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Concept of Research ABK

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CONCEPT OF RESEARCH INTRODUCTION Research is defined as human activity based on intellectual application in the investigation of matter. The primary purpose for applied research is discovering, interpreting and the development of methods and systems for the advancement of human knowledge on a wide variety of scientific matter of our world and the universe. Research comprises "creative work undertaken on a systematic basis in order to increase the stock of knowledge, including knowledge of man, culture and society, and the use of this stock of knowledge to devise new applications." MEANING Research a systematic investigation designed to develop or contribute to generalizable knowledge about the variable one is intrested. Research is an art of scientific investigation. Research means to a search for a knowledge. Research as a scientific & systematic search for information on a specific topic. Research has been defined in a number of different ways. A broad definition of research is given by Martyn Shuttleworth - "In the broadest sense of the word, the definition of research includes any gathering of data,
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Research is defined as human activity based on intellectual application in the investigation of matter. The primary purpose for applied research is discovering, interpreting and the development of methods and systems for the advancement of human knowledge on a wide variety of scientific matter of our world and the universe.

Research comprises "creative work undertaken on a systematic basis in order to increase the stock of knowledge, including knowledge of man, culture and society, and the use of this stock of knowledge to devise new applications."


Research a systematic investigation designed to develop or contribute to generalizable knowledge about the variable one is intrested. Research is an art of scientific investigation. Research means to a search for a knowledge. Research as a scientific & systematic search for information on a specific topic.

Research has been defined in a number of different ways.

A broad definition of research is given by Martyn Shuttleworth - "In the broadest sense of the word, the definition of research includes any gathering of data, information and facts for the advancement of knowledge."

Another definition of research is given by Creswell who states - "Research is a process of steps used to collect and analyze information to increase our understanding of a topic or issue". It consists of three steps: Pose a question, collect data to answer the question, and present an answer to the question.

The Merriam-Webster Online Dictionary defines research in more detail as "a studious inquiry or examination; especially  : investigation or experimentation aimed at the discovery and interpretation of facts, revision of accepted theories or laws in the light of new facts, or practical application of such new or revised theories or laws".

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According to Marry and Redman,

“Research is a careful & systematic affort of gaining new knowledge”.

According to Cifford Wooay,“Research comprises of defining & redefining problem formulating hypothesis (suggested solution), collecting, organising & evaluating data, making, deduction & reaching conclusion & atlist carefully testing the conclusion to determine whether they fit the formulating hypothesis or not”.

Types of research

Quantitative research

Quantitative research is generally associated with the positivist/postpositivist paradigm. It usually involves collecting and converting data into numerical form so that statistical calculations can be made and conclusions drawn.

The process

Researchers will have one or more hypotheses. These are the questions that they want to address which include predictions about possible relationships between the things they want to investigate (variables). In order to find answers to these questions, the researchers will also have various instruments and materials (e.g. paper or computer tests, observation check lists etc.) and a clearly defined plan of action.

Data is collected by various means following a strict procedure and prepared for statistical analysis. Nowadays, this is carried out with the aid of sophisticated statistical computer packages. The analysis enables the researchers to determine to what extent there is a relationship between two or more variables. This could be a simple association (e.g. people who exercise on a daily basis have lower blood pressure) or a causal relationship (e.g. daily exercise actually leads to lower blood pressure). Statistical analysis permits researchers to discover complex causal relationships and to determine to what extent one variable influences another.

The results of statistical analyses are presented in journals in a standard way, the end result being a P value. For people who are not familiar with scientific research jargon, the discussion sections at the end of articles in peer reviewed journals

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usually describe the results of the study and explain the implications of the findings in straightforward terms


Objectivity is very important in quantitative research. Consequently, researchers take great care to avoid their own presence, behaviour or attitude affecting the results (e.g. by changing the situation being studied or causing participants to behave differently). They also critically examine their methods and conclusions for any possible bias.

Researchers go to great lengths to ensure that they are really measuring what they claim to be measuring. For example, if the study is about whether background music has a positive impact on restlessness in residents in a nursing home, the researchers must be clear about what kind of music to include, the volume of the music, what they mean by restlessness, how to measure restlessness and what is considered a positive impact. This must all be considered, prepared and controlled in advance.

External factors, which might affect the results, must also be controlled for. In the above example, it would be important to make sure that the introduction of the music was not accompanied by other changes (e.g. the person who brings the CD player chatting with the residents after the music session) as it might be the other factor which produces the results (i.e. the social contact and not the music). Some possible contributing factors cannot always be ruled out but should be acknowledged by the researchers.

The main emphasis of quantitative research is on deductive reasoning which tends to move from the general to the specific. This is sometimes referred to as a top down approach. The validity of conclusions is shown to be dependent on one or more premises (prior statements, findings or conditions) being valid. Aristotle’s famous example of deductive reasoning was: All men are mortal àSocrates is a man à Socrates is mortal. If the premises of an argument are inaccurate, then the argument is inaccurate. This type of reasoning is often also associated with the fictitious character Sherlock Holmes. However, most studies also include an element of inductive reasoning at some stage of the research (see section on qualitative research for more details).

Researchers rarely have access to all the members of a particular group (e.g. all people with dementia, carers or healthcare professionals). However, they are usually interested in being able to make inferences from their study about these

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larger groups. For this reason, it is important that the people involved in the study are a representative sample of the wider population/group. However, the extent to which generalizations are possible depends to a certain extent on the number of people involved in the study, how they were selected and whether they are representative of the wider group. For example, generalizations about psychiatrists should be based on a study involving psychiatrists and not one based on psychology students. In most cases, random samples are preferred (so that each potential participant has an equal chance of participating) but sometimes researchers might want to ensure that they include a certain number of people with specific characteristics and this would not be possible using random sampling methods. Generalizability of the results is not limited to groups of people but also to situations. It is presumed that the results of a laboratory experiment reflect the real life situation which the study seeks to clarify.

When looking at results, the P value is important. P stands for probability. It measures the likelihood that a particular finding or observed difference is due to chance. The P value is between 0 and 1. The closer the result is to 0, the less likely it is that the observed difference is due to chance. The closer the result is to 1, the greater the likelihood that the finding is due to chance (random variation) and that there is no difference between the groups/variables.

Qualitative research

Qualitative research is the approach usually associated with the social constructivist paradigm which emphasises the socially constructed nature of reality. It is about recording, analysing and attempting to uncover the deeper meaning and significance of human behaviour and experience, including contradictory beliefs, behaviours and emotions. Researchers are interested in gaining a rich and complex understanding of people’s experience and not in obtaining information which can be generalized to other larger groups.

The process

The approach adopted by qualitative researchers tends to be inductive which means that they develop a theory or look for a pattern of meaning on the basis of the data that they have collected. This involves a move from the specific to the general and is sometimes called a bottom-up approach. However, most research projects also involve a certain degree of deductive reasoning (see section on quantitative research for more details).

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Qualitative researchers do not base their research on pre-determined hypotheses. Nevertheless, they clearly identify a problem or topic that they want to explore and may be guided by a theoretical lens - a kind of overarching theory which provides a framework for their investigation.

The approach to data collection and analysis is methodical but allows for greater flexibility than in quantitative research. Data is collected in textual form on the basis of observation and interaction with the participants e.g. through participant observation, in-depth interviews and focus groups. It is not converted into numerical form and is not statistically analysed.

Data collection may be carried out in several stages rather than once and for all. The researchers may even adapt the process mid-way, deciding to address additional issues or dropping questions which are not appropriate on the basis of what they learn during the process. In some cases, the researchers will interview or observe a set number of people. In other cases, the process of data collection and analysis may continue until the researchers find that no new issues are emerging.


Researchers will tend to use methods which give participants a certain degree of freedom and permit spontaneity rather than forcing them to select from a set of pre-determined responses (of which none might be appropriate or accurately describe the participant’s thoughts, feelings, attitudes or behaviour) and to try to create the right atmosphere to enable people to express themselves. This may mean adopting a less formal and less rigid approach than that used in quantitative research.

It is believed that people are constantly trying to attribute meaning to their experience. Therefore, it would make no sense to limit the study to the researcher’s view or understanding of the situation and expect to learn something new about the experience of the participants. Consequently, the methods used may be more open-ended, less narrow and more exploratory (particularly when very little is known about a particular subject). The researchers are free to go beyond the initial response that the participant gives and to ask why, how, in what way etc. In this way, subsequent questions can be tailored to the responses just given.

