This document discusses sampling methods for research. It explains that sampling is necessary when studying the entire population is not feasible due to constraints of time and cost. The main types of sampling discussed are probability sampling, which allows statistical generalization, and purposive sampling, which is used for qualitative research. Specific probability sampling methods explained include simple random sampling, stratified random sampling, cluster sampling, and quota sampling. Purposive sampling methods described are a priori criteria sampling and snowball sampling. Factors to consider when choosing a sampling method, such as the research question and desired level of detail or generalization, are also covered.

1. By:
Engr. Jayson M. Narito,
REE, RMP
M.S. Construction
Management
Student No.: 2014 -
11475-MN-O
Po lytechnic U niversity
o f t he Philippines
( Open U niversity)
SAMPLING (FOR
RESEARCH)

2. PURPOSE OF
SAMPLING
WHY SAMPLE?
SAMPLING

3. WHY SAMPLE?
DIFFERENT KINDS OF SAMPLES
DETERMINATION OF SAMPLING SIZE

4. PURPOSE
OF
SAMPLE
WHY SAMPLE?

5.  SAMPLING is used when it is not possible or
practical to include the entire research
population in your study, which is usually the
case.

6. WHAT IS SAMPLING?
 SAMPLING is the process of selecting a few from
many the many in order to carry out empirical
research. It needs to be accepted from the outset
that a sample represents a form of trade-off
between the desirable and the attainable, but this
is more often the case and the goal is rarely to
make inferences about the wider population based
on this discovery.

7.  In most quantitative research the point is to take
a sample and make inferences about the rest of
the population based on that sample. With both
approaches it may well be much more
informative to study the entire population but
this would almost always be impossible based on
cost and time. For this reason we sample.
PURPOSE OF SAMPLING

8. NOTE:
 _METHOD OF SAMPLING USED PLAYS A MAJOR ROLE IN
ANY RESEARCH INVESTIGATION.
 _VERY OFTEN IT IS THE CHARACTERISTICS, COMPOSITION
AND SCALE OF THE SAMPLE THAT GIVE WEIGHT TO ANY
FINDINGS THAT EMERGE FROM THE INVESTIGATION.

9.  _WHEN SELECTING A SAMPLING TECHNIQUE:
 There are a number of different approaches to sampling and
choice of approach should be influenced largely by the
purpose of the investigation.
 There is a need to demonstrate the appropriateness of the
chosen sample to the nature and output of the research.
 It is totally inappropriate to engage in a small scale,
localized qualitative study then attempt to generalize from
the findings.
 It is inappropriate to engage in large scale, broad study and
attempt to provide any real detail concerning individuals.

10. WHAT SAMPLE TECHNIQUE?
 _A SIMPLISTIC RULE OF THUMB:
Assume that quantitative research will tend to
use probability sampling techniques and
qualitative research will tend to use purposive
sampling.

11.  Qualitative Research may produce theoretical generalization, which
means it is possible to generalize from, for example, a case study to a
wider theory based on the findings of the case study.
 Your sample selection must be directly related to the type of study you
intend to conduct, the research question you are asking, and the type
of evidence you need to present in order to respond to that question.
 For example: Would you take a random sample of 13 to 16 year old
teenagers from a particular region if you wanted to investigate their
use of the internet? What if, when your random sample was decided,
none had ever touched a computer?
 Think carefully about the research question you are asking and the
nature of the response you want: do you want to be in the position to
make general statements or do you want to provide detailed insight?

