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SINDHU SEBASTIAN
LECTURER
FMCON ,2013
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.
It is a process of obtaining information regarding a
phenomenon about entire population by examining
a part of it 3/2/20152
TERMINOLO
GY USED IN
SAMPLING
3/2/20153
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. 3/2/20154
Population is defined as The Entire Group under study. Sometimes it is also called as
the “Universe.”
Population
Target Population:
3/2/20156
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.
For example, a researcher is interested in
identifying the complication of diabetes mellitus
type-II among people who have migrated to
Canada. In this instance, the target population
are all the migrants at Canada suffering with
diabetes mellitus type-II
Accessible population:
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It is the aggregate of cases that conform to
designated criteria & are also accessible as
subjects for a study.
For example, ‘a researcher is conducting a study
on the registered nurses (RN) working in Father
Muller Hospital, Mangalore ’. In this case, the
population for this study is all the RNs working in
Father Muller Hospital, but some of them may be
on leave & may not be accessible for research
study. Therefore, accessible population for this
study will be RNs who meet the designated
criteria & who are also available for the research
study.
Sampling:
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Sampling is the process of selecting
a representative segment of the
population under study.
Sample:
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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
Element:
3/2/201510
The individual entities that comprise the
samples & population are known as
elements, & an element is the most basic
unit about whom/which information is
collected. An elements is also known as
subject in research. The most common
element in nursing research is an
individual. The sample or population
depends on phenomenon under study
Sampling frame:
3/2/201511
It is a list of all the elements or subjects in the population from which the sample is drawn.
Sampling frame could be prepared by the researcher or an existing frame may be used.
For example, a researcher may prepare a list of the all the households of a locality which have pregnant women or may used a register of pregnant women for antenatal care available with the
Sampling error:
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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:
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Distortion that arises when a
sample is not representative of the
population from which it was
drawn.
Sampling plan
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The formal plan specifying a
sampling method, a sample
size, & the procedure of
selecting the subjects.
Sample size
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Size of sample should be determined by a researcher keeping in view:
1. Nature of universe: -homo (small sample)
- hetero (large sample).
2. No. of classes proposed: directly proportional to the sample size .
3. Nature of study: general (large)
intensive (small).
4.Type of sampling: small random sample is better than a large but bad one.
Contd…
3/2/201516
Standard of accuracy: high level of precision large
sample.
6. Availability of finance: sample size =amount of
money available.
7. Other considerations: size of population,
size of questionnaire,
nature of units,
conditions.
SAMPLING BREAKDOWN
Purposes of sampling
3/2/201520
Economical
Improved quality of data
Quick study results
Precision and accuracy of
data
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.
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 an entire population provides researchers with
voluminous data, & maintaining precision of that data
becomes a cumbersome task.
3/2/201521
CHARACTERISTICS OF GOOD SAMPLE
True Representative
Free from sample bias and errors
No substitution and incompleteness
Appropriate sample size(Optimum
size (adequately large)3/2/201522
Contd…
Has all characteristics that are present
in population
Economically viable
Results can be applied to the universe
in general with a reasonable level of
confidence or reliability
SAMPLING PROCESSIdentifying and defining the target
population
Describing the accessible population &
ensuring sampling frame
Specifying the sampling unit
Specifying sampling selection methods
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Count…
Determining the sample size
Specifying the sampling plan
Selecting a desired sample
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Sampling Design Process
Define Population
Determine Sampling Frame
Determine Sampling Procedure
Probability Sampling Simple Random SamplingStratified SamplingCluster SamplingSystematic SamplingMultistage Sampling
Non-Probability SamplingConvenientJudgmentalQuotaSnow ball Sampling
Determine AppropriateSample Size
Execute SamplingDesign
Steps in Sampling Process
1. Define the population
2. Identify the sampling frame
3. Select a sampling design or procedure
4. Determine the sample size
5. Draw the sample
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FACTORS
INFUENCING
SAMPLING
PROCESS
FACTORS INFUENCING SAMPLING
PROCESS
Nature of the researcher:
Inexperienced investigator
Lack of interest
Lack of honesty
Intensive workload
Inadequate supervision
Nature of sample
Inappropriate sampling technique
Sample size
Defective sampling frame
Circumstances:
Lack of time
Large geographical area
Lack of cooperation
Natural calamities
FACTORS INFLUENCING SAMPLING
PROCESS
3/2/201531
32
METHODS OF SAMPLING
The methods of sampling can be divided
on the basis of the element of probability
associated with the sampling technique.