Qualitative research often involves a smaller number of participants. This may be because the methods used such as in-depth interviews are time and labour intensive but also because a large number of people are not needed for the purposes of statistical analysis or to make generalizations from the results.

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The smaller number of people typically involved in qualitative research studies and the greater degree of flexibility does not make the study in any way “less scientific” than a typical quantitative study involving more subjects and carried out in a much more rigid manner. The objectives of the two types of research and their underlying philosophical assumptions are simply different. However, as discussed in the section on “philosophies guiding research”, this does not mean that the two approaches cannot be used in the same study.

Pragmatic approach to research (mixed methods)

The pragmatic approach to science involves using the method which appears best suited to the research problem and not getting caught up in philosophical debates about which is the best approach. Pragmatic researchers therefore grant themselves the freedom to use any of the methods, techniques and procedures typically associated with quantitative or qualitative research. They recognise that every method has its limitations and that the different approaches can be complementary.

They may also use different techniques at the same time or one after the other. For example, they might start with face-to-face interviews with several people or have a focus group and then use the findings to construct a questionnaire to measure attitudes in a large scale sample with the aim of carrying out statistical analysis.

Depending on which measures have been used, the data collected is analysed in the appropriate manner. However, it is sometimes possible to transform qualitative data into quantitative data and vice versa although transforming quantitative data into qualitative data is not very common.

Being able to mix different approaches has the advantages of enabling triangulation. Triangulation is a common feature of mixed methods studies. It involves, for example:

the use of a variety of data sources (data triangulation) the use of several different researchers (investigator triangulation) the use of multiple perspectives to interpret the results (theory triangulation) the use of multiple methods to study a research problem (methodological


In some studies, qualitative and quantitative methods are used simultaneously. In others, first one approach is used and then the next, with the second part of the study perhaps expanding on the results of the first. For example, a qualitative study involving in-depth interviews or focus group discussions might serve to obtain

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information which will then be used to contribute towards the development of an experimental measure or attitude scale, the results of which will be analysed statistically.

Advocacy/participatory approach to research (emancipatory)

To some degree, researchers adopting an advocacy/participatory approach feel that the approaches to research described so far do not respond to the needs or situation of people from marginalised or vulnerable groups. As they aim to bring about positive change in the lives of the research subjects, their approach is sometimes described as emancipatory. It is not a neutral stance. The researchers are likely to have a political agenda and to try to give the groups they are studying a voice. As they want their research to directly or indirectly result in some kind of reform, it is important that they involve the group being studied in the research, preferably at all stages, so as to avoid further marginalising them.

The researchers may adopt a less neutral position than that which is usually required in scientific research. This might involve interacting informally or even living amongst the research participants (who are sometimes referred to as co-researchers in recognition that the study is not simply about them but also by them). The findings of the research might be reported in more personal terms, often using the precise words of the research participants. Whilst this type of research could by criticised for not being objective, it should be noted that for some groups of people or for certain situations, it is necessary as otherwise the thoughts, feelings or behaviour of the various members of the group could not be accessed or fully understood.

Vulnerable groups are rarely in a position of power within society. For this reason, researchers are sometimes members of the group they are studying or have something in common with the members of the group.


To gain familiratiy with a phenomenon to achieve new insight into it.

To portray accurately the characteristics of particular individual, situation or a group.

To determine the frequency with which something occurs or with which it is associated with something else.

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To test a hypothesis of casual relationship between variable.


Originates with a question or problem.

Require clear articulation of a goal.

Follows a scientific plan or procedure.

Often divides main problem into sub-problems.

Guided by specific poblem, question or hypothesis.

Accepts certain critical assumptions.

Requires collection and interpretation of data.

Returning in nature.


Personal Interests and Curiosities

Practical Problems or Questions

·    Unsystematic Observation (casual)

Observation of everyday behavior

Observation of animal behavior

·    Systematic Observation

Observation of behavior under naturally occurring conditions  

Published research reports


Your own previous or ongoing research

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Theory and Fact

Fact vs Theory

The terms fact and theory are words with different meanings. Although both are used in many different fields of studies, they still manage to have their own distinct definitions that separate one from the other. One particular field, wherein both terms are commonly used is in Science.

In the scientific world, facts (or scientific facts) are what one can readily observe. It can pertain to any objective and real phenomenon may it be the falling of the ball after being thrown upwards or other simple observable occurrences. In this regard, the fact is that the ball will fall. More so, if this test is being done repeatedly under a controlled environment that cancels all unnecessary variables the phenomenon would have become a very obvious and undeniable fact. It is considered a fact because it will remain as true even after several centuries unless there is a more rigid and precise way of measuring a certain phenomenon.

On the contrary, theories in science are likened to the explanations to what has been observed. It is relatively greater in weight to what a hypothesis is. If a hypothesis (an intelligent guess) is the first base of formulating a scientific law then theories are placed at the second base. These are the statements that are assumed to be true (because they seem so) even if there are no hundred percent concrete evidences. Nevertheless, theories are always presented to be true even if the claims in the said theories are mere speculations or a general agreement between a significant numbers of experts. Moreover, theories are the statements that often undergo a series of tests to nullify the claims maid by those who propose them.

To display the difference between fact and theory, a good example is when a report will state that a certain hurricane killed thousands in a particular state in America yesterday because of the reckless mass evacuation spearheaded by the local officials. In this aspect, the fact is that many were killed by the hurricane while the theory is the reason behind the death of these people. Was it only because of the haphazard evacuation plan or was it also because of the intensity of the hurricane among many other reasons? Hence, facts are really the real deal while theories are still unclear although presumed to be true.

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1. Facts are observations whereas theories are the explanations to those observations.

2. Theories are vague truths or unclear facts whereas facts are really facts.


A hypothesis (plural hypotheses) is a proposed explanation for a phenomenon. For a hypothesis to be a scientific hypothesis, the scientific method requires that one can test it. Scientists generally base scientific hypotheses on previous observations that cannot satisfactorily be explained with the available scientific theories. Even though the words "hypothesis" and "theory" are often used synonymously, a scientific hypothesis is not the same as a scientific theory. A scientific hypothesis is a proposed explanation of a phenomenon which still has to be rigorously tested. In contrast, a scientific theory has undergone extensive testing and is generally accepted to be the accurate explanation behind an observation.[1] A working hypothesis is a provisionally accepted hypothesis proposed for further research.[2]

A different meaning of the term hypothesis is used in formal logic, to denote the antecedent of a proposition; thus in the proposition "If P, then Q", P denotes the hypothesis (or antecedent); Q can be called a consequent. P is the assumption in a (possibly counterfactual) What If question.

The adjective hypothetical, meaning "having the nature of a hypothesis", or "being assumed to exist as an immediate consequence of a hypothesis", can refer to any of these meanings of the term "hypothesis".

Scientific hypothesis

People refer to a trial solution to a problem as a hypothesis, often called an "educated guess" because it provides a suggested solution based on the evidence. Some scientists reject the term "educated guess" as incorrect, however. Experimenters may test and reject several hypotheses before solving the problem.

According to Schick and Vaughn, researchers weighing up alternative hypotheses may take into consideration:

Testability (compare falsifiability as discussed above) Parsimony (as in the application of "Occam's razor", discouraging the

postulation of excessive numbers of entities)

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Scope – the apparent application of the hypothesis to multiple cases of phenomena

Fruitfulness – the prospect that a hypothesis may explain further phenomena in the future

Conservatism – the degree of "fit" with existing recognized knowledge-systems.



Reality is very complex, and economists, like other scientists, use models to analyze reality. A model is a simplified version of the real world and only includes the elements that we believe are the most important. For example, we think that prices are the most important factor in determining the demand for bread. We also think that income and population are important. Other economic factors will be left out of the model.

Each of these elements of a model is a variable. Our model for the demand for bread has four variables: (1) the quantity of bread demanded, (2) the price of bread, (3) the income of consumers, and (4) the number of consumers. Each variable can be expressed by numbers.

The dependent variable is the tail of the dog -- its number value depends on the number values of the other variables. In our model, the dependent variable is the quantity of bread demanded (#1). The other three variables are the independent variables and their number values, taken together, will determine the quantity of bread that consumers want to buy.

