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Sampling
By
Mr. Ravi Rai Dangi
Assistant Professor
Fellowship in Neonatal Nursing
MSc. Child Health Nursing

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INTRODUCTION
 Sampling is a process of selecting representative units from
an entire population of a study.
 Sample is not always possible to study an entire population;
therefore, the researcher draws a representative part of a
population through sampling process.
 In other words, sampling is the selection of some part of an
aggregate or a whole on the basis of which judgments or
inferences about the aggregate or mass is made.

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Population:
Population is the aggregation of all the units
in which a researcher is interested. In other words,
population is the set of people or entire to which the
results of a research are to be generalized. For
example, a researcher needs to study the problems
faced by postgraduate nurses of India; in this the
‘population’ will be all the postgraduate nurses who
are Indian citizen.

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Target Population:
A target population consist of the total number of
people or objects which are meeting the designated set
of criteria. In other words, it is the aggregate of all the
cases with a certain phenomenon about which the
researcher would like to make a generalization.
Accessible population:
It is the aggregate of cases that conform to
designated criteria & are also accessible as subjects for a
study.

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Sample:
Sample may be defined as representative unit
of a target population, which is to be worked upon
by researchers during their study. In other words,
sample consists of a subset of units which comprise
the population selected by investigators or
researchers to participates in their research project

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Sampling error:
There may be fluctuation in the values of the
statistics of characteristics from one sample to another,
or even those drawn from the same population.
Sampling bias:
Distortion that arises when a sample is not
representative of the population from which it was
drawn.

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PURPOSES OF SAMPLING
 Economical: In most cases, it is not possible &
economical for researchers to study an entire
population. With the help of sampling, the
researcher can save lots of time, money, & resources
to study a phenomenon.
 Improved quality of data: It is a proven fact that
when a person handles less amount the work of
fewer number of people, then it is easier to ensure
the quality of the outcome.

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 Quick study results: Studying an entire
population itself will take a lot of time, & generating
research results of a large mass will be almost
impossible as most research studies have time limits
 Precision and accuracy of data: Conducting a
study on entire population giver you a voluminous
data and maintain the precision of that data become
more complex task. So sampling is necessary.

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Characteristics of Good Sample
 Representative
 Free from bias and errors
 No substitution and incompleteness
 Appropriate sample size

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Sampling Process
 Sampling process of selecting a part of the assigned
population to represent the entire population.

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Identifying and defining the target population
Describing the accessible population &
ensuring sampling frame
Specifying the sampling unit
Specifying sampling selection methods
Determining the sample size
Specifying the sampling plan
Selecting a desired sample

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FACTORS INFLUENCING SAMPLINGPROCESS

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Sampling Techniques

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Two Types
• Probability Sampling Techniques
• Non- Probability sampling techniques

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PROBABILITY SAMPLING TECHNIQUE
 It is based on the theory of probability.
 It involve random selection of the elements/members
of the population.
 In this, every subject in a population has equal chance
to be selected sampling for a study.
 In probability sampling techniques, the chances of
systematic bias are relatively less because subjects are
randomly selected.

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Features Of The Probability Sampling
 It is a technique wherein the sample are gathered in a
process that given all the individuals in the
population equal chances of being selected.
 In this sampling technique, the researcher must
guarantee that every individual has an equal
opportunity for selection.

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 The advantage of using a random sample is the
absence of both systematic & sampling bias.
 The effect of this is a minimal or absent systematic
bias, which is a difference between the results from
the sample & those from the population.

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Simple random sampling
 This is the most pure & basic probability sampling
design.
 In this type of sampling design, every member of
population has an equal chance of being selected as
subject.
 The entire process of sampling is done in a single
step, with each subject selected independently of the
other members of the population

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 There is need of two essential prerequisites to
implement the simple random technique: population
must be homogeneous & researcher must have list of
the elements/members of the accessible population

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 The first step of the simple random sampling
technique is to identify the accessible population &
prepare a list of all the elements/members of the
population.
 The list of the subjects in population is called as
sampling frame & sample drawn from sampling frame
by using following methods:

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The lottery method
 It is most primitive & mechanical method.
 Each member of the population is assigned a unique
number.
 Each number is placed in a bowel or hat & mixed
thoroughly.

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The use of table of random numbers
 This is most commonly & accurately used method in
simple random sampling.
 Random table present several numbers in rows &
columns.
 Researcher initially prepare a numbered list of the
members of the population, & then with a blindfold
chooses a number from the random table.

