sample and sampling design gives an insight knowledge on sample and various techniques of sampling for m.d.u rohtak students

1. BY…RAGHAV JHA
(M.B.A / M.COM)RAGHAVJHA45@GMAIL.COM(80
10969972)

2. • POPULATION – universe is meant that groups of
unit which is being studied for the purpose of
investigation. Example - students in a class.
• SAMPLE - Smaller representation of a large whole.
Example – to check the quality of rice and milk.
• SAMPLING FRAME/SOURCE LIST- list of all
the items in your population. Example – Telephone
book.
• SAMPLING UNIT – is a geographical one ( state,
districts).
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3. • SAMPLE SIZE – number of items selected for study. Example –
when we want to study about the smart phones, we consider people
age above 18.
• SAMPLE DESIGN– Methods the researcher adopts in selecting
the sampling units from the frame or population.
• SAMPLING ERROR – is the difference between population value
and sample value.
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4. Census and sampling
census sampling
Definition Census: Census refers to a periodic
collection of information about the
populace from the entire population.
Sampling: Sampling is a method of
collecting information from a sample that is
representative of the entire population.
Reliability Census: Data from the census is reliable
and accurate.
Sampling: there is a margin of error in data
obtained from sampling.
Time Census: Census is very time-consuming. Sampling: Sampling is quick.
Cost Census: Census is very expensive Sampling: Sampling is inexpensive.
Convenience Census: Census is not very convenient as
the researcher has to allocate a lot of effort
in collecting data.
Sampling: Sampling is the most convenient
method of obtaining data about the
population.
Sampling is taking any portion of a
population or universe as representative of
that population.
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5. benefit of sampling in research
• Saves lot of time
• Provides accuracy
• Controls unlimited data
• Studies individual
• Reduces cost
• Gives greater speed / help to complete in stipulated time
• Assists to collect intensive and exhaustive data
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6. SAMPLING PROCESS
• Define the population ( element, units, extent and
time)
• Specify sampling frame (telephone directory)
• Specify sampling unit (retailers, our product,
students, unemployed)
• Specify sampling method/ technique
• Determine sampling size
• Specify sampling size (optimum sample )
• Specify sampling plan
• Select the sample
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7. Good sampling
• The sample should be true representative of universe.
• No bias in selecting sample.
• Quality of the sample should be same.
• Sampling needs to be adequate.
• Accurate- Estimate the sampling errors.
• It is necessary that complete, correct, practical and clear
instructions should be given to the researcher.
RAGHAVJHA45@GMAIL.COM(80
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8. Methods of sampling
Probability
• Random /simple
• Stratified random
• Cluster
• Systematic
Non probability
• Quota
• Purposive
• Snowball
• Convenience
RAGHAVJHA45@GMAIL.COM(80
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9. Probability sample
• Probability sampling technique is one in
which every unit in the population has a
chance of being selected in the sample.
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10. Simple random sampling
• Simplest type of sampling, in which we draw a sample of size in
such a way that each of the ‘N’ members of the population has the
same chance of being included in the sample.
• Each unit of the population must have equal probability of being
selected.
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11. Systematic sampling
• In this method the selection of unit depends upon the selection
of a preceding unit.
• First unit is selected on random basis then follow a specific order
• It is best when elements are randomly ordered with no
cyclic variation.
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12. Stratified or mixed sampling
• Divide / split the population into homogenous sub groups or assigned to
strata on the basis of some characteristic , and a simple random sample is
drawn from each stratum.
• In stratified random sampling, we randomly sample elements from each
layer, or stratum of the population.
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13. Cluster sampling
• Divide the population into sub groups
• Each sub group is representative of the population
• Select a random set of sub groups
• Select a random sample from within the chosen sub groups
• It is best when elements within strata are heterogeneous.
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14. Non probability sampling
• Non probability sampling in any sampling method
where some elements of the population have no
chance of selection, or where the probability of
selection can’t be accurately determined.
• It involves the selection of elements based on
assumptions.
• The selection of elements is non random.
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15. Purposive/ Judgment sampling
• In judgment sampling the judgment or opinion of some experts
forms the basis of the sampling method.
• In Purposive/ Judgment sampling , selecting sample with a
purpose in mind.
• Purposive sampling can be very useful for situations where
we need to reach a targeted sample quickly
and where proportionality is not the primary concern.
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16. Quota sampling
• Quota sampling , the population is the first segmented into mutually
exclusive sub groups, just as in stratified sampling.
• This judgment is used to select the subjects or units from each segment
based on a specified proportion. For example , an interviewer may be
told to sample 200 females and 300 males between the age of 45 and
60.
• It is very popular for market survey and opinion poll.
RAGHAVJHA45@GMAIL.COM(80
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17. Snowball sampling
• Identifying someone who meets criteria for inclusion in the study.
• Snowball sampling is especially useful when we are trying to reach
populations that are inaccessible or hard to find
• This method would hardly lead to representative samples.
• Initially certain members and add few members latter.
RAGHAVJHA45@GMAIL.COM(80
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18. Convenience sampling
• The researcher normally interviews the person in groups at
some retail shop, supermarkets may stand at a prominent point
and interview the person who happen to be there.
• More suitable for exploratory research where focus is on
getting new ideas into a given problem.
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19. Sampling errors
• The errors which arise due to
incomplete coverage of the population
inaccurate information provided by the participants
errors occurring during editing, tabulating and mathematical manipulation of
data
• Two types of sampling errors-
sampling errors
Non sampling errors
• Sampling errors which arise due to drawing of faulty interferences about the
population based on results obtained from the samples.
• Non sampling errors/ random sampling errors arise due to technically faulty
observations or calculations during the processing of the data.
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