Probability means chances available to
members of the population for getting
selected in the sample. Accordingly, the
methods of sampling are classified into two
broad types:
Probability Sampling
Non Probability Sampling
3/2/201533
Classification of Sampling Methods
SamplingMethods
ProbabilitySamples
SimpleRandom
Cluster
Systematic Stratified
Non-probability
QuotaJudgment
Convenience Snowball
Multistage
PROBABILITY
SAMPLING
TECHNIQUE3/2/201535
36
Probability Sampling Method
Probability Sampling is also known as Random
Sampling or formal sampling
Probability means chance
Therefore element of the population has the equal chance or opportunity of being selected in the
sample
Probability samples are more accurate
Eg. If a sample of 100 students is to be selected
from a population of 1000 students, then it is
known to every one that each student has 1000 /
100 i.e. 1 chance in 10 being selected
3/2/201537
Concept… 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 is relatively
less because subjects are randomly
selected.
Features of the Probability
sampling
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It is the only systematic and objective method of sampling that
provides equal chance to every element of the population in
getting selected in the sample
The results of probability sampling more accurate and reliable
It helps in the formulation of a true representative sample by
eliminating human biases
Under probability method each element of population known
in advance about the possibility of being included in the sample
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.
Probability Techniques
Probablitytechniques
Simple Random Sampling
Systemic Sampling
Stratified Sampling
Disproportionate
Proportionate
Cluster Sampling Others
Types of the probability sampling
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1.Simple random sampling
2.Stratified random sampling
3.Systematic random sampling
4.Cluster/multistage sampling
5.Sequential sampling
Simple random
sampling
DR G K KALKOTI42
Simple Random Sampling
It is the basic probability sampling technique and all other methods are variations of simple random method.
It can be defined as the method of sampling which provides every element in the population an equal and known chance of being selected in the sample.
Simple random can be done by
A) Lottery Method
B) Random Tables
c)The use of computer
Simple random sampling
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The entire process of sampling is done
in a single step, with each subject
selected independently of the other
members of the population
There is need of two essential
prerequisites to implement the simple
random technique:
1.Population must be homogeneous &
2.Researcher must have list of the
elements/members of the accessible
population.
Steps
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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:
The lottery method
The use of table of random numbers
The use of computer
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1.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.
The blind-folded researcher then picks
numbered tags from the hat.
All the individuals bearing the numbers
picked by the researcher are the subjects
for the study.
3/2/201547
The use of table of random numbers…
This is most commonly & accurately used method in simple random sampling.
It provides use of random numbers specially designed for sampling purposes
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.
The same procedure is continued until the desired number of the subject is achieved.
If repeatedly similar numbers are encountered, they are ignored & next numbers are considered until desired numbers of the subject are achieved.
Such type of random table are mostly found at the end of statistical textbooks
Random numbers of table
6 8 4 2 5 7 9 5 4 1 2 5 6 3 2 1 4 05 8 2 0 3 2 1 5 4 7 8 5 9 6 2 0 2 4 3 6 2 3 3 3 2 5 4 7 8 9 1 2 0 3 2 59 8 5 2 6 3 0 1 7 4 2 4 5 0 3 6 8 6
3/2/201549
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.
From a sample frame
Randomlyselect
elements
The selected elements form the sample
Types of Simple Random Sample
With replacement
Without replacement
With replacement
The unit once selected has the chance for againselection
Without replacement
The unit once selected can not be selectedagain
REPLACEMENT OF SELECTED
UNITS
53
Sampling schemes may be without replacement('WOR' - no element can be selected more than once
in the same sample) or with replacement ('WR' -
an element may appear multiple times in the one
sample).
