Sampling

PRESENTED BY:
MS. MONIKA KANWAR
M.Sc. (N) MENTAL HEALTH
NURSING
SHIMLA NUSRING COLLEGE

INTRODUCTION
Sampling is a key step in the research process. Where the
researcher obtains a sample for data collection. Sampling
is not a new development, but in recent times it is used
by people in all fields, even in day-to-day life, to get an
understanding about societies, opinions, or situations. It
is not always possible to study an entire population,
therefore, the researcher draws a representative part of a
population through sampling process.

DEFINITION
Sampling is the action or process of taking samples of
something for analysis.
OR
Sampling is the process of selecting a sample from a target
population.
OR
It is the process of selecting a group of people, events,
behaviors, or other elements that are representative of the
population being studied.
OR
It is defined as a process by which a sample, which is the
representative unit of the population under the research
study is selected and it represents the entire target
population

CONTD….
 POPULATION:
Population is the aggregation of all
the units in which a researcher
is interested.
Composed of two groups: Target
population and Accessible
population
 SAMPLE :
Sample is a group of people,
objects, or items that are taken
from a larger population for
measurement.

• A target population is the
entire population, or group,
that a researcher is interested
in researching and analyzing.
TARGET
POPULATION
• It is the aggregate of cases
that conform to designated
criteria and are also
accessible as subjects for a
study.
ACCESSIBLE
POPULATION

PURPOSE OF SAMPLING
Following are the purposes of sampling:
 Economical: With the help of sampling, researcher
can save lots of time, money, and resources.
 Improved quality of data: It is a proven fact that
when a person handles less amount the work or
fewer no. of people, then it is easier to ensure the
quality of the outcome.

CONTD….
 Quick study results: With a sample, it is possible to
generate study results faster, which is one of the most
important objective of every researcher.
 Precision and accuracy of data: Conducting study in
the entire population provides voluminous data to the
researcher, due to which precision of data becomes a
burden to the researcher. It is always easy to establish
better rapport with a sample (Part of population) thus
to collect more accurate data. Thus, sample helps to
generate precise and accurate data in research study.

CHARACTERISTICS OF
GOOD SAMPLING
A good sample has the following characteristics:
 A sample should be representative of the population,
from where it is drawn.
 A sample should posses the same key attributes or
the same key characteristics of the population, from
where it is drawn.
 A sample should be optimum in size.
 A sample should be free from sampling biases.
 It must posses the least sampling error.

PRINCIPLES OF
SAMPLING
1. Principles of Statistical Regularity:
This law is based upon mathematical theory of
probability. It is based upon the following two
conditions:
- Large sample size: As the sample size increases, the
true characteristics of the population are more likely
to reveal.
- Random selection: The sample should be selected
randomly in which each and every unit of the
universe has an equal chance of being selected.

CONTD….
2. Principles of large numbers:
 It is based upon the concept as sample size increases,
the better result we will get.
 For example if we have to study the non-compliance
among tuberculosis patients in a hospital, then fairly
adequate sample of the patients help researcher to
arrive at good results

CONTD….
3. Principles of validity:
 If valid tests are derived, only then sampling design
is termed as valid.
4. Principle of optimization:
 One must be able to get optimum results with
maximum efficiency and minimum cost.

FACTORS INFLUENCING
SAMPLING PROCESS
1 NATURE OF THE RESEARCHER:
 Inexperienced investigator
 Lack of interest
 Lack of honesty
 Intensive workload
 Inadequate supervision
2 NATURE OF THE SAMPLE:
 Inappropriate sampling technique
 Sample size
 Defective sample frame

CONTD….
3 CIRCUMSTANCES:
 Lack of time
 Large geographic area
 Lack of cooperation
 Natural calamities

SAMPLING
ELIGIBILITY CRITERIA
In identifying a population the researcher should be
specific about the criteria that defines who will be
included in the study. There are two important terms to
describe the sample eligibility criteria:
 Inclusion Criteria: The criteria that specify the
characteristics that people in the population must
possess are referred to as eligibility criteria or
inclusion criteria.
• For example to be eligible for the study, the patient
must be visiting to the out-patient department of the
hospital at the time of data collection, must be older
than 60 years of age and be able to speak and
understand English.

