meaning of study in research

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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:


Approach used	Unstructured	Structured	Highly structured
Conducted through	Asking questions	Asking questions	By using hypotheses.
Time	Early stages of decision making	Later stages of decision making	Later stages of decision making

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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The Savvy Scientist

Experiences of a London PhD student and beyond

What is the Significance of a Study? Examples and Guide

Significance of a study graphic, showing a female scientist reading a book

If you’re reading this post you’re probably wondering: what is the significance of a study?

No matter where you’re at with a piece of research, it is a good idea to think about the potential significance of your work. And sometimes you’ll have to explicitly write a statement of significance in your papers, it addition to it forming part of your thesis.

In this post I’ll cover what the significance of a study is, how to measure it, how to describe it with examples and add in some of my own experiences having now worked in research for over nine years.

If you’re reading this because you’re writing up your first paper, welcome! You may also like my how-to guide for all aspects of writing your first research paper .

Looking for guidance on writing the statement of significance for a paper or thesis? Click here to skip straight to that section.

What is the Significance of a Study?

For research papers, theses or dissertations it’s common to explicitly write a section describing the significance of the study. We’ll come onto what to include in that section in just a moment.

However the significance of a study can actually refer to several different things.

Graphic showing the broadening significance of a study going from your study, the wider research field, business opportunities through to society as a whole.

Working our way from the most technical to the broadest, depending on the context, the significance of a study may refer to:

Within your study: Statistical significance. Can we trust the findings?
Wider research field: Research significance. How does your study progress the field?
Commercial / economic significance: Could there be business opportunities for your findings?
Societal significance: What impact could your study have on the wider society.
And probably other domain-specific significance!

We’ll shortly cover each of them in turn, including how they’re measured and some examples for each type of study significance.

But first, let’s touch on why you should consider the significance of your research at an early stage.

Why Care About the Significance of a Study?

No matter what is motivating you to carry out your research, it is sensible to think about the potential significance of your work. In the broadest sense this asks, how does the study contribute to the world?

After all, for many people research is only worth doing if it will result in some expected significance. For the vast majority of us our studies won’t be significant enough to reach the evening news, but most studies will help to enhance knowledge in a particular field and when research has at least some significance it makes for a far more fulfilling longterm pursuit.

Furthermore, a lot of us are carrying out research funded by the public. It therefore makes sense to keep an eye on what benefits the work could bring to the wider community.

Often in research you’ll come to a crossroads where you must decide which path of research to pursue. Thinking about the potential benefits of a strand of research can be useful for deciding how to spend your time, money and resources.

It’s worth noting though, that not all research activities have to work towards obvious significance. This is especially true while you’re a PhD student, where you’re figuring out what you enjoy and may simply be looking for an opportunity to learn a new skill.

However, if you’re trying to decide between two potential projects, it can be useful to weigh up the potential significance of each.

Let’s now dive into the different types of significance, starting with research significance.

Research Significance

What is the research significance of a study.

Unless someone specifies which type of significance they’re referring to, it is fair to assume that they want to know about the research significance of your study.

Research significance describes how your work has contributed to the field, how it could inform future studies and progress research.

Where should I write about my study’s significance in my thesis?

Typically you should write about your study’s significance in the Introduction and Conclusions sections of your thesis.

It’s important to mention it in the Introduction so that the relevance of your work and the potential impact and benefits it could have on the field are immediately apparent. Explaining why your work matters will help to engage readers (and examiners!) early on.

It’s also a good idea to detail the study’s significance in your Conclusions section. This adds weight to your findings and helps explain what your study contributes to the field.

On occasion you may also choose to include a brief description in your Abstract.

What is expected when submitting an article to a journal

It is common for journals to request a statement of significance, although this can sometimes be called other things such as:

Impact statement
Significance statement
Advances in knowledge section

Here is one such example of what is expected:

Impact Statement: An Impact Statement is required for all submissions. Your impact statement will be evaluated by the Editor-in-Chief, Global Editors, and appropriate Associate Editor. For your manuscript to receive full review, the editors must be convinced that it is an important advance in for the field. The Impact Statement is not a restating of the abstract. It should address the following: Why is the work submitted important to the field? How does the work submitted advance the field? What new information does this work impart to the field? How does this new information impact the field? Experimental Biology and Medicine journal, author guidelines

Typically the impact statement will be shorter than the Abstract, around 150 words.

Defining the study’s significance is helpful not just for the impact statement (if the journal asks for one) but also for building a more compelling argument throughout your submission. For instance, usually you’ll start the Discussion section of a paper by highlighting the research significance of your work. You’ll also include a short description in your Abstract too.

How to describe the research significance of a study, with examples

Whether you’re writing a thesis or a journal article, the approach to writing about the significance of a study are broadly the same.

I’d therefore suggest using the questions above as a starting point to base your statements on.

Why is the work submitted important to the field?
How does the work submitted advance the field?
What new information does this work impart to the field?
How does this new information impact the field?

Answer those questions and you’ll have a much clearer idea of the research significance of your work.

When describing it, try to clearly state what is novel about your study’s contribution to the literature. Then go on to discuss what impact it could have on progressing the field along with recommendations for future work.

Potential sentence starters

If you’re not sure where to start, why not set a 10 minute timer and have a go at trying to finish a few of the following sentences. Not sure on what to put? Have a chat to your supervisor or lab mates and they may be able to suggest some ideas.

This study is important to the field because…
These findings advance the field by…
Our results highlight the importance of…
Our discoveries impact the field by…

Now you’ve had a go let’s have a look at some real life examples.

Statement of significance examples

A statement of significance / impact:

Impact Statement This review highlights the historical development of the concept of “ideal protein” that began in the 1950s and 1980s for poultry and swine diets, respectively, and the major conceptual deficiencies of the long-standing concept of “ideal protein” in animal nutrition based on recent advances in amino acid (AA) metabolism and functions. Nutritionists should move beyond the “ideal protein” concept to consider optimum ratios and amounts of all proteinogenic AAs in animal foods and, in the case of carnivores, also taurine. This will help formulate effective low-protein diets for livestock, poultry, and fish, while sustaining global animal production. Because they are not only species of agricultural importance, but also useful models to study the biology and diseases of humans as well as companion (e.g. dogs and cats), zoo, and extinct animals in the world, our work applies to a more general readership than the nutritionists and producers of farm animals. Wu G, Li P. The “ideal protein” concept is not ideal in animal nutrition. Experimental Biology and Medicine . 2022;247(13):1191-1201. doi: 10.1177/15353702221082658

And the same type of section but this time called “Advances in knowledge”:

Advances in knowledge: According to the MY-RADs criteria, size measurements of focal lesions in MRI are now of relevance for response assessment in patients with monoclonal plasma cell disorders. Size changes of 1 or 2 mm are frequently observed due to uncertainty of the measurement only, while the actual focal lesion has not undergone any biological change. Size changes of at least 6 mm or more in T 1 weighted or T 2 weighted short tau inversion recovery sequences occur in only 5% or less of cases when the focal lesion has not undergone any biological change. Wennmann M, Grözinger M, Weru V, et al. Test-retest, inter- and intra-rater reproducibility of size measurements of focal bone marrow lesions in MRI in patients with multiple myeloma [published online ahead of print, 2023 Apr 12]. Br J Radiol . 2023;20220745. doi: 10.1259/bjr.20220745

Other examples of research significance

Moving beyond the formal statement of significance, here is how you can describe research significance more broadly within your paper.

Describing research impact in an Abstract of a paper:

Three-dimensional visualisation and quantification of the chondrocyte population within articular cartilage can be achieved across a field of view of several millimetres using laboratory-based micro-CT. The ability to map chondrocytes in 3D opens possibilities for research in fields from skeletal development through to medical device design and treatment of cartilage degeneration. Conclusions section of the abstract in my first paper .

In the Discussion section of a paper:

We report for the utility of a standard laboratory micro-CT scanner to visualise and quantify features of the chondrocyte population within intact articular cartilage in 3D. This study represents a complimentary addition to the growing body of evidence supporting the non-destructive imaging of the constituents of articular cartilage. This offers researchers the opportunity to image chondrocyte distributions in 3D without specialised synchrotron equipment, enabling investigations such as chondrocyte morphology across grades of cartilage damage, 3D strain mapping techniques such as digital volume correlation to evaluate mechanical properties in situ , and models for 3D finite element analysis in silico simulations. This enables an objective quantification of chondrocyte distribution and morphology in three dimensions allowing greater insight for investigations into studies of cartilage development, degeneration and repair. One such application of our method, is as a means to provide a 3D pattern in the cartilage which, when combined with digital volume correlation, could determine 3D strain gradient measurements enabling potential treatment and repair of cartilage degeneration. Moreover, the method proposed here will allow evaluation of cartilage implanted with tissue engineered scaffolds designed to promote chondral repair, providing valuable insight into the induced regenerative process. The Discussion section of the paper is laced with references to research significance.

How is longer term research significance measured?

Looking beyond writing impact statements within papers, sometimes you’ll want to quantify the long term research significance of your work. For instance when applying for jobs.

The most obvious measure of a study’s long term research significance is the number of citations it receives from future publications. The thinking is that a study which receives more citations will have had more research impact, and therefore significance , than a study which received less citations. Citations can give a broad indication of how useful the work is to other researchers but citations aren’t really a good measure of significance.

Bear in mind that us researchers can be lazy folks and sometimes are simply looking to cite the first paper which backs up one of our claims. You can find studies which receive a lot of citations simply for packaging up the obvious in a form which can be easily found and referenced, for instance by having a catchy or optimised title.

Likewise, research activity varies wildly between fields. Therefore a certain study may have had a big impact on a particular field but receive a modest number of citations, simply because not many other researchers are working in the field.

Nevertheless, citations are a standard measure of significance and for better or worse it remains impressive for someone to be the first author of a publication receiving lots of citations.

Other measures for the research significance of a study include:

Accolades: best paper awards at conferences, thesis awards, “most downloaded” titles for articles, press coverage.
How much follow-on research the study creates. For instance, part of my PhD involved a novel material initially developed by another PhD student in the lab. That PhD student’s research had unlocked lots of potential new studies and now lots of people in the group were using the same material and developing it for different applications. The initial study may not receive a high number of citations yet long term it generated a lot of research activity.

That covers research significance, but you’ll often want to consider other types of significance for your study and we’ll cover those next.

Statistical Significance

What is the statistical significance of a study.

Often as part of a study you’ll carry out statistical tests and then state the statistical significance of your findings: think p-values eg <0.05. It is useful to describe the outcome of these tests within your report or paper, to give a measure of statistical significance.

Effectively you are trying to show whether the performance of your innovation is actually better than a control or baseline and not just chance. Statistical significance deserves a whole other post so I won’t go into a huge amount of depth here.

Things that make publication in The BMJ impossible or unlikely Internal validity/robustness of the study • It had insufficient statistical power, making interpretation difficult; • Lack of statistical power; The British Medical Journal’s guide for authors

Calculating statistical significance isn’t always necessary (or valid) for a study, such as if you have a very small number of samples, but it is a very common requirement for scientific articles.

Writing a journal article? Check the journal’s guide for authors to see what they expect. Generally if you have approximately five or more samples or replicates it makes sense to start thinking about statistical tests. Speak to your supervisor and lab mates for advice, and look at other published articles in your field.

How is statistical significance measured?

Statistical significance is quantified using p-values . Depending on your study design you’ll choose different statistical tests to compute the p-value.

A p-value of 0.05 is a common threshold value. The 0.05 means that there is a 1/20 chance that the difference in performance you’re reporting is just down to random chance.

p-values above 0.05 mean that the result isn’t statistically significant enough to be trusted: it is too likely that the effect you’re showing is just luck.
p-values less than or equal to 0.05 mean that the result is statistically significant. In other words: unlikely to just be chance, which is usually considered a good outcome.

Low p-values (eg p = 0.001) mean that it is highly unlikely to be random chance (1/1000 in the case of p = 0.001), therefore more statistically significant.

It is important to clarify that, although low p-values mean that your findings are statistically significant, it doesn’t automatically mean that the result is scientifically important. More on that in the next section on research significance.

How to describe the statistical significance of your study, with examples

In the first paper from my PhD I ran some statistical tests to see if different staining techniques (basically dyes) increased how well you could see cells in cow tissue using micro-CT scanning (a 3D imaging technique).

In your methods section you should mention the statistical tests you conducted and then in the results you will have statements such as:

Between mediums for the two scan protocols C/N [contrast to noise ratio] was greater for EtOH than the PBS in both scanning methods (both p < 0.0001) with mean differences of 1.243 (95% CI [confidence interval] 0.709 to 1.778) for absorption contrast and 6.231 (95% CI 5.772 to 6.690) for propagation contrast. … Two repeat propagation scans were taken of samples from the PTA-stained groups. No difference in mean C/N was found with either medium: PBS had a mean difference of 0.058 ( p = 0.852, 95% CI -0.560 to 0.676), EtOH had a mean difference of 1.183 ( p = 0.112, 95% CI 0.281 to 2.648). From the Results section of my first paper, available here . Square brackets added for this post to aid clarity.

From this text the reader can infer from the first paragraph that there was a statistically significant difference in using EtOH compared to PBS (really small p-value of <0.0001). However, from the second paragraph, the difference between two repeat scans was statistically insignificant for both PBS (p = 0.852) and EtOH (p = 0.112).

By conducting these statistical tests you have then earned your right to make bold statements, such as these from the discussion section:

Propagation phase-contrast increases the contrast of individual chondrocytes [cartilage cells] compared to using absorption contrast. From the Discussion section from the same paper.

Without statistical tests you have no evidence that your results are not just down to random chance.

Beyond describing the statistical significance of a study in the main body text of your work, you can also show it in your figures.

