Research definition (1) word

Research
Systematic investigative process employed to increase or revise current knowledge by
discovering new facts. It is divided into two general categories:
(1) Basic research is inquiry aimed at increasing scientific knowledge, and
(2) Applied research is effort aimed at using basic research for solving problems or developing
new processes, products, or techniques.
In the broadest sense of the word, the definition of research includes any gathering of data,
information and facts for the advancement of knowledge.
The Scientific Definition
The strict definition of scientific research is performing a methodical study in order to prove a
hypothesis or answer a specific question. Finding a definitive answer is the central goal of any
experimental process.
Research must be systematic and follow a series of steps and a rigid standard protocol. These
rules are broadly similar but may vary slightly between the different fields of science.
Scientific research must be organized and undergo planning, including performing literature
reviews of past research and evaluating what questions need to be answered.
Any type of ‘real’ research, whether scientific, economic or historical, requires some kind of
interpretation and an opinion from the researcher. This opinion is the underlying principle, or
question, that establishes the nature and type of experiment.
The scientific definition of research generally states that a variable must be manipulated,
although case studies and purely observational science do not always comply with this norm.

Empirical Research
Empirical Research can be defined as "research based on experimentation or observation
(evidence)". Such research is conducted to test a hypothesis.
The word empirical means information gained by experience, observation, or experiment. The
central theme in scientific method is that all evidence must be empirical which means it is based
on evidence. In scientific method the word "empirical" refers to the use of working hypothesis
that can be tested using observation and experiment.
Empirical data is produced by experiment and observation.
Objectives of the Scientific Research Process
 Capture contextual data and complexity
 Identify and learn from the collective experience of others from the field
 Identification, exploration, confirmation and advancing the theoretical concepts.
 Further improve educational design
Objectives of the Empirical Research
 Go beyond simply reporting observations
 Promote environment for improved understanding
 Combine extensive research with detailed case study
 Prove relevancy of theory by working in a real world environment (context)
Reasons for Using Empirical Research Methods
 Traditional or superstitional knowledge has been trusted for too long
 Empirical Research methods help integrating research and practice
 Educational process or Instructional science needs to progress
Advantages of Empirical Methods

 Understand and respond more appropriately to dynamics of situations
 Provide respect to contextual differences
 Help to build upon what is already known
 Provide opportunity to meet standards of professional research
In real case scenario, the collection of evidence to prove or counter any theory involves planned
research designs in order to collect empirical data. Several types of designs have been
suggested and used by researchers. Also accurate analysis of data using standard statistical
methods remains critical in order to determine legitimacy of empirical research.
Various statistical formulas such uncertainty coefficient, regression, t-test, chi-square and
different types of ANOVA (analysis of variance) have been extensively used to form logical and
valid conclusion.
However, it is important to remember that any of these statistical formulas don't produce proof
and can only support a hypothesis, reject it, or do neither.
Empirical Cycle

Empirical cycle consists of following stages:
1. Observation
Observation involves collecting and organizing empirical facts to form hypothesis
2. Induction
Induction is the process of forming hypothesis
3. Deduction
Deduct consequences with newly gained empirical data
4. Testing
Test the hypothesis with new empirical data
5. Evaluation
Perform evaluation of outcome of testing

The Scientific Definition
The strict definition of scientific research is performing a methodical study in order to
prove a hypothesis or answer a specific question. Finding a definitive answer is the
central goal of any experimental process.
Research must be systematic and follow a series of steps and a rigid standard protocol.
These rules are broadly similar but may vary slightly between the different fields of
science.
Scientific research must be organized and undergo planning, including performing literature
reviews of past research and evaluating what questions need to be answered.
Any type of ‘real’ research, whether scientific, economic or historical, requires some kind of
interpretation and an opinion from the researcher. This opinion is the underlying principle, or
question, that establishes the nature and type of experiment.
The scientific definition of research generally states that a variable must be manipulated,
although case studies and purely observational science do not always comply with this norm.
What is the Scientific Method?
Martyn Shuttleworth 194.4K reads 1 Comment
The scientific method, as defined by various scientists and philosophers, has a fairly rigorous
structure that should be followed.
In reality, apart from a few strictly defined physical sciences, most scientific disciplines have to
bend and adapt these rules, especially sciences involving the unpredictability of natural
organisms and humans.
In many ways, it is not always important to know the exact scientific method, to the letter, but
any scientist should have a good understanding of the underlying principles.

If you are going to bend and adapt the rules, you need to understand the rules in the first place.
Empirical
Science is based purely around observation and measurement, and the vast majority of
research involves some type of practical experimentation.
This can be anything, from measuring the Doppler Shift of a distant galaxy to handing out
questionnaires in a shopping center. This may sound obvious, but this distinction stems back to
the time of the Ancient Greek Philosophers.
Cutting a long story short, Plato believed that all knowledge could be reasoned; Aristotle that
knowledge relied upon empirical observation and measurement.
This does bring up one interesting anomaly. Strictly speaking, the great physicists, such as
Einstein and Stephen Hawking, are not scientists. They generate sweeping and elegant theories
and mathematical models to describe the universe and the very nature of time, but measure
nothing.
In reality, they are mathematicians, occupying their own particular niche, and they should
properly be referred to as theoreticians.
Still, they are still commonly referred to as scientists and do touch upon the scientific method in
that any theory they have can be destroyed by a single scrap of empirical evidence.
The Scientific Method Relies Upon Data
The scientific method uses some type of measurement to analyze results, feeding these
findings back into theories of what we know about the world. There are two major ways of
obtaining data, through measurement and observation. These are generally referred to as
quantitative and qualitative measurements.
Quantitative measurements are generally associated with what are known as ‘hard' sciences,
such as physics, chemistry and astronomy. They can be gained through experimentation or
through observation.

For Example:
 At the end of the experiment, 50% of the bacteria in the sample treated with penicillin
were left alive.
 The experiment showed that the moon is 384403 km away from the earth.
 The pH of the solution was 7.1
As a rule of thumb, a quantitative unit has a unit of measurement after it, some scientifically
recognized (SI) or SI derived unit. Percentages and numbers fall into this category.
Qualitative measurements are based upon observation and they generally require some type of
numerical manipulation or scaling.
As an example, a social scientist interviewing drug addicts in a series of case studies, and
documenting what they see, is not really performing science, although the research is still
useful.
However, if he performs some sort of manipulation, such as devising a scale to assess the
intensity of the response to specific questions, then he generates qualitative results.
 On average, the subjects showed an anxiety level of four.
 91% of respondents stated that they preferred Hershey bars.
Generally, qualitative measurements are arbitrary, a scale designed to measure abstract
responses and constructs. Measuring anxiety, preference, pain and aggression are some
examples of concepts measured qualitatively. For a small group of long-established tests, the
results are often regarded as quantitative, such as IQ (Intelligence Quotient) and EQ (Emotional
Quotient).
Both types of data are extremely important for understanding the world around us and the
majority of scientists use both types of data.
A medical researcher might design experiments to test the effectiveness of a drug, using a
placebo to contrast.

However, she might perform in depth case studies on a few of the subjects, a pilot study, to
ensure that her experiment has no problems.
The Scientific Method is Intellectual and Visionary
Science requires vision, and the ability to observe the implications of results. Collecting data is
part of the process, and it also needs to be analyzed and interpreted.
However, the visionary part of science lies in relating the findings back into the real world. Even
pure sciences, which are studied for their own sake rather than any practical application, are
visionary and have wider goals.
The process of relating findings to the real world is known as induction, or inductive reasoning,
and is a way of relating the findings to the universe around us.
For example, Wegener was the first scientist to propose the idea of continental drift. He noticed
that the same fossils were found on both sides of the Atlantic, in old rocks, and that the
continental shelves of Africa and South America seemed to fit together.
He induced that they were once joined together, rather than joined by land bridges, and faced
ridicule for his challenge to the established paradigm. Over time, the accumulated evidence
showed that he was, in fact, correct and he was shown to be a true visionary.

Science Uses Experiments to Test Predictions
This process of induction and generalization allows scientists to make predictions about how
they think that something should behave, and design an experiment to test it.
This experiment does not always mean setting up rows of test tubes in the lab or designing
surveys. It can also mean taking measurements and observing the natural world.
Wegener's ideas, whilst denigrated by many scientists, aroused the interest of a few. They
began to go out and look for other evidence that the continents moved around the Earth.
From Wegener's initial idea of continents floating through the ocean floor, scientists now
understand, through a process of prediction and measurement, the process of plate tectonics.
The exact processes driving the creation of new crust and the subduction of others are still not
fully understood but, almost 100 years after Wegener's idea, scientists still build upon his initial
work.

Null Hypothesis
Martyn Shuttleworth 592.7K reads 9 Comments
The null hypothesis, H0, is an essential part of any research design, and is always tested, even
indirectly.
The simplistic definition of the null is as the opposite of the alternative hypothesis, H1, although
the principle is a little more complex than that.
The null hypothesis (H0) is a hypothesis which the researcher tries to disprove, reject or nullify.
The 'null' often refers to the common view of something, while the alternative hypothesis is what
the researcher really thinks is the cause of a phenomenon.
The simplistic definition of the null is as the opposite of the alternative hypothesis, H1, although
the principle is a little more complex than that.
The null hypothesis (H0) is a hypothesis which the researcher tries to disprove, reject or nullify.
The 'null' often refers to the common view of something, while the alternative hypothesis is what
the researcher really thinks is the cause of a phenomenon.
An experiment conclusion always refers to the null, rejecting or accepting H0 rather than H1.
Despite this, many researchers neglect the null hypothesis when testing hypotheses, which is
poor practice and can have adverse effects.
Examples of the Null Hypothesis
A researcher may postulate a hypothesis:
H1: Tomato plants exhibit a higher rate of growth when planted in compost rather than in soil.
And a null hypothesis:

H0: Tomato plants do not exhibit a higher rate of growth when planted in compost rather than
soil.
It is important to carefully select the wording of the null, and ensure that it is as specific as
possible. For example, the researcher might postulate a null hypothesis:
H0: Tomato plants show no difference in growth rates when planted in compost rather than soil.
There is a major flaw with this H0. If the plants actually grow more slowly in compost than in soil,
an impasse is reached. H1 is not supported, but neither is H0, because there is a difference in
growth rates.
If the null is rejected, with no alternative, the experiment may be invalid. This is the reason why
science uses a battery of deductive and inductive processes to ensure that there are no flaws in
the hypotheses.
Many scientists neglect the null, assuming that it is merely the opposite of the alternative, but it
is good practice to spend a little time creating a sound hypothesis. It is not possible to change
any hypothesis retrospectively, including H0.

Significance Tests
If significance tests generate 95% or 99% likelihood that the results do not fit the null
hypothesis, then it is rejected, in favor of the alternative.
Otherwise, the null is accepted. These are the only correct assumptions, and it is incorrect to
reject, or accept, H1.
Accepting the null hypothesis does not mean that it is true. It is still a hypothesis, and must
conform to the principle of falsifiability, in the same way that rejecting the null does not prove the
alternative.
Perceived Problems With the Null
The major problem with the H0 is that many researchers, and reviewers, see accepting the null
as a failure of the experiment. This is very poor science, as accepting or rejecting any
hypothesis is a positive result.
Even if the null is not refuted, the world of science has learned something new. Strictly
speaking, the term ‘failure’, should only apply to errors in the experimental design, or incorrect
initial assumptions.
Development of the Null
The Flat Earth model was common in ancient times, such as in the civilizations of the Bronze
Age or Iron Age. This may be thought of as the null hypothesis, H0, at the time.
H0: World is Flat
Many of the Ancient Greek philosophers assumed that the sun, moon and other objects in the
universe circled around the Earth. Hellenistic astronomy established the spherical shape of the
earth around 300 BC.
H0: The Geocentric Model: Earth is the centre of the Universe and it is Spherical

Copernicus had an alternative hypothesis, H1 that the world actually circled around the sun, thus
being the center of the universe. Eventually, people got convinced and accepted it as the null,
H0.
H0: The Heliocentric Model: Sun is the centre of the universe
Later someone proposed an alternative hypothesis that the sun itself also circled around the
something within the galaxy, thus creating a new H0. This is how research works - the H0 gets
closer to the reality each time, even if it isn't correct, it is better than the last H0.
Systematic and Methodical
Scientists are very conservative in how they approach results and they are naturally very
skeptical.
It takes more than one experiment to change the way that they think, however loud the
headlines, and any results must be retested and repeated until a solid body of evidence is built
up. This process ensures that researchers do not make mistakes or purposefully manipulate
evidence.
In Wegener's case, his ideas were not accepted until after his death, when the amount of
evidence supporting continental drift became irrefutable.
This process of changing the current theories, called a paradigm shift, is an integral part of the
scientific method. Most groundbreaking research, such as Einstein's Relativity or Mendel's
Genetics, causes a titanic shift in the prevailing scientific thought.
Summary
The scientific method has evolved, over many centuries, to ensure that scientists make
meaningful discoveries, founded upon logic and reason rather than emotion.
The exact process varies between scientific disciplines, but they all follow the above principle of
observe - predict - test - generalize.

Purpose of Research
The purpose of research can be a complicated issue and varies across different scientific fields
and disciplines. At the most basic level, science can be split, loosely, into two types, 'pure
research' and 'applied research'.
Both of these types follow the same structures and protocols for propagating and testing
hypotheses and predictions, but vary slightly in their ultimate purpose.
An excellent example for illustrating the difference is by using pure and applied mathematics.
Pure maths is concerned with understanding underlying abstract principles and describing them
with elegant theories. Applied maths, by contrast, uses these equations to explain real life
phenomena, such as mechanics, ecology and gravity.
Pure Scientific Research
Some science, often referred to as 'pure science', is about explaining the world around us and
trying to understand how the universe operates. It is about finding out what is already there

without any greater purpose of research than the explanation itself. It is a direct descendent of
philosophy, where philosophers and scientists try to understand the underlying principles of
existence.
Whilst offering no direct benefits, pure research often has indirect benefits, which can contribute
greatly to the advancement of humanity.
For example, pure research into the structure of the atom has led to x-rays, nuclear power and
silicon chips.
Applied Scientific Research
Applied scientists might look for answers to specific questions that help humanity, for example
medical research or environmental studies. Such research generally takes a specific question
and tries to find a definitive and comprehensive answer.
The purpose of research is about testing theories, often generated by pure science, and
applying them to real situations, addressing more than just abstract principles.
Applied scientific research can be about finding out the answer to a specific problem, such as 'Is
global warming avoidable?' or 'Does a new type of medicine really help the patients?'
Generating Testable Data
However, they all involve generating a theory to explain why something is happening and using
the full battery of scientific tools and methods to test it rigorously.
This process opens up new areas for further study and a continued refinement of the
hypotheses.
Observation is not accurate enough, with statistically testable and analyzable data the only
results accepted across all scientific disciplines. The exact nature of the experimental process
may vary, but they all adhere to the same basic principles.

Scientists can be opinionated, like anybody else, and often will adhere to their own theories,
even if the evidence shows otherwise. Research is a tool by which they can test their own, and
each others' theories, by using this antagonism to find an answer and advance knowledge.
The purpose of research is really an ongoing process of correcting and refining hypotheses,
which should lead to the acceptance of certain scientific truths.
Whilst no scientific proof can be accepted as ultimate fact, rigorous testing ensures that proofs
can become presumptions. Certain basic presumptions are made before embarking on any
research project, and build upon this gradual accumulation of knowledge.
Research Hypothesis
A research hypothesis is the statement created by researchers when they speculate upon the
outcome of a research or experiment.
Every true experimental design must have this statement at the core of its structure, as the
ultimate aim of any experiment.
The hypothesis is generated via a number of means, but is usually the result of a process of
inductive reasoning where observations lead to the formation of a theory. Scientists then use a
large battery of deductive methods to arrive at a hypothesis that is testable, falsifiable and
realistic.

The precursor to a hypothesis is a problem, usually framed as a question.
The precursor to a hypothesis is a research problem, usually framed as a question. It might ask
what, or why, something is happening.
For example, to use a topical subject, we might wonder why the stocks of cod in the North
Atlantic are declining. The problem question might be ‘Why are the numbers of Cod in the North
Atlantic declining?’
This is too broad as a statement and is not testable by any reasonable scientific means. It is
merely a tentative question arising from literature reviews and intuition. Many people would think
that instinct and intuition are unscientific, but many of the greatest scientific leaps were a result
of ‘hunches’.
The research hypothesis is a paring down of the problem into something testable and falsifiable.
In the aforementioned example, a researcher might speculate that the decline in the fish stocks
is due to prolonged over fishing. Scientists must generate a realistic and testable hypothesis
around which they can build the experiment.
This might be a question, a statement or an ‘If/Or’ statement. Some examples could be:
 Is over-fishing causing a decline in the stocks of Cod in the North Atlantic?
 Over-fishing affects the stocks of cod.
 If over-fishing is causing a decline in the numbers of Cod, reducing the amount of
trawlers will increase cod stocks.
These are all acceptable statements and they all give the researcher a focus for constructing a
research experiment. Science tends to formalize things and use the ‘If’ statement, measuring
the effect that manipulating one variable has upon another, but the other forms are perfectly
acceptable. An ideal research hypothesis should contain a prediction, which is why the more
formal ones are favored.
A hypothesis must be testable, but must also be falsifiable for its acceptance as true science.

A scientist who becomes fixated on proving a research hypothesis loses their impartiality and
credibility. Statistical tests often uncover trends, but rarely give a clear-cut answer, with other
factors often affecting the outcome and influencing the results.
Whilst gut instinct and logic tells us that fish stocks are affected by over fishing, it is not
necessarily true and the researcher must consider that outcome. Perhaps environmental factors
or pollution are causal effects influencing fish stocks.
A hypothesis must be testable, taking into account current knowledge and techniques, and be
realistic. If the researcher does not have a multi-million dollar budget then there is no point in
generating complicated hypotheses. A hypothesis must be verifiable by statistical and analytical
means, to allow a verification or falsification.
In fact, a hypothesis is never proved, and it is better practice to use the terms ‘supported’ or
‘verified’. This means that the research showed that the evidence supported the hypothesis and
further research is built upon that.
A research hypothesis, which stands the test of time, eventually becomes a theory, such as
Einstein’s General Relativity. Even then, as with Newton’s Laws, they can still be falsified or
adapted.
True Experimental Design
True experimental design is regarded as the most accurate form of experimental research, in
that it tries to prove or disprove a hypothesis mathematically, with statistical analysis.
For some of the physical sciences, such as physics, chemistry and geology, they are standard
and commonly used. For social sciences, psychology and biology, they can be a little more
difficult to set up.
For an experiment to be classed as a true experimental design, it must fit all of the following
criteria.
 The sample groups must be assigned randomly.

 There must be a viable control group.
 Only one variable can be manipulated and tested. It is possible to test more than one,
but such experiments and their statistical analysis tend to be cumbersome and difficult.
 The tested subjects must be randomly assigned to either control or experimental groups.
Advantages
The results of a true experimental design can be statistically analyzed and so there can be little
argument about the results.
It is also much easier for other researchers to replicate the experiment and validate the results.
For physical sciences working with mainly numerical data, it is much easier to manipulate one
variable, so true experimental design usually gives a yes or no answer.
Disadvantages
Whilst perfect in principle, there are a number of problems with this type of design. Firstly, they
can be almost too perfect, with the conditions being under complete control and not being
representative of real world conditions.
For psychologists and behavioral biologists, for example, there can never be any guarantee that
a human or living organism will exhibit ‘normal’ behavior under experimental conditions.
True experiments can be too accurate and it is very difficult to obtain a complete rejection or
acceptance of a hypothesis because the standards of proof required are so difficult to reach.
True experiments are also difficult and expensive to set up. They can also be very impractical.
While for some fields, like physics, there are not as many variables so the design is easy, for
social sciences and biological sciences, where variations are not so clearly defined it is much
more difficult to exclude other factors that may be affecting the manipulated variable.

Summary
True experimental design is an integral part of science, usually acting as a final test of a
hypothesis. Whilst they can be cumbersome and expensive to set up, literature reviews,
qualitative research and descriptive research can serve as a good precursor to generate a
testable hypothesis, saving time and money.
Whilst they can be a little artificial and restrictive, they are the only type of research that is
accepted by all disciplines as statistically provable.
Random Sampling Error
Random sampling errors are one type of experimental error that everybody should know.
Anyone who reads polls on the internet, or in newspapers, should be aware that sampling errors
could vastly influence the data and lead people to draw incorrect conclusions.
To further compound the random sampling errors, many survey companies, newspapers and
pundits are well aware of this, and deliberately manipulate polls to give favorable results.
In any experiment where it is impossible to sample an entire population, usually due to
practicality and expense, a representative sample must be used.
Of course, when you use a sample group, it can never fully match the entire population, and
there will always be some likelihood of random sampling error.
Any researcher must strive to ensure that the sample is as representative as possible, and
statistical tests have inbuilt checks and balances to take this into account.
To illustrate how to ensure that your statistics are as accurate as possible, we are going to use
the example of an opinion poll. These are one of the most commonly misinterpreted
representations of data, and failure to take into account the nuances of statistics can paint an
incorrect picture.

Margin of Error - A False Picture
The problem is, when you see an opinion poll in a newspaper or internet site, you will usually
see a margin of error, such a + or - 3%. The temptation is to think that the polls will be accurate
within this figure.
For example, if a poll gives one political party (A) a 42% share of the vote, and the other (B)
39%, this opens up a number of possible results. (A) could have 45%, (B) 36%. Both could be
39% or (B) could actually be ahead, 42% versus 39%. Of course, the results could show any
variation in between those extremes. Complicated enough?
To complicate the picture further, even this random sampling error can be wildly inaccurate. Any
opinion poll may give the margin of error, but this can convey a false sense of security and
make people assume that the results 'must' lie within this range.
In fact, these figures could actually be completely wrong, and the numbers are only ever an
estimate.
The Problem With Random Sampling Error
The problem is that these results only show the random sampling error within that specific
group. They show the chances of the results in that group occurring purely by chance, exactly
like the 95% confidence margin employed by many scientific researchers.
However, this is a very narrow definition and is often misunderstood.
In an opinion poll, there is no guarantee that the sample of 1000 or 10 000 people is truly
representative of the larger population as a whole.
There have been many extremely inaccurate polls conducted over the years, and they fell down
due to poor design and not understanding all of the relevant factors.
For example, an opinion poll company conducting telephone polls may make the mistake of only
telephoning during office hours, when most of the population is at work, skewing the data.

In addition, poorer families do not always have a fixed line telephone and use unregistered cell
phones, again leaving a huge potential for inaccuracy. The margins of error would be perfectly
acceptable, in these cases, but the overall findings would still be horribly wrong.
Modern polling companies are very skilled at designing polls to select samples from many
elements of the population, and via various media, so big errors rarely happen. Despite this,
opinion polls must always be taken as a guide only, not an exact representation of how an
election is likely to unfold.
Random Sampling Error and Experimental Design
The mistakes made by pollsters relate directly to any type of experiment involving random
sample groups.
Statistics can only work with the data provided and, if your design is poorly thought out, will not
be able to cover up these errors. Garbage in definitely equals garbage out.
Bibliography
Husch, B. (1971). Planning a Forest Inventory. Rome, Italy: Food and Agriculture Organization
of the United Nations
Urdan, T.C. (2005). Statistics in Plain English, Mahwah, NJ: Lawrence Erlbaum
Weisberg, H.F. (2005).The Total Survey Error Approach: A Guide to the New Science of Survey
Research. Chicago: University of Chicago Press
Scientists frequently use statistics to analyze their results. Why do researchers use statistics?
Statistics can help understand a phenomenon by confirming or rejecting a hypothesis. It is vital
to how we acquire knowledge to most scientific theories.
You don't need to be a scientist though; anyone wanting to learn about how researchers can get
help from statistics may want to read this statistics tutorial for the scientific method.

What is Statistics?
Research Data
This section of the statistics tutorial is about understanding how data is acquired and used.
The results of a science investigation often contain much more data or information than the
researcher needs. This data-material, or information, is called raw data.
To be able to analyze the data sensibly, the raw data is processed into "output data". There are
many methods to process the data, but basically the scientist organizes and summarizes the
raw data into a more sensible chunk of data. Any type of organized information may be called a
"data set".
Then, researchers may apply different statistical methods to analyze and understand the data
better (and more accurately). Depending on the research, the scientist may also want to use
statistics descriptively or for exploratory research.
What is great about raw data is that you can go back and check things if you suspect something
different is going on than you originally thought. This happens after you have analyzed the
meaning of the results.
The raw data can give you ideas for new hypotheses, since you get a better view of what is
going on. You can also control the variables which might influence the conclusion (e.g. third
variables). In statistics, a parameter is any numerical quantity that characterizes a given
population or some aspect of it.
Central Tendency and Normal Distribution
This part of the statistics tutorial will help you understand distribution, central tendency and how
it relates to data sets.
Much data from the real world is normal distributed, that is, a frequency curve, or a frequency
distribution, which has the most frequent number near the middle. Many experiments rely on
assumptions of a normal distribution. This is a reason why researchers very often measure the

central tendency in statistical research, such as the mean(arithmetic mean or geometric mean),
median or mode.
The central tendency may give a fairly good idea about the nature of the data (mean, median
and mode shows the "middle value"), especially when combined with measurements on how the
data is distributed. Scientists normally calculate the standard deviation to measure how the data
is distributed.
But there are various methods to measure how data is distributed: variance, standard deviation,
standard error of the mean, standard error of the estimate or "range" (which states the
extremities in the data).
To create the graph of the normal distribution for something, you'll normally use the arithmetic
mean of a "big enough sample" and you will have to calculate the standard deviation.
However, the sampling distribution will not be normally distributed if the distribution is skewed
(naturally) or has outliers (often rare outcomes or measurement errors) messing up the data.
One example of a distribution which is not normally distributed is the F-distribution, which is
skewed to the right.
So, often researchers double check that their results are normally distributed using range,
median and mode. If the distribution is not normally distributed, this will influence which
statistical test/method to choose for the analysis.
Other Tools
 Quartile
 Trimean
Hypothesis Testing - Statistics Tutorial
How do we know whether a hypothesis is correct or not?
Why use statistics to determine this?

Using statistics in research involves a lot more than make use of statistical formulas or getting to
know statistical software.
Making use of statistics in research basically involves
1. Learning basic statistics
2. Understanding the relationship between probability and statistics
3. Comprehension of the two major branches in statistics: descriptive statistics and
inferential statistics.
4. Knowledge of how statistics relates to the scientific method.
Statistics in research is not just about formulas and calculation. (Many wrong conclusions have
been conducted from not understanding basic statistical concepts)
Statistics inference helps us to draw conclusions from samples of a population.
When conducting experiments, a critical part is to test hypotheses against each other. Thus, it is
an important part of the statistics tutorial for the scientific method.
Hypothesis testing is conducted by formulating an alternative hypothesis which is tested against
the null hypothesis, the common view. The hypotheses are tested statistically against each
other.
The researcher can work out a confidence interval, which defines the limits when you will regard
a result as supporting the null hypothesis and when the alternative research hypothesis is
supported.
This means that not all differences between the experimental group and the control group can
be accepted as supporting the alternative hypothesis - the result need to differ significantly
statistically for the researcher to accept the alternative hypothesis. This is done using a
significance test (another article).
Caution though, data dredging, data snooping or fishing for data without later testing your
hypothesis in a controlled experiment may lead you to conclude on cause and effect even
though there is no relationship to the truth.

Depending on the hypothesis, you will have to choose between one-tailed and two tailed tests.
Sometimes the control group is replaced with experimental probability - often if the research
treats a phenomenon which is ethically problematic, economically too costly or overly time-
consuming, then the true experimental design is replaced by a quasi-experimental approach.
Often there is a publication bias when the researcher finds the alternative hypothesis correct,
rather than having a "null result", concluding that the null hypothesis provides the best
explanation.
If applied correctly, statistics can be used to understand cause and effect between research
variables.
It may also help identify third variables, although statistics can also be used to manipulate and
cover up third variables if the person presenting the numbers does not have honest intentions
(or sufficient knowledge) with their results.
Misuse of statistics is a common phenomenon, and will probably continue as long as people
have intentions about trying to influence others. Proper statistical treatment of experimental data
can thus help avoid unethical use of statistics. Philosophy of statistics involves justifying proper
use of statistics, ensuring statistical validity and establishing the ethics in statistics.
Here is another great statistics tutorial which integrates statistics and the scientific method.
Reliability and Experimental Error
Statistical tests make use of data from samples. These results are then generalized to the
general population. How can we know that it reflects the correct conclusion?
Contrary to what some might believe, errors in research are an essential part of significance
testing. Ironically, the possibility of a research error is what makes the research scientific in the
first place. If a hypothesis cannot be falsified (e.g. the hypothesis has circular logic), it is not
testable, and thus not scientific, by definition.
If a hypothesis is testable, to be open to the possibility of going wrong. Statistically this opens up
the possibility of getting experimental errors in your results due to random errors or other

problems with the research. Experimental errors may also be broken down into Type-I error and
Type-II error. ROC Curves are used to calculate sensitivity between true positives and false
positives.
A power analysis of a statistical test can determine how many samples a test will need to have
an acceptable p-value in order to reject a false null hypothesis.
The margin of error is related to the confidence interval and the relationship between statistical
significance, sample size and expected results. The effect size estimate the strength of the
relationship between two variables in a population. It may help determine the sample size
needed to generalize the results to the whole population.
Replicating the research of others is also essential to understand if the results of the research
were a result which can be generalized or just due to a random "outlier experiment". Replication
can help identify both random errors and systematic errors (test validity).
Cronbach's Alpha is used to measure the internal consistency or reliability of a test score.
Replicating the experiment/research ensures the reliability of the results statistically.
What you often see if the results have outliers, is a regression towards the mean, which then
makes the result not be statistically different between the experimental and control group.
Statistical Tests
Here we will introduce a few commonly used statistics tests/methods, often used by
researchers.
Relationship Between Variables
The relationship between variables is very important to scientists. This will help them to
understand the nature of what they are studying. A linear relationship is when two variables
varies proportionally, that is, if one variable goes up, the other variable will also go up with the
same ratio. A non-linear relationship is when variables do not vary proportionally. Correlation is
a a way to express relationship between two data sets or between two variables.

Measurement scales are used to classify, categorize and (if applicable) quantify variables.
Pearson correlation coefficient (or Pearson Product-Moment Correlation) will only express the
linear relationship between two variables. Spearman rho is mostly used for linear relationships
when dealing with ordinal variables. Kendall's tau (τ) coefficient can be used to measure
nonlinear relationships.
Partial Correlation (and Multiple Correlation) may be used when controlling for a third variable.
Predictions
The goal of predictions is to understand causes. Correlation does not necessarily mean
causation. With linear regression, you often measure a manipulated variable.
What is the difference between correlation and linear regression? Basically, a correlational
study looks at the strength between the variables whereas linear regression is about the best fit
line in a graph.
Regression analysis and other modeling tools
 Linear Regression
 Multiple Regression
 A Path Analysis is an extension of the regression model
 A Factor Analysis attempts to uncover underlying factors of something.
 The Meta-Analysis frequently make use of effect size
Bayesian Probability is a way of predicting the likelihood of future events in an interactive way,
rather than to start measuring and then get results/predictions.
Testing Hypotheses Statistically
Student's t-test is a test which can indicate whether the null hypothesis is correct or not. In
research it is often used to test differences between two groups (e.g. between a control
group and an experimental group).

The t-test assumes that the data is more or less normally distributed and that the variance is
equal (this can be tested by the F-test).
Student's t-test:
 Independent One-Sample T-Test
 Independent Two-Sample T-Test
 Dependent T-Test for Paired Samples
Wilcoxon Signed Rank Test may be used for non-parametric data.
A Z-Test is similar to a t-test, but will usually not be used on sample sizes below 30.
A Chi-Square can be used if the data is qualitative rather than quantitative.
Comparing More Than Two Groups
An ANOVA, or Analysis of Variance, is used when it is desirable to test whether there are
different variability between groups rather than different means. Analysis of Variance can also
be applied to more than two groups. The F-distribution can be used to calculate p-values for the
ANOVA.
Analysis of Variance
 One way ANOVA
 Two way ANOVA
 Factorial ANOVA
 Repeated Measures and ANOVA
Nonparametric Statistics
Some common methods using nonparametric statistics:
 Cohen's Kappa
 Mann-Whitney U-test
 Spearman's Rank Correlation Coefficient

Other Important Terms in Statistics
Research Methodology
Key Concepts of the Scientific Method
There are several important aspects to research methodology. This is a summary of the key
concepts in scientific research and an attempt to erase some common misconceptions in
science.
Research Methodology
key Concepts of the Scientific Method

There are several important aspects to research methodology. This is a summary of the key
concepts in scientific research and an attempt to erase some common misconceptions in
science.
General Question
The starting point of most new research is to formulate a general question about an area of
research and begin the process of defining it.
This initial question can be very broad, as the later research, observation and narrowing down
will hone it into a testable hypothesis.
For example, a broad question might ask 'whether fish stocks in the North Atlantic are declining
or not', based upon general observations about smaller yields of fish across the whole area.
Reviewing previous research will allow a general overview and will help to establish a more
specialized area.
Unless you have an unlimited budget and huge teams of scientists, it is impossible to research
such a general field and it needs to be pared down. This is the method of trying to sample one
small piece of the whole picture and gradually contribute to the wider question.

Narrowing Down
The research stage, through a process of elimination, will narrow and focus the research area.
This will take into account budgetary restrictions, time, available technology and practicality,
leading to the proposal of a few realistic hypotheses.
Eventually, the researcher will arrive at one fundamental hypothesis around which the
experiment can be designed.
Designing the Experiment
This stage of the scientific method involves designing the steps that will test and evaluate the
hypothesis, manipulating one or more variables to generate analyzable data.
The experiment should be designed with later statistical tests in mind, by making sure that the
experiment has controls and a large enough sample group to provide statistically valid results.
Observation

This is the midpoint of the steps of the scientific method and involves observing and recording
the results of the research, gathering the findings into raw data.
The observation stage involves looking at what effect the manipulated variables have upon the
subject, and recording the results.
Analysis
The scope of the research begins to broaden again, as statistical analyses are performed on the
data, and it is organized into an understandable form.
The answers given by this step allow the further widening of the research, revealing some
trends and answers to the initial questions.
Conclusions and Publishing
This stage is where, technically, the hypothesis is stated as proved or disproved.
However, the bulk of research is never as clear-cut as that, and so it is necessary to filter the
results and state what happened and why. This stage is where interesting results can be
earmarked for further research and adaptation of the initial hypothesis.
Even if the hypothesis was incorrect, maybe the experiment had a flaw in its design or
implementation. There may be trends that, whilst not statistically significant, lead to further
research and refinement of the process.
The results are usually published and shared with the scientific community, allowing verification
of the findings and allowing others to continue research into other areas.
Cycles
This is not the final stage of the steps of the scientific method, as it generates data and ideas to
recycle into the first stage.
The initial and wider research area can again be addressed, with this research one of the many
individual pieces answering the whole question.

Building up understanding of a large area of research, by gradually building up a picture, is the
true path of scientific advancement. One great example is to look at the work of J J Thomson,
who gradually inched towards his ultimate answer.
Research Variables
The research variables, of any scientific experiment or research process, are factors that can be
manipulated and measured.
Any factor that can take on different values is a scientific variable and influences the outcome of
experimental research.
Gender, color and country are all perfectly acceptable variables, because they are inherently
changeable.
Most scientific experiments measure quantifiable factors, such as time or weight, but this is not
essential for a component to be classed as a variable.
As an example, most of us have filled in surveys where a researcher asks questions and asks
you to rate answers. These responses generally have a numerical range, from ‘1 - Strongly
Agree’ through to ‘5 - Strongly Disagree’. This type of measurement allows opinions to be
statistically analyzed and evaluated.
Dependent and Independent Variables
The key to designing any experiment is to look at what research variables could affect the
outcome.
There are many types of variable but the most important, for the vast majority of research
methods, are the independent and dependent variables.
A researcher must determine which variable needs to be manipulated to generate quantifiable
results.

The independent variable is the core of the experiment and is isolated and manipulated by the
researcher. The dependent variable is the measurable outcome of this manipulation, the results
of the experimental design. For many physical experiments, isolating the independent variable
and measuring the dependent is generally easy.
If you designed an experiment to determine how quickly a cup of coffee cools, the manipulated
independent variable is time and the dependent measured variable is temperature.
In other fields of science, the variables are often more difficult to determine and an experiment
needs a robust design. Operationalization is a useful tool to measure fuzzy concepts which do
not have one obvious variable.
The Difficulty of Isolating Variables
In biology, social science and geography, for example, isolating a single independent variable is
more difficult and any experimental design must consider this.
For example, in a social research setting, you might wish to compare the effect of different foods
upon hyperactivity in children. The initial research and inductive reasoning leads you to
postulate that certain foods and additives are a contributor to increased hyperactivity. You
decide to create a hypothesis and design an experiment, to establish if there is solid evidence
behind the claim.

The type of food is an independent variable, as is the amount eaten, the period of time
and the gender and age of the child. All of these factors must be accounted for during
the experimental design stage. Randomization and controls are generally used to
ensure that only one independent variable is manipulated.
To eradicate some of these research variables and isolate the process, it is essential to
use various scientific measurements to nullify or negate them.
For example, if you wanted to isolate the different types of food as the manipulated
variable, you should use children of the same age and gender.
The test groups should eat the same amount of the food at the same times and the
children should be randomly assigned to groups. This will minimize the physiological
differences between children. A control group, acting as a buffer against unknown
research variables, might involve some children eating a food type with no known links
to hyperactivity.
In this experiment, the dependent variable is the level of hyperactivity, with the resulting
statistical tests easily highlighting any correlation. Depending upon the results, you
could try to measure a different variable, such as gender, in a follow up experiment.
ConvertingResearch Variables Into Constants
Ensuring that certain research variables are controlled increases the reliability and
validity of the experiment, by ensuring that other causal effects are eliminated. This
safeguard makes it easier for other researchers to repeat the experiment and
comprehensively test the results.
What you are trying to do, in your scientific design, is to change most of the variables
into constants, isolating the independent variable. Any scientific research does contain
an element of compromise and inbuilt error, but eliminating other variables will ensure
that the results are robust and valid.

Dependent Variable
Martyn Shuttleworth 64.9K reads
In any true experiment, a researcher manipulates an independent variable, to influence a
dependent variable, or variables.
A well-designed experiment normally incorporate one or two independent variables, with every
other possible factor eliminated, or controlled. There may be more than two dependent variables
in any experiment.
For example, a researcher might wish to establish the effect of temperature on the rate of plant
growth; temperature is the independent variable. They could regard growth as height, weight,
number of fruits produced, or all of these. A whole range of dependent variables arises from one
independent variable.
In any experimental design, the researcher must determine that there is a definite causal link
between the independent and dependent variable.
This reduces the risk of 'correlation and causation' errors. Controlled variables are used to
reduce the possibility of any other factor influencing changes in the dependent variable, known
as confounding variables.
In the above example, the plants must all be given the same amount of water, or this factor
could obscure any link between temperature and growth.
The relationship between the independent variable and dependent variable is the basis of most
statistical tests, which establish whether there is a real correlation between the two. The results
of these tests allow the researcher to accept or reject the null hypothesis, and draw conclusions.
Independent Variable

The independent variable, also known as the manipulated variable, lies at the heart of any
quantitative experimental design.
This is the factor manipulated by the researcher, and it produces one or more results, known as
dependent variables. There are often not more than one or two independent variables tested in
an experiment, otherwise it is difficult to determine the influence of each upon the final results.
There may be more than several dependent variables, because manipulating the independent
can influence many different things.
For example, an experiment to test the effects of a certain fertilizer, upon plant growth, could
measure height, number of fruits and the average weight of the fruit produced. All of these are
valid analyzable factors, arising from the manipulation of one independent variable, the amount
of fertilizer.
Potential Complexities of the Independent Variable
The term independent variable is often a source of confusion; many people assume that the
name means that the variable is independent of any manipulation.
The name arises because the variable is isolated from any other factor, allowing experimental
manipulation to establish analyzable results.
Some research papers appear to give results manipulating more than one experimental
variable, but this is usually a false impression.
Each manipulated variable is likely to be an experiment in itself, one area where the words
'experiment' and 'research' differ. It is simply more convenient for the researcher to bundle them
into one paper, and discuss the overall results.
The botanical researcher above might also study the effects of temperature, or the amount of
water on growth, but these must be performed as discrete experiments, with only the conclusion
and discussion amalgamated at the end.
Independent Variables - Examples

As an example of an experiment with easily defined experimental variables, Mendel's famous
Pea Plant Experiment is a good choice.
The Austrian monk cross-pollinated pea plants, trying to establish which characteristics were
passed down through the generations. In this case, the inheritable characteristic of the parent
plant was the independent variable. For example, when plants with green seedpods were
crossed with plants with yellow seedpods, pod color was the independent variable.
In the Bandura Bobo Doll experiment, whether the children were exposed to an aggressive
adult, or to a passive adult, was the independent variable.
This experiment is a prime example of how the concept of experimental variables can become a
little complex. He also studied the differences between boys and girls, with gender as an
independent variable. Surely, this is breaking the rules of only having one manipulated variable!
In fact, this is a prime example of performing multiple experiments at the same time. If you study
carefully the structure of the research design, you will see that the Bobo Doll Experiment should
have been called the Bobo Doll Experiments.
It was actually four experiments, each with their own hypothesis and variables, running
concurrently. It would have been expensive, and possibly unethical, to test the children four
times and, if the same children were used each time, their behavior may have changed with
repetition.
Careful design allowed Bandura to test different hypotheses as part of the same research.
Statistically Significant Results
Statistically significant results are those that are interpreted not likely to have occurred purely by
chance and thereby have other underlying causes for their occurrence.
Whenever a statistical analysis is performed and results interpreted, there is always a finite
chance that the results are purely by chance. This is an inherent limitation of any statistical

analysis and cannot be done away with. Also, mistakes such as measurement errors may cause
the experimenter to misinterpret the results.
However, the probability that the process was simply a chance encounter can be calculated,
and a minimum threshold of statistical significance can be set. If the results are obtained such
that the probability that they are simply a chance process is less than this threshold of
significance, then we can say the results are not due to chance.
Common statistically significant levels are 5%, 1%, etc.
In terms of null hypothesis, the concept of statistical significance can be understood to be the
minimum level at which the null hypothesis can be rejected. This means if the experimenter sets
his statistical significance level at 5% and the probability that the results are a chance process is
3%, then the experimenter can claim that the null hypothesis can be rejected.
In this case, the experimenter will call his results to be statistically significant. Lower the
significance level, higher the confidence.
Statistically significant results are required for many practical cases of experimentation in
various branches of research. The choice of the statistical significance level is influenced by a
number of parameters and changes with different experiments.
In most cases of practical consideration, however, the distribution of parameters or qualities
follows a normal distribution, which is also the simplest case under consideration. However,
care should always be taken to account for other distributions within the given population.
While determining significant results statistically, it is important to note that it is impossible to
use statistics to prove that the difference in levels of two parameters is zero. This means that
the results of a significant analysis should not be interpreted as meaning there was no
difference. The only thing that the statistical analysis can state is that the experiment failed to
find any difference.
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 Controlled Variables
Controlled Variables
Controlled variables are variables that is sometimes overlooked by researchers, but it is usually
far more important than the dependent or independent variables.
A failure to isolate the controlled variables, in any experimental design, will seriously
compromise the internal validity. This oversight may lead to confounding variables ruining the
experiment, wasting time and resources, and damaging the researcher's reputation.
In any experimental design, a researcher will be manipulating one variable, the independent
variable, and studying how that affects the dependent variables.
A failure to isolate the controlled variables will compromise the internal validity.
Most experimental designs measures only one or two variables at a time. Any other factor,
which could potentially influence the results, must be correctly controlled. Its effect upon the
results must be standardized, or eliminated, exerting the same influence upon the different
sample groups.
For example, if you were comparing cleaning products, the brand of cleaning product would be
the only independent variable measured. The level of dirt and soiling, the type of dirt or stain,
the temperature of the water and the time of the cleaning cycle are just some of the variables
that must be the same between experiments. Failure to standardize even one of these
controlled variables could cause a confounding variable and invalidate the results.

Control Groups
In many fields of science, especially biology and behavioral sciences, it is very difficult to ensure
complete control, as there is a lot of scope for small variations.
Biological processes are subject to natural fluctuations and chaotic rhythms. The key is to use
established operationalization techniques, such as randomization and double blind experiments.
These techniques will control and isolate these variables, as much as possible. If this proves
difficult, a control group is used, which will give a baseline measurement for the unknown
variables.
Sound statistical analysis will then eliminate these fluctuations from the results. Most statistical
tests have a certain error margin built in, and repetition and large sample groups will eradicate
the unknown variables.
There still needs to be constant monitoring and checks, but due diligence will ensure that the
experiment is as accurate as is possible.
The Value of Consistency
Controlled variables are often referred to as constants, or constant variables.
It is important to ensure that all these possible variables are isolated, because a type III error
may occur if an unknown factor influences the dependent variable. This is where the null
hypothesis is correctly rejected, but for the wrong reason.

In addition, inadequate monitoring of controlled variables is one of the most common causes of
researchers wrongly assuming that a correlation leads to causality.
Controlled variables are the road to failure in an experimental design, if not identified and
eliminated. Designing the experiment with controls in mind is often more crucial than
determining the independent variable.
Poor controls can lead to confounding variables, and will damage the internal validity of the
experiment.
Operationalization
Operationalization is the process of strictly defining variables into measurable factors. The
process defines fuzzy concepts and allows them to be measured, empirically and quantitatively.
For experimental research, where interval or ratio measurements are used, the scales are
usually well defined and strict.
Operationalization also sets down exact definitions of each variable, increasing the quality of the
results, and improving the robustness of the design.

For many fields, such as social science, which often use ordinal measurements,
operationalization is essential. It determines how the researchers are going to measure an
emotion or concept, such as the level of distress or aggression.
Such measurements are arbitrary, but allow others to replicate the research, as well as perform
statistical analysis of the results.
Fuzzy Concepts
Fuzzy concepts are vague ideas, concepts that lack clarity or are only partially true. These are
often referred to as "conceptual variables".
It is important to define the variables to facilitate accurate replication of the research process.
For example, a scientist might propose the hypothesis:
“Children grow more quickly if they eat vegetables.”
What does the statement mean by 'children'? Are they from America or Africa. What age are
they? Are the children boys or girls? There are billions of children in the world, so how do you
define the sample groups?

How is 'growth' defined? Is it weight, height, mental growth or strength? The statement does not
strictly define the measurable, dependent variable.
What does the term 'more quickly'mean? What units, and what timescale, will be used to
measure this? A short-term experiment, lasting one month, may give wildly different results than
a longer-term study.
The frequency of sampling is important for operationalization, too.
If you were conducting the experiment over one year, it would not be practical to test the weight
every 5 minutes, or even every month. The first is impractical, and the latter will not generate
enough analyzable data points.
What are 'vegetables'? There are hundreds of different types of vegetable, each containing
different levels of vitamins and minerals. Are the children fed raw vegetables, or are they
cooked? How does the researcher standardize diets, and ensure that the children eat their
greens?
Operationalization
The above hypothesis is not a bad statement, but it needs clarifying and strengthening, a
process called operationalization.
The researcher could narrow down the range of children, by specifying age, sex, nationality, or
a combination of attributes. As long as the sample group is representative of the wider group,
then the statement is more clearly defined.
Growth may be defined as height or weight. The researcher must select a definable and
measurable variable, which will form part of the research problem and hypothesis.
Again, 'more quickly' would be redefined as a period of time, and stipulate the frequency of
sampling. The initial research design could specify three months or one year, giving a
reasonable time scale and taking into account time and budget restraints.

Each sample group could be fed the same diet, or different combinations of vegetables. The
researcher might decide that the hypothesis could revolve around vitamin C intake, so the
vegetables could be analyzed for the average vitamin content.
Alternatively, a researcher might decide to use an ordinal scale of measurement, asking
subjects to fill in a questionnaire about their dietary habits.
Already, the fuzzy concept has undergone a period of operationalization, and the hypothesis
takes on a testable format.
The Importance of Operationalization
Of course, strictly speaking, concepts such as seconds, kilograms and centigrade are artificial
constructs, a way in which we define variables.
Pounds and Fahrenheit are no less accurate, but were jettisoned in favor of the metric system.
A researcher must justify their scale of scientific measurement.
Operationalization defines the exact measuring method used, and allows other scientists to
follow exactly the same methodology. One example of the dangers of non-operationalization is
the failure of the Mars Climate Orbiter.
This expensive satellite was lost, somewhere above Mars, and the mission completely failed.
Subsequent investigation found that the engineers at the sub-contractor, Lockheed, had used
imperial units instead of metric units of force.
A failure in operationalization meant that the units used during the construction and simulations
were not standardized. The US engineers used pound force, the other engineers and software
designers, correctly, used metric Newtons.
This led to a huge error in the thrust calculations, and the spacecraft ended up in a lower orbit
around Mars, burning up from atmospheric friction. This failure in operationalization cost
hundreds of millions of dollars, and years of planning and construction were wasted.

Conceptual Variables
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Conceptual variables are often expressed in general, theoretical, qualitative, or subjective terms
and important in hypothesis building process.
Two levels of abstraction exist for our research activities and our understanding of research
outcomes. Everyone understands at conceptual level. For example, if you say "Computer
games sharpen children's minds" expresses a belief about a causal relationship at a conceptual
level. At this level of abstraction, the variables are called constructs or conceptual variables.
Constructs are the mental definitions of properties of events of objects that can vary. Definitions
of computer games and mental sharpness are examples of such constructs.
Now, computer games and mental sharpness need be defined and explained. It is important to
note that the empirical research activities are carried out at an operational level of abstraction
and empirical research acquire scores from cases on measures. These measures represent
operational variables. The variables can be made operational by the measures used to acquire
scores from the cases studied. For example, a question that asks children how many hours a
day they play computer games is an operational measure children's interest in computer games.
Conceptual variables are often expressed in general, theoretical, subjective, or qualitative
terms. The research hypothesis is usually starts at this level, for example. "Effect of nicotine
patch is poorer among people lacking mental determination to quit smoking".
To measure conceptual variables, an objective definition is often required. This may involve
having an easily available validated instrument, inferring an operational variable from theory,
establishing consensus or all three. In above example, we need to have a definition of effect of
nicotine patch and mental determination.
During this process, one needs to decide on measurement scale. The researcher may decide to
make effect of nicotine patch: yes/no" (nominal), or "none/low/moderate/high (ordinal) based on
definition of potency of a patch. For mental determination to quit smoking, you may need to do

the same: present/absent, or, more likely, use some ordinal scale based on a predesigned
questionnaire or third party evaluation.
Another example: if this is stated that
"The recovery in diabetic patient was quick among those patients without concurrent
cardiovascular problems"
Now, the recovery needs to be converted into some measureable variable e.g.
"maintenance of glucose levels over one year (continuous scale), as does cardiovascular
problems, e.g."
No history of previous heart attack, normal findings of ECG/Echocardiography/Color Doppler
and cardiac enzymes etc for evaluation of cardiovascular status (continuous scale).
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Experimental Research
Oskar Blakstad 1 read 15 Comments
Experimental research is commonly used in sciences such as sociology and
psychology, physics, chemistry, biology and medicine etc.
It is a collection of research designs which use manipulation and controlled
testing to understand causal processes. Generally, one or more variables
are manipulated to determine their effect on a dependent variable.
The experimental method

is a systematic and scientific approach to research in which the researcher manipulates one or
more variables, and controls and measures any change in other variables.
Experimental Research is often used where:
1. There is time priority in a causal relationship (cause precedes effect)
2. There is consistency in a causal relationship (a cause will always lead to the same
effect)
3. The magnitude of the correlation is great.
(Reference: en.wikipedia.org)
The word experimental research has a range of definitions. In the strict sense, experimental
research is what we call a true experiment.
This is an experiment where the researcher manipulates one variable, and control/randomizes
the rest of the variables. It has a control group, the subjects have been randomly assigned
between the groups, and the researcher only tests one effect at a time. It is also important to
know what variable(s) you want to test and measure.
A very wide definition of experimental research, or a quasi experiment, is research where the
scientist actively influences something to observe the consequences. Most experiments tend to
fall in between the strict and the wide definition.
A rule of thumb is that physical sciences, such as physics, chemistry and geology tend to define
experiments more narrowly than social sciences, such as sociology and psychology, which
conduct experiments closer to the wider definition.
Aims of Experimental Research
Experiments are conducted to be able to predict phenomenons. Typically, an experiment is
constructed to be able to explain some kind of causation. Experimental research is important to
society - it helps us to improve our everyday lives.
Identifying the Research Problem

After deciding the topic of interest, the researcher tries to define the research problem. This
helps the researcher to focus on a more narrow research area to be able to study it
appropriately. Defining the research problem helps you to formulate a research hypothesis,
which is tested against the null hypothesis.
The research problem is often operationalizationed, to define how to measure the research
problem. The results will depend on the exact measurements that the researcher chooses and
may be operationalized differently in another study to test the main conclusions of the study.
An ad hoc analysis is a hypothesis invented after testing is done, to try to explain why the
contrary evidence. A poor ad hoc analysis may be seen as the researcher's inability to accept
that his/her hypothesis is wrong, while a great ad hoc analysis may lead to more testing and
possibly a significant discovery.
Constructing the Experiment
There are various aspects to remember when constructing an experiment. Planning ahead
ensures that the experiment is carried out properly and that the results reflect the real world, in
the best possible way.
Sampling Groups to Study
Sampling groups correctly is especially important when we have more than one condition in the
experiment. One sample group often serves as a control group, whilst others are tested under
the experimental conditions.
Deciding the sample groups can be done in using many different sampling techniques.
Population sampling may chosen by a number of methods, such as randomization, "quasi-
randomization" and pairing.
Reducing sampling errors is vital for getting valid results from experiments. Researchers often
adjust the sample size to minimize chances of random errors.
Here are some common sampling techniques:
 probability sampling

 non-probability sampling
 simple random sampling
 convenience sampling
 stratified sampling
 systematic sampling
 cluster sampling
 sequential sampling
 disproportional sampling
 judgmental sampling
 snowball sampling
 quota sampling
Creating the Design
The research design is chosen based on a range of factors. Important factors when choosing
the design are feasibility, time, cost, ethics, measurement problems and what you would like to
test. The design of the experiment is critical for the validity of the results.
Typical Designs and Features in Experimental Design
 Pretest-Posttest Design
Check whether the groups are different before the manipulation starts and the effect of
the manipulation. Pretests sometimes influence the effect.
 Control Group
Control groups are designed to measure research bias and measurement effects, such
as the Hawthorne Effect or the Placebo Effect. A control group is a group not receiving
the same manipulation as the experimental group. Experiments frequently have 2
conditions, but rarely more than 3 conditions at the same time.
 Randomized Controlled Trials
Randomized Sampling, comparison between an Experimental Group and a Control
Group and strict control/randomization of all other variables
 Solomon Four-Group Design
With two control groups and two experimental groups. Half the groups have a pretest

and half do not have a pretest. This to test both the effect itself and the effect of the
pretest.
 Between Subjects Design
Grouping Participants to Different Conditions
 Within Subject Design
Participants Take Part in the Different Conditions - See also: Repeated Measures
Design
 Counterbalanced Measures Design
Testing the effect of the order of treatments when no control group is available/ethical
 Matched Subjects Design
Matching Participants to Create Similar Experimental- and Control-Groups
 Double-Blind Experiment
Neither the researcher, nor the participants, know which is the control group. The results
can be affected if the researcher or participants know this.
 Bayesian Probability
Using bayesian probability to "interact" with participants is a more "advanced"
experimental design. It can be used for settings were there are many variables which are
hard to isolate. The researcher starts with a set of initial beliefs, and tries to adjust them
to how participants have responded
Pilot Study
It may be wise to first conduct a pilot-study or two before you do the real experiment. This
ensures that the experiment measures what it should, and that everything is set up right.
Minor errors, which could potentially destroy the experiment, are often found during this
process. With a pilot study, you can get information about errors and problems, and improve the
design, before putting a lot of effort into the real experiment.
If the experiments involve humans, a common strategy is to first have a pilot study with
someone involved in the research, but not too closely, and then arrange a pilot with a person
who resembles the subject(s). Those two different pilots are likely to give the researcher good
information about any problems in the experiment.
Conducting the Experiment

An experiment is typically carried out by manipulating a variable, called the independent
variable, affecting the experimental group. The effect that the researcher is interested in, the
dependent variable(s), is measured.
Identifying and controlling non-experimental factors which the researcher does not want to
influence the effects, is crucial to drawing a valid conclusion. This is often done by controlling
variables, if possible, or randomizing variables to minimize effects that can be traced back to
third variables. Researchers only want to measure the effect of the independent variable(s)
when conducting an experiment, allowing them to conclude that this was the reason for the
effect.
Analysis and Conclusions
In quantitative research, the amount of data measured can be enormous. Data not prepared to
be analyzed is called "raw data". The raw data is often summarized as something called "output
data", which typically consists of one line per subject (or item). A cell of the output data is, for
example, an average of an effect in many trials for a subject. The output data is used for
statistical analysis, e.g. significance tests, to see if there really is an effect.
The aim of an analysis is to draw a conclusion, together with other observations. The researcher
might generalize the results to a wider phenomenon, if there is no indication of confounding
variables "polluting" the results.
If the researcher suspects that the effect stems from a different variable than the independent
variable, further investigation is needed to gauge the validity of the results. An experiment is
often conducted because the scientist wants to know if the independent variable is having any
effect upon the dependent variable. Variables correlating are not proof that there is causation.
Experiments are more often of quantitative nature than qualitative nature, although it happens.
Examples of Experiments
This website contains many examples of experiments. Some are not true experiments, but
involve some kind of manipulation to investigate a phenomenon. Others fulfill most or all criteria
of true experiments.

Here are some examples of scientific experiments:
Social Psychology
 Stanley Milgram Experiment - Will people obey orders, even if clearly dangerous?
 Asch Experiment - Will people conform to group behavior?
 Stanford Prison Experiment - How do people react to roles? Will you behave differently?
 Good Samaritan Experiment - Would You Help a Stranger? - Explaining Helping
Behavior
Genetics
 Law Of Segregation - The Mendel Pea Plant Experiment
 Transforming Principle - Griffith's Experiment about Genetics
Defining a Research Problem
Martyn Shuttleworth 382.4K reads 13 Comments
Defining a research problem is the fuel that drives the scientific process, and is the foundation of
any research method and experimental design, from true experiment to case study.
It is one of the first statements made in any research paper and, as well as defining the
research area, should include a quick synopsis of how the hypothesis was arrived at.
Operationalization is then used to give some indication of the exact definitions of the variables,
and the type of scientific measurements used.
This will lead to the proposal of a viable hypothesis. As an aside, when scientists are putting
forward proposals for research funds, the quality of their research problem often makes the
difference between success and failure.
Structuring the Research Problem

Look at any scientific paper, and you will see the research problem, written almost like a
statement of intent.
Defining a research problem is crucial in defining the quality of the answers, and determines the
exact research method used. A quantitative experimental design uses deductive reasoning to
arrive at a testable hypothesis.
Qualitative research designs use inductive reasoning to propose a research statement.
Defining a Research Problem
Formulating the research problem begins during the first steps of the scientific process.
As an example, a literature review and a study of previous experiments, and research, might
throw up some vague areas of interest.
Many scientific researchers look at an area where a previous researcher generated some
interesting results, but never followed up. It could be an interesting area of research, which
nobody else has fully explored.
A scientist may even review a successful experiment, disagree with the results, the tests used,
or the methodology, and decide to refine the research process, retesting the hypothesis.

This is called the conceptual definition, and is an overall view of the problem. A science report
will generally begin with an overview of the previous research and real-world observations. The
researcher will then state how this led to defining a research problem.
The Operational Definitions
The operational definition is the determining the scalar properties of the variables.
For example, temperature, weight and time are usually well known and defined, with only the
exact scale used needing definition. If a researcher is measuring abstract concepts, such as
intelligence, emotions, and subjective responses, then a system of measuring numerically
needs to be established, allowing statistical analysis and replication.
For example, intelligence may be measured with IQ and human responses could be measured
with a questionnaire from ‘1- strongly disagree’, to ‘5 - strongly agree’.
Behavioral biologists and social scientists might design an ordinal scale for measuring and
rating behavior. These measurements are always subjective, but allow statistics and replication
of the whole research method. This is all an essential part of defining a research problem.
Examples of Defining a Research Problem
An anthropologist might find references to a relatively unknown tribe in Papua New Guinea.
Through inductive reasoning, she arrives at the research problem and asks,
‘How do these people live and how does their culture relate to nearby tribes?’
She has found a gap in knowledge, and she seeks to fill it, using a qualitative case study,
without a hypothesis.
The Bandura Bobo Doll Experiment is a good example of using deductive reasoning to arrive at
a research problem and hypothesis.
Anecdotal evidence showed that violent behavior amongst children was increasing. Bandura
believed that higher levels of violent adult role models on television, was a contributor to this

rise. This was expanded into a hypothesis, and operationalization of the variables, and scientific
measurement scale, led to a robust experimental design.

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