12. POPULATION AND SAMPLE

13.  How do we go about selecting a sample and what do we select
the sample from?
 Your research population is the entire set of individuals about
which inference will be made.
 For example: before a political election opinion polls are used to
gauge the general trends that are emerging within the
population; these opinion polls are drawn from a small section of
the entire population in order to make inferences concerning the
likely outcome of the election, when every member of the
population should cast their own vote.
 So, the expressed opinion of a small number of the population is
used to infer political preferences within the entire population.
POPULATION AND SAMPLE

14.  Be wary of making over-exaggerated claims or extended
generalizations based on relatively small samples.
 Remember you are making a trade off but that does not mean
your discovery has no significance; you just need to be honest
with your reader when you put forward your inferences, make
aware of the sample and also make them aware that it is
impossible to account for all individual traits. This is discovery
based on a ‘best possible estimation’.
POPULATION AND SAMPLE

15. Simple
Random
sampling
Stratified
Random
sampling
Cluster
sampling
Quota
sampling
PROBABILITY SAMPLING

16.  Probability sampling is applied in order to provide a statistical
basis for generalizing from a research study to a wider
population.
 There are a number of techniques available that allow or
statistical generalizations but remember that the logic of
statistical generalization demands that certain conditions are
met.
 The conditions are as follows:
 Sample is representative of a wider population
 Wider population is properly defined
 Sample was drawn from a population using probability sampling methods
PROBABILITY SAMPLING

17.  Even where a sample is obtained using probability
sampling methods, the ability of such sampling to produce
a representative sample will depend on:
 The adequacy of the sampling frame from which the sample was
drawn;
 Any bias in response and non-response from the selected sample
units
(De Vaus, 2002a, 149)
 These conditions apply regardless of the sampling
procedure you choose and you must always be aware of the
limitations of your final sample when you discuss the
nature and consequences of your research findings
PROBABILITY SAMPLING

18.  It is always preferable to calculate the sampling error present
in your research as this provides your reader with a more
realistic understanding of the significance of your findings.
‘Error’ in this case does not mean a ‘mistake’, it is the term
used to demonstrate the likely variance between results
obtained from the sample and characteristics of the
population as a whole. A general rule is that the larger the
sample size the smaller the sampling error.
 Example: Assume that the research population is the entire
membership of professional organization
PROBABILITY SAMPLING

19. Random sampling is a
procedure of creating a sample
where each member of the
defined population has an equal
chance of being selected for
inclusion and the selection of
one participant depends on the
selection of any other from that
population. A simple random
sample can be drawn in several
ways.
Probability
sampling
SIMPLE
RANDOM
SAMPLING

20.  Example:
We could write the name of every
member of the professional association
on a separate slip of paper; all of the
names could then be placed in a large
container. We then draw out random
slips of paper until we have the number
required for our sample.
Remember that each slip would need to
be replaced after the name was noted in
order to ensure the number of slips in
the container named constant. If these
number decreases (if we did not replace
the slips) then the probability of
selection would improve for each new
participant.
Probability
sampling
SIMPLE
RANDOM
SAMPLING

21.  This method is the most basic way of
selecting a simple random sample, but it can
also be the most unwieldy; if the research
population is very large this could prove a
very arduous task.
 An alternative is to use a random number
table, a table of numbers where the numbers
are listed in no particular order and no order
and no number occurs any more frequently
than any other. Using the example by
assuming the association had a membership
of 3000 we would give each association
member a number from 1 to 3000 on a
separate population list.
 Using the random number table and entering
the table at any point we would work
horizontally or vertically through the table
until we had drawn the required sample,
ticking off the membership number on our
population list as they were selected. This
process can be performed using a computer
program that numbers each member of the
population, generates a list of random
numbers and then produces a sample list
based on those numbers.
Probability
sampling
SIMPLE
RANDOM
SAMPLING

22.  Stratified random sampling allows
for random selection within each
group or strata. It has a two stage
process. First, the group is
identified and the research
population is listed within their
groups. Once this list has been
prepared a random sample is
taken from the group in the same
way as a simple random sample
would be drawn from the entire
research population.
 It is important to remember that
each group should be represented
in the sample in equal proportion
to the size of that group in relation
to the entire research population.
Probability
Sampling
STRATIFIED
RANDOM
SAMPLING