In figures such as bar charts you’ll often see asterisks to represent statistical significance, and “n.s.” to show differences between groups which are not statistically significant. Here is one such figure, with some subplots, from the same paper:

Figure from a paper showing the statistical significance of a study using asterisks

In this example an asterisk (*) between two bars represents p < 0.05. Two asterisks (**) represents p < 0.001 and three asterisks (***) represents p < 0.0001. This should always be stated in the caption of your figure since the values that each asterisk refers to can vary.

Now that we know if a study is showing statistically and research significance, let’s zoom out a little and consider the potential for commercial significance.

Commercial and Industrial Significance

What are commercial and industrial significance.

Moving beyond significance in relation to academia, your research may also have commercial or economic significance.

Simply put:

Commercial significance: could the research be commercialised as a product or service? Perhaps the underlying technology described in your study could be licensed to a company or you could even start your own business using it.
Industrial significance: more widely than just providing a product which could be sold, does your research provide insights which may affect a whole industry? Such as: revealing insights or issues with current practices, performance gains you don’t want to commercialise (e.g. solar power efficiency), providing suggested frameworks or improvements which could be employed industry-wide.

I’ve grouped these two together because there can certainly be overlap. For instance, perhaps your new technology could be commercialised whilst providing wider improvements for the whole industry.

Commercial and industrial significance are not relevant to most studies, so only write about it if you and your supervisor can think of reasonable routes to your work having an impact in these ways.

How are commercial and industrial significance measured?

Unlike statistical and research significances, the measures of commercial and industrial significance can be much more broad.

Here are some potential measures of significance:

Commercial significance:

How much value does your technology bring to potential customers or users?
How big is the potential market and how much revenue could the product potentially generate?
Is the intellectual property protectable? i.e. patentable, or if not could the novelty be protected with trade secrets: if so publish your method with caution!
If commercialised, could the product bring employment to a geographical area?

Industrial significance:

What impact could it have on the industry? For instance if you’re revealing an issue with something, such as unintended negative consequences of a drug , what does that mean for the industry and the public? This could be:

Reduced overhead costs
Better safety
Faster production methods
Improved scaleability

How to describe the commercial and industrial significance of a study, with examples

Commercial significance.

If your technology could be commercially viable, and you’ve got an interest in commercialising it yourself, it is likely that you and your university may not want to immediately publish the study in a journal.

You’ll probably want to consider routes to exploiting the technology and your university may have a “technology transfer” team to help researchers navigate the various options.

However, if instead of publishing a paper you’re submitting a thesis or dissertation then it can be useful to highlight the commercial significance of your work. In this instance you could include statements of commercial significance such as:

The measurement technology described in this study provides state of the art performance and could enable the development of low cost devices for aerospace applications. An example of commercial significance I invented for this post

Industrial significance

First, think about the industrial sectors who could benefit from the developments described in your study.

For example if you’re working to improve battery efficiency it is easy to think of how it could lead to performance gains for certain industries, like personal electronics or electric vehicles. In these instances you can describe the industrial significance relatively easily, based off your findings.

For example:

By utilising abundant materials in the described battery fabrication process we provide a framework for battery manufacturers to reduce dependence on rare earth components. Again, an invented example

For other technologies there may well be industrial applications but they are less immediately obvious and applicable. In these scenarios the best you can do is to simply reframe your research significance statement in terms of potential commercial applications in a broad way.

As a reminder: not all studies should address industrial significance, so don’t try to invent applications just for the sake of it!

Societal Significance

What is the societal significance of a study.

The most broad category of significance is the societal impact which could stem from it.

If you’re working in an applied field it may be quite easy to see a route for your research to impact society. For others, the route to societal significance may be less immediate or clear.

Studies can help with big issues facing society such as:

Medical applications : vaccines, surgical implants, drugs, improving patient safety. For instance this medical device and drug combination I worked on which has a very direct route to societal significance.
Political significance : Your research may provide insights which could contribute towards potential changes in policy or better understanding of issues facing society.
Public health : for instance COVID-19 transmission and related decisions.
Climate change : mitigation such as more efficient solar panels and lower cost battery solutions, and studying required adaptation efforts and technologies. Also, better understanding around related societal issues, for instance this study on the effects of temperature on hate speech.

How is societal significance measured?

Societal significance at a high level can be quantified by the size of its potential societal effect. Just like a lab risk assessment, you can think of it in terms of probability (or how many people it could help) and impact magnitude.

Societal impact = How many people it could help x the magnitude of the impact

Think about how widely applicable the findings are: for instance does it affect only certain people? Then think about the potential size of the impact: what kind of difference could it make to those people?

Between these two metrics you can get a pretty good overview of the potential societal significance of your research study.

How to describe the societal significance of a study, with examples

Quite often the broad societal significance of your study is what you’re setting the scene for in your Introduction. In addition to describing the existing literature, it is common to for the study’s motivation to touch on its wider impact for society.

For those of us working in healthcare research it is usually pretty easy to see a path towards societal significance.

Our CLOUT model has state-of-the-art performance in mortality prediction, surpassing other competitive NN models and a logistic regression model … Our results show that the risk factors identified by the CLOUT model agree with physicians’ assessment, suggesting that CLOUT could be used in real-world clinicalsettings. Our results strongly support that CLOUT may be a useful tool to generate clinical prediction models, especially among hospitalized and critically ill patient populations. Learning Latent Space Representations to Predict Patient Outcomes: Model Development and Validation

In other domains the societal significance may either take longer or be more indirect, meaning that it can be more difficult to describe the societal impact.

Even so, here are some examples I’ve found from studies in non-healthcare domains:

We examined food waste as an initial investigation and test of this methodology, and there is clear potential for the examination of not only other policy texts related to food waste (e.g., liability protection, tax incentives, etc.; Broad Leib et al., 2020) but related to sustainable fishing (Worm et al., 2006) and energy use (Hawken, 2017). These other areas are of obvious relevance to climate change… AI-Based Text Analysis for Evaluating Food Waste Policies

The continued development of state-of-the art NLP tools tailored to climate policy will allow climate researchers and policy makers to extract meaningful information from this growing body of text, to monitor trends over time and administrative units, and to identify potential policy improvements. BERT Classification of Paris Agreement Climate Action Plans

Top Tips For Identifying & Writing About the Significance of Your Study

Writing a thesis? Describe the significance of your study in the Introduction and the Conclusion .
Submitting a paper? Read the journal’s guidelines. If you’re writing a statement of significance for a journal, make sure you read any guidance they give for what they’re expecting.
Take a step back from your research and consider your study’s main contributions.
Read previously published studies in your field . Use this for inspiration and ideas on how to describe the significance of your own study
Discuss the study with your supervisor and potential co-authors or collaborators and brainstorm potential types of significance for it.

Now you’ve finished reading up on the significance of a study you may also like my how-to guide for all aspects of writing your first research paper .

Writing an academic journal paper

I hope that you’ve learned something useful from this article about the significance of a study. If you have any more research-related questions let me know, I’m here to help.

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Methodology

Research Methods | Definitions, Types, Examples

Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

Qualitative vs. quantitative : Will your data take the form of words or numbers?
Primary vs. secondary : Will you collect original data yourself, or will you use data that has already been collected by someone else?
Descriptive vs. experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyze the data .

For quantitative data, you can use statistical analysis methods to test relationships between variables.
For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.

Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs. quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .


Qualitative		to broader populations. .
Quantitative		.

You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.

Primary vs. secondary research

Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.


Primary	.	methods.
Secondary

Descriptive vs. experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.


Descriptive		. .
Experimental

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Research methods for collecting data
Research method	Primary or secondary?	Qualitative or quantitative?	When to use
	Primary	Quantitative	To test cause-and-effect relationships.
	Primary	Quantitative	To understand general characteristics of a population.
Interview/focus group	Primary	Qualitative	To gain more in-depth understanding of a topic.
Observation	Primary	Either	To understand how something occurs in its natural setting.
	Secondary	Either	To situate your research in an existing body of work, or to evaluate trends within a research topic.
	Either	Either	To gain an in-depth understanding of a specific group or context, or when you don’t have the resources for a large study.

Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.

Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:

From open-ended surveys and interviews , literature reviews , case studies , ethnographies , and other sources that use text rather than numbers.
Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias .

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that was collected either:

During an experiment .
Using probability sampling methods .

Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.

Research methods for analyzing data
Research method	Qualitative or quantitative?	When to use
	Quantitative	To analyze data collected in a statistically valid manner (e.g. from experiments, surveys, and observations).
Meta-analysis	Quantitative	To statistically analyze the results of a large collection of studies. Can only be applied to studies that collected data in a statistically valid manner.
	Qualitative	To analyze data collected from interviews, , or textual sources. To understand general themes in the data and how they are communicated.
	Either	To analyze large volumes of textual or visual data collected from surveys, literature reviews, or other sources. Can be quantitative (i.e. frequencies of words) or qualitative (i.e. meanings of words).

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

Chi square test of independence
Statistical power
Descriptive statistics
Degrees of freedom
Pearson correlation
Null hypothesis
Double-blind study
Case-control study
Research ethics
Data collection
Hypothesis testing
Structured interviews

Research bias

Hawthorne effect
Unconscious bias
Recall bias
Halo effect
Self-serving bias
Information bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

The research methods you use depend on the type of data you need to answer your research question .

If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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What is the Background of a Study and How to Write It (Examples Included)

Have you ever found yourself struggling to write a background of the study for your research paper? You’re not alone. While the background of a study is an essential element of a research manuscript, it’s also one of the most challenging pieces to write. This is because it requires researchers to provide context and justification for their research, highlight the significance of their study, and situate their work within the existing body of knowledge in the field.

Despite its challenges, the background of a study is crucial for any research paper. A compelling well-written background of the study can not only promote confidence in the overall quality of your research analysis and findings, but it can also determine whether readers will be interested in knowing more about the rest of the research study.

In this article, we’ll explore the key elements of the background of a study and provide simple guidelines on how to write one effectively. Whether you’re a seasoned researcher or a graduate student working on your first research manuscript, this post will explain how to write a background for your study that is compelling and informative.

Table of Contents

What is the background of a study ?

Typically placed in the beginning of your research paper, the background of a study serves to convey the central argument of your study and its significance clearly and logically to an uninformed audience. The background of a study in a research paper helps to establish the research problem or gap in knowledge that the study aims to address, sets the stage for the research question and objectives, and highlights the significance of the research. The background of a study also includes a review of relevant literature, which helps researchers understand where the research study is placed in the current body of knowledge in a specific research discipline. It includes the reason for the study, the thesis statement, and a summary of the concept or problem being examined by the researcher. At times, the background of a study can may even examine whether your research supports or contradicts the results of earlier studies or existing knowledge on the subject.

How is the background of a study different from the introduction?

It is common to find early career researchers getting confused between the background of a study and the introduction in a research paper. Many incorrectly consider these two vital parts of a research paper the same and use these terms interchangeably. The confusion is understandable, however, it’s important to know that the introduction and the background of the study are distinct elements and serve very different purposes.

The basic different between the background of a study and the introduction is kind of information that is shared with the readers . While the introduction provides an overview of the specific research topic and touches upon key parts of the research paper, the background of the study presents a detailed discussion on the existing literature in the field, identifies research gaps, and how the research being done will add to current knowledge.
The introduction aims to capture the reader’s attention and interest and to provide a clear and concise summary of the research project. It typically begins with a general statement of the research problem and then narrows down to the specific research question. It may also include an overview of the research design, methodology, and scope. The background of the study outlines the historical, theoretical, and empirical background that led to the research question to highlight its importance. It typically offers an overview of the research field and may include a review of the literature to highlight gaps, controversies, or limitations in the existing knowledge and to justify the need for further research.
Both these sections appear at the beginning of a research paper. In some cases the introduction may come before the background of the study , although in most instances the latter is integrated into the introduction itself. The length of the introduction and background of a study can differ based on the journal guidelines and the complexity of a specific research study.

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To put it simply, the background of the study provides context for the study by explaining how your research fills a research gap in existing knowledge in the field and how it will add to it. The introduction section explains how the research fills this gap by stating the research topic, the objectives of the research and the findings – it sets the context for the rest of the paper.

Where is the background of a study placed in a research paper?

T he background of a study is typically placed in the introduction section of a research paper and is positioned after the statement of the problem. Researchers should try and present the background of the study in clear logical structure by dividing it into several sections, such as introduction, literature review, and research gap. This will make it easier for the reader to understand the research problem and the motivation for the study.

So, when should you write the background of your study ? It’s recommended that researchers write this section after they have conducted a thorough literature review and identified the research problem, research question, and objectives. This way, they can effectively situate their study within the existing body of knowledge in the field and provide a clear rationale for their research.

Creating an effective background of a study structure

Given that the purpose of writing the background of your study is to make readers understand the reasons for conducting the research, it is important to create an outline and basic framework to work within. This will make it easier to write the background of the study and will ensure that it is comprehensive and compelling for readers.

While creating a background of the study structure for research papers, it is crucial to have a clear understanding of the essential elements that should be included. Make sure you incorporate the following elements in the background of the study section :

Present a general overview of the research topic, its significance, and main aims; this may be like establishing the “importance of the topic” in the introduction.
Discuss the existing level of research done on the research topic or on related topics in the field to set context for your research. Be concise and mention only the relevant part of studies, ideally in chronological order to reflect the progress being made.
Highlight disputes in the field as well as claims made by scientists, organizations, or key policymakers that need to be investigated. This forms the foundation of your research methodology and solidifies the aims of your study.
Describe if and how the methods and techniques used in the research study are different from those used in previous research on similar topics.

By including these critical elements in the background of your study , you can provide your readers with a comprehensive understanding of your research and its context.