CONTD….
 Exclusion Criteria: Sometimes, a population is
defined in terms of characteristics that people must
not possess that is known as exclusion criteria.
• For example the population may be defined to
exclude people who are denied to participate and
mentally unstable or unsound.

CRITERIA FOR GOOD
SAMPLING
 Homogeneous: Selected samples from the
population should be homogeneous and should not
have any difference when compared with the
populations.
 Optimum Sample Size: Reasonable number of items
is to be included in the sample to make the result
more reliable.
 True representative: The selected sample should
have the similar characteristics as the original
population from which it has been selected.
 Mutually exclusive: The individual items composing
the sample should be independent from each other.

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

CONTD….
1. Identifying and defining the target population: The
first step of the sampling process is the
identification and defining the target population.
Target population consist of the total group of
people or objects that meet the designated set of
criteria of interest of the researcher. Target
population is the aggregate of cases about which the
researcher would generalize the information.
Therefore, it is the first and the most essential stage
of the sampling process

CONTD….
2. Describing the accessible population and ensuring
sampling frame: It is not always possible to have
access to each subject induced in the target
population. Therefore, researcher must establish a
description about the accessible population, which is
readily available for research. In addition, after
establishing a complete description of the accessible
population, researchers must have a sampling frame
available to select a sample from accessible
population.

CONTD….
3. Specifying the sampling unit: Next, the researcher
must establish the specific inclusion and exclusion
criteria to select a particular sampling unit.
Therefore, specifying the sampling units helps the
researcher eliminate the confusion while the
selection of the sample.

CONTD….
4. Specifying sample selection methods: It is one of
the important stages of sampling process, where the
researcher decides whether sample will be drawn
from the population by using probability and non-
probability sampling techniques. Selection of the
specific method for selection of the sample depends
upon several factors, such as type of population,
kind of phenomenon under study, and availability of
resources and knowledge of the researcher.

CONTD….
5. Determining the sample size: It is very essential to
determine the size of sample, so that the researcher
can plan the implementation of the sampling process
accordingly.
6. Specifying sampling plan: Before the selection of a
particular sample, the researcher must have a final
sampling process can be implemented without any
due problems.

CONTD….
7. Selecting a desired sample: Finally a researcher
draws a representative sample from the accessible
population, which requires the implementation of
the plan of sampling process, and ultimately selects
a representative part of population, which is used by
the researcher for data collection in research study.

TYPES OF SAMPLING
TECHNIQUES
Probability
sampling technique
1Simple random sampling
2 Stratified random
sampling
3 Systematic random
sampling
4 Cluster/ Multistage
sampling
5 Sequential sampling
Nonprobability
sampling technique
1 Purposive sampling
2 Convenient sampling
3 Consecutive sampling
4 Quota sampling
5 Snowball sampling
6 Volunteer sampling
7 Genealogy sampling

PROBABILITY SAMPLING
Probability sampling technique is a technique in
which each subject has an equal opportunity or
probability of being included in the sample. In
this technique, the sample members are selected
by randomization.

CONTD….
TYPES OF PROBABILITY SAMPLING:
1. Simple Random Sampling Technique:
 In this type of sampling design, every population
member has a similar chance of being picked as the
subject.
 For example: We wish to draw a sample of 50 students
from a population of 400 students. Place all the 400
names of students in a container and draw out the 50
names one by one randomly.

CONTD….
The first step of simple random sampling technique is to
identify the accessible population and prepare a list of
all elements/members of the population.
Sample is drawn from sampling frame by using following
methods:
METHODS
The lottery
method
The use of table
of random
numbers
The use of
computer

CONTD….
MERITS:
 Fair way of selecting a sample.
 Require minimum knowledge about the population in
advance.
 Free from sampling errors.
 Reduce the risk of sampling bias
DEMERITS:
 Does not make use of knowledge about a population
which researchers may already have.
 Lots of procedure need to be done before sampling.
 Expensive and time consuming.

CONTD….
2. Stratified random sampling:
 This is a technique where the entire accessible
population is first divided into two or more sub-groups
or sub-strata of population from where the subjects are
selected randomly.
 For example: A 100 students of the research population
may be sub-divided into two groups of male and female
students. Then the samples are selected from each sub-
group by random method from whom the information or
data is gathered.

