This document discusses sampling techniques used in educational research. It begins by defining key terms like population, sample, and sampling techniques. It then describes probability sampling methods like systematic sampling and non-probability sampling methods like purposive sampling. For systematic sampling, every kth unit is selected from an ordered population. Purposive sampling involves selecting units that are relevant to the research objectives. The document outlines the advantages and limitations of these sampling methods.

1. Assignment in
EDUCATIONAL RESEARCH AND STATISTICS
[M.Ed 2011-12]
Assigned by:
Dr. Archna Dubey
Presented by:
• Neeraj Singh
• Rekha Boriwal
• Siddhi Sood
• Varsha Kapse

3. INDEX
1. SAMPLING TECHNIQUES
(by Siddhi Sood)
2. SYSTEMATIC SAMPLING
(by Varsha Kapse)
3. PURPOSIVE SAMPLING
(by Neeraj Singh and Rekha Boriwal)

5. Sampling is a concept that is more
often used in researches. Lets know
something essential herein about
RESEARCH.
PURPOSE OF THE RESEARCH
The primary purpose of a research is to discover
principles that have universal application.

6. BASIC PROBLEM IN RESEARCH
 Most of the educational phenomena consist of a
large number of units.
 Impractical to test or observe each unit of the
population under controlled conditions for
arriving at generalizations.
 Such a study is expensive in terms of time,
money, effort, and manpower.

7. PRIMARY SOLUTION
 Process of Sampling makes it possible to draw
valid inferences.
 It includes careful observation of variables
within a relatively small proportion of the
population.
 The process of sampling involves the use of
SAMPLE.

8. WHAT IS A SAMPLE?
 A sample is a small proportion of a population
selected for observation and analysis.
 By observing the characteristics of the sample,
one can make inferences about the
characteristics othe population from which it is
drawn.
 For example, if all public schools teachers
represent a Population, the teachers of public
schools at Indore, may be taken as Sample.

9. PRINCIPAL ADVANTAGES OF SAMPLING
 Sampling-
 Reduces expenditure cost.
 Saves time and energy.
 Permits measurement of greater scope.
 Produces greater precision and accuracy.
 Applying a process effectively always involve
some techniques, and so does sampling.

10. SAMPLING TECHNIQUES
 Samples cannot be drawn haphazardly.
 Its adequacy depends upon our knowledge of
the population and also on the methods or
techniques used in drawing the sample.
 Before moving on to various techniques of
sampling, we should be aware of some
components of sampling.

11. KEY TERMS IN SAMPLING
 SAMPLING ELEMENT is the entity from the
population about which information is collected.
 SAMPLING UNIT can be a single member
(element) or collection of members subject to
data analysis in the sample.
 SAMPLING FRAME is the complete list of all
units/elements from which sample is drawn. It is
also known as working population.
…cont.

12.  TARGET POPULATION is the one to which the
researcher would like to generalize his results
on.
 SAMPLING TRAIT acts as a base of taking out
the sample out of the population. It may be
quantitative or qualitative.
 SAMPLING FRACTION is the proportion of the
total population to be included in the sample.
…cont.

13. Formula:
Size of sample or n
Total population N
 SAMPLING ESTIMATE is an estimate from a
sample value of what the value would be in the total
population from which the sample is drawn.
 SAMPLING ERROR is the difference between total
population value and the sampling value.
…cont.

14. It is the degree to which the ‘sample
characteristics’ approximate the ‘characteristics
of the total population’.
Smaller the sample, greater will be the sampling
error.
NOTE:
• Parameter is the summary description of a variable for a
population.
• Statistic is a measured value based upon sample data.

15. Parameter
20 years
Statistic
21 years
Statistic
24 years
Statistic
22 years
Error= 1 year
Error= 4 years
Error= 2 years
SAMPLE 1
SAMPLE 2
SAMPLE 3
SAMPLING ERROR

16. TYPES OF SAMPLING
There are basically two types of sampling:
SAMPLING
Probability Sampling Non-probability sampling

17. PROBABILITY SAMPLING
 It is one in which every unit of the population
has an equal probability of being selected for
the sample.
 It offers a high degree of representativeness.
 Today, it remains the primary method for
selecting large, representative samples for
social science and business researches.

18. CONDITIONS TO BE SATISFIED
According to Black and Champion (1976):
1. complete list of the subjects to be studied is
available;
2. size of the universe must be known;
3. desired sample size must be specified, and
4. each element must have an equal chance of being
selected.

