The document discusses the concept of sampling, which involves selecting a subset of a larger population for research purposes when studying the entire population is impractical. It outlines different classifications of populations, sampling methods, and the importance of accurate representation and sample size in the research process. Additionally, it addresses potential errors in sampling and non-sampling, along with guidelines for effective sampling design.

2. Sampling refers to selecting a subset of
the entire population for the purpose of
research study. Sampling is more helpful in
cases where it is neither possible nor feasible
to study each and every element of the
population. Sampling is done if population is
massive and contains several hundreds and
even thousands of elements. In such a case,
the data collection from each and every
element of population is either impossible or
requires huge time, cost, efforts and other
resources.

3.  "Sampling is the selection of certain
percentage of a group of items according to
a predetermined plan"-Bogardus
 "A statistical sample is a miniature picture
of cross selection of the entire group or
aggregate from which the sample is taken"-
P.V.Young

4. POPULATION
It refers to the complete group containing
all elements on which the research is based. It
can be classified into four categories-
 Finite,
 Infinite,
 Real,
 Hypothetical.

5. The population is said to be finite if it
consists of a fixed number of elements so
that it is possible to enumerate in its totality.
For example-populations of a city, the
number of workers in a factory, etc.

6. An infinite population is that population
in which it is theoretically impossible to
observe all the elements. In an infinite
population the number of items is infinite.
From practical consideration, we use the
term infinite population for a population that
cannot be enumerated in a reasonable period
of time.
For example- number of stars in sky,
water in a pond etc.

7. Population consisting of the items which
are all present physically is termed as real
population.
For example- number of court case in a
day, number of covid-19 patients admitted in
the hospital etc.

8. Population consisting of the results of
repeated trials is named as hypothetical
population.
For example: the tossing of coin, rolling
of a dice again and again etc.

9. A sample is a selection of units from the
entire group called the population or
universe of interest. It refers to the subset of
a large population.
For example: - if population contains
1000 elements, 200 selected members would
be considered as the sample of the entire
population.

10. It is the process of selecting the
sample for estimating the population
characteristics. In other words, it is the
process of obtaining information about an
entire population by examining only a part of
it.

11. If a consumer wishes to judge the
quality of a bucket of grains before buying;
then it would not be practically possible for
him/her to examine each and every morsel
present in the bucket. Therefore, in such a
situation, the consumer would examine only
a handful of grains to judge the quality of
the entire bucket. In this case, the whole
bucket of grains represents the population;
the handful of grains represents the sample
and this process is known as sampling.

12.  Goal-oriented:
A sample design should be goal oriented.
It is means and should be oriented to the
research objectives and fitted to the survey
conditions.
 Accurate representative of the universe:
A sample should be an accurate
representative of the universe from which it is
taken. There are different methods for selecting
a sample. It will be truly representative only
when it represents all types of units or groups in
the total population in fair proportions.

13.  Proportional:
A sample should be proportional. It should be
large enough to represent the universe properly. The
sample size should be sufficiently large to provide
statistical stability or reliability. The sample size
should give accuracy required for the purpose of
particular study.
 Random selection:
A sample should be selected at random. This
means that any item in the group has a full and
equal chance of being selected and included in the
sample. This makes the selected sample truly
representative in character.

14.  Economical:
A sample should be economical. The
objectives of the survey should be achieved
with minimum cost and effort.
 Practical:
A sample design should be practical.
The sample design should be simple i.e. it
should be capable of being understood and
followed in the fieldwork.

15.  Actual information provider:
A sample should be designed so as to
provide actual information required for the
study and also provide adequate basis for the
measurement of its own reliability.

16.  Economical: Reduce the cost compare to
entire population.
 Increased speed: Collection of data, analysis
and Interpretation of data etc take less time
than the population.
 Accuracy: Due to limited area of coverage,
completeness and accuracy is possible.
 Rapport: Better rapport is established with
the respondents, which helps in validity and
reliability of the results.

17.  Biasedness: Chances of biased selection
leading to incorrect conclusion
 Selection of true representative sample:
Sometimes it is difficult to select the right
representative sample.
 Need for specialized knowledge: The
researcher needs knowledge, training and
experience in sampling technique, statistical
analysis and calculation of probable error.
 Impossibility of sampling: Sometimes
population is too small or too heterogeneous
to select a representative sample.

18. 1. Defining the target population.
2. Specifying the sampling frame.
3. Specifying the sampling unit.
4. Selection of the sampling
method.
5. Determination of sample size.
6. Specifying the sampling plan.
7. Selecting the sample.
Defining the target population
Specifying the sampling frame
Specifying the sampling unit
Selection of the sampling method
Determination of sample size
Specifying the sampling plan
Selecting the sample

19. Population must be defined in terms of
elements, sampling units, extent and time.
Because there is very rarely enough time or
money to gather information from everyone
or everything in a population, the goal
becomes finding a representative sample
(or subset) of that population.

20. A sampling frame is the list of elements
from which the sample may be drawn. It
refers to the list of all elements in a
population. If the sampling frame is not
available, then it should be prepared by the
researcher. It is better, if the sampling frame
is comprehensive, correct and appropriate.

21. • A sampling unit is a basic unit that contains a
single element or a group of elements of the
population to be sampled. The sampling unit
selected is often dependent upon the sampling
frame. For example- suppose a researcher wants
to survey the whole country for some purpose. In
this case, sampling unit may be states, districts,
blocks, and villages.