CONTD….
It is further divide into two categories:
 Proportionate: The number of elements
chosen from each of the strata is proportionate
to the size of a particular strata relative to the
overall sample size.
 Disproportionate: The number of elements
chosen from each of the strata is not based on
the size of the stratum relative to the target
population size, but rather is based either on
the importance of a particular stratum or its
variability.

CONTD….
MERITS:
 Ensures representation of all groups in a population.
 Comparison is possible between subgroups.
 Save much time, money, and effort.
 Reduces bias in the sample
DEMERITS:
 Requires accurate information on the proportion of
population in each stratum.
 Possibility of faulty classification.
 Large population must be available from which to
select subjects.
 Knowledge about complete sampling frame is
required.

CONTD….
3. Systematic Random Sampling:
 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.
 For example: If a researcher has to obtain a sample of
25 students from the 100 student-population, the
researcher selects every 4th student for the sample,
maintaining an equal sampling interval and sampling
distance.

CONTD….
MERITS:
 Simple to carry out.
 Distribution of sample is spread evenly over the
entire given population.
 Time-consuming and is cheaper than simple random
sampling technique.
DEMERITS:
 If first subject is not randomly selected, then it
becomes a nonrandom sampling technique.
 Sometimes this may result in biased sample.

CONTD….
4. Cluster sampling:
 It is also called as Multistage or Multi-step sampling.
 This sampling techniques is used where a widespread,
large population is to be studied and in cases where
the population elements are scattered over a wide area
and is impossible to obtain the list of all the elements.

CONTD….
Types of cluster samples:
1. ONE-STAGE CLUSTER SAMPLING: One-stage
cluster sample occurs when the researcher includes
all the high school students from all the randomly
selected clusters as sample.
2. TWO-STAGE CLUSTER SAMPLING: Two-stage
cluster sample is obtained when the researcher only
selects a number of students from each cluster by
using simple or systematic random sampling.
3. MULTISTAGE CLUSTER SAMPLE: The
sampling is done at more than the two levels by
initially identifying clusters as population at
different levels.

CONTD….
 For example:
• POPULATION: 5000 hospitals in the country
• FIRST RANDOM SAMPLE:1000 hospitals are
randomly selected from 5000 hospitals
• SECOND RANDOM SAMPLE: 200 hospitals are
randomly selected from 1000 hospitals
• THIRD RANDOM SAMPLE: Finally, 10 hospitals
are randomly selected from 200 hospitals

CONTD….
MERITS:
 This is cheap, quick, and easy for a large
population.
 Large population can be studied, and require only
list of the members.
 Same clusters can be used again for study.
DEMERITS:
 Not useful in small homogenous group.
 The cost to reach an element to sample is very high.

CONTD….
5. Sequential sampling:
 This is slightly different from other methods. Here
the sample size is not fixed.
 The investigator initially selects small samples and
ties out to make inferences; if not able to draw
results, he or she then adds more subjects until
clear-cut inferences can be drawn.

CONTD….
MERITS:
 Facilitates to conducts a study on best-possible
smallest representative sample.
 Helping in ultimately finding the inferences of
the study.
DEMERITS:
 It is not possible to study a phenomenon which
needs to be studied at one point of time.
 Requires repeated entries.

NONPROBABILITY
SAMPLING
It is a technique wherein the samples are gathered in a
process that does not give all the individuals in the
population equal chances of being selected in the
sample.

CONTD….
TYPES OF NONPROBABILITY SAMPLING:
1. Purposive sampling:
 More commonly known as ”judgmental sampling” ,
“authoritative sampling” or “ Theoretical sampling” .
 This is a sampling technique, where the researcher draws a
sample with a specific purpose that is associated with the
research study at hand, such as where the sample-
participants will be able to generate data regarding the issues
relevant to research study undertaken.

CONTD….
 Purposive sampling technique can be further classified
as follow:
• Homogeneous sampling
• Variation sampling
• Extreme sampling
• Intensity sampling
• Typical case sampling
• Critical case sampling
 For example: If the researcher is interested to learn the
sexual relationship of the breast cancer patients who
have undergone radical mastectomy, the researcher has
to select a sample purposefully from a typical
population of “ breast cancer patients with radical
mastectomy

CONTD….
MERITS:
 Simple to draw samples.
 Save resources as it requires less feedback
DEMERITS:
 Requires considerable knowledge about population
under study.
 Conscious bias may occur.

