study
iii) Large size of many populations: Sampling can be the only choice
when population contains infinite numbers.
iv) Inaccessibility of population: Sampling helps in the case of
geographically scattered diverse population
v) Accuracy: Sampling may help to get
more accurate measurements if conducted by trained and experienced
investigators.
Characteristic
of a Good Sample: Validity
of research results depends highly upon the quality of the sample drawn. If the
sample is biased and does not truly represent the population, then the results
cannot be trusted or generalized. There are certain factors that can affect the
inferences drawn from a sample. For example: (a) the larger the sample, the
more is the chance of accuracy (b) the higher the variations in population, the
greater is the chance of uncertainty in outcome, however on the contrary, the
higher the consistency in population, the greater is the chance for quality of
outcome, and (c) the higher the variations in population, the larger should be
the size of the sample.
A
sample should essentially possess the following characteristics:

i) True representative of the Population: A sample should homogeneously represent
the population it is taken from.

ii) Goaloriented: A sample should be oriented to research objectives and fitted
to the survey conditions

iii) Accuracy: It should be free from error and bias

iv) Economical & Practical: The sample should be such
that the objectives of research can be obtained with minimum cost and effort.
At the same time, it should be simple and practical to be followed easily.

v) Proportional: Sample should be adequate and proportional in size and reliable

Types of Sampling: Sampling
techniques/methods can be categorized into the following two broad types:
PROBABILITY SAMPLING: Probability sampling utilizes Random Sampling
Techniques to create a sample. This group of sampling methods gives all the
members of a population equal chance of being selected. This
means that the selection of sample is independent of the person making the
study and the sampling operation is controlled so objectively that all items
will be chosen strictly at random. Probability sampling is more complex,
timeconsuming and generally more costly than nonprobability sampling. Probability
sampling can be further grouped into following types:
a)
Simple Random Sampling: Simple
Random Sampling (SRS) refers to that technique in which each and every member
of the population has an equal opportunity of being selected in the sample. In
simple random sampling, the item gets selected in the sample depends on a
matter of chance – personal bias of the researcher does not influence the
selection. One of the popular and simplest methods under Simple Random Sampling
is Lottery Method of Sampling in which all the items of the population are
numbered on separate slips of paper of same size, shape and color and are mixed
up in a box or a container before a blindfold selection is made. The desired
number of slips are then selected which wholly depends on a matter of chance.
Advantage of
Simple Random Sampling:

i.
The method is quite simple to use
ii.
It requires minimum knowledge of the population in
advance
iii.
It lacks personal bias
iv.
It is more representative of the population as
compared to judgmental sampling

Disadvantage:

i.
A complete list of all units/members in the whole
population is needed
ii.
Time and cost involve can be high if the
units/members are geographically scattered

