Advance Research Techniques; How to make samples Abdurrahman Abdalla .. كيف تؤخد العينة في طرق البحث المتقدم .. إعداد عبدالرحمن المهدي نصير جامعة الشرق الادنى - قبرص الشمالية

1. Prepared By: Abdurrahman Abdalla
ID Number: 20145652
Supervised by: Dr. ÇİSE ÇAVUŞOĞLU
Fall 2014/2015

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3. What is the difference between probability
and non-probability sampling?

4. The difference between Probability and Non-
probability sampling
Probability sampling
Probability sampling involves the
selection of a sample from a
population, based on the principle
of randomization or chance.
Probability sampling is more
complex, more time-consuming and
usually more costly than non-
probability sampling. There are
several different ways in which a
probability sample can be selected.
The method chosen depends on a
number of factors, such as the
available sampling frame, how
spread out the population is, how
costly it is to survey members of the
population and how users will
analyze the data.
In non-probability sampling
there is an assumption that
there is an even distribution of
characteristics within the
population. This is what
makes the researcher believe
that any sample would be
representative and because of
that, results will be accurate.
In non-probability there is no
way to estimate the probability
of any one element being
included in the sample.
Non-probability sampling

5. Simple random sampling
Systematic sampling
Sampling with probability proportional to size
Stratified sampling
Cluster sampling
Multi-stage sampling
Multi-phase sampling
The most Common Types of
Probability Sampling

6. Convenience or haphazard sampling
Volunteer sampling
Judgment sampling
Quota sampling
The most common types of Non-
probability sampling :

7. The differences between sampling in
quantitative and qualitative research
Quantitative research
In quantitative research
you attempt to select a
sample in such a way that
it is unbiased and
represents the population
from where it is selected.
In qualitative research,
number considerations may
influence the selection of a
sample such as: the ease in
accessing the potential
respondents; your judgement
that the person has extensive
knowledge about an event or
a situation of interest to you;
how typical the case is of a
category of individuals or
simply that it is totally
different from the others.
Qualitative research

8. The purpose of sampling in
quantitative research is to draw
inferences about the group
from which you have selected
the sample.
In quantitative research you are
guided by a predetermined
sample size that is based upon a
number of other considerations
in addition to the resources
available.
In qualitative research it is designed
either to gain in-depth knowledge
about a situation/ event or to know
as much as possible about different
aspects of an individual on the
assumption that the individual is
typical of the group and hence will
provide insight into the group.
In qualitative research you do not
have a predetermined sample size
but during the data collection
phase you wait to reach a point of
data saturation.
Quantitative research Qualitative research

9. Considerable importance is placed on
the sample size in quantitative research,
depending upon the type of study and
the possible use of the findings. This is
based upon the principle that a larger
sample size will ensure the inclusion of
people with diverse backgrounds, thus
making the sample representative of the
study population.
In quantitative research,
randomization is used to avoid
bias in the selection of a sample
and is selected in such a way that
it represents the study population.
The sample size in qualitative
research does not play any
significant role as the purpose
is to study only one or a few
cases in order to identify the
spread of diversity and not its
magnitude.
In qualitative research no such
attempt is made in selecting a
sample .You purposely select '
information-rich' respondents
who will provide you with the
information you need.
Quantitative research Qualitative research

10. Statistical Sampling Techniques
Statistical sampling techniques: are the
strategies applied by researchers during the
statistical sampling process.
This process is done when the researcher aims
to draw conclusions for the entire population
after conducting a study on a sample taken
from the same population.

11. Concerns in Statistical Sampling
Representativeness
Practicability
Sampling Risks

12. Representativeness
This is the primary concern in statistical sampling. The sample
obtained from the population must be representative of the
same population.
This can be accomplished by using randomized statistical
sampling techniques or probability sampling like cluster sampling
and stratified sampling. The reason behind representativeness
being the primary concern in statistical sampling is that it allows
the researcher to draw conclusions for the entire population. If
the sample is not representative of the population, conclusions
cannot be drawn since the results that the researcher obtained
from the sample will be different from the results if the entire
population is to be tested.

13. Practicability of statistical sampling techniques allows
the researchers to estimate the possible number of
subjects that can be included in the sample, the type of
sampling technique, the duration of the study, the
number of materials, ethical concerns, availability of
the subjects/samples, the need for the study and the
amount of workforce that the study demands.
All these factors contribute to the decisions of the
researcher regarding to the study design.
Practicability

14.  There are two types of sampling risks, first is the risk of incorrect
acceptance of the research hypothesis and the second is the risk for
incorrect rejection. These risks pertain to the possibility that when a
test is conducted to a sample, the results and conclusions may be
different from the results and conclusions when the test is conducted
to the entire population.
 The risk of incorrect acceptance pertains to the risk that the sample can
yield a conclusion that supports a theory about the population when it
is actually not existent in the population.
 On the other hand, the risk of incorrect rejection pertains to the risk
that the sample can yield a conclusion that rejects a theory about the
population when in fact, the theory holds true in the population.
Sampling Risks

15. The sample size of a statistical sample is the
number of observations that constitute it:
The sample size is typically denoted by
(n) and it is always a positive integer. No
exact sample size can be mentioned here
and it can vary in different research
settings. However, all else being equal,
large sized sample leads to increased
precision in estimates of various
properties of the population.

16. What should be the sample size?
• Determining the sample size to be selected is an important step
in any research study. For example let us suppose that some
researcher wants to determine prevalence of eye problems in
school children and wants to conduct a survey.
• The important question that should be answered in all sample
surveys is "How many participants should be chosen for a
survey"? However, the answer cannot be given without
considering the objectives and circumstances of investigations.
• The choosing of sample size depends on non-statistical and
statistical considerations. The non-statistical considerations may
include availability of resources, manpower, budget, ethics
and sampling frame. The statistical considerations will include
the desired precision of the estimate of prevalence and the
expected prevalence of eye problems in school children.

18. Sample Error
Sampling error: is the deviation of the selected sample
from the true characteristics, traits, behaviors, qualities or
figures of the entire population.
Why Does This Error Occur?
Researchers draw different subjects from the same population
but still, the subjects have individual differences. Keep in mind
that when you take a sample, it is only a subset of the entire
population; therefore, there may be a difference between the
sample and population.

19. The most frequent cause of the said error is a biased sampling
procedure. Every researcher must seek to establish a sample
that is free from bias and is representative of the entire
population. In this case, the researcher is able to minimize or
eliminate sampling error.
Another possible cause of this error is chance. The process
of randomization and probability sampling is done to
minimize sampling process error but it is still possible that all
the randomized subjects are not representative of the
population. The most common result of sampling error
is systematic error wherein the results from the sample differ
from the results of the entire population. It follows logic that if
the sample is not representative of the entire population, the
results from it will most likely differ from the results taken
from the entire population.

20. Sample Size and Sampling Error
Given two exactly the same studies, same sampling
methods, same population, the study with a larger
sample size will have less sampling process error
compared to the study with smaller sample size.
Keep in mind that as the sample size increases, it
approaches the size of the entire population,
therefore, it also approaches all the characteristics
of the population, thus, decreasing sampling
process error.

21. THE MEANING OF “DATA ETHICS”
Ethics: (derived from the Greek ethos, meaning character, custom,
or usage), or morality (from the Latin synonym meaning manner,
custom, or habit), is the philosophical study of normative behavior, the
"shoulds" and "oughts," the "rights" and "wrongs," of our conduct.
Research ethics is a kind of applied of practical ethics, meaning that it
attempts to resolve not merely general issues but also specific
problems that arise in the conduct of research. Its goal is to determine
the moral acceptability or appropriateness of specific conduct and to
establish the actions that moral agents ought to take in particular
situation. Research ethics is therefore not merely theoretical. It aims to
establish practical moral norms and standards for the conduct of the
research. This is the most common way of defining "ethics": norms for
conduct that distinguish between acceptable and unacceptable
behavior.(David B. Resnik, J.D., Ph.D.).

22. Ethics: is commonly defined as the rule of
behavior or norms of conduct that
differentiate between acceptable and
unacceptable practices. Guidance of scientists
by an appropriate ethical compass is
paramount in research because scientists
occupy a unique position of trust with the
readers/users of their data.(Mike Brown 2014)
THE MEANING OF “DATA ETHICS”

23. Ethics may be divided into three
major areas of study:
Meta-ethics, about the theoretical meaning and reference of
moral propositions and how their truth values (if any) may
be determined
Normative ethics, about the practical means of determining
a moral course of action
Applied ethics draws upon ethical theory in order to ask
what a person is obligated to do in some very specific
situation, or within some particular domain of action (such
as business)

24. Ethical Decision Making in Research
Although codes, policies, and principals are very
important and useful, like any set of rules, they do
not cover every situation, they often conflict, and
they require considerable interpretation. It is
therefore important for researchers to learn how to
interpret, assess, and apply various research rules
and how to make decisions and to act in various
situations. The vast majority of decisions involve
the straightforward application of ethical rules.

25. Participants’ rights
Participants have a right to:
• consent to participate, withdraw from, or refuse to take part in
research projects; confidentiality: personal information or identifiable
data should not be disclosed without participants’ consent;
• security: data and samples collected should be kept secure and
anonymous where appropriate; and safety: participants should not be
exposed to unnecessary or disproportionate levels of risk.
Researchers’ obligations
Researchers have an obligation to ensure that their research is
conducted with:
• honesty;
• integrity;
• minimal possible risk to participants and to themselves; and
• cultural sensitivity.
Principles of research ethics

27. References
 Creswell, J.W.& Miller, D,. (2002). Determining validity in qualitative inquiry. Theory into
Practice, 39(3), 124-130.
 Creswell, J. W. (2002). Educational research: Planning conducting , and quantitative and
qualitative approaches to research Merrill/Education.
 Explorable Psychology Experiments Published on Explorable.com
(https://explorable.com/statisticalsampling-techniques)
 Mike Brown. (2014). Importance and impact of research ethics on industry. Kansas City
Convention Center.
 Ranjit Kumar. (2011). Research Methodology, Third Edition.