Models can be expressed using mathematical notation. We often use y for the dependent variable and x for the independent variables. We use f to represent the actual mathematical relationship (usually a linear polynomial).

y = f (x)

In the demand for bread, we would use Qd for quantity demanded, P for price, Y for income, and N for population. The + and - signs show direct and inverse relationships.

Qd = f (-P,+Y,+N)

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Among the independent variables, the price of bread (#2) is the most important, so we match the quantity (#1) and price (#2) variables together in tables and graphs. The table containing these numbers is called a schedule, and the graph of these numbers is called a curve. Since we are not including income and population, we have to assume that these variables don't vary! We call this condition "ceteris paribus" which means that income and population are held constant. If income changes, for example, we will need a new set of quantity numbers for our schedule, and the location of our curve will change.

The relationship of the dependent variable and each of the independent variables can be direct or inverse. In a direct relationship, a higher value of the independent variable is related to a higher value of the dependent variable (or vice-versa). Mathematically, a direct relationship is also a positive relationship.

In an inverse relationship, a higher value of the independent variable is related to a lower value of the dependent variable (or vice-versa). Mathematically, an inverse relationship is also a negative relationship. [The word "indirect" does not mean inverse!]

In our example, the quantity of bread demanded (#1) is inversely related to the price of bread (#2). These two variables are used for the demand schedule and the demand curve. In the schedule, higher values of price are linked to lower values of quantity demanded. In the demand curve, the curve will slope downward to the right (a "negative" slope). When there is a change in price, we say there has been a "change in the quantity demanded".

The demand for bread is directly related to income (#3). If income takes higher values, then the demand for bread will also take higher values. In the demand schedule, the quantity demanded at each price will be higher. In the demand curve, the quantity demanded will be further to the right at each price level. We say that there is an "increase in demand" and "the curve shifts to the right". If income takes lower values, the process is reversed. We say that there is a "decrease in demand" and "the curve shifts to the left". We call these shifts in the demand curve a "change in demand". [The demand for bread is also directly related to changes in population (#4).]


Statistics lets economists use real world data to identify these types of relationships for our models. But sometimes, data can be misleading. For example, consumption spending by households and gross domestic product move up and

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down together. This is a positive (direct) correlation. Variables that are directly related will also show a positive correlation. There are two questions: (1) are these variables related, and (2) which are the independent and dependent variables? Economists believe consumption spending and GDP are related, and that consumption is the dependent variable. In fact, this relationship of consumption to output is the "consumption function" developed by John Maynard Keynes to help explain the causes of the Great Depression of the 1930s.

Natural gas use and ice cream sales show a negative (inverse) correlation -- when gas sales are high, ice cream sales are low, and vice-versa. Are these two variables inversely related? Economists argue that these two variables are not related to each other at all. If anything, we are observing the impact of seasonal changes in the weather.

Related variables will be correlated variables; correlated variables may not be related variables.

Another type of data problem arises from the timing of events. This is sometimes called the post hoc, ergo propter hoc fallacy. It assumes that a later event is always due to an earlier event.

If event A is followed by event B, are we observing related events or just a coincidence? For example, we observe (A) an increase the in the money supply, followed by (B) an increase in the price level. Can we conclude that the price level is a dependent variable which is directly related to the money supply which is an independent variable? Economists assert that this relationship does exist, and it is the important "equation of exchange" which we use to explain the power of monetary policy.

In early 1997, (A) Madonna had a baby. In late 1997, (B) the economies of Southeast Asia collapsed. This is a an inverse correlation. Is there any relationship of event A to event B? Probably not! Consider these arguments, however.

In 1948, England established their National Health Service, which pays all the medical expenses of their citizens. A writer noted that in 1948, the life expectancy in England was 67 years, but after half a century of socialized medicine, the life expectancy had risen to 74 years. This is a direct correlation, but is there a causal linkage?

In 1981, the Reagan Administration cut personal income taxes by 25 percent. By the mid-1980s, the federal government deficit was over $200 billion

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per year. Did the Reagan tax cuts create the later deficit? In 1981 and 1982, the U.S. economy endured a severe recession, and tax cuts are one way to fight a recession. Economists still debate the effects of the Reagan tax cuts.

A dependent event will be a later event; not all later events are dependent events.

Causal Relationships Between Variables

What do we mean when we talk about a “relationship” between variables? In psychological research, we are referring to a connection between two or more factors that we can measure or systematically vary.

One of the most important distinctions to make when discussing the relationship between variables is the meaning of causation.

A causal relationship is when one variable causes a change in another variable. These types of relationships are investigated by experimental research in order to determine if changes in one variable actually result in changes in another variable.

A correlation is the measurement of the relationship between two variables. These variables already occur in the group or population and are not controlled by the experimenter.

A positive correlation is a direct relationship where as the amount of one variable increases, the amount of a second variable also increases.

In a negative correlation, as the amount of one variable goes up, the levels of another variable go down.

In both types of correlation, there is no evidence or proof that changes in one variable cause changes in the other variable. A correlation simply indicates that there is a relationship between the two variables.

The most important concept to take from this is that correlation does not equal causation. Many popular media sources make the mistake of assuming that simply because two variables are related, a causal relationship exists.


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A research design is a systematic plan to study a scientific problem. The design of a study defines the study type (descriptive, correlational, semi-experimental, experimental, review, meta-analytic) and sub-type (e.g., descriptive-longitudinal case study), research question, hypotheses, independent and dependent variables, experimental design, and, if applicable, data collection methods and a statistical analysis plan.

Design types and sub-types

There are many ways to classify research designs, but sometimes the distinction is artificial and other times different designs are combined. Nonetheless, the list below offers a number of useful distinctions between possible research designs.[1]

Descriptive (e.g., case-study, naturalistic observation, Survey[disambiguation needed]) Correlational (e.g., case-control study, observational study) Semi-experimental (e.g., field experiment, quasi-experiment) Experimental (Experiment with random assignment) Review (Literature review, Systematic review) Meta-analytic (Meta-analysis)

Sometimes a distinction is made between "fixed" and "flexible" or, synonymously, "quantitative" and "qualitative" research designs.[2] However, fixed designs need not be quantitative, and flexible design need not be qualitative. In fixed designs, the design of the study is fixed before the main stage of data collection takes place. Fixed designs are normally theory driven; otherwise it is impossible to know in advance which variables need to be controlled and measured. Often, these variables are measured quantitatively. Flexible designs allow for more freedom during the data collection process. One reason for using a flexible research design can be that the variable of interest is not quantitatively measurable, such as culture. In other cases, theory might not be available before one starts the research. However, these distinctions are not recognized by many researchers, such as Stephen Gorard who presents a simpler and cleaner definition of research design


The choice of how to group participants depends on the research hypothesis and on how the participants are sampled. In a typical experimental study, there will be at least one "experimental" condition (e.g., "treatment") and one "control" condition ("no treatment"), but the appropriate method of grouping may be depend on factors such as the duration of measurement phase and participant characteristics:

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Cohort study Cross-sectional study Cross-sequential study Longitudinal study

Confirmatory versus exploratory research

Confirmatory research tests a priori hypotheses—outcome predictions that are made before the measurement phase begins. Such a priori hypotheses are usually derived from a theory or the results of previous studies. The advantage of confirmatory research is that the result is more meaningful, in the sense that it is much harder to claim that a certain result is statistically significant. The reason for this is that in confirmatory research, one ideally strives to reduce the probability of falsely reporting a non-significant result as significant. This probability is known as α-level or a type I error. Loosely speaking, if you know what you are looking for, you should be very confident when and where you will find it; accordingly, you only accept a result as significant if it is highly unlikely to have been observed by chance.

Exploratory research on the other hand seeks to generate a posteriori hypotheses by examining a data-set and looking for potential relations between variables. It is also possible to have an idea about a relation between variables but to lack knowledge of the direction and strength of the relation. If the researcher does not have any specific hypotheses beforehand, the study is exploratory with respect to the variables in question (although it might be confirmatory for others). The advantage of exploratory research is that it is easier make new discoveries due to the less stringent methodological restrictions. Here, the researcher does not want to miss a potentially interesting relation and therefore aims to minimize the probability of rejecting a real effect or relation, this probability is sometimes referred to as β and the associated error is of type II. In other words, if you want to see whether some of your measured variables could be related, you would want to increase your chances of finding a significant result by lowering the threshold of what you deem to be significant.

