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                        WHAT IS RESEARCH?


There are several authors writing articles for the website. We are not well-renowned researchers, nor do we wish to profile the website as authoritative. We aim to provide content which is accurate and easy to understand.

The founder and administrator is a Psychologist who has worked on many research-projects. Other authors' special areas include Biology, Statistics, Physic, Bioenergy, Orgonomy and Quantum medicine,Psychology,Medicine.....

In the broadest sense of the word, the definition of research includes any gathering of data, information and facts for the advancement of knowledge.
Whilst any definition of the scientific method is always a little difficult, due to the vast number of scientific disciplines and subtypes, there are a few basic fundamentals that are common to them all.
Research is an often-misused term, its usage in everyday language very different from the strict scientific meaning.
Research process deals with the ways and strategies used by researchers to understand the world around us.
The steps of the scientific method are a structure that has been developed over the millennia, since the time of the ancient Greek and Persian philosophers.
Experimental research is commonly used in sciences such as sociology and psychology, physics, chemistry, biology and medicine etc.
The definition of science is the observation, identification, description, experimental investigation, and theoretical explanation of phenomena. It is in particular used in activities applied to an object of inquiry or study.




Research Methodology
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.

Steps of the scientific method are shaped like an hourglass - starting from general questions, narrowing down to focus on one specific aspect and designing research where we can observe and analyze this aspect. At last, we conclude and generalize to the real world.


A variable is something that changes. It changes according to different factors. Some variables changes easily, like the stock-exchange value, while other variables are almost constant, like the name of someone. Researchers are often seeking to measure variables.

The variable can be a number, a name or anything where the value can change.

An example of a variable is temperature. The temperature varies according to other variable and factors. You can measure different temperature inside and outside. If it is a sunny day, chances are that the temperature will be higher than if it's cloudy. Another thing that can make the temperature change is whether something has been done to manipulate the temperature, like lighting a fire in the chimney.

In research, you typically define variables according to what you're measuring. The independent variable is the variable which the researcher would like to measure (the cause), while the dependent variable is the effect (or assumed effect), dependent on the independent variable. These variables are often stated in experimental research, in a hypothesis, e.g. "what is the effect of personality on helping behavior?"

In explorative research methodology, e.g. in some qualitative research, the independent and the dependent variables might not be identified beforehand. They might not be stated because the researcher does not have a clear idea yet on what is really going on.

Confounding variables are variables with a significant effect on the dependent variable that the researcher failed to control or eliminate - sometimes because the researcher is not aware of the effect of the confounding variable. The key is to identify possible confounding variables and somehow try to eliminate or control them.


Researchers organize their research by formulating and defining a research problem. This helps them focus the research process so that they can draw conclusions reflecting the real world in the best possible way.


In research, a hypothesis is a suggested explanation of a phenomenon.

A null hypothesis is a hypothesis which a researcher tries to disprove. Normally, the null hypothesis represents the current view/explanation of an aspect of the world that the researcher wants to challenge.

Research methodology involves the researcher providing an alternative hypothesis, a research hypothesis, as an alternate way to explain the phenomenon.

The researcher tests the hypothesis to disprove the null hypothesis, not because he/she loves the research hypothesis, but because it would mean coming closer to finding an answer to a specific problem. The research hypothesis is often based on observations that evoke suspicion that the null hypothesis is not always correct.

In the Stanley Milgram Experiment, the a null hypothesis was that the personality determined whether a person would hurt another person, while the research hypothesis was that the role, instructions and orders were much more important in determining whether people would hurt others.


Operationalization is to take a fuzzy concept, such as 'helping behavior', and try to measure it by specific observations, e.g. how likely are people to help a stranger with problems.


Validity refers to what degree the research reflects the given research problem.

Types of validity:

  • External Validity
  • Population Validity
  • Ecological Validity
  • Internal Validity
  • Content Validity
  • Face Validity
  • Construct Validity
  • Convergent and Discriminant Validity
  • Test Validity
  • Criterion Validity
  • Concurrent Validity
  • Predictive Validity

Reliability refers to how consistent a set of measurements are. Reliability may be defined as "Yielding the same or compatible results in different clinical experiments or statistical trials" (the free dictionary). Research methodology lacking reliability cannot be trusted. Replication studies are a way to test reliability.

Types of Reliability:

  • Test-Retest Reliability
  • Interrater Reliability
  • Internal Consistency Reliability
  • Instrument Reliability
  • Statistical Reliability
  • Reproducability

Both validity and reliability are important aspects of the research methodology to get better explanations of the world.


Generalization is to which extent the research and the conclusions of the research apply to the real world. It is not always so that good research will reflect the real world, since we can only measure a small portion of the population at a time.


The selection of the research method is crucial for what conclusions you can make about a phenomenon. It affects what you can say about the cause and factors influencing the phenomenon.

It is also important to choose a research method which is within the limits of what the researcher can do. Time, money, feasibility, ethics and availability to measure the phenomenon correctly are examples of issues constraining the research.


Choosing the scientific measurements are also crucial for getting the correct conclusion. Some measurements might not reflect the real world, because they do not measure the phenomenon as it should.


To test a hypothesis, quantitative research uses significance tests to determine which hypothesis is right.

The significance test can show whether the null hypothesis is more likely correct than the research hypothesis. Research methodology in a number of areas like social sciences depends heavily on significance test.

A significance test may even drive the research process in a whole new direction, based on the findings.

The t-test (also called the Student's T-Test) is one of many statistical significance tests, which compares two supposedly equal sets of data to see if they really are alike or not. The t-test helps the researcher conclude whether a hypothesis is supported or not.


Drawing a conclusion is based on several factors of the research process, not just because the researcher got the expected result. It has to be based on the validity and reliability of the measurement, how good the measurement was to reflect the real world and what more could have affected the results.

The observations are often referred to as 'empirical evidence' and the logic/thinking leads to the conclusions. Anyone should be able to check the observation and logic, to see if they also reach the same conclusions.

Errors of the observations may stem from measurement-problems, misinterpretations, unlikely random events etc.


Logically, there are possible to make two types of errors when drawing conclusions in research:

Type 1 error is when we accept the research hypothesis when the null hypothesis is in fact correct.

Type 2 error is when we reject the research hypothesis even if the null hypothesis is wrong.



Empirical Research can be defined as ”research based on experimentation or observation (evidence)". Such research is conducted to test a hypothesis.
by Dr. Hani (2009)

Empirical Research - Cycle

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.




  • 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


  • 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)


  • 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


  • 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 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 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.

In many ways, 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 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.


Visionary Sciencerequires 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.


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.


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.

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.

Illustration of the Scientific Method
Illustration of the Scientific Method (by A. Keita)


We evaluate the cost and benefits for most decisions in life, whether we are aware of it or not.

This can be quite a dilemma in some experiments. Stem cell research is one example of an area with difficult ethical considerations.

As a result, stem cell research is restricted in many countries, because of the major and problematic ethical issues.


  • avoid any risk of considerably harming people, the environment, or property unnecessarily. The Tuskegee Syphilis Study is an example of a study which seriously violated these standards.
  • not use deception on people participating, as was the case with the ethics of the Stanley Milgram Experiment
  • obtain informed consent from all involved in the study.
  • preserve privacy and confidentiality whenever possible.
  • take special precautions when involving populations or animals which may not be considered to understand fully the purpose of the study.
  • not offer big rewards or enforce binding contracts for the study. This is especially important when people are somehow reliant on the reward.
  • not plagiarize the work of others
  • not skew their conclusions based on funding.
  • not commit science fraud, falsify research or otherwise conduct scientific misconduct. A con-study, which devastated the public view of the subject for decades, was the study of selling more coke and popcorn by unconscious ads.

    The researcher said that he had found great effects from subliminal messages, whilst he had, in fact, never conducted the experiment.

  • not use the position as a peer reviewer to give sham peer reviews to punish or damage fellow scientists.

Basically, research must follow all regulations given, and also anticipate possible ethical problems in their research.

Competition is an important factor in research, and may be both a good thing and a bad thing.

Whistleblowing is one mechanism to help discover misconduct in research.
















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