good research work meaning

What Constitutes a Good Research?

The Declining Art of Good Research

We seem to be compromising our commitment to good research in favor of publishable research, and there are a combination of trends that are accountable for this.

The first is the continued pressure of “publish or perish” for young academics seeking to move forward on the track for fewer and fewer tenured positions (or increasingly draconian renewable contracts).

Secondly, the open access model of research publication has created a booming population of academic journals with pages to fill and new researchers willing to pay article publication fees (APFs).

Thirdly, budget-strapped institutions have been aggressively targeting doctoral research candidates and the higher fees they bring to the table.

When these three trends are combined, the resulting onslaught of quantity over quality leads us to question what “good” research looks like anymore.

Is it the institution from which the research originated, or the debatable rank of the journal that published it?

Good Research as a Methodological Question

When looking to learn how to recognize what “good” research looks like, it makes sense to start at the beginning with the basic scope of the project:

• Does the research have a solid hypothesis?
• Is there evidence of a comprehensive literature review from reputable sources that clearly defines a target area for valuable research?
• Is the research team allocating sufficient time/resources to do the job properly, or were compromises made in order to accommodate the available funding?
• Is there evidence of a willingness to refine the hypothesis and research strategy if needed?
• Are the expectations of the implications of the research realistic?

Characteristics of a Good Research

For conducting a systematic research, it is important understand the characteristics of a good research.

• Its relevance to existing research conducted by other researchers.
• A good research is doable and replicable in future.
• It must be based on a logical rationale and tied to theory.
• It must generate new questions or hypotheses for incremental work in future.
• It must directly or indirectly address some real world problem.
• It must clearly state the variables of the experiment.
• It must conclude with valid and verifiable findings.

Good Research as an Ethical Question

The question as to whether or not the research is worth conducting at all could generate an extended and heated debate. Researchers are expected to publish, and research budgets are there to be spent.

We can hope that there was some degree of discussion and oversight before the research project was given the green light by a Principal Investigator or Research Supervisor, but those decisions are often made in a context of simple obligation rather than perceived need.

Consider the example of a less than proactive doctoral student with limited time and resources to complete a dissertation topic. A suggestion is made by the departmental Research Supervisor to pick a dissertation from a decade ago and simply repeat it. The suggestion meets the need for expediency and simplicity, but raises as many questions as it answers:

• What is the validity of the study – just because it can be repeated, should it?
• What was the contribution of the original study to the general body of knowledge? Will this additional data be an improvement?
• Given the lack of interest among academic journals in replicated studies, is the suggestion denying the student the opportunity to get published?
• Is directing a student to replication in the interests of expediency meeting a broader academic goal of graduating proficient researchers?

The Building Blocks of “Good” Research

There is no shortage of reputable, peer-reviewed journals that publish first-rate research material for new researchers to model.

That doesn’t mean you should copy the research topic or the methodology, but it wouldn’t hurt to examine the protocol in detail and make note of the specific decisions made and criteria put in place when that protocol was developed and implemented.

The challenge lies in sticking to those tried-and-true methodologies when your research data doesn’t prove to be as rich and fruitful as you had hoped.

Have you ever been stuck while in the middle of conducting a research? How did you cope with that? Let us know your approach while conducting a good research in the comments section below!

You can also visit our  Q&A forum  for frequently asked questions related to different aspects of research writing and publishing answered by our team that comprises subject-matter experts, eminent researchers, and publication experts.

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15 Steps to Good Research

• Define and articulate a research question (formulate a research hypothesis). How to Write a Thesis Statement (Indiana University)
• Identify possible sources of information in many types and formats. Georgetown University Library's Research & Course Guides
• Judge the scope of the project.
• Reevaluate the research question based on the nature and extent of information available and the parameters of the research project.
• Select the most appropriate investigative methods (surveys, interviews, experiments) and research tools (periodical indexes, databases, websites).
• Plan the research project. Writing Anxiety (UNC-Chapel Hill) Strategies for Academic Writing (SUNY Empire State College)
• Retrieve information using a variety of methods (draw on a repertoire of skills).
• Refine the search strategy as necessary.
• Write and organize useful notes and keep track of sources. Taking Notes from Research Reading (University of Toronto) Use a citation manager: Zotero or Refworks
• Evaluate sources using appropriate criteria. Evaluating Internet Sources
• Synthesize, analyze and integrate information sources and prior knowledge. Georgetown University Writing Center
• Revise hypothesis as necessary.
• Use information effectively for a specific purpose.
• Understand such issues as plagiarism, ownership of information (implications of copyright to some extent), and costs of information. Georgetown University Honor Council Copyright Basics (Purdue University) How to Recognize Plagiarism: Tutorials and Tests from Indiana University
• Cite properly and give credit for sources of ideas. MLA Bibliographic Form (7th edition, 2009) MLA Bibliographic Form (8th edition, 2016) Turabian Bibliographic Form: Footnote/Endnote Turabian Bibliographic Form: Parenthetical Reference Use a citation manager: Zotero or Refworks

Adapted from the Association of Colleges and Research Libraries "Objectives for Information Literacy Instruction" , which are more complete and include outcomes. See also the broader "Information Literacy Competency Standards for Higher Education."

Research Question 101 📖

Everything you need to know to write a high-quality research question

By: Derek Jansen (MBA) | Reviewed By: Dr. Eunice Rautenbach | October 2023

If you’ve landed on this page, you’re probably asking yourself, “ What is a research question? ”. Well, you’ve come to the right place. In this post, we’ll explain what a research question is , how it’s differen t from a research aim, and how to craft a high-quality research question that sets you up for success.

Research Question 101

What is a research question.

• Research questions vs research aims
• The 4 types of research questions
• How to write a research question
• Frequently asked questions
• Examples of research questions

As the name suggests, the research question is the core question (or set of questions) that your study will (attempt to) answer .

In many ways, a research question is akin to a target in archery . Without a clear target, you won’t know where to concentrate your efforts and focus. Essentially, your research question acts as the guiding light throughout your project and informs every choice you make along the way.

Let’s look at some examples:

What impact does social media usage have on the mental health of teenagers in New York?

How does the introduction of a minimum wage affect employment levels in small businesses in outer London?

How does the portrayal of women in 19th-century American literature reflect the societal attitudes of the time?

What are the long-term effects of intermittent fasting on heart health in adults?

As you can see in these examples, research questions are clear, specific questions that can be feasibly answered within a study. These are important attributes and we’ll discuss each of them in more detail a little later . If you’d like to see more examples of research questions, you can find our RQ mega-list here .

Research Questions vs Research Aims

At this point, you might be asking yourself, “ How is a research question different from a research aim? ”. Within any given study, the research aim and research question (or questions) are tightly intertwined , but they are separate things . Let’s unpack that a little.

A research aim is typically broader in nature and outlines what you hope to achieve with your research. It doesn’t ask a specific question but rather gives a summary of what you intend to explore.

The research question, on the other hand, is much more focused . It’s the specific query you’re setting out to answer. It narrows down the research aim into a detailed, researchable question that will guide your study’s methods and analysis.

Let’s look at an example:

Research Aim: To explore the effects of climate change on marine life in Southern Africa.

Research Question: How does ocean acidification caused by climate change affect the reproduction rates of coral reefs?

As you can see, the research aim gives you a general focus , while the research question details exactly what you want to find out.

Need a helping hand?

Types of research questions

Now that we’ve defined what a research question is, let’s look at the different types of research questions that you might come across. Broadly speaking, there are (at least) four different types of research questions – descriptive , comparative , relational , and explanatory . 

Descriptive questions ask what is happening. In other words, they seek to describe a phenomena or situation . An example of a descriptive research question could be something like “What types of exercise do high-performing UK executives engage in?”. This would likely be a bit too basic to form an interesting study, but as you can see, the research question is just focused on the what – in other words, it just describes the situation.

Comparative research questions , on the other hand, look to understand the way in which two or more things differ , or how they’re similar. An example of a comparative research question might be something like “How do exercise preferences vary between middle-aged men across three American cities?”. As you can see, this question seeks to compare the differences (or similarities) in behaviour between different groups.

Next up, we’ve got exploratory research questions , which ask why or how is something happening. While the other types of questions we looked at focused on the what, exploratory research questions are interested in the why and how . As an example, an exploratory research question might ask something like “Why have bee populations declined in Germany over the last 5 years?”. As you can, this question is aimed squarely at the why, rather than the what.

Last but not least, we have relational research questions . As the name suggests, these types of research questions seek to explore the relationships between variables . Here, an example could be something like “What is the relationship between X and Y” or “Does A have an impact on B”. As you can see, these types of research questions are interested in understanding how constructs or variables are connected , and perhaps, whether one thing causes another.

Of course, depending on how fine-grained you want to get, you can argue that there are many more types of research questions , but these four categories give you a broad idea of the different flavours that exist out there. It’s also worth pointing out that a research question doesn’t need to fit perfectly into one category – in many cases, a research question might overlap into more than just one category and that’s okay.

The key takeaway here is that research questions can take many different forms , and it’s useful to understand the nature of your research question so that you can align your research methodology accordingly.

How To Write A Research Question

As we alluded earlier, a well-crafted research question needs to possess very specific attributes, including focus , clarity and feasibility . But that’s not all – a rock-solid research question also needs to be rooted and aligned . Let’s look at each of these.

A strong research question typically has a single focus. So, don’t try to cram multiple questions into one research question; rather split them up into separate questions (or even subquestions), each with their own specific focus. As a rule of thumb, narrow beats broad when it comes to research questions.

Clear and specific

A good research question is clear and specific, not vague and broad. State clearly exactly what you want to find out so that any reader can quickly understand what you’re looking to achieve with your study. Along the same vein, try to avoid using bulky language and jargon – aim for clarity.

Unfortunately, even a super tantalising and thought-provoking research question has little value if you cannot feasibly answer it. So, think about the methodological implications of your research question while you’re crafting it. Most importantly, make sure that you know exactly what data you’ll need (primary or secondary) and how you’ll analyse that data.

A good research question (and a research topic, more broadly) should be rooted in a clear research gap and research problem . Without a well-defined research gap, you risk wasting your effort pursuing a question that’s already been adequately answered (and agreed upon) by the research community. A well-argued research gap lays at the heart of a valuable study, so make sure you have your gap clearly articulated and that your research question directly links to it.

As we mentioned earlier, your research aim and research question are (or at least, should be) tightly linked. So, make sure that your research question (or set of questions) aligns with your research aim . If not, you’ll need to revise one of the two to achieve this.

FAQ: Research Questions

Research question faqs, how many research questions should i have, what should i avoid when writing a research question, can a research question be a statement.

Typically, a research question is phrased as a question, not a statement. A question clearly indicates what you’re setting out to discover.

Can a research question be too broad or too narrow?

Yes. A question that’s too broad makes your research unfocused, while a question that’s too narrow limits the scope of your study.

Here’s an example of a research question that’s too broad:

“Why is mental health important?”

Conversely, here’s an example of a research question that’s likely too narrow:

“What is the impact of sleep deprivation on the exam scores of 19-year-old males in London studying maths at The Open University?”