Sometimes, a researcher may conduct exploratory research but report it as if it had been confirmatory (HARKing); this is a questionable research practice bordering fraud.

State problems versus process problems

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A distinction can be made between state problems and process problems. State problems aim to answer what the state of a phenomena is at a given time, while process problems deal with the change of phenomena over time. Examples of state problems are the level of mathematical skills of sixteen year old children or the level, computer skills of the elderly, the depression level of a person, etc. Examples of process problems are the development of mathematical skills from puberty to adulthood, the change in computer skills when people get older and how depression symptoms change during therapy.

State problems are easier to measure than process problems. State problems just require one measurement of the phenomena of interest, while process problems always require multiple measurements. Research designs like repeated measures and longitudinal study are needed to address process problems.

Examples of fixed designs

In an experimental design, the researcher actively tries to change the situation, circumstances, or experience of participants (manipulation), which may lead to a change in behavior or outcomes for the participants of the study. The researcher randomly assigns participants to different conditions, measures the variables of interest and tries to control for confounding variables. Therefore, experiments are often highly fixed even before the data collection starts.

In a good experimental design, a few things are of great importance. First of all, it is necessary to think of the best way to operationalize the variables that will be measured. Therefore, it is important to consider how the variable(s) will be measured, as well as which methods would be most appropriate to answer the research question. In addition, the statistical analysis has to be taken into account. Thus, the researcher should consider what the expectations of the study are as well as how to analyse this outcome. Finally, in an experimental design the researcher must think of the practical limitations including the availability of participants as well as how representative the participants are to the target population. It is important to consider each of these factors before beginning the experiment.[3] Additionally, many researchers employ power analysis before they conduct an experiment, in order to determine how large the sample must be to find an effect of a given size with a given design at the desired probability of making a Type I or Type II error.

Non-experimental research designs

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Non-experimental research designs do not involve a manipulation of the situation, circumstances or experience of the participants. Non-experimental research designs can be broadly classified into three categories. First, relational designs, in which a range of variables is measured. These designs are also called correlational studies, because correlational data are most often used in analysis. It is important to clarify here that correlation does not imply causation, and rather identifies dependence of one variable on another. Correlational designs are helpful in identifying the relation of one variable to another, and seeing the frequency of co-occurrence in two natural groups (See correlation and dependence). The second type is comparative research. These designs compare two or more groups on one or more variable, such as the effect of gender on grades. The third type of non-experimental research is a longitudinal design. A longitudinal design examines variables such as performance exhibited by a group or groups over time. See Longitudinal study.

Examples of flexible research designs

Case study

Famous case studies are for example the descriptions about the patients of Freud, who were thoroughly analysed and described.

Bell (1999) states “a case study approach is particularly appropriate for individual researchers because it gives an opportunity for one aspect of a problem to be studied in some depth within a limited time scale”.

Ethnographic study

This type of research is involved with a group, organization, culture, or community. Normally the researcher shares a lot of time with the group.

Grounded Theory study

Grounded theory research is a systematic research process that works to develop "a process, and action or an interaction about a substantive topic".


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10 Steps to Writing an Academic Research Proposal

This hub discusses some of the common elements in a research proposal. Whether you are doing quantitative or qualitative research, it is important that you outline the reasons why you propose doing the study and what process or procedures you will follow to complete the proposed study.

Some of the important parts of a good quantitative or qualitative research proposal include:

1. Determining the general topic;2. Performing a Literature review on the topic;3. Identifying a gap in the literature;4. Identifying a problem highlighted by the gap in the literature and framing a

purpose for the study;5. Writing an Introduction to the study;6. Framing research hypotheses and or research questions to investigate or

guide the study;7. Determine the method of investigation8. Outline the research design9. Define the Sample size and the characteristics of the proposed sample;10.Describe the procedures to follow for data collection and data analyses.

Determine a General Topic

The first step in writing an academic research proposal is to idenitfy a general topic or subject area to investigate. Usually this first point is the easiest because the research proposal will be tied to the overall theme of a course. In such a case, the the general subject for investigation is normally determined by a professor who is leading the class, the school's department chair, or academic advisory committee.

Perform a Literature Review

The next step is to read as much literature on the general subject matter as time will allow. While you read the literature it is advised to take copious notes and then summarize the purpose and findings of each study relevant to the general subject matter of the eventual research proposal.

Identify a Gap in the Literature

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The general purpose of the literature review is not to have notes on a whole bunch of different journal articles and books on a particular subject. The purpose is to understand what studies have already been done on the subject and then to identify any glaring gaps in the literature. Identifying gaps in the literature will open up opportunities to add to the body of knowledge within the general subject area.

For instance, both Kimura and Coggins found that servant leadership is actively admired and taught in the Cambodian Christian community which makes up only a small percentage of the Cambodian population. However, no one has yet investigated attitudes towards servant leadership in the non-Christian Cambodian community which makes up over 90% of the population. This is an obvious gap in the literature.

Identify a Problem and Frame a Purpose Statement

After you have performed the literature review and hopefully identified an obvious gap in the literature, next you need to identify a problem related to the gap and frame a purpose statement as to why you are investigating what you propose and why other should care about the study. If your readers cannot answer the question so what? Or your answer the question why should I care? Then it may be interesting to you, but not relevant to anyone else. 

Write an Introduction

After you have identified a pertinent problem and framed a purpose statement, then you need to craft an introduction. Among other things, the introduction to the proposal will include

The Problem Statement A brief summary of the literature A brief description of the gap in the literature A Purpose statement as to why you are proposing the study and why others

should care about the subject matter tied to your research proposal.

Determine Research Hypotheses and or Reseach Questions

Next, you need to identify and craft carefully defined research hypotheses and or research questions. Research hypotheses identify what you are actually going to investigate and what you expect to find from your research study. Research hypotheses are normally found in quantitative research proposals which compare differences and/or relationships between independent variables (or causes of

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phenomena) and dependent variables (or the effects that result from causes). Research questions are normally found in qualitative research studies. Most importantly, in good academic writing, research hypotheses and questions must be informed or flow from the literature review.

Determine the Method of Investigation

The method section is the second of the two main parts of the research proposal. In good academic writing it is important to include a method section that outlines the procedures you will follow to complete your proposed study. The method section generally includes sections on the following:

Research design; Sample size and characteristics of the proposed sample; Data collection and data analysis procedures

Determine the Research Design

The next step in good academic writing is to outline the research design of the research proposal. For each part of the design, it is highly advised that you describe two or three possible alternatives and then tell why you propose the particular design you chose. For instance, you might describe the differences between experimental, quasi-experimental, and non-experimental designs before you elaborate on why you propose a non-experimental design.

Determine the Sample Size and the Characteristics of the Sample

In this section of your research proposal, you will describe the sample size and the characteristics of the participants in the sample size. Describe how you determined how many people to include in the study and what attributes they have which make them uniquely suitable for the study.

Determine the Data Collection and Data Analysis Procedures

The last section highlighted in this hub is the data collection and analysis procedures. In this section you will describe how you propose to collect your data e.g. through a questionnaire survey if you are performing a quantitative analysis or through one-on-one interviews if you are performing a qualitative or mixed methods study.

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After you collect the data, you also need to follow a scheme as how to analyze the data and report the results. In a quantitative study you might run the data through Excel or better yet SPSS and if you are proposing a qualitative study you might use a certain computer program like ATLAi. to perform a narrative study or grounded theory study that exposes the main themes from the proposed interviews.


Documentary research is the use of outside sources, documents, to support the viewpoint or argument of an academic work. The process of documentary research often involves some or all of conceptualising, using and assessing documents. The analysis of the documents in documentary research would be either quantitative or qualitative analysis (or both).[1] The key issues surrounding types of documents and our ability to use them as reliable sources of evidence on the social world must be considered by all who use documents in their research. The paucity of sources available until now means that this compendium will be invaluable to social researchers.

Type of documents

Examples of documents include government publications, newspapers, certificates, census publications, novels, film and video, paintings, personal photographs, diaries and innumerable other written, visual and pictorial sources in paper, electronic, or other `hard copy' form. Along with surveys and ethnography, documentary research is one of the three major types of social research and arguably has been the most widely used of the three throughout the history of sociology and other social sciences. It has been the principal method for leading sociologists.


In the social sciences and life sciences, a case study (or case report) is a descriptive, exploratory or explanatory analysis of a person, group or event. An explanatory case study is used to explore causation in order to find underlying principles. Case studies may be prospective (in which criteria are established and cases fitting the criteria are included as they become available) or retrospective (in which criteria are established for selecting cases from historical records for inclusion in the study).

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Thomas[3] offers the following definition of case study: "Case studies are analyses of persons, events, decisions, periods, projects, policies, institutions, or other systems that are studied holistically by one or more methods. The case that is the subject of the inquiry will be an instance of a class of phenomena that provides an analytical frame — an object — within which the study is conducted and which the case illuminates and explicates."

Another suggestion is that case study should be defined as a research strategy, an empirical inquiry that investigates a phenomenon within its real-life context. Case study research can mean single and multiple case studies, can include quantitative evidence, relies on multiple sources of evidence, and benefits from the prior development of theoretical propositions. Case studies should not be confused with qualitative research and they can be based on any mix of quantitative and qualitative evidence. Single-subject research provides the statistical framework for making inferences from quantitative case-study data.[2][4] This is also supported and well-formulated in (Lamnek, 2005): "The case study is a research approach, situated between concrete data taking techniques and methodologic paradigms."

Case selection and structure

An average, or typical, case is often not the richest in information. In clarifying lines of history and causation it is more useful to select subjects that offer an interesting, unusual or particularly revealing set of circumstances. A case selection that is based on representativeness will seldom be able to produce these kinds of insights. When selecting a subject for a case study, researchers will therefore use information-oriented sampling, as opposed to random sampling. Outlier cases (that is, those which are extreme, deviant or atypical) reveal more information than the potentially representative case. Alternatively, a case may be selected as a key case, chosen because of the inherent interest of the case or the circumstances surrounding it. Or it may be chosen because of researchers' in-depth local knowledge; where researchers have this local knowledge they are in a position to “soak and poke” as Fenno[5] puts it, and thereby to offer reasoned lines of explanation based on this rich knowledge of setting and circumstances.

Three types of cases may thus be distinguished:

1. Key cases2. Outlier cases3. Local knowledge cases

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Whatever the frame of reference for the choice of the subject of the case study (key, outlier, local knowledge), there is a distinction to be made between the subjestorical unity [6] through which the theoretical focus of the study is being viewed. The object is that theoretical focus – the analytical frame. Thus, for example, if a researcher were interested in US resistance to communist expansion as a theoretical focus, then the Korean War might be taken to be the subject, the lens, the case study through which the theoretical focus, the object, could be viewed and explicated.[7]

Beyond decisions about case selection and the subject and object of the study, decisions need to be made about purpose, approach and process in the case study. Thomas[3] thus proposes a typology for the case study wherein purposes are first identified (evaluative or exploratory), then approaches are delineated (theory-testing, theory-building or illustrative), then processes are decided upon, with a principal choice being between whether the study is to be single or multiple, and choices also about whether the study is to be retrospective, snapshot or diachronic, and whether it is nested, parallel or sequential. It is thus possible to take many routes through this typology, with, for example, an exploratory, theory-building, multiple, nested study, or an evaluative, theory-testing, single, retrospective study. The typology thus offers many permutations for case study structure.

A closely related study in medicine is the case report, which identifies a specific case as treated and/or examined by the authors as presented in a novel form. These are, to a differentiable degree, similar to the case study in that many contain reviews of the relevant literature of the topic discussed in the thorough examination of an array of cases published to fit the criterion of the report being presented. These case reports can be thought of as brief case studies with a principal discussion of the new, presented case at hand that presents a novel interest.

Generalizing from case studies

A critical case is defined as having strategic importance in relation to the general problem. A critical case allows the following type of generalization, ‘If it is valid for this case, it is valid for all (or many) cases.’ In its negative form, the generalization would be, ‘If it is not valid for this case, then it is not valid for any (or only few) cases.’

The case study is also effective for generalizing using the type of test that Karl Popper called falsification, which forms part of critical reflexivity. Falsification is

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one of the most rigorous tests to which a scientific proposition can be subjected: if just one observation does not fit with the proposition it is considered not valid generally and must therefore be either revised or rejected. Popper himself used the now famous example of, "All swans are white," and proposed that just one observation of a single black swan would falsify this proposition and in this way have general significance and stimulate further investigations and theory-building. The case study is well suited for identifying "black swans" because of its in-depth approach: what appears to be "white" often turns out on closer examination to be "black."

Galileo Galilei’s rejection of Aristotle’s law of gravity was based on a case study selected by information-oriented sampling and not random sampling. The rejection consisted primarily of a conceptual experiment and later on of a practical one. These experiments, with the benefit of hindsight, are self-evident. Nevertheless, Aristotle’s incorrect view of gravity dominated scientific inquiry for nearly two thousand years before it was falsified. In his experimental thinking, Galileo reasoned as follows: if two objects with the same weight are released from the same height at the same time, they will hit the ground simultaneously, having fallen at the same speed. If the two objects are then stuck together into one, this object will have double the weight and will according to the Aristotelian view therefore fall faster than the two individual objects. This conclusion seemed contradictory to Galileo. The only way to avoid the contradiction was to eliminate weight as a determinant factor for acceleration in free fall.[8]

History of the case study

It is generally believed that the case-study method was first introduced into social science by Frederic Le Play in 1829 as a handmaiden to statistics in his studies of family budgets. (Les Ouvriers Europeens (2nd edition, 1879).[9]

The use of case studies for the creation of new theory in social sciences has been further developed by the sociologists Barney Glaser and Anselm Strauss who presented their research method, Grounded theory, in 1967.

The popularity of case studies in testing hypotheses has developed only in recent decades. One of the areas in which case studies have been gaining popularity is education and in particular educational evaluation.[10]

Case studies have also been used as a teaching method and as part of professional development, especially in business and legal education. The problem-based learning (PBL) movement is such an example. When used in (non-business)

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education and professional development, case studies are often referred to as critical incidents.

Ethnography is an example of a type of case study, commonly found in communication case studies. Ethnography is the description, interpretation, and analysis of a culture or social group, through field research in the natural environment of the group being studied. The main method of ethnographic research is through observation where the researcher observes the participants over an extended period of time within the participants own environment.[11]

When the Harvard Business School was started, the faculty quickly realized that there were no textbooks suitable to a graduate program in business. Their first solution to this problem was to interview leading practitioners of business and to write detailed accounts of what these managers were doing. Cases are generally written by business school faculty with particular learning objectives in mind and are refined in the classroom before publication. Additional relevant documentation (such as financial statements, time-lines, and short biographies, often referred to in the case as "exhibits"), multimedia supplements (such as video-recordings of interviews with the case protagonist), and a carefully crafted teaching note often accompany cases


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.

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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.

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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.

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 DesignCheck whether the groups are different before the manipulation starts and the effect of the manipulation. Pretests sometimes influence the effect.

Control GroupControl 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 TrialsRandomized Sampling, comparison between an Experimental Group and a Control Group and strict control/randomization of all other variables

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Solomon Four-Group DesignWith 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 DesignGrouping Participants to Different Conditions

Within Subject DesignParticipants Take Part in the Different Conditions - See also: Repeated Measures Design

Counterbalanced Measures DesignTesting the effect of the order of treatments when no control group is available/ethical

Matched Subjects DesignMatching Participants to Create Similar Experimental- and Control-Groups

Double-Blind ExperimentNeither the researcher, nor the participants, know which is the control group. The results can be affected if the researcher or participants know this.

Bayesian ProbabilityUsing 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

Survey/descriptive RESEARCH

Descriptive research, is used to describe characteristics of a population or phenomenon being studied. It does not answer questions about how/when/why the characteristics occurred. Rather it addresses the "what" question (What are the characteristics of the population or situation being studied?) The characteristics used to describe the situation or population are usually some kind of categorical scheme also known as descriptive categories. For example, the periodic table categorizes the elements. Scientists use knowledge about the nature of electrons, protons and neutrons to devise this categorical scheme. We now take for granted the periodic table, yet it took descriptive research to devise it. Descriptive research generally precedes explanatory research. For example, over time the periodic table’s description of the elements allowed scientists to explain chemical reaction and make sound prediction when elements were combined.

Hence, research cannot describe what caused a situation. Thus, Descriptive research cannot be used to as the basis of a causal relationship, where one variable

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affects another. In other words, descriptive research can be said to have a low requirement for internal validity.