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 The same procedure is continued until the desired
number of the subject is achieved.
 If repeatedly similar numbers are encountered,
they22 are ignored & next numbers are considered
until desired number of subject are achieved.

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The use of computer
 Nowadays random tables may be generated from
the computer , & subjects may be selected as
described in the use of random table.
 For populations with a small number of members,
it is advisable to use the first method, but if the
population has many members, a computer-aided
random selection is preferred.

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Merits
 Every member have equal opportunity to select
 It requires minimum knowledge about the
population in advances.
 Unbiased
 Sample error can be computed

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Demerits
 Requires all members list
 Expensive and time consuming process

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Stratified Random Sampling
 This method is used for heterogeneous population.
 It is a probability sampling technique wherein the
researcher divides the entire population into
different homogeneous subgroups or strata, & then
randomly selects the final subjects proportionally
from the different strata.

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 The strata are divided according selected traits of
the population such as age, gender, religion, socio-
economic status, diagnosis, education, geographical
region, type of institution, type of care, type of
registered nurses, nursing area.

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Merits
 It ensures the representation of all group .
 To observe the existed fact with two or more groups.
 Higher statistical precision .
 Save time, money and efforts.

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Demerits
 It requires accurate information on the portion of
population in each stream
 Large population needed
 Possibility of faulty classification

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Systematic Random Sampling
 It can be likened to an arithmetic progression,
wherein the difference between any two consecutive
numbers is the same.
 It involves the selection of every Kth case from list of
group, such as every 10th person on a patient list or
every 100th person from a phone directory.
 Systematic sampling is sometimes used to sample
every Kth person entering a bookstore, or passing
down the street or leaving a hospital & so forth

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 Systematic sampling can be applied so that an
essentially random sample is drawn.
 If we had a list of subjects or sampling frame, the
following procedure could be adopted.
 The desired sample size is established at some number
(n) & the size of population must know or estimated
(N).

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 For example, a researcher wants to choose about 100
subjects from a total target population of 500 people.
Therefore,
500/100=5.
 Therefore, every 5th person will be selected.

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Merits
 Convenient and simple to carry out.
 Distributed of sample is spread evenly over the entire
population.
 Less time consuming and cost effective also.
 Statistically more significant.

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Demerits
 Subject is not randomly selected so it become non
random sampling techniques.
 Sometimes this may result in biasness

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Cluster or multistage Sampling
 It is done when simple random sampling is almost
impossible because of the size of the population.
 Cluster sampling means random selection of sampling
unit consisting of population elements.
 Then from each selected sampling unit, a sample of
population elements is drawn by either simple random
selection or stratified random sampling.

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 This method is used in cases where the population
elements are scattered over a wide area, & it is
impossible to obtain a list of all the elements.
 The important thing to remember about this
sampling technique is to give all the clusters equal
chances of being selected.

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Merits
 Cheap, quick and easy for large population
 Investigator can use existing division like district,
villages or towns.
 Same clusters can be used for further studies.

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Demerits
 Least representative
 Some time same characteristics can present in two
clusters
 Possibility of high sample error
 If homogenous then not possible

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Sequential Sampling
 It is slightly different from others.
 Here the sample size is no fixed.
 The researcher initially select the small sample and
tries out to make inferences ; if not able to draw the
result researcher can add more samples.

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Merits
 Best possible smallest representative sample
 Helps in ultimately finding the inference in the study.
Demerits
 One point of time study cannon be done
 Requires repeated entries

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Nonprobability Sampling Technique
 Non probability sampling is the techniques wherein
the samples are gathered in a process that does not
give all the individual in the population equal chance
of being selected in the sample.

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Features of the nonprobability sampling
 Does not give equal chance to select each sample.
 Saves time, money and workforce
 Samples will be selected by purpose or personal
judgement.
 If population is unknown then research cannot be
used in generalized for entire population.

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Uses of non probability sampling
 This type of sampling can be used when
demonstrating that a particular trait exists in the
population.
 It can also be used when researcher aims to do a
qualitative, pilot , or exploratory study.
 It can be used when randomization is not possible like
when the population is almost limitless.