CONTD….
2. Convenience sampling:
 Convenience sampling is also known as
“Accidental sampling technique”.
 This is a sampling technique where the researcher
selects a sample which is most readily or more
conveniently available to the researcher for the
research study or at the time of the research study.

CONTD….
 For example: A researcher would like to study the
patients attending an outdoor department of a
hospital. So, the researcher has to pick up the
subjects whoever have come to attend the outpatient
department. When a sample is obtained in this way,
such as whoever available is included in the sample,
this is called convenient sampling technique.

CONTD….
MERITS:
 It is easiest, cheapest and least time
consuming.
 This technique help in saving time, money,
and resources.
DEMERITS:
 Chances of sampling bias.
 Findings generated from these samples
cannot be generalized on the population.

CONTD….
3. Consecutive sampling:
 This sampling technique can be considered as best of
all Nonprobability samples because it includes all the
subjects that are available, which makes the sample a
better representation of the entire population.
It is also known as “ total enumerative sampling”.
 It is used for continuously changing population, such
as patients admitted in the hospital.

CONTD….
MERITS:
 Not expensive, not time consuming.
 There is very little effort on the part of the researcher
when performing this sampling technique.
DEMERITS:
 Limit option about the sample size.
 Does not guarantee the representative sample

CONTD….
4. Quota sampling:
 This is a sampling technique in which the researcher
divides the population into various homogeneous
segments or quotas or sub-groups. Homogeneity is a
status where the amount of variability is restricted.
 The bases of quota are usually age, generation,
education, race, religion, socio-economic status, etc.

CONTD….
 For example: Researcher might be need data from
40 adults and 20 adolescents in order to study
students television viewing habits
Selection will be:
 20 adult men and 20 adult women
 10 adolescent girls and 10 adolescent boys

CONTD….
MERITS:
 Economically cheap.
 Very extensively used/understood.
 DEMERITS:
 Not possible to estimate errors.
 Time consuming.

CONTD….
5. Snowball sampling:
It is also known as “chain referral sampling”,
“network sampling” , “Nominated sampling” .
 This is sampling technique in which the
participants or the members of the samples are
requested to refer other people who may meet the
eligibility criteria set by the researcher to
participate in the research study but are
inaccessible to the researcher but who should be
included in the sample.

CONTD….
 For example: When a researcher is interested to
study “ drug-addicts” , the researcher may not have
the direct accessibility to such “ Drug-addicts”. The
researcher may identify one or two “Drug-addicts”
and then ask them to get more “Drug-addicts”
through their contacts. This process may be
continued till desired sample size of the “Drug-
addicts” are obtained.

CONTD….
i. Linear snowball sampling: In this, each selected
sample is asked to provide reference of only one
similar subject, where a linear chain is created by
the completion of desired sample.

CONTD….
ii. Exponential snowball sampling: In this, each
sample member is asked to provide reference of
atleast two similar subject, because of which the
size of the sample grows exponentially and a
large sample size can be achieved.

CONTD….
iii. Exponential discriminative snowball sampling:
In this, initially one sample is selected and asked
for two references and another could be nonactive
in providing references. Similarly, each active
reference subject is further asked for two
references for similar subjects; out of them one
should be active for further reference.

CONTD….
MERITS:
 Facilitate sampling for people difficult to
locate.
 Low cost.
 Need little planning and lesser workforce.
DEMERITS:
 Researcher has less control over the
sampling method.
 Chances of poor coverage of entire
population.

CONTD….
6. Volunteer sampling:
 It is a type of non-probability sampling technique, in
which participants themselves volunteer to
participate in the study and they only approach the
researcher to be the part of the study Sample.
 Researcher publishes an advertisement or informs
target population through mass media to participate
in the study and interested participants may
voluntarily contact research to participate in the
study.

CONTD….
 For example: A nurse researcher is interested to
assess the effectiveness of a selected yoga
technique on the reduction of blood pressure. She
may advertise in newspaper to inform target people
to participate in this research to take the scheduled
yoga classes and pre and post assessment of blood
pressure. In this instance, interested people may
voluntarily contact researcher to participate in the
study.