For example, if we catch fish, measure them, and
immediately return them to the water before
continuing with the sample, this is a WR design,
because we might end up catching and measuring
the same fish more than once. However, if we do not
return the fish to the water (e.g. if we eat the fish), this
becomes a WOR design.
Merits and Demerits
3/2/201554
Merits Ease of assembling
the sample
Fair way of selecting a sample
Require minimum knowledge about the population in advance
It unbiased probability method
Free from sampling errors
Demerits It requirement of a
complete & up-to-date list of all the members of the population.
Does not make use of knowledge about a population which researchers may already have.
Lots of procedure need to be done before sampling
Expensive & time-consuming
Stratified
samplingpopulation
strata
strata
strata
strata
STRATIFIED SAMPLING
Population is divided on the basis of
characteristic of interest in the population e.g.
male and female may have different
consumption patterns.
Stratified Random Sampling
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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.
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
specialization, site of care, etc.
DR G K KALKOTI58
Stratified Random Sampling
In this method, the population is divided and subdivided with homogeneous or similar characteristics
For example, a group of 200 college teachers can be first divided into teachers in Arts faculty, Commerce Faculty and Science Faculty.
After dividing the entire population of teachers into such classes called strata, a sample is selected from each stratum of teachers at random. These samples are put together to form a single sample.
Stratified random sampling is more accurate and representative as compared to simple random sampling because under this the population is divided into homogeneous groups.
Stratified Random Sampling
Population is divided into two or more groupscalled strata, according to some criterion, such asgeographic location, grade level, age, or income,and subsamples are randomly selected from eachstrata.
Elements within each strata are homogeneous, butare heterogeneous across strata
Stratified Random Sampling
Types of Stratified Random Sampling
Proportionate Stratified Random Sampling
Equal proportion of sample unit are selected from each strata
Disproportionate Stratified Random Sampling
Also called as equal allocation technique and sample unit
decided according to analytical consideration
From a population
Based on an attribute divide into
strata
Selectelements
from strata by
randomization
Merits and Demerits
3/2/201563
systemic
sampling
SYSTEMATIC SAMPLING
If a sample size of n is desired from a population
containing N elements, we might sample one
element for every n/N elements in the population.
Systematic Random Sampling
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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
Systematic sampling can be applied so that an
essentially random sample is drawn.
Count…
3/2/201567
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).
Number of subjects in target
population (N)
K = N/n or K=
Size of sample
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.
DR G K KALKOTI68
Systematic Sampling
It is modification of simple random
sampling. It is called as quasi-random
sampling.
It is called quasi because it is in between
probability and non-probability sampling.
The procedure of quasi sampling begins
with finding out the sample interval. This
can be found out by the ratio of the
population to the sample. Afterwards a
random number is selected from the
sample interval.
DR G K KALKOTI69
Illustration of Systematic
Sampling Selecting a sample of 100 students out of 1000, the
sample interval will be 1000 divided by 100 i.e.10.
Then make small chits bearing numbers 1to 10 and put them into a box
Then by using lottery method withdraw one slip and suppose we get number 5 then proceed to select numbers starting with 5 with a regular interval of 10.
The selected sample consists of elements bearing nos. 5,15,25,..........105,115 and so on .
It should be noted that up to selecting no.5,Systematic sampling can be treated as probability sampling and afterwards it is non-probability because the chances of other elements are certainly affected
In this example numbers other than 5 have no chance of being selected
Systematic Random Sampling
Order all units in the sampling frame based on somevariable and then every nth number on the list isselected
Gaps between elements are equal and ConstantThere is periodicity.
