Basics of sampling

PRESENED BY
AKHIL C A
PG FIRSTYEAR
DEPT. OF PUBLIC HEALTH DENTISTRY
SCB DENTAL COLLEE ,CUTTACK 2
SEMINAR-1

 INTRODUCTION
 BASIC CONSIDERATIONS
 HISTORY
 NEED FOR SAMPLING
 IDEAL REQUISITIES OF A SAMPLE
 CONSIDERATIONS IN SAMPLING DESIGN
 SAMPLING PROCESS
- METHODS OF SAMPLING
- NON PROBABILITY SAMPLING
- PROBABILITY SAMPLING
- OTHER SAMPLING METHODS
 ERRORS IN SAMPLING
 CONCLUSION
 REFERENCES
3

 In order to answer the research questions, it is doubtful that
researcher should be able to collect data from all cases.
 Since, researchers neither have time nor the resources to
analyse the entire population, there is the need for a much
simpler way of collecting data.
 And there is the relevance of studying a part of the population
rather than the entire population to produce reliable data in a
more practical way.
4

SAMPLING
 Sampling may be defined as the selection of
some part of an aggregate or totality on the basis
of which a judgement or inference about the
aggregate or totality is made.
 In other words, it is the process of obtaining
information about an entire population by
examining only a part of it.
KothariC R. Research methodology: methods and techniques. 2nd ed. New Delhi: NewAge
International (P) Limited, Publishers; 2004, 55-67,152-155.
6

SAMPLE
 A sample is “a smaller (but hopefully representative)
collection of units from a population used to determine truths
about that population” (Field, 2005)
 Source of data in a research
KothariC R. Research methodology: methods and techniques. 2nd ed. New Delhi: NewAge
International (P) Limited, Publishers; 2004, 55-67,152-155.
7

Universe/Population
 ‘Universe’refers to the total of the items or units in any field of
inquiry, whereas the term ‘population’ refers to the total of
items about which information is desired
 The attributes that are the object of study are referred to as
characteristics and the units possessing them are called as
elementary units. The aggregate of such units is generally
described as population.
KothariC R. Research methodology: methods and techniques. 2nd ed. New Delhi: NewAge International (P)
Limited, Publishers; 2004, 55-67,152-155.
8
basic considerations

 Thus, all units in any field of inquiry constitute universe and all
elementary units (on the basis of one characteristic or more)
constitute population
 The population or universe can be finite or infinite.
 From a practical consideration, we then use the term infinite
population for a population that cannot be enumerated in a
reasonable period of time
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census and sample survey
 A complete enumeration of all items in the ‘population’ is
known as a census inquiry.
 practically beyond the reach of ordinary researchers.
 mostly it is possible to obtain sufficiently accurate results by
studying only a part of total population, provided the
respondents selected should be as representative of the total
population as possible.
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 The selected respondents constitute the ‘sample’ and the
selection process is called ‘sampling technique.’
 The survey so conducted is known as ‘sample survey’.
Sampling frame:
 It is a listing of the members of the population from which the
sample is to be drawn.
 Collection of sampling units
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Sampling design:
 It refers to the technique or the procedure the researcher
would adopt in selecting some sampling units from which
inferences about the population is drawn.
 Sampling design is determined before any data are collected
Statisitc(s) and parameter(s):
A statistic is a characteristic of a sample, whereas a parameter is
a characteristic of a population.
Eg; population mean “ µ” is a parameter
sample mean ( X ) is a statistic.
12
KothariC R. Research methodology: methods and techniques. 2nd ed. New Delhi: NewAge International (P) Limited, Publishers; 2004, 55-67,152-155.

