The document discusses various sampling techniques used in research including probability and non-probability sampling. It explains key concepts like population, sample, sampling frame, sampling error, systematic error. It describes different probability sampling designs such as simple random sampling, stratified sampling, cluster sampling and multistage sampling. It also discusses non-probability sampling techniques like convenience sampling and quota sampling. The document provides advantages and limitations of different sampling methods and guidelines for selecting an appropriate sampling design.

2.  A sample design is a definite plan for
obtaining a sample from a given population.
Sample constitutes a certain portion of the
population or universe. Sampling design refers
to the technique or the procedure the
researcher adopts for selecting items for the
sample from the population or universe.

3. Meaning of sample
A part of population is called sample
In other words , selected or
Sorted units from the
Population
or
a sample is a smaller
Collection of units from a
Population used to determine
Truth to about the population

8.  To obtain the 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

9.  There are two causes of incorrect
inferences:
(1) Systematic Error
(2) Sampling Error.

10.  Systematic bias arises out of errors in the sampling
procedure. They cannot be reduced or eliminated by
increasing the sample size. Utmost, the causes of these
errors can be identified and corrected. Generally, a
systematic bias arises out of one or more of the following
factors:
a. Inappropriate sampling frame,
b. Defective measuring device,
c. Non-respondents,
d. Indeterminacy principle, and
e. Natural bias in the reporting of data.

11.  Sampling error refers to the random variations in the sample
estimates around the true population parameters.
 Sampling error tends to decrease with the increase in the size of
the sample.
 It also becomes smaller in magnitude when the population is
homogenous.
 Sampling error can be computed for a given sample size and
design.
 The measurement of sampling error is known as ‘precision of the
sampling plan’.
 When the sample size is increased, the precision can be
improved.
 However, increasing the sample size has its own limitations.
 The large sized sample not only increases the cost of data
collection, but also increases the systematic bias..

12.  Representativeness
 Adequacy
 Unbiasedness
 No substitution
 High precision

13.  Census survey is that survey in which information or
data is collected from each and every unit of the
population relating to the problem under investigation
and conclusion are drawn on their basis
 Sample survey is that in which data is collected from
the sample of items selected from population and
conclusion are drawn from them

14.  If the size of population is small
 Researcher is interested in gathering the information
from every individual
When a sample is appropriate
 If the size of population is large
 When time and cost are the main consideration in
reseach
 if population is homogeneous
 Reduces non-sampling error

15.  Reliable and accurate data
 Extensive information
 Suitability
Demerits of census survey
 More expensive
 More time
 More labour
 Not suitable for specific problems

16.  Saving of time and money
 Intensive study
 Organizational convenience
 More reliable results
 More scientific
 Only method

17.  less accurate
 Wrong conclusion
 Less reliable
 Need of specified knowledge
 Not suitable

18.  Scope
 Cost
 Field of investigation
 Homogeneity
 Type of universe

21.  Sample designs may be classified into different
categories based on two factors, namely, the
representation basis and the element selection
technique. Under the representation basis, the sample
may be classified as:
I. Non-probability sampling
II. Probability sampling
 While probability sampling is based on random
selection, the non-probability sampling is based on
‘non-random’ selection of samples.

22.  Probability sampling is also known as ‘choice
sampling’ or ‘random sampling’. Under this sampling
design, every item of the universe has an equal chance
of being included in the sample. In a way, it is a lottery
method under which individual units are selected from
the whole group, not deliberately, but by using some
mechanical process. Therefore, only chance would
determine whether an item or the other would be
included in the sample or not.

23.  Restricted Sampling Design (Complex Random
Sampling Design)
a) Systematic Sampling
b) Stratafied Sampling
c) Cluster Sampling
d) Area Sampling
e) Multi-Stage sampling
f) Proportionate Sampling
 Unrestricted Sampling Design (Simple Random
sampling Design)

24.  The process of selecting a random sample 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.
Then they have to be thoroughly mixed and then the
required number of slips for the sample can 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 an
equal chance of being chosen.

25.  Under restricted sampling technique, the probability
sampling may result in complex random sampling
designs. Such designs are known as mixed sampling
designs. Many of such designs may represent a
combination of non-probability and probability
sampling procedures in choosing a sample.