N= Sampling Interval
Systematic Sampling
Order all units in the sampling frame
based on some variable and number
them from 1 to N
Choose a random starting place from 1
to N and then sample every k units after
that
3. Selecting a Sampling
Design
systematic random sample
number the units in the population from 1 to
N
decide on the n (sample size) that you want
or need
k = N/n = the interval size
randomly select an integer between
1 to k
then take
every kth unit
Merits and Demerits
3/2/201574
3/2/201575
Cluster
sampling
Cluster or Area Random Sampling
Clusters of population units are selected at random by dividing the population into clusters (usually along geographic boundaries) and then all or some randomly chosen units in the selected clusters are studied.
Cluster or multistage Sampling
3/2/201577
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.
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.
Geographical units are the most commonly used
ones in research. For example, a researcher wants
to survey academic performance of high school
students in India.
He can divide the entire population (of India) into
different clusters (cities).
Then the researcher selects a number of clusters
depending on his research through simple or
systematic random sampling.
Then, from the selected clusters (random selected
cities), the researcher can either include all the high
school students as subjects or he can select a
number of subjects from each cluster through
simple or systematic sampling
Count…
3/2/201578
DR G K KALKOTI79
Cluster Sampling Cluster means group, therefore, sampling
units are selected in groups. Cluster sampling is an improvement over
stratified sampling. Both simple random and stratified random sampling are not suitable while dealing with large and geographically scattered populations. Therefore, large-scale sample surveys are conducted on cluster sampling basis.
The working of cluster sampling is based on the principle that it is beneficial to use a large sample of units closer to each other than to select a small group of sample scattered over a wider area.
DR G K KALKOTI80
Illustration of Cluster Sampling
Suppose researcher wants to study the learning habits of the college students from Mumbai. He may select the sample as under
1)First prepare a list of all colleges in Mumbai city
2)Then, select a sample of colleges on random basis. Suppose there are 200 colleges in Mumbai, then he may select 20 colleges by random method.
DR G K KALKOTI81
3)From the 20 sampled colleges, prepare
a list of all students. From these lists
select the required number of say 1000
students on random basis]
In this example the researcher gets a
sample 1000 students from 20 colleges
only otherwise if researcher decides to
select 1000 students on random basis,
then he would have to select them out of
200 colleges which would have been
expensive and time consuming
Cluster Sampling• The target population is first divided into mutually exclusive and
collectively exhaustive subpopulations, or clusters.
• Then a random sample of clusters is selected, based on a
probability sampling technique such as SRS.
• For each selected cluster, either all the elements are included in
the sample (one-stage) or a sample of elements is drawn
probabilistically (two-stage).
• Elements within a cluster should be as heterogeneous as
possible, but clusters themselves should be as homogeneous
as possible. Ideally, each cluster should be a small-scale
representation of the population.
• In probability proportionate to size sampling, the clusters are
sampled with probability proportional to size. In the second
stage, the probability of selecting a sampling unit in a selected
cluster varies inversely with the size of the cluster.
Types of Cluster SamplingCluster Sampling
One-Stage
Sampling
Multistage
Sampling
Two-Stage
Sampling
Simple Cluster
SamplingProbability
Proportionate
to Size Sampling
The population is divided into subgroups (clusters) likefamilies. A simple random sample is taken of the subgroupsand then all members of the cluster selected are surveyed.
Cluster Sampling
Cluster sampling
Section 4
Section 5
Section 3
Section 2Section 1
Merits and Demerits
3/2/201587
Sequential Sampling
3/2/201588
3/2/201589
DR G K KALKOTI90
Multistage and Multi Phase
Sampling
As the name suggests, multistage sampling is carried out in steps. This method is regularly used in conducting national surveys on large scale. It is an economical and time saving method of selecting a sample out of widely spread population.
In this method first the population will be divided on state basis, then districts, then cities, then locality, wards, individuals who are sampled at different stages until a final sample unit.
DR G K KALKOTI91
Multiphase sampling is slightly different
from multi-stage sampling. With multi-
phase sampling, the sampling unit at
each phase is the same, but some of
them are interviewed in detail or asked
more questions than others ask. In other
words, all the members of the sample
provide basic information and some of
them provide more and detailed
information.