Sampling error:
 Sample surveys study small portion of the population and as
such there would naturally be a certain amount of inaccuracy
in the information collected.This inaccuracy may be termed
as sampling error or error variance.
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 The Dutch word for sample is “steekproef”.The origin of this word
is unclear
 Some believe it is a translation of the German word “Stichprobe”.
 “Stich” means to dig, stab or cut, and “Probe” means to test or to
try.
 Literature “Stichprobe” as a technique used in mining. A kind of
spoon ( test spoon) was used to take a small amount from a
melted substance to determine the amount of metal contained in
it
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 To gather data about the population in order to make an
inference that can be generalized to the population
 It reduces the cost of the investigation, the time required and
the number of personnel involved
 It allows thorough investigation of the units of observation
 It helps to provide adequate and in-depth coverage of the
sample units
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 Sampling remains the only way when population contains
infinitely many members.
 Sampling remains the only choice when a test involves the
destruction of the item under study.
 Sampling usually enables to estimate the sampling errors
and, thus, assists in obtaining information concerning some
characteristic of the population.
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need for sampling

1. Efficiency:
It is the ability of the sample to yield the desired
information.
2. Representativeness:
A sample should be representative of the parent
population so that inferences drawn from the sample can be
generalized to that population with , measurable precision and
confidence. Peter S. Research MethodologyAnd Biostatistics. In: Essentials of Public Health Dentistry. 5th ed. New Delhi: Arya
(Medi); 2004. p. 590–594.
17

3. Measurability:
The design of the sample should be such that valid
estimates of its variability can be made, that is, the investigator
should be able to estimate the extent to which findings from the
sample are likely to differ from the parent population.
4. Size:
A sample should be large enough to minimize sample
variability and to allow estimates of the population characteristics to
be made with measurable precision.
18
ideal requisities
Peter S. Research MethodologyAnd Biostatistics. In: Essentials of Public Health Dentistry. 5th ed. New Delhi: Arya (Medi); 2004. p. 590–594.

5. Coverage:
Adequate coverage is essential if the sample has
to remain representative. High rates of refusal / non-response,
loss to follow-up and other missing data can make a sample un
representative of the parent population.
6. Goal orientation:
Sample selection should be oriented towards the
study objectives and research design.
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ideal requisities

7. Feasibility:
The design should be simple enough to be carried
out in practice
8. Economy and cost-efficiency:
The sample design should be such that it should
yield the desired information with appreciable savings in
time and cost and with least sampling error.
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ideal requisities

(i)Type of universe:
 The first step in developing any sample design is to clearly
define the set of objects, technically called the Universe, to be
studied.
 The universe can be finite or infinite.
(ii) Sampling unit:
 A decision has to be taken concerning a sampling unit before
selecting sample.
 Sampling unit may be a geographical ,may be an individual.
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iii) Source list:
 It is also known as ‘sampling frame’ from which sample is to
be drawn.
 If source list is not available, researcher has to prepare it.
 Such a list should be comprehensive, correct, reliable and
appropriate.
(iv) Size of sample:
 This refers to the number of items to be selected from the
universe to constitute a sample.
22
considerations in sampling design

 The size of sample should neither be excessively large, nor
too small. It should be optimum.
 The size of population variance needs to be considered as in
case of larger variance usually a bigger sample is needed.
 The parameters of interest in a research study must be kept in
view, while deciding the size of the sample.
 Costs too dictate the size of sample that we can draw.
23

(v) Parameters of interest:
 we may be interested in estimating the proportion of persons
with some characteristic in the population, or we may be
interested in knowing some average or the other measure
concerning the population.
 There may also be important sub-groups in the population
about whom we would like to make estimates.
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(vi) Budgetary constraint:
 Cost considerations, from practical point influence the size
and type of sample.
 This fact can even lead to the use of a non-probability sample.
(vii) Sampling procedure:
 Finally, the researcher must decide about the technique to be
used in selecting the items for the sample.
 he must select that design which, for a given sample size and
for a given cost, has a smaller sampling error.
25

Clearly DefineTheTarget Population
SelectThe Sampling Frame
ChooseThe SamplingTechnique
Hamed taherdoost,Sampling Methods In Reseaarch Methodology;How to choose a sampling technique for research.ijarm; 2016;volume 5: issue 2,p.18-27.
26
DetermineThe Sample Size
Collect Data
AssessThe Response Rate