What is the background of a study and how to write it

How to write a background of the study in research papers ?

Now that you know the essential elements to include, it’s time to discuss how to write the background of the study in a concise and interesting way that engages audiences. The best way to do this is to build a clear narrative around the central theme of your research so that readers can grasp the concept and identify the gaps that the study will address. While the length and detail presented in the background of a study could vary depending on the complexity and novelty of the research topic, it is imperative to avoid wordiness. For research that is interdisciplinary, mentioning how the disciplines are connected and highlighting specific aspects to be studied helps readers understand the research better.

While there are different styles of writing the background of a study , it always helps to have a clear plan in place. Let us look at how to write a background of study for research papers.

Identify the research problem: Begin the background by defining the research topic, and highlighting the main issue or question that the research aims to address. The research problem should be clear, specific, and relevant to the field of study. It should be framed using simple, easy to understand language and must be meaningful to intended audiences.
Craft an impactful statement of the research objectives: While writing the background of the study it is critical to highlight the research objectives and specific goals that the study aims to achieve. The research objectives should be closely related to the research problem and must be aligned with the overall purpose of the study.
Conduct a review of available literature: When writing the background of the research , provide a summary of relevant literature in the field and related research that has been conducted around the topic. Remember to record the search terms used and keep track of articles that you read so that sources can be cited accurately. Ensure that the literature you include is sourced from credible sources.
Address existing controversies and assumptions: It is a good idea to acknowledge and clarify existing claims and controversies regarding the subject of your research. For example, if your research topic involves an issue that has been widely discussed due to ethical or politically considerations, it is best to address them when writing the background of the study .
Present the relevance of the study: It is also important to provide a justification for the research. This is where the researcher explains why the study is important and what contributions it will make to existing knowledge on the subject. Highlighting key concepts and theories and explaining terms and ideas that may feel unfamiliar to readers makes the background of the study content more impactful.
Proofread to eliminate errors in language, structure, and data shared: Once the first draft is done, it is a good idea to read and re-read the draft a few times to weed out possible grammatical errors or inaccuracies in the information provided. In fact, experts suggest that it is helpful to have your supervisor or peers read and edit the background of the study . Their feedback can help ensure that even inadvertent errors are not overlooked.

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How to avoid mistakes in writing the background of a study

While figuring out how to write the background of a study , it is also important to know the most common mistakes authors make so you can steer clear of these in your research paper.

Write the background of a study in a formal academic tone while keeping the language clear and simple. Check for the excessive use of jargon and technical terminology that could confuse your readers.
Avoid including unrelated concepts that could distract from the subject of research. Instead, focus your discussion around the key aspects of your study by highlighting gaps in existing literature and knowledge and the novelty and necessity of your study.
Provide relevant, reliable evidence to support your claims and citing sources correctly; be sure to follow a consistent referencing format and style throughout the paper.
Ensure that the details presented in the background of the study are captured chronologically and organized into sub-sections for easy reading and comprehension.
Check the journal guidelines for the recommended length for this section so that you include all the important details in a concise manner.

By keeping these tips in mind, you can create a clear, concise, and compelling background of the study for your research paper. Take this example of a background of the study on the impact of social media on mental health.

Social media has become a ubiquitous aspect of modern life, with people of all ages, genders, and backgrounds using platforms such as Facebook, Instagram, and Twitter to connect with others, share information, and stay updated on news and events. While social media has many potential benefits, including increased social connectivity and access to information, there is growing concern about its impact on mental health. Research has suggested that social media use is associated with a range of negative mental health outcomes, including increased rates of anxiety, depression, and loneliness. This is thought to be due, in part, to the social comparison processes that occur on social media, whereby users compare their lives to the idealized versions of others that are presented online. Despite these concerns, there is also evidence to suggest that social media can have positive effects on mental health. For example, social media can provide a sense of social support and community, which can be beneficial for individuals who are socially isolated or marginalized. Given the potential benefits and risks of social media use for mental health, it is important to gain a better understanding of the mechanisms underlying these effects. This study aims to investigate the relationship between social media use and mental health outcomes, with a particular focus on the role of social comparison processes. By doing so, we hope to shed light on the potential risks and benefits of social media use for mental health, and to provide insights that can inform interventions and policies aimed at promoting healthy social media use.

To conclude, the background of a study is a crucial component of a research manuscript and must be planned, structured, and presented in a way that attracts reader attention, compels them to read the manuscript, creates an impact on the minds of readers and sets the stage for future discussions.

A well-written background of the study not only provides researchers with a clear direction on conducting their research, but it also enables readers to understand and appreciate the relevance of the research work being done.

Frequently Asked Questions (FAQs) on background of the study

Q: How does the background of the study help the reader understand the research better?

The background of the study plays a crucial role in helping readers understand the research better by providing the necessary context, framing the research problem, and establishing its significance. It helps readers:

understand the larger framework, historical development, and existing knowledge related to a research topic
identify gaps, limitations, or unresolved issues in the existing literature or knowledge
outline potential contributions, practical implications, or theoretical advancements that the research aims to achieve
and learn the specific context and limitations of the research project

Q: Does the background of the study need citation?

Yes, the background of the study in a research paper should include citations to support and acknowledge the sources of information and ideas presented. When you provide information or make statements in the background section that are based on previous studies, theories, or established knowledge, it is important to cite the relevant sources. This establishes credibility, enables verification, and demonstrates the depth of literature review you’ve done.

Q: What is the difference between background of the study and problem statement?

The background of the study provides context and establishes the research’s foundation while the problem statement clearly states the problem being addressed and the research questions or objectives.

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What are the Best Research Funding Sources

Inductive vs. Deductive Research Approach

Research: Meaning and Purpose

First Online: 27 October 2022

Cite this chapter

Kazi Abusaleh 4 &
Akib Bin Anwar 5

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The objective of the chapter is to provide the conceptual framework of the research and research process and draw the importance of research in social sciences. Various books and research papers were reviewed to write the chapter. The chapter defines ‘research’ as a deliberate and systematic scientific investigation into a phenomenon to explore, analyse, and predict about the issues or circumstances, and characterizes ‘research’ as a systematic and scientific mode of inquiry, a way to testify the existing knowledge and theories, and a well-designed process to answer questions in a reliable and unbiased way. This chapter, however, categorizes research into eight types under four headings, explains six steps to carry out a research work scientifically, and finally sketches the importance of research in social sciences.

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Research Design and Methodology

Research Questions and Research Design

Looking Back

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Abusaleh, K., Anwar, A.B. (2022). Research: Meaning and Purpose. In: Islam, M.R., Khan, N.A., Baikady, R. (eds) Principles of Social Research Methodology. Springer, Singapore. https://doi.org/10.1007/978-981-19-5441-2_2

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How to Write the Rationale of the Study in Research (Examples)

What is the Rationale of the Study?

The rationale of the study is the justification for taking on a given study. It explains the reason the study was conducted or should be conducted. This means the study rationale should explain to the reader or examiner why the study is/was necessary. It is also sometimes called the “purpose” or “justification” of a study. While this is not difficult to grasp in itself, you might wonder how the rationale of the study is different from your research question or from the statement of the problem of your study, and how it fits into the rest of your thesis or research paper.

The rationale of the study links the background of the study to your specific research question and justifies the need for the latter on the basis of the former. In brief, you first provide and discuss existing data on the topic, and then you tell the reader, based on the background evidence you just presented, where you identified gaps or issues and why you think it is important to address those. The problem statement, lastly, is the formulation of the specific research question you choose to investigate, following logically from your rationale, and the approach you are planning to use to do that.

The basis for writing a research rationale is preliminary data or a clear description of an observation. If you are doing basic/theoretical research, then a literature review will help you identify gaps in current knowledge. In applied/practical research, you base your rationale on an existing issue with a certain process (e.g., vaccine proof registration) or practice (e.g., patient treatment) that is well documented and needs to be addressed. By presenting the reader with earlier evidence or observations, you can (and have to) convince them that you are not just repeating what other people have already done or said and that your ideas are not coming out of thin air.

Once you have explained where you are coming from, you should justify the need for doing additional research–this is essentially the rationale of your study. Finally, when you have convinced the reader of the purpose of your work, you can end your introduction section with the statement of the problem of your research that contains clear aims and objectives and also briefly describes (and justifies) your methodological approach.

When is the Rationale for Research Written?

The author can present the study rationale both before and after the research is conducted.

Before conducting research : The study rationale is a central component of the research proposal . It represents the plan of your work, constructed before the study is actually executed.
Once research has been conducted : After the study is completed, the rationale is presented in a research article or PhD dissertation to explain why you focused on this specific research question. When writing the study rationale for this purpose, the author should link the rationale of the research to the aims and outcomes of the study.

What to Include in the Study Rationale

Although every study rationale is different and discusses different specific elements of a study’s method or approach, there are some elements that should be included to write a good rationale. Make sure to touch on the following:

A summary of conclusions from your review of the relevant literature
What is currently unknown (gaps in knowledge)
Inconclusive or contested results from previous studies on the same or similar topic
The necessity to improve or build on previous research, such as to improve methodology or utilize newer techniques and/or technologies

There are different types of limitations that you can use to justify the need for your study. In applied/practical research, the justification for investigating something is always that an existing process/practice has a problem or is not satisfactory. Let’s say, for example, that people in a certain country/city/community commonly complain about hospital care on weekends (not enough staff, not enough attention, no decisions being made), but you looked into it and realized that nobody ever investigated whether these perceived problems are actually based on objective shortages/non-availabilities of care or whether the lower numbers of patients who are treated during weekends are commensurate with the provided services.

In this case, “lack of data” is your justification for digging deeper into the problem. Or, if it is obvious that there is a shortage of staff and provided services on weekends, you could decide to investigate which of the usual procedures are skipped during weekends as a result and what the negative consequences are.

In basic/theoretical research, lack of knowledge is of course a common and accepted justification for additional research—but make sure that it is not your only motivation. “Nobody has ever done this” is only a convincing reason for a study if you explain to the reader why you think we should know more about this specific phenomenon. If there is earlier research but you think it has limitations, then those can usually be classified into “methodological”, “contextual”, and “conceptual” limitations. To identify such limitations, you can ask specific questions and let those questions guide you when you explain to the reader why your study was necessary:

Methodological limitations

Did earlier studies try but failed to measure/identify a specific phenomenon?
Was earlier research based on incorrect conceptualizations of variables?
Were earlier studies based on questionable operationalizations of key concepts?
Did earlier studies use questionable or inappropriate research designs?

Contextual limitations

Have recent changes in the studied problem made previous studies irrelevant?
Are you studying a new/particular context that previous findings do not apply to?

Conceptual limitations

Do previous findings only make sense within a specific framework or ideology?

Study Rationale Examples

Let’s look at an example from one of our earlier articles on the statement of the problem to clarify how your rationale fits into your introduction section. This is a very short introduction for a practical research study on the challenges of online learning. Your introduction might be much longer (especially the context/background section), and this example does not contain any sources (which you will have to provide for all claims you make and all earlier studies you cite)—but please pay attention to how the background presentation , rationale, and problem statement blend into each other in a logical way so that the reader can follow and has no reason to question your motivation or the foundation of your research.

Background presentation

Since the beginning of the Covid pandemic, most educational institutions around the world have transitioned to a fully online study model, at least during peak times of infections and social distancing measures. This transition has not been easy and even two years into the pandemic, problems with online teaching and studying persist (reference needed) .

While the increasing gap between those with access to technology and equipment and those without access has been determined to be one of the main challenges (reference needed) , others claim that online learning offers more opportunities for many students by breaking down barriers of location and distance (reference needed) .

Rationale of the study

Since teachers and students cannot wait for circumstances to go back to normal, the measures that schools and universities have implemented during the last two years, their advantages and disadvantages, and the impact of those measures on students’ progress, satisfaction, and well-being need to be understood so that improvements can be made and demographics that have been left behind can receive the support they need as soon as possible.

Statement of the problem

To identify what changes in the learning environment were considered the most challenging and how those changes relate to a variety of student outcome measures, we conducted surveys and interviews among teachers and students at ten institutions of higher education in four different major cities, two in the US (New York and Chicago), one in South Korea (Seoul), and one in the UK (London). Responses were analyzed with a focus on different student demographics and how they might have been affected differently by the current situation.

How long is a study rationale?

In a research article bound for journal publication, your rationale should not be longer than a few sentences (no longer than one brief paragraph). A dissertation or thesis usually allows for a longer description; depending on the length and nature of your document, this could be up to a couple of paragraphs in length. A completely novel or unconventional approach might warrant a longer and more detailed justification than an approach that slightly deviates from well-established methods and approaches.

Consider Using Professional Academic Editing Services

Now that you know how to write the rationale of the study for a research proposal or paper, you should make use of Wordvice AI’s free AI Grammar Checker , or receive professional academic proofreading services from Wordvice, including research paper editing services and manuscript editing services to polish your submitted research documents.

You can also find many more articles, for example on writing the other parts of your research paper , on choosing a title , or on making sure you understand and adhere to the author instructions before you submit to a journal, on the Wordvice academic resources pages.

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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.

What Is A Research (Scientific) Hypothesis? A plain-language explainer + examples

By: Derek Jansen (MBA) | Reviewed By: Dr Eunice Rautenbach | June 2020

If you’re new to the world of research, or it’s your first time writing a dissertation or thesis, you’re probably noticing that the words “research hypothesis” and “scientific hypothesis” are used quite a bit, and you’re wondering what they mean in a research context .

“Hypothesis” is one of those words that people use loosely, thinking they understand what it means. However, it has a very specific meaning within academic research. So, it’s important to understand the exact meaning before you start hypothesizing.