NONPROBABILITY
SAMPLING
TECHNIQUE
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Non-probability techniques
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Non-probability
techniques
Purposive
sampling
Convenie
nt
sampling
Consecuti
ve
sampling
Quota
sampling
Show ball
sampling
Non Probability Sampling
Involves non random methods in selection of sample
All have not equal chance of being selected
Selection depend upon situation
Considerably less expensive
Convenient
Sample chosen in many ways
DR G K KALKOTI95
Non-Probability Sampling
Non-probability sampling is also called as judgment sampling.
In case of non-probability sampling, units in the population do not have an equal chance or opportunity of being selected in the sample. The non-probability method believes in selecting the sample by choice and not by chance.
Non-probability sampling suffers defects like personal bias and sampling error cannot be estimated.
This is an unscientific and less accurate method of sampling, hence it is only occasionally used in research activities.
3/2/201596
Uses of Nonprobability Sampling
3/2/201598
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.
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 (pilot study)
3/2/201599
Purposive Sampling
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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.
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
Count…
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For example, a researcher wants to study the lived
experiences of postdisaster depression among
people living in earthquake affected areas of
Gujarat.
In this case, a purposive sampling technique is
used to select the subjects who were the victims of
the earthquake disaster & have suffered
postdisaster depression living in earthquake-
affected areas of Gujarat.
In this study, the researcher selected only those
people who fulfill the criteria as well as particular
subjects that are the typical & representative part
of population as per the knowledge of the
researcher.
DR G K KALKOTI102
Purposive Sampling
Purposive sampling means deliberate
selection of sample units confirm to some
predetermined criteria. This is also known
as judgment sampling
It involves selection of cases when we
judge as most appropriate ones for a given
study. It is based on the judgment of a
researcher. It does not aim at securing a
cross section of a population. The
selection of samples depends upon the
subjective judgment of researcher.
Merits and Demerits
3/2/2015103
Convenience Sampling
3/2/2015104
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.
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.
3/2/2015105
CONVENIENCE SAMPLINGSometimes known as grab or opportunity sampling or accidental or haphazard sampling.
A type of non probability sampling which involves the sample being drawn from that part of the population which is close to hand. That is, readily available and convenient.
DR G K KALKOTI107
Convenience Sampling In convenience sampling, the sample is selected
as per the convenience of the researcher.
For example, the producer may add a reply coupon along with product to collect responses from consumers. The duly returned coupons are conveniently available to the researcher for the survey purpose.
Manufacturers of consumer goods like Titan watches and Philips provide a questionnaire along with the product purchased and collect information relating to name of retail store, income group etc., similarly sample selected from the telephone directory, pay-roll register, register of members is a type of convenience sampling.
Merits and Demerits
3/2/2015108
Merits
This technique is
considered
easiest, cheapest,
& least time
consuming.
This sampling
technique may
help in saving
time, money, &
resources.
Demerits
Sampling bias, & the
sample is not
representative of the entire
population.
It does not provide the
representative sample
from the population of the
study.
Findings generated from
these sampling cannot be
generalized on the
population.
Consecutive Sampling
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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.
It is also known as total enumerative sampling.
3/2/2015110
Count…
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.
Merits and Demerits
3/2/2015111
Merits Little effort for
sampling
It is not expensive, not time consuming, & not workforce intensive.
Ensures more representativeness of the selected sample.
Demerits Researcher has not set
plans about the sample size & sampling schedule.
It always does not guarantee the selection of representative sample.
Results from this sampling technique cannot be used to create conclusions & interpretations pertaining to the entire population.
Quota Sampling
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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.
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,
25 third-year, & 25 fourth-year students.
DR G K KALKOTI113
Quota Sampling Quota sampling is the frequently used method of
sampling in marketing research. The basic objective of quota sampling is to control biases arising out of non-probability method by stratification and the setting of quotas for each stratum.
