sampling methods and techniques

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SAMPLING
Introduction
Sampling is a complex and technical topic to whichentire have been devoted. At the same
time, the basic features of sampling are familiar to us all. In the course of our daily activities, we
gather knowledge, make decision and make decisions and make predictions through sampling.
Researchers too, generally derive knowledge from samples. For example, in testing the
efficacy of a medication for asthma patients, a researcher must reach a conclusionwithout
administering the drug to every asthmatic patient. However, researchers cannot affordto draw
conclusion about the effectiveness of interventions or the validity of relationships based on a
sample of only three or four subjects.
Sampling is an important step in the research process forquantitative studies. It is the
selection of some part of an aggregate or totally of population on the basis of whicha judgment or
inference about the aggregate or totality is made.
Definitions:-
Sampling is a process of selecting representative units of a population forstudy in a research.
-B. T. Basavanthappa.
Sampling refers to the process of selecting a position of the population to represents the entire
population.
-Polit.
Sampling is the process of selecting a representative segment of the population under study
Statistical method of obtaining representative data or observations from a group. (Clot, batch,
population or universe).
www.dictionary.com.

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Terminology
Some of the main terms used in sampling process are
Population
Population is the aggregate of all the units in which a researcher is interested. In other
words, population is the set of people or entities to whichthe result of a research are to be
generalized.
For example, a researcher needs to study the problems faced by post graduate nurse of
India; in this the population willbe all the post graduate nurses who are Indian citizens.
Target population
A target population consists of the total number of people or objectswhich are meeting the
designates set of criteria. In other words, it is aggregate of all the cases with a certain
phenomenon.
For example, a researcher is interested in identifying the complicationof DM typeII among
people whohave migrated to Ludhiana. Here, the target population is all the migrants at
Ludhiana suffering with DM- type II.
Accessible population
It is the aggregate of cases that conformto designated criteria and are subject fora study.
For example, ‘a researcher is conducting a study on the registered nurses(RN) workingin
Dayanand medical college and hospital (DMCH), Ludhuana. Here, the population forthis
study is all the RNS who meet the designated criteria and whoare available for the research
study.
Sampling
Sampling is the process of selecting a representative segment of the population under
study.
Sample
Sample may be defined as representative unit of a target population, whichis to be worked
upon by researcher during their study.
Elements
The individual entities that compromise the samples and population are knownas
elements and an element is the most basic unit about which information is collected.An
element is also known as subject in research.

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Sampling frame
It is a list of all the elements or subjects in the population from whichthe sample is drawn.
Sample frame could be prepared by the researcher or an existing frame may be used.
Population
Sample frame
Available units
Sample
For example, a research may prepare a list of the all the household of a locality whichhave
pregnant women or may used a register of pregnant women forantenatal care available
with the local Anganwadi worker.
Sampling error
There may be fluctuations in the values of the statistics of characteristics from one sample
to another, or even those drawn the same population.
Sampling bias
Distortion that arises when a sample is not representative of the population from it was
drawn.

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Sampling plan
The formal plan specifying a sampling method, a sample size, and the procedure of the
selecting the subjects.
Sample planning
A sampling plan is a detailed outline of whichmeasurements will be taken at what times, on
whichmaterial, in what manner, and by whom?
Assignment
Having drawn the sample, these may be assigned to different groups.
A common grouping is an experimental group whichreceive the treatment under study and a
controlgroup that gives a standard against whichexperimental results can be compared. To
sustain internal validity, this is usually random assignment. Non-random assignment is for
example where two schoolclasses are selected as coherent groups and one chosen as the control.
Sampling distribution
If the sample is described as a histogram (a bar chart showing numbers in different
measurement ranges) it will have a particular shape. Multiple samples should have similar
shapes, although random variation means each may be slightly different. The larger the
sample size, the more similar sample distributions will be.
Generalizing
After sampling youthen generalize in order to make conclusions about the rest of the
population.
Validity
Validity is about truth and accuracy.A valid sample is representative of the population and
will allow you to generalize to valid conclusions. This aligns with external validity.
A valid sample is both big enough and is selected withoutbias so it is representative of the
population.
Strata
Strata (singular: stratum) are sub-groups within a population or sample frame. These can
be random groups, but often are natural groupings, such as men and women or age-range
groups. Stratification helps reduce error.