Can I change my research question during the research process?

How do i know if my research question is good.

A good research question is focused, specific, practical, rooted in a research gap, and aligned with the research aim. If your question meets these criteria, it’s likely a strong question.

Is a research question similar to a hypothesis?

Not quite. A hypothesis is a testable statement that predicts an outcome, while a research question is a query that you’re trying to answer through your study. Naturally, there can be linkages between a study’s research questions and hypothesis, but they serve different functions.

How are research questions and research objectives related?

The research question is a focused and specific query that your study aims to answer. It’s the central issue you’re investigating. The research objective, on the other hand, outlines the steps you’ll take to answer your research question. Research objectives are often more action-oriented and can be broken down into smaller tasks that guide your research process. In a sense, they’re something of a roadmap that helps you answer your research question.

Need some inspiration?

If you’d like to see more examples of research questions, check out our research question mega list here .  Alternatively, if you’d like 1-on-1 help developing a high-quality research question, consider our private coaching service .

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This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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What is Scientific Research and How Can it be Done?

Scientific researches are studies that should be systematically planned before performing them. In this review, classification and description of scientific studies, planning stage randomisation and bias are explained.

Research conducted for the purpose of contributing towards science by the systematic collection, interpretation and evaluation of data and that, too, in a planned manner is called scientific research: a researcher is the one who conducts this research. The results obtained from a small group through scientific studies are socialised, and new information is revealed with respect to diagnosis, treatment and reliability of applications. The purpose of this review is to provide information about the definition, classification and methodology of scientific research.

Before beginning the scientific research, the researcher should determine the subject, do planning and specify the methodology. In the Declaration of Helsinki, it is stated that ‘the primary purpose of medical researches on volunteers is to understand the reasons, development and effects of diseases and develop protective, diagnostic and therapeutic interventions (method, operation and therapies). Even the best proven interventions should be evaluated continuously by investigations with regard to reliability, effectiveness, efficiency, accessibility and quality’ ( 1 ).

The questions, methods of response to questions and difficulties in scientific research may vary, but the design and structure are generally the same ( 2 ).

Classification of Scientific Research

Scientific research can be classified in several ways. Classification can be made according to the data collection techniques based on causality, relationship with time and the medium through which they are applied.

• Observational
• Experimental
• Descriptive
• Retrospective
• Prospective
• Cross-sectional
• Social descriptive research ( 3 )

Another method is to classify the research according to its descriptive or analytical features. This review is written according to this classification method.

I. Descriptive research

• Case series
• Surveillance studies

II. Analytical research

• Observational studies: cohort, case control and cross- sectional research
• Interventional research: quasi-experimental and clinical research
• Case Report: it is the most common type of descriptive study. It is the examination of a single case having a different quality in the society, e.g. conducting general anaesthesia in a pregnant patient with mucopolysaccharidosis.
• Case Series: it is the description of repetitive cases having common features. For instance; case series involving interscapular pain related to neuraxial labour analgesia. Interestingly, malignant hyperthermia cases are not accepted as case series since they are rarely seen during historical development.
• Surveillance Studies: these are the results obtained from the databases that follow and record a health problem for a certain time, e.g. the surveillance of cross-infections during anaesthesia in the intensive care unit.

Moreover, some studies may be experimental. After the researcher intervenes, the researcher waits for the result, observes and obtains data. Experimental studies are, more often, in the form of clinical trials or laboratory animal trials ( 2 ).

Analytical observational research can be classified as cohort, case-control and cross-sectional studies.

Firstly, the participants are controlled with regard to the disease under investigation. Patients are excluded from the study. Healthy participants are evaluated with regard to the exposure to the effect. Then, the group (cohort) is followed-up for a sufficient period of time with respect to the occurrence of disease, and the progress of disease is studied. The risk of the healthy participants getting sick is considered an incident. In cohort studies, the risk of disease between the groups exposed and not exposed to the effect is calculated and rated. This rate is called relative risk. Relative risk indicates the strength of exposure to the effect on the disease.

Cohort research may be observational and experimental. The follow-up of patients prospectively is called a prospective cohort study . The results are obtained after the research starts. The researcher’s following-up of cohort subjects from a certain point towards the past is called a retrospective cohort study . Prospective cohort studies are more valuable than retrospective cohort studies: this is because in the former, the researcher observes and records the data. The researcher plans the study before the research and determines what data will be used. On the other hand, in retrospective studies, the research is made on recorded data: no new data can be added.

In fact, retrospective and prospective studies are not observational. They determine the relationship between the date on which the researcher has begun the study and the disease development period. The most critical disadvantage of this type of research is that if the follow-up period is long, participants may leave the study at their own behest or due to physical conditions. Cohort studies that begin after exposure and before disease development are called ambidirectional studies . Public healthcare studies generally fall within this group, e.g. lung cancer development in smokers.

• Case-Control Studies: these studies are retrospective cohort studies. They examine the cause and effect relationship from the effect to the cause. The detection or determination of data depends on the information recorded in the past. The researcher has no control over the data ( 2 ).

Cross-sectional studies are advantageous since they can be concluded relatively quickly. It may be difficult to obtain a reliable result from such studies for rare diseases ( 2 ).

Cross-sectional studies are characterised by timing. In such studies, the exposure and result are simultaneously evaluated. While cross-sectional studies are restrictedly used in studies involving anaesthesia (since the process of exposure is limited), they can be used in studies conducted in intensive care units.

• Quasi-Experimental Research: they are conducted in cases in which a quick result is requested and the participants or research areas cannot be randomised, e.g. giving hand-wash training and comparing the frequency of nosocomial infections before and after hand wash.
• Clinical Research: they are prospective studies carried out with a control group for the purpose of comparing the effect and value of an intervention in a clinical case. Clinical study and research have the same meaning. Drugs, invasive interventions, medical devices and operations, diets, physical therapy and diagnostic tools are relevant in this context ( 6 ).

Clinical studies are conducted by a responsible researcher, generally a physician. In the research team, there may be other healthcare staff besides physicians. Clinical studies may be financed by healthcare institutes, drug companies, academic medical centres, volunteer groups, physicians, healthcare service providers and other individuals. They may be conducted in several places including hospitals, universities, physicians’ offices and community clinics based on the researcher’s requirements. The participants are made aware of the duration of the study before their inclusion. Clinical studies should include the evaluation of recommendations (drug, device and surgical) for the treatment of a disease, syndrome or a comparison of one or more applications; finding different ways for recognition of a disease or case and prevention of their recurrence ( 7 ).

Clinical Research

In this review, clinical research is explained in more detail since it is the most valuable study in scientific research.

Clinical research starts with forming a hypothesis. A hypothesis can be defined as a claim put forward about the value of a population parameter based on sampling. There are two types of hypotheses in statistics.

• H 0 hypothesis is called a control or null hypothesis. It is the hypothesis put forward in research, which implies that there is no difference between the groups under consideration. If this hypothesis is rejected at the end of the study, it indicates that a difference exists between the two treatments under consideration.
• H 1 hypothesis is called an alternative hypothesis. It is hypothesised against a null hypothesis, which implies that a difference exists between the groups under consideration. For example, consider the following hypothesis: drug A has an analgesic effect. Control or null hypothesis (H 0 ): there is no difference between drug A and placebo with regard to the analgesic effect. The alternative hypothesis (H 1 ) is applicable if a difference exists between drug A and placebo with regard to the analgesic effect.

The planning phase comes after the determination of a hypothesis. A clinical research plan is called a protocol . In a protocol, the reasons for research, number and qualities of participants, tests to be applied, study duration and what information to be gathered from the participants should be found and conformity criteria should be developed.

The selection of participant groups to be included in the study is important. Inclusion and exclusion criteria of the study for the participants should be determined. Inclusion criteria should be defined in the form of demographic characteristics (age, gender, etc.) of the participant group and the exclusion criteria as the diseases that may influence the study, age ranges, cases involving pregnancy and lactation, continuously used drugs and participants’ cooperation.

The next stage is methodology. Methodology can be grouped under subheadings, namely, the calculation of number of subjects, blinding (masking), randomisation, selection of operation to be applied, use of placebo and criteria for stopping and changing the treatment.

I. Calculation of the Number of Subjects

The entire source from which the data are obtained is called a universe or population . A small group selected from a certain universe based on certain rules and which is accepted to highly represent the universe from which it is selected is called a sample and the characteristics of the population from which the data are collected are called variables. If data is collected from the entire population, such an instance is called a parameter . Conducting a study on the sample rather than the entire population is easier and less costly. Many factors influence the determination of the sample size. Firstly, the type of variable should be determined. Variables are classified as categorical (qualitative, non-numerical) or numerical (quantitative). Individuals in categorical variables are classified according to their characteristics. Categorical variables are indicated as nominal and ordinal (ordered). In nominal variables, the application of a category depends on the researcher’s preference. For instance, a female participant can be considered first and then the male participant, or vice versa. An ordinal (ordered) variable is ordered from small to large or vice versa (e.g. ordering obese patients based on their weights-from the lightest to the heaviest or vice versa). A categorical variable may have more than one characteristic: such variables are called binary or dichotomous (e.g. a participant may be both female and obese).

If the variable has numerical (quantitative) characteristics and these characteristics cannot be categorised, then it is called a numerical variable. Numerical variables are either discrete or continuous. For example, the number of operations with spinal anaesthesia represents a discrete variable. The haemoglobin value or height represents a continuous variable.

Statistical analyses that need to be employed depend on the type of variable. The determination of variables is necessary for selecting the statistical method as well as software in SPSS. While categorical variables are presented as numbers and percentages, numerical variables are represented using measures such as mean and standard deviation. It may be necessary to use mean in categorising some cases such as the following: even though the variable is categorical (qualitative, non-numerical) when Visual Analogue Scale (VAS) is used (since a numerical value is obtained), it is classified as a numerical variable: such variables are averaged.

Clinical research is carried out on the sample and generalised to the population. Accordingly, the number of samples should be correctly determined. Different sample size formulas are used on the basis of the statistical method to be used. When the sample size increases, error probability decreases. The sample size is calculated based on the primary hypothesis. The determination of a sample size before beginning the research specifies the power of the study. Power analysis enables the acquisition of realistic results in the research, and it is used for comparing two or more clinical research methods.

Because of the difference in the formulas used in calculating power analysis and number of samples for clinical research, it facilitates the use of computer programs for making calculations.

It is necessary to know certain parameters in order to calculate the number of samples by power analysis.

• Type-I (α) and type-II (β) error levels
• Difference between groups (d-difference) and effect size (ES)
• Distribution ratio of groups
• Direction of research hypothesis (H1)

a. Type-I (α) and Type-II (β) Error (β) Levels

Two types of errors can be made while accepting or rejecting H 0 hypothesis in a hypothesis test. Type-I error (α) level is the probability of finding a difference at the end of the research when there is no difference between the two applications. In other words, it is the rejection of the hypothesis when H 0 is actually correct and it is known as α error or p value. For instance, when the size is determined, type-I error level is accepted as 0.05 or 0.01.