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 It can be used when the research does not aim to
generate results that will be used to create
generalizations.
 It is also useful when the researcher has limited
budget, time, & workforce.
 This technique can also be used in an initial study

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Purposive Sampling
 It is more commonly known as ‘judgmental’ or
‘authoritative sampling’.
 In this type of sampling, subjects are chosen to be part
of the sample with a specific purpose in mind.
 In purposive sampling, the researcher believes that
some subjects are fit for research compared to other
individual. This is the reason why they are purposively
chosen as subject.

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 In this sampling technique, samples are chosen by
choice not by chance, through a judgment made the
researcher based on his or her knowledge about the
population
 For example, a researcher wants to study the lived
experiences of post disaster depression among people
living in earthquake affected areas of Gujarat.

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Merits
 Simple to draw
 Useful in exploratory studies
 Save resources
 Requires less field work.

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Demerits
 Requires considerable knowledge abut the
population
 Not always reliable sample
 Purposiveness lead to biasness
 Misrepresentation of sample may be possible

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Convenience Sampling
 It is probably the most common of all sampling
techniques because it is fast, inexpensive, easy, & the
subject are readily available.
 It is a nonprobability sampling technique where
subjects are selected because of their convenient
accessibility & proximity to the researcher.

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 The subjects are selected just because they are
easiest to recruit for the study & the researcher did
not consider selecting subjects that are
representative of the entire population.
 It is also known as an accidental sampling.
 Subjects are chosen simply because they are easy to
recruit.

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Merits
 Easiest, cheapest and least time consuming.
 In pilots study we use this sampling technique
Demerits
 Sample is not representative of entire population
 Result cannot be generalized for entire population.

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Consecutive Sampling
 It is very similar to convenience sampling except that it
seeks to include all accessible subjects as part of the
sample.
 This nonprobability sampling technique can be
considered as the best of all nonprobability samples
because it include all the subjects that are available,
which makes the sample a better representation of the
entire population.

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 In this sampling technique, the investigator pick up all
the available subjects who are meeting the preset
inclusion & exclusion criteria.
 This technique is generally used in small-sized
populations.
 For example, if a researcher wants to study the activity
pattern of postkidney-transplant patient, he can selects
all the postkideney transplant patients who meet the
designed inclusion & exclusion criteria, & who are
admitted in post- transplant ward during a specific
time period.

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Merits
 Little effort
 Saves time, money and material
 Ensure more representative from population
Demerit
 Researcher has not set sampling plans about the
sample
 Not guarantee for representative sample
 Sampling technique cannot be used to create
conclusion and interpretation for entire population.

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Quota Sampling
 It is nonprobability sampling technique wherein the
researcher ensures equal or proportionate
representation of subjects, depending on which trait is
considered as the basis of the quota.
 The bases of the quota are usually age, gender,
education, race, religion, & socio-economic status.

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 For example,
 if the basis of the quota is college level & the research
needs equal representation, with a sample size of 100, he
must select 25 first- year students, another 25 second-
year students,49 25 third-year, & 25 fourth-year
students.

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Merits
 Economically cheap, as there is no need to approach all
the candidates.
 Suitable for studies where the field work has to be
carried out, like studies related to market & public
opinion polls.
Demits
 Not represent the entire population
 In the process of sampling these subgroups, other traits
in the sample may be overrepresented.
 Bias is possible, as investigator/interviewer can select
persons known to him.

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Snowball Sampling
 It is a nonprobability sampling technique that is used
by researchers to identify potential subjects in studies
where subjects are hard to locate such as commercial
sex workers, drug abusers, etc.
 For example, a researcher wants to conduct a study on
the prevalence of HIV/AIDS among commercial sex
workers.

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 This type of sampling technique works like chain
 After observing the initial subject, the researcher asks
for assistance from the subject to help in identify
people with a similar trait of interest.
 The process of snowball sampling is much like asking
subjects to nominate another person with the same
trait.
 The researcher then observes the nominated subjects
& continues in the same way until obtaining
sufficient number of subjects.

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Merits
 The chain referral process allows the researcher to
reach populations that are difficult to sample when
using other sampling methods.
 The process is cheap, simple, & cost-efficient.
 Need little planning & lesser workforce
Demerits
 Researcher has little control over the sampling
method.
 Representativeness of the sample is not guaranteed.
 Sampling bias is also a fear of researchers when using
this sampling technique.

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Problems of sampling
 Sampling errors
 Lack of sample representativeness
 Difficulty in estimation of sample size
 Lack of knowledge about the sampling process
 Lack of resources
 Lack of cooperation
 Lack of existing appropriate sampling frames for
larger population