CONTD….
MERITS:
 Cost-effect sampling technique
 Needs very limited efforts and time to locate the
study participants
 This technique helps to collect large size data in
limited time period.
DEMERITS:
 Only interested people contact to participate, so
there are very chances that sample may not be
representative sample
 Since there are very high chances of non-
representatives of sample and systematic bias, the
study results lack of generalizability.

CONTD….
7. Genealogy sampling:
 It is a type of non-probability sampling technique, in
which all the member of entire related families are
selected rather than selecting the different
households in the village or area.
 It begins with identifying a first participant, who is
convinced to participate in the study and then further
he/she is asked to refer to close relatives of his
family, who even may be living in other areas of
village or area.

CONTD….
 Sampling technique is primarily used in rural
population, which are socio-culturally and
economically homogeneous, and it is also frequently
used in genetic studies to identify trends of genes in
traditional families and so on.
 This sampling technique provides significant cross-
section of selected community by age, gender, and
so on.

CONTD….
MERITS:
 This sampling technique is useful in drawing a
representative sample from traditional rural
communities, which are socio-culturally and
economically homogeneous.
 Saves the time and efforts
DEMERITS:
 Problem of systematic errors or bias.
 Lacks the diversity of sample participant
characteristics because subjects are selected from a
family or related families.

SAMPLE SIZE
 Sample size is a direct count of the number of
samples measured or observations being made.
Sample size should be adequate to represent the
population.
 It is the number of subjects, events, behaviours, or
situations that are examined in a study.
 The larger the sample size the more accurate the
findings from a study.

FACTORS AFFECTING
SAMPLE SIZE
 Design effect: The study design factors
influencing the sample size includes the type of
study and the number of variables under study and
the sampling strategy. Quantitative studies require
larger sample as compared to qualitative studies.
The sample size variation is also considered
within qualitative studies.
• For example A longitudinal study design requires
larger sample size then cross-sectional studies,
because longitudinal studies may lose the subject
over time

CONTD….
 Resources available: A large sample may ensure
precision, but it may prove to be costly as it may make
the researcher realize at some time or other that they
are running short or out of money and other resources.
So before carrying out a study with a large sample, the
researcher needs to decide if they have sufficient
resources.
 Nature of study: The sample size depends upon the
type of study to be carried out. Small samples can
make intensive and longitudinal studies successful, but
for extensive and one-time studies, large samples are
required.

CONTD….
 Sampling methods used: Smaller but efficiently
selected samples proved to be far better than badly
selected, spurious large samples. Probability sampling
has been able to prove its worth in the research more
as compared to non-probability sampling.
 Homogeneity: If the population is homogeneous, then
a small sample may be sufficient. Since in a
homogeneous population, their answers are going to
be close, taking a large sample would be unwise.

CONTD….
 Effect Size: If the relationship between the
independent and dependent variables is strong then a
sample size will be sufficient. For example, to find the
effect of drug abuse on family disorganization, a
sample that is small will be enough.
 Degree of Confidence: Confidence refers to our
desires that we donot want the error to go beyond a
certain limit. Therefore, the higher the degree of
confidence, the larger is the sample size required. For
example, if we need a degree of confidence of 99%,
then it means we do not want the error to be beyond
1%.

CONTD….
 Degree of accuracy desired from the estimate:
Precision is the limit if tolerable errors exist in the
sample estimates. For example, a study is conducted
on the number of patient’s satisfied with the diet
provided at the hospital. In this study, all that the
researcher want is the number of people satisfied to
be within ›50% of the true population (patient on
hospital diet)

CONTD….
 Cooperation with attrition: Attrition refers to the
condition when people initially willing to participate,
fail to continue participation and leave the sample. If
the data is collected from multiple points and there is
less chance of communication of the researcher with
the sample due to long time gap, there can be a
possibility of attrition. So, sample size should be
selected including expected refusals.

CONTD….
 Subgroup Analysis: If the hypothesis are to be tested
not only for the population, but also for specific
subgroups, then the sample size should be large
enough to make the generalization of the result
possible to those subgroups too. For example, if we
want to study the use of an internet-equipped library
to the nursing students, and then utilize the same
results for the final-year students; who are expected
to carry-out internet searching exercises.

CONTD….
 Measurement factors: Measurement factors that
influence sample include the sensitivity of the
research instruments and the effect that the process
has on the outcome. Data collection instruments in
which measurement error is minimal are said to be
precise. The less precise the instrument, the larger
the sample size perspective, it is best to measure at
that level if at all possible, because a smaller sample
size may be used.