23.  Each group within the research
population should be taken as a
separate population, then simple
random sampling can be carried
out to draw the correct number
from the group using one of the
techniques discussed in the
previous section.
Probability
Sampling
STRATIFIED
RANDOM
SAMPLING

24. CLUSTER
SAMPLING
 Clusters may be selected by the
researcher when the research
population is very large and often
spread over a wide geographical
area, or groups demonstrate a
common characteristic that a
direct relationship with a main
variable in the research question
(similar to stratified sampling).
 Clusters can be identified based
on geographic location. In the case
of our example – professional
association this could be done
based on regional groups if it were
a national association.
Probability
Sampling

25.  If we were identifying clusters based
on the nature of professional activity,
clusters could be identified as sub-
groups within the association. This
type of sampling is most common in
educational research (Burns, 2000)
where it would be impossible to take
a sample based on, for example, the
entire population of children in
compulsory education.
 Based on the assumption that all
state run schools will be following a
similar curriculum it is possible to
select individual schools based on
geographic location.
Probability
Sampling
CLUSTER
SAMPLING

26. QUOTA
SAMPLING
 Quota sampling is sometimes referred
to as convenience sampling as it is
based on the researcher’s ease of
access to the sample.
 With quota sampling, a required
percentage of the total research
population is identified (quota). There
may be some visible characteristics
that are used to guide the sample, for
example the researcher wishes to draw
a sample that is 50% female, 50%
male. The researcher then takes up
position in a convenient location and
asks all possible participants who pass
to be involved in the research. This is
often the technique used by markets
researchers when identifying random
members of the public in shopping
centers or other such public places.
Probability
Sampling

27.  Using the example – professional
association, the researcher would
set a quota, that is determine the
size of sample required for the
research. The researcher would
then seek permission to take up
position in one of the central
common rooms in the association
headquarters. The researcher
would approach every member
who enters the common room until
the quota for the sample has been
achieved.
Probability
Sampling
QUOTA
SAMPLING

28. A Priori
Criteria
Sampling
Snowball
Sampling
PURPOSIVE SAMPLING

29.  There are two possible approaches to purposive sampling: a
priori sampling, which establishes sample framework before
sampling begins; and snowball sampling which takes an
inductive approach to ‘growing’ the sample as the research
progresses.
 If this is your first attempt at qualitative research, or if you
are very restricted by time, you may want to create some
boundaries to your sample by applying a more rigid structure
such as a Priori sampling. It is not strictly speaking,
consistent with the concept of emerging theory but from a
practical sense it offers some security while still allowing for
theoretical sampling within the structure.
PURPOSIVE SAMPLING

30.  As with all sampling, it is the purpose of the research that
should drive the choice of sampling technique; a priori criteria
sampling is more useful for ‘analysing, differentiating and
perhaps testing assumptions about common features and
differences between groups’ (Flick,2002,63).
 Snowball and theoretical sampling are processes that allow
for ‘on-going joint collection and analysis of data associated
with the generation of theory.’ (Glaser and Strauss, 1967, 48).
PURPOSIVE SAMPLING

31.  A priori criteria sampling may
represent a trade off between a
totally emergent research design
and a more structured a priori
design but it also allows for an
element of inductive design within
the framework that is created. In a
similar way to probability sampling
criteria are identified from the
conceptual framework of the
research study, those cognitive
signposts developed from the
literature review.
Purposive
Sampling
A PRIORI
CRITERIA
SAMPLING

32.  Criteria are identified and used to
create a grid. Once this is done
each cell within that grid needs to
be represented in the final sample.
This is as far as the a priori
determination of the sample goes;
within each cell sampling can be
done in an inductive way. The
researcher can now engage in
snowball sampling to populate
each cell.
Purposive
Sampling
A PRIORI
CRITERIA
SAMPLING

33. Associate Affiliate Fellow
Male Female Male Female Male Female
NE
NW
MID
SE
SW
Purposive
Sampling
A PRIORI
CRITERIA
SAMPLING
•From our example: we have investigated the literature on the issue
and have discovered that the significant criteria that appear to
influence a professional’s attitude towards their association are
gender type of membership and location. Using this information, we
construct a sample grid for our investigation. We know we have to
identify members who fit these cells and attempt to fill each cell as
evenly as possible to build our sample.
•Once the overall structure has been determined we can identify
individuals in an inductive manner more appropriate to qualitative
research until the cells are evenly populated.

