good

1. 5. Sampling Design
Census and Sampling:
• Census inquiry-includes all cases (100 %).
Highest accuracy is possible.
• 100 % population survey is not practical in
all cases.
• Therefore, reasonable representatives
among the population is considered
practical and selected representatives are
called sample.

2. Sampling:
It is a technique of data collection in which
some samples are taken for the study
which represents the whole population.
Sample:
A sample is a small proportion of
population , selected for observation
/analysis.

3. Population or Universe
A population is any group of individuals that
have one or more characteristics in common,
which are of interest of the research fellow.
Universe is the boundary, within which the
study is confined.
-finite universe-population of a city, number of
workers in factory, are some examples of finite
universe.
-infinite universe-number of stars in the sky,
listeners of a specific radio program, are some
of the examples of infinite universe

4. Characteristics of a sample
• Sample should be as correct as possible,
• It should represent the whole population,
• Quality of sample is important than
quantity,
• Sample should not be biased,
• Sample should fulfill the purpose of the
study,

5. Sample Design
In sample design, following three points
has to be considered;
a. Sampling frame (List of population),
b. Selection of sampling item
(Method of sampling)
c. Size of sampling (Larger the sample size,
more accurate it is)

6. Sampling Methods:
The methods by which units of
observation are selected, are broadly
classified as;
i. Probably Sampling and,
ii. Non-probability Sampling

7. i. Probability Sampling
Probability sampling believes that each
element in the universe has equal chance
of selection.
The implications of simple random
sampling are: (a) equal probability of
getting into the sample, (b) all choices are
independent of one another, (c) gives each
possible sample combination an equal
probability of being chosen.

8. Probability Sampling is further
classified as;
a. Simple Random
b. Systematic Random
c. Stratified Random and,
d. Cluster or Multi-stage Random

9. a. Simple Random Sampling
This is very simple and basic methods of
sampling. In this method, any item may be
selected from the mass.
Example: Any 50 student from the college.
Probability of selection = n/N where, n =
sample size, and N = population size.

10. b. Systematic Random sampling
• Every Kth element in the population is
sampled, beginning with random start of an
element in the range of 1 to k.
• Skip internal (or kth element), k = N/n, where
N is population size and n is sample size.
• Advantages :It is simple and flexible. No need
of random numbers table. Easy to instruct
field workers to choose sample unit. To draw
systematic sample, do following: (a) identify,
list , and number the elements in the
population, (b) identify skip interval, K, (c )
identify the random start, (d) draw a sample
by choosing every kth entry.

11. c. Stratified Random Sampling
• The process by which the sample is constrained to include
elements from each of the segments is called stratified
random sampling.
• If population characteristics are heterogeneous, then
simple random sampling does not serve as a good design
so as to represent the sample units from each
characteristics.
• In this condition, entire population is divided/sub-divided
into homogenous groups or class called strata.
• For example-university students can be divided by their
class level, major, gender, etc.
• After a population is divided into the appropriate strata, a
simple random sample process can be taken within each
stratum.

12. d. Cluster or Multi-stage Sampling
Cluster sampling is a plan that involves dividing the
population into subgroups and then draws a
sample from each subgroup.
This is a multistage sampling related to the
geographical region.
* Example: If we have to study about the conditions
of schools in Lalitpur district, we can take sample
as:
- Select 4 schools from municipality and 12 schools
from VDCs
- Take 4 schools from each constituency among 12
- Take 1 secondary school, 1 lower secondary school
and 2 primary schools.

13. Differences between stratified sampling
and cluster sampling
• Division of the population into a few sub groups (each
subgroups has many elements in it) in case of Stratified
Sampling, but in case of Cluster Sampling, division of
population into many subgroups (each subgroup has
few elements in it).
• We try to secure homogeneity within subgroup in case
of Stratified Sampling, but try to secure heterogeneity
within subgroups in case of Cluster Sampling.
• Try to secure heterogeneity between subgroups in case
of Stratified Sampling, but try to secure homogeneity
between subgroups in case of Cluster Sampling.
• Randomly chose elements from within subgroups in
Stratified Sampling but in Cluster Sampling, randomly
chose several subgroups to study in depth.

14. ii. Non-probability Sampling
This method does not believe in equal chance.
Some elements have more chance of being
selected . This depends upon
• Convenience sampling
• Judgment or purposive sampling
• Quota sampling (male and female for
example)
• Snowball sampling (in case of network or
interconnectedness studies).

15. a. Convenience Sampling
In this method, the selection of sample depends
upon the convenience of the researcher.
Example: Taking interviews with the drivers,
pasangers or pedestrians in the street.

16. b. Judgment or purposive sampling
• In this method samples are taken at the
judgment of the researcher as that fulfils
his purpose.
Example: Taking views of 10 doctors about
the eradication of T.B.

17. C. Quota sampling
• This is better method of non-probability
sampling. In this method some quota are
divided to each group of items and required
samples are selected. (more priority to the
small nos.)
Example:
Subject No. of Student Quota (25 nos.)
Civil 100 12
Computer 50 8
Electronics 25 5

18. d. Snowball Sampling
This method is used where it is difficult to
identify respondents. The respondents are
located through referral network.
In the beginning, individuals are discovered and
this group is then used to locate others who
possesses similar characteristics and who, in
turn, identify others.

19. Advantages/Merits of sampling
• Speed or less time
• Economy (reduced cost of the study).
• Administrative convenience (complete census
study requires very huge administrative setup
including human resources)
• Reliability
• Greater scope (more practical than census study
with reference to time, money, and man hours)
• For infinite of too large population, sampling is
only the way
• For destructive testing, sampling is more economic
than census study.
(source: S.C. Gupta, page 1048-1049)

20. Disadvantages/limitations of
Sampling
• If not properly planned, the results obtained
will not be reliable.
• Efficient sampling requires the services of
qualified, skilled, and experienced person.
• If the sample size is the large proportion of
the population size, it may require more time
and money.
• In case we want to have information about
each and every unit of the population,
sampling is useless.

21. Sampling error
• There would naturally be a certain amount of
inaccuracy in the information collected. Such
inaccuracy may be termed as sampling error or
error variance.
• Differently, sampling errors are those errors which
arise on account of sampling and they generally
happen to be random variations (in case of
random sampling) in the sample estimates around
the true population value.
• Sampling error = frame error + chance error +
Response error.
• Sampling error can be reduced by increasing
sample size.
• (Source: Kothari, 2011, page 153-154)

22. Size of the Sample
• Size of sample-is the number of items to be
selected from the universe.
- Sample size should neither be excessively large
nor too small. It should be optimum.
- Optimum sample fulfills the requirement of
efficiency, representatives, reliability, and
flexibility.
- Decide the level of confidence or significance
level (precision of study).
- Size of population and costs for research also need
to be considered.

23. Approximate Sample Size
• One principle of sample sizes is, the smaller the
population, the bigger the sampling ratio.
• For example, for small populations (under 1,000), a
large sampling ratio (about 30 %) is recommended.
• For moderately large populations (10,000), a smaller
sampling ratio (about 10 %) is recommended.
• For large populations (over 150,000), smaller sampling
ratio (about 1 %) is recommended.
• For very large populations (over 10 million), tiny
sampling ration (about 0.025 %) is adequate
(source: w. Lawrence Neuman, page 220-221)
Cohen J (1988 or latest). Statistical power analysis for the behavioral
sciences. Hillsdale, NJ: Lawrence Erlbaum Associates. It provides
tables for determining sample size.