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Oversampling
Oversampling occurswhen you study the same person twice. For example if you selected
people by their telephone number and someone had twophone numbers, then youcould
end up calling them twice.This can cause bias.
Purposes ofsampling
Economical
It is not possible and economicalfor researcher to study an entire population. The
researcher can save lots of time, money and resources to study a phenomenon.
Improved quality data
When a researcher is handling the information from only a part of the population under
study, it is easier to maintain the quality of the research work, whichwould not be possible
in case the entire population is involved.
Quickstudy results
Studying an entire population itself will take a lot of time and generating research results of
a large mass will be almost possible as most research studies have time limits. It is possible
to generate study results faster, whichis one of the important objectivesof every
researcher.
Precision and accuracy
of data conducting a study on an entire population provides researchers with voluminous
data, and maintaining precision of that data becomes a cumbersome task, while carrying a
study on a part of the population helps the researcher to generate more precise data; where
formulation of the interpretations of the data becomes much.
Need forSampling
 Saves time and energy
 Enable more accuratemeasurements
 Only way when population is infinite
 Only choice when test involves destruction of item under the study
 Enables toestimate sampling errors – more information on population

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Characteristicofgood sampling
There are various qualities and characteristic features that make a sample good. To
generalize the finding for an entire population, a good sample forthe research study must
have followingcharacteristics.
Representative
a representative sample is one that the key characteristic of whichare closely related to
those of the population. Representativeness of the sample makes it possible togeneralize
the findings for the population.
Free from bias and errors
a good sample is one whichis free from deliberate selection of the subjects forstudy.
Sample should be free from simple random sampling errors or sampling bias.
No substitution and in completeness
A Sample is the said to be good if once a subject is selected for the study, it is neither
replaced nor it is incomplete in any aspects of researcher’s interest.
Appropriate sample size:
Generally it is believed that in quantitative studies the larger the sample size better is the
probability of the goodness of the sample.
Low Sampling Error
Every time you poll a sample of a population (as opposed to asking everyone), you'regoing to
get some statistics that are a little different from the "true" statistics. This is called sampling
error, and is often expressed as percentage points. For example, a poll might be plus or minus
"ten points." In other words, if a pollster finds that 55 percent of people willvote fora certain
candidate, plus or minus ten points, they are really saying that somewhere between 45 and 65
percent will vote forthat candidate. A good sample will have a low sampling error (a point or
two).
High Confidence Level
Population, the more the data resembles a bell curve. Confidence levels are expressed as a
percentage, such as a "90 percent confidencelevel." The higher the confidencelevel, the more
sure a researcher is that his data lookslike a bell curve: a 99 percent confidencelevel is
desirable and likely to have better results than a 90 percent (or lower) confidencelevel.
Degree of Variability
The degree of variability refers to how diverse a population is. Forexample, a poll of all
political parties about health care is likely to result a more widespread variation in
responses than a simple poll of a single party. The higher the stated proportion, the greater

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the level of variability,with .5 being the highest (and possibly, least desirable) value. For
smaller samples, youwould want to see a low degree of variability
Factors influencingsamplingprocess
The factors whichmay affectthe sampling process are-
Natureof the researcher
Inexperienced investigator
If the investigator lacksadequate knowledge and experience about the conditions of the
researcher process, the sample selection be adversely affected.
Lackof the interest
Lackof the self motivation and appreciation forcarrying out task or establishing research
methodology on the part of the research also affectthe drawing of the sample.
Lackof honest
Lackof the honesty will affectsampling process in research. Research should be honestly
involvedin each step of the research process.
Intensive work load
Lackof adequate resources and ability to carry out the research process result in
inadequate selection and application of all resource process including the sampling process.
Inadequate supervision
There should be adequate supervision of the research activity to ensure the appropriate
implementation of the research process including the sampling process.
Natureof the sample-
Inappropriatesamplingasampling technique,thewholesamplingprocess maygetaffected.
Sample size:
Very large samples become heterogeneous and do not exhibit characteristics of whole
population in general; if the sample is too small, a researcher may not be able to generalize
the study findings to the whole population.
Definitivesampling frame
Defectivesampling frames leads to faculty sampling process. Researcher should have
adequate knowledge about population under study to have an appropriate sampling frame.