Another error that can be made during a hypothesis test is a type-II error. It is the acceptance of a wrongly hypothesised H 0 hypothesis. In fact, it is the probability of failing to find a difference when there is a difference between the two applications. The power of a test is the ability of that test to find a difference that actually exists. Therefore, it is related to the type-II error level.

Since the type-II error risk is expressed as β, the power of the test is defined as 1–β. When a type-II error is 0.20, the power of the test is 0.80. Type-I (α) and type-II (β) errors can be intentional. The reason to intentionally make such an error is the necessity to look at the events from the opposite perspective.

b. Difference between Groups and ES

ES is defined as the state in which statistical difference also has clinically significance: ES≥0.5 is desirable. The difference between groups is the absolute difference between the groups compared in clinical research.

c. Allocation Ratio of Groups

The allocation ratio of groups is effective in determining the number of samples. If the number of samples is desired to be determined at the lowest level, the rate should be kept as 1/1.

d. Direction of Hypothesis (H1)

The direction of hypothesis in clinical research may be one-sided or two-sided. While one-sided hypotheses hypothesis test differences in the direction of size, two-sided hypotheses hypothesis test differences without direction. The power of the test in two-sided hypotheses is lower than one-sided hypotheses.

After these four variables are determined, they are entered in the appropriate computer program and the number of samples is calculated. Statistical packaged software programs such as Statistica, NCSS and G-Power may be used for power analysis and calculating the number of samples. When the samples size is calculated, if there is a decrease in α, difference between groups, ES and number of samples, then the standard deviation increases and power decreases. The power in two-sided hypothesis is lower. It is ethically appropriate to consider the determination of sample size, particularly in animal experiments, at the beginning of the study. The phase of the study is also important in the determination of number of subjects to be included in drug studies. Usually, phase-I studies are used to determine the safety profile of a drug or product, and they are generally conducted on a few healthy volunteers. If no unacceptable toxicity is detected during phase-I studies, phase-II studies may be carried out. Phase-II studies are proof-of-concept studies conducted on a larger number (100–500) of volunteer patients. When the effectiveness of the drug or product is evident in phase-II studies, phase-III studies can be initiated. These are randomised, double-blinded, placebo or standard treatment-controlled studies. Volunteer patients are periodically followed-up with respect to the effectiveness and side effects of the drug. It can generally last 1–4 years and is valuable during licensing and releasing the drug to the general market. Then, phase-IV studies begin in which long-term safety is investigated (indication, dose, mode of application, safety, effectiveness, etc.) on thousands of volunteer patients.

II. Blinding (Masking) and Randomisation Methods

When the methodology of clinical research is prepared, precautions should be taken to prevent taking sides. For this reason, techniques such as randomisation and blinding (masking) are used. Comparative studies are the most ideal ones in clinical research.

Blinding Method

A case in which the treatments applied to participants of clinical research should be kept unknown is called the blinding method . If the participant does not know what it receives, it is called a single-blind study; if even the researcher does not know, it is called a double-blind study. When there is a probability of knowing which drug is given in the order of application, when uninformed staff administers the drug, it is called in-house blinding. In case the study drug is known in its pharmaceutical form, a double-dummy blinding test is conducted. Intravenous drug is given to one group and a placebo tablet is given to the comparison group; then, the placebo tablet is given to the group that received the intravenous drug and intravenous drug in addition to placebo tablet is given to the comparison group. In this manner, each group receives both the intravenous and tablet forms of the drug. In case a third party interested in the study is involved and it also does not know about the drug (along with the statistician), it is called third-party blinding.

Randomisation Method

The selection of patients for the study groups should be random. Randomisation methods are used for such selection, which prevent conscious or unconscious manipulations in the selection of patients ( 8 ).

No factor pertaining to the patient should provide preference of one treatment to the other during randomisation. This characteristic is the most important difference separating randomised clinical studies from prospective and synchronous studies with experimental groups. Randomisation strengthens the study design and enables the determination of reliable scientific knowledge ( 2 ).

The easiest method is simple randomisation, e.g. determination of the type of anaesthesia to be administered to a patient by tossing a coin. In this method, when the number of samples is kept high, a balanced distribution is created. When the number of samples is low, there will be an imbalance between the groups. In this case, stratification and blocking have to be added to randomisation. Stratification is the classification of patients one or more times according to prognostic features determined by the researcher and blocking is the selection of a certain number of patients for each stratification process. The number of stratification processes should be determined at the beginning of the study.

As the number of stratification processes increases, performing the study and balancing the groups become difficult. For this reason, stratification characteristics and limitations should be effectively determined at the beginning of the study. It is not mandatory for the stratifications to have equal intervals. Despite all the precautions, an imbalance might occur between the groups before beginning the research. In such circumstances, post-stratification or restandardisation may be conducted according to the prognostic factors.

The main characteristic of applying blinding (masking) and randomisation is the prevention of bias. Therefore, it is worthwhile to comprehensively examine bias at this stage.

Bias and Chicanery

While conducting clinical research, errors can be introduced voluntarily or involuntarily at a number of stages, such as design, population selection, calculating the number of samples, non-compliance with study protocol, data entry and selection of statistical method. Bias is taking sides of individuals in line with their own decisions, views and ideological preferences ( 9 ). In order for an error to lead to bias, it has to be a systematic error. Systematic errors in controlled studies generally cause the results of one group to move in a different direction as compared to the other. It has to be understood that scientific research is generally prone to errors. However, random errors (or, in other words, ‘the luck factor’-in which bias is unintended-do not lead to bias ( 10 ).

Another issue, which is different from bias, is chicanery. It is defined as voluntarily changing the interventions, results and data of patients in an unethical manner or copying data from other studies. Comparatively, bias may not be done consciously.

In case unexpected results or outliers are found while the study is analysed, if possible, such data should be re-included into the study since the complete exclusion of data from a study endangers its reliability. In such a case, evaluation needs to be made with and without outliers. It is insignificant if no difference is found. However, if there is a difference, the results with outliers are re-evaluated. If there is no error, then the outlier is included in the study (as the outlier may be a result). It should be noted that re-evaluation of data in anaesthesiology is not possible.

Statistical evaluation methods should be determined at the design stage so as not to encounter unexpected results in clinical research. The data should be evaluated before the end of the study and without entering into details in research that are time-consuming and involve several samples. This is called an interim analysis . The date of interim analysis should be determined at the beginning of the study. The purpose of making interim analysis is to prevent unnecessary cost and effort since it may be necessary to conclude the research after the interim analysis, e.g. studies in which there is no possibility to validate the hypothesis at the end or the occurrence of different side effects of the drug to be used. The accuracy of the hypothesis and number of samples are compared. Statistical significance levels in interim analysis are very important. If the data level is significant, the hypothesis is validated even if the result turns out to be insignificant after the date of the analysis.

Another important point to be considered is the necessity to conclude the participants’ treatment within the period specified in the study protocol. When the result of the study is achieved earlier and unexpected situations develop, the treatment is concluded earlier. Moreover, the participant may quit the study at its own behest, may die or unpredictable situations (e.g. pregnancy) may develop. The participant can also quit the study whenever it wants, even if the study has not ended ( 7 ).

In case the results of a study are contrary to already known or expected results, the expected quality level of the study suggesting the contradiction may be higher than the studies supporting what is known in that subject. This type of bias is called confirmation bias. The presence of well-known mechanisms and logical inference from them may create problems in the evaluation of data. This is called plausibility bias.

Another type of bias is expectation bias. If a result different from the known results has been achieved and it is against the editor’s will, it can be challenged. Bias may be introduced during the publication of studies, such as publishing only positive results, selection of study results in a way to support a view or prevention of their publication. Some editors may only publish research that extols only the positive results or results that they desire.

Bias may be introduced for advertisement or economic reasons. Economic pressure may be applied on the editor, particularly in the cases of studies involving drugs and new medical devices. This is called commercial bias.

In recent years, before beginning a study, it has been recommended to record it on the Web site www.clinicaltrials.gov for the purpose of facilitating systematic interpretation and analysis in scientific research, informing other researchers, preventing bias, provision of writing in a standard format, enhancing contribution of research results to the general literature and enabling early intervention of an institution for support. This Web site is a service of the US National Institutes of Health.

The last stage in the methodology of clinical studies is the selection of intervention to be conducted. Placebo use assumes an important place in interventions. In Latin, placebo means ‘I will be fine’. In medical literature, it refers to substances that are not curative, do not have active ingredients and have various pharmaceutical forms. Although placebos do not have active drug characteristic, they have shown effective analgesic characteristics, particularly in algology applications; further, its use prevents bias in comparative studies. If a placebo has a positive impact on a participant, it is called the placebo effect ; on the contrary, if it has a negative impact, it is called the nocebo effect . Another type of therapy that can be used in clinical research is sham application. Although a researcher does not cure the patient, the researcher may compare those who receive therapy and undergo sham. It has been seen that sham therapies also exhibit a placebo effect. In particular, sham therapies are used in acupuncture applications ( 11 ). While placebo is a substance, sham is a type of clinical application.

Ethically, the patient has to receive appropriate therapy. For this reason, if its use prevents effective treatment, it causes great problem with regard to patient health and legalities.

Before medical research is conducted with human subjects, predictable risks, drawbacks and benefits must be evaluated for individuals or groups participating in the study. Precautions must be taken for reducing the risk to a minimum level. The risks during the study should be followed, evaluated and recorded by the researcher ( 1 ).

After the methodology for a clinical study is determined, dealing with the ‘Ethics Committee’ forms the next stage. The purpose of the ethics committee is to protect the rights, safety and well-being of volunteers taking part in the clinical research, considering the scientific method and concerns of society. The ethics committee examines the studies presented in time, comprehensively and independently, with regard to ethics and science; in line with the Declaration of Helsinki and following national and international standards concerning ‘Good Clinical Practice’. The method to be followed in the formation of the ethics committee should be developed without any kind of prejudice and to examine the applications with regard to ethics and science within the framework of the ethics committee, Regulation on Clinical Trials and Good Clinical Practice ( www.iku.com ). The necessary documents to be presented to the ethics committee are research protocol, volunteer consent form, budget contract, Declaration of Helsinki, curriculum vitae of researchers, similar or explanatory literature samples, supporting institution approval certificate and patient follow-up form.

Only one sister/brother, mother, father, son/daughter and wife/husband can take charge in the same ethics committee. A rector, vice rector, dean, deputy dean, provincial healthcare director and chief physician cannot be members of the ethics committee.

Members of the ethics committee can work as researchers or coordinators in clinical research. However, during research meetings in which members of the ethics committee are researchers or coordinators, they must leave the session and they cannot sign-off on decisions. If the number of members in the ethics committee for a particular research is so high that it is impossible to take a decision, the clinical research is presented to another ethics committee in the same province. If there is no ethics committee in the same province, an ethics committee in the closest settlement is found.

Thereafter, researchers need to inform the participants using an informed consent form. This form should explain the content of clinical study, potential benefits of the study, alternatives and risks (if any). It should be easy, comprehensible, conforming to spelling rules and written in plain language understandable by the participant.