Research Hypothesis 101

What is a hypothesis ?
What is a research hypothesis (scientific hypothesis)?
Requirements for a research hypothesis
Definition of a research hypothesis
The null hypothesis

What is a hypothesis?

Let’s start with the general definition of a hypothesis (not a research hypothesis or scientific hypothesis), according to the Cambridge Dictionary:

Hypothesis: an idea or explanation for something that is based on known facts but has not yet been proved.

In other words, it’s a statement that provides an explanation for why or how something works, based on facts (or some reasonable assumptions), but that has not yet been specifically tested . For example, a hypothesis might look something like this:

Hypothesis: sleep impacts academic performance.

This statement predicts that academic performance will be influenced by the amount and/or quality of sleep a student engages in – sounds reasonable, right? It’s based on reasonable assumptions , underpinned by what we currently know about sleep and health (from the existing literature). So, loosely speaking, we could call it a hypothesis, at least by the dictionary definition.

But that’s not good enough…

Unfortunately, that’s not quite sophisticated enough to describe a research hypothesis (also sometimes called a scientific hypothesis), and it wouldn’t be acceptable in a dissertation, thesis or research paper . In the world of academic research, a statement needs a few more criteria to constitute a true research hypothesis .

What is a research hypothesis?

A research hypothesis (also called a scientific hypothesis) is a statement about the expected outcome of a study (for example, a dissertation or thesis). To constitute a quality hypothesis, the statement needs to have three attributes – specificity , clarity and testability .

Let’s take a look at these more closely.

Need a helping hand?

Hypothesis Essential #1: Specificity & Clarity

A good research hypothesis needs to be extremely clear and articulate about both what’ s being assessed (who or what variables are involved ) and the expected outcome (for example, a difference between groups, a relationship between variables, etc.).

Let’s stick with our sleepy students example and look at how this statement could be more specific and clear.

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.

As you can see, the statement is very specific as it identifies the variables involved (sleep hours and test grades), the parties involved (two groups of students), as well as the predicted relationship type (a positive relationship). There’s no ambiguity or uncertainty about who or what is involved in the statement, and the expected outcome is clear.

Contrast that to the original hypothesis we looked at – “Sleep impacts academic performance” – and you can see the difference. “Sleep” and “academic performance” are both comparatively vague , and there’s no indication of what the expected relationship direction is (more sleep or less sleep). As you can see, specificity and clarity are key.

A good research hypothesis needs to be very clear about what’s being assessed and very specific about the expected outcome.

Hypothesis Essential #2: Testability (Provability)

A statement must be testable to qualify as a research hypothesis. In other words, there needs to be a way to prove (or disprove) the statement. If it’s not testable, it’s not a hypothesis – simple as that.

For example, consider the hypothesis we mentioned earlier:

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.

We could test this statement by undertaking a quantitative study involving two groups of students, one that gets 8 or more hours of sleep per night for a fixed period, and one that gets less. We could then compare the standardised test results for both groups to see if there’s a statistically significant difference.

Again, if you compare this to the original hypothesis we looked at – “Sleep impacts academic performance” – you can see that it would be quite difficult to test that statement, primarily because it isn’t specific enough. How much sleep? By who? What type of academic performance?

So, remember the mantra – if you can’t test it, it’s not a hypothesis 🙂

A good research hypothesis must be testable. In other words, you must able to collect observable data in a scientifically rigorous fashion to test it.

Defining A Research Hypothesis

You’re still with us? Great! Let’s recap and pin down a clear definition of a hypothesis.

A research hypothesis (or scientific hypothesis) is a statement about an expected relationship between variables, or explanation of an occurrence, that is clear, specific and testable.

So, when you write up hypotheses for your dissertation or thesis, make sure that they meet all these criteria. If you do, you’ll not only have rock-solid hypotheses but you’ll also ensure a clear focus for your entire research project.

What about the null hypothesis?

You may have also heard the terms null hypothesis , alternative hypothesis, or H-zero thrown around. At a simple level, the null hypothesis is the counter-proposal to the original hypothesis.

For example, if the hypothesis predicts that there is a relationship between two variables (for example, sleep and academic performance), the null hypothesis would predict that there is no relationship between those variables.

At a more technical level, the null hypothesis proposes that no statistical significance exists in a set of given observations and that any differences are due to chance alone.

And there you have it – hypotheses in a nutshell.

If you have any questions, be sure to leave a comment below and we’ll do our best to help you. If you need hands-on help developing and testing your hypotheses, consider our private coaching service , where we hold your hand through the research journey.

Psst... there’s more!

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 ...

17 Comments

Very useful information. I benefit more from getting more information in this regard.

Very great insight,educative and informative. Please give meet deep critics on many research data of public international Law like human rights, environment, natural resources, law of the sea etc

In a book I read a distinction is made between null, research, and alternative hypothesis. As far as I understand, alternative and research hypotheses are the same. Can you please elaborate? Best Afshin

This is a self explanatory, easy going site. I will recommend this to my friends and colleagues.

Very good definition. How can I cite your definition in my thesis? Thank you. Is nul hypothesis compulsory in a research?

It’s a counter-proposal to be proven as a rejection

Please what is the difference between alternate hypothesis and research hypothesis?

It is a very good explanation. However, it limits hypotheses to statistically tasteable ideas. What about for qualitative researches or other researches that involve quantitative data that don’t need statistical tests?

In qualitative research, one typically uses propositions, not hypotheses.

could you please elaborate it more

I’ve benefited greatly from these notes, thank you.

This is very helpful

well articulated ideas are presented here, thank you for being reliable sources of information

Excellent. Thanks for being clear and sound about the research methodology and hypothesis (quantitative research)

I have only a simple question regarding the null hypothesis. – Is the null hypothesis (Ho) known as the reversible hypothesis of the alternative hypothesis (H1? – How to test it in academic research?

this is very important note help me much more

Hi” best wishes to you and your very nice blog”

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What Is Research Methodology? Simple Definition (With Examples) - Grad Coach - […] Contrasted to this, a quantitative methodology is typically used when the research aims and objectives are confirmatory in nature. For example,…

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Tailored market research approaches mean better audience understanding.

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Founder and CEO of market research consultancy, Alter Agents ; believer that powerful insights can change businesses.

As a market researcher, I am a student of human behavior. Over the past few years, my coursework has accelerated; keeping up with an ever-changing consumer means going far beyond traditional research approaches. Our clients need more than a shiny, PR-worthy statistic; they need to deeply understand where their target audiences are spending time, when they are most engaged and how to best reach them. This requires a tailored approach to research projects, where many layers and data points can come together to build the picture a brand needs to make better decisions.

Tailored Research Approaches In Action: Two Examples

Two recent projects we completed with clients illustrated the need for tailored research approaches perfectly. While very different from one another, the projects each required a multimodal approach to deliver actionable audience insights.

Diving Deep To Go Beyond Surface Vanity Metrics

We worked with our long-time client Audacy to evaluate consumer sentiment surrounding podcasts. As podcasts continue to rise in popularity, with 42% of Americans over age 12 listening monthly in 2023 (versus only 12% in 2013), according to Edison Research, it is important to understand this segment of the media landscape. However, brands have been cautious about publishing content and advertising in this medium due to its potentially controversial content.

To find out exactly how consumers feel about brand content in the podcast environment, we knew it wasn’t enough to simply send out a questionnaire to the general population. The study instead consisted of interviews with experts in the industry, followed by a representative 6,000-person survey of current listeners and other tasks to obtain data surrounding individuals’ deeper, subconscious perceptions. This study design is illustrative of how we must approach projects to obtain insights that go beyond surface vanity metrics.

A 10,000-Person Study Embedded With In-Context Testing

In a different study, we worked with Snap to find out just how engaged and receptive individuals were while using various social media platforms, including Snapchat. With all the controversy and discontent being poured through social media today, and its propensity to inflame social polarization, it can be hard for brands to find a positive place to connect with audiences inside the social ecosystem. Our previous neuroscience research with Snap clearly showed that people feel more joy, friendship and connection with Snapchat than with other social media platforms.

With the newest study, we wanted to find out how the positive emotions associated with using Snapchat to connect with family and friends impacted how people feel about brands. We explored this by conducting a 10,000-person study across eight countries. In the survey, we embedded in-context testing to collect data about the respondents’ emotions while using various social media platforms. In this way, we were able to test how people’s emotional states affected brand reception and outcomes.

Tailoring Your Research Approach

Adopting a multimodal approach in research projects like these proves invaluable for gaining a nuanced understanding of audience behavior, and places us far beyond the limitations of a single, traditional research method. In your own approach to research, follow some of these basic steps to gain a more holistic understanding:

• Complement the “what” with the “why.” Surveys, or quantitative research, generally provide data about things like what your customers might be doing, how satisfied they are and perhaps what they plan to do in the future. Qualitative approaches, like focus groups and other newer innovative methodologies, can provide the reasons behind the consumer’s actions, and how they feel about things—called sentiment.

• Don’t use a one-size-fits-all approach. Just because a certain mix of methodologies worked for one project doesn’t mean it will work for the next one. Take the time upfront to deeply understand the business objective behind the research project. Lay the groundwork by familiarizing yourself with existing data, such as that uncovered in previous studies, and asking stakeholders what their goals are. Then, choose the methods you will use based on this foundation of knowledge—whether it is neuroscience, behavioral data, trends, qualitative, quantitative, analytics, biometrics or another approach.

• Remember that your audience is complex. No matter the category, no matter the audience, the consumer landscape is becoming more and more complicated. And your research needs to reflect that level of complexity. A simple one-and-done survey can only get you so far, providing a one-dimensional view of topics, people, actions and feelings that are decidedly not that simple. Every decision is made within a web of circumstances, context and much more—you need to understand it all to confidently take action.

A customized, layered approach to market research can help brands continue to find ways to build audience understanding, make well-informed decisions and create meaningful brand connections. Approach research with the mindset of a student—there is always new information to uncover, new data to explore and new discoveries about human behavior just around the corner.

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Stonehenge's 'altar stone' originally came from Scotland and not Wales, new research shows

Stonehenge altar stone.

WASHINGTON (AP) — The ancient ritual meaning of Stonehenge is still a mystery, but researchers are one step closer to understanding how the famous stone circle was created.

The unique stone lying flat at the center of the monument was brought to the site in southern England from near the tip of northeast Scotland, researchers reported Wednesday in the journal Nature. It’s not clear whether the 16-foot (5-meter) stone was carried by boat or through land — a journey of more than 460 miles (740 kilometers).

“It’s a surprise that it’s come from so far away,” said University of Exeter archaeologist Susan Greaney, who was not involved in the study.

For more than a hundred years, scientists believed that Stonehenge’s central sandstone slab — long called the “altar stone" — came from much closer Wales. But a study last year by some of the same researchers showed that the stone didn’t match the geology of Wales’ sandstone formations. The actual source of the stone remained unknown until now.

For the study, the team was not permitted to chip away rocks at the site, but instead analyzed minerals in bits of rock that had been collected in previous digs, some dating back to the 1840s. They found a match in the sandstone formations of Orcadian Basin in northeast Scotland, a region that includes parts of the tip of the Scottish peninsula as well as the Orkney Islands.

“That geological ‘fingerprint’ isn’t repeated in any other area of sediment in the U.K.,” said Aberystwyth University geologist Nick Pearce, a study co-author.

Greaney said the difficult logistics of moving the stone such a long distance show a high level of coordination and cultural connection between these two regions of ancient Britain.

Stonehenge was constructed around 5,000 years ago, with stones forming different circles brought to the site at different times. The placement of stones allows for the sun to rise through a stone “window” during summer solstice. The ancient purpose of the altar stone — which lies flat at the heart of Stonehenge, now beneath other rocks — remains a mystery.

“Stonehenge isn’t a settlement site, but a place of ceremony or ritual,” said Heather Sebire, senior curator at English Heritage, who was not involved in the study. She said that past archaeological excavations had not uncovered evidence of feasting or daily living at the site.

Previous research has shown cultural connections — such as similarities in pottery styles — between the area around Stonehenge and Scotland’s Orkney Islands. Other stones at Stonehenge came from western Wales.

While Britain is dotted with other Neolithic stone circles, “the thing that’s unique about Stonehenge is the distance from which the stones have been sourced,” said Aberystwyth University’s Richard Bevins, a study co-author.

The Associated Press Health and Science Department receives support from the Howard Hughes Medical Institute’s Science and Educational Media Group. The AP is solely responsible for all content.

Home » Research – Types, Methods and Examples

Research – Types, Methods and Examples

Table of Contents

Definition:

Research refers to the process of investigating a particular topic or question in order to discover new information , develop new insights, or confirm or refute existing knowledge. It involves a systematic and rigorous approach to collecting, analyzing, and interpreting data, and requires careful planning and attention to detail.

History of Research

The history of research can be traced back to ancient times when early humans observed and experimented with the natural world around them. Over time, research evolved and became more systematic as people sought to better understand the world and solve problems.

In ancient civilizations such as those in Greece, Egypt, and China, scholars pursued knowledge through observation, experimentation, and the development of theories. They explored various fields, including medicine, astronomy, and mathematics.

During the Middle Ages, research was often conducted by religious scholars who sought to reconcile scientific discoveries with their faith. The Renaissance brought about a renewed interest in science and the scientific method, and the Enlightenment period marked a major shift towards empirical observation and experimentation as the primary means of acquiring knowledge.

The 19th and 20th centuries saw significant advancements in research, with the development of new scientific disciplines and fields such as psychology, sociology, and computer science. Advances in technology and communication also greatly facilitated research efforts.

Today, research is conducted in a wide range of fields and is a critical component of many industries, including healthcare, technology, and academia. The process of research continues to evolve as new methods and technologies emerge, but the fundamental principles of observation, experimentation, and hypothesis testing remain at its core.