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Circumstances
Lackof time
Adequate time should be available with the researcher to have adequate planning and
implementation of the sampling process.
Large Geographic area
A large Geographic needs lots of time and resource to accomplish the sampling process. In
addition, large Geographical areas can also lead to mental and physical exhaustion and thus
the sampling process can get adversely affected.
Large of cooperation
During sample process, researcher needs cooperation from competent authorities as well as
from competent authorities as wellas from the subjects.
In the absences of cooperation of the requisite authorities and study subjects, the sampling
process may be affected.
Natural calamities
Sometimes the sampling process is affectedby natural calamities such as floods and other
natural distress, death , or other environmental constrains.
Types of samplingtechniques.
Sampling is the process of selecting a representative part of the population. There are
several methods or techniques of sampling; basically sampling techniques are classified into
twobroad categories, i.e., probability and non probability sampling techniques.
Types of sampling techniques
Probabilitysampling Nonprobabilitysampling
Simple random Purposive Sampling
Stratified random convenient sampling
Systematic random Consecutive sampling
Cluster/multistage sampling Quota sampling
Sequential sampling Snow ball sampling
Probability Proportional to Size Sampling
PROBABILITY SAMPLING
It is based on the theory of probability. It involvesrandom selection of the
elements/members of the population.

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 Probability sampling includes techniques that select samples based on the conceptof
random selection
 It is a scheme in whichevery unit in the population has a chance (greater than zero) of
being selected in the sample, and this probability can be accurately determined
Probability sampling is a technique where in the samples are gathered in a process that gives all
NON-PROBABILITY SAMPLING
 Non-probability sampling techniques are not based on random selection
It is any sampling method where some elements of the population have no chance of
selection (these are sometimes referred to as 'out of coverage'),or where the probability of
selection can't be accurately determined.
SimpleRandomSampling
This is the most pure and basic probability sampling design. In this type of sampling design,
every member of the population has the equal chance of being selected as subject. Each
choiceof sampling unit is independent of all other choices.
There is a need of twoessential prerequisites toimplement the simple random
technique.
1. The population must be homogeneous and
2. Researcher must have list of members/elements of the accessible population
The first step of the simple random sampling technique is to identify the accessible population and
prepare a list of all element / members of the population.
The list of the subject in population is called as sample frame and a sample drawn fromsampling
frame by using followingmethods:
The lottery method:
There are many methods to proceed withsimple random sampling. The most primitive and
mechanical would be the lottery method. Eachmember of the population is assigned a unique
number. Eachnumber is placed in a bowl and mixed thoroughly.
The blind- folded researcher then picks numbered tags. All the individuals bearing
the numbers picked by the researcher are the subjects for the population.
The use oftable randomnumbers
This is the most commonly and accurately used method in simple random sampling .
Random table present several numbers in rows and columns. Researcher initially prepares a
numbered list of elements /members of population, and then withblind folded chooses a number
from the random table.

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The same procedure is continued until the desired number of the subjects is
achieved. If repeatedly similar numbers are considered until desired numbers of subjects are
achieved.
10 09 73 25 33 76 52 1
37 54 20 48 05 64 89 47
8 42 26 89 53 19 64 50
9 1 90 25 29 9 37 67
12 80 79 99 70 80 15 73
66 6 57 47 17 34 7 27
31 6 1 8 5 45 57 18
85 26 97 76 2 2 5 16
63 57 33 21 35 5 32 54
73 79 64 57 53 3 52 96
The use ofcomputer
For population with a small number of members, it is advisable to use the first method, but if
the population has many members, a computer aide random selection preferred.
This sampling technique gives each element an equal and independent chance of being
selected. An equal chance means equal probability of section e.g., in a population of 300 each
element theoretically has 11300th chanceof being selected .
Equal probability selection method is described as epsin sampling. An independent
chance means that the draw of one element willnot affectthe chances of other elements being
selected.
Merits-
 One of the best things about simple random sampling is the ease of assembling the sample.
 It is also considered a fair way of selecting a sample from a given population. Since every
member is given equal opportunity of being selected.