STATISTICAL
CALCULATION OF SAMPLE
SIZE
 The Yamane Formula: It was formulated by the
statistician Tara Yamane in 1967 to determine the
sample size from a given population.
n = N/1+N(e)²
Where,
• n signifies the sample size
• N signifies the population under study
• e signifies the margin error (it could be 0.10, 0.05 or
0.01)

CONTD….
Example: A student have a total population of about
400 respondents and wishes to determine the sample
size.
Solution: n = N/1+N(e)²
n = 400/1+400 (0.05)²
n = 400/1+400 (0.0025)
n = 400/1+1
n = 400/2
n = 200

CONTD….
 Sample Size for infinite Sample size:
SS = [Z²p (1-p)]/ C²
 Sample Size for finite Sample size:
SS/ [1+{(SS-1)/Pop}]
Where,
• SS = Sample size
• Z = Given Z value
• P = Percentage of population
• C = Confidence level
• Pop = Population

CONTD….
FOR INFINITE POPULATION:
Example: Find the sample size for infinite population
when the percentage of 4300 population is 5,
confidence level 99 and confidence level is 0.01?
Solution: SS = [Z²p (1-p)]/ C²
SS = (2.58)²x0.05x(1-0.05)/ (0.01)²
SS = (6.656x0.05x0.95)/ 0.0001
SS = 0.6564x0.0475/0.0001
SS = 0.316/0.0001
SS = 316

CONTD….
FOR FINITE POPULATION:
Example: Find the sample size for a finite population
when the percentage of 4300 population is 5,
confidence level 99 and confidence level is 0.01?
Solution: SS/ [1+{(SS-1)/Pop}]
316/ 1+{(316-1)/4300}
316/ 1+ 315/4300
316/ 1+0.0732
316/ 1.0732
294
New SS = 294

CONTD….
 Cochran’s Formula: It is considered especially
appropriate in situations with large populations.
n= Z²pq/e²
Where,
• e is the desired level of precision (i.e. the margin of
error)
• p is the (estimated) proportion of the population
which as the attribute in question
• q is 1-p

CONTD….
Example: Suppose we are doing a study on the inhabitants
of a large town, and want to find out how many
households serve breakfast in the mornings. We don’t have
much information on the subject to begin with, so we’re
going to assume that half of the families serve breakfast:
this gives us maximum variability. So p=0.5. Now let’s say
we want 95% Confidence, and atleast 5 percent- plus or
minus-precision. A 95% confidence level gives us Z values
of 1.96, per the normal tables, so we get
Solution: (1.96)² (0.5) (0.5)/ (0.05)²
3.842 x0 0.25 / 0.0025
0.9605/0.0025
385

CONTD….
 Modification for the Cochran’s Formula for
sample size calculation in smaller populations: It is
considered especially appropriate in situations with
large populations.
Where,
• n Cochran’s sample size
• N is the population size
• n is the new, adjusted sample size

CONTD….
Example: If there were just 1000 households in the
target population, we could calculate
Solution: 385/ (1+ (385-1) / 1000
385/ (1+ (384) / 1000
385/ 1+0.384
385/1.384
278

PROBABILITY AND
SAMPLING ERRORS
Sampling error is the error caused by observing sample
instead of the whole population. Sampling error is the
deviation of the selected sample from the true
characteristics, traits, behaviours, qualities, or figures of
the entire population.
Reasons of sampling errors:
TWO BASIC REASONS
CHANCE
ERROR
SAMPLING
BIAS

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
 Callous approach of the researcher towards
sampling process

ADVANTAGES OF
SAMPLING
Sampling has following advantages:
 Sampling is economical in terms of cost and time.
 Sampling is economical in terms of resources.
 Sampling helps in accurate data collection.
 Sampling facilitates the establishment of close rapport
between the researcher and the sample-subjects or the
participants, because the size of the sample is much
smaller than the population.

DISADVANTAGES OF
SAMPLING
Sampling has following disadvantages:
 There is risk of biased selection of sample. Sampling
biasness occurs due to over representation or due to
under representation of the population.
 It is difficult to select a sample which is truly
representative.
 It is necessary for the researcher to have or to develop
skills in sampling techniques.

Sampling

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Sampling