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Research methodology is a collective term for the structured process of
conducting research. There are many different methodologies used in various
types of research and the term is usually considered to include research
design, data gathering and data analysis.
Research methodologies can be quantitative (for example, measuring the
number of times someone does something under certain conditions) or
qualitative (for example, asking people how they feel about a certain
situation). Ideally, comprehensive research should try to incorporate both
qualitative and quantitative methodologies but this is not always possible,
usually due to time and financial constraints.
Research methodologies are generally used in academic research to test
hypotheses or theories. A good design should ensure the research is valid, i.e.
It clearly tests the hypothesis and not extraneous variables, and that the
research is reliable, i.e. It yields consistent results every time.
Part of the research methodology is concerned with the how the research is
conducted. This is called the study design and typically involves research
conducted using questionnaires, interviews, observation and/or experiments.
The term research methodology, also referred to as research methods, usually
encompasses the procedures followed to analyze and interpret the data
gathered. These often use a range of sophisticated statistical analyses of the
data to identify correlations or statistical significance in the results.
Objective, representative research can be difficult to conduct because tests can
normally only be conducted on a small sample (e.g. You cannot test a drug on
every person in the world so a sample needs to be used in research). This
means that researchers need to have a very detailed understanding of the types
and limitations of research methodologies which they are using.
Community Answer
Real-time nucleic acid detection system meeting your budget &
There are two main types of research methodology, 1- Quantitative
methodology, 2- Qualitative methodology.1- Quantitative methodology
is the type by which you test the significance of your hypothesis, in
other words you answer the words: How much? Is there a
relationship?Quantitative methods tend to be systematic and use
numbers... Actually it is a deep sea. However, 2- Qualitative
methodology is the type by which you are depending on your
observations and descriptions. It is subjectively and descriptive, no
facts.... This kind of method is used to assess knowledges, attitudes,
behaviours, and opinions of people depending on the topic of your
research. Researcher, in this type of method use his opinion and
experience which are not allowed to be used in quantitative method at
all.About the types of sample and sample size, I think they are apart of
research design not apart of the methodology.... For further reading
please do not hesitate to send an email to me: Orwa Alabdulla
Research Methods
To understand the use of statistics, one needs to know a little bit about
experimental design or how a researcher conducts investigations. A little
knowledge about methodology will provide us with a place to hang our
statistics. In other words, statistics are not numbers that just appear out of
nowhere. Rather, the numbers (data) are generated out of research. Statistics
are merely a tool to help us answer research questions. As such, an
understanding of methodology will facilitate our understanding of basic
A key concept relevant to a discussion of research methodology is that of
validity. When an individual asks, "Is this study valid?", they are questioning
the validity of at least one aspect of the study. There are four types of validity
that can be discussed in relation to research and statistics. Thus, when
discussing the validity of a study, one must be specific as to which type of
validity is under discussion. Therefore, the answer to the question asked
above might be that the study is valid in relation to one type of validity but
invalid in relation to another type of validity.
Each of the four types of validity will be briefly defined and described below.
Be aware that this represents a cursory discussion of the concept of validity.
Each type of validity has many threats which can pose a problem in a research
study. Examples, but not an exhaustive discussion, of threats to each validity
will be provided. For a comprehensive discussion of the four types of validity,
the threats associated with each type of validity, and additional validity issues
see Cook and Campbell (1979).
Statistical Conclusion Validity: Unfortunately, without a background in
basic statistics, this type of validity is difficult to understand. According to
Cook and Campbell (1979), "statistical conclusion validity refers to inferences
about whether it is reasonable to presume covariation given a specified alpha
level and the obtained variances (p. 41)." Essentially, the question that is
being asked is - "Are the variables under study related?" or "Is variable A
correlated (does it covary) with Variable B?". If a study has good statistical
conclusion validity, we should be relatively certain that the answer to these
questions is "yes". Examples of issues or problems that would threaten
statistical conclusion validity would be random heterogeneity of the research
subjects (the subjects represent a diverse group - this increases statistical
error) and small sample size (more difficult to find meaningful relationships
with a small number of subjects).
Internal Validity: Once it has been determined that the two variables (A &
B) are related, the next issue to be determined is one of causality. Does A
cause B? If a study is lacking internal validity, one can not make cause and
effect statements based on the research; the study would be descriptive but not
causal. There are many potential threats to internal validity. For example, if a
study has a pretest, an experimental treatment, and a follow-up posttest,
history is a threat to internal validity. If a difference is found between the
pretest and posttest, it might be due to the experimental treatment but it might
also be due to any other event that subjects experienced between the two times
of testing (for example, a historical event, a change in weather, etc.).