b)
Systematic
Sampling: Systematic sampling
is a probability sampling method where the items are chosen from a target
population by selecting a random starting point and then selecting other
members after a fixed ‘sampling interval’. Sampling interval is calculated by
dividing the entire population size by the desired sample size. In simple
words, systematic sampling can be used if a population is accurately listed or
is finite. In this method, at first the list is prepared in some order and then
the first item is selected randomly and subsequent items are chosen by taking
every n^{th} item from the list.
c)
Stratified
Sampling: Stratified sampling is a probability sampling
technique wherein the the entire population is divided into different subgroups
or strata, then randomly selects the final sample proportionally from the
different strata or subgroups. In stratified random sampling or
stratification, the strata are formed based on members' shared characteristics
or attributes such as age, gender, socioeconomic status, religion, nationality
and educational attainment.
Stratified random sampling is
also called proportional random sampling or quota random sampling. The population
within a strata or subgroups is homogeneous with respect to the
characteristics under study. Thus, in contrast to Cluster sampling, in
Stratified sampling, the
groups/cluster/strata are externally heterogeneous but internally homogeneous.
d)
Cluster Sampling: Cluster sampling is a sampling
method in which the entire population of the study is divided into externally homogeneous but internally
heterogeneous groups called clusters. Essentially, each cluster is a
minirepresentation of the entire population. After identifying the clusters, certain clusters are
chosen using simple random sampling as the sample of the proposed study. The
population within a cluster should
ideally be as heterogeneous as possible, but there should be homogeneity between clusters.
Simple Random
Sampling and Stratified Sampling methods are usually chosen in situations when
population size is small and units are identifiable. However, cluster sampling
is suitable if the population is larger.
e)
Multistage Sampling: Multistage sampling is a
complex form of cluster sampling where samples are taken in different stages by
dividing the population into smaller groups (or clusters) at each stage. For
example, in a national level survey, at first researcher may select a few
states as sample. At the second stage, within the states, a few districts may
be selected and then within each district, some blocks and then some villages
and so on. In short, multistage sampling divides large populations into stages
to make the sampling process easy and more practical.
NONPROBABILITY SAMPLING: It is a group of
sampling techniques where the samples are collected in a way that doesn't give
all the units in the population equal chances of being selected. The selection process in this method is at least
partially subjective. NonProbability
sampling doesn't involve random selection at all. NonProbability sampling can
again be categorized into the following types:
a) Purposive
sampling: It is a
nonprobability sample design in which the
researcher purposively or deliberately selects certain units of the universe to
form a sample that would represent the whole universe. In other words, Purposive sampling is the one in which the researcher
chooses the sample on the basis of subject being appropriate for the study.
This is used primarily when there are a limited number of people who have
expertise in the area being researched. The researcher in this technique relies
on his/her own judgment when choosing a sample. Purposive sampling is different from convenience sampling and is also known as judgmental,
deliberate, selective, or subjective sampling.
b) Convenience sampling: Convenience sampling (also known as grab sampling, accidental
sampling, or opportunity
sampling) is a type of nonprobability sampling in which sample is taken from a
group of people easy to contact or to reach. This type of sampling is
most useful for pilot testing.
c) Quota sampling: Quota sampling
is a nonprobabilistic sampling method where the researchers divide the survey
population into mutually exclusive subgroups. These subgroups are
selected with respect to certain known features, traits, or interests. People
in each subgroup are then selected by the researcher by fixing a quota and thus
the assembled sample has
the same proportions of individuals as the entire population with respect to
known characteristics, traits or focused phenomenon.
d) Snowball sampling: Also known as referrals, the
sample is made up of referrals from subjects who identified other suitable
subjects, usually in areas that are difficult to conduct research in. In other
words, this sampling technique involves tracing rare sample through referrals
from the already identified members of the sample. In short, in this method,
the research participants recruit other possible participants for the study.
Difference between
Probability & NonProbability sampling


Key elements

Probability sampling

NonProbability sampling

Meaning

This group of sampling
methods gives all the members of a population equal chance of being selected.
Also known as Random sampling.

It is a group of sampling
techniques where the samples are collected in a way that doesn't give all the
units in the population equal chances of being selected. Also known as
Nonrandom sampling.

Basis of selection

Randomly

Arbitrarily

Opportunity of selection

Information about the entire
population known & available

Information about the entire
population is not specified and unknown

Research

Conclusive

Exploratory

Method

Objective

Subjective

Result

Less prone to bias and sampling
errors

More prone to bias and
sampling errors

Inferences

Statistical

Analytical

Hypothesis

Tested

Generated

Cost of procedure

Expensive, timeconsuming

Less expensive, more
convenient

SELF CHECK EXERCISES
1. Which
of the following can be considered as probability sampling?
A. Quota sampling
B. Lottery
method of sampling
C. Purposive
sampling
D. Convenience
sampling
2. Which
of the following sampling techniques can be used to locate a sample of rare
talents?
A. Quota sampling
B. Purposive sampling
C. Snowball
sampling
D. Convenience sampling
3. Which
of the following cannot be considered as probability sampling?
A. Lottery method of sampling
B. Snowball sampling
C. Systematic
sampling
D. Cluster
sampling
4. Which
of the following techniques is used when the population is finite?
A. Deliberate
sampling
B. Systematic
sampling
C. Area
sampling
D. Cluster
sampling
5. An
alternative term for “Population” in sampling is:
A. The sample size
B. The Universe
C. Cluster
D. None of the above
6. In which of the following sampling techniques is
the ultimate size of the sample not fixed in advance?
A. Quota sampling
B. Cluster sampling
C. Sequential
sampling
D. Systematic
sampling
2 Comments
Thank you sir
ReplyDeleteMy pleasure indeed! stay connected!!
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