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 The simple random sampling requires minimum knowledge about the population in
advance.
 This is one of the most unbiased probability methods of sampling.
 This is method sampling which is free from sampling errors.
 Sample errors can be easily computed and the accuracy of estimate easily assessed.
Demits-
 One of the most obviouslimitations of simple random sampling method is the requirement
of and an up-to-date list of all the members of the population
 This method does not make use of knowledgeabout population that researcher already
have.
 Lots of procedures need to be done before sampling is accomplished.
 The cases selected by random sampling tend to be widely dispersed geographically and the
time and cost of collectingdata becomes too large.
Stratifiedrandomsampling
This method is used forheterogeneous population. Stratified sampling is a
probability sampling technique where the researcher divides the entire population into different
homogenous subgroups or strata and then randomly selects the final subjects proportionally from
the different strata.
The strata are divided according to selected traits of population such as age, gender, religion,
socioeconomic status, diagnosis etc.
According to the weight age of the sample and proportion; stratified random sampling is further
divided into twocategories:
1. Proportionate stratified random sampling
2. Disproportionate stratified random sampling.
Proportionatestratified randomsampling
In this the sample chosen fromeach stratum is in proportion to the size of total
population. The sample size of each stratum in this technique is proportionate to the population
size of the stratum when vied against the entire population. This means that each stratum has
the same sampling fraction.
The important thing in this technique is to use the sample fractionforeach stratum regard less
of the differences in proportion size of strata.
For example, researcher has three strata with 100, 200, 300 population size respectively and
the researcher choose a sampling fraction of ½. Then the researcher must randomly sample
50,100 and150 subjects from each stratum respectively.
Disproportionatestratified randomsampling

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In this sub type,the sample chosen from each stratum are not in proportion to
size of total population in that stratum. The only difference between proportionate and
disproportionate sampling is their sampling fraction.
The precision of this design is highly dependent on the sampling fractionallocation of the
researcher. If the researcher commits mistake in allotting sampling fractions,a fractionmay be
over represented or underrepresented.
For example if the researcher wants to study biophysicalprofiles of nursing students, in a CON,
which contains 100 students of from Himachal, 200 students fromHaryana and 300 students
from Punjab. The researcher choose different sampling fraction and then randomly select
sample of 50 subjects fromeach strata.
Merits
 It ensures representation of all groups in a population
 Researcher also employs stratified random sampling when they wantto observe
existing relationship between 2 or more subgroups. Therefore comparisons is possible
 With stratified sampling the researcher representatively sample even the smallest and
most in accessible subgroups in the population. This allows the researcher to sample
the rare extremes of a given population.
 With stratified sampling technique, there is a higher statistical precision compared to
simple random sampling. This is because of variability within the subgroups is lower
compared to the variations when dealing with entire population.
 Because this technique has high statistical precision, it also means that it requires a
simple sample size whichcan save lot of time money and effortof the researcher.
Demerits
 Proportionate stratification requires accurate information on the proportion of
population in each stratum.
 Large population must be available from whichto select subjects.
 There is always possibility for faulty classification and increase in variability.
Systematicrandomsampling
Systematic random sampling can linked to an arithmetic progression, where in
the difference between any two consecutivemembers is the same it involvesthe selection of every
k th list of group such as every tenth person or a patient list or every 100 th person from a phone
directory.
Systematic sampling is sometimes used to sample every K th person entering the a bookstore etc.
Systematic sampling can be applied so that an essentially random sample is drawn. If we had a list
of subjects or sample frame, then, the desired sample can be sample size is established at some (n)
and the size of population must be knownor estimated (N).
K=N/n (or)

