This document discusses different sampling methods used in research. It defines sampling as selecting a portion of a population to make generalizations about the whole population. There are two main types of sampling: probability sampling, where every member of the population has a known chance of being selected, and non-probability sampling, where not every member has a chance of selection. Some common probability sampling methods described are simple random sampling, systematic sampling, and stratified sampling. Non-probability sampling methods include convenience sampling and purposive sampling. The document provides examples and steps for implementing different sampling designs.

1. SAMPLING
DESIGN
PROBABILITY SAMPLING &
NON-PROBABILITY
SAMPLING

2. Definition of Sampling:
 Measuring a small portion of something
and then making a general statement
about the whole thing.
 Process of selecting a number of units for
a study in such a way that the units
represent the larger group from which
they are selected.

3. Why We Need Sampling (Purposes and
Advantages of Sampling)
 Sampling makes possible the study of a
large, population.
- The universe or population to be studied
maybe too large or unlimited that it is almost
impossible to reach all of them. Sampling
makes possible this kind of study because in
sampling only a small portion of the population
maybe involved in the study, enabling the
researcher to reach all through this small portion
of the population.

4. Why We Need Sampling (Purposes and
Advantages of Sampling)
1. Sampling is for economy.
- Research without sampling may be too
costly. Sampling reduces the study
population to a reasonable size that
expenses are greatly reduced.
 Sampling is for speed.
- Research without sampling might be
too time consuming.

5. Why We Need Sampling (Purposes and
Advantages of Sampling)
1. Sampling is for accuracy.
- If it takes too long a time to cover the
whole study population, there maybe
inaccuracy. The research must be
finished within a reasonable period of
time so that the data are still true, valid
and reasonable.

6. Why We Need Sampling (Purposes and
Advantages of Sampling)
1. Sampling saves the sources of data
from being all consumed.
- The act of gathering data may
consume all the sources of information
without sampling. In such a case, there is
no more data to apply the conclusion to.

7. Disadvantages of Sampling (Defective
Sampling)
1. If sampling is biased, or not representative, or
too small, the conclusion may not be valid and
reliable.
2. In research, the respondents to a study must
have a common characteristics which is the
basis of the study.

8. Disadvantages of Sampling (Defective
Sampling)
1. If the population is very large and there are
many sections and subsections, the sampling
procedure becomes very complicated.
2. If the researcher does not possess the
necessary skill and technical knowhow in
sampling procedure.

9. WHAT IS A GOOD SAMPLE?
 The sample must be valid.
 Validity depends on 2 considerations:
1. Accuracy – bias is absent from the sample
(ex. A company is thinking of lowering its price for its
soap bar product. After making a survey in the sales
of their product in a known mall in Makati they
concluded that they will not cut down the price of the
soap bar since there was an increased in sales
compared to last year. Bias is present in this study
since the company based its decision for the sales of a
known mall which have consumers who can afford
high price products. They did not consider the sales of
their products in other area wherein they have middle
class or low class consumers.)

10. WHAT IS A GOOD SAMPLE?
2. Precision – sample represents the population
(ex. Customers who visited a particular dress shop
are requested to log in their phone numbers so
that they will receive information for discounts
and new arrivals. Management wish to study
customers satisfaction for that shop. By means
of interviewing thru phone they get comments
and reactions of their client. Samples used are
not an exact representative of the population
since it is limited only to those customers who
log in their phone numbers and they did not
consider customers without phone numbers
indicated.

11. STEPS IN SAMPLING DESIGN
1. What is the target population?
- Target population is the aggregation of elements (members
of the population) from which the sample is actually selected.
3. What are the parameters of interest?
- Parameters are summary description of a given variable in a
population.
5. What is the sampling frame?
- Sampling frame is the list of elements from which the sample
is actually drawn. Complete and correct list of population
members only.
7. What is the appropriate sampling method?
- Probability or Non-Probability sampling method

12. STEPS IN SAMPLING DESIGN
1. What size sample is needed?
There are no fixed rules in determining the size of a sample
needed. There are guidelines that should be observed in
determining the size of a sample.
 When the population is more or less homogeneous and
only the typical, normal, or average is desired to be
known, a smaller sample is enough. However, if
differences are desired to be known, a larger sample is
needed.
 When the population is more or less heterogeneous and
only the typical, normal or average is desired to be
known a larger sample is needed. However, if only their
differences are desired to be known, a smaller sample is
sufficient.

