This slide presentation discuss about sampling techniques for research application

1. By :
Dr. Surendra Pal
Associate Professor
D.A.V. (P.G.) College
Muzaffarnagar, U.P.

2.  Population : Aggregate of all people or items
with same characteristics.
 Sample : The smaller unit of population that
is representation of population.
 Probability: Chance of occurrence of an item.
 Randomization: is a method of sampling in
which each item has equal chance or
probability of selection in sample.

3.  True Representative
 Free from Bias
 Objective
 Accurate
 Comprehensive
 Economical
 Approachable
 Good Size
 Feasible
 Practical

4. 1. Probability Sampling Techniques :
Every item chosen has a known probability to
be selected in sample.
2. Non-Probability sampling Techniques :
The items are chosen in absence of any
probability method. (or Probability of being
chosen is unknown)

6.  Each element of population has an equal and
independent chance of being included in
sample.
Examples:
1. Tossing a coin
2. Throwing a dice
3. Lottery method
4. Blind folded method
5. By using random table

7. Strengths Weaknesses
 Essay to calculate
Minimum Knowledge of
population
Free from personal Error
 Observations can be
used for inferential
purposes
 Accuracy depends on
size of sample.
 Applicable to small and
homogeneous samples.
 Minor subgroups of
interest may not be
present in the sample.

8.  Arranging the target population according to some
ordering scheme and than selecting elements and
regular intervals.
 Involves a random start and then proceeds with the
selection of every kth element from then onwards. (
select each N/n th element, where N=population size
&n= sample size)
Example:
Select every 10th name from telephone directory.

9. Strengths Weaknesses
 Improvement over
Simple random sampling.
Simple method
 Reduce the field cost
 Sample is
comprehensive &
representative
 Observation is used for
conclusion and
 There are different ways
of systematic list by
different peoples.
 Knowledge of
population is essential.
 Risk in drawing
conclusion.
 It cannot ensure
representativeness.

10.  Where the population is having number of distinct
categories i.e. separate strata then from each of this
homogeneous groups sample is constituted.
 Using same sampling fraction for all strata ensures
proportionate representation in the sample.

11. Strengths Weaknesses
 Good representation of
population.
 It is improvement over
the earlier.
Objective method of
sampling.
Observation can be used
for inferential purposes
 Adequate representation
of minority subgroups is
ensured.
 difficult to decide
relevant criterion fro
stratification.
 it is costly and time
consuming.
 Require large sample
size as compare to other
method.
 Sample is
representative with used
criterion and not with
other.
 Complicating the

12.  It is an example of 'two-stage sampling’, In first stage
a sample of areas is chosen & in Second stage a
sample of respondents within those areas is
selected.
 Population divided into clusters of homogeneous
units, usually based on geographical contiguity.
 Sampling units are groups rather than individuals.
 A sample of such clusters is then selected.
 All units from the selected clusters are studied

13. Strengths Weaknesses
 Good representation of
Sampling
 Easy and Economical
Method
 Highly applicable in
education
 Observation can be used
for inferential purpose.
 It is not free from errors
 It is not comprehensive.

14.  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.
 All ultimate units (houses, for instance) selected at
last step are surveyed.

15. Strengths Weaknesses
 Good representation of
Population.
 It is improvement over
earlier methods.
 An objective method of
sampling.
 Observation may be
used for inferential
purpose.
 It is difficult and
complex.
 It involves errors while
considering primary and
secondary stages.

16.  In this Samples are taken that are readily
available and convenient.
 These are mostly used in pilot studies.
Example:
Interviewing person coming at Mall in given
time interval.

17. Strengths Weaknesses
 Easy method of
Sampling.
 Frequently used in
behavioural Science.
 Economical method
as regards to time,
money and energy.
 Not representative of
population.
 Not free from errors.
 in it parametric
statistics cannot be
used.

18.  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.

19. Strengths Weaknesses
 Knowledge of the
investigator can be best
used.
 It is Economical.
 It is Objective.
 Not free from error.
 It include uncontrolled
variation.
Inferential Statistics
cannot be used for
observations. Therefore
generalization is not
possible.

20.  It combines both judgement Sampling and
probability sampling.
 Selecting participant in numbers
proportionate to their numbers in the larger
population, no randomization.
 Select people non randomly according to
some quota
Example:
For example you include exactly 50 males
and 50 females in a sample of 100.

21. Strengths Weaknesses
 improvement over
judgement sampling.
 easy sampling
technique.
 most frequently used in
social surveys.
 Not a representative
sample.
 Not free from error.
 It has influence of
regional geographical and
social factors.

22.  Selecting participants by finding one or
two participants and then asking them to
refer you to others. One person
recommends another, who recommends
another, who recommends another, etc
 Example:
Good way to identify hard-to-reach
populations, for example, homeless
persons, Cancer patient etc.

23. Strengths Weaknesses
 Easy sampling technique.
 Most frequently used where
population size is small.
 Not a representative sample.
 not free from error.