1. SAMPLES
in research methodology
quantitative AND qualititative
approach
Thushara RANASINGHE (ttr)
FACULTY OF LAW
INTAKE 29
RESEARCH METHODOLOGY

2. What is sampling
 Once the researcher has chosen a hypothesis to
test in a study, the next step is to select a pool of
participants to be in that study.
 Sampling, as it relates to research, refers to the
selection of individuals, units, and/or settings to be
studied.
 Whereas quantitative studies strive for random
sampling,
 qualitative studies often use purposeful or criterion-
based sampling, that is, a sample that has the
characteristics relevant to the research questions.

3. Why Sample for researches?
 First, it is usually too costly to test the entire population
 The second reason to sample is that it may be impossible to
test the entire population.
 The third reason to sample is that testing the entire population
often produces error.
 The final reason to sample is that testing may be destructive
 To draw conclusions about populations from samples.

4. Sampling methods in qualitative and
quantitative research
Assumptions of quantitative
sampling……….
 We want to generalize to
the population.
 Random events are
predictable.
 We can compare
random events to our
results.
Therefore
 Probability sampling is
the best approach.
Assumptions of qualitative
sampling………
Social actors are not
predictable like objects.
Randomized events are
irrelevant to social life.
Probability sampling is
expensive and inefficient.
therefore
Non-probability sampling
is the best approach.

5. size of the Sample
• sample size not matter in qualitative research Because
of the assumptions that qualitative researchers make,
namely, that the social world is not predictable.
Qualitative researchers believe that people are not like
molecules or other objects; people’s actions are not
predictable.
• But quantitative researchers believe that social
activity is predictable. So when they compare their
observations of social activity to what would happen in
purely random results, the difference says something.

6. Types of Samples
6
• Probability (Random) Samples
• Simple random sample
– Systematic random sample
– Stratified random sample
– Multistage sample
– Multiphase sample
– Cluster sample
• Non-Probability Samples
– Convenience sample
– Purposive sample
– Quota
– Snowball
– Theoretical

7. Simple random sample
• Simple random sampling is the most straight forward
of the random sampling strategies. We use this
strategy when we believe that the population is
relatively homogeneous for the characteristic of
interest.
• For example, let's say you were surveying first-time
parents about their attitudes toward mandatory seat
belt laws. You might expect that their status as new
parents might lead to similar concerns about safety.
On campus, those who share a major might also have
similar interests and values; we might expect
psychology majors to share concerns about access to
mental health services on campus.

8. Systematic sampling
• Systematic sampling yields a probability sample
but it is not a random sampling strategy (it is one
of our exceptions). Systematic sampling strategies
take every nth person from the sampling frame.
• For example, you choose a random start page
and take every 45th name in the directory until
you have the desired sample size. Its major
advantage is that it is much less cumbersome to
use than the procedures outlined for simple
random sampling.

9. Stratified random sampling
• Stratified random sampling is used when we have
subgroups in our population that are likely to differ
substantially in their responses or behavior. This sampling
technique treats the population as though it were two or
more separate populations and then randomly samples
within each.
• For example, you are interested in visual-spatial reasoning
and previous research suggests that men and women will
perform differently on these types of task. So, you divide
your sample into male and female members and randomly
select equal numbers within each subgroup (or "stratum").
With this technique, you are guaranteed to have enough of
each subgroup for meaningful analysis.

10. Multistage sampling
• Multistage sampling. This is our most
sophisticated sampling strategy and it is often
used in large epidemiological studies. To obtain a
representative national sample, researchers may
select zip codes at random from each state.
Within these zip codes, streets are randomly
selected. Within each street, addresses are
randomly selected. While each zip code
constitutes a cluster, which may not be as
accurate as other probability sampling strategies,
it still can be very accurate.

11. Cluster sampling
• Cluster sampling is useful when it would be impossible or
impractical to identify every person in the sample.
• Suppose a college does not print a student directory. It would
be most practical in this instance to sample students from
classes. Rather than randomly sample 10% of students from
each class, which would be a difficult task, randomly sampling
every student in 10% of the classes would be easier.
Sampling every student in a class is not a random procedure.
However, by randomly selecting the classes, you have a
greater probability of capturing a representative sample of the
population. Many students believe that it is not possible to
gather a representative sample for a class project or a thesis.
However, this type of cluster sampling is easily done,
especially since all colleges publish lists of classes for
registration.

12. Convenience sampling
• Convenience sampling selects a particular group
of people but it does not come close to sampling all
of a population.
• Convenience sampling is widely used in student
research projects. Students contact professors that
they know and ask if they can use their classes to
recruit research subjects. Convenience sampling
looks just like cluster sampling. The major difference
is that the clusters of research participants are
selected by convenience rather than by a random
process.

13. Purposive sampling
• Purposive sampling targets a particular group of people. When the
desired population for the study is rare or very difficult to locate and
recruit for a study, purposive sampling may be the only option.
• For example, you are interested in studying cognitive processing
speed of young adults who have suffered closed head brain injuries
in automobile accidents. This would be a difficult population to find.
Your city has a well-established rehabilitation hospital and you
contact the director to ask permission to recruit from this population.
The major problem with purposive sampling is that the type of
people who are available for study may be different from those in the
population who can't be located and this might introduce a source of
bias. For example, those available for study through the
rehabilitation hospital may have more serious injuries requiring
longer rehabilitation, their families may have greater education and
financial resources (which resulted in their choosing this hospital for
care).