22. The choice of the sampling method is
influenced by the objectives of the business
research, availability of financial resources, time
constraints, and the nature of the problem to be
investigated. All sampling methods can be grouped
under two distinct heads, that is, probability and
non-probability sampling.

23. The sample size plays a crucial role in the
sampling process. It refers to the number of
items in a sample. The researcher should pay a
good deal of attention in deciding sample size.
Sample size should be neither too large nor too
small. Before selecting a sample size, following
points should be considered:
• Flexibility
• Population variance
• Parameters of interest
• Budgetary Constraint

24. The specifications and decisions
regarding the implementation of the
research process are outlined. As the
interviewers and their co-workers will be on
field duty of most of the time, a proper
specification of the sampling plans would
make their work easy and they would not
have to reverting operational problems.

25. It involves execution of the operational
sampling plan discussed in the previous
steps. It is important that this step include
adequate checking to make sure that
specified procedures are adhered to.

26. Sampling errors also known as random
errors, refers to the errors that occur due to
observing only a sample from the whole
population. only a specific part of population
cannot show the true picture of the whole
population. However, sampling error can be
reduced to an extent by designing sample in
a better way.

27. suppose a researcher is interested in
calculating average yield of rice in a village in a
particular year. There are 10,000 farmers in the
village involved in rice production. The
researcher selects a sample of 1000 farmers
from the complete universe (population) and
calculates the average yield of rice based on the
figures given by 1000 farmers. There are
maximum chances that average obtained from
the sample would be different from the actual
average of complete population. This deviation
from the actual average in the preceding
example refers to sampling error.

28.  Population specification error
 Sample frame error
 Selection error
 Sampling error

29. A population specification error occurs
when researchers don’t know precisely who
to survey. For example, imagine a research
study about kid’s apparel. Who is the right
person to survey? It can be parents, only the
mother, or the child. The parents make
purchase decisions, but the kids may
influence their choice.

30. Sampling frame errors arise when
researchers target the sub-population
wrongly while selecting the sample. For
example, picking a sampling frame from the
telephone white pages book may have
erroneous inclusions because people shift
their cities. Erroneous exclusions occur when
people prefer to un-list their numbers.
Wealthy households may have more than one
connection, thus leading to multiple
inclusions.

31. A selection error occurs when
respondents self-select themselves to
participate in the study. Only the interested
ones respond. You can control selection
errors by going the extra step to request
responses from the entire sample. Pre-survey
planning, follow-ups, and a neat and clean
survey design will boost respondents’
participation rate.

32. It majorly happens when the researcher
does not plan his sample carefully. These
sampling errors can be controlled and
eliminated by creating a careful sample
design, having a large enough sample to
reflect the entire population, or using an
online sample or survey audiences to collect
responses.

33.  Increasing Sample Size
 Stratification
 Know your population

34. It implies that increase in size of the sample
may reduce sampling error. If the sample size is
equal to complete population, sampling error is
zero.

35. It refers to dividing the given population
into homogenous units [known as stratum] to
make the sample more representative. For
example, if people of a specific demographic
make up 20% of the population, make sure
20% of the study is made up of this variable.

36. The error of population specification is
when a research team selects an
inappropriate population to obtain data.
Know who buys your product, uses it works
with you, and so forth.

37. Non-sampling errors are the errors that
do not occur because of sampling. These
errors would occur even if each and every
element of the population would be
considered in the research study. For
example, suppose a population contains 1000
elements. The researcher intends to find the
average income of the population. Even if
the researcher considers all 1000 elements to
find the average, he/she may get inaccurate
results because of non-sampling errors.

38.  Improper division of sampling units of a
population
 Poor response of respondents
 Bias - Intentional Bias,
Un-intentional Bias.

39.  Coverage error
 Non-response error
 Response error
 Interviewer error
 processing error

40. This occurs when a unit in the sample is
incorrectly excluded or included, or is
duplicated in the sample (e.g. a field
interviewer fails to interview a selected
household or some people in a household).

41. This refers to the failure to obtain a
response from some unit because of absence,
non-contact, refusal, or some other reason.
Non-response can be complete non-response
(.e. no data has been obtained at all from a
selected unit) or partial non-response (i.e.
the answers to some questions have not been
provided by a selected unit).

42. This refers to a type of error caused by
respondents intentionally or accidentally
providing inaccurate responses. This occurs
when concepts, questions or instructions are
not clearly understood by the respondent;
when there are high levels of respondent
burden and memory recall required; and
because some questions can result in a
tendency to answer in a socially desirable
way (giving a response which they feel is
more acceptable rather than being an
accurate response).

43. This occurs when interviewers
incorrectly record information; are not
neutral or objective; influence the
respondent to answer in a particular way; or
assume responses based on appearance or
other characteristics.

44. This refers to errors that occur in the
process of data collection, data entry,
coding, editing and output.

45. Using a sample in research saves mainly
on money and time, is
suitable sampling strategy is used;
appropriate sample size selected and
necessary precautions taken to reduce
on sampling and measurement errors, then
a sample should yield valid and reliable
information.

47.  RESEARCH METHODOLOGY, G.C. Ramamurthy
& kogent learning solutions lnc.
 Basic Guidelines for Research: An
Introductory Approach for All Disciplines,
Syed Muhammad Sajjad Kabir.
 https://www.examrace.com/Research/
 https://youtu.be/bkJfJTDfgrw