13. STEPS IN SAMPLING DESIGN
 The size of a sample varies inversely as the size of the
population. A larger proportion is required of a smaller
population and a smaller proportion may do for a bigger
population.
 For a greater accuracy and reliability of results, a greater
sample is desirable.
 In biological and chemical experiments, the use of few
persons is more desirable to determine the reactions of
humans.
 When subjects are likely to be destroyed during
experiment, it is more feasible to use non-humans.

14. STEPS IN SAMPLING DESIGN
Example:
A Company would like to make a study in the quality of digital
cameras it manufactured.
3. Target population – consumers of digital cameras
4. Parameters of interest – quality of digital cameras (scale of 1 to
5 , 5 being the most satisfactory)
5. Sampling frame – database of stores in which digital cameras
are sold, usually customers gives information about them for
warranty purposes
6. Sampling method – Probability sampling (Stratified sampling).
7. Size of sample – it is more on heterogeneous population,
average responses would like to know by the manufacturer, so
large proportion will be needed from the population.

15. STEPS IN COMPUTING THE
SIZE OF A SAMPLE
 Determine the size of the target population.
 Decide on the margin of error. As much as possible
the margin of error should not be higher than 5%.
Probably 3% is an ideal one.
 Use the formula n = N
1 + Ne2 (pagoso , et al. p.46)
n = sample size
N = the size of the population
e = the margin of error
8. Compute the sample proportion by dividing the result
in number 3 by the population.

16. STEPS IN COMPUTING THE
SIZE OF A SAMPLE
1. Population is 5,346
2. Margin of error is 3%
3. Using the formula
n = ___5,346_
1+ 5346(.03)2
n = 920
7. Sample proportion (%) = 920 / 5346
= 17%

17. General Types of Sampling
 Probability sampling
 the sample is a proportion (a certain percent)
of the population and such sample is selected
from the population by means of some
systematic way in which every element of the
population has a chance of being included in
the sample.

18. PROBABILITY SAMPLING
 randomization is a feature of the selection
process rather than an assumption about the
structure of the population.
 more complex, time consuming and more costly

19. General Types of Sampling
 Non-probability sampling
 the sample is not a proportion of the
population and there is no system in selecting
the sample. The selection depends upon the
situation.

20. NON-PROBABILITY SAMPLING
 no assurance is given that each item has a
chance of being included as a sample
 there is an assumption that there is an even
distribution of characteristics within the
population, believing that any sample would be
representative.

21. TYPES OF PROBABILITY
SAMPLING

22. A. PURE RANDOM
SAMPLING
 this type of sampling is one in which every one
in the population of the inquiry has an equal
chance of being selected to be included in the
sample.
 also called the lottery or raffle type of sampling.
 this may be used if the population has no
differentiated levels, sections, or classes.
 done with or without replacement

23. PURE RANDOM SAMPLING
 main advantage of this technique of sampling
is that, it is easy to understand and it is easy
to apply too.
 disadvantage is that, it is hard to use with too
large a population because of the difficulty
encountered in writing the names of the
persons involved.

24. PURE RANDOM SAMPLING
 Steps in selecting sample using a table of random numbers:
 Define the population
 Determine the desired sample size
 List all the members of the population
 Assign each of the individuals on the list a
consecutive number from zero to the required
number, ex. 01-89 or 001-249
 Select an arbitrary number in the table of random
numbers (Close your eyes and point)
 For the selected number, look at only the
appropriate number of digits
 If the selected number corresponds to the number
assigned to any individual in the population, then
that individual is in the sample
 Repeat the steps until the desired sample size is
reached.

25. B. SYSTEMATIC SAMPLING
 a technique of sampling in which every kth
name (old system of counting off) in a list may
be selected to be included in a sample.
 also called as interval sampling, there is a gap or
interval, between each selected unit in the
sample.
 used when the subjects or respondents in the
study are arrayed or arranged in some systematic
or logical manner such as alphabetical
arrangement and geographical placement from
north to south.

26. SYSTEMATIC SAMPLING
 Steps in systematic sampling:
 Define the population
 Determine the desired sample size
 Obtain a list (preferably randomized) of the population
 Determine what K is equal to by dividing the size of the
population by the desired sample size
 Select some random place at the top of the population list
 Starting at that point, take every Kth name on the list until
desired sample size is reached
 If the end of the list is reached before the desired sample is
reached, go back to the top of the list.