The description is used for frequencies, averages and other statistical calculations. Often the best approach, prior to writing descriptive research, is to conduct a survey investigation. Qualitative research often has the aim of description and researchers may follow-up with examinations of why the observations exist and what the implications of the findings are.


Data collection is the process of gathering and measuring information on variables of interest, in an established systematic fashion that enables one to answer stated research questions, test hypotheses, and evaluate outcomes. The data collection component of research is common to all fields of study including physical and social sciences, humanities, business, etc. While methods vary by discipline, the emphasis on ensuring accurate and honest collection remains the same.

Regardless of the field of study or preference for defining data (quantitative, qualitative), accurate data collection is essential to maintaining the integrity of research. Both the selection of appropriate data collection instruments (existing, modified, or newly developed) and clearly delineated instructions for their correct use reduce the likelihood of errors occurring.

A formal data collection process is necessary as it ensures that data gathered are both defined and accurate and that subsequent decisions based on arguments embodied in the findings are valid.[1] The process provides both a baseline from which to measure and in certain cases a target on what to improve.

Consequences from improperly collected data include:

Inability to answer research questions accurately. Inability to repeat and validate the study.


Data presentation is a critical portion of making proposals, reports and other essential demonstrations during the course of daily meetings and important presentations. Most presentations are either visual in nature or rely on strong visual

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elements for clarity and information conveyance. In the past, this visual transfer of data was accomplished through projectors and slides, with crude handouts supplementing the data where possible. In the present, computer projectors and programs like Microsoft PowerPoint make transferring a visual form of data easier than ever, and also make it easier to produce a tangible form of the data faithful to the presentation with printed slides.Data presentation methods vary depending on the form the data taken within the presentation. When using graphs to display information, including items that list what each axis of the graph is meant to represent aids the audience in immediate understanding, and does not require the speaker to field constant questions about the basic message of the graph. Standard bar graphs compare a relatively small number of objects and are useful for showing a direct visual relationship for a small number of items. Pie graphs offer an easily seen method for dissecting pieces of a whole, relating amounts to each other in comparison to the entire total.Multiple graphs displayed on a single slide or sheet may require additional explanation or interpretation from the presenter. For complex data, graphs may require multiple parts with complex relationships. If the graphs are presented in a clear and concise manner, questions from interested and impacted parties should further illuminate the data and create additional usefulness for the presentation as a whole. Printed copies of the presentation are best available at the start of the presentation. Any party who asks for a copy of the presentation should have it made available to them when asked.

Data analysis

Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science domains.

Data mining is a particular data analysis technique that focuses on modeling and knowledge discovery for predictive rather than purely descriptive purposes. Business intelligence covers data analysis that relies heavily on aggregation, focusing on business information. In statistical applications, some people divide data analysis into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA). EDA focuses on discovering new features in the data and CDA on confirming or falsifying existing hypotheses. Predictive analytics focuses on application of statistical or structural models for predictive

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forecasting or classification, while text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a species of unstructured data. All are varieties of data analysis.

Data integration is a precursor to data analysis, and data analysis is closely linked to data visualization and data dissemination. The term data analysis is sometimes used as a synonym for data modeling.

The process of data analysis

Data analysis is a process, within which several phases can be distinguished:[1] Processing of Data Refers to concentrating, recasting and dealing with data in such a way that they becomes as amenable to analysis as possible

Data cleaning

The need for data cleaning will arise from problems in the way that data is entered and stored. Data cleaning is process of preventing and correcting these errors. Common tasks include record matching, deduplication, and column segmentation. There are several types of data cleaning that depend on the type of data. For Quantitative data methods for outlier detection can be used to get rid of likely incorrectly entered data. For textual data spellcheckers can used to lessen the amount of mistyped words, but it is harder to tell if the word themselves are correct.

Initial data analysis

The most important distinction between the initial data analysis phase and the main analysis phase, is that during initial data analysis one refrains from any analysis that are aimed at answering the original research question. The initial data analysis phase is guided by the following four questions:

Quality of data

The quality of the data should be checked as early as possible. Data quality can be assessed in several ways, using different types of analyses: frequency counts, descriptive statistics (mean, standard deviation, median), normality (skewness, kurtosis, frequency histograms, n: variables are compared with coding schemes of variables external to the data set, and possibly corrected if coding schemes are not comparable.

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Test for common-method variance.

The choice of analyses to assess the data quality during the initial data analysis phase depends on the analyses that will be conducted in the main analysis phase.

Quality of measurements

The quality of the measurement instruments should only be checked during the initial data analysis phase when this is not the focus or research question of the study. One should check whether structure of measurement instruments corresponds to structure reported in the literature.There are two ways to assess measurement

Analysis of homogeneity (internal consistency), which gives an indication of the reliability of a measurement instrument. During this analysis, one inspects the variances of the items and the scales, the Cronbach's α of the scales, and the change in the Cronbach's alpha when an item would be deleted from a scale.

Initial transformations

After assessing the quality of the data and of the measurements, one might decide to impute missing data, or to perform initial transformations of one or more variables, although this can also be done during the main analysis phase. Possible transformations of variables are:

Square root transformation (if the distribution differs moderately from normal)

Log-transformation (if the distribution differs substantially from normal) Inverse transformation (if the distribution differs severely from normal) Make categorical (ordinal / dichotomous) (if the distribution differs severely

from normal, and no transformations help)

Did the implementation of the study fulfill the intentions of the research design?

One should check the success of the randomization procedure, for instance by checking whether background and substantive variables are equally distributed within and across groups.If the study did not need and/or use a randomization procedure, one should check the success of the non-random sampling, for instance by checking whether all

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subgroups of the population of interest are represented in sample.Other possible data distortions that should be checked are:

dropout (this should be identified during the initial data analysis phase) Item nonresponse (whether this is random or not should be assessed during

the initial data analysis phase) Treatment quality (using manipulation checks).

Characteristics of data sample

In any report or article, the structure of the sample must be accurately described. It is especially important to exactly determine the structure of the sample (and specifically the size of the subgroups) when subgroup analyses will be performed during the main analysis phase.The characteristics of the data sample can be assessed by looking at:

Basic statistics of important variables Scatter plots Correlations and associations Cross-tabulations[10]

Final stage of the initial data analysis

During the final stage, the findings of the initial data analysis are documented, and necessary, preferable, and possible corrective actions are taken.Also, the original plan for the main data analyses can and should be specified in more detail and/or rewritten.In order to do this, several decisions about the main data analyses can and should be made:

In the case of non-normals: should one transform variables; make variables categorical (ordinal/dichotomous); adapt the analysis method?

In the case of missing data: should one neglect or impute the missing data; which imputation technique should be used?

In the case of outliers: should one use robust analysis techniques? In case items do not fit the scale: should one adapt the measurement

instrument by omitting items, or rather ensure comparability with other (uses of the) measurement instrument(s)?

In the case of (too) small subgroups: should one drop the hypothesis about inter-group differences, or use small sample techniques, like exact tests or bootstrapping?

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In case the randomization procedure seems to be defective: can and should one calculate propensity scores and include them as covariates in the main analyses?[11]


Several analyses can be used during the initial data analysis phase:

Univariate statistics (single variable) Bivariate associations (correlations) Graphical techniques (scatter plots)

It is important to take the measurement levels of the variables into account for the analyses, as special statistical techniques are available for each level:

Nominal and ordinal variables o Frequency counts (numbers and percentages)o Associations

circumambulations (crosstabulations) hierarchical loglinear analysis (restricted to a maximum of 8

variables) loglinear analysis (to identify relevant/important variables and

possible confounders)o Exact tests or bootstrapping (in case subgroups are small)o Computation of new variables

Continuous variables o Distribution

Statistics (M, SD, variance, skewness, kurtosis) Stem-and-leaf displays Box plots

Nonlinear analysis

Nonlinear analysis will be necessary when the data is recorded from a nonlinear system. Nonlinear systems can exhibit complex dynamic effects including bifurcations, chaos, harmonics and subharmonics that cannot be analyzed using simple linear methods. Nonlinear data analysis is closely related to nonlinear system identification.

Main data analysis

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In the main analysis phase analyses aimed at answering the research question are performed as well as any other relevant analysis needed to write the first draft of the research report.[15]

Exploratory and confirmatory approaches

In the main analysis phase either an exploratory or confirmatory approach can be adopted. Usually the approach is decided before data is collected. In an exploratory analysis no clear hypothesis is stated before analysing the data, and the data is searched for models that describe the data well. In a confirmatory analysis clear hypotheses about the data are tested.

Exploratory data analysis should be interpreted carefully. When testing multiple models at once there is a high chance on finding at least one of them to be significant, but this can be due to a type 1 error. It is important to always adjust the significance level when testing multiple models with, for example, a Bonferroni correction. Also, one should not follow up an exploratory analysis with a confirmatory analysis in the same dataset. An exploratory analysis is used to find ideas for a theory, but not to test that theory as well. When a model is found exploratory in a dataset, then following up that analysis with a comfirmatory analysis in the same dataset could simply mean that the results of the comfirmatory analysis are due to the same type 1 error that resulted in the exploratory model in the first place. The comfirmatory analysis therefore will not be more informative than the original exploratory analysis.[16]

Stability of results

It is important to obtain some indication about how generalizable the results are.[17] While this is hard to check, one can look at the stability of the results. Are the results reliable and reproducible? There are two main ways of doing this:

Cross-validation: By splitting the data in multiple parts we can check if analyzes (like a fitted model) based on one part of the data generalize to another part of the data as well.

Sensitivity analysis: A procedure to study the behavior of a system or model when global parameters are (systematically) varied. One way to do this is with bootstrapping.

Statistical methods

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Many statistical methods have been used for statistical analyses. A very brief list of four of the more popular methods is:

General linear model: A widely used model on which various methods are based (e.g. t test, ANOVA, ANCOVA, MANOVA). Usable for assessing the effect of several predictors on one or more continuous dependent variables.

Generalized linear model: An extension of the general linear model for discrete dependent variables.

Structural equation modelling: Usable for assessing latent structures from measured manifest variables.

Item response theory: Models for (mostly) assessing one latent variable from several binary measured variables (e.g. an exam).

Free software for data analysis

Data Applied - an online data mining and data visualization solution. DevInfo - a database system endorsed by the United Nations Development

Group for monitoring and analyzing human development. ELKI - data mining framework in Java with data mining oriented

visualization functions. KNIME - the Konstanz Information Miner, a user friendly and

comprehensive data analytics framework. PAW - FORTRAN/C data analysis framework developed at CERN SCaVis - Java (multi-platform) data analysis framework developed at ANL R - a programming language and software environment for statistical

computing and graphics. ROOT - C++ data analysis framework developed at CERN


In education, most educators have access to a data system for the purpose of analyzing student data. These data systems present data to educators in an over-the-counter data format (embedding labels, supplemental documentation, and a help system and making key package/display and content decisions) to improve the accuracy of educators’ data analyses.[19]

Nuclear and particle physics

In nuclear and particle physics the data usually originate from the experimental apparatus via a data acquisition system. They are then processed, in a step usually called data reduction, to apply calibrations and to extract physically significant

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information. Data reduction is most often, especially in large particle physics experiments, an automatic, batch-mode operation carried out by software written ad-hoc. The resulting data n-tuples are then scrutinized by the physicists, using specialized software tools like ROOT or PAW, comparing the results of the experiment with theory.

The theoretical models are often difficult to compare directly with the results of the experiments, so they are used instead as input for Monte Carlo simulation software like Geant4, in order to predict the response of the detector to a given theoretical event, thus producing simulated events which are then compared to experimental data.

4. Data Analysis and Interpretation

The purpose of the data analysis and interpretation phase is to transform the data collected into credible evidence about the development of the intervention and its performance.

Analysis can help answer some key questions:

·         Has the program made a difference?

·         How big is this difference or change in knowledge, attitudes, or behavior?

This process usually includes the following steps:

·         Organizing the data for analysis (data preparation)

·         Describing the data

·         Interpreting the data (assessing the findings against the adopted evaluation criteria)

Where quantitative data have been collected, statistical analysis can:

·         help measure the degree of change that has taken place

·         allow an assessment to be made about the consistency of data

Where qualitative data have been collected, interpretation is more difficult.

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·         Here, it is important to group similar responses into categories and identify common patterns that can help derive meaning from what may seem unrelated and diffuse responses.

·         This is particularly important when trying to assess the outcomes of focus groups and interviews.

It may be helpful to use several of the following 5 evaluation criteria as the basis for organizing and analyzing data:

Relevance: Does the intervention address an existing need? (Were the outcomes achieved aligned to current priorities in prevention? Is the outcome the best one for the target group—e.g., did the program take place in the area or the kind of setting where exposure is the greatest?)

Effectiveness: Did the intervention achieve what it was set out to achieve? Efficiency: Did the intervention achieve maximum results with given

resources? Results/Impact: Have there been any changes in the target group as a result

of the intervention? Sustainability: Will the outcomes continue after the intervention has


Particularly in outcomes-based and impact-based evaluations, the focus on impact and sustainability can be further refined by aligning data around the intervention’s

Extent: How many of the key stakeholders identified were eventually covered, and to what degree have they absorbed the outcome of the program? Were the optimal groups/people involved in the program?

Duration: Was the project’s timing appropriate? Did it last long enough? Was the repetition of the project’s components (if done) useful? Were the outcomes sustainable?

4.1 Association, Causation, and ConfoundingOne of the most important issues in interpreting research findings is understanding how outcomes relate to the intervention that is being evaluated. This involves making the distinction between association and causation and the role that can be played by confounding factors in skewing the evidence.

4.1.1 Association

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An association exists when one event is more likely to occur because another event has taken place. However, although the two events may be associated, one does not necessarily cause the other; the second event can still occur independently of the first.

·         For example, some research supports an association between certain patterns of drinking and the incidence of violence. However, even though harmful drinking and violent behavior may co-occur, there is no evidence showing that it is drinking that causes violence.

4.1.2 Causation

A causal relationship exists when one event (cause) is necessary for a second event (effect) to occur. The order in which the two occur is also critical. For example, for intoxication to occur, there must be heavy drinking, which precedes intoxication.

Determining cause and effect is an important function of evaluation, but it is also a major challenge. Causation can be complex:

·         Some causes may be necessary for an effect to be observed, but may not be sufficient; other factors may also be needed.

·         Or, while one cause may result in a particular outcome, other causes may have the same effect.


Being able to correctly attribute causation is critical, particularly when conducting an evaluation and interpreting the findings.

4.1.3 Confounding 

To rule out that a relationship between two events has been distorted by other, external factors, it is necessary to control for confounding. Confounding factors may actually be the reason we see particular outcomes, which may have nothing to do with what is being measured.

To rule out confounding, additional information must be gathered and analyzed. This includes any information that can possibly influence outcomes.

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When evaluating the impact of a prevention program on a particular behavior, we must know whether the program may have coincided with any of the following:

·         Other concurrent prevention initiatives and campaigns

·         New legislation or regulations in relevant areas

·         Relevant changes in law enforcement

·         For example, when mounting a campaign against alcohol-impaired driving, it is important to know whether other interventions aimed at road traffic safety are being undertaken at the same time. Similarly, if the campaign coincides with tighter regulations around BAC limits and with increased enforcement and roadside testing by police, it would be difficult to say whether any drop in the rate of drunk-driving crashes was attributable to the campaign or to these other measures.

Addressing possible confounders is an important element for proper interpretation of results.

·         However, it is often impossible to rule out entirely the influence of confounders.

·         Care must be taken not to misinterpret the results of an evaluation and to avoid exaggerated or unwarranted claims of effectiveness. This will inevitably lead to loss of credibility.

·         Any potential confounders should be openly acknowledged in the analysis of the evaluation results.

·         It is important to state all results in a clear and unambiguous way so that they are easy to interpret.

4.2 Short- and Long-term Outcomes 

The outcomes resulting from an intervention may be seen in a number of different areas, including changes in skills, attitudes, knowledge, or behaviors.

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·         Outcomes require time to develop. As a result, while some are likely to become apparent in the short term, immediately following an intervention, others may not be obvious until time has passed.

·         It is often of interest to see whether short-term outcomes will continue to persist over the medium- and long-term.



Evaluators should try to address short-, medium-, and long-term outcomes of an intervention separately.

·         If the design of a program allows, it is desirable to be able to monitor whether its impact is sustained beyond the short term.

·         Care should be taken to apply an intervention over a sufficiently long period of time so that outcomes (and impact) can be observed and measured.

Short- and long-term outcomes can be measured by using different methodologies for collecting data.

·         Cross-sectional studies involve measurement at a single point in time after the intervention has been applied and allow short-term results to be measured

·         Longitudinal study designs, on the other hand, follow progress over longer periods and allow measurements to be taken at two or more different points in time. They can help assess outcomes into the medium- and long-term

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Unfortunately, the reality is that, for most projects, resources and time frames available are likely to allow only for the measurement of short- and perhaps medium-term outcomes.

 4.3 Providing the Proper Context 

Interpreting results is only possible in the proper context. This includes knowing what outcomes one can reasonably expect from implementing a particular intervention based on similar interventions that have been conducted previously. 

For instance, when setting up a server training program, it is useful to know that such interventions have in the past helped reduce the incidence of violence in bars.

Therefore, once the intervention is over, if the results are at odds with what others have observed, it is likely that the program was not implemented correctly or that some other problem has occurred.

How to Write a Research Report

Parts of a report

An objective of organizing a research paper is to allow people to read your work selectively. When I research a topic, I may be interested in just the methods, a specific result, the interpretation, or perhaps I just want to see a summary of the paper to determine if it is relevant to my study.

For most studies, a proper research report includes the following sections, submitted in the order listed, each section to start on a new page. Some journals request a summary to be placed at the end of the discussion. Some techniques articles include an appendix with equations, formulas, calculations, etc. Some journals deviate from the format, such as by combining results and discussion, or combining everything but the title, abstract, and literature as is done in the journal Science. Your reports will adhere to the standard format.

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Title page Abstract Introduction Materials and Methods Results Discussion Literature Cited Examples

For detailed guidelines with examples, consult a text that is dedicated to scientific communication, such as McMillan, VE. "Writing Papers in the Biological Sciences (2nd edition)." Boston: Bedford Books, 1994.

Common errors in student research reports have been collected and summarized, to help you avoid a number of pitfalls. You may also want to keep in mind how lab reports are usually graded as you prepare your work.


In all sections of your paper, use paragraphs to separate each important point (except for the abstract), and present your points in logical order. Use present tense to report background that is already established. For example, 'the grass is green.' Always use past tense to describe results of a specific experiment, especially your own. For example, 'When weed killer was applied, the grass was brown.' Remember - present tense for background, and past tense for results.

Title Page

Select an informative title, such as "Role of temperature in determination of the rate of development of Xenopus larvae." A title such as "Biology lab #1" is not informative. Include the name(s) and address(es) of all authors, and date submitted.


Summarize the study, focusing on the results and major conclusions, including relevant quantitative data. It must be a single paragraph, and concise. It should stand on its own, therefore do not refer to any other part of the report, such as a figure or table. Avoid long sections of introductory or explanatory material. As a summary of work done, it is written in past tense.


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Introduce the rationale behind the study, including

The overall question and its relevance to science Suitability of the experimental model to the overall question Experimental design and specific hypothesis or objective Significance of the anticipated results to the overall question

Include appropriate background information (but please do not write everything you know about the subject).

Methods and Materials

The purpose of this section is to document all of your procedures so that another scientist could reproduce all or part of your work. It is not designed to be a set of instructions. As awkward as it may seem, it is standard practice to report methods and materials in past tense, third person passive. Your laboratory notebook should contain all of the details of everything you do in lab, plus any additional information needed in order to complete this section.

While it is tempting to report methods in chronological order in a narrative form, it is usually more effective to present them under headings devoted to specific procedures or groups of procedures. Some examples of separate headings are "sources of materials," "assay procedures,"cell fractionation protocol," and "statistical methods." Try to be succinct without sacrificing essential information. Omit any background information or comments. If you must explain why a particular procedure was chosen, do so in the discussion.

Omit information that is irrelevant to a third party. For example, no third party cares what color ice bucket you used, or which individual logged in the data. You need not report sources of basic chemicals that would be found in any supply cabinet, such as sodium chloride or potassium phosphate. Report how procedures were done, not how they were specifically performed on a particular day. For example, report "samples were diluted to a final concentration of 2 mg/ml protein;" don't report that '135 microliters of sample one was diluted with 330 microliters of buffer to make the proteins concentration 2 mg/ml."


Raw data are never included in a research paper. Analyze your data, then present the analyzed (converted) data in the form of a figure (graph), table, or in narrative

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form. Present the same data only once, in the most effective manner. By presenting converted data, you make your point succinctly and clearly.

Figures are preferable to tables, and tables are preferable to straight text. However, many times a figure is inappropriate, or the data come across more clearly if described in narrative form.

To give your results continuity, describe the relationship of each section of converted data to the overall study. For example, rather than just putting a table in the paper and going on to the discussion, write, 'In order to test the null hypothesis that dust particles are responsible for the blue color of the sky, we observed the results of filtering air through materials of decreasing pore size. Table 1 lists the spectrum of transmitted light at right angles to the light path through air filtered through different pore sizes.' Then present your table, complete with title and headings.

All converted data go into the body of the report, after the methods and before the discussion. Do not stick graphs or other data onto the back of the report just because you printed or prepared them separately.

Do not draw conclusions in the results section. Reserve data interpretation for the discussion.


Interpret your data in the discussion. Decide if each hypothesis is supported, rejected, or if you cannot make a decision with confidence. Do not simply dismiss a study or part of a study as "inconclusive." Make what conclusions you can, then suggest how the experiment must be modified in order to properly test the hypothesis(es).

Explain all of your observations as much as possible, focusing on mechanisms. When you refer to information, distinguish data generated by your own studies from published information or from information obtained from other students. Refer to work done by specific individuals (including yourself) in past tense. Refer to generally accepted facts and principles in present tense. For example, "Doofus, in a 1989 survey, found that anemia in basset hounds was correlated with advanced age. Anemia is a condition in which there is insufficient hemoglobin in the blood."

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Decide if the experimental design adequately addressed the hypothesis, and whether or not it was properly controlled. One experiment will not answer an overall question, so keeping the big picture in mind, where do you go next? The best studies open up new avenues of research. What questions remain? Did the study lead you to any new questions? Try to think up a new hypothesis and briefly suggest new experiments to further address the main question. Be creative, and don't be afraid to speculate.

Literature Cited

List all literature cited in your report, in alphabetical order, by first author. In a proper research paper, only primary literature is used (original research articles authored by the original investigators). Some of your reports may not require references, and if that is the case simply state "no references were consulted."

Example (title, abstract, introduction)

Title: Evaluation of two models for predicting membrane potential, using crayfish extensor muscle


Through measurement of steady state transmembrane potentials (Em) using an intracellular microelectrode recording system, we studied the possible direct role of the sodium/potassium pump in maintenance of Em in crayfish extensor muscles. We varied extracellular sodium ([Na+]out) and potassium ion ([K+]out) concentrations in order to test the predictability of the equilibrium potential model (using the Nernst equation for potassium) and the diffusion potential model as described by the Goldman/Hodgkin/Katz equation. Combined Em measurements from four preparations before and after treatment with 6 mM ouabain showed no significant difference (-59.2 +/- 5.8 before treatment, -56.8 +/- 5/3 after treatment, p=0.06). The Nernst equation for potassium failed to predict Em at low [K+]out but was adequate when [K+]out was elevated to five times control values (+100% error at 0.3 x [K+]out, +22% error at 5 x [K+]out). The Goldman equation was off by +20% and +2.5% respectively, for the same conditions. At [Na+]out of 1x, 0.5x, 0.2x, and 0.05x normal the Goldman equation prediction was off -2%, +4%, +11%, and +7%, respectively. Since measured Em was consistently lower than predicted Em part of the error may be due to a slight electrogenic contribution by the pump. Although the diffusion potential model is a better predictor of Em than the equilibrium potential model pump activity is not sufficient to account for all of the deviation of predicted from measured values.

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A cell's ability to sustain an electrical potential difference across its membrane is essential for signal transduction as well as the maintenance of structures within the lipid bilayer, such as protein complexes. Studies have shown that this potential difference is due to ion gradients across the membrane, created and maintained by an ATP-dependent sodium-potassium pump. The pump is an antiporter that exchanges three sodium ions from the cytosol for two extracellular potassium ions with each ATP hydrolysis, thus maintaining a high intracellular potassium ion concentration and low intracellular sodium ion concentration. The cell membrane is selectively permeable, so that these ion gradients can maintain an asymmetric distribution of charge across the membrane, leading to a potential difference.