26.  In some cases, the best way of sampling is to select
every first item on a list. Sampling of this kind is called
as systematic sampling. An element of randomness is
introduced in this type of sampling by using random
numbers to select the unit with which to start. For
example, if a 10 per cent sample is required out of 100
items, the first item would be selected randomly from
the first low of item and thereafter every 10th item. In
this kind of sampling, only the first unit is selected
randomly, while rest of the units of the sample is
chosen at fixed intervals.

27.  When a population from which a sample is to be selected
does not comprise a homogeneous group, stratified
sampling technique is generally employed for obtaining a
representative sample. Under stratified sampling, the
population is divided into many sub-populations in such a
manner that they are individually more homogeneous than
the rest of the total population. Then, items are selected
from each stratum to form a sample. As each stratum is
more homogeneous than the remaining total population, the
researcher is able to obtain a more precise estimate for each
stratum and by estimating each of the component parts
more accurately; he/she is able to obtain a better estimate of
the whole. In sum, stratified sampling method yields more
reliable and detailed information.

28.  When the total area of research interest is large, a
convenient way in which a sample can be selected is to
divide the area into a number of smaller non-overlapping
areas and then randomly selecting a number of such smaller
areas. In the process, the ultimate sample would consist of
all the units in these small areas or clusters. Thus in cluster
sampling, the total population is sub-divided into numerous
relatively smaller subdivisions, which in themselves
constitute clusters of still smaller units. And then, some of
such clusters are randomly chosen for inclusion in the
overall sample.

29.  When clusters are in the form of some geographic
subdivisions, then cluster sampling is termed as area
sampling. That is, when the primary sampling unit
represents a cluster of units based on geographic area,
the cluster designs are distinguished as area sampling.
The merits and demerits of cluster sampling are equally
applicable to area sampling.

30.  A further development of the principle of cluster sampling is multi-
stage sampling. When the researcher desires to investigate the working
efficiency of nationalized banks in India and a sample of few banks is
required for this purpose, the first stage would be to select large
primary sampling unit like the states in the country. Next, certain
districts may be selected and all banks interviewed in the chosen
districts. This represents a two-stage sampling design, with the ultimate
sampling units being clusters of districts.
 On the other hand, if instead of taking census of all banks within the
selected districts, the researcher chooses certain towns and interviews
all banks in it, this would represent three-stage sampling design. Again,
if instead of taking a census of all banks within the selected towns, the
researcher randomly selects sample banks from each selected town,
then it represents a case of using a four-stage sampling plan. Thus, if
the researcher selects randomly at all stages, then it is called as multi-
stage random sampling design.

31.  When the case of cluster sampling units does not have
exactly or approximately the same number of elements,
it is better for the researcher to adopt a random
selection process, where the probability of inclusion of
each cluster in the sample tends to be proportional to
the size of the cluster. For this, the number of elements
in each cluster has to be listed, irrespective of the
method used for ordering it. Then the researcher should
systematically pick the required number of elements
from the cumulative totals.

32.  Non-probability sampling is a sampling technique where the
samples are gathered in a process that does not give all the
individuals in the population equal chances of being selected.
 Judgment Sampling
 Quota Sampling
 Convenience Sampling

33.  Judgment sample is a type of nonrandom sample
that is selected based on the opinion of an expert.
Results obtained from a judgment sample are
subject to some degree of bias, due to the frame and
population not being identical.
 For eg; if a sample of 10 students is to be selected
from the sample of 60 students for analyzing the
spending habits of the students, the investigators
would select 10 students who, in his opinion, are
representation of the class.

34.  A sampling method of gathering representative data
from a group. As opposed to random sampling,
quota sampling requires that representative
individuals are chosen out of a specific subgroup.
For example, a researcher might ask for a sample of
100 females, or 100 individuals between the ages of
20-30.

35.  A convenience sample is a type of non-probability
sampling method where the sample is taken from a
group of people easy to contact or to reach. For
example, standing at a mall or a grocery store and
asking people to answer questions would be an
example of a convenience sample.

36.  A private bank is interested in knowing the level of
satisfaction of its customers. For this purpose it decided to
conduct a survey and designed a framework for conducting
the same. It included the research objectives and the
methodology for carrying out the survey.
Research objectives : The study is being conducted to find
out the satisfaction level of customers on the following
counts :
(a) The work efficiency of the bank
(b) The customer care service in the bank
(c) The value added services provided by the bank

37. (i) What in your opinion should be the population of
study, sampling unit, sample size, and data collection
method ? Justify your answer in each case.
(ii) What should be the research design ? Explain

38. Thanku