The document contains information about a presentation by a group of students on various sampling topics. It includes the names and roll numbers of 12 presentation members and 3 paragraphs written by 4 of the members on the topics of census, sample, and sampling survey. It provides basic definitions and examples for each topic.
5. Census
A census is the procedure of systematically acquiring and
recording information about the members of a given
population.
It is a regularly occurring and official count of a particular
population.
6. The term is used mostly in connection with national population
and housing censuses; other common censuses include
agriculture, business, and traffic censuses.
A survey that measures the entire target population is called a
census.
8. What is Sample
A sample is a subset of the population.
It comprises some members selected from it. In other words,
some, but not all, elements of the population would form the
sample.
According to E.R Babble “A sample is a special subset of
population that is observed for purpose of making inference
about the nature of the total population itself .”
9. Example
if there are 145 in-patients in a hospital
and 40 of them are to be surveyed by the
hospital administrator to assess their level
of satisfaction with the treatment received,
then these 40 members will be the sample.
11. The sample survey was only a small size but it did give us a
window of knowledge and understanding . The sample
survey that we gave to the public was given in order to
determine what we needed to do.
A sample survey can often change the design of a product or
even the entire product as a whole, depending on current
consumer opinions.
Sampling Survey
12. According to the black and champion-
“The process of drawing those elements from the larger
population or universe is called sampling”
According to the bhandarker-
“The method of selecting for study a portion of the
universe with a view to drawing conclusion about the
universe is know as sampling.”
Sampling is to reduce the cost or the amount of work that it
would take to survey the entire target population.
14. In Census, each and every unit of population is studied.
But only few units of the population studied is studied in Sampling.
Census refers to periodic collection of information about the populace from
the entire population.
However, if the next Census is far away, Sampling is the most convenient
method of obtaining data about the population.
Census Method demands a large amount of finance, time and labor.
Relatively less amount of finance, till labor is required for sampling.
Difference between Census and
sampling
15. Difference between Census and
sampling
Results obtain by the Census are quit reliable.
Results obtained by the Sampling are less reliable.
It is more suitable to use Census Method if population is heterogeneous in nature.
and it is more suitable to use Sampling Method if population is
homogeneous in nature.
Samples have a margin of error though, which gets lower as the sample size
increases. In other words sampling more people means obtaining better data.
Instead, this type of error not present in Census as each and every part of the
geographical area has to be approached for data collection .
18. A sample design is the framework, or road map, that serves as the basis for
the selection of a survey sample and affects many other important aspects
of a survey as well.
In broad context survey researchers are interested in obtaining some types
of information through a survey for some population or universe. One must
define a sampling frame that represents the population of interest from
which a sample is to be drawn.
Sample Design
19. Preparing a sample design:
1. Types of universe (set of objects) /finite or
non finite
2. Sample unit (district, school, age)
3. Sampling frame
4. Size of sample
5. Sampling technique
20. Abu Sadat Md. Sakib
Roll: 7143
Topic: Characteristics of Good Sample Design
21. Following are the characteristics of
good sample design:
1. Sample design should be a representative sample: A researcher selects a
relatively small number for a sample from an entire population. If the sample
used in an experiment is a representative sample then it will help generalize the
results from a small group to large universe being studied.
2. Sample design should have marginal systematic bias: Systematic bias results
from errors in the sampling procedures which cannot be reduced or eliminated
by increasing the sample size. The best bet for researchers is to detect the
causes and correct them.
3. Results obtained from the sample should be generalized and applicable to
the whole universe: The sampling design should be created keeping in mind
that samples that it covers the whole universe of the study and is not limited to
a part.
22. 4. Sample design should have small sampling error:
Sampling error is the error caused by taking a small sample instead
of the whole population for study. Sampling error refers to the
discrepancy that may result from judging all on the basis of a small
number. Sampling error is reduced by selecting a large sample and
by using efficient sample design and estimation strategies.
5. Sample design should be economically viable:
Studies have a limited budget called the research budget. The
sampling should be done in such a way that it is within the research
budget and not too expensive to be replicated.
Conti….
24. Importance of Having Good Sample Design
Efficient business research : Sample research
design is a pre-condition of business research.
If we want to make our business research
efficient we should make proper use of sample
design.
Cost minimization: As a principle of
minimization cost in every research project we
should prepare a sample design at first.
25. Backbone of Research: Sample design is just
like CPU and brain of a research. Because when
we prepare a research design we will collect
information from sample design.
Smooth running of business : If we want to
run the activities of business continuously we
need a research design and this research design
is totally dependent on sample design.
27. EFFICIENCY IN SAMPLING
Efficiency in sampling is attained when for a given level of precision (standard
error), the sample size could be reduced, or for a given sample size (n), the level of
precision could be increased.
Some probability sampling designs are more efficient than others.
The simple random sampling procedure is not always the most efficient plan to
adopt; some other probability sampling designs are often more efficient.