This form assists the participants in taking a decision regarding participation in the study. It should aim to protect the participants. The participant should be included in the study only after it signs the informed consent form; the participant can quit the study whenever required, even when the study has not ended ( 7 ).

Peer-review: Externally peer-reviewed.

Author Contributions: Concept - C.Ö.Ç., A.D.; Design - C.Ö.Ç.; Supervision - A.D.; Resource - C.Ö.Ç., A.D.; Materials - C.Ö.Ç., A.D.; Analysis and/or Interpretation - C.Ö.Ç., A.D.; Literature Search - C.Ö.Ç.; Writing Manuscript - C.Ö.Ç.; Critical Review - A.D.; Other - C.Ö.Ç., A.D.

Conflict of Interest: No conflict of interest was declared by the authors.

Financial Disclosure: The authors declared that this study has received no financial support.

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What is Research: Definition, Methods, Types & Examples

The search for knowledge is closely linked to the object of study; that is, to the reconstruction of the facts that will provide an explanation to an observed event and that at first sight can be considered as a problem. It is very human to seek answers and satisfy our curiosity. Let’s talk about research.

Content Index

What is Research?

What are the characteristics of research.

• Comparative analysis chart

Qualitative methods

Quantitative methods, 8 tips for conducting accurate research.

Research is the careful consideration of study regarding a particular concern or research problem using scientific methods. According to the American sociologist Earl Robert Babbie, “research is a systematic inquiry to describe, explain, predict, and control the observed phenomenon. It involves inductive and deductive methods.”

Inductive methods analyze an observed event, while deductive methods verify the observed event. Inductive approaches are associated with qualitative research , and deductive methods are more commonly associated with quantitative analysis .

Research is conducted with a purpose to:

• Identify potential and new customers
• Understand existing customers
• Set pragmatic goals
• Develop productive market strategies
• Address business challenges
• Put together a business expansion plan
• Identify new business opportunities
• Good research follows a systematic approach to capture accurate data. Researchers need to practice ethics and a code of conduct while making observations or drawing conclusions.
• The analysis is based on logical reasoning and involves both inductive and deductive methods.
• Real-time data and knowledge is derived from actual observations in natural settings.
• There is an in-depth analysis of all data collected so that there are no anomalies associated with it.
• It creates a path for generating new questions. Existing data helps create more research opportunities.
• It is analytical and uses all the available data so that there is no ambiguity in inference.
• Accuracy is one of the most critical aspects of research. The information must be accurate and correct. For example, laboratories provide a controlled environment to collect data. Accuracy is measured in the instruments used, the calibrations of instruments or tools, and the experiment’s final result.

What is the purpose of research?

There are three main purposes:

• Exploratory: As the name suggests, researchers conduct exploratory studies to explore a group of questions. The answers and analytics may not offer a conclusion to the perceived problem. It is undertaken to handle new problem areas that haven’t been explored before. This exploratory data analysis process lays the foundation for more conclusive data collection and analysis.

LEARN ABOUT: Descriptive Analysis

• Descriptive: It focuses on expanding knowledge on current issues through a process of data collection. Descriptive research describe the behavior of a sample population. Only one variable is required to conduct the study. The three primary purposes of descriptive studies are describing, explaining, and validating the findings. For example, a study conducted to know if top-level management leaders in the 21st century possess the moral right to receive a considerable sum of money from the company profit.

LEARN ABOUT: Best Data Collection Tools

• Explanatory: Causal research or explanatory research is conducted to understand the impact of specific changes in existing standard procedures. Running experiments is the most popular form. For example, a study that is conducted to understand the effect of rebranding on customer loyalty.

Here is a comparative analysis chart for a better understanding:

It begins by asking the right questions and choosing an appropriate method to investigate the problem. After collecting answers to your questions, you can analyze the findings or observations to draw reasonable conclusions.

When it comes to customers and market studies, the more thorough your questions, the better the analysis. You get essential insights into brand perception and product needs by thoroughly collecting customer data through surveys and questionnaires . You can use this data to make smart decisions about your marketing strategies to position your business effectively.

To make sense of your study and get insights faster, it helps to use a research repository as a single source of truth in your organization and manage your research data in one centralized data repository .

Types of research methods and Examples

Research methods are broadly classified as Qualitative and Quantitative .

Both methods have distinctive properties and data collection methods .

Qualitative research is a method that collects data using conversational methods, usually open-ended questions . The responses collected are essentially non-numerical. This method helps a researcher understand what participants think and why they think in a particular way.

Types of qualitative methods include:

• One-to-one Interview
• Focus Groups
• Ethnographic studies
• Text Analysis

Quantitative methods deal with numbers and measurable forms . It uses a systematic way of investigating events or data. It answers questions to justify relationships with measurable variables to either explain, predict, or control a phenomenon.

Types of quantitative methods include:

• Survey research
• Descriptive research
• Correlational research

LEARN MORE: Descriptive Research vs Correlational Research

Remember, it is only valuable and useful when it is valid, accurate, and reliable. Incorrect results can lead to customer churn and a decrease in sales.

It is essential to ensure that your data is:

• Valid – founded, logical, rigorous, and impartial.
• Accurate – free of errors and including required details.
• Reliable – other people who investigate in the same way can produce similar results.
• Timely – current and collected within an appropriate time frame.
• Complete – includes all the data you need to support your business decisions.

Gather insights

• Identify the main trends and issues, opportunities, and problems you observe. Write a sentence describing each one.
• Keep track of the frequency with which each of the main findings appears.
• Make a list of your findings from the most common to the least common.
• Evaluate a list of the strengths, weaknesses, opportunities, and threats identified in a SWOT analysis .
• Prepare conclusions and recommendations about your study.
• Act on your strategies
• Look for gaps in the information, and consider doing additional inquiry if necessary
• Plan to review the results and consider efficient methods to analyze and interpret results.

Review your goals before making any conclusions about your study. Remember how the process you have completed and the data you have gathered help answer your questions. Ask yourself if what your analysis revealed facilitates the identification of your conclusions and recommendations.

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What is quality research? A guide to identifying the key features and achieving success

Every researcher worth their salt strives for quality. But in research, what does quality mean?

Simply put, quality research is thorough, accurate, original and relevant. And to achieve this, you need to follow specific standards. You need to make sure your findings are reliable and valid. And when you know they're quality assured, you can share them with absolute confidence.

You’ll be able to draw accurate conclusions from your investigations and contribute to the wider body of knowledge in your field.

Importance of quality research

Quality research helps us better understand complex problems. It enables us to make decisions based on facts and evidence. And it empowers us to solve real-world issues. Without quality research, we can't advance knowledge or identify trends and patterns. We also can’t develop new theories and approaches to solving problems.

With rigorous and transparent research methods, you’ll produce reliable findings that other researchers can replicate. This leads to the development of new theories and interventions. On the other hand, low-quality research can hinder progress by producing unreliable findings that can’t be replicated, wasting resources and impeding advancements in the field.

In all cases, quality control is critical. It ensures that decisions are based on evidence rather than gut feeling or bias.

Standards for quality research

Over the years, researchers, scientists and authors have come to a consensus about the standards used to check the quality of research. Determined through empirical observation, theoretical underpinnings and philosophy of science, these include:

1. Having a well-defined research topic and a clear hypothesis

This is essential to verify that the research is focused and the results are relevant and meaningful. The research topic should be well-scoped and the hypothesis should be clearly stated and falsifiable .

For example, in a quantitative study about the effects of social media on behavior, a well-defined research topic could be, "Does the use of TikTok reduce attention span in American adolescents?"

This is good because:

• The research topic focuses on a particular platform of social media ( TikTok ). And it also focuses on a specific group of people (American adolescents).
• The research question is clear and straightforward, making it easier to design the study and collect relevant data.
• You can test the hypothesis and a research team can evaluate it easily. This can be done through the use of various research methods, such as survey research, experiments or observational studies.
• The hypothesis is focused on a specific outcome (the attention span). Then, this can be measured and compared to control groups or previous research studies.

2. Ensuring transparency

Transparency is crucial when conducting research. You need to be upfront about the methods you used, such as:

• Describing how you recruited the participants.
• How you communicated with them.
• How they were incentivized.

You also need to explain how you analyzed the data, so other researchers can replicate your results if necessary. re-registering your study is a great way to be as transparent in your research as possible. This  involves publicly documenting your study design, methods and analysis plan before conducting the research. This reduces the risk of selective reporting and increases the credibility of your findings.

3. Using appropriate research methods

Depending on the topic, some research methods are better suited than others for collecting data. To use our TikTok example, a quantitative research approach, such as a behavioral test that measures the participants' ability to focus on tasks, might be the most appropriate.

On the other hand, for topics that require a more in-depth understanding of individuals' experiences or perspectives, a qualitative research approach, such as interviews or focus groups, might be more suitable. These methods can provide rich and detailed information that you can’t capture through quantitative data alone.

4. Assessing limitations and the possible impact of systematic bias

When you present your research, it’s important to consider how the limitations of your study could affect the result. This could be systematic bias in the sampling procedure or data analysis, for instance. Let’s say you only study a small sample of participants from one school district. This would limit the generalizability and content validity of your findings.

5. Conducting accurate reporting

This is an essential aspect of any research project. You need to be able to clearly communicate the findings and implications of your study . Also, provide citations for any claims made in your report. When you present your work, it’s vital that you describe the variables involved in your study accurately and how you measured them.

Curious to learn more? Read our Data Quality eBook .

How to identify credible research findings

To determine whether a published study is trustworthy, consider the following:

• Peer review: If a study has been peer-reviewed by recognized experts, rest assured that it’s a reliable source of information. Peer review means that other scholars have read and verified the study before publication.
• Researcher's qualifications: If they're an expert in the field, that’s a good sign that you can trust their findings. However, if they aren't, it doesn’t necessarily mean that the study's information is unreliable. It simply means that you should be extra cautious about accepting its conclusions as fact.
• Study design: The design of a study can make or break its reliability. Consider factors like sample size and methodology.
• Funding source: Studies funded by organizations with a vested interest in a particular outcome may be less credible than those funded by independent sources.
• Statistical significance: You've heard the phrase "numbers don't lie," right? That's what statistical significance is all about. It refers to the likelihood that the results of a study occurred by chance. Results that are statistically significant are more credible.

Achieve quality research with Prolific

Want to ensure your research is high-quality? Prolific can help.

Our platform gives you access to a carefully vetted pool of participants. We make sure they're attentive, honest, and ready to provide rich and detailed answers where needed. This helps to ensure that the data you collect through Prolific is of the highest quality.

Streamline your research process and feel confident in the results you receive. Our minimum pay threshold and commitment to fair compensation motivate participants to provide valuable responses and give their best effort. This ensures the quality of your research and helps you get the results you need. 

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Literature Searching

Characteristics of a good research question

The first step in a literature search is to construct a well-defined question.  This helps in ensuring a comprehensive and efficient search of the available literature for relevant publications on your topic.  The well-constructed research question provides guidance for determining search terms and search strategy parameters.