Construct Validity: One is examining the issue of construct validity when
one is asking the questions "Am I really measuring the construct that I want to
study?" or "Is my study confounded (Am I confusing constructs)?". For
example, if I want to know a particular drug (Variable A) will be effective for
treating depression (Variable B) , I will need at least one measure of
depression. If that measure does not truly reflect depression levels but rather
anxiety levels (Confounding Variable X), than my study will be lacking
construct validity. Thus, good construct validity means the we will be
relatively sure that Construct A is related to Construct B and that this is
possibly a causal relationship. Examples of other threats to construct validity
include subjects apprehension about being evaluated, hypothesis guessing on
the part of subjects, and bias introduced in a study by expectencies on the part
of the experimenter.
External Validity: External validity addresses the issue of being able to
generalize the results of your study to other times, places, and persons. For
example, if you conduct a study looking at heart disease in men, can these
results be generalized to women? Therefore, one needs to ask the following
questions to determine if a threat to the external validity exists: "Would I find
these same results with a difference sample?", "Would I get these same results
if I conducted my study in a different setting?", and "Would I get these same
results if I had conducted this study in the past or if I redo this study in the
future?" If I can not answer "yes" to each of these questions, then the external
validity of my study is threatened.
Types of Research Studies
There are four major classifications of research designs. These include
observational research, correlational research, true experiments, and quasi-
experiments. Each of these will be discussed further below.

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Observational research: There are many types of studies which could be
defined as observational research including case studies, ethnographic studies,
ethological studies, etc. The primary characteristic of each of these types of
studies is that phenomena are being observed and recorded. Often times, the
studies are qualitative in nature. For example, a psychological case study
would entail extensive notes based on observations of and interviews with the
client. A detailed report with analysis would be written and reported
constituting the study of this individual case. These studies may also be
qualitative in nature or include qualitative components in the research. For
example, an ethological study of primate behavior in the wild may include
measures of behavior durations ie. the amount of time an animal engaged in a
specified behavior. This measure of time would be qualitative.
Surveys are often classified as a type of observational research.
Correlational research: In general, correlational research examines the
covariation of two or more variables. For example, the early research on
cigarette smoking examine the covariation of cigarette smoking and a variety
of lung diseases. These two variable, smoking and lung disease were found to
covary together.
Correlational research can be accomplished by a variety of techniques which
include the collection of empirical data. Often times, correlational research is
considered type of observational research as nothing is manipulated by the
experimenter or individual conducting the research. For example, the early
studies on cigarette smoking did not manipulate how many cigarettes were
smoked. The researcher only collected the data on the two variables. Nothing
was controlled by the researchers.
It is important to not that correlational research is not causal research. In other
words, we can not make statements concerning cause and effect on the basis
of this type of research. There are two major reasons why we can not make
cause and effect statements. First, we don¹t know the direction of the cause.
Second, a third variable may be involved of which we are not aware. An
example may help clarify these points.
In major clinical depressions, the neurotransmitters serotonin and/or
norepinephrine have been found to be depleted (Coppen, 1967; Schildkraut &
Kety, 1967). In other words, low levels of these two neurotransmitters have
been found to be associated with increased levels of clinical depression.
However, while we know that the two variables covary - a relationship exists -
we do not know if a causal relationship exists. Thus, it is unclear whether a
depletion in serotonin/norepinephrine cause depression or whether depression
causes a depletion is neurotransmitter levels. This demonstrates the first
problem with correlational research; we don't know the direction of the cause.
Second, a third variable has been uncovered which may be affecting both of
the variables under study. The number of receptors on the postsynaptic neuron
has been found to be increased in depression (Segal, Kuczenski, & Mandell,
1974; Ventulani, Staqarz, Dingell, & Sulser, 1976). Thus, it is possible that
the increased number of receptors on the postsynaptic neuron is actually
responsible for the relationship between neurotransmitter levels and
depression. As you can see from the discussion above, one can not make a
simple cause and effect statement concerning neurotransmitter levels and
depression based on correlational research. To reiterate, it is inappropriate in
correlational research to make statements concerning cause and effect.
Correlational research is often conducted as exploratory or beginning
research. Once variables have been identified and defined, experiments are
True Experiments: The true experiment is often thought of as a laboratory
study. However, this is not always the case. A true experiment is defined as an
experiment conducted where an effort is made to impose control over all other
variables except the one under study. It is often easier to impose this sort of
control in a laboratory setting. Thus, true experiments have often been
erroneously identified as laboratory studies.
To understand the nature of the experiment, we must first define a few terms:
1. Experimental or treatment group - this is the group that receives
the experimental treatment, manipulation, or is different from
the control group on the variable under study.
2. Control group - this group is used to produce comparisons. The
treatment of interest is deliberately withheld or manipulated to
provide a baseline performance with which to compare the
experimental or treatment group's performance.
3. Independent variable - this is the variable that the experimenter
manipulates in a study. It can be any aspect of the environment
that is empirically investigated for the purpose of examining its
influence on the dependent variable.
4. Dependent variable - the variable that is measured in a study. The
experimenter does not control this variable.
5. Random assignment - in a study, each subject has an equal
probability of being selected for either the treatment or control
6. Double blind - neither the subject nor the experimenter knows
whether the subject is in the treatment of the control condition.
Now that we have these terms defined, we can examine further the structure
of the true experiment. First, every experiment must have at least two
groups: an experimental and a control group. Each group will receive a level
of the independent variable. The dependent variable will be measured to
determine if the independent variable has an effect. As stated previously, the
control group will provide us with a baseline for comparison. All subjects
should be randomly assigned to groups, be tested a simultaneously as
possible, and the experiment should be conducted double blind. Perhaps an
example will help clarify these points.
Wolfer and Visintainer (1975) examined the effects of systematic preparation
and support on children who were scheduled for inpatient minor surgery. The
hypothesis was that such preparation would reduce the amount of
psychological upset and increase the amount of cooperation among thee
young patients. Eighty children were selected to participate in the study.
Children were randomly assigned to either the treatment or the control
condition. During their hospitalization the treatment group received the
special program and the control group did not. Care was take such that kids in
the treatment and the control groups were not roomed together. Measures that
were taken included heart rates before and after blood tests, ease of fluid
intake, and self-report anxiety measures. The study demonstrated that the
systematic preparation and support reduced the difficulties of being in the
hospital for these kids.
Let us examine now the features of the experiment described above. First,
there was a treatment and control group. If we had had only the treatment
group, we would have no way of knowing whether the reduced anxiety was
due to the treatment or the weather, new hospital food, etc. The control group
provides us with the basis to make comparisons The independent variable in
this study was the presence or absence of the systematic preparation program.
The dependent variable consisted of the heart rates, fluid intake, and anxiety
measures. The scores on these measures were influenced by and depended on
whether the child was in the treatment or control group. The children were
randomly assigned to either group. If the "friendly" children had been placed
in the treatment group we would have no way of knowing whether they were
less anxious and more cooperative because of the treatment or because they
were "friendly". In theory, the random assignment should balance the number
of "friendly" children between the two groups. The two groups were also
tested at about the same time. In other words, one group was not measured
during the summer and the other during the winter. By testing the two groups
as simultaneously as possible, we can rule out any bias due to time. Finally,
the children were unaware that they were participants in an experiment (the
parents had agreed to their children's participation in research and the
program), thus making the study single blind. If the individuals who were
responsible for the dependent measures were also unaware of whether the
child was in the treatment or control group, then the experiment would have
been double blind.
A special case of the true experiment is the clinical trial. A clinical trial is
defined as a carefully designed experiment that seeks to determine the clinical
efficacy of a new treatment or drug. The design of a clinical trial is very
similar to that of a true experiment. Once again, there are two groups: a
treatment group (the group that receives the therapeutic agent) and a control
group (the group that receives the placebo). The control group is often called
the placebo group. The independent variable in the clinical trial is the level of
the therapeutic agent. Once again, subjects are randomly assigned to groups,
they are tested simultaneously, and the experiment should be conducted
double blind. In other words, neither the patient or the person administering
the drug should know whether the patient is receiving the drug or the placebo.
Quasi-Experiments: Quasi-experiments are very similar to true experiments
but use naturally formed or pre-existing groups. For example, if we wanted to
compare young and old subjects on lung capacity, it is impossible to randomly
assign subjects to either the young or old group (naturally formed groups).
Therefore, this can not be a true experiment. When one has naturally formed
groups, the variable under study is a subject variable (in this case - age) as
opposed to an independent variable. As such, it also limits the conclusions we
can draw from such an research study. If we were to conduct the quasi-
experiment, we would find that the older group had less lung capacity as
compared to the younger group. We might conclude that old age thus results
in less lung capacity. But other variables might also account for this result. It
might be that repeated exposure to pollutants as opposed to age has caused the
difference in lung capacity. It could also be a generational factor. Perhaps
more of the older group smoked in their early years as compared to the
younger group due to increased awareness of the hazards of cigarettes. The
point is that there are many differences between the groups that we can not
control that could account for differences in our dependent measures. Thus,
we must be careful concerning making statement of causality with quasi-
experimental designs.
Quasi-experiments may result from studying the differences between naturally
formed groups (ie. young & old; men & women). However, there are also
instances when a researcher designs a study as a traditional experiment only to
discover that random assignment to groups is restricted by outside factors. The
researcher is forced to divide groups according to some pre-existing criteria.
For example, if a corporation wanted to test the effectiveness of a new
wellness program, they might decide to implement their program at one site
and use a comporable site (no wellness program) as a control. As the
employees are not shuffled and randomly assigned to work at each site, the

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Study in Australia Enjoy Beaches, Climate, Lifestyle & Excellent Education Research methodology is a collective term for the structured process of conducting research.  There are many different methodologies used in various types of research and the term is usually considered to include research design, data gathering and data analysis. Research methodologies can be quantitative (for example, measuring the number of times someone does something under certain conditions) or qualitative (for example, asking people how they feel about a certain situation).  Ideally, comprehensive research should try to incorporate both qualitative and quantitative methodologies but this is not always possible, usually due to time and financial constraints. Research methodologies are generally used in academic research to test hypotheses or theories.  A good desig ...
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