34. SNOWBALL
SAMPLING
 Snowball sampling or Interactive
sampling is the technique that is
most commonly used to identify a
theoretical sample and it can be
accomplished in two ways.
 The first and original method of
this type of sampling is to make
initial contact with key informants
who, in turn, point to information-
rich cases. The second is to begin
with an initial participant who
issues that needs further inquiry.
Purposive
Sampling

35.  These characteristics form the criteria
used to identify subsequent cases in order
to provide a suitable sample (Lincoln and
Guba, 1985; Patton, 2002).
 ‘Purposive and directed sampling through
human instrumentation increases the
range of data exposed and maximizes the
researcher’s ability to identify emerging
themes’ (Erlandson et al., 1993, 82)
 ‘ The sample was not chosen on the basis
of some “a priori” criteria but inductively
in line with the developing conceptual
requirements of the study’ (Ellis, 1993,
473).
 This type of sampling demands a viable
exit strategy. As there are no ‘a priori’
numerical restrictions placed on the
sample, the danger of over-saturation
could become highly significant.
Purposive
Sampling
SNOWBALL
SAMPLING

36.  Lincoln and Guba do suggest that a
dozen or so interviews, if properly
selected, will exhaust most available
information; to include as many as
twenty will surely reach well beyond
the point of redundancy’.
 As they relate this suggestion only to
the interview situation, it could not be
as readily applied to long term
observation and multiple interviews,
which may be a part of an in-depth
study of each case.
 The researcher makes the decision to
terminate sampling, based on
information redundancy and other
restrictions on the study, such as time
and resources. Like any form of
sampling, snowball sampling may also
be subject to compromise.
Purposive
Sampling
SNOWBALL
SAMPLING

37.  Snowball sampling can be applied
to building various types of
sample. Patton (2002) provides
definitions of six types of sample
that can be built applying snowball
sampling techniques: extreme or
deviant cases, typical cases,
maximum variation cases, critical
cases, politically relevant cases, or
convenience samples. The type of
case (sample unit) that is
identified depends on the purpose
of the research.
Purposive
Sampling
SNOWBALL
SAMPLING

38.  Theoretical sampling follows a
very similar process to snowball
sampling; the difference is in the
purpose of sample selection. With
theoretical sampling emerging
theory drives the selection of
subsequent participants. This
technique is particular to
grounded theory, where the
purpose of the research is to
generate theory, not to produce
generalizations about a wider
population outside the study
sample;
Purposive
Sampling
SNOWBALL
SAMPLING

39.  ‘Theoretical sampling is the
process of data collection for
generating theory whereby the
analyst jointly collects, codes and
analyses his data and decides
what data to collect next and
where to find them, in order to
develop his theory as it emerges.
This process of data collection is
controlled by the emerging the
theory’ (Glaser and Strauss, 1967,
45).
Purposive
Sampling
SNOWBALL
SAMPLING

40. The size of a sample is usually determined before
the conduct of any study. There are no fixed rules in
determining the size of a sample needed. However,
there are broad guidelines that should be observed
in determining the size of a sample (J.F. Calderon
1993 p.175):
GUIDELINES FOR DETERMINING
ADEQUATE SAMPLING

41. When the universe or
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.
GUIDELINES
FOR
DETERMINING
ADEQUATE
SAMPLING