Types of Research

Types of Research are as follows:

Applied Research : This type of research aims to solve practical problems or answer specific questions, often in a real-world context.
Basic Research : This type of research aims to increase our understanding of a phenomenon or process, often without immediate practical applications.
Experimental Research : This type of research involves manipulating one or more variables to determine their effects on another variable, while controlling all other variables.
Descriptive Research : This type of research aims to describe and measure phenomena or characteristics, without attempting to manipulate or control any variables.
Correlational Research: This type of research examines the relationships between two or more variables, without manipulating any variables.
Qualitative Research : This type of research focuses on exploring and understanding the meaning and experience of individuals or groups, often through methods such as interviews, focus groups, and observation.
Quantitative Research : This type of research uses numerical data and statistical analysis to draw conclusions about phenomena or populations.
Action Research: This type of research is often used in education, healthcare, and other fields, and involves collaborating with practitioners or participants to identify and solve problems in real-world settings.
Mixed Methods Research : This type of research combines both quantitative and qualitative research methods to gain a more comprehensive understanding of a phenomenon or problem.
Case Study Research: This type of research involves in-depth examination of a specific individual, group, or situation, often using multiple data sources.
Longitudinal Research: This type of research follows a group of individuals over an extended period of time, often to study changes in behavior, attitudes, or health outcomes.
Cross-Sectional Research : This type of research examines a population at a single point in time, often to study differences or similarities among individuals or groups.
Survey Research: This type of research uses questionnaires or interviews to gather information from a sample of individuals about their attitudes, beliefs, behaviors, or experiences.
Ethnographic Research : This type of research involves immersion in a cultural group or community to understand their way of life, beliefs, values, and practices.
Historical Research : This type of research investigates events or phenomena from the past using primary sources, such as archival records, newspapers, and diaries.
Content Analysis Research : This type of research involves analyzing written, spoken, or visual material to identify patterns, themes, or messages.
Participatory Research : This type of research involves collaboration between researchers and participants throughout the research process, often to promote empowerment, social justice, or community development.
Comparative Research: This type of research compares two or more groups or phenomena to identify similarities and differences, often across different countries or cultures.
Exploratory Research : This type of research is used to gain a preliminary understanding of a topic or phenomenon, often in the absence of prior research or theories.
Explanatory Research: This type of research aims to identify the causes or reasons behind a particular phenomenon, often through the testing of theories or hypotheses.
Evaluative Research: This type of research assesses the effectiveness or impact of an intervention, program, or policy, often through the use of outcome measures.
Simulation Research : This type of research involves creating a model or simulation of a phenomenon or process, often to predict outcomes or test theories.

Data Collection Methods

Surveys : Surveys are used to collect data from a sample of individuals using questionnaires or interviews. Surveys can be conducted face-to-face, by phone, mail, email, or online.
Experiments : Experiments involve manipulating one or more variables to measure their effects on another variable, while controlling for other factors. Experiments can be conducted in a laboratory or in a natural setting.
Case studies : Case studies involve in-depth analysis of a single case, such as an individual, group, organization, or event. Case studies can use a variety of data collection methods, including interviews, observation, and document analysis.
Observational research : Observational research involves observing and recording the behavior of individuals or groups in a natural setting. Observational research can be conducted covertly or overtly.
Content analysis : Content analysis involves analyzing written, spoken, or visual material to identify patterns, themes, or messages. Content analysis can be used to study media, social media, or other forms of communication.
Ethnography : Ethnography involves immersion in a cultural group or community to understand their way of life, beliefs, values, and practices. Ethnographic research can use a range of data collection methods, including observation, interviews, and document analysis.
Secondary data analysis : Secondary data analysis involves using existing data from sources such as government agencies, research institutions, or commercial organizations. Secondary data can be used to answer research questions, without collecting new data.
Focus groups: Focus groups involve gathering a small group of people together to discuss a topic or issue. The discussions are usually guided by a moderator who asks questions and encourages discussion.
Interviews : Interviews involve one-on-one conversations between a researcher and a participant. Interviews can be structured, semi-structured, or unstructured, and can be conducted in person, by phone, or online.
Document analysis : Document analysis involves collecting and analyzing written documents, such as reports, memos, and emails. Document analysis can be used to study organizational communication, policy documents, and other forms of written material.

Data Analysis Methods

Data Analysis Methods in Research are as follows:

Descriptive statistics : Descriptive statistics involve summarizing and describing the characteristics of a dataset, such as mean, median, mode, standard deviation, and frequency distributions.
Inferential statistics: Inferential statistics involve making inferences or predictions about a population based on a sample of data, using methods such as hypothesis testing, confidence intervals, and regression analysis.
Qualitative analysis: Qualitative analysis involves analyzing non-numerical data, such as text, images, or audio, to identify patterns, themes, or meanings. Qualitative analysis can be used to study subjective experiences, social norms, and cultural practices.
Content analysis: Content analysis involves analyzing written, spoken, or visual material to identify patterns, themes, or messages. Content analysis can be used to study media, social media, or other forms of communication.
Grounded theory: Grounded theory involves developing a theory or model based on empirical data, using methods such as constant comparison, memo writing, and theoretical sampling.
Discourse analysis : Discourse analysis involves analyzing language use, including the structure, function, and meaning of words and phrases, to understand how language reflects and shapes social relationships and power dynamics.
Network analysis: Network analysis involves analyzing the structure and dynamics of social networks, including the relationships between individuals and groups, to understand social processes and outcomes.

Research Methodology

Research methodology refers to the overall approach and strategy used to conduct a research study. It involves the systematic planning, design, and execution of research to answer specific research questions or test hypotheses. The main components of research methodology include:

Research design : Research design refers to the overall plan and structure of the study, including the type of study (e.g., observational, experimental), the sampling strategy, and the data collection and analysis methods.
Sampling strategy: Sampling strategy refers to the method used to select a representative sample of participants or units from the population of interest. The choice of sampling strategy will depend on the research question and the nature of the population being studied.
Data collection methods : Data collection methods refer to the techniques used to collect data from study participants or sources, such as surveys, interviews, observations, or secondary data sources.
Data analysis methods: Data analysis methods refer to the techniques used to analyze and interpret the data collected in the study, such as descriptive statistics, inferential statistics, qualitative analysis, or content analysis.
Ethical considerations: Ethical considerations refer to the principles and guidelines that govern the treatment of human participants or the use of sensitive data in the research study.
Validity and reliability : Validity and reliability refer to the extent to which the study measures what it is intended to measure and the degree to which the study produces consistent and accurate results.

Applications of Research

Research has a wide range of applications across various fields and industries. Some of the key applications of research include:

Advancing scientific knowledge : Research plays a critical role in advancing our understanding of the world around us. Through research, scientists are able to discover new knowledge, uncover patterns and relationships, and develop new theories and models.
Improving healthcare: Research is instrumental in advancing medical knowledge and developing new treatments and therapies. Clinical trials and studies help to identify the effectiveness and safety of new drugs and medical devices, while basic research helps to uncover the underlying causes of diseases and conditions.
Enhancing education: Research helps to improve the quality of education by identifying effective teaching methods, developing new educational tools and technologies, and assessing the impact of various educational interventions.
Driving innovation: Research is a key driver of innovation, helping to develop new products, services, and technologies. By conducting research, businesses and organizations can identify new market opportunities, gain a competitive advantage, and improve their operations.
Informing public policy : Research plays an important role in informing public policy decisions. Policy makers rely on research to develop evidence-based policies that address societal challenges, such as healthcare, education, and environmental issues.
Understanding human behavior : Research helps us to better understand human behavior, including social, cognitive, and emotional processes. This understanding can be applied in a variety of settings, such as marketing, organizational management, and public policy.

Importance of Research

Research plays a crucial role in advancing human knowledge and understanding in various fields of study. It is the foundation upon which new discoveries, innovations, and technologies are built. Here are some of the key reasons why research is essential:

Advancing knowledge: Research helps to expand our understanding of the world around us, including the natural world, social structures, and human behavior.
Problem-solving: Research can help to identify problems, develop solutions, and assess the effectiveness of interventions in various fields, including medicine, engineering, and social sciences.
Innovation : Research is the driving force behind the development of new technologies, products, and processes. It helps to identify new possibilities and opportunities for improvement.
Evidence-based decision making: Research provides the evidence needed to make informed decisions in various fields, including policy making, business, and healthcare.
Education and training : Research provides the foundation for education and training in various fields, helping to prepare individuals for careers and advancing their knowledge.
Economic growth: Research can drive economic growth by facilitating the development of new technologies and innovations, creating new markets and job opportunities.

When to use Research

Research is typically used when seeking to answer questions or solve problems that require a systematic approach to gathering and analyzing information. Here are some examples of when research may be appropriate:

To explore a new area of knowledge : Research can be used to investigate a new area of knowledge and gain a better understanding of a topic.
To identify problems and find solutions: Research can be used to identify problems and develop solutions to address them.
To evaluate the effectiveness of programs or interventions : Research can be used to evaluate the effectiveness of programs or interventions in various fields, such as healthcare, education, and social services.
To inform policy decisions: Research can be used to provide evidence to inform policy decisions in areas such as economics, politics, and environmental issues.
To develop new products or technologies : Research can be used to develop new products or technologies and improve existing ones.
To understand human behavior : Research can be used to better understand human behavior and social structures, such as in psychology, sociology, and anthropology.

Characteristics of Research

The following are some of the characteristics of research:

Purpose : Research is conducted to address a specific problem or question and to generate new knowledge or insights.
Systematic : Research is conducted in a systematic and organized manner, following a set of procedures and guidelines.
Empirical : Research is based on evidence and data, rather than personal opinion or intuition.
Objective: Research is conducted with an objective and impartial perspective, avoiding biases and personal beliefs.
Rigorous : Research involves a rigorous and critical examination of the evidence and data, using reliable and valid methods of data collection and analysis.
Logical : Research is based on logical and rational thinking, following a well-defined and logical structure.
Generalizable : Research findings are often generalized to broader populations or contexts, based on a representative sample of the population.
Replicable : Research is conducted in a way that allows others to replicate the study and obtain similar results.
Ethical : Research is conducted in an ethical manner, following established ethical guidelines and principles, to ensure the protection of participants’ rights and well-being.
Cumulative : Research builds on previous studies and contributes to the overall body of knowledge in a particular field.

Advantages of Research

Research has several advantages, including:

Generates new knowledge: Research is conducted to generate new knowledge and understanding of a particular topic or phenomenon, which can be used to inform policy, practice, and decision-making.
Provides evidence-based solutions : Research provides evidence-based solutions to problems and issues, which can be used to develop effective interventions and strategies.
Improves quality : Research can improve the quality of products, services, and programs by identifying areas for improvement and developing solutions to address them.
Enhances credibility : Research enhances the credibility of an organization or individual by providing evidence to support claims and assertions.
Enables innovation: Research can lead to innovation by identifying new ideas, approaches, and technologies.
Informs decision-making : Research provides information that can inform decision-making, helping individuals and organizations make more informed and effective choices.
Facilitates progress: Research can facilitate progress by identifying challenges and opportunities and developing solutions to address them.
Enhances understanding: Research can enhance understanding of complex issues and phenomena, helping individuals and organizations navigate challenges and opportunities more effectively.
Promotes accountability : Research promotes accountability by providing a basis for evaluating the effectiveness of policies, programs, and interventions.
Fosters collaboration: Research can foster collaboration by bringing together individuals and organizations with diverse perspectives and expertise to address complex issues and problems.

Limitations of Research

Some Limitations of Research are as follows:

Cost : Research can be expensive, particularly when large-scale studies are required. This can limit the number of studies that can be conducted and the amount of data that can be collected.
Time : Research can be time-consuming, particularly when longitudinal studies are required. This can limit the speed at which research findings can be generated and disseminated.
Sample size: The size of the sample used in research can limit the generalizability of the findings to larger populations.
Bias : Research can be affected by bias, both in the design and implementation of the study, as well as in the analysis and interpretation of the data.
Ethics : Research can present ethical challenges, particularly when human or animal subjects are involved. This can limit the types of research that can be conducted and the methods that can be used.
Data quality: The quality of the data collected in research can be affected by a range of factors, including the reliability and validity of the measures used, as well as the accuracy of the data entry and analysis.
Subjectivity : Research can be subjective, particularly when qualitative methods are used. This can limit the objectivity and reliability of the findings.
Accessibility : Research findings may not be accessible to all stakeholders, particularly those who are not part of the academic or research community.
Interpretation : Research findings can be open to interpretation, particularly when the data is complex or contradictory. This can limit the ability of researchers to draw firm conclusions.
Unforeseen events : Unexpected events, such as changes in the environment or the emergence of new technologies, can limit the relevance and applicability of research findings.

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Attention-Deficit/Hyperactivity Disorder: What You Need to Know

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What is ADHD?

Attention-deficit/hyperactivity disorder (ADHD) is a developmental disorder marked by persistent symptoms of inattention, hyperactivity, and impulsivity. Some people mostly have symptoms of inattention. Others mostly have symptoms of hyperactivity and impulsivity. Some people have both types of symptoms.

Symptoms begin in childhood and can interfere with daily life, including social relationships and school or work performance. ADHD is well-known among children and teens, but many adults also have the disorder. Effective treatments are available to manage symptoms.

What are the symptoms of ADHD?

