This document discusses different sampling methods used in educational research. It defines key terms like population, sample, and target population. The main sampling methods covered are random sampling techniques like simple random sampling, stratified random sampling, and cluster random sampling. It also discusses non-random sampling techniques like systematic sampling, convenience sampling, and purposive sampling. For each method, the document provides the definition, steps to implement it, and advantages and disadvantages. The goal of the document is to explain the basic sampling approaches available to researchers in selecting participants for a study.

1. EDU702: RESEARCH
METHODOLOGY
THE BASIC OF EDUCATIONAL
RESEARCH
SAMPLING

2. DEFINITION: SAMPLING
The process of selecting a number of
individuals for a study in such a way
that the individuals represent the
larger group from which they were
selected.

3. DEFINITION: POPULATION
The larger group from which
individuals are selected to participate.

4. TARGET VERSUS
ACCESSIBLE POPULATIONS:
1.
The Target Population is the ideal selection of actual
population which researcher really like to generalize:
- is rarely available.
- Researcher’s ideal choice.
2. The Accessible or ‘available’ population is the
population to which a researcher is able to generalize:
- Researcher’s realistic selection

5. SAMPLING:
1)
2)
RANDOM SAMPLING METHOD
NONRANDOM SAMPLING
METHOD

6. RANDOM SAMPLING
METHODS
1.
2.
3.
4.
Simple Random Sampling
Stratified Random Sampling
Cluster Random Sampling
Two-Stage Random Sampling

7. SIMPLE RANDOM SAMPLING
The proces of selecting a sample that
allows induvidual in the defined
population to have an equal and
independent chance of being selected
for the sample.

8. STEPS IN RANDOM SAMPLING:
1. Identify and define the population.
2. Determine the desired sample size.
3. List all members of the population.
4. Assign all individuals on the list consecutive
number from zero to the required number.
Each individual must have the same number
of digits as each other individual.

9. STEPS IN RANDOM SAMPLING:
5.
Select an arbitrary number in the table of
random numbers.
6. For the selected number, look only at the
number of digits assigned to each population
member.

10. STEPS IN RANDOM SAMPLING:
7.
8.
If the number corresponds to the number
assigned to any of the individual in the
population, then that individual is included
in the sample.
Go to the next number in the column and
repeat step #7 until the desired number of
individuals has been selected for the
sample.

11. ADVANTAGES OF SIMPLE
RANDOM SAMPLING:

Easy to conduct

Strategy requires minimum
knowledge of the population to be
sampled

12. DISADVATAGES OF SIMPLE
RANDOM SAMPLING:

Need names of all population members.

May over-represent or under-estimate
sample members.

There is difficulty in reaching all selected
in the sample.

13. STRATIFIED RANDOM
SAMPLING
The process of selecting a sample
that allows identified subgroups in
the defined population to be
represented in the same proportion
that they exist in the population.

14. STEPS IN STRATIFIED
SAMPLING:
1. Identify and define the population.
2. Determine the desired sample size.
3. Identify the variable and subgroups (strata)
for which you want to guarantee appropriate,
equal representation.

15. STEPS IN STRATIFIED RANDOM
SAMPLING
4.
Classify all members of the population as
members of the one identified subgroup.
5.
Randomly select, using a table of random
numbers; an “appropriate” number of
individuals from each of the subgroups,
appropriate meaning an equal number of
individuals.

16. ADVANTAGES OF STRATIFIED
RANDOM SAMPLING:
 More precise sample.

Can be used both proportions and
stratification sampling.

Sample represents the desired strta.

17. DISADVANTAGES OF
STRATIFIED RANDOM
SAMPLING:

Need names of all population members.

There is difficulty in reaching all selected
in the sample.

Researcher must have names of all
populations.

18. CLUSTER SAMPLING
The process of randomly selecting
intact groups, not individuals, within
the defined population sharing
similar characteristics.

19. STEPS IN CLUSTER RANDOM
SAMPLING:
1. Identify and define the population.
2. Determine the desired sample size.
3. Identify and define a logical cluster.

20. STEPS IN CLUSTER RANDOM
SAMPLING:
4. List all clusters (or obtain a list) that make up
the population of clusters.
5. Estimate the average number of population
members per cluster.
6. Determine the number of clusters needed by
dividing the sample size by the estimated
size of a cluster.

21. STEPS IN CLUSTER RANDOM
SAMPLING:
7. Randomly select the needed number of
clusters by using a table of random
numbers.
8. Include in your study all population
members in each selected cluster.

22. ADVANTAGES OF CLUSTER
RANDOM SAMPLING:
Efficient.
 Researcher does not need nemes of
all population members.
 Reduces travel to site.
 Useful for educational research.


23. DISADVANTAGES OF CLUSTER
RANDOM SAMPLING:
 Fewer sampling points make it less like
that the sample is representative.

24. TWO-STAGE RANDOM
SAMPLING
The process of COMBINING Cluster
Random Sampling with an Individual
Random Sampling.

25. STEPS IN TWO-STAGE RANDOM
SAMPLING:
1.

26. ADVANTAGES OF TWO-STAGE
RANDOM SAMPLING:
 Less time-consuming

27. NONRANDOM SAMPLING
METHODS
1.
2.
3.
Systematic Sampling
Convenience Sampling
Purposive Sampling

28. SYSTEMATIC SAMPLING
The process of selecting individuals
within the defined population from a
list by taking every Kth name.

29. STEPS IN SYSTEMATIC
SAMPLING:
1. Identify and define the population.
2. Determine the desired sample size.
3. Obtain a list of the population.
4. Determine what K is equal to by dividing the size of
the population by the desired sample size.

30. STEPS IN SYSTEMATIC
SAMPLING:
5. Start at some random place in the population
list. Close your eyes and point your finger to
a name.
6. Starting at that point, take every Kth name on
the list until the desired sample size is
reached.
7. If the end of the list is reached before the
desired sample is reached, go back to the top
of the list.

31. ADVANTAGES OF SYSTEMATIC
SAMPLING:
 Sample selection is simple

32. DISADVANTAGES OF
SYSTEMATIC SAMPLING:

All members of the population do not
have an equal chance of being selected.

The Kth person may be related to a
periodical order in the population list,
producing unrepresentativeness in the
sample.

33. CONVENIENCE SAMPLING
The process of including whoever
happens to be available at the time .
It is also called “accidental” or
“haphazard” sampling.

34. DISADVANTAGES OF
CONVENIENCE SAMPLING:
 Difficulty in determining how much
of the effect (dependent variable)
results from the cause (independent
variable)

35. PURPOSIVE SAMPLING
The process whereby the researcher
selects a sample based on experience
or knowledge of the group to be
sampled. It is also called “judgment”
sampling.

36. DISADVANTAGES OF
PURPOSIVE SAMPLING:
 Potential for inaccuracy in the
researcher’s criteria and resulting
sample selection.

37. END
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