Stage 1: Clearly DefineTarget Population
 The first stage in the sampling process is to clearly define target
population.
 Population is commonly related to the number of people living in
a particular geographical area or sharing a common experience or
charectiristic.
Stage2: Select Sampling Frame
 A sampling frame is a list of the actual cases from which sample
will be drawn.The sampling frame must be representative of the
population.
27
Sampling process

Stage 3: Choose SamplingTechnique
 Taking a subset from chosen sampling frame or entire
population is called sampling. Sampling can be used to make
inference about a population or to make generalization in
relation to existing theory.
 In essence, this depends on choice of the researcher.
 Different sampling techniques are available depending upon
the type and nature of the population and the objectives of
the investigation.
28
Sampling process

Stage 4: Determine Sample Size
 In order to generalize from a random sample and avoid sampling
errors or biases, a random sample needs to be of adequate size
 Factors to be considered
(i) Nature of universe:
 Universe may be either homogenous or heterogenous in nature.
 If the items of the universe are homogenous, a small sample can
serve the purpose.
 But if the items are heterogenous, a large sample would be
required.
29
Sampling process

(ii) Number of classes proposed:
 If many class-groups (groups and sub-groups) are to be formed, a
large sample would be required because a small sample might not
be able to give a reasonable number of items in each class-group.
(iii) Nature of study:
 If items are to be intensively and continuously studied, the sample
should be small.
 For a general survey the size of the sample should be large, but a
small sample is considered appropriate in technical surveys.
30
Sampling process

(iv)Type of sampling:
 Sampling technique also determines the size of the sample.
 A simple random sample is best for a small population.
(v) Standard of accuracy and acceptable confidence level:
 If the standard of accuracy or the level of precision is to be
kept high, we shall require relatively larger sample.
31
Sampling process

(vi) Availability of finance:
 In practice, size of the sample depends upon the amount of
money available for the study purposes.
(vii) Other considerations:
Nature of units, size of the population, size of questionnaire,
availability of trained investigators, the conditions under which
the sample is being conducted and the time available.
32
Sampling process

Stage 5: Collect Data
 Once target population, sampling frame, sampling technique
and sample size have been established, the next step is to
collect data.
Stage 6: Assess Response Rate
 Response rate is the number of cases agreeing to take part in
the study.These cases are taken from original sample.
 In reality, most researchers never achieve a 100 percent
response rate.
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Sampling process

SAMPLINGTECHNIQUES
NON-PROBABILITY SAMPLING
PURPOSIVE
SAMPLING
QUOTA
SAMPLING
SNOW BALL
SAMPLING
CONVENIENCE
SAMPLING
PROBABILITY SAMPLING
SIMPLE
RANDOM
SAMPLING
CLUSTER
SAMPLING
SYSTEMATIC
SAMPLING
OTHER
SAMPLING
TECHNIQUES
34

35

 Not truly representative and less desirable
 Used when researcher is not able to get a random or
stratified sample
 When it is not necessary to generalize to a large
population
 when it is too expensive to obtain random or stratified
sample
36

 General composition of the sample is decided in
advance
 Only requirement is to fill the required number
 Done to ensure inclusion of a particular group
 Then its no longer a true representative
37
Non-probability Sampling

 Non-representative subset of a larger population
 Constructed to serve a very specific need or purpose
 Researcher have a Specific group in mind(may not be possible
to specify the population)
 Researcher zero in on target group .
38

 A subset of purposive sample
 chain referral/network sampling
 One picks up sample along the way ,analogous to a snow ball
accumulating snow
 One participant links to ,or recommends another
 Useful in hard to track populations
 Eg;drug users,sex workers,homeless people …
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Merit
 access to difficult to reach populations
(other methods may not yield any results).
Demerit
 not representative of the population and will result in a biased
sample as it is self-selecting
40