42. 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
GUIDELINES
FOR
DETERMINING
ADEQUATE
SAMPLING

43. The size of a sample varies
inversely as with the size of the
population. A larger proportion
is required of a smaller
population and smaller
proportion may do for a bigger
population (i.e. For a
population of 5000, a sample
of 10% may do but for a
population of 500, a proportion
of 30% may be required)
GUIDELINES
FOR
DETERMINING
ADEQUATE
SAMPLING

44. For a greater accuracy and
reliability of results, a greater
sample is desirable.
In science experiments such as
testing the effects of
chemicals, the use of a few
persons is enough. If the
chemical is harmful to humans,
animals such as mice may do
as a replacement of sample.
GUIDELINES
FOR
DETERMINING
ADEQUATE
SAMPLING

45. 1. Determine the size of the
study population.
2. Decide the margin of error
(3%-5% is enough)
3. Use the formula:
𝑛 =
𝑁
1+(𝑁𝑒2)
Where: n= Size of Sample
N= Size of Population
e= Margin Error
STEPS IN
COMPUTING
THE SIZE OF A
SAMPLE

46. 4. If the sampling is multistage
or if the population is
stratified (to arrange in
layers), compute the sample
proportion or percent by
dividing the result in step 3 by
the population.
5. Multiply the number of
sampling units in each final
sampling stratum by the rate
or percent to find the amount
of sample from each final
sampling stratum.
6. Add the samples from all the
final sampling strata to find
the total sample.
STEPS IN
COMPUTING
THE SIZE OF A
SAMPLE

47. EXAMPLE:
Suppose an investigation of the
teaching of Science Subject in
High School of a region is to be
conducted, in which the science
teachers are to be made
respondents.
There are 2243 teachers
handling biological sciences,
1406 handling chemical
sciences, and 992 teachers
handling physical sciences, a
total of 4641 teachers.
STEPS IN
COMPUTING
THE SIZE OF A
SAMPLE

48. SOLUTION:
Step 1: The population is
4641
Step 2: The margin error to
be used is 3%
Step 3:
𝑛 =
𝑁
1+(𝑁𝑒2)
=
4641
1+(4641 𝑥 0.032)
𝑛 = 896.4825 ≈ 900
STEPS IN
COMPUTING
THE SIZE OF A
SAMPLE

49. SOLUTION:
Step 4: The teachers are
grouped into three
categories according to the
branch of science they are
handling, so we use
stratified sampling.
Sample Proportion = n/N =
900/4641 = 0.1939 ≈ 0.2 or
20%
STEPS IN
COMPUTING
THE SIZE OF A
SAMPLE

50. SOLUTION:
Step 5 & 6:
The answer is 928
STEPS IN
COMPUTING
THE SIZE OF A
SAMPLE
Teaching Handling Number Percentage Sample
Biological Sciences 2243 20% 449
Chemical Sciences 1406 20% 281
Physical Sciences 992 20% 198
Total 4641 - 928

51. SUMMARY
 Sampling is a vital stage in the research process; the
outcomes , rigour and trustworthiness of your research all rely
on the robustness of the sample and how that sample was
identified. The sampling technique you applied must be
appropriate to your research goals and conform to the
research tradition you have chosen for your investigation. Any
claims you make concerning generalization, applicability,
transferability and significance will all be judged in view of
your empirical evidence and the source of that evidence.

52.  But we are all aware that reality and theory are rarely a
perfect match; compromise is inevitable in research and all
sampling has limitations. What is important is that you
choose a technique that matches your research design, you
are open and honest about your sample composition and you
provide your reader with sufficient detail to understand the
significance of your findings and any bias that may exist as a
result of your sampling: ‘ These techniques are, of course, the
ideal. Few researchers, apart from government bodies, have
the resources and time to obtain truly representative samples.
For most research, investigators often have to make do with
whatever subjects they can gain access to.
SUMMARY

53. -END-