A good or well-constructed research question is:

• Original and of interest to the researcher and the outside world
• It is clear and focused: it provides enough specifics that it is easy to understand its purpose and it is narrow enough that it can be answered. If the question is too broad it may not be possible to answer it thoroughly. If it is too narrow you may not find enough resources or information to develop a strong argument or research hypothesis.  
• The question concept is researchable in terms of time and access to a suitable amount of quality research resources.
• It is analytical rather than descriptive.  The research question should allow you to produce an analysis of an issue or problem rather than a simple description of it.  In other words, it is not answerable with a simple “yes” or “no” but requires a synthesis and analysis of ideas and sources.
• The results are potentially important and may change current ideas and/or practice
• And there is the potential to develop further projects with similar themes

The question you ask should be developed for the discipline you are studying. A question appropriate for Physical Therapy, for instance, is different from an appropriate one in Sociology, Political Science or Microbiology .

The well-constructed question provides guidance for determining search terms and search strategy parameters. The process of developing a good question to research involves taking your topic and breaking each aspect of it down into its component parts. 

One well-established way that can be used both for creating research questions and developing strategies is known as PICO(T). The PICO framework was designed primarily for questions that include clinical interventions and comparisons, however other types of questions may also be able to follow its principles.  If the PICO framework does not precisely fit your question, using its principles can help you to think about what you want to explore even if you do not end up with a true PICO question.

References/Additional Resources

Fandino W. (2019). Formulating a good research question: Pearls and pitfalls.   Indian journal of anaesthesia ,  63 (8), 611–616. 

Vandenbroucke, J. P., & Pearce, N. (2018). From ideas to studies: how to get ideas and sharpen them into research questions .  Clinical epidemiology ,  10 , 253–264.

Ratan, S. K., Anand, T., & Ratan, J. (2019). Formulation of Research Question - Stepwise Approach .  Journal of Indian Association of Pediatric Surgeons ,  24 (1), 15–20.

Lipowski, E.E. (2008). Developing great research questions. American Journal of Health-System Pharmacy, 65(17) , 1667–1670.

FINER Criteria

Another set of criteria for developing a research question was proposed by Hulley (2013) and is known as the FINER criteria. 

FINER stands for:

Feasible – Writing a feasible research question means that it CAN be answered under objective aspects like time, scope, resources, expertise, or funding. Good questions must be amenable to the formulation of clear hypotheses.

Interesting – The question or topic should be of interest to the researcher and the outside world. It should have a clinical and/or educational significance – the “so what?” factor. 

Novel – In scientific literature, novelty defines itself by being an answer to an existing gap in knowledge. Filling one of these gaps is highly rewarding for any researcher as it may represent a real difference in peoples’ lives.

Good research leads to new information. An investigation which simply reiterates what is previously proven is not worth the effort and cost. A question doesn’t have to be completely original. It may ask whether an earlier observation could be replicated, whether the results in one population also apply to others, or whether enhanced measurement methods can make clear the relationship between two variables.  

Ethical – In empirical research, ethics is an absolute MUST. Make sure that safety and confidentiality measures are addressed, and according to the necessary IRB protocols.

Relevant – An idea that is considered relevant in the healthcare community has better chances to be discussed upon by a larger number of researchers and recognized experts, leading to innovation and rapid information dissemination.

The results could potentially be important and may change current ideas and/or practice.

Cummings, S.R., Browner, W.S., & Hulley, S.B. (2013). Conceiving the research question and developing the study plan. In: Designing clinical research (Hulley, S. R. Cummings, W. S. Browner, D. Grady, & T. B. Newman, Eds.; Fourth edition.). Wolters Kluwer/Lippincott Williams & Wilkins. Pp. 14-22.    

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Research quality – not all research is good research, but how do you tell?

Blog, Evidence-Based Practice, Oxford Review, Research

• “Research says…”

“Research says…”, but does it? How to tell how good a study is. Research quality is an important factor in deciding to use a study but how do you know how good a particular bit of research really is?

Science proves nothing 

There are lots of factors that influence things, science is useless, confirmation bias, how to tell how good a piece of research is, the 4 levels of evidence, the 5 grade domains , risk of bias, imprecision, inconsistency, indirectness, publication bias, what we are looking for, example review panel, citing evidence, other references used.

Lots of people like quoting research. Usually the quote is vague, something like “There is a study that proves x”. There are a number of problems with using research in this way. The first and most obvious issue here is ‘what research’? A vague reference to some study could refer to a 4 th grade children’s school project, someone’s ideas written on the back of an envelope or a major international study conducted by subject matter experts and professional researchers. There is usually no way of telling what the research quality is until we know which study it actually is, how the research was conducted and by whom.

For example there is a big difference in research quality between a study that interviews 5 people in the same office and a study that observes the actual behaviour of 2,000 employees over time and not what they tell you their behaviour is.

The other issue here is the idea that research is there to ‘prove’ things. It isn’t. It is trying to get to as close to the truth about a particular topic as possible, but that is very different to ‘proving something to be true’. To say something has been ‘proved’ means that there is no doubt left, that this is the truth.

Science doesn’t work like that [i] . Life is complex. There are rarely simple causes of things and finding real causal relationships is notoriously hard. For example, ‘smoking causes lung cancer’. Firstly, not everyone who smokes will get lung cancer. There are other factors which, when combined with smoking, make the chances of developing cancer more or less likely, like excessive drinking, exercise, living in a polluted environment, having a diet of fast food and fizzy drinks etc. Some people have a greater genetic susceptibility to the carcinogens contained in smoke than others. Even then, all the smoke does is to help to create the conditions within the lungs that start changes in the person’s physiology that can then result in cancer. Additionally, there are many types of cancer.

The best we can say is that smoking significantly increases the chance of developing cancer compared to people who don’t smoke. But it’s complex and we don’t have all the answers.

There are rarely simple causes to things – life is complex

On an organisational note, saying something like ‘it has been proved that transformational leadership is better than transactional leadership’ has the same problem. There are so many variables involved that it is impossible to ‘prove’. For example, a poor transformational leader could be worse than a good transactional leader, or transformational leadership may not work in certain situations or with certain people (which appears to be the case). Notice I say, ‘appears to be the case’, not ‘has been proved’. There is always an element of doubt and, as research progresses and gets better, we start to see flaws in our research.

The problem with life, organisations, people etc. is that the number of variables or factors involved in the relationship between most things is so complex it is really hard to unravel.

Does that mean science is useless if it doesn’t prove things? No, not at all. Think about all the life-saving drugs that have been developed, for example. Does it mean the drugs work all the time and in every case? No, because there are so many variables, which is why most medicines come with a leaflet to tell you to stop taking them if you get certain reactions.

So, what researchers do when they are testing something is, firstly, they try to work out what factors may be involved (usually from previous research) and then they don’t test it. They form a hypothesis, something like we think that x is related to y in z direction or way. For example, higher levels of work engagement result in higher levels of productivity. That’s the hypothesis. However, the researcher will turn that statement around and test the null hypothesis. So, for example something like engagement (however that is measured) doesn’t result in higher productivity (however that is measured). If we then find that the null hypothesis can’t be accepted, we  accept  the hypothesis.

The reason for this is that it prevents things like confirmation bias, where people start looking for the answer they want / expect. This way, researchers are trying to break their hypothesis. If they can’t break it then the hypothesis is accepted. And this is important. It is only accepted – for now. It is always possible (and this happens all the time) that someone else comes along and either finds a flaw in your findings or research method or discovers a relationship or intervening factor you hadn’t. Science and research is dynamic and constantly changing. 

The problem is that just looking at findings doesn’t tell you how good or what quality a study is. For example, there is a big difference between someone publishing a single case study based on one particular situation, say a factory or one office or even one person, and a study taking in 1,000s of people across the world and using robust and valid research and analysis methods. 

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In evidence-based practice circles, the accuracy and reliability of the research (research quality) is an important factor when deciding to include a study in the evidence-base for a decision, especially clinical or engineering decisions where people’s lives are on the line.

One way that evidence-based practitioners judge the quality of research is to use the GRADE system or framework [i] . GRADE stands for Grading of Recommendations, Assessment, Development and Evaluations, and is used extensively in medical scenarios. 

GRADE is now being used in engineering, organisational evidence-based practice to judge the research and is the basis of many systematic reviews. 

When choosing studies to include in a decision GRADE has four levels of evidence, or four levels of certainty, in the quality of the evidence/study:

• Very low – The real effect is probably very different from the reported findings.
• Low – The true effect is quite likely to be different from the findings of this study.
• Moderate – The true effect is probably close to the findings of this study.
• High – A high level of confidence that, the findings represent the true effect / represents reality.

There are five overall factors which are used to help a practitioner work out what the level of evidence a particular study is using :

• Publication bias    All of these potential biases downgrade the research quality of the study.

Tool for Assessing Risk of Bias 

The Cochrane Collaboration’s Tool for Assessing Risk of Bias usually uses a 3-point grading system for judging bias and is an important factor in working the quality of  research :

• Low risk of bias
• High risk of bias
• Unclear risk of bias

In 2005 The Cochrane Institute in Oxford produced a tool that has become the standard bias risk assessment tool – The Cochrane Collaboration’s Tool for Assessing Risk of Bias – and looks specifically at a range of different biases that can affect a study’s findings. The tool looks for whether the study being looked at:

• Uses quality scales. Quality scales are often inherently biased and based on opinions.
• Looks at the internal validity of the study. What this means is that the researchers extensively look for and report any potential biases the method or study might have. In other words, are the methods used in the study likely to lead to bias or less so?
• Has actively looked for sources of bias or influence in their results. Double blind trials, where neither the participants (subjects) nor the researchers know which subject is getting which treatment or is in which group are considered to be at the least risk of bias.
• That the assessor / evaluator understands the methods used and can make a good judgement about the methods used.
• Is there a risk of bias in the way the data is being presented or represented?
• Does the use or group to which you are putting the study introduce a risk of bias not associated to the study?

How precise are the data and methods used for the study? 

Does the study show inconsistences openly or do they try to mask them? Are there inherent inconsistencies that haven’t been reported?

How close is the situation / population of the study being looked at to the one it is being applied to? Is this study talking directly into and about the organisation or population of the study, or is this being used in a more indirect way?

• Does the publisher have a stake in the findings? For example, is this a study by a consultancy attempting to show how good its own methods are? 

All of these potential biases downgrade the quality of the research study.

When assessing a study, we are looking for good research that is applicable to the situation to which the findings are being put. It needs to be valid (the methods used actually give good data and is measuring what we think it is measuring) and reliable (you keep getting the same results). Being able to spot bias in all its forms in research studies is essential if you are using research to inform organisational decisions.

Our research briefings, research quality and the review panel

At the end of all of all of our research briefings we have an assessment/review of the study being reported. We use the following simplified panel to grade the quality of the study under consideration:

• Research Quality – 3/5 A good literature review and overview of the subject. Based on a meta-analysis, rather than primary research.
• Confidence – 4/5 Consistent with the current research thinking and developments.
• Usefulness – 4/5 Particularly useful to HR practitioners.
• Comments – The current focus on differentiated HR architecture (structures and systems) to support talent management is showing a lot of promise in improving organisational performance generally. 