 Taking what you can get easily
 Accidental sample/ haphazard”sampling
 Merit – useful in pilot studies
 Demerit – results usually biased and unsatisfactory.
 Volunteers would constitute a convenience sample
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42

 Also known as ‘random sampling’ or ‘chance sampling’
 Recommended method of sampling
 Each unit has a known probability of being selected
 Random sampling ensures the law of “Statistical Regularity”
which states that if on an average the sample chosen is a
random one, the sample will have the same composition and
characteristics as the universe.
 Generalisations can be made to parent population with
precision and confidence
43

 It gives each element in the population an equal probability of
getting into the sample.
 All choices are independent of one another.
 4TYPES
A)simple random sampling
B)systematic sampling
C)stratified sampling
D)cluster sampling
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Probability SamplingTechniques

 It is a technique whereby each sampling unit has the same
probability of being selected(unrestricted random sampling)
 Basic procedure:
• Prepare a sampling frame
• Decide on the size of the sample
• Select the required number of units
45

 Chance determines which items shall be included.
 All items selected independently.
 At each selection , all remaining items have same chance
of being selected.
46

 we can define a simple random sample(size; n) from a finite
population(N) as a sample which is chosen in such a way that
each of the NCn possible samples has the same probability,
1/NCn , of being selected.
 six elements (say a, b, c, d, e, f ) i.e., N = 6. Suppose that we
want to take a sample of size n = 3 from it.
c r kothari,research methodology,second edition,2004 47

 Then there are 6 C3 = 20 possible distinct samples of the
required size
 ( abc, abd, abe, abf, acd, ace, acf, ade, adf, aef, bcd, bce, bcf,
bde, bdf, bef, cde, cdf, cef, and def).
 Each has the probability 1/20 of being chosen.

 Two methods of simple random sampling
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A)Lottery method
B)Table of random numbers

 Most primitive and mechanical method
 Here the population units are numbered on separate slips of
paper of identical size and shape
 These slips are then shuffled and blind fold selection of the
number of slips is made to constitute the desired sample size.
 This cannot be used for large or infinite population
Peter S. Research MethodologyAnd Biostatistics. In: Essentials of Public Health Dentistry. 5th ed.
New Delhi:Arya (Medi); 2004. p. 590–594.
50

 Two types
(1)With replacement
In this type of sampling an observation has a chance to
be selected at each draw.
Probability each item: 1/N
(2)Without replacement
In this type of sampling an observation is included in the
sample only once and is selected randomly without any preference.
Probability 1st draw: 1/N Probability 2nd draw: 1/N-1
51

This table consists of a series of digits that are generated
randomly.
The numbers are arranged in rows and columns and can be read
in any direction. All the digits are equally probable.
 Number each member of the population 1 to N.
 Determine the population size and sample size.
 Select a starting point on the random number table.
52

 Choose a direction in which to read (up to down, left to right,
or right to left).
 Continue this way through the table until you have selected
your entire sample, whatever your n is.
 The numbers selected then correspond to the numbers
assigned to the members of population, and those selected
become sample.
53

 Various statisticians likeTippett,Yates, Fisher have prepared
tables of random numbers which can be used for selecting a
random sample.
 Generally,Tippett’s random number tables are used for the
purpose.
 Tippett gave10400 four figure numbers.
 He selected 41600 digits from the census reports and
combined them into fours to give his random numbers which
may be used to obtain a random sample.

 first thirty sets ofTippett’s numbers
2952 6641 3992 9792 7979 5911
3170 5624 4167 9525 1545 1396
7203 5356 1300 2693 2370 7483
3408 2769 3563 6107 6913 7691
0560 5246 1112 9025 6008 8126
 Suppose we are interested in taking a sample of 10 units from a
population of 5000 units, bearing numbers from 3001 to 8000.
from left to right, starting from the first row itself, we obtain the
following numbers:
 6641, 3992, 7979,5911, 3170, 5624, 4167, 7203, 5356, and 7483.

Merits
 Ease of task in smaller populations
 No personal bias.
 Sample more representative of population.
 Accuracy can be assessed as sampling errors
follow principals of chance.
Demerits
 Requires completely catalogued universe.
 Cases too widely dispersed - more time and
cost.
56

 A systematic sample is obtained by selecting one unit at random and then
selecting
additional units at evenly spaced interval till the sample of required size has
been got.
 applied to field studies when the population is large, scattered and not
homogeneous.
 Systematic procedure is followed to choose a sample by taking every Kth
house or patient where K refers to the sample interval,
K=sampling interval/fraction
57

Steps to form/select the sample using systematic sampling:
 First develop a well-defined structural population to
start.
 Figure out the ideal size of sample
 After deciding the sample size, assign number to every
member of sample
 Then, the interval of the sample is decided.
 And successive sampling units are added in accordance
with sampling interval.
58

 For example, we want to select a total of ten patients from a
group of forty, then the Kth element will be selected by;
 40/10 = 4
 so every 4th patient will be taken for sampling – 4, 8, 12, 16,
20, 24, 28, 32, 36, and 40.
59
K=N/n

 Types of systematic sampling
 Linear systematic sampling
 A list is made in a sequential manner of the whole population
list.
 Decide the sample size and find the sampling interval
 Now, choose random number between 1 and K and then to
the number what we got add K to that to get the next sample.
Types of Sampling in Research
Pooja Bhardwaj, ReviewArticle 60
• Linear
• Circular

 Circular systematic sampling
 In this, first, we will determine sample interval and then select
number nearest to N/n.
 For example, if N = 17 and n = 4, then k is taken as 4 not 5 and
then start selecting randomly between 1 to N
 Skip K units each time when we select the next unit until we
get n units. In this type, there will be N number of samples
unlike K samples in linear systematic sampling method.
Types of Sampling in Research
Pooja Bhardwaj, ReviewArticle 61

MERITS
 The systematic design is simple, convenient to adopt.
 The time and labor involved in the collection of sample is
relatively small.
 If the population is sufficiently large, homogeneous and each
unit is numbered, this method can yield accurate results
DEMERITS
 Sample may be biased if hidden periodicity in population
coincides with that of selection.
Soben peter,essentials of public health dentistry,5th edition
62

 Followed when the population is not homogeneous.
 The population under study is first divided into homogeneous
groups or classes called strata and the sample is drawn from
each stratum at random in proportion to its size.
 For giving representation to all strata of society or population
such as selecting sample from defined areas, classes, ages,
sexes, etc.
 More of a representative sample than a random sample
Hamed taherdoost,Sampling Methods In Reseaarch Methodology;How to choose a sampling technique for
research.ijarm; 2016;volume 5: issue 2,p.18-27.
63

64
First, we will define target population.
Recognize the stratification variables which should match with the
research objective
Figure out the number of strata to be used
The whole population is then divided in to different strata

65
Assign random, unique number to each element
Then divide the number of samples to be taken with the total
number of population into number of people in that group.
The number now what we got is the number of samples to be
selected for that particular strata.
Here, we will use the simple random technique.

Merits
 1. Proportionate representative sample from each strata is
secured
 2. It gives greater accuracy
Demerits
 1. Utmost care in dividing strata.
 2. Skilled sampling supervisors.
66

 A cluster is a randomly selected group.
 used when units of population are natural groups or clusters
such as villages,wards, blocks, slums of a town, factories,
work-shops or children of a school, etc.
Peter S. Research MethodologyAnd Biostatistics. In: Essentials of Public Health Dentistry. 5th ed. New Delhi: Arya
(Medi); 2004. p. 590–594.
67

 Used to evaluate vaccination coverage in expanded
Programme of Immunization (EPI) and Universal
Immunization Programme(UIP)
 For example,suppose we want to measure the proportion of
defective machine parts in an inventory ,at apoint of time
stored in 400 cases 50 each.
 Now we consider 400 cases as clusters and randomly select ‘n’
cases in first stage and in second stage examine all the the
machine parts in each randomly selected case.
KothariC R. Research methodology: methods and techniques. 2nd ed. New Delhi: NewAge International (P) Limited, Publishers; 2004, 55-67,152-155 68

