This document discusses different sampling methods used in research studies. It describes probability sampling methods like simple random sampling, stratified random sampling, cluster sampling, and systematic sampling. It explains that probability sampling allows for statistical measurement of random error. The document also covers non-probability sampling methods including convenience sampling, quota sampling, judgmental sampling, snowball sampling, and self-selection sampling. These do not allow for statistical measurement of variability and bias. The key sampling methods and their advantages and disadvantages are summarized.

1. Sampling Methods
Dr Surjya Kumar Saikia
Department of Zoology
Visva-Bharati University

2. The process of selecting a number of individuals
(sample) for a study in such a way that the individuals
represent the larger group (Population) from which
they were selected

3.  The sampling frame
A list of all elements or other units
containing the elements in a
population.
 A sample is “a smaller (but
hopefully representative) collection
of units from a population used to
determine truths about that
population” (Field, 2005)
Population: Birds that
are pink.
Sampling Frame:
Brown-capped Rosy-
Finch.
White-winged
Crossbill.
American Flamingo.
Roseate Spoonbill.
Black Rosy-Finch.
Cassin’s Finch.
Sample and Sampling Frame

5. Population…
The larger group from which
individuals are selected to
participate in a study
A set of elements larger than or
different from the population sampled
and to which the researcher would like
to generalize the study findings
E.g. Diabetic patients, anemic women

7.  To gather data about the population in order to
make an inference that can be generalized to the
population
Sampling
frame
Data Inference
Population
is diabetic

8. STEPS OF SAMPLING

9.  Probability sampling is also called random
sampling
 Best method to achieve a representative
sample
 Four techniques
1. Simple Random sampling
2. Stratified random sampling
3. Cluster sampling
4. Systematic sampling
PROBABILITY SAMPLING

10. 1. Simple random sampling
Selecting subjects so that all members of a
population have an
equal and independent chance of being selected
Advantages
1. Easy to conduct
2. High probability of achieving a
representative sample
3. Meets assumptions of many statistical
procedures
Disadvantages
1. Identification of all members of the
population can be difficult
2. Contacting all members of the sample
can be difficult
PROBABILITY SAMPLING

11. ◦ Selection process
 Identify and define the
population
 Determine the desired sample
size
 List all members of the
population and assign all
members on the list a
consecutive number
 Select an arbitrary starting point
from a table of random
numbers and read the
appropriate number of digits
1
2
3
4
5
6
7
8
9
10 11 12
N=12
PROBABILITY SAMPLING

12. 2. Stratified random sampling
◦ The population is divided into two or more
groups called strata, according to some
criterion, such as geographic location, grade
level, age, or income, and subsamples are
randomly selected from each strata.
PROBABILITY SAMPLING

13. PROBABILITY SAMPLING

14.  Stratified random sampling (continued)
◦ Advantages
 More accurate sample
 Can be used for both proportional and non-
proportional samples
 Representation of subgroups in the sample
◦ Disadvantages
 Identification of all members of the population can
be difficult
 Identifying members of all subgroups can be
difficult
PROBABILITY SAMPLING

15. 3. Cluster sampling
PROBABILITY SAMPLING
 The population is divided into subgroups (clusters) like
families.
 A simple random sample is taken from each cluster

16.  Cluster sampling
◦ Advantages
 Very useful when populations are large and spread over a
large geographic region
 Convenient and expedient
 Do not need the names of everyone in the population
◦ Disadvantages
 Representation is likely to become an issue
PROBABILITY SAMPLING

17. PROBABILITY SAMPLING

18. 4. Systematic sampling
 Order all units in the sampling frame
 Then every nth number on the list is selected
PROBABILITY SAMPLING

19.  Systematic sampling procedure
 Identify and define the population
 Determine the desired sample size
 Determine what K is equal to by dividing the
size of
 the population by the desired sample size
 Start at some random place in the population
list
 Take every Kth individual on the list
PROBABILITY SAMPLING

20.  Example, to select a sample of 25 dorm rooms in your college
dorm, makes a list of all the room numbers in the dorm. For
example there are 100 rooms, divide the total number of rooms
(100) by the number of rooms you want in the sample (25). The
answer is 4. This means that you are going to select every
fourth dorm room from the list. First of all, we have to
determine the random starting point. This step can be done by
picking any point on the table of random numbers, and read
across or down until you come to a number between 1 and 4.
This is your random starting point. For instance, your random
starting point is "3". This means you select dorm room 3 as
your first room, and then every fourth room down the list (3, 7,
11, 15, 19, etc.) until you have 25 rooms selected.

21.  The probability of each case being selected from the
total population is not known.
 Units of the sample are chosen on the basis of
personal judgment or convenience.
 There are NO statistical techniques for measuring
random sampling error in a non-probability sample.
NON-PROBABILITY
SAMPLING

22.  Convenience Sampling
 Quota Sampling
 Judgmental Sampling (Purposive Sampling)
 Snowball sampling
 Self-selection sampling
Non-probability sampling

23. 1. Convenience sampling:
the process of including whoever happens to
be available at the time …called “accidental”
or “haphazard” sampling.
Simply, convenience sampling involves
choosing respondents at the convenience of
the researcher.
Non-probability sampling

24. Advantages
 Very low cost
 Extensively used/understood
Disadvantages
 Variability and bias cannot be measured or
controlled
 Projecting data beyond sample not justified
 Restriction of Generalization.
Non-probability sampling

25. 2. Quota sampling
The process whereby a
researcher gathers data
from individuals
possessing identified
characteristics and
quotas.The population is
first segmented into
mutually exclusive sub-
groups, just as in
stratified sampling.
Non-probability sampling

26. Advantages
 Used when research budget is limited
 Very extensively used/understood
 No need for list of population elements
Disadvantages
 Variability and bias cannot be
measured/controlled
 Time Consuming
 Projecting data beyond sample not justified
Quota sampling
Non-probability sampling

27.  Researcher employs his or her own "expert”
judgment about.
Advantages
 There is a assurance of Quality response
 Meet the specific objective.
Disadvantages
 Bias selection of sample may occur
 Time consuming process.
3. Judgmental sampling
Non-probability sampling

28.  The research starts with a key person and
introduce the next one to become a chain
Advantages
 Low cost
 Useful in specific circumstances & for locating rare
populations
Disadvantages
 Not independent
 Projecting data beyond sample not justified
4. Snowball sampling
Non-probability sampling

29.  It occurs when you allow each case usually
individuals, to identify their desire to take part in the
research.
Advantages
 More accurate
 Useful in specific circumstances to serve the purpose.
Disadvantages
 More costly due to Advertizing
 Mass are left
5. Self-selection sampling
Non-probability sampling