For instance, a sample of 40 students can be selected from a group of 200 students comprising of 120 boys and 80 girls. To make the sample representative, the group of 40 should include 24 boys and 16 girls (i.e. 120: 80 = 3: 2).
Quota sampling offer benefits of speed, economy and simplicity. It is widely used in market surveys and public opinion polls.
Merits and Demerits
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Merits
Economically cheap,
as there is no need
to approach all the
candidates.
Suitable for studies
where the fieldwork
has to be carried out,
like studies related to
market & public
opinion polls.
Demerits
It not represent all
population
In the process of sampling
these subgroups, other
traits in the sample may be
overrepresented.
Not possible to estimate
errors.
Bias is possible, as
investigator/interviewer can
select persons known to
him.
Snowball Sampling
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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.
In this situation, snowball sampling is the best
choice for such studies to select a sample.
This type of sampling technique works like chain
referral. Therefore it is also known as chain
referral sampling.
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Count…
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.
SNOWBALL SAMPLING
Selection of additional respondents is
based on referrals from the initial
respondents.
- friends of friends
Used to sample from low incidence or rare
populations.
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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.
PROBLEMS OF SAMPLING
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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
In A Nut Shell
Probability Sampling
- Simple Random – Selection at Random
- Systematic – Selecting every nth case
- Stratified – Sampling w/n groups of Populn
- Cluster – Surveying whole clusters of P/n
- Multistage – Sub samples from large smpl
Non- Probability Sampling
- Accidental – Sampling those most convnt
- Voluntary – Sample is self selected
- Purposive – Handpicking typical cases
- Quota – Sampling w/n groups of Ppln
- Snowball – building sample thru informnts
Sampling Error
Sampling error refers to differences between thesample and the population that exist only becauseof the observations that happened to be selected forthe sample
Increasing the sample size will reduce this type oferror
Types of Sampling Error
Sample Errors
Non Sample Errors
Sample Errors
Error caused by the act of taking a sample
They cause sample results to be different from the results ofcensus
Differences between the sample and the population that existonly because of the observations that happened to be selected forthe sample
Statistical Errors are sample error
We have no control over
Non Sample Errors
Non Response Error
Response Error
Not Control by Sample Size
Non Response Error
A non-response error occurs when unitsselected as part of the sampling procedure donot respond in whole or in part
Response Errors
Respondent error (e.g., lying, forgetting, etc.)
Interviewer bias
Recording errors
Poorly designed questionnaires
Measurement error
A response or data error is any systematic bias that occursduring data collection, analysis or interpretation
Respondent error
respondent gives an incorrect answer, e.g. due to prestige or competenceimplications, or due to sensitivity or social undesirability of question
respondent misunderstands the requirements
lack of motivation to give an accurate answer
“lazy” respondent gives an “average” answer
question requires memory/recall
proxy respondents are used, i.e. taking answers from someone other thanthe respondent
Interviewer bias
Different interviewers administer a survey in different ways
Differences occur in reactions of respondents to differentinterviewers, e.g. to interviewers of their own sex or own ethnicgroup
Inadequate training of interviewers
Inadequate attention to the selection of interviewers
There is too high a workload for the interviewer
Measurement Error
The question is unclear, ambiguous or difficult to answer
The list of possible answers suggested in the recording instrumentis incomplete
Requested information assumes a framework unfamiliar to therespondent
The definitions used by the survey are different from those used bythe respondent (e.g. how many part-time employees do you have?See next slide for an example)
Key Points on Errors
Non-sampling errors are inevitable in production of nationalstatistics. Important that:-
At planning stage, all potential non-sampling errors are listed and stepstaken to minimise them are considered.
If data are collected from other sources, question procedures adoptedfor data collection, and data verification at each step of the data chain.
Critically view the data collected and attempt to resolve queriesimmediately they arise.
Document sources of non-sampling errors so that results presented canbe interpreted meaningfully.
Sample vs. Population
Population
Sample
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Questions???
Thank
You
Further communication….