27. SYSTEMATIC SAMPLING
k = skip interval = population size
sample size
population size = 64
sample size = 8
k=8

28. SYSTEMATIC SAMPLING
 main advantage is that it is more convenient,
faster, and more economical
 disadvantage is that the sample becomes biased
if the persons in the list belong to a class by
themselves whereas the investigation requires
that all sectors of the population are to be
involved.

29. C. STRATIFIED SAMPLING
 the process of selecting randomly, samples from the
different strata of the population used in the study.
 advantage is that it contributes much to the
representative of the sample
 Steps involves in stratified sampling:
 Define the population
 Determine the desired sample size
 Identify the variable and subgroups (strata) for which you want to
guarantee appropriate representation (either proportion or equal)
 Classify all members of the population as members of one of the
identified subgroups
 Randomly select (using table of random numbers) an appropriate
number of individuals from subgroups.

30. STRATIFIED SAMPLING
Example: A call center company wants to seek suggestions of their agents for a
new marketing strategy for their new services.
1. Population 5,000 agents.
2. Desired sample size 500
3. Variable of interest is age and there are three subgroups under 30, 30 to 45 and
over 45
4. We classify the agents into the subgroups
20% or 1,000 are under age 30
65% or 3,250 are age 30 to 45
15% or 750 are over age 45
5. We want 500 agents. Since we want proportional representation.
20% of the sample (100) under age 30
65% (325) should be age 30 to 45
15% (75) should be over age 45
Therefore, using table of random numbers,
100 of the 1000 under age 30 are selected
325 of the 3250 age 30 to 45 are selected
75 of the 750 over age are selected

31. D. CLUSTER SAMPLING
 also called as multistage cluster sampling
 used when the population is so big or the geographical
area of the research is so large.
 Advantage : Efficiency
 Disadvantage: reduced accuracy or representativeness,
on the account of the fact that in every stage there is a
sampling error.

32. CLUSTER SAMPLING
 Steps in cluster sampling:
 Define the population
 Determine the desired sample size
 Identify and define a logical cluster
 Obtain, or make a list of all clusters in the population
 Estimate the average number of population members per
cluster
 Determine the number of clusters needed by dividing the
sample size by the estimated size of the cluster
 Randomly select the needed number of clusters (using a
table of random numbers)
 Include in the sample all population members in selected
cluster

33. CLUSTER SAMPLING
 Same example in the stratified sampling:
2. Population 5,000 agents
3. Desired sample size 500
4. Logical cluster is a branch
5. 50 branches all over the country
6. Although the branch vary in number of agents , there is
an average of 100 agents per branch.
7. The number of clusters (branch) needed equals the
desired sample size, 500 divided by the average size of a
cluster, 100. Thus, the number of branch needed is 5.
8. Therefore, we randomly select 5 of the 50 branch
9. All the agents in each of the 5 selected branch are in
the sample.

34. TYPES OF NON-
PROBABILITY SAMPLING

35. A. ACCIDENTAL SAMPLING /
CONVENIENCE SAMPLING
 no system of selection but only those whom the
researcher or interviewer meet by chance are
included in the sample.
 process of picking out people in the most
convenient and fastest way to immediately get
their reactions to a certain hot and controversial
issue.

36. ACCIDENTAL / CONVENIENCE SAMPLING
 not representative of target population because
sample are selected if they can be accessed easily
and conveniently.
 Advantage : easy to use
 Disadvantage: bias is present
 it could deliver accurate results when the
population is homogeneous.

37. ACCIDENTAL / CONVENIENCE SAMPLING
Examples:
 the female moviegoers sitting in the first row of a
movie theatre
 the first 100 customers to enter a department
store
 the first three callers in a radio contest
 use of volunteers

38. B. PURPOSIVE SAMPLING
 the respondents are chosen on the basis of their
knowledge of the information desired.

39. TYPES OF PURPOSIVE
SAMPLING
1. QUOTA SAMPLING
 specified number of persons of certain types are
included in the sample.
 advantage over accidental sampling is that many
sectors of the population are represented. But
its representativeness is doubtful because there
is no proportional representation and there are
no guidelines in the selection of the
respondents.

40. PURPOSIVE SAMPLING
2. JUDGEMENT SAMPLING
 sample is taken based on certain judgements
about the overall population.
 Critical issue: Objectivity “how much can
judgement be relied upon to arrive at a typical
sample?”
 Advantage: reduced cost and time involved in
acquiring the sample