People with ADHD may experience an ongoing pattern of:

Inattention : Difficulty paying attention
Hyperactivity : Showing too much energy or moving and talking too much
Impulsivity : Acting without thinking or having difficulty with self-control

Signs of inattention can include frequent difficulty with :

Paying attention to details, leading to careless mistakes at school, work, or during other activities
Concentrating on tasks or activities, for instance, while having conversations, taking tests, completing assignments, or reading papers
Listening when spoken to directly
Following instructions or finishing tasks at school, work, or home
Organizing tasks and activities, managing time, and meeting deadlines
Completing tasks that require sustained attention, such as homework, large projects, and complicated forms
Losing things, such as backpacks, books, keys, wallets, and phones
Getting easily distracted by unrelated thoughts or stimuli
Forgetting about daily activities, such as chores, errands, and events, or other important things, like assignments, appointments, and phone calls

Signs of hyperactivity and impulsivity can include often :

Fidgeting, tapping hands or feet, or squirming while seated
Moving around when expected to remain seated, such as in the classroom or office, or feeling restless in these situations
Running, climbing, or moving around at times when it is not appropriate
Being constantly “on the go” and acting as if driven by a motor
Being unable to quietly play or take part in hobbies and activities
Talking excessively
Answering questions before they are fully asked or finishing other people’s sentences
Struggling to wait or be patient, such as when playing a game or waiting in line
Interrupting or intruding on others, for example, in conversations, games, or meetings

What causes ADHD?

Researchers are not sure what causes ADHD, although many studies suggest that genes play a large role. Like many other disorders, ADHD probably results from a combination of factors.

In addition to genetics, researchers are looking at differences in brain development and neurobiology among people with ADHD compared to those without the disorder. They are also studying environmental factors that might increase the risk of developing ADHD, including brain injuries, nutrition, and social environments.

How is ADHD diagnosed?

Based on their specific symptoms, a person can be diagnosed with one of three types of ADHD:

Inattentive : Mostly symptoms of inattention but not hyperactivity or impulsivity
Hyperactive-impulsive : Mostly symptoms of hyperactivity and impulsivity but not inattention
Combined : Symptoms of both inattention and hyperactivity and impulsivity

ADHD symptoms must begin in childhood (before age 12). Symptoms often continue into the teen years and adulthood. The criterion for a diagnosis differs slightly based on age.

Children up to 16 years must show at least six symptoms of inattention, hyperactivity and impulsivity, or both.
Adults and youth over 16 years must show at least five symptoms of inattention, hyperactivity and impulsivity, or both.

To be diagnosed with ADHD, a person’s symptoms must also:

Occur for at least 6 months
Be present in two or more settings (for example, at home, at work, in school, or with friends)
Interfere with or impair social, school, or work functioning

Stress, sleep disorders, anxiety, depression, and other physical conditions or illnesses can cause similar symptoms to those of ADHD. A health care provider needs to do a thorough evaluation to determine the cause of symptoms, make a diagnosis, and identify effective treatments.

Primary care providers sometimes diagnose and treat ADHD, or they may refer the person to a mental health professional. During an evaluation, a provider usually:

Examines the person’s mental health and medical history, including their mood and past or current health conditions.
Looks at the person’s current or, if an adult, childhood behavior and school experiences. To obtain this information, the provider may ask for permission to talk with family, friends, partners, teachers, and others who know the person well and have seen them in different settings to learn about behaviors and experiences at home, school, or elsewhere.
Uses standardized behavior rating scales or ADHD symptom checklists to determine whether the person meets the criteria for a diagnosis of ADHD.
Administers psychological tests that look at cognitive skills, such as working memory, executive functioning (abilities such as planning and decision-making), visual and spatial abilities, or reasoning. Such tests can help identify psychological or cognitive (thinking-related) strengths and challenges and identify or rule out possible learning disabilities.

Does ADHD look the same in everyone?

Anyone can have ADHD. However, boys and men tend to display more hyperactive and impulsive symptoms, while girls and women are more likely to be diagnosed with inattentive ADHD.

ADHD can also be diagnosed at any age, although symptoms must have begun in childhood (before age 12). Adults with ADHD often have a history of problems with school, work, and relationships.

ADHD symptoms may change as a person gets older.

Children show hyperactivity and impulsivity as the most common symptoms. As academic and social demands increase, symptoms of inattention often become more prominent and begin to interfere with academic performance and peer relationships.
Adolescents usually show less hyperactivity and may appear as restless or fidgeting. Symptoms of inattention and impulsivity typically continue and may cause academic, organizational, or relationship challenges. Teens with ADHD are more likely to engage in impulsive, risky behaviors, such as substance use and unsafe sexual activity.
Adults, including older adults , can show inattention, restlessness, and impulsivity, although, in some people, those symptoms become less severe and less impairing. They may also be irritable, have a low tolerance for frustration and stress, or experience frequent or intense mood changes.

Some adults may not have been diagnosed with ADHD when younger because their teachers or family did not recognize the disorder, they had a mild form of the disorder, or they managed well until experiencing the demands of adulthood. But it is never too late to seek a diagnosis and treatment for ADHD and other mental health conditions that may co-occur with it. Effective treatment can make day-to-day life easier for people with ADHD and their families.

How is ADHD treated?

Although there is no cure for ADHD, current treatments may help reduce symptoms and improve functioning. Common treatments for ADHD are medication, psychotherapy, and other behavioral interventions. For children, treatment often includes parent education and school-based programs.

Researchers are studying new treatments for people with ADHD, such as cognitive training and neurofeedback. These options are usually explored only after medication and psychotherapy have already been tried. For many people, treatment involves a combination of elements.

Stimulants are the most common type of medication used to treat ADHD, and research shows them to be highly effective. They work by increasing levels of brain chemicals involved in thinking and attention.

Like all medications, stimulants can have side effects and must be prescribed and monitored by a health care provider. Tell the provider about other medications you or your child are taking. Medications for common health problems, such as diabetes, anxiety, and depression, can interact with stimulants, in which case, a provider can suggest other medication options.

Health care providers sometimes prescribe nonstimulant medications like antidepressants to treat ADHD. However, the U.S. Food and Drug Administration (FDA) has not approved these medications specifically for ADHD. Sometimes, a person must try several different medications or dosages before finding the one that works for them.

Learn more about stimulants and other mental health medications . You can learn more about specific medications, including the latest approvals, side effects, warnings, and patient information, on the FDA website .

Psychotherapy and behavioral interventions

Psychological interventions for ADHD can take many forms and be combined with medication and other elements for parents, families, and teachers. Adding therapy to an ADHD treatment plan can help some people better cope with daily challenges, gain confidence, or manage impulsive and risky behaviors.

Therapy is especially helpful if ADHD co-occurs with other mental disorders, such as anxiety, depression, conduct problems, or substance use disorders. Learn about other mental disorders .

Several psychosocial interventions have been shown to help manage symptoms and improve functioning.

Behavioral therapy helps a person change their behavior. It might involve practical assistance, such as organizing tasks or completing schoolwork, learning social skills, or monitoring one’s behavior.
Cognitive behavioral therapy helps a person become aware of attention and concentration challenges and work on skills to improve focus and organization and complete daily tasks (for instance, by breaking large tasks into smaller, more manageable steps).
Family and marital therapy helps family members learn to handle disruptive behaviors, encourage behavior changes, and improve interactions with children and partners.

Some people find it helpful to get support from a professional life coach or ADHD coach who can teach them skills to improve daily functioning.

Learn more about psychotherapy .

Parent education and support

Therapy for children and teens requires parents to play an active role. Treatment sessions with the child alone are more likely to be effective for treating symptoms of anxiety or depression that may co-occur with ADHD than for managing core symptoms of the disorder.

Mental health professionals can educate parents about the disorder and how it affects a family. They also can help parents develop new skills, attitudes, and ways of relating to their child. Examples include parenting skills training, stress management techniques for parents, and support groups that help parents and families connect with others who have similar concerns.

School-based programs

Many children and teens with ADHD benefit from school-based behavioral interventions and academic accommodations. Interventions include behavior management plans or classroom-taught organizational and study skills. Accommodations include preferential seating in the classroom, reduced classwork, and extended time on tests and exams. Schools may provide accommodations through what is called a 504 Plan or, for children who qualify for special education services, an Individualized Education Plan (IEP).

Learn more about special education services and the Individuals with Disabilities Education Act .

Cognitive training

Cognitive training approaches involve repeatedly using a program or activity over several weeks to improve specific functions, such as memory or attention. Exercises are tailored to the person’s ongoing performance.

Cognitive training is shown to modestly improve the tasks being practiced. For instance, research shows the training can help memory, attention, inhibition, planning, and cognitive flexibility in people with ADHD. However, these improvements don’t usually translate to changes in core ADHD symptoms of impulsivity and hyperactivity.

Neurofeedback

Neurofeedback is a noninvasive technique in which an electronic device monitors and records a person’s brain activity, providing them with immediate feedback to support self-regulation. The device measures brain activity through such means as EEG or fMRI scans and feeds the information back to the person, usually in the form of a computer screen or visual cue. Through this feedback, people learn to self-regulate their brain activity to directly alter the associated behavior. The assumption is that, with repeated, real-time information, people can change their internal brain activity, with observable effects on behavior and cognition.

For people with ADHD, neurofeedback is used to train and improve specific cognitive functions. Although it is shown to help reduce some ADHD symptoms, the effects of neurofeedback remain lower than those seen from medication and psychotherapy. Additional research is needed to refine the treatment and determine for whom it works and under what conditions.

Complementary health approaches

Some people may explore complementary health approaches to manage symptoms of ADHD. These can include natural products, vitamins and supplements, diet changes, and acupuncture. Others find it helpful to make lifestyle changes, like adding more physical exercise to their daily schedule.

Unlike psychotherapy and medication that are scientifically shown to improve ADHD symptoms, complementary health approaches generally have not been found to treat ADHD effectively and do not qualify as evidence-supported interventions.

Find more information from the National Center for Complementary and Integrative Health .

How can I find help?

If you’re unsure of where to get help, a health care provider is a good place to start. They can refer you to a qualified mental health professional, such as a psychologist, psychiatrist, or clinical social worker, who can help figure out the next steps. Find tips for talking with a health care provider about your or your child’s mental health.

The Centers for Disease Control and Prevention (CDC) has information about ADHD symptoms, diagnosis, and treatment, as well as additional resources for families and providers.

You can learn more about getting help on the NIMH website. You can also learn about finding support and locating mental health services in your area on the Substance Abuse and Mental Health Services Administration (SAMHSA) website.

How can I help myself?

Medication and therapy are the most effective treatments for ADHD. Other strategies may also help manage symptoms.

Get regular exercise, especially when feeling hyperactive or restless.
Eat regular, healthy meals.
Get plenty of sleep. Try to turn off screens at least 1 hour before bedtime and get between 7–9 hours of sleep every night.
Stick to a consistent routine.
Work on time management and organization. Prioritize time-sensitive tasks and write down assignments, messages, appointments, reminders, and important thoughts.
Take short breaks during tasks that require sustained attention to help maintain focus and prevent burnout. Break large tasks into smaller, more manageable steps.
Connect with people and maintain relationships. Schedule activities with friends, particularly supportive people who understand your challenges with ADHD.
Take medications as directed. Avoid alcohol, tobacco, and drugs not prescribed for you.

How can I help my child?

Be patient, flexible, and understanding. ADHD can be frustrating both for people who have it and the people in their lives. ADHD may make it hard for your child to perform certain tasks or behaviors. Some children may need to use different strategies to help them succeed.
Use clear, simple, direct language to explain rules and expectations. Reward behaviors that meet these expectations with positive reinforcement. Provide consistent praise or rewards for acting in a desired way.
Offer practical help, such as on tasks like cleaning and organizing, or simply be present and engaged while your child works, which can give them a sense of accountability and motivation and help them stay focused and on track.
Provide opportunities to explore different activities and interests. Help your child discover their unique talents and build confidence in their abilities.

What are clinical trials and why are they important?

Clinical trials are research studies that look at ways to prevent, detect, or treat diseases and conditions. These studies help show whether a treatment is safe and effective in people. Some people join clinical trials to help doctors and researchers learn more about a disease and improve health care. Other people, such as those with health conditions, join to try treatments that aren’t widely available.

NIMH supports clinical trials across the United States. Talk to a health care provider about clinical trials and whether one is right for you. Learn more about participating in clinical trials .

For more information

Learn more about mental health disorders and topics . For information about various health topics, visit the National Library of Medicine’s MedlinePlus .

The information in this publication is in the public domain and may be reused or copied without permission. However, you may not reuse or copy images. Please cite the National Institute of Mental Health as the source. Read our copyright policy to learn more about our guidelines for reusing NIMH content.

U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES National Institutes of Health NIH Publication No. 24-MH-8300 Revised 2024

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Published: 07 August 2024

Highest ocean heat in four centuries places Great Barrier Reef in danger

Benjamin J. Henley ORCID: orcid.org/0000-0003-3940-1963 1 , 2 , 3 ,
Helen V. McGregor ORCID: orcid.org/0000-0002-4031-2282 1 , 2 ,
Andrew D. King ORCID: orcid.org/0000-0001-9006-5745 4 , 5 ,
Ove Hoegh-Guldberg ORCID: orcid.org/0000-0001-7510-6713 6 ,
Ariella K. Arzey 1 , 2 ,
David J. Karoly 4 ,
Janice M. Lough 7 ,
Thomas M. DeCarlo ORCID: orcid.org/0000-0003-3269-1320 8 , 9 &
Braddock K. Linsley ORCID: orcid.org/0000-0003-2085-0662 10

Nature volume 632 , pages 320–326 ( 2024 ) Cite this article

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Climate change
Environmental impact
Palaeoclimate

Mass coral bleaching on the Great Barrier Reef (GBR) in Australia between 2016 and 2024 was driven by high sea surface temperatures (SST) 1 . The likelihood of temperature-induced bleaching is a key determinant for the future threat status of the GBR 2 , but the long-term context of recent temperatures in the region is unclear. Here we show that the January–March Coral Sea heat extremes in 2024, 2017 and 2020 (in order of descending mean SST anomalies) were the warmest in 400 years, exceeding the 95th-percentile uncertainty limit of our reconstructed pre-1900 maximum. The 2016, 2004 and 2022 events were the next warmest, exceeding the 90th-percentile limit. Climate model analysis confirms that human influence on the climate system is responsible for the rapid warming in recent decades. This attribution, together with the recent ocean temperature extremes, post-1900 warming trend and observed mass coral bleaching, shows that the existential threat to the GBR ecosystem from anthropogenic climate change is now realized. Without urgent intervention, the iconic GBR is at risk of experiencing temperatures conducive to near-annual coral bleaching 3 , with negative consequences for biodiversity and ecosystems services. A continuation on the current trajectory would further threaten the ecological function 4 and outstanding universal value 5 of one of Earth’s greatest natural wonders.