A stratified random sampling plan is often the most effi- cient, and a disproportionate
stratified random sampling design has been shown to be more efficient than a
proportionate sampling design in many cases.
Importance of Having Good Sample Design
28. MANAGERIAL RELEVANCE
Awareness of sampling designs and sample size helps managers to understand why a
particular method of sampling is used by researchers.
It also facilitates understanding of the cost implications of different designs, and the
trade-off between precision and confidence vis-à-vis the costs.
This enables managers to understand the risk they take in implementing changes based on
the results of the research study.
While reading journal articles, this knowledge also helps managers to assess the
generalizability of the findings and analyze the implications of trying out the
recommendations made therein in their own system.
Cont…..
30. The researcher have to consider the following factors while preparing a sample design.
1.Universe: While preparing a sample design, it is required to define the set of objects
to be studied. Technically, it is also known as the Universe, which can be finite or
infinite.
2.Sampling unit: It is necessary to decide a sampling unit before selecting a sample
design. It can be a geographical one (state, district, village, etc.), a construction unit
(house, flat, etc.), a social unit (family, club, school, etc.), or an individual.
3 Sample size: Whether you are using a probability sampling or non-probability
sampling technique to help you create your sample, you will need to decide how large
your sample should be (i.e., your sample size). . The sample size should not be too
large or too small, but optimum. Your sample size becomes an ethical issue for two
reasons:
(a) Over-sized samples and (b) under-sized samples.
Factors considered in sample design
31. 4. Source list: In other words, it is called the ‘sampling frame’ from which the
sample is drawn. It is a list of the items or people forming a population from which
a sample is taken. . If source list/sampling frame is unavailable, the researcher has
to prepare it by himself.
5. Parameters of interest: While determining a sample design, it is required to
consider the question of the specific population parameters of interest.
6. Budgetary constraint: Practically, cost considerations have a major impact
upon the decisions concerning not only the sample size but also the sample type.
7. Sampling procedure: The researcher, at last, decides the techniques to be used
in selecting the items for the sample. In fact, this technique/procedure stands for the
sample design itself. So such a design should be selected, which for a provided
sample size and cost, has a smaller sampling error
Factors considered in sample design
35. Each element or each combination of elements has
equal probability of selection.
Applicable when population is small , homogeneous &
readily available.
A table of random number or lottery system is used to
determine which are to be selected
Simple Random Sampling Design
36. Systematic sampling relies on arranging the target population
according to some ordering scheme & then selecting elements at
regular intervals.
Each element has equal probability of selection but combination
of elements have different probabilities.
Population size N, desired sample size n , sampling interval k=N/n.
Randomly select a number j between 1 & k ,sample element j &
every kth element thereafter j+k,j+2k etc.
Example: N=64,n=8, k=64/8=8.now assume j=3.
Here starting with case number chosen in j=3 & taking every 3rd
number recode(such as 3,11,19 etc).
Systematic Sampling Design
38. Where population embraces a number of distinct categories
then the population is broken down into separate groups in
which each group is sampled as sub-population & a random
sample is taken of each category.
Every unit in a group has same chance of being selected.
Probabilities of selection may be different for different groups.
Stratified Sampling Design
39.
40. Cluster sampling is a sampling technique used when "natural" but relatively
homogeneous groupings are evident in a statistical population.
It is often used in marketing research.
In this technique, the total population is divided into these groups (or clusters)
and a simple random sample of the groups is selected.
Cluster sampling
43. Complex form of cluster sampling in which two or more levels of units are
embedded one in the other.
First stage, random number of districts chosen in all states.
Followed by random number of talukas , villages.
Then third stage units will be houses
This technique, is essentially the process of taking random samples of
preceding random samples.
Multistage sampling used frequently when a complete list of all members of
population not exists is appropriate.
Multistage sampling
44.
45. In multiphase sampling the different phase of observations
relate to the sample units of the same type.
Part of the information collected from sample & subsample .
Survey by such procedure is less costly , less laborious &
more purposeful
Multiphase sampling :
49. A type of non probability sampling which
involves the sample being drawn from that
part of the population which is close to hand.
That is, readily available and convenient.
Cont….
50. Purposive sampling:
the process whereby
the researcher selects a
sample based on
experience or
knowledge of the group
to be sampled
…called “purposive”
sampling
51. The researcher chooses the sample
based on who they think would be
appropriate for the study.
This is used primarily when there is a
limited number of people that have
expertise in the area being researched.
Judgmental Sampling
52. Cont…
Can not scientifically make
generalization about the total population
from this sample because it would not be
representative enough
53. The population is first segmented into mutually
exclusive sub-groups, just as in stratified sampling.
Then judgment used to select 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.
QUOTA SAMPLING
54. The problem is that these samples may be
biased because not everyone gets a
chance of selection. This random element
is its greatest weakness and quota versus
probability has been a matter of
controversy for many years
Cont……