And we always fully cite which studies we have used, so that you can check it and do further reading

[i] Guyatt GH, Oxman AD, Kunz R, Vist GE, Falck-Ytter Y, Schunemann HJ. What is “quality of evidence” and why is it important to clinicians? BMJ (Clinical research ed). 2008;336(7651):995-8. [i]  

Guyatt GH, Oxman AD, Vist GE, Kunz R, Falck-Ytter Y, Alonso-Coello P, et al. GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ (Clinical research ed). 2008;336(7650):924-6.

Guyatt G, Oxman AD, Akl EA, Kunz R, Vist G, Brozek J, et al. GRADE guidelines: 1. Introduction-GRADE evidence profiles and summary of findings tables. Journal of clinical epidemiology. 2011;64(4):383-94.

Guyatt GH, Oxman AD, Kunz R, Atkins D, Brozek J, Vist G, et al. GRADE guidelines: 2. Framing the question and deciding on important outcomes. Journal of clinical epidemiology. 2011;64(4):395-400.

Balshem H, Helfand M, Schunemann HJ, Oxman AD, Kunz R, Brozek J, et al. GRADE guidelines: 3. Rating the quality of evidence. Journal of clinical epidemiology. 2011;64(4):401-6.

Guyatt, G. H., Oxman, A. D., Kunz, R., Vist, G. E., Falck-Ytter, Y., & Schünemann, H. J. (2008). What is “quality of evidence” and why is it important to clinicians?.  Bmj ,  336 (7651), 995-998.

BMJ Best practice series: What is GRADE? Accessed at:  https://bestpractice.bmj.com/info/toolkit/learn-ebm/what-is-grade/  on 3 rd  November 2019

Guyatt, G., Oxman, A. D., Akl, E. A., Kunz, R., Vist, G., Brozek, J., … & Jaeschke, R. (2011). GRADE guidelines: 1. Introduction—GRADE evidence profiles and summary of findings tables.  Journal of clinical epidemiology ,  64 (4), 383-394.

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What is a Research Problem? Characteristics, Types, and Examples

A research problem is a gap in existing knowledge, a contradiction in an established theory, or a real-world challenge that a researcher aims to address in their research. It is at the heart of any scientific inquiry, directing the trajectory of an investigation. The statement of a problem orients the reader to the importance of the topic, sets the problem into a particular context, and defines the relevant parameters, providing the framework for reporting the findings. Therein lies the importance of research problem s.  

The formulation of well-defined research questions is central to addressing a research problem . A research question is a statement made in a question form to provide focus, clarity, and structure to the research endeavor. This helps the researcher design methodologies, collect data, and analyze results in a systematic and coherent manner. A study may have one or more research questions depending on the nature of the study.   

Identifying and addressing a research problem is very important. By starting with a pertinent problem , a scholar can contribute to the accumulation of evidence-based insights, solutions, and scientific progress, thereby advancing the frontier of research. Moreover, the process of formulating research problems and posing pertinent research questions cultivates critical thinking and hones problem-solving skills.   

Table of Contents

What is a Research Problem ?  

Before you conceive of your project, you need to ask yourself “ What is a research problem ?” A research problem definition can be broadly put forward as the primary statement of a knowledge gap or a fundamental challenge in a field, which forms the foundation for research. Conversely, the findings from a research investigation provide solutions to the problem .  

A research problem guides the selection of approaches and methodologies, data collection, and interpretation of results to find answers or solutions. A well-defined problem determines the generation of valuable insights and contributions to the broader intellectual discourse.  

Characteristics of a Research Problem  

Knowing the characteristics of a research problem is instrumental in formulating a research inquiry; take a look at the five key characteristics below:  

Novel : An ideal research problem introduces a fresh perspective, offering something new to the existing body of knowledge. It should contribute original insights and address unresolved matters or essential knowledge.   

Significant : A problem should hold significance in terms of its potential impact on theory, practice, policy, or the understanding of a particular phenomenon. It should be relevant to the field of study, addressing a gap in knowledge, a practical concern, or a theoretical dilemma that holds significance.  

Feasible: A practical research problem allows for the formulation of hypotheses and the design of research methodologies. A feasible research problem is one that can realistically be investigated given the available resources, time, and expertise. It should not be too broad or too narrow to explore effectively, and should be measurable in terms of its variables and outcomes. It should be amenable to investigation through empirical research methods, such as data collection and analysis, to arrive at meaningful conclusions A practical research problem considers budgetary and time constraints, as well as limitations of the problem . These limitations may arise due to constraints in methodology, resources, or the complexity of the problem.  

Clear and specific : A well-defined research problem is clear and specific, leaving no room for ambiguity; it should be easily understandable and precisely articulated. Ensuring specificity in the problem ensures that it is focused, addresses a distinct aspect of the broader topic and is not vague.  

Rooted in evidence: A good research problem leans on trustworthy evidence and data, while dismissing unverifiable information. It must also consider ethical guidelines, ensuring the well-being and rights of any individuals or groups involved in the study.

Types of Research Problems  

Across fields and disciplines, there are different types of research problems . We can broadly categorize them into three types.  

• Theoretical research problems

Theoretical research problems deal with conceptual and intellectual inquiries that may not involve empirical data collection but instead seek to advance our understanding of complex concepts, theories, and phenomena within their respective disciplines. For example, in the social sciences, research problem s may be casuist (relating to the determination of right and wrong in questions of conduct or conscience), difference (comparing or contrasting two or more phenomena), descriptive (aims to describe a situation or state), or relational (investigating characteristics that are related in some way).  

Here are some theoretical research problem examples :   

• Ethical frameworks that can provide coherent justifications for artificial intelligence and machine learning algorithms, especially in contexts involving autonomous decision-making and moral agency.  
• Determining how mathematical models can elucidate the gradual development of complex traits, such as intricate anatomical structures or elaborate behaviors, through successive generations.  
• Applied research problems

Applied or practical research problems focus on addressing real-world challenges and generating practical solutions to improve various aspects of society, technology, health, and the environment.  

Here are some applied research problem examples :   

• Studying the use of precision agriculture techniques to optimize crop yield and minimize resource waste.  
• Designing a more energy-efficient and sustainable transportation system for a city to reduce carbon emissions.  
• Action research problems

Action research problems aim to create positive change within specific contexts by involving stakeholders, implementing interventions, and evaluating outcomes in a collaborative manner.  

Here are some action research problem examples :   

• Partnering with healthcare professionals to identify barriers to patient adherence to medication regimens and devising interventions to address them.  
• Collaborating with a nonprofit organization to evaluate the effectiveness of their programs aimed at providing job training for underserved populations.  

These different types of research problems may give you some ideas when you plan on developing your own.  

How to Define a Research Problem  

You might now ask “ How to define a research problem ?” These are the general steps to follow:   

• Look for a broad problem area: Identify under-explored aspects or areas of concern, or a controversy in your topic of interest. Evaluate the significance of addressing the problem in terms of its potential contribution to the field, practical applications, or theoretical insights.
• Learn more about the problem: Read the literature, starting from historical aspects to the current status and latest updates. Rely on reputable evidence and data. Be sure to consult researchers who work in the relevant field, mentors, and peers. Do not ignore the gray literature on the subject.
• Identify the relevant variables and how they are related: Consider which variables are most important to the study and will help answer the research question. Once this is done, you will need to determine the relationships between these variables and how these relationships affect the research problem . 
• Think of practical aspects : Deliberate on ways that your study can be practical and feasible in terms of time and resources. Discuss practical aspects with researchers in the field and be open to revising the problem based on feedback. Refine the scope of the research problem to make it manageable and specific; consider the resources available, time constraints, and feasibility.
• Formulate the problem statement: Craft a concise problem statement that outlines the specific issue, its relevance, and why it needs further investigation.
• Stick to plans, but be flexible: When defining the problem , plan ahead but adhere to your budget and timeline. At the same time, consider all possibilities and ensure that the problem and question can be modified if needed.

Key Takeaways  

• A research problem concerns an area of interest, a situation necessitating improvement, an obstacle requiring eradication, or a challenge in theory or practical applications.   
• The importance of research problem is that it guides the research and helps advance human understanding and the development of practical solutions.  
• Research problem definition begins with identifying a broad problem area, followed by learning more about the problem, identifying the variables and how they are related, considering practical aspects, and finally developing the problem statement.  
• Different types of research problems include theoretical, applied, and action research problems , and these depend on the discipline and nature of the study.  
• An ideal problem is original, important, feasible, specific, and based on evidence.  

Frequently Asked Questions  

Why is it important to define a research problem?  

Identifying potential issues and gaps as research problems is important for choosing a relevant topic and for determining a well-defined course of one’s research. Pinpointing a problem and formulating research questions can help researchers build their critical thinking, curiosity, and problem-solving abilities.   

How do I identify a research problem?  

Identifying a research problem involves recognizing gaps in existing knowledge, exploring areas of uncertainty, and assessing the significance of addressing these gaps within a specific field of study. This process often involves thorough literature review, discussions with experts, and considering practical implications.  

Can a research problem change during the research process?  

Yes, a research problem can change during the research process. During the course of an investigation a researcher might discover new perspectives, complexities, or insights that prompt a reevaluation of the initial problem. The scope of the problem, unforeseen or unexpected issues, or other limitations might prompt some tweaks. You should be able to adjust the problem to ensure that the study remains relevant and aligned with the evolving understanding of the subject matter.

How does a research problem relate to research questions or hypotheses?  

A research problem sets the stage for the study. Next, research questions refine the direction of investigation by breaking down the broader research problem into manageable components. Research questions are formulated based on the problem , guiding the investigation’s scope and objectives. The hypothesis provides a testable statement to validate or refute within the research process. All three elements are interconnected and work together to guide the research.  

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When Dreams Merge With Your Waking Life—What to Know About Deja Reve

It's more common than you think

Verywell Mind / Getty Images

• What It Feels Like

Different Types of Deja Reve

• Causes and Triggers
• What the Research Say

Deja Reve vs. Deja Vu

Coping with deja reve, enjoying the ride.

I wouldn’t say it happens often, but I’d say it happens enough to take note. I’ll be in a conversation with my husband, pulling up to a restaurant I’ve never been to before, or meeting someone for the first time and I’ll have this overwhelming feeling of familiarity. However, this isn’t a sense of familiarity where I feel I’m repeating myself or retracing my steps. Instead, I am convinced I have dreamed what I am experiencing at that very moment. 

This wasn’t a phenomenon that only cropped up in adulthood. I remember similar instances from my preteen years and, when I asked others if they could relate, I was met with stories of shared experiences. I’ve come to learn this experience has a name: deja reve.

Deja reve quite literally translates into, “already dreamed,” adding a romantic touch of linguistics to this already dreamy (no pun intended) experience. Sometimes confused with deja vu , which we will get into a bit later, this phenomenon is a common experience that typically isn’t any cause for concern. Read on to learn more about what deja reve feels like, what causes it, and if you need to seek out professional support. 

What It Feels Like to Experience Deja Reve

Deja reve feels similar to deja vu, in the sense that it provides an overwhelming sense of familiarity that you just quite can’t put your finger on. However, it is quite different. Rather than feeling like you’ve experienced something before when you know you haven’t, as is the case in deja vu, deja reve is when you feel as if you’ve dreamed something before. 

Here are some examples of what it feels like to experience deja reve:

• You’re at a party and someone you’ve never met before introduces themselves. You are overcome with a sense that you’ve met this person, in this exact same scenario, before. Yet, you know that is impossible because you’ve never been to a party at this location and you know you haven’t met this person.
• A friend invites you to dinner at a restaurant you haven’t heard of before. As she says the name of it, you're almost certain you've had this conversation before. 
• Deja reve doesn’t only happen in our waking life. I’ve had a dream set at a place in France that I’ve never been to. During the dream, I was positive I’d dreamt of this exact same place before. 

There are three different types of deja reve:

• Episodic: When your memory jumps back to a specific dream.
• Familiarity : When something is vague yet quite familiar, meaning you can’t put your finger on the specific dream your mind is harkening back to, but you’re certain it was a dream you experienced nonetheless.
• Dreamy state : Dreamy states are particularly interesting because it feels as though you’re literally dreaming – so you might be in a situation from a dream that is so overwhelming, that you feel as if you’re dreaming again.

Causes and Triggers of Deja Reve

To better understand the causes and triggers of deja reve, I turned to movement disorders neurologist and assistant clinical professor of neurology at Vanderbilt University Britt Stone . “[Deja reve] seems to occur in younger people,” she explains. She continued by sharing that some folks with epilepsy also report deja reve as a common occurrence. Finally, good old-fashioned fatigue can trigger deja reve, too. 

Intrigued by her statement that those with epilepsy can often experience deja reve, I asked her if it could be an indicator of other conditions. “This phenomenon can be associated with seizure disorders , but also are just isolated occurrences and are not necessarily something to be concerned about,” she explained.

What the Research Says About Deja Reve

To Stone's point, research drives home that deja reve can be linked to epilepsy. A 2018 study published in Frontiers Journal found that some can experience deja reve during a seizure. However, that doesn’t mean deja reve is a direct indicator of epilepsy. There is nuance to this experience and anyone, those with epilepsy or not, can experience this phenomenon. 

While deja reve calls back to a dream you’ve had, deja vu calls back to an experience you’ve had. While deja reve may have you shaking someone’s hand and feeling as if you’ve already done so in a dream , deja vu would have you shaking the person’s hand and feeling insistent you’ve met them before – even if you know you haven’t.

Similar to deja reve, deja vu is also associated with epilepsy. But, just like deja reve, anyone can have this experience.

Generally speaking, deja reve isn’t anything to fear. Though it can feel mystical and otherworldly, it is a rather common experience. If you’re doubting that, ask those in your life if they’ve experienced it – they likely have. “I’ve had it before myself – the majority of people have had the experience before,” explained Dr. Stone before following her statement up with a statistic that 97% of folks have experienced deja reve. 

Considering there is a link to epilepsy, some readers may feel a bit spooked. I asked Dr. Stone for her insight on when she thinks someone should seek professional help for deja reve. “If it is associated with losing time or loss of awareness, confusion, headaches, or vision changes, then they should let your doctor know,” she shared. 

If you experience deja reve occasionally and are not experiencing any of the aforementioned symptoms that would make you a candidate to seek out medical support, we recommend leaning into the experience. Take notes of your dreams in your dream journal and notice what dreams your mind calls back to with deja reve.

If you’re working with a therapist , this can be powerful material to process together. Alternatively, you may look into dream symbolism to see what wisdom these occurrences may hold. Or, you might just take notice of what’s happening as a passing experience and keep on with your day.

Curot J, Valton L, Denuelle M, et al. Déjà-rêvé: Prior dreams induced by direct electrical brain stimulation . Brain Stimul . 2018;11(4):875-885. doi:10.1016/j.brs.2018.02.016

de la Chapelle A, Frauscher B, Valomon A, Ruby PM, Peter-Derex L. Relationship between epilepsy and dreaming: current knowledge, hypotheses, and perspectives . Front Neurosci . 2021;15:717078. doi:10.3389/fnins.2021.717078

Zatloukalova E, Mikl M, Shaw DJ, et al. Insights into déjà vu: Associations between the frequency of experience and amplitudes of low-frequency oscillations in resting-state functional magnetic resonance imaging . Eur J Neurosci . 2022;55(2):426-437. doi:10.1111/ejn.15570

By Julia Childs Heyl, MSW Julia Childs Heyl, MSW, is a clinical social worker and writer. As a writer, she focuses on mental health disparities and uses critical race theory as her preferred theoretical framework. In her clinical work, she specializes in treating people of color experiencing anxiety, depression, and trauma through depth therapy and EMDR (eye movement desensitization and reprocessing) trauma therapy.

More From Forbes

‘fauxductivity’: your boss might fake it more than you, new survey finds.

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New research shows that your boss might be faking productivity more than you are.

You’ve probably heard the popular phrase, “Dance like nobody’s watching,” but the opposite trend has gained momentum in the U.S. workplace. Threats of layoffs, the rise of surveillance tools and concerns over the effectiveness of remote work have led to “productivity theater” and “the mouse shuffle, ” in which employees work like their employers are watching.

‘Work Like Everybody’s Watching’

Last year Visier surveyed 1,000 U.S. based full-time employees to better understand their need to “play productive” and the factors that drive decision-making in the workplace. They found that when businesses pressure employees to perform, workers react by prioritizing tasks that make them appear productive and visible to management instead of impactful work.

This troubling pattern has emerged because employees feel pressured to “look busy” instead of “being busy.” They want to prove they’re working by constantly moving their mouse, appearing online by keeping their laptop screen awake or prioritizing tasks that make them appear productive and visible to their organization as opposed to actually working. On the surface, this pattern might not seem harmful, but continuing to take on “visible” tasks for the sake of appearing productive is a productivity killer that threats a company’s efficiency and bottom line.

Even more troubling is a study by BambooHR in June of this year that reveals productivity theater is still alive and well in the 2024 workplace. The results show that visibility is more important than actual productivity. Over 79% of in-office employees and 88% of remote workers say they must use performative tactics to show they’re working. In that study, a quarter of executives actually admitted they hoped for employee turnover when implementing recent return-to-office policies.

‘Fauxductivity’: A Toxic Culture Of Performative Work

Even more shocking, a new study from Workhuman uncovers what they call “fauxductivity”—or fake productivity, highlighting how misaligned perceptions and top-down pressures are creating a toxic culture of performative work.

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According to their findings, the majority of employees (67%) deny faking activity. But almost half of managers (48%) say it’s a common issue on their team. The biggest head scratcher, though, is that it’s happening among managers and the C-suite executives at higher rates. Additional key findings include:

• 37% of managers and 38% of C-suite executives admit to faking activity, versus just 32% of individual contributors, higher than the 33% average of all respondents and 32% of non-managers.
• Top reasons for faking productivity across the board include better work-life balance, appeasing management and burnout.
• Of the managers who admit to faking activity, 69% say faking activity is a common issue on their team versus 37% of managers who do not fake activity.
• Over 50% of respondents feel pressured to respond immediately to messages and be available for after-hours meetings.
• 54% of disengaged employees report they do the bare minimum to get through the day.

What’s behind “fauxductivity”? The majority of managers blame either distractions (56%) or burnout/low well-being (53%) for faking, and 40% cite personal responsibilities while 33% say it’s laziness. The managers who admit faking productivity say it was a desire for work-life balance, to appease management or burnout.

A Final Word: Addressing Psychological Safety

“Productivity anxiety”—the feeling employees have that they must be “always on” and that there’s always more they should be doing—is pervasive in the workplace in this country. Over 50% of respondents report they’re expected to immediately respond to all Slacks, messages or emails, and 52% say they’re expected to be flexible with their working hours to accommodate after-hours meetings.

A previous Workhuman analysis found that 61% of U.S. workers say they’re productive at work, but it comes at a cost. A total of 80% report they have “productivity anxiety” and over one-third have it multiple times a week. Obviously, this strain impacts individual workers and permeates team dynamics—ultimately shaping the culture and bottom line of an entire organization, according to Meisha-ann Martin, senior director of people analytics and research at Workhuman.

Martin believes it’s essential to cultivate a culture of psychological safety to offset productivity anxiety and fauxductivity. “It’s an understatement to say that today’s employees are up against a lot: both professional and personal stressors, burnout, overwork and disengagement can contribute to low well-being,” she notes in the report.

The Workhuman report concludes that low productivity and fauxductivity are symptoms of poor culture, “creating a toxic cycle of performative productivity and performance anxiety.” It further suggests that the solution resides in addressing systemic cultural issues rather than scrutinizing individual workers.

“Managers especially are in the position to promote a workplace culture that allows employees to be human and say when they’re struggling—not turn to performative productivity,” Martin points out. “That means managers themselves need to resist the urge to keep up appearances and instead be vocal about when they’re taking a break. The re-energized, re-committed people that return to work after recharging will achieve better outcomes and better well-being than those who stay quiet and stay online.”

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• Knowledge Base
• Starting the research process
• Research Objectives | Definition & Examples

Research Objectives | Definition & Examples

Published on July 12, 2022 by Eoghan Ryan . Revised on November 20, 2023.

Research objectives describe what your research is trying to achieve and explain why you are pursuing it. They summarize the approach and purpose of your project and help to focus your research.

Your objectives should appear in the introduction of your research paper , at the end of your problem statement . They should:

• Establish the scope and depth of your project
• Contribute to your research design
• Indicate how your project will contribute to existing knowledge

Table of contents

What is a research objective, why are research objectives important, how to write research aims and objectives, smart research objectives, other interesting articles, frequently asked questions about research objectives.

Research objectives describe what your research project intends to accomplish. They should guide every step of the research process , including how you collect data , build your argument , and develop your conclusions .

Your research objectives may evolve slightly as your research progresses, but they should always line up with the research carried out and the actual content of your paper.

Research aims

A distinction is often made between research objectives and research aims.

A research aim typically refers to a broad statement indicating the general purpose of your research project. It should appear at the end of your problem statement, before your research objectives.

Your research objectives are more specific than your research aim and indicate the particular focus and approach of your project. Though you will only have one research aim, you will likely have several research objectives.

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Research objectives are important because they:

• Establish the scope and depth of your project: This helps you avoid unnecessary research. It also means that your research methods and conclusions can easily be evaluated .
• Contribute to your research design: When you know what your objectives are, you have a clearer idea of what methods are most appropriate for your research.
• Indicate how your project will contribute to extant research: They allow you to display your knowledge of up-to-date research, employ or build on current research methods, and attempt to contribute to recent debates.

Once you’ve established a research problem you want to address, you need to decide how you will address it. This is where your research aim and objectives come in.

Step 1: Decide on a general aim

Your research aim should reflect your research problem and should be relatively broad.

Step 2: Decide on specific objectives

Break down your aim into a limited number of steps that will help you resolve your research problem. What specific aspects of the problem do you want to examine or understand?

Step 3: Formulate your aims and objectives

Once you’ve established your research aim and objectives, you need to explain them clearly and concisely to the reader.

You’ll lay out your aims and objectives at the end of your problem statement, which appears in your introduction. Frame them as clear declarative statements, and use appropriate verbs to accurately characterize the work that you will carry out.

The acronym “SMART” is commonly used in relation to research objectives. It states that your objectives should be:

• Specific: Make sure your objectives aren’t overly vague. Your research needs to be clearly defined in order to get useful results.
• Measurable: Know how you’ll measure whether your objectives have been achieved.
• Achievable: Your objectives may be challenging, but they should be feasible. Make sure that relevant groundwork has been done on your topic or that relevant primary or secondary sources exist. Also ensure that you have access to relevant research facilities (labs, library resources , research databases , etc.).
• Relevant: Make sure that they directly address the research problem you want to work on and that they contribute to the current state of research in your field.
• Time-based: Set clear deadlines for objectives to ensure that the project stays on track.

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

Methodology

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

Research objectives describe what you intend your research project to accomplish.

They summarize the approach and purpose of the project and help to focus your research.

Your objectives should appear in the introduction of your research paper , at the end of your problem statement .

Your research objectives indicate how you’ll try to address your research problem and should be specific:

Once you’ve decided on your research objectives , you need to explain them in your paper, at the end of your problem statement .

Keep your research objectives clear and concise, and use appropriate verbs to accurately convey the work that you will carry out for each one.

I will compare …

A research aim is a broad statement indicating the general purpose of your research project. It should appear in your introduction at the end of your problem statement , before your research objectives.

Research objectives are more specific than your research aim. They indicate the specific ways you’ll address the overarching aim.

Scope of research is determined at the beginning of your research process , prior to the data collection stage. Sometimes called “scope of study,” your scope delineates what will and will not be covered in your project. It helps you focus your work and your time, ensuring that you’ll be able to achieve your goals and outcomes.

Defining a scope can be very useful in any research project, from a research proposal to a thesis or dissertation . A scope is needed for all types of research: quantitative , qualitative , and mixed methods .

To define your scope of research, consider the following:

• Budget constraints or any specifics of grant funding
• Your proposed timeline and duration
• Specifics about your population of study, your proposed sample size , and the research methodology you’ll pursue
• Any inclusion and exclusion criteria
• Any anticipated control , extraneous , or confounding variables that could bias your research if not accounted for properly.

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• Published: 16 September 2024

From whole-slide image to biomarker prediction: end-to-end weakly supervised deep learning in computational pathology

• Omar S. M. El Nahhas   ORCID: orcid.org/0000-0002-2542-2117 1 , 2 ,
• Marko van Treeck 1 ,
• Georg Wölflein   ORCID: orcid.org/0000-0002-0407-7617 3 ,
• Michaela Unger 1 ,
• Marta Ligero 1 ,
• Tim Lenz 1 ,
• Sophia J. Wagner 4 , 5 ,
• Katherine J. Hewitt 1 ,
• Firas Khader 2 , 6 ,
• Sebastian Foersch 7 ,
• Daniel Truhn 2 , 6 &
• Jakob Nikolas Kather   ORCID: orcid.org/0000-0002-3730-5348 1 , 2 , 8 , 9  

Nature Protocols ( 2024 ) Cite this article

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• Bioinformatics
• Cancer imaging
• Image processing

Hematoxylin- and eosin-stained whole-slide images (WSIs) are the foundation of diagnosis of cancer. In recent years, development of deep learning-based methods in computational pathology has enabled the prediction of biomarkers directly from WSIs. However, accurately linking tissue phenotype to biomarkers at scale remains a crucial challenge for democratizing complex biomarkers in precision oncology. This protocol describes a practical workflow for solid tumor associative modeling in pathology (STAMP), enabling prediction of biomarkers directly from WSIs by using deep learning. The STAMP workflow is biomarker agnostic and allows for genetic and clinicopathologic tabular data to be included as an additional input, together with histopathology images. The protocol consists of five main stages that have been successfully applied to various research problems: formal problem definition, data preprocessing, modeling, evaluation and clinical translation. The STAMP workflow differentiates itself through its focus on serving as a collaborative framework that can be used by clinicians and engineers alike for setting up research projects in the field of computational pathology. As an example task, we applied STAMP to the prediction of microsatellite instability (MSI) status in colorectal cancer, showing accurate performance for the identification of tumors high in MSI. Moreover, we provide an open-source code base, which has been deployed at several hospitals across the globe to set up computational pathology workflows. The STAMP workflow requires one workday of hands-on computational execution and basic command line knowledge.

STAMP (solid tumor associative modeling in pathology) is a practical workflow for end-to-end weakly supervised deep learning in computational pathology, enabling prediction of biomarkers directly from whole-slide images.

This protocol differentiates itself from others by providing a collaborative framework through which clinical researchers can work with engineers to set up a complete computational pathology project.

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Artificial intelligence for digital and computational pathology

Deep learning in cancer pathology: a new generation of clinical biomarkers

Pacpaint: a histology-based deep learning model uncovers the extensive intratumor molecular heterogeneity of pancreatic adenocarcinoma

Data availability.

Histopathology slides and genomics data from TCGA and CPTAC were used to train and validate the models. The slides for TCGA are available at https://portal.gdc.cancer.gov/ . The slides for CPTAC are available at https://proteomics.cancer.gov/data-portal . The molecular and clinical data for TCGA and CPTAC used in the experiments are available at https://github.com/KatherLab/cancer-metadata . Source data are provided with this paper.

Code availability

The open-source STAMP software for the implementation of the MSI experiments is available on GitHub ( https://github.com/KatherLab/STAMP ).

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Acknowledgements

We thank the testers of the protocol, S. Sainath, O. L. Saldanha, L. Žigutytė, C. Kummer, G. Serna, K. Boehm and L. Shaktah, who executed the STAMP protocol on various systems at cancer centers around the world. O.S.M.E.N. is supported by the German Federal Ministry of Education and Research (BMBF) through grant 1IS23070, Software Campus 3.0 (TU Dresden), as part of the Software Campus project ’MIRACLE-AI’. J.N.K. is supported by the German Federal Ministry of Health (DEEP LIVER, ZMVI1-2520DAT111), the German Cancer Aid (DECADE, 70115166), the German Federal Ministry of Education and Research (PEARL, 01KD2104C; CAMINO, 01EO2101; SWAG, 01KD2215A; TRANSFORM LIVER, 031L0312A; TANGERINE, 01KT2302 through ERA-NET Transcan), the German Academic Exchange Service (SECAI, 57616814), the German Federal Joint Committee (TransplantKI, 01VSF21048), the European Union’s Horizon Europe and innovation programme (ODELIA, 101057091; GENIAL, 101096312) and the National Institute for Health and Care Research (NIHR, NIHR213331) Leeds Biomedical Research Centre. G.W. is supported by Lothian NHS. D.T. is supported by the German Federal Ministry of Education and Research (SWAG, 01KD2215A; TRANSFORM LIVER) and the European Union’s Horizon Europe and innovation programme (ODELIA, 101057091). S.F. is supported by the German Federal Ministry of Education and Research (SWAG, 01KD2215A), the German Cancer Aid (DECADE, 70115166) and the German Research Foundation (504101714). S.J.W. was supported by the Helmholtz Association under the joint research school ‘Munich School for Data Science – MUDS’ and the Add-on Fellowship of the Joachim Herz Foundation. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. This work was funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.

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Authors and affiliations.

Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany

Omar S. M. El Nahhas, Marko van Treeck, Michaela Unger, Marta Ligero, Tim Lenz, Katherine J. Hewitt & Jakob Nikolas Kather

StratifAI GmbH, Dresden, Germany

Omar S. M. El Nahhas, Firas Khader, Daniel Truhn & Jakob Nikolas Kather

School of Computer Science, University of St Andrews, St Andrews, UK

Georg Wölflein

Helmholtz Munich–German Research Center for Environment and Health, Munich, Germany

Sophia J. Wagner

School of Computation, Information and Technology, Technical University of Munich, Munich, Germany

Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany

Firas Khader & Daniel Truhn

Institute of Pathology–University Medical Center Mainz, Mainz, Germany

Sebastian Foersch

Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany

Jakob Nikolas Kather

Department of Medicine 1, University Hospital and Faculty of Medicine Carl Gustav Carus, Technical University Dresden, Dresden, Germany

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Contributions

O.S.M.E.N. and J.N.K. designed the protocol. O.S.M.E.N., M.v.T., G.W. and T.L. developed the software and wrote technical documentation. O.S.M.E.N., M.v.T., G.W., T.L., M.L., M.U., S.J.W., F.K., S.F. and D.T. tested the software. O.S.M.E.N., J.N.K. and K.J.H. interpreted and analyzed the data. All authors wrote and reviewed the protocol and approved the final version for submission.

Corresponding author

Correspondence to Jakob Nikolas Kather .

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Competing interests.

O.S.M.E.N., F.K. and D.T. hold shares in StratifAI GmbH. J.N.K. declares consulting services for Owkin, France; DoMore Diagnostics, Norway; Panakeia, UK,; Scailyte, Switzerland; Mindpeak, Germany; and Histofy, UK; furthermore, he holds shares in StratifAI GmbH, Germany, and has received honoraria for lectures by AstraZeneca, Bayer, Eisai, MSD, BMS, Roche, Pfizer and Fresenius. D.T. received honoraria for lectures by Bayer and holds shares in StratifAI GmbH, Germany. S.F. has received honoraria from MSD and BMS.

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Key references using this protocol

Wagner, S. J. et al. Cancer Cell 41 , 1650–1661.e4 (2023): https://doi.org/10.1016/j.ccell.2023.08.002

El Nahhas, O. S. M. et al. Nat. Commun . 15 , 1253 (2024): https://doi.org/10.1038/s41467-024-45589-1

Jiang, X. et al. Lancet Digit. Health 6 , e33–e43 (2024): https://doi.org/10.1016/S2589-7500(23)00208-X

Hewitt, K. J. et al. Neurooncol. Adv . 5 , vdad139 (2023): https://doi.org/10.1093/noajnl/vdad139

Saldanha, O. L. et al. npj Precis. Onc . 7 , 35 (2023): https://doi.org/10.1038/s41698-023-00365-0

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El Nahhas, O.S.M., van Treeck, M., Wölflein, G. et al. From whole-slide image to biomarker prediction: end-to-end weakly supervised deep learning in computational pathology. Nat Protoc (2024). https://doi.org/10.1038/s41596-024-01047-2

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Columbia university is dedicated to increasing diversity in its workforce, its student body, and its educational programs. achieving continued academic excellence and creating a vibrant university community require nothing less. in fulfilling its mission to advance diversity at the university, columbia seeks to hire, retain, and promote exceptionally talented individuals from diverse backgrounds.  , share this job.

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