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Sample &Sampling
DesignDR.G.SINGARAVELU
Associate Professor
UGC-ASC
BHARATHIAR UNIVERSITY
COIMBATORE
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DEFINITIONS
Population-totality of the objects orindividuals regarding inferences are
made in a sampling study.
Sample-smaller representation of a
large whole.
Sampling- is a process of selecting a
subset of randomised number of the
members of the population of a study
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Sampling frame /Source list -complete list of all themembers/ units of the population from which eachsampling unit
Sample design / sample plan-is a definite plan for obtaininga sample from a given population.
Sampling unit-is a geographical one (state,district)
Sample size-number of items selected for the study
Sampling Error-is the difference between population valueand sample value.
Sampling distribution-is the relative frequency distributionof samples.
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CENSUS/SAMPLING
Census-collection of data from
whole population.
Sampling is taking any portion of apopulation or universe as
representative of that population.
Sampling method has been usingin social science research since
1754 by A.L.BOWLEY
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Indispensable of sampling in
Research
Saves lot of time
Provides accuracy
Controls unlimited data
Studies individualReduces cost
Gives greater speed /helps to complete instipulated time
Assists to collect intensive and exhaustive data
Organises conveniences
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Steps in Sampling Process /
Procedures
Define the population (element,units,extent andtime)
Specify sampling frame(Telephone directory)
Specify sampling unit (retailers, ourproduct,students,unemployed)
Specify sampling method/technique
Determine sampling size
Specify sampling size-(optimum sample)
Specify sampling plan
Select the sample
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PRINCIPLES OF SAMPLING
Two important principles
Principles of Statistical Regularity-random
(sufficient representative of the sample),
Principles of Large Numbers-(steadiness ,
stability and consistency)
Principles are referred to as the laws ofsampling
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Good sampling
The sample should be true representative of
universe.
No bias in selecting sample
Quality of the sample should be same
Regulating conditions should be same for all
individual
Sampling needs to be adequateEstimate the sampling error
Sample study should be applicable to all items
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Preparing a sampling design
Type of universe (set of objects)
Finite/Non-finite
Sampling unit (district,school,products)
Sampling frame
Sampling size
Sampling technique
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Methods of sampling
Bloomers and Lindquist
Probability Non Probability
Random/simple Quota
Stratified random Purposive
Cluster Accidental
Systematic Incidental
Multistage
Proportionate Snow ball
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Probability
Probability sampling technique is one
in which every unit in the population has a
chance of being selected in the sample
This probability can be accurately
determined.
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Nonprobability sampling
Nonprobability sampling is any samplingmethod where some elements of the populationhave no chance of selection (these aresometimes referred to as 'out of
coverage'/'undercovered'), or where theprobability of selection can't be accuratelydetermined.
It involves the selection of elements based onassumptions regarding the population of interest,which forms the criteria for selection.The selection of elements is non random
.
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Simple random sampling
In a simple random sample ('SRS') of a given size, allsuch subsets of the frame are given an equal probability.
Method of chance selection. Lottery method,Tippetstable, Kendall and Babington smith, Fisher and Yates
numbers.Simple random sampling with replacement:- equalprobability selection of each unit=1/N (Monte-Carlosimulation)
Simple random without replacement -varying probabilityselection of each. First unit=1/N , Second unit=1/N-1,Probality of selection of the nth unit=1/N-(n-1)(Monte-Carlo simulation
http://en.wikipedia.org/wiki/Simple_random_samplehttp://en.wikipedia.org/wiki/Simple_random_sample7/27/2019 sampling design.ppt
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Systematic
Systematic sampling involves a randomstart and then proceeds with the selectionof every kth element from then onwards. In
this case, k=(population size/sample size).It is important that the starting point is notautomatically the first in the list, but isinstead randomly chosen from within thefirst to the kth element in the list
Sampling interval width=I=N/n=800/40=20
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Stratified or Mixed sampling
Where the population embraces a number ofdistinct categories, the frame can be organizedby these categories into separate "strata." Eachstratum is then sampled as an independent sub-
population, out of which individual elements canbe randomly selected .(homogenous group)
Two types-Proportionate (equal number of unit
from each stratum proportion to size of thestrata) and Disproportionate (not equal numberof unit from each stratum proportion to size ofthe strata)
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Cluster sampling
Cluster sampling is an example of 'two-stage sampling' or 'multistage sampling/Multi phase sampling'
in the first stage a sample of areas ischosen
in the second stage a sample of
respondents within those areas isselected.(several stages)- State level,Distlevel,Village level,Hosehold level.
http://en.wikipedia.org/wiki/Cluster_samplinghttp://en.wikipedia.org/wiki/Multistage_samplinghttp://en.wikipedia.org/wiki/Multistage_samplinghttp://en.wikipedia.org/wiki/Cluster_sampling7/27/2019 sampling design.ppt
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Cluster Sampling
This stepwise process is useful for those whoknow little about the population theyre studying.
First, the researcher would divide the population
into clusters (usually geographic boundaries).Then, the researcher randomly samples theclusters.
Finally, the researcher must measure all units
within the sampled clusters.Researchers use this method when economy of
administration is important.
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Sequential sampling
Single sampling
Double sampling
Multiple sampling
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Non probability
Non probability sampling does not
involve random selection and
probability sampling does .
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Multistage sampling
Multistage sampling is a complex form of clustersampling in which two or more levels of units areembedded one in the other.
The first stage consists of constructing the clusters that
will be used to sample frame.In the second stage, a sample of primary units israndomly selected from each cluster (rather than usingall units contained in all selected clusters).
In following stages, in each of those selected clusters,additional samples of units are selected and so on.
All ultimate units (individuals, for instance) selected atthe last step of this procedure are surveyed.
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Purposive/Judgment Sampling
In purposive sampling, selecting samplewith a purpose in mind
Purposive sampling can be very useful for
situations where we need to reach atargeted sample quickly and wheresampling for proportionality is not theprimary concern.
It is for pilot study
Questions / questionnaires may be tested.
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Quota sampling
Quota sampling, the population is firstsegmented into mutually exclusive sub-groups,
just as in stratified sampling.
Then judgment is used to select the subjects or
units from each segment based on a specifiedproportion. For example, an interviewer may betold to sample 200 females and 300 malesbetween the age of 45 and 60.
Proportional quota samplingNonproportional quota sampling
It is very popular for market survey and opinionpoll.
http://en.wikipedia.org/wiki/Mutually_exclusivehttp://en.wikipedia.org/wiki/Stratified_samplinghttp://en.wikipedia.org/wiki/Stratified_samplinghttp://en.wikipedia.org/wiki/Mutually_exclusive7/27/2019 sampling design.ppt
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Snowball Sampling
Identifying someone who meets thecriteria for inclusion in the study.
Snowball sampling is especially useful
when we are trying to reach populationsthat are inaccessible or hard to find
This method would hardly lead to
representative samplesIntially certain members and add fewmembers latter
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Convenience sampling
Convenience sampling (sometimes
known as grab oropportunity sampling)
is a type of nonprobability sampling which
involves the sample being drawn from thatpart of the population which is close to
hand
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Accidental Sampling
The researcher can select any sample in
any place, can collect the data from
pedestrian also.
It can be used for exploratory studies
It has sample error.
It has less accuracy
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Combination of Probability sampling
and Non Probability sampling
If sampling is carried out in series of
stages, it is possible to combine probability
and non-probability sampling in one
design
Users of particular product in one street for
the particular group of people.
Utility of the particular product in the town.
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Sampling Errors
The errors which arise due to the use ofsampling surveys are known as the samplingerrors.
Two types of sampling errors-Biased Errors,
Unbiased ErrorsBiased Errors-Which arise due to selection ofsampling techniques.-size of the sample
Unbiased Errors / Random sampling errors-arise
due to chance differences between the membersof the population included in the sample and notincluded.
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Methods of reducing Sampling
Errors
Specific problem selection
Systematic documentation of related
research
Effective enumeration
Effective pre testing
Controlling methodological biasSelection of appropriate sampling
techniques.
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Non-sampling Errors
Non-sampling errors refers to biases and mistakes inselection of sample.
CAUSES FOR NON-SAMPLING ERRORS
Sampling operations
Inadequate of response
Misunderstanding the concept
Lack of knowledge
Concealment of the truth.
Loaded questions
Processing errors
Sample size
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Factors related to Sample size
The nature of population
Complexity of tabulation
Problems relating to collection of data
Selection of sampling techniquesLimitation of accuracy
Calculating sample size=(SZ / T)2
S-preliminary SD of the universe
Z-number of standard errors
T-errors to be tolerated
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