14. Theoretical Sampling
• Theory-Based or Operational Construct or Theoretical
Sampling—dentifies manifestations of a theoretical
construct of interest so as to elaborate and examine
the construct. This strategy is similar to criterion
sampling, except it is more conceptually focused. This
strategy is used in grounded theory studies. You would
sample people/incidents, etc., based on whether or
not they manifest/represent an important theoretical
or operational construct. For example, if you were
interested in studying the theory of “resiliency” in
adults who were physically abused as children, you
would sample people who meet theory-driven criteria
for “resiliency.”

15. Snowball or Chain Sampling
• Snowball or Chain Sampling—Identifies cases of
interest from people who know people who know
what cases are information-rich, that is, who would be
a good interview participant. Thus, this is an approach
used for locating information-rich cases. You would
begin by asking relevant people something like: “Who
knows a lot about ?”
• For example, you would ask for nominations, until the
nominations snowball, getting bigger and bigger.
Eventually, there should be a few key names that are
mentioned repeatedly.

16. Advantages & disadvantages of
samples.
Technique advantages disadvantages
Quota Ensures selection of
adequate numbers of
subjects with appropriate
characteristics
Not possible to prove
that the sample is
representative of
designated population
Snowball Possible to include
members of groups
where no lists or
identifiable clusters even
exist (e.g., drug abusers,
criminals)
No way of knowing
whether the sample is
representative of the
population
convenience Inexpensive way of
ensuring sufficient
numbers of a study
Can be highly
unrepresentative

17. Technique Advantages disadvantages
Simple
random
Highly representative if all subjects
participate; the ideal
Not possible without
complete list of
population members;
Stratified
random
Can ensure that specific groups are
represented, even proportionally, in
the sample(s) (e.g., by gender), by
selecting individuals from strata list
More complex, requires
greater effort than simple
random; strata must be
carefully defined
Cluster Possible to select randomly when no
single list of population members
exists, but local lists do; data
collected on groups may avoid
introduction of confounding by
isolating members
Clusters in a level must be
equivalent and some natural
ones are not for essential
characteristics (e.g.,
geographic: numbers equal,
but unemployment rates
differ)
Purposive Ensures balance of group sizes when
multiple groups are to be selected
Samples are not easily
defensible as being
representative of populations
due to potential subjectivity
of researcher

18. Process
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• The sampling process comprises several stages:
– Defining the population of concern
– Specifying a sampling frame, a set of items or
events possible to measure
– Specifying a sampling method for selecting items
or events from the frame
– Determining the sample size
– Implementing the sampling plan
– Sampling and data collecting
– Reviewing the sampling process

19. Sampling Frame
• The list or procedure defining the POPULATION.
(From which the sample will be drawn.)
• Distinguish sampling frame from sample.
Examples:
Telephone book
Voter list
Random digit dialing
• Essential for probability sampling, but can be
defined for non-probability sampling

20. The time factor of sampling
• A sample may provide to us with needed information quickly.
Ex: you are a Doctor and a disease has broken out in a village
within your area of jurisdiction, the disease is contagious and it
is killing within hours nobody knows what it is. You are
required to conduct quick tests to help save the situation. If
you try a census of those affected, they may be long dead
when you arrive with your results. In such a case just a few of
those already infected could be used to provide the required
information.
• Accuracy and sampling A sample may be more accurate
than a census. A sloppily conducted census can provide less
reliable information than a carefully obtained sample

21. Sampling Problems
missing elements - individuals who should be on the list but for some
reason are not on the list.
Ex: if my population consists of all individuals living in a particular city
and I use the phone directory as my sampling frame or list, I will
miss individuals with unlisted numbers or who can not afford a
phone.
Foreign elements Elements which should not be included in
population and sample appear on the sampling list.
Ex: if I were to use property records to create my list of individuals
living within a particular city, landlords who live elsewhere would be
foreign elements. In this case, renters would be missing elements.
Duplicates These are elements who appear more than once on the
sampling frame.
Ex: if I am a researcher studying patient satisfaction with emergency
room care, I may potentially include the same patient more than
once in my study. If the patients are completing a patient satisfaction
questionnaire, I need to make sure that patients are aware that if
they have completed the questionnaire previously, they should not
complete it again. If they complete it more that once, their second
set of data respresents a duplicate.

22. References
• Camic, P. M, Rhodes, J. E., & & Yardley, L. (Ed.). (2003). Qualitative
research in psychology: Expanding perspectives in methodology and
design. Washington, DC: American Psychological Association.
• Creswell, J. W. (1998). Qualitative Inquiry & Research Design: Choosing
Among Five Traditions. Thousand Oaks: CA. Sag Publications, Inc.
• Dey, I. (1999). Grounding grounded theory: Guidelines for qualitative
inquiry. San Diego, CA: Academic Press.
• Harter, S. (1978). Effectance motivation reconsidered: Toward a
developmental model. Human Development, 21, 34–64.
• Harter, S. (1999). The construction of the self: A developmental perspective.
New York: Guilford.
• Hitchcock, J. H, Nastasi, B. K., Dai, D. C., Newman, J., Jayasena, A.,
Bernstein-Moore, R., Sarkar, S., & Varjas, K. (2004). Illustrating a mixed-
method approach for identifying and validating culturally specific constructs.
Accepted for publication in Journal of School Psychology.

23. Thushara RANASINGHE (ttr)
FACULTY OF LAW
INTAKE 29
RESEARCH METHODOLOGY