2. SAMPLING
Procedure by which some members
of a given population are selected as
representatives of the entire
population.
3. UNIVERSE
the larger group from which
individuals are selected to
participate in a study
SAMPLE
the representatives selected for a
study whose characteristics
exemplify the larger group from
which they were selected
4. PURPOSE OF SAMPLING
TO GATHER DATA ABOUT THE POPULATION IN
ORDER TO MAKE AN INFERENCE THAT CAN BE
GENERALIZED TO THE POPULATION
POPULATION
SAMPLE
INFERENCE
5. PROCESS OF SAMPLING
Define the Population
Develop Sampling Frame
Select a Sampling Method
Determine Sample Size
Execute the Sampling Process
9. PROBABILITY
SAMPLING
Every element in the target
population or universe
[sampling frame] has equal
probability of being chosen in
the sample for the survey being
conducted.
Scientific, operationally
convenient and simple in
theory.
Results may be generalized.
NON-PROBABILITY
SAMPLING
Every element in the universe
[sampling frame] does not have
equal probability of being chosen
in the sample.
Operationally convenient and
simple in theory.
Results may not be generalized.
11. SIMPLE RANDOM SAMPLING
Simple random sampling is a method of probability
sampling in which every unit has an equal non zero chance
of being selected for the sample.
Methods of selecting random sample:
1. Lottery Method
2. Tables of Random Numbers
12. STRATIFIED RANDOM
SAMPLING
Stratified random sampling is a method of probability sampling in
which the population is divided into different subgroups and samples
are selected from each of them.
Steps:-
All units of population are divided into different stratas in accordance
with their characteristics.
Using random sampling, sample items are selected from each
stratum.
13. SYSTEMATIC RANDOM SAMPLING
OR QUASI-RANDOM SAMPLING
Systematic random sampling is a method of
probability sampling in which the defined target
population is ordered and the 1st unit of sample is
selected at random and rest of the sample is
selected according to position using a skip interval
(every Kth item)
K = N
n
Where, K = Sampling/ Skip interval
N = Universe/ Population Size
n = Sample Size
14. MULTISTAGE RANDOM SAMPLING
Used in large scale investigations
First stage- preparation of large sized sampling units
Randomly selecting a certain number
Second stage- Another list prepared from them
Sub-samples drawn by random sampling
15. CLUSTER SAMPLING
The process of randomly selecting intact groups, not individuals,
within the defined population sharing similar characteristics
Steps :-
1. Defined population is divided into number of mutually exclusive
and collectively exhaustive subgroups or clusters
2. Select an independent simple random sample of clusters.
16. AREA SAMPLING
One special type of cluster sampling is called area sampling, where
pieces of geographical areas such as districts, housing blocks or
townships are selected.
Area sampling could be one-stage, two-stage, or multi-stage.
Generally used by Govt. agencies and agricultural statistics.
18. CONVENIENCE SAMPLING
the process of including
whoever happens to be available at the
time…called “accidental” or
“haphazard” sampling.
19. PURPOSIVE SAMPLING
the process whereby the
researcher selects a sample based on
experience or knowledge of the group
to be sampled…called “judgment”
sampling
20. QUOTA SAMPLING
the process whereby a researcher
gathers data from individuals possessing
identified characteristics and quotas
21. OTHER NON-PROBABILITY SAMPLING
METHODS
Intensity sampling: selecting participants who
permit study of different levels of the research topic
Homogeneous sampling: selecting participants who
are very similar in experience, perspective, or outlook
Criterion sampling: selecting all cases
that meet some pre-defined characteristic
Snowball sampling relies upon respondent
referrals of others with like characteristics
22. FACTORS TO CONSIDER IN SAMPLE
DESIGN
Research objectives Degree of accuracy
Resources Time frame
Knowledge of
target population Research scope
Statistical analysis needs