19. DISADVANTAGE
This method is expensive, time consuming, and
relatively complicated since it requires a large
sample size and the units selected are usually
widely scattered.

20. NON-PROBABILITY SAMPLING
 This sampling makes no claim for
representativeness.
 Every unit does not get the chance of being
selected
 Researcher decides which sample should be
chosen.

21. WHEN TO USE NON- PROBABILIYTY SAMPLING
 In many research situations, especially where
there is no list of persons to be studied,
probability sampling is inappropriate to use.
 In such researches, non-probability sampling is
the most appropriate one.
 It is usually used for qualitative exploratory
analysis.

22. DISADVANTAGES
 The informants do not represent the population.
 Informants’ observation and opinions may be misleading.
 Unwillingness to help and cooperate.
 Selecting informants who are convenient for study
 Personal leanings of the researchers of being prejudiced
against certain types of persos or groups, say bias in
choosing a caste, community, social strata, etc.

23. FORMS OF PROBABILITY AND NON-PROBABILITY
SAMPLING
Probability Sampling
1. Simple random
2. Stratified random
3. Systematic (or
interval)
4. Cluster
5. Multi-stage
6. Multi-phase
Non-probability
Sampling
 Convenience
 Purposive
 Quota
 Snowball
 Volunteer

25. DEFINITION
 According to Joan Joseph Castillo (2009),
“ Systematic sampling is a random sampling
technique which is frequently chosen by the
researchers for its simplicity and periodic
quality.”
 i.e., the researcher first randomly picks the first
item or subject from the population and then he
will select each ‘kth’ item or subject from the list.
Here, k = Population size / sample size

26. USE
 It is an improved version of simple random
sampling and used in a research, were the
information about the population is known.
 This method is used when group is to be
arranged in a sequence.
 It is used where the ordering of the population
is essentially random or contains at most a mild
stratification.
…cont.

27.  Used for sub-sampling clustered units.
 Used for sampling populations with variations of
a continuous type, provided that an estimate of
the sampling error is not regularly required.

28. ADVANTAGES
 It is a simple method of sampling.
 Systematic samples are convenient to draw
and execute.
 It ensures that the population will be evenly
sampled and so reliable conclusions can be
drawn.
 This method is comparatively economic in
terms of time, labor and money.
 It systematically eliminates the clustered
selection of the subjects.

29. DISADVANTAGES
 Poor precision when unsuspected periodicity is
present.
 No trustworthy method for estimating from the
sample data, is known.
 Possibility of illusionary conclusions if there is a
circular change within the intervals of
population.

30.  Rare possibility of using this method in
economic and social surveys.
 Ignores all persons between two kth nos.
 Possibility of over or under representation of
several groups is greater.

31. LIMITATIONS
 Information about every unit and population
should be known.
 No complete assurance for the representative
sample.
 Risk in extracting conclusions on the basis of
obtained results from samples.
 Since, each element has no chance of being
selected, it is not probability random sampling
(Black and Champion, 1976)
Thus, this technique cannot be free from errors.

33. MEANING AND OBJECTIVE
 Purposive sampling is a sampling method in
which elements are chosen from among the
whole population based on purpose of the
study.
 The main objective of purposive sampling is
that the researcher, with his good decision and
appropriate policy, chooses those elements
which are meant for fulfilling the research
objective.

34. DEFINITION
 In the words of J.P. Guilford:
“ A purposive sample is the one arbitrarily selected
because there is good evidence that it is very
representative of the total population.”
 According to Jahoda and Cook:
“The basic assumption behind purposive sampling
is the good judgment and appropriate strategy, one
can hand-pick the class to be included in the sample
and thus develops sample that are satisfactory in
relation to one’s need.

35. CHARACTERISTICS
 The researcher is well aware of the attributes
and characteristics of the population units.
 The sample is selected keeping the problem of
study in mind for its purposeful solution.
 The results obtained are policy affected and bit
biased.

36. ADVANTAGES
 Moderately economic in terms of cost and time
because of small sample size.
 The selected units from the population are
closely related to the problem of study
 Pre-knowledge of the population units makes
the selected sample more representative.
 Guarantees meeting specific objectives.
 Useful for certain types of forecasting.

37. DISADVANTAGES
 This method is useful only when the researcher has the
complete information and knowledge about the
population.
 More biased.
 Impurities of the sample cannot be judged.
 Understanding the whole group is not that easy.
 Statistically , the obtained results through this techniques
are less reliable.