samle design

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2.  Population or Universe is any complete group
of people, companies, hospitals, stores,
college students or like that who share some
set of characteristics.
 Population and universe can also be
distinguished as:
 If complete set of element is finite it is known as
Population.
 If complete set of element is infinite it is known
as Universe.
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3.  All those primary units which constitute
universe or population, consisting some
common set of characteristics are know as
elements.
 In a survey when elements are human being
we call them respondents.
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4.  It is a process of using small portion of the
population or universe to make conclusions
about the whole population.
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Population
Sample
A subset or part of
population capable
of representing
almost in same
ratio the
characteristics
which are present
in the population or
universe

6.  An investigation of all the individual
elements making up a population.
 It should be noticed that universe cannot be
studied through census method.
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7.  A complete enumeration of all items in the
‘population’ is known as a census inquiry.
 A Sample survey is a sub group of population.
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8.  If the size of population is small
 Researcher is interested in gathering the
information from every individual.
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9.  When the size of population is large.
 When time and cost are the main
consideration in research
 If population is homogenous
 Sampling reduces the labour requirements
and gathers vital information.
 Reduces non sampling errors
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10. A sample must have following things which are
very essential for drawing valid conclusions:
 It should be representative
 It should be independent
 It should be homogenous
 It should be adequate
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11.  To obtain reliable information about the
population.
 To arrive at the characteristics of the parent
population.
 To test the reliability of difference between
the sample estimates and population
parameters.
 To test the validity
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Define the Target
population
Select a
Sampling Frame
Determine if a probability
or non-probability sampling
method will be chosen
Plan procedure for
selecting sampling units
Determine sample size
Select actual sampling units
Conduct fieldwork
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13.  The specific, complete group relevant to the
research project.
 Who has the information/data you need?
 How do you define your target
population?
 Geography
 Demographics
 Use
 Awareness
 Reason: To define a proper source from
which the data are to be collected.
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14.  The target group should be clearly delineated.
Thus, population is defined as:
 Elements
 Sampling unit
 Extent
 Time
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15.  A sampling frame is the list of elements from
which the sample may be drawn.
 Sampling frame is also known as working
population.
 Examples of sampling frames are a
student telephone directory, the list of
companies on the stock exchange, the
yellow pages (for businesses).
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16.  Generally, it is not feasible to compile a list
that includes the entire population, leading
to sampling frame error.
 Sampling frame error - Error that occurs
when certain sample elements are not listed
or available and are not represented in the
sampling frame
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17. Sampling
Technique
Probability
sampling
Simple
Random
Sampling
Complex
Random
Sampling
Non-
Probability
sampling
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18.  A Probability Sampling is one in which
every unit in the population has an equal
chance or a non zero probability of being
selected in the sample.
 A Non-Probability Sampling is one in
which units of the sample are chosen on
the basis of personal judgment or
convenience
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19.  Purest form of probability sampling
 It is a process in which every item of the
population has an equal probability of being
chosen.
 Applicable when population is small,
homogeneous & readily available.
 A table of random number or lottery system
is used to determine which units are to be
selected.
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20.  It involves writing the name of each element
of a finite population on a slip of paper and
putting them into a box or a bag.
 After this, mix them thoroughly and then the
required number of slips for the sample shall
be picked one after the other without
replacement.
 While doing this, it has to be ensured that in
successive drawings each of the remaining
elements of the population has the same
chance of being selected
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21.  Also known as mixed sampling design.
 Under restricted sampling techniques, the
probability sampling may result in complex
random sampling designs.
 such designs may represent a combination of
probability and non-probability sampling
procedures in selecting a sample.
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Complex Random
Sampling
Systematic
sampling
Stratified
Sampling
Cluster
Sampling
Area
sampling
Multi
stage
Sampling
Sequenti
al
Sampling
Sampling
with
probability
proportion
al to size

23.  A sampling procedure in which an initial
starting point is selected by a random
process and then every nth number on the
list is selected.
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24.  This method is an improvement over a simple
random sample.
 Easier and less costlier method
 Can be conveniently used even in case of
large populations.
 Problem of Systematic Sampling is
Periodicity.
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25.  A procedure in which simple random
subsamples are drawn from within different
strata that are more or less equal on some
characteristics.
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26. Reasons for stratified sampling:
 If population does not constitute a
homogeneous group.
 To have more efficient sampling
 Reducing random sampling error
 Assuring that sample would correctly reflect
the population
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27.  Under stratified sampling the population is
divided into several sub-populations that are
individually more homogeneous than the total
population.
 Then items are selected from each stratum to
constitute a sample.
 Since each stratum is more homogeneous than
the total population, research is able to get
more precise estimates for each stratum and by
estimating more accurately each of the
component parts.
 Stratified sampling results yield more reliable
and detailed information.
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28. Reason:
 When the total area of research interest is
large
Process:
 Firstly, population is divided into a number of
smaller non-overlapping areas, which are
clusters of homogeneous units
 Secondly, few clusters are selected by using
a simple random sampling method.
 Finally, all the units in the selected clusters
are studied.
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29.  Advantages
 Low cost/high frequency of use
 Requires list of all clusters, but only of
individuals within chosen clusters
 Can estimate characteristics of both
cluster and population
 Disadvantages
 Larger error for comparable size than
other probability methods
 Multistage very expensive and validity
depends on other methods used
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30.  Sampling that involves using a combination of
two or more probability sampling techniques.
 Complex form of cluster sampling in which two
or more levels of units are embedded one in the
other.
Process:
 First stage, random number of districts chosen in
all states.
 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.
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31.  In case the cluster sampling units do not
have the same number or approximately the
same number of elements, it is considered
appropriate to use a random selection
process where the probability of each cluster
being included in the sample is proportional
to the size of the cluster.
 The actual numbers selected in this way do
not refer to individual elements, but indicate
which clusters and how many from the
cluster are to be selected by simple random
sampling or by systematic sampling.
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32.  A complex sample design
 The ultimate size of the sample is not fixed
in advance
 When the number of samples is more than
two but it is neither certain nor decided in
advance, this type of system is often
referred to as sequential sampling.
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Non
Probability
Sampling
Convenience
Sampling
Judgement
Sampling
Quota
Sampling
Snowball
Sampling

34. Convenience Sampling:
 The sampling procedure of obtaining those
people or units that are most conveniently
available.
Judgement Sampling:
 A technique in which an experienced
individual selects the sample based on
personal judgement about some appropriate
characteristic of the sample member.
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35.  Quota Sampling:
 This procedure ensures that various sub
groups of a population will be represented on
pertinent characteristics to the exact extent
that the investigator desires.
 Snowball Sampling:
 A sampling procedure in which initial
respondents are selected by probability
methods and additional respondents are
obtained from information provided by the
initial respondents.
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36.  In a sample survey, since only a small portion
of the population is studied and its results
are bounded to differ from census results
and thus having a certain amount of error.
 In Statistics, the word error is used to denote
the difference between the true value and
the estimated or approximated value.
 Sampling error is the gap between the
sample mean and population mean.
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37.  Sampling Frame Error: A sampling frame is a
specific list of population units, from which
the sample for a study being chosen.
 Non Response Error: This occurs because the
planned sample and final sample vary
significantly.
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Total Population
Sampling Frame Error
Random Sampling Error
Sampling Frame
Planned
Sample
Non-Response Error
Respondents
(actual
sample)

39.  Errors in sampling can be reduced if the size
of sample is increased.
 Avoid leading questions
 Pre-test the questionnaire
 Train the interviewer to establish good
rapport with the respondents.
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