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Number of subjects target population (N)
Size of the sample (n)
For example a researcher wants to choose about 100 subjects from a total target population of 500
people, 500/100=5. Therefore 5th person willbe selected.
In this method list of subjects is prepared forthe target population (sample frame) and then the
first subject is randomly selected; later every Kth subject is selected from the sampling frame.
Merits
 This technique is convenient and simple to carry out.
 Distribution of sample is spread evenly overthe entire given population.
 Less cumbersome, time consuming, and is cheaper than simple random sampling.
 Statistically more efficient and provides a better representative sample when population
elements are randomly distributed.
Demerits
 If first subject is not randomly selected,, then it becomes a non random sampling technique.
 This may result in biased sample.
 If sample frame has nonrandomly distributed subjects, this sampling technique may not be
appropriate to select a representative sample.
ClusterorMultistagesampling
Cluster random sampling is done when simple random sampling is almost impossible
because of size of the population.
Cluster sampling means random selection of sampling unit consisting of population
elements. Then fromeach selected sampling unit, a sample population element 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, and it is not possible to list all the elements.
Geographical units are the most commonly used. 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 the number of clusters depending on the research based on simple
random sampling.
 Then from the selected clusters the researcher can either include all the high school students as
subjects or he can select a number of subjects fromeach cluster through or systematic random
sampling.
TYPESOF CLUSTERSAMPLES
One stage cluster sample
Twostage cluster sampling

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One stage cluster sample
It occurswhen the researcher includes all the high school students from all the selected clusters as
sample
Twostage cluster sampling
From the cluster sample selected, the researcher selects few number of students from each cluster
by using simple or systematic random sampling technique.
Merits
 This sampling technique enables the investigator to use existing divisions such as
districts, villages, towns etc.
 Can be used when there is no exhaustive list of all elements.
 Reduces the cost and workload.
 Some clusters can also used again forsampling
Demerits
 Less precise than other random sampling techniques.
 If clusters chosen are biased in anyway,inferences drawn about population will not be
accurate.
 Possibility of sampling bias and errors.
Sequential sampling
This sampling technique is slightly different from other methods. Here the sample size is not fixed.
The investigator initially selects a small sample and tries out to take inferences; if not able to draw
results, he or she then adds more subjects until clear cutinference can be drawn.
Merits
 Facilitates to conduct study on a best possible smallest representative sample
 Helps ultimately in finding the inferences of the study
Demerits
 A phenomena cannot be studied at one point of time
 Requires repeated entries into the field to collectthe sample
Probabilityproportionateto sizesampling
In this procedure if cluster has large a population as other, it is given twicethe chance of being
selected. A sampling technique, commonly used in multi-stage cluster sampling, in which the
probability that a particular sampling unit will be selected in the sample is proportional to itssize.
The selection procedure is
1. Draw a list of clusters withtheir size measures
2. Cumulate the size measures in sequences

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3. Divide the list into a certain appropriate equal strata with reference to cumulated measures
example if the cumulative total is 600 the list may be divided into 3 equal zones 1-200,201-
400and 401-600.
4. Select the required equal number of sample in each zone , applying preferable systematic
selection with a random start &
5. Draw a same fixed number of population elements fromeach selected cluster at random
Advantages
- Pps lead to greater precision than simple random sample of clusters.
- Equal sized samples from each selected primary cluster is convinent
- Pps cannot be used if the sizes of the primary clusters rae not known.
Nonprobabilitysampling:
Every subject does not have equal chance to be selected necause elements are chosen by choice
not by chance through non random sampling methods.
It is believed that non random `methods of sampling are more likely to produce a biared sample
than random methods. In aprobability sampling certain elements have more probability to be the
part of sampling while other may have no chance of being included in the sample. This restricts the
generalization that can be made abord the study findings.
Featuresof the nonprobabilitysampling:
Non probability sampling is a technique where in the samples are gathered in a process that does
not give all the individuals in the population equal chances of being selected.
Most researchers are bound by time money and workforce and because of these limitations it is
almost impossible to randomly sample the entire population and it is often necessary to employ
another sampling technique the non probability sampling technique.
In contrast with probability sampling , non probability sample is not a product of a randomized
selection process subjects in a non probability sample are usually selected on basis of their
accessibility or by the purposive personal judgment of the researcher.
Usesof nonprobabilitysampling:
Non probability sampling is used in followingsituations.
 This type of sampling can be used when demonstrating that a particular trait exists in the
population.
 It can also be used when the researcher aims to do a qualitative, pilot or exploratory study
 It can be used when the research is not possible when the population is almost limitless

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