Jayesh sampling technique semi (1) (1)

Sampling techniques
Mr. Jayesh Patidar,
Assistant Professor,
MTIN,CHARUSAT
1/31/2020 Mr. JAYESH PATIDAR MTIN

SAMPLING TECHNIQUES
Methods used for obtaining subjects for a
research study.

Refers to number of subjects drawn out
deliberately in a planned representative manner
from a population.
Sample
Mr. JAYESH PATIDAR MTIN1/31/2020

Listing of members of the population is known as the
sampling frame. The researcher then selects
subjects from the sampling frame using a sampling
plan.
Sampling frame

Sampling criteria are the characteristics of the
study subjects that are considered as eligible
to get into the study.
Sampling Criteria

 Is it possible to study entire population??
 Sampling is a process of selecting representative units of
entire population of a study.
 For example, a grain buyer takes handful of gains to make
an idea about entire bag of grains; at another instance.
 Sampling is the process of selecting a subset of a
population in order to obtain information regarding a
phenomenon in a way that represents the entire
population.

TERMINOLOGY USED IN SAMPLING
 Population: Population is the entire aggregation of cases in
which a researcher is interested. For example, a researcher
needs to study problem among post-graduate nurses of India; in
this the population will be all the post-graduate nurses who are
Indian citizens.
 Target population: A target population consists of the total
group of people or object, which are meeting the designated
set of criteria of interest of the researcher. For example, A
researcher in interested in identifying the complication of DM
Type-II among migrated people in City Vadodara. In this instance
the target population with migrated people at Vadodara
suffering with DM Type-II since last a decade.

• TARGET POPULATON
Is the aggregate of cases about which the researcher
would like to make the generalization
Example:
 All mother with under five children
 All the nurses working in the pediatric ward
 All antenatal mothers in tarapur
• ACCESSIBLE POULATION
Is the aggregate of cases that confirm to the
designated criteria and are accessible to the
researcher as a pool of subject for study.
• Example : All the antenatal mothers who are
admitted in a Tarapur CHC

CONTD…
 Accessible population: It is the aggregate of cases that
confirm to designated criteria and that are accessible as
subjects for a study.
For example, ‘A researcher is conducting a study on the
nurses working in Govt. hospitals of Gujarat’.
Population: Nurses working in Govt. hospitals of Gujarat
Target population: Who meets designated criteria (only SN).
Accessible population: Who are available at the time of
study (Who were available and were not on leave).

Sampling: Sampling is the process of selecting a representative
portion of population under study.
Sample: A representative unit of target population, which is to
be worked upon by researchers during their study.
It is a subset of population selected by researcher to participate
in research project.

SAMPLE - Consists of subset of the units
that compose the population. E.g. Out of
a population of 1000 rural mothers, a
subset of 300
Element - The most basic unit about
which information is collected. ( E.g.
Individual Rural Mother)

CONTD…
 Element: An element is the most basic unit about which
information is collected. Also known as subject or study
participant. Individual/ places/ objects etc.
 Sampling frame: It is the list of all the elements or subjects in
the population from which the sample is drown.
o Sampling frame could be framed by the researcher or an
existing frame may be used.
o For example, a research may prepare the list of the entire
household, which have a pregnant women or may used a
antenatal register available with Anganwari worker.

CONTD…
 Sampling error: The fluctuations of the value of the statistic
from one sample to another drown from the same
population.
 Sampling bias: Distortion that arises when a sample is not
representative of the population from which it was drown.
 Sampling plan: The formal plan specifying a sampling
method, a sample size, and procedure of selecting the
subjects.

Schematic presentation of the sampling

TERMS
Sampling Unit – is the element or
set of elements used for selecting
the sample.
E.g. The researcher wants to study
the health status of rural mothers
and finds there are Hindu, Muslim
and Christian mothers from which
she can select the sample ; hence
each group becomes a sampling unit

PURPOSES OF SAMPLING
Economical: Many times it is not possible and economical for
researchers to study entire population. With the help of
sampling researcher can save lots of time, money and
resources to study a phenomenon.
Improved quality of data: In research, when a research is
handling the information from a part of population it is
possible to maintain the quality in research work but in case
of handling the information from entire population, the
quality of information may come down.

CONTD…
Quick study results: Studying entire population take lots of time
and generating research results take more and more time, but in
case of conducting research on a sample rather than a entire
population, it is possible to generate study results faster.
Precision and accuracy of data: Carrying a study on the par of
the population (sample) helps the researcher to generate more
précised data, where formulation of the interpretations of the
data become more easy. It is always easy to establish better
rapport with the sample number of subject to collect more
accurate data.

CHARACTERISTICS OF GOOD SAMPLE
• Representative
• Free from bias and errors
• No substitution and incompleteness
• Appropriate sample size

SAMPLING PROCESS
Sampling process entails the
formulation of specific criteria for
selection ensures that the
characteristics of phenomenon of
interest will be present in all units
being studied. The sampling process
consists of following stages:

FACTORS INFLUENCING SAMPLING PROCESS
• Inexperienced investigator
• Lack of interest
• Lack of honesty
• Intensive workload
• Inadequate supervision
Nature of the
Researcher
• Inappropriate sampling
technique
• Sample size
• Defective sampling frame
Nature of the
sample
• Lack of time
• Large geographic area
• Lack of cooperation
• Natural calamities
Circumstances

TYPES OF SAMPLING
PROBABILITY
SAMPLING
Are those in which
sample elements are
automatically selected
by some scheme, under
which a particular
sample size from a
specific population has
a known probability of
being selected.
Elements are chosen
randomly
NON-PROBABILITY
SAMPLING
Are those in which the
sample elements are
selected by the
researcher because in
his judgment the
element chosen will
most effectively
represent the population
Elements are chosen
Non randomly

Probability Sampling Technique
It involves random selection in choosing the
elements/ member of the population.
In this, every subject in a population has equal
chance to be selected as study sample.
The chances of systemic bias is relatively less
because subjects are randomly selected.

PROBABILITY SAMPLING
Hallmark of probability sampling is
the random selection process
A Random selection process is one in
which each element in the population
has an equal & independent chance
of being selected.

Probability sampling
Uses a random process to guarantee
that each subject in the population has a
specific chance of selection.

TYPES OF SAMPLING TECHNIQUES
Types of sampling technique
1. Simple random sampling
2. Stratified random sampling
3. Systematic random sampling
4. Cluster/multistage sampling
5. Sequential sampling
1. Convenient sampling
2. Purposive sampling
3. Quota sampling
4. Snow ball sampling
5. Consecutive sampling
6. Volunteer sampling
7.Genealogy sampling
Probability sampling
technique
Non-probability
sampling technique

Features of Probability Sampling
• This is feasible only if the researcher used
randomization.
• The absence of both sampling and systematic bias is
the advantage of utilizing a random sample. If
random selection is made in a proper manner ,the
sample is representative of the whole population.
• The effect of this is an absent or minimal systematic
bias & is a different between the results from the
sample & those from the population .Since the
subjects are randomly selected ,therefore, sampling
bias is eliminated.

SIMPLE RANDOM SAMPLING
- Most pure &Basic probability Sampling design every
population member has a similar chance of being
picked as the subject. The whole process of sampling is
carried out in in a single step ,with each subject chosen
independently of the other members of the population.
- Two essential Prerequisites 1.Population must be
homogeneous.2.Must have list of element/members of
accessible population.
The first step is to identify the accessible population
and prepare a list of all elements/members of the
population .List of subjects in population is known as
sampling frame & sample can be drawn from sampling
by using following methods.
 The lottery method
The use of table of random numbers
The use of computer1/31/2020 Mr. JAYESH PATIDAR MTIN

The lottery method
• Oldest and mechanical (Variety of methods)
• Each member of the population is attributed a
unique number.
• Each member is placed inside a hat or bowl &
mixed in a thorough manner.
• Blind-folded researcher then chooses numbered
tags from the hat. All individuals who bear the
numbers that are chosen by the researcher
become subject for the study.

If the sampling frame is small, names can be written
on slips of paper, placed in a container, mixed
well, and then can be drawn out one at a time
until the desired sample size has been reached.
 Random sampling with replacement
 Random sampling without replacement

2.USING A TABLE OF RANDOM NUMBERS
-Most commonly & accurately used method
-Random table present several numbers in rows &
columns.
-Researcher initially prepares a numbered list of the
element/members of the population & then with
blindfold chooses a number from the random
table.
- Same procedure is continue until desired numbers
of the subject are achieved.however ,if
repeatedly similar number are encountered ,they
are ignored & next numbered.1/31/2020 Mr. JAYESH PATIDAR MTIN

SECTION FROM A RANDOM NUMBERS
TABLE
06 84 10 22 56 72 25 70 69 43
07 63 10 34 66 39 54 02 33 85
03 19 63 93 72 52 13 30 44 40
77 32 69 58 25 15 55 38 19 62
20 01 94 54 66 88 43 91 34 28

The use of computer
• Nowadays random tables may be generated from
the computer & subject may be selected as
described in the use of random table . Population
with small number – Lottery method.
• Population with many number – A computer
aided random selection is preferred.
• www.ranadmization.com

 Advantages
 Most reliable & unbiased method.
 Require minimum knowledge of study
population.
 Free from sampling error /bias
 Disadvantages
 Need up to date complete list of all the
member of the population.
 Expensive and time consuming

Merits
• -Ease of assembling the sample.
• Fair way of selecting a sample from a population
since very member is given equal opportunity of
being selected.
• Requires minimum knowledge about population
in advance.
• Most unbiased Probability method
• Method of sampling is free from sampling error.
• Sampling error can be easily computed &
accuracy of estimate easily assessed.

Demerits
-Requirement of a complete & up to date list of all
the members of the population.
-This list is usually not available for large
population
- This method does not make use of knowledge
about a population which researcher may already
have .
- Lot of procedure ned to be done before sampling
accomplished.
- Expensive and time consuming

Stratified Random Sampling
 Used for heterogeneous group.
 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 to age, gender, diagnosis or education,
geographical region, type of institution, type of care, type of registered
nurses, nursing area specialization and site of care etc.
According to weightage of sample &Proportion
a) Proportionate stratified random sampling: Sample chosen from each
stratum are in proportion to size of total population of that stratum.
b) Disproportionate stratified random sampling: In this subtype the
sample chosen from each stratum are not in proportion to size of total
population in that stratum .

Approaches of Stratified Random
sampling
Two approaches:
• Proportional stratified sampling
• Disproportional stratified sampling.
1 2

Proportional Stratified Random Sampling
Obtain a sample from each stratum that is in
proportion to the size of that stratum in the
total population.

Disproportional Stratified Random
Sampling
subjects chosen from each stratum is not in
proportion to the size of the stratum.

 Advantages
-Ensures representative sample in heterogeneous
population.
-Comparison is possible in two groups.
 Disadvantages
-Require complete information of population
-Large population is required.
-Chances of faulty classification of strata.

 Merits
-It ensure representation of all group in a population
-Researcher also employ stratified random sampling when they
want to observe existing relationship between two or more
group subgroup.Therfore comparison is possible between
subgroup with this technique.
-The researcher can representatively sample the most
inaccessible subgroup & even the smallest in the population
with stratified sampling .This permits researcher to sample
rare extremes of a provided population.
- Higher statistical precision as compared to simple random
sampling. This is due to fact that the variability within the
subgroup is less as compared to the variation while dealing
with entire population.
- As this technique Higher statistical precision that it need
small sample size that can save much time, money,& effort of
the researchers.

 Demerits
-Proportionate stratification requires accurate
information on the proportion of population in each
stratum.
-Large population must be available from which to
select subjects.
-Always possibility of faulty classification & hence
increase variability.

Systematic Random Sampling
 SRS can be likened to arithmetic progression, wherein
the difference between any two consecutive numbers
is the same. It involves the selection of every kth case
from our 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 Random Sampling
Systematic sampling can be applied so that an
essentially random sample is drawn. If we had a list
or sampling frame, the following procedure could be
adopted. The desired sample size is established at
some no. (n). the size of population must be known
or estimated (N).
K=N/n OR K = No. of target population
Size of sample

Example
* If a researcher was selecting a sample (n) of 50 from a
population (N) of 500, sampling interval is
K = N/n 500/50 = 10
Every 10th Person will be selected of the population list
would be selected for the sample.
*In this method list of subject is prepared for the target
population (Sampling frame)&then 1st subject is randomly
selected :later very k th subject is selected from the
sampling frame. Before selecting sample through SRST keep
in mind that subjects/elements of population must be in
random order, which means mixture of elements rather
than segregation &first subject is chosen by help of random
number table.

Merits
• Convenient & simple to carry out.
• Distribution of sample spread evenly over the entire population .
• Less cumbersome ,time-consuming & cheaper than Simple RST .
• Statistically more efficient &Provides better representative sample
Demerits
-If first subject is not randomly select then it becomes a
nonrandom sampling technique.
-Sometime this may result in biased sample
-If sampling frame has nonrandomly distributed subject ,this
sampling technique may not be appropriate to select a
representative sample.

Cluster or Multistage Sampling
 When simple random sampling is not possible due to the size of the
population ,cluster random sampling carried out .A simple random
sampling cannot be imaginated when the population under study is the
entire population of Asia.
 Cluster sampling means random selection of sampling unit consisting of
population element.
 Then from each selected sampling unit ,a sampling unit consisting of
population elments.Then from each selected sampling unit, a sample of
population elements is drawn by either simple random selection or
stratified random sampling .This method Used at that place where the
population elements are scattered over a wider area.& it is impossible to
obtain a list of all the elements.
 Important thing is to give all the clusters equal chances of being
selectd.Geographically units are most commonly used ones in research.
 Example, Survey academic performance of Indian high school students.

• -Divide the entire India population into different clusters(Cities)
• Depending on research through simple or systematic random
sampling ,the researcher then selects a number of clusters.
• Then researcher can either include all the high school students as
subject or can chose a number of subject from each cluster through
simple or systematic random sampling from the selected clusters
(Randomly selected cities)

Types of cluster samples
• One-stage cluster sample;- Include all the high school students
from all the randomly selected cluster as sample.
• Two-stage cluster sample;-Researcher lists all cluster appearing in
the population. after this cluster are selected normally by simple
random sampling .then usually by simple random sampling or
often by systematic sampling in 2nd stage
• Multistage cluster sample – sampling is done at more than two
level of initially identifying cluster as population at different levels
and selecting them using simple random sampling technique &
finally selecting unit (Elements)using simple random sampling or
Systematic sampling .
• Probability proportion to size cluster sampling ;- It is a variant of
cluster sampling when size of cluster is not same& risk of over-
sampling from smaller size cluster & under sampling from larger
size cluster .

• Then it is most useful when the sampling units vary considerably in
size because it assures that those in larger sites have the same
probability of getting into the sample as those in the smaller
sites,& vice versa.
Merits
- Cheap, quick & easy for large population.
- Large population can studied & require only list of members.
- Enable investigators to use existing division, Such as districts,
villages/towns etc.
- Same cluster can be used again for study.
Demerits ;- least representative of the population from various kind
of Probability sampling.
• Tendency of individuals within a cluster is to get similar
characterstics.Morever ,chance researcher can have over-
represented or under-represented cluster that can skew the result
of study with cluster sample.
• Possibility of high sampling error.
• If Samll homogeneous population is under study not at all usefull.

Sequential Sampling
 Sample size is not fixed. The investigator initially select small sample and
tries out to make inferences; if not able to draw results he/she then adds
subjects until clear cut inferences can be drawn.
 Eg.Researcher is studying association between smoking & lung cancer
.Initially researcher takes a smallest sample & tries to draw inferences. if
unable to draw any inferences he or she continue to draw the sample
until meaningful inferences are drawn.
Above table depicts progressively increase sample size until
inferences are drawn. it can be sad that out of 50 subjects ,28
smokers had almost double incidence of lung cancer as compared
to 22 smokers.
No of
subjects
Smokers
(A)
Nonsmoke
rs (B)
Having lung Cancer
A B
20 8 12 2 1
40 18 22 5 3
50 28 22 10 4

Merits
-Facilitates to conduct a study on best – possible
smallest representative sample.
-Helping in ultimately finding the inferences of
study.
Demerits- Not possible to study a phenomenon
which needs to be studied at one point of time.
Require repeated entries into field to collect sample.

Non Probability Sampling
• A process that does not give all the individuals in the
population equal chances of being selected in sample.
• Elements are chosen by choice not the by chance.
• Generally it is believed that nonrandom method of
sampling more likely to produce a biased sample than
random method.
• Certain elements have more probability to be part of
sample while others may have no chance of being
included in the sample. This restricts the generalization
that can be made about the study findings.

Features Of Non Probability Sampling
• Samples are gathered in a process that does not give all
individuals in the population equal chance of being
selected.
• Most researcher bound by time, money& workforce
because of these limitation difficult to randomly sample the
entire population & it is often necessary to employ another
technique.
• Subject in a non-probability sample are usually selected on
basis of their accessibility or by purposive personal
judgment of researcher.
• Downside of this is that an unknown proportion of the
entire population is not sampled. This entails that sample
may or may not represent entire population accurately.
Therefore ,result of research cannot b used in
generalization pertaining to entire population.
• 1/31/2020 Mr. JAYESH PATIDAR MTIN

Uses of Non Probability Sampling
• It utilized when it is needed to show that a
particular trait is existent in population.
• It utilized when researcher targets to make
qualitative ,pilot, or exploratory study.
• When random sampling is impossible when
population is limitless.
• Moreover ,when research does not aim to
produce results that will be utilized to generate
generalization pertaining to entire population.
• Limited budget ,time,& workforce

NON PROBABILITY SAMPLING
TYPES
1. Convenient sampling
2. Purposive sampling
3. Quota sampling
4. Snow ball sampling
5. Consecutive sampling
6. Volunteer sampling
7.Genealogy sampling

PURPOSIVE SAMPLING
• Is also called as judgmental or authoritative
sampling.
• It involves handpicking of subjects. Subjects are
chosen that the researcher believes are typical or
representative of the accessible population
• It is Subjective.

• Subject are chosen to be part of the sample with
a specific purpose in mind .
• Researcher believe some subject are fit for
research compared to other individuals .
• Samples are chosen by choice not by chance,
through a judgment .
• Researcher might decide purposely to select
subject who are judged or believed to be typical
or representative of accessible population.
• Particularly experts who have depth knowledge
able about accessible population under study.

Example
• Researcher want to study lived experiences of
post –disaster depression among people living in
earthquake-affected area of Gujarat .
• In this used to select subject who were victims of
earthquake disaster & have suffered post –
disaster depression living in earthquake-affected
area of Gujarat .
• Researcher selected only those people who fulfill
criteria as well as particular subject that are
typical & representative part of population as per
knowledge of researcher.

Uses of purposive sampling
• Used when limited number of individuals
possess trait of interest.
• Only viable sampling obtaining information
from a very specific group of people.
• It is also possible to use purposive sampling if
researcher knows a reliable or authority that
he or she think is capable of assembling a

Example
• E.g.- If you are studying cardiac patients finding
typical cardiac patient would be the approach
• If the researcher is interested in the play pattern
of four year old village boys, then studying a
small sample of typical four year olds attending
the anganwadi or pre nursery schools would
comprise the purposive study sample.

Merits
• Simple to draw sample & useful in explorative
studies.
• Save resources ,requires less framework.
Demerits
• Require considerable knowledge about
population under study.
• Not always reliable sample, as conscious bias may
exist.
• Two weakness of PS stay with authority & in
sampling process .both of which relate to bias
&reliability that accompanies the sampling
technique.

Convenience Sampling
Most common of all technique. It is fast ,inexpensive ,easy &
subjects are readily available.also called accidental sampling
Subject are selected due to their convenient accessibility &
proximity to researcher.
Subject are chosen because of the fact that they are very easy to
recruit for study.Morever ,researcher does not opt for choosing
subject that are representative of entire populations.
For example, if a researcher wants to conduct a study on the older
people residing in the city Ludhiana and researchers observes that
he can meets several older people coming for morning walk in a
park located near by his residence in Ludhiana. These subjects are
readily accessible for the researcher and may help him to save the
time, money and resources.

Use of Convenience Sampling
• Researchers use convenience sampling not just because it is easy
to use ,but also has other research advantages.
• In pilot studies, convenience sample is usually used because it
allows researchers to obtain basic data &trends for study
without complication of using random sample selection
methods.
• Also useful in documenting a particular quality of a substance or
phenomenon that occurs within a given sample.Such studies
very useful for detecting relationship among different
phenomena.

Merits
-Easiest,chepest ,& least time consuming
- Help in saving time ,money & resources.
Demerits
-Sampling bias & that the sample is not
representative of entire population.
-Not Provide representative sample from
population of study.
-Finding generate from these sample cannot be
generalized on population.

Volunteer Sampling
• Participate themselves volunteer to participate in
study& then only approach researcher to be part
of study sample.
• Researcher publish an advertisement or informs
target population through mass media to
participate in study & interested participant may
voluntary contact researcher.
• Eg. Nurse Researcher is interested to assess
effectiveness of a selected yoga technique on
reduction of blood pressure .she may advertise in
news paper to inform target people to participate
in research to take scheduled yoga classes & pre
and post assessment of blood pressure.
• Interested people may volunteer contact resarcher.1/31/2020 Mr. JAYESH PATIDAR MTIN

Characteristics of Volunteer Sampling
• Researcher only disseminate information about
the research activity to target population
• Interested participate themselves volunteer to
participate .
• However encounters problem of systematic error
,non-representative of sample & lack of
generalizability of study results.

Merits
• Cost effective
• Needs very limited efforts & time to locate study
participants.
• Help to collect large size data in limited time
period .
Demerits
-Only Interested people can participants.
-Encounters systematic error or bias because who
have access published advt.may get chance.
-High chances of non representatives of sample
-Lack of genralizability

Consecutive Sampling
 Very similar to convenience sampling except that it seeks to
include all accessible subject as part of sample.
 considered as Best of all non probability
 It is also known as total enumerative sampling.
 Picks up all the available subjects who are meeting the preset
inclusion and exclusion criteria.
 This technique is generally used in small size population.
 Eg. Researcher want to study activity pattern of post-kidney –
transplant pt.who can select all post-kidney –transplant pt.who
meet designed inclusion & exclusion criteria & who are admitted
in Post transplant ward during a specific time.

• Merits
Little effort on the part of researcher .
Not expensive, not time consuming & not
workforce intensive.
Demerits
-No set plan about sample size &sampling
schedule
-Always does not guarantee selection of
-Results from this cannot be used to create
conclusions &Interpretations pertaining to
entire population .

Quota Sampling
 Researcher ensures equal or proportionate representation of
subjects depending on which trait is considered as basis of the
quota. The bases of the quota are usually age, gender, education,
race, religion and socioeconomic status.
For example, if basis of the quota is college year level and the
researcher needs equal representation, with a sample size of 100,
he must select 25 1st year students, another 25 ,2nd year students,
25, 3rd year and 25 4th year students.

Steps and uses of quota sampling:
– The first step: Divide the population into exclusive
subgroups.
– Then, identify the proportions of these subgroups in the
population; this same proportion will be applied in the
sampling process.
– Finally, selects subjects from the various subgroups while
taking into consideration the proportions noted in the
previous step.
– The final step ensures that the sample is representative of
the entire population.
– It also allows the researcher to study traits and
characteristics that are noted for each subgroup.

QUOTA SAMPLING
• Is similar to stratified random sampling in the
sense that the first step involves dividing the
population into homogenous strata and selecting
the sample subjects from these strata
• The difference lies in the method of selecting the
subjects from these strata. In stratified sampling,
random method is used where as in quota ,
subjects are obtained by convenient method

Example
• Researcher is interested in studying the attitude of UG
students towards working with the AIDS patients
• Accessible Population – single CON with 400 enrolled
students
• Sample size of 100 students is desired
• Here researcher can use convenience sampling and
select first 100 students entering the library or by just
distributing the questionnaire in the classroom . But the
sample may not be proportionate in the sense there may
be too many or too few students from 1st, 2nd, 3rd, 4th
year.
• So under Quota sampling first the researcher forms 4
strata of four year students, Then select equal No. Say
(25) from each strata by convenience sampling.

• Merits
-Economically cheap ,as there is no need to approach all
candidates.
Suitable for studies where the fieldwork has to be carried out
,like studies related to market & Public opinion polls.
Demerits
-It is seen to be wholly representative of population ,still in
some cases it is not so. Keeping in mind only chosen traits of
population were taken into consideration while forming
subgroups.
-Other traits in sample may over representative in process of
sampling these sub group .Final sample may have skewed
representations of race ,age, educational
attainment,marrital status& lot more in study that considers
gender,socio-economic status & religion as the bases of sub
group. Bias is possible.1/31/2020 Mr. JAYESH PATIDAR MTIN

Snowball Sampling
• Snowball sampling is used by researchers to identify potential
subjects in studies where subjects are hard to locate such as
commercial sex workers, drug abusers etc.
• Very rare or is limited to a very small subgroup of the population.
• This type of sampling technique works like chain referral. Therefore
it is also known as chain referral sampling.
• Researcher ask for assistance from subject to identify people with
a similar traits of interest after observing initial subject.
• Eg.Researcher wants to conduct a study on the prevalence of
HIV/AIDS among commercial sex worker .
• Asking subject to nominate another person with same trait is
similar to process sampling Until he or she obtains sufficient
number of subjects ,researcher then observes nominated subject
& continue in same way.

• For example ,researcher may choose to use
snowball sampling while obtaining subjects for a
study to observe a rare disease.
• Observing one of the members as your initial
subject then leads to more subject for the study.

Types of the snowball sampling
1. Linear snowball sampling: 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.

2.Exponential Non-Discriminative Snowball Sampling:
Each sample is asked to provide reference of at least
two similar subjects, where size of the sample size
grow exponentially and a large sample size can be
achieved.

3. Exponential Discriminative Snowball Sampling: Initially one
sample is selected and asked for two references of similar
subjects, out of which at least one subjects must be active to
provide further reference and another could be non-active for
providing references. Similarly each active reference subject is
further asked for two reference for similar subjects; out of
then one should be active for further references.

Merits
• When utilizing other sampling method ,the chain referral process
permits researcher to reach populations that are difficult to
sample.
• The process is simple,cheap,and cost – efficient.
• Require lesser workforce & little planning as compared to other
method.
Demerits
• Less control over the sampling method.subjects that
researcher can get depend chiefly on the previous
subject were observed.
• Sample representation is not guaranteed .
• Sample bias is also a matter of great concern for
researchers.1/31/2020 Mr. JAYESH PATIDAR MTIN

Genealogy Sampling
• This technique in which all the member of entire
• Related families are selected rather then selecting the
different household in village or area.
• Genealogy sampling begins with identifying a first
participant ,who is convinced to participate in the study &
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.
• It is primarily used in rural population which are socio-
culturally & economically homogenous & it is also frequently
used in genetic studies to identify trends of genes in
traditional families & so on.
• Provide significant cross section of selected community b y
age,gender,and so on.

• Merits
- Useful in drawing a representative sample from
traditional rural communities, which are socio-
culturally & economically homogenous.
- Save time & efforts in locating the study subject
because participants are identified through
reference from previous participants .
Demerits
Encounter problem of systematic errors or bias.
Lacks the diversity of sample.

Sampling in Qualitative Studies
• A small & nonrandom sample is used in qualitative studies.
• However qualitative research are also concerned to obtain a
representative sample ,so that measurement obtained from
study sample accurately reflects population & thus result can
generalize to population.
• Most of qualitative studies intend to identify meaning and to
search the unknown attributes & phenomenon but not to
generalize results target populations.
• Generally ,qualitative researcher approaches a participant ,who
is information rich to provide in depth information about
phenomenon under study.
• Thus qualitative researcher uses emergent sampling
technique(Opportunistic)which involves adding a new sampling
technique as study advances & changes in circumstances that
occur when data are being collected.

• Main criteria to chose a study participant in
qualitative study are richness of participants
knowledge or experience about particular
culture or phenomenon under study.
• Commonly used sampling technique
1.Convenince sampling technique
2.Snowball sampling technique
3.Purposive sampling technique- Patton (2002)
identified more than a dozen purposive
sampling stratigies,which can be used in
selecting sample in qualitative studies.

Purposive sampling technique strategies
1. Maximum variation sampling – Participant with diverse
attributes are included in study with purpose to study different
variations on dimension of interest.
2. Homogenous sampling ; Purposefully ,participants with same
culture or similar attribute are included in sample to study
phenomenon of interest in depth through in depth group
interviews & focused group discussion.
3. Typical case sampling – A peculiar case or participant ,who
represent a particular culture or social setting ,is included in study
sample to obtain specific known attributes or phenomenon of
interest is also as stratified Purposive sampling .
4. Extreme (Deviant)Case sampling – A most useful or extreme
participant or informant (Such as commonly unavailable)is
purposefully included as study participant considering these
participant rich in specific population .

4.Intensity sampling – It is different from extreme
casa sampling ,as, these information's are rich in
experience of particular phenomenon of interest.
It is quite frequently used in phenomenological
studies.
5.Reputational Case sampling – Key informants
refer further cases which are included as study
participants as they believed to be the
supplemental informants.
-This sampling technique is commonly used in
ethnographic studies.

SAMPLING ERROR
• Sampling error may be defined as the
difference between the data obtained
from a random sample and the data that
would be obtained if the entire
population was measured.
• It is not under the researchers control
• It is caused by the chance variations that
may occur when a sample is chosen to
represent the population.

SAMPLING BIAS
• Sampling bias is caused by the researcher
• It occurs when samples are not carefully selected
E.g. If names are written on slips of paper and placed
in a hat, each piece of paper has to be of same size
and thickness or bias could occur.
• If the slips of paper stuck together bias could occur
• All Non probability sampling methods are subject
to sampling bias
• Random sampling procedures are subject to bias if
some elements of the selected sample decide not to
participate in the study.

STEPS IN SAMPLING
1. Identify the target population- This is the group
to which you want to generalize your result
2. Identify the accessible population
3. Specify the eligibility criteria
4. Identify the elements in the population to be
studied
5. Choose a sampling approach : Probability non
probability
6. Determine the sample size
7. Contact the sample
8. Evaluate the sampling approach

CONCLUSION
• Sampling is one of the indispensable step in
the research process.
• Sampling is a complex process. To avoid
inappropriate generalizations, the researcher
needs to take appropriate steps to ensure
that the sample is a true representation of
the population

References
• Suresh k. Sharma 3rd ed ,Nursing research and
statistics,Elsevier,Pg 251-73.

Jayesh sampling technique semi (1) (1)

More Related Content

What's hot

Similar to Jayesh sampling technique semi (1) (1)

Recently uploaded

Jayesh sampling technique semi (1) (1)