ADVANTAGES:
▪ Very useful when populations are large and spread over a
large geographic region
▪ Convenient and more time and cost effective.
DISADVANTAGES:
▪ Representation is likely to become an issue
▪ Standard error is higher
Peter S. Research MethodologyAnd Biostatistics. In: Essentials of Public Health Dentistry. 5th ed. New Delhi: Arya
(Medi); 2004. p. 590–594.
69

[internet].2020[cited 3rd october]Available from: https://www.researchgate.net/publication/325846982_ Sample
_And_Sampling_Designs.
70

 Part of the information collected from whole sample & part
from subsample.
In a tuberculosis survey,
 First phase - Mantoux test may be done in all cases of
the sample
 Second phase - X-ray of the chest may be done in Mantoux
positive cases
 Third phase -sputum examined in X-ray positive cases
 Survey by such procedure is less costly, less laborious and more
purposeful.
71
other SamplingTechniques

 The first stage is to select the groups or clusters.
 Then subsamples are taken in as many subsequent stages as
necessary to obtain the desired sample size.
 Employed in large country surveys.
 First stage, random number of districts chosen in all states.
 In Second stage followed by random number of talukas,
villages.
 Then third stage units will be houses.
 All ultimate units (houses, for instance) selected at last step
are surveyed.
Peter S. Research Methodology And Biostatistics. In: Essentials of Public Health Dentistry. 5th ed. New Delhi: Arya (Medi); 2004. p. 590–594.
72

 Multistage sampling used frequently when a complete list of
all members of the population does not exist and is
inappropriate.
 This technique, is essentially the process of taking random
samples of preceding random samples.
 By avoiding the use of all sample units in all selected clusters,
multistage sampling avoids the unnecessary, costs associated
with traditional cluster sampling.
Peter S. Research Methodology And Biostatistics. In: Essentials of Public Health Dentistry. 5th ed. New Delhi: Arya (Medi); 2004. p. 590–594.
73

Matched Random Sampling:
 Also called matched pairs,paired samples,or dependant
samples.
 A method of assigning participants to groups in which pairs of
participants are first matched on some characteristic and then
individually assigned randomly to groups.
[internet].2020[cited 3rd october]Available from: https://www.researchgate.net/publication/325846982_ Sample _And_Sampling_Designs.
74

 Matched samples are paired up so that participants share
every characteristics ,except for the one under investigation.
75

 Mechanical Sampling:
Mechanical sampling is typically used in sampling solids,
liquids and gases, using devices such as grabs, scoops; thief
probes etc. Care is needed in ensuring that the sample is
representative of the frame.
[internet].2020[cited 3rd october]Available from: https://www.researchgate.net/publication/325846982_ Sample _And_Sampling_Designs. 76

 Line-intercept Sampling:
 Line-intercept sampling is a method of sampling elements in a
region whereby an element is sampled if a chosen line
segment, called a ‘transect’, intersects the element.
77

 Panel Sampling:
 Method of first selecting a group of participants through a
random sampling method and then asking that group for the
same information again several times over a period of time.
 Therefore, each participant is given the same survey or
interview at two or more time points; each period of data
collection is called a ‘wave’.
 chosen for large scale or nation-wide studies in order to study
changes in the population with regard to problems like
chronic illness , job stress, weekly food expenditures.
78

 Voluntary Sample:
 A voluntary sample is made up of people who self-select into
the survey.
 Often,these folks have a strong interest in the main topic of
the survey.
 Suppose, for example, that a news show asks viewers to
participate in an online poll.This would be a volunteer sample.
 The sample is chosen by the viewers, not by the survey
administrator.
79

 River sampling
 Invites respondents to take a survey via online
banners,promotions,offers, and invitations placed on a variety
of websites.
 And after some screening questions they are routed to a
survey based on their answers.
 The surveyers have no idea that who will respond.
 They have no demographic information of the participants
and can’t contact them after survey completion.
80

Kothari C R. Research methodology: methods and techniques. 2nd ed. New Delhi: New Age International (P) Limited, Publishers; 2004, 55-67,152-155.
81

 Two costs are involved in a sampling analysis viz. the cost of
collecting the data and the cost of an incorrect inference
resulting from the data
 There are two types of errors that arise in sampling,
82
-Sampling error
-Systematic bias
Errors In Sampling

systematic bias
Results from errors in the sampling procedures, and it cannot be
reduced or eliminated by increasing the sample size.it results from;
 1.Inappropriate sampling frame:
If the sampling frame is inappropriate i.e., a biased representation of
the universe, it will result in a systematic bias.
 2. Defective measuring device:
If the measuring device is constantly in error, it will result in
systematic bias or In survey if the questionnaire or the interviewer is
biased.
83
Errors In Sampling

 3. Non-respondents(coverage error)
If we are unable to sample all the individuals initially included in the
sample, there may arise a systematic bias.
 4. Indeterminancy principle:
Sometimes we find that individuals act differently when kept under
observation than what they do when kept in non-observed
situations.
 5. Natural bias in the reporting of data:
Natural bias of respondents in the reporting of data is often the
cause of a systematic bias in many inquiries.
84
Errors In Sampling

Sampling errors
 Are the random variations in the sample estimates around the
true population parameters.
 Since they occur randomly and are equally likely to be in
either direction, their nature happens to be of compensatory
type and the expected value happens to be equal to zero.
 Sampling error decreases with the increase in the size of the
sample, and it happens to be of a smaller magnitude in case of
homogeneous population
85
Errors In Sampling

 Sampling error can be measured for a given sample design
and size.
 The measurement of sampling error is usually called the
‘precision of the sampling plan’
 If we increase the sample size, the precision can be improved.
 But increasing the size of the sample increases the cost of
collecting data and also enhances the systematic bias.
 In brief, researcher must ensure that the procedure balances
both.
86
Errors In Sampling

 Sampling is the process by which elements are selected
from population for a research study.
 There are mainly two types of sampling; probability and
non-probability sampling
 Probabilty sampling is the ideal method of sampling,but
is not practical always and in such situation resaercher
has to go for non-probability sampling
 Appropriate sample size depends on various factors such
as population characteristics,statistical issues,economic
constraints,availabilty of resources time etc..
87

 Kothari C R. Research methodology: methods and techniques.
2nd ed. New Delhi: New Age International (P) Limited,
Publishers; 2004.p. 55-67,152-155.
 Peter S. Research Methodology And Biostatistics. In:
Essentials of Public Health Dentistry. 5th ed. New Delhi:Arya
(Medi); 2013. p. 590–594.
 DanielWW, Cross CL. In: Biostatistics: basic concepts and
methodology for the Health Sciences. 10th ed . New Delhi :
Wiley; 2014. p. 7–13.
88

 Mahajan BK.Methods in Biostatistics For Medical Students
And ResearchWorkers. 6th ed. New Delhi :Jay pee
Publishers;1997.p.88-102.
 WHO Health Research Methodology,A Guide ForTraining In
Research Methods.2nd ed.Manila; 2001.p.71-82
 Park K. Park's textbook of Preventive and social medicine.
25th ed. India: Bhanot Publishers; 2017. p. 112–23.
 Bhardwaj P.Types of sampling in research. J Pract Cardiovasc
Sci 2019;volume5: issue3.p.157-63.
89

 Syed Mohammed Sajjad Kabir. SampleAnd Sampling Designs
[internet].2020[updated 2016 July;cited 2020 october
3]Available
from:https://www.researchgate.net/publication/325846982_
Sample _And_Sampling_Designs.

Basics of sampling

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