This document discusses sampling methods for research. It defines key terms like population, sample, and sampling. It covers the main types of sampling: 1. Probability sampling methods like simple random sampling, stratified sampling, and systematic sampling which give all units an equal chance of selection. 2. Non-probability sampling methods like judgement, quota, and convenience sampling which do not give all units an equal chance and can be biased. 3. Factors to consider for good sampling include accuracy, precision, defining the target population, determining the sampling method and size. The document provides details on each sampling technique and their advantages and disadvantages.

1. BY
BALJINDER SINGH
M.SC. MSN

2. Definition:
Sample: A fraction of population selected in any
manner is known as sample
Population: It is the totality of persons, objects, items
or anything conceivable pertaining to certain
characteristics.
Sampling: It is a process of selecting subjects who
are representative of population events, behaviours
or other elements with which to conduct study.

3. Population is so large and scattered.
It offers high degree of accuracy.
Results can be obtained shortly.
Needs small portions.
Economical one.
Purposes of sampling:

4. Principles of sampling: -
Based on the objectives
Systematic
Clearly defined and easily identifiable
Used throughout the study
Based on sound criteria and avoid errors and
bias.

5. Selection of Good Sample:
Accuracy – Defined as the degree to which
bias is absent from the sample
Precision – Sample size

6. Define the target population

Select sampling frame

Determine whether probability or non probability sampling method
will be chosen

Plan procedure for selecting sampling units

Determine sample size and actual sampling units

Conduct field work
Sampling process (Steps in
Selection of a sample): -

7. 1. Defining the target population
Once the decision to sample has been made, the first
question related to sample, concerns identifying the target
population, that is the complete group of specific
population elements related to research project it is
important to carefully define the target population.
2. Sampling frame
This is the list of elements from which a sample may
be drawn.
E.g. Class attendance register.

8. 3. Methodof sampling
Method should be important because the total student body is
geographically concentrated and their reasonably accurate list of their
population.
4. Procedure for sampling
During the actual sampling process the elements of the population
according to the certain procedure sample units are selected. The
sampling units is a single element or group of elements.

9. 5. Select actual sampling Units
Sample of 30 and more are consider as large
sample, less than 30 is known as small sample.
After determining the sample size the actual
sampling units are selected for the study.
6. Conduct field work
After selecting actual sampling units field work are
carried out.

10. When the sample is so chosen that some
elements are more likely to be represented their other
elements, it is called biased sample.
Sampling error: -
Type – I (alpha) - Rejection of null hypothesis if its
true
Type – II (beta) - Acceptance of a null hypothesis
if its actually false.
Biased Sample: -

11. Types of Sampling
SAMPLING
NON PROBABILITY
SAMPLING
PROBABILITY
SAMPLING
JUDGEMENTAL
SAMPLING
QUOTA SAMPLING
CONVENIENCE
SAMPLING
SNOWBALL SAMPLING
SIMPLE RANDOM SAMPLING
STRATIFIED SAMPLING
MULTIPHASE SAMPLING
SYSTEMATIC SAMPLING
CLUSTER SMPLING
MULTI STAGE SAMPLING

12. Is one in which every unit of the population has
an equal probability of being selected for the
sample.
I. Simple Random sampling (Random sampling)
Random sample is a sample selected in such a
way that every item in the population has an
equal chance.
(a) Lottery method
(b) Table of random number method
A. Probability sampling

13. Merits
Scientific method
More economical
More representative
Demerits
Need complete list of study
If sample is small size means it will not be a true
representative

14. This method of sampling is used when the
population is composed of diverse segment.
When the population be divided into strata or
subgroups and then it is divided into
homogenous such as defined areas, classes,
ages, sexes etc.
Size of the sample from each strata can
either be
1. Proportional
2. Disproportional
II Stratified random sampling

15. Merits
More representative
Greater accuracy
Administrative convenience
Stratification is more advantages
Demerits
It is difficult to divide the population into
homogenous strata.
Supplementary information to set up is not
available some time.
Sometime the difficult strata may overlap makes
the sampling would not be representative

16. III. Systematic sampling: -
This sampling is obtaining collection of elements
by drawing every k th person from a pre-
determined list of person.
K = N n = sample size
n K = sample interval
N = target population
Merits
Simple and convenient
Rapid method

17. Demerits
It is suitable when there is no unique variation.
IV. Multistage sampling
Sampling is selected in various stages but only the last sample of subject is studied.

18. NORTH ZONEEAST ZONE WEST ZONESOUTH ZONE
TAMIL NADUKERALA KARNADAKA
CHENNAISALEMTRICHY
VEERAPONDI
MAGUDANCHAVADI
SEERAGAPADI
E.G. – FOR STUDYING THE PANCHAYAT SYSTEM IN
VILLAGES
INDIA

19. Merit
Helpful in large scale survey less expensive
V. Multiphase sampling
This type is same as multi stage sampling,
however, each sample is adequately studied before
another sample is drawn from it.
Merits
Less cost
Less laborious
More purposeful

20. VI. Cluster sampling
This sampling implies dividing population into
clusters and drawing random sample either from
all clusters or selected clusters.
Small units (or) group of element in the population
is called as cluster. E.g. wards, villages, slums,
schools

21. Merits
It is easy to apply large sample size
Less cost
Respondents can be readily substituted
Flexible
It is administratively simple
Used all the individuals (inconvenient or unethical)
Demerits
Each cluster is not equal size
Sample error is grater
Same individual can study twice.

22. B.NON-PROBABILITY SAMPLING: -
The elements are chosen by non random
methods.
Judgement sampling: -
This sampling also known as purposive
sampling. Sampling based on judgement of the
person entrusted with the job.
Quota sampling: -
This is stratified sampling but it worked on ‘Quotas’
fixed by the researcher
E.g. fixing 10 patients from each ward.

23. Convenience sampling
This also known as accidental on ‘haphazard’ sampling, the researcher studies all
those persons who are most conveniently available or who accidentally come in his contact
during data collection.
Snow ball sampling
The researcher begins the research with the few respondents who are known and
available to him, subsequently, there respondents give other names.

24. Sample size
Sample size can be calculated by using
standard formula
I.e. n
1+n(e2)
n - Total number of population
e – Error (90%) ie 0.05 level)

25. Disadvantages/critique
Not carefully planned one will be inaccurate
Need qualified and skilled person.
Inadequate sample size indicate false characteristics of population.