Mesophotic coral bleaching associated with changes in thermocline depth

Atypical weather patterns cause coral bleaching on the Great Barrier Reef, Australia during the 2021–2022 La Niña

Internal tides can provide thermal refugia that will buffer some coral reefs from future global warming

Like many coral reefs globally, the World Heritage-listed GBR in Australia is under threat 4 , 6 . Mass coral bleaching, declining calcification rates 5 , 7 , outbreaks of crown-of-thorns starfish ( Acanthaster spp.) 8 , severe tropical cyclones 9 and overfishing 10 have placed compounding detrimental pressures on the reef ecosystem. Coral bleaching typically occurs when heat stress triggers the breakdown of the symbiosis between corals and their symbiotic dinoflagellates 11 . Although coral bleaching can occur locally as a result of low salinity, cold waters or pollution, regional and global mass bleaching events, in which the majority of corals in one or more regions bleach at once, are strongly associated with increasing SST linked to global warming 2 .

The first modern observations of mass coral bleaching on the GBR occurred in the 1980s, but these events were less widespread and generally less severe 3 than the bleaching events in the twenty-first century 4 . Stress bands in coral skeletal cores have provided potential evidence for pre-1980s bleaching in the GBR and Coral Sea, such as during the 1877–78 El Niño 12 . However, stress bands are evident in relatively few cores before 1980 (ref. 12 ), suggesting that severe mass bleaching did not occur in the 1800s and most of the 1900s.

As the oceans have warmed, however, mass coral bleaching events have become increasingly lethal to corals 4 . Coral bleaching on the GBR 1 in 1998 coincided with a strong eastern-Pacific El Niño, and in 2002 with a weak El Niño. El Niño events can induce lower cloud cover and increased solar irradiance over the GBR 13 , increasing the risk of thermal stress and mass bleaching events 14 . In 2004, water temperatures were anomalously warm, and although bleaching occurred in the Coral Sea 15 , it was not widespread in the GBR, probably because there was reduced upwelling and an associated reduced influence of nutrients on symbiotic dinoflagellate expulsion 16 .

However, in the nine January–March periods from 2016 to 2024 (inclusive) there were five mass coral bleaching events on the GBR. Each was associated with high SSTs and affected large sections of the reef. GBR mass bleaching occurred in both 2016 and 2017, influenced by the presence of an El Niño event in 2016, and led to the death of at least 50% of shallow-water (depths of 5–10 m) reef-building corals 4 . Major bleaching events occurred again in quick succession in 2020 and 2022, with the accumulated heat stress for large sections of the GBR reaching levels conducive to widespread bleaching but lower levels of coral mortality 1 . The bleaching event in 2022 occurred, unusually, during a La Niña event, which is typically associated with cooler summer SSTs, higher than average rainfall and higher cloud cover on the GBR 1 . At the time of writing, researchers are assessing the impacts of the 2024 mass bleaching event.

The frequency of recent mass coral bleaching and mortality on the GBR is cause for concern. In 2021, the World Heritage Committee of the United Nations Educational, Scientific and Cultural Organization (UNESCO) drafted 17 a decision to inscribe the GBR on the List of World Heritage in Danger, stating that the reef is “facing ascertained danger”, citing recent mass coral bleaching events and insufficient progress by the State Party (Australia) in countering climate change, improving water quality and land management issues. The committee’s adopted decisions 18 have not included inscription of the ‘in danger’ status, but the draft inscription highlights the seriousness of the recent mass coral bleaching events. Authorities in Australia 5 have noted that climate change and coral bleaching have deteriorated the integrity of the outstanding universal value of the GBR, a defining feature of its World Heritage status.

Although rapidly rising SSTs are attributed to human activities with virtual certainty 19 , understanding the multi-century SST history of the GBR is critical to understanding the influence of SST on mass coral bleaching and mortality in recent decades. Putting aside a problematic attempt to do this 20 , which was discredited 21 , 22 , knowledge of the long-term context for GBR SSTs comes primarily from two multi-century reconstructions based on the geochemistry of coral cores collected from the inner shelf 23 and outer shelf 24 (Flinders Reef) in the central GBR. These reconstructions showed that SSTs in the early 2000s were not unusually high relative to levels in the past three centuries, with five-year mean SSTs (and salinities) estimated to be higher in the 1700s than in the 1900s. However, these records were limited by their relatively coarse five-year sampling resolution and their most recent data point being from the early 2000s. After these studies were published, SSTs in the GBR have continued to rise. Updated analysis of coral data from Flinders Reef provides valuable improved temporal resolution 25 , but interpretations of these records remain limited spatially.

Here, we investigate the recent high SST events in the GBR region in the context of the past four centuries. We combine a network of 22 coral Sr/Ca and δ 18 O palaeothermometer series (Supplementary Tables 1 and 2 ) located in and near to the Coral Sea region to infer spatial mean SST anomalies (SSTAs) for January–March, the months when maximum SST and thermal bleaching are most likely to occur in the Coral Sea 16 , 26 , each year from 1618 to 1995 ( Methods and Supplementary Information ). Anthropogenic climate change began and proceeded entirely within the multi-century lives of some of these massive coral colonies, offering a continuous multi-century record covering the industrial era. We use this 1618–1995 reconstruction and the available 1900–2024 instrumental data to contextualize the modern trend and rank four centuries of January–March SSTAs with greater precision than was previously possible. We then assess the degree of human influence on ocean temperatures in the region using climate model simulations run both with and without anthropogenic forcing.

The instrumental period (1900–present)

Mass coral bleaching on the GBR in 2016, 2017, 2020, 2022 and 2024 during January–March coincided with widespread warm SSTAs in the surrounding seas 1 , including the Coral Sea (Fig. 1a–e , using ERSSTv5 data 27 ). The Coral Sea and GBR have experienced a strong warming trend since 1900 (Fig. 1f ). January–March SSTAs averaged over the GBR are strongly correlated ( ρ = 0.84, P ≪ 0.01) with those in the broader Coral Sea (Fig. 1f ), including when the long-term warming trend is removed from both time series ( ρ = 0.69, P < 0.01; Supplementary Fig. 4 ). Based on the strength of this correlation, we associate high January–March area-averaged Coral Sea SSTAs with increased thermal bleaching risk in the GBR.

a – e , SSTAs (using ERSSTv5 data) for January–March in the Australasian region relative to the 1961–90 average for the five recent GBR mass coral bleaching years: 2016, 2017, 2020, 2022 and 2024. The black box shows the Coral Sea region (4° S–26° S, 142° E–174° E). f , Coral Sea and GBR mean SSTAs for 1900–2024 in January–March relative to the 1961–90 average. The black vertical lines indicate the five recent GBR mass coral bleaching years.

Record temperatures were set in 2016 and 2017 in the Coral Sea, and in 2020 they peaked fractionally below the record high of 2017. The January–March of 2022 was another warm event, the fifth warmest on record at the time. Recent data (ERSSTv5) indicate that 2024 set a new record by a margin of more than 0.19 °C above the previous record for the region. The January–March mean SSTs averaged over the five mass bleaching years during the period 2016–2024 are 0.77 °C higher than the 1961–90 January–March averages in both the Coral Sea and the GBR. The multidecadal warming trend, extreme years and association between GBR and Coral Sea SSTs are similar for the HadISST 28 gridded SST dataset, with some notable differences in the 1900–40 period (Supplementary Fig. 3 ). Furthermore, analysis of modern temperature-sensitive Sr/Ca series from GBR corals for 1900–2017 provides coherent independent evidence of statistically significant multi-decadal warming trends in January–March SSTs in the central and southern GBR (Supplementary Information section 4.2 ).

A multi-century context (1618–present)

Reconstructing Coral Sea January–March SSTs from 1618 to 1995 extends the century-long instrumental record back in time by an additional three centuries (Fig. 2a and Methods ). The reconstruction (calibrated to ERSSTv5) shows that multi-decadal SST variability was a persistent feature in the past. At the centennial timescale, there is relative stability before 1900, with the exception that cooler temperatures prevailed in the 1600s. Warming during the industrial era has been evident since the early 1900s (Fig. 2a ). There is a warming trend for January–March of 0.09 °C per decade for 1900–2024 and 0.12 °C per decade for 1960–2024 (Fig. 1f ) using ERSSTv5 data. Calibrating our reconstruction to HadISST1.1 yields similar results, with some differences in the degree of pre-1900 variability at both multi-decadal and centennial timescales (Supplementary Information section 5.2.6 ).

a , Reconstructed and observed mean January–March SSTAs in the Coral Sea for 1618–2024 relative to 1961–90. Dark blue, highest skill (maximum coefficient of efficiency) reconstruction with the full proxy network; light blue, 5th–95th-percentile reconstruction uncertainty; black, observed (ERSSTv5) data. Red crosses indicate the five recent mass bleaching events. Dashed lines indicate the best estimate (highest skill, red) and 95th-percentile (pink) uncertainty bound for the maximum pre-1900 January–March SSTA. b , Central GBR SSTA for the inner shelf 23 in thick orange and outer shelf 25 (Flinders Reef) in thin orange lines; these series are aligned here (see Methods ) with modern observations of mean GBR SSTAs for January–March relative to 1961–90. Observed data are shown at annual (grey line) and five-year (black line with open circles, plotted at the centre of each five-year period and temporally aligned with the five-year coral series 23 ) resolution. Dashed lines indicate best-estimate pre-1900 January–March maxima for refs. 23 (red) and 25 (pink). Orange shading indicates 5th–95th-percentile uncertainty bounds. Red crosses indicate the five recent mass bleaching events. c , Evaluation metrics for the Coral Sea reconstruction (Supplementary Information section 3.1 ); RE, reduction of error; CE, coefficient of efficiency; Rsq-cal, R-squared in the calibration period; Rsq-ver, R-squared in the verification (evaluation) period. d , Coral data locations relative to source data region (orange box) and Coral Sea region (red box). Coral proxy metadata are given in Supplementary Tables 1 and 2 .

Our best-estimate (highest skill; Methods ) annual-resolution Coral Sea reconstruction (Fig. 2a ), using the full coral network calibrated to the ERSSTv5 instrumental data, indicates that the January–March mean SSTAs in 2016, 2017, 2020, 2022 and 2024 were, respectively, 1.50 °C, 1.54 °C, 1.53 °C, 1.46 °C and 1.73 °C above the 1618–1899 (hereafter ‘pre-1900’) reconstructed average. Using the same best-estimate reconstruction, Coral Sea January–March SSTs during these GBR mass bleaching years were five of the six warmest years the region has experienced in the past 400 years (Fig. 2a ).

By comparing the recent warm events to the reconstruction’s uncertainty range ( Methods ), we quantify, using likelihood terminology consistent with recent reports from the Intergovernmental Panel on Climate Change 19 , that the recent heat extremes in 2017, 2020 and 2024 are ‘extremely likely’ (>95th percentile; Fig. 2a ) to be higher than any January–March in the period 1618–1899. Furthermore, the heat extremes in 2016 and 2022 are (at least) ‘very likely’ (>90th percentile) to be above the pre-1900 maximum. We perform a series of tests that verify that our findings are not simply an artefact of the nature of the coral network itself (Supplementary Information section 5.2 ). In a network perturbation test, we generate 22 subsets of the reconstruction by adding proxy records incrementally in order from the highest to the lowest correlation with the target (Supplementary Information section 5.2.5 ). We confirm that 2017, 2020 and 2024 were ‘extremely likely’ (>95th percentile) to have been warmer than any year pre-1900 (using ERSSTv5 data) for all of these proxy subsets. Furthermore, in 20 of the 22 subsets, 2016 was also ‘extremely likely’ (>95th percentile), rather than ‘very likely’, to be warmer (2022 was ‘extremely likely’ in 14 of the 22 subsets). All our additional tests, including a reconstruction with only Sr/Ca coral data (thereby omitting the possibility of any non-temperature signal in δ 18 O coral on the reconstruction), achieve high reconstruction skill and confirm the extraordinary nature of recent extreme temperatures in the multi-century context (Supplementary Information section 5.2 ). Analyses using HadISST1.1 generally show lower correlations with the coral data and reconstructions with slightly warmer regional SSTs before 1900, along with more-muted centennial and multi-decadal variability in the pre-instrumental period. Nevertheless, the HadISST1.1-calibrated reconstructions show that the recent thermal extremes are well above the best estimate (highest skill) of the pre-1900 maximum of reconstructed January–March SSTAs (Supplementary Fig. 42 ). Furthermore, lower SSTAs (in the HadISST1.1 data) relative to the previous three centuries (as in our reconstructions calibrated to HadISST1.1), coupled with the recently observed mass coral bleaching events, could indicate that long-lived corals have a greater sensitivity to warming than is currently recognized.

Reconstructed regional GBR SSTAs based on a five-year-resolution, multi-century coral δ 18 O record from the central inshore GBR 23 (Fig. 2b ) show similarly strong warming since 1900 but more multi-decadal-to-centennial variability than the Coral Sea reconstruction. Recent five-year mean January–March GBR SSTAs narrowly exceed the best estimate of the maximum pre-1900 five-year mean since the early 1600s (Fig. 2b ). The averages for the five-year periods centred on 2018 and 2022 exceed the pre-1900 maximum by 0.11 °C and 0.06 °C, respectively. Results are similar using the five-year-resolution Flinders Reef (central outer shelf) 24 record (Supplementary Fig. 24 ), although its interpretation is limited by the lack of uncertainty estimates available for that record. Our Coral Sea reconstruction incorporates an updated (annual resolution) record from Flinders Reef 25 , which indicates similar centennial trends (thin orange line in Fig. 2b ) and shows that the recent high January–March SSTA events have approached the estimated local pre-1900 maximum SSTA. Although contiguous multi-century cores from within the GBR are limited in their spatial extent, twentieth-century warming is evident in these records.

The extraordinary nature of the recent Coral Sea January–March SSTs in the context of the past 400 years is further illustrated by comparing the ranked temperature anomalies (Fig. 3 ) for the combined reconstructed and instrumental period from 1618–2024, incorporating reconstruction uncertainty ( Methods ). The mass coral bleaching years of 2016, 2017, 2020, 2022 and 2024, and the heat event of 2004, stand out as the warmest events across the whole 407-year record. The warmest three years (2024, 2017 and 2020) exceed the upper uncertainty bound (95th percentile) of the warmest reconstructed January–March in the pre-1900 period (pink (upper) dashed line in Fig. 3 ); 2016, 2004 and 2022 exceed the 90th percentile bound (red (lower) dashed line in Fig. 3 ). The warming trend is clear in the association between the ascending rank of the temperature anomalies and the year (shown as the colour of the filled circles in Fig. 3 ). Despite high interannual variability, 78 of the warmest 100 January–March periods between 1618 and 2024 occurred after 1900, and the 23 warmest all occur after 1900. The warmest 20 January–March periods all occur after 1950, coinciding with accelerated global warming.

Ranked January–March SSTAs for 1618–2024 relative to 1961–90 (coloured circles) from the best-estimate (highest skill, full coral network) reconstruction (1618–1899) and instrumental (ERSSTv5) data (1900–2024). The year is indicated by the colour of the filled circles. The 5th–95th-percentile uncertainty bounds of the pre-1900 reconstructed SSTAs are shown by small grey dots. The year labels indicate the warmest six years on record, five of which were mass coral bleaching years on the GBR. The pink (upper) dashed line indicates the 95th-percentile uncertainty bound of the maximum pre-1900 reconstructed SSTA; the red (lower) dashed line indicates the 90th-percentile limit.

Assessing anthropogenic influence

Using climate model simulations from the most recent (sixth) phase of the Coupled Model Intercomparison Project 29 (CMIP6), we assess the human influence on January–March SSTAs in the Coral Sea. The model simulations are from two experiments in the Detection and Attribution Model Intercomparison Project (DAMIP) 30 . The first set of simulations represents historical climate conditions, including both the natural and human influences on the climate system over the 1850–2014 period (‘historical’; red in Fig. 4 ). The second experiment is a counterfactual climate that spans the same period and uses the same models but includes only natural influences on the climate, omitting all human influences (‘historical-natural’; blue in Fig. 4 ). The historical experiment includes anthropogenic emissions of greenhouse gases and aerosols, stratospheric ozone changes and anthropogenic land-use changes; the historical-natural experiment does not. Variations in natural climate forcings, such as from volcanic eruptions and solar variability, are incorporated in both experiments. We include models that have a transient climate response (the global mean surface-temperature anomaly at the time of a doubling of atmospheric CO 2 concentration) in the range 1.4–2.2 °C, which is deemed ‘likely’ by the science community 31 ( Methods and Supplementary Information ).

Climate-model simulations of Coral Sea January–March SSTAs relative to the 1850–1900 average for the period 1850–2014, for models within the ‘likely’ range for their transient climate response 31 . The blue line (median) and light blue shading (5th–95th-percentile limits) are from the ‘historical-natural’ climate model simulations (no anthropogenic climate forcing); the red line and light red shading are from the ‘historical’ simulations (anthropogenic influences on the climate included) using the same set of climate models. The climate-model-derived time of emergence of anthropogenic climate change, shown by the grey and black vertical lines (1976 and 1997), is when the ratio of the climate change signal to the standard deviation of noise/variability 32 across model ensemble members first rises above 1 and 2, respectively. All models are represented equally in the model ensemble.

It is only with the incorporation of anthropogenic influences on the climate that the model simulations capture the modern-era warming of the Coral Sea January–March SSTA (Fig. 4 ). The median of the historical simulations has statistically significant warming trends of 0.05 °C, 0.10 °C and 0.15 °C per decade for the periods from 1900, 1950 and 1970 to 2014, respectively; the equivalent historical-natural trends are smaller in magnitude than ±0.01 °C per decade. To further explore the centennial-scale trends, we use a bootstrap ensemble ( Methods ) of the two sets of 165-year simulations from 1850–2014. We found that 100% of the historical bootstrap ensemble has statistically significant positive trends ( Methods ) for 1900–2014, but this value is 0% for the historical-natural ensemble. The observed (ERSSTv5) mean SSTA for 2016–2024 of 0.60 °C relative to 1961–90 is warmer than any nine-year sequence in the 7,095 simulated years in the historical-natural experiments from models with transient climate responses in the ‘likely’ range 31 .

We also use the simulations to estimate the time of emergence of the anthropogenic influence on January–March Coral Sea SSTAs above the natural background variability. The anthropogenic warming signal 32 increases from near zero in 1900 to around 0.5 standard deviations of the variability (‘noise’) in 1960. The climate change signal-to-noise ratio then increases rapidly from 1960 to 2014, exceeding 1.0 in 1976, 2.0 in 1997 and around 2.8 by 2014, the end of these simulations (Fig. 4 , Methods and Supplementary Fig. 50 ). Anthropogenic impacts on the climate are virtually certain to be the primary driver of this long-term warming in the Coral Sea.

Previously, our knowledge of the SST history of the GBR and the Coral Sea region has been highly dependent on instrumental observations, with the exception of the five-year-resolution multi-century coral Sr/Ca and U/Ca SST reconstructions from the two point locations in the central GBR 23 , 24 , an update at one of these locations 25 , seasonal resolution ‘floating’ (in time) chronologies from the GBR in the Holocene 33 , 34 and point SST estimates further back in time 35 . Thus, the context of recent warming trends in the Coral Sea and GBR and their relation to natural variability on decadal to centennial timescales is largely unknown without reconstructions such as the one we developed here.

Our coral proxy network is located mostly beyond the GBR, in the Coral Sea, and some series are located outside the Coral Sea region (Fig. 2d ). The selection of the Coral Sea as a study region allowed for a larger sample of contributing coral proxy data than exists for the GBR. However, coral bleaching on the GBR can be influenced by factors other than large-scale SST, including local oceanic and atmospheric dynamics that can modulate the occurrence and severity of thermal bleaching and mortality events 13 . Nonetheless, warming of seasonal SSTs over the larger Coral Sea region is likely to prime the background state and increase the likelihood of smaller spatio-temporal-scale heat anomalies. Furthermore, where we use only the five-year resolution series directly from the GBR to reconstruct GBR SSTAs, we draw similar conclusions about the long-term trajectory of SSTAs as for our full coral network (Fig. 2b and Supplementary Fig. 24 ). Furthermore, short modern coral series from within the GBR, analysed in this study, document a multi-decadal warming signal that is coherent with instrumental data (Supplementary Figs. 29 and 30 ). Nonetheless, additional high-resolution, multi-century, temperature-sensitive coral geochemical series from within the GBR would help unravel the local and remote ocean–atmosphere contributions to past bleaching events and reduce uncertainties.

The focus on the larger Coral Sea study region also takes advantage of the global modelling efforts of CMIP6. The large number of ensemble members available for CMIP6 means that greater climate model diversity, and therefore greater certainty in our attribution analysis, is possible compared with most single model analyses. There is also a methodological benefit in having high replication of the same experiments run with multiple climate models. However, coarse-resolution global-scale models do not accurately simulate smaller-scale processes, such as inshore currents and mesoscale eddies in the Coral Sea or the Gulf of Carpentaria, which probably affect local surface temperatures and variations in nutrient upwelling in the GBR 36 , 37 . Upwelling on the GBR is linked to the strength of the East Australian Current 16 , the southward branch of the South Pacific subtropical gyre. The CMIP-scale models we use do capture these gyre dynamics. The models show that the East Australian Current is expected to increase in strength as the climate continues to warm through this century 38 , and this may lead to more nutrient inputs that can exacerbate coral sensitivity to rising heat stress 39 , 40 . As well as focusing our model analysis on the larger Coral Sea region, we use a three-month time step. In doing so, we minimize the impact of model spatio-temporal resolution on our inferences about the role of anthropogenic greenhouse-gas emissions on the SST conditions that give rise to GBR mass bleaching.

Remaining uncertainties

We present analyses and interpretations that are as robust as possible given currently available data and methods. However, several sources of remaining uncertainty mean that future reconstructions of past Coral Sea and GBR SSTs could differ from those presented here. Although bias corrections are applied to observational SST datasets such as ERSST and HadISST, these datasets probably retain biases, especially for the period during and before 1945 (ref. 41 ), and these may not be fully accounted for in the uncertainty estimates 42 . Because our reconstructions are calibrated directly to these datasets, future observational-bias corrections are likely to improve proxy-based reconstructions.

Reconstructions of SST that use coral δ 18 O records may be susceptible to the influence of changes in the coral δ 18 O–SST relationship on time periods longer than the instrumental training period, along with non-SST changes in the δ 18 O of seawater, which can covary with salinity. As such, new coral records of temperature-sensitive trace-element ratios such as Sr/Ca, Li/Mg or U/Ca may prove influential in future efforts to distinguish between changes in past temperature and hydroclimate. Owing to the limited availability of multi-century coral data from within the GBR itself, the reconstructed low-frequency variability of GBR SSTs in recent centuries is likely to change as more temperature proxy data become available. It is also likely that new sub-annual resolution records would aid in removing potential signal damping or bias from our use of some annual-resolution records to reconstruct seasonal SSTAs.

Ecological consequences

With global warming of 0.8–1.1 °C above pre-industrial levels 19 there has been a marked increase in mass coral bleaching globally 43 . Even limiting global warming to the Paris Agreement’s ambitious 1.5 °C level would be likely to lead to the loss of 70–90% of corals that are on reefs today 44 . If all current international mitigation commitments are implemented, global mean surface temperature is still estimated to increase in the coming decades, with estimates varying between 1.9 °C (ref. 45 ) and 3.2 °C (ref. 46 ) above pre-industrial levels by the end of this century. Global warming above 2 °C would have disastrous consequences for coral ecosystems 19 , 44 and the hundreds of millions of people who currently depend on them.

Coral reefs of the future, if they can persist, are likely to have a different community structure to those in the recent past, probably one with much less diversity in coral species 4 . This is because mass bleaching events have a differential impact on different coral species. For example, fast-growing branching and tabulate corals are affected more than slower-growing massive species because they have different thermal tolerance 4 . The simplification of reef structures will have adverse impacts on the many thousands of species that rely on the complex three-dimensional structure of reefs 4 . Therefore, even with an ambitious long-term international mitigation goal, the ecological function 4 of the GBR is likely to deteriorate further 5 before it stabilizes.

Coral adaptation and acclimatization may be the only realistic prospect for the conservation of some parts of the GBR this century. However, although adaptation opportunities may be plausible to some extent 47 , they are no panacea because evolutionary changes to fundamental variables such as temperature take decades, if not centuries, to occur, especially in long-lived species such as reef-building corals 48 . There is currently no clear evidence of the real-time evolution of thermally tolerant corals 48 . Most rapid changes depend on a history of exposure to key genetic types and extremes, and there are limitations to genetic adaptation that prevent species-level adaptation to environments outside of their ecological and evolutionary history 19 . Model projections also indicate that rates of coral adaptation are too slow to keep pace with global warming 49 . In a rapidly warming world, the temperature conditions that give rise to mass coral bleaching events are likely to soon become commonplace. So, although we may see some resilience of coral to future marine heat events through acclimatization, thermal refugia are likely to be overwhelmed 50 . Global warming of more than 1.5 °C above pre-industrial levels will probably be catastrophic for coral reefs 44 .

Our new multi-century reconstruction illustrates the exceptional nature of ocean surface warming in the Coral Sea today and the resulting existential risk for the reef-building corals that are the backbone of the GBR. The reconstruction shows that SSTs were relatively cool and stable for hundreds of years, and that recent January–March ocean surface heat in the Coral Sea is unprecedented in at least the past 400 years. The coral colonies and reefs that have lived through the past several centuries, and that yielded the valuable Sr/Ca and δ 18 O data on which our reconstruction is based, are themselves under serious threat. Our analysis of climate-model simulations confirms that human influence is the driver of recent January–March Coral Sea surface warming. Together, the evidence presented in our study indicates that the GBR is in danger. Given this, it is conceivable that UNESCO may in the future reconsider its determination that the iconic GBR is not in danger. In the absence of rapid, coordinated and ambitious global action to combat climate change, we will likely be witness to the demise of one of Earth’s great natural wonders.

Instrumental observations

The Coral Sea and GBR area-averaged monthly SSTAs relative to 1961–90 for January–March are obtained from version 5 of the Extended Reconstructed Sea Surface Temperature dataset (ERSSTv5) 27 . We compare our results using ERSSTv5 with those generated using the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST1.1) 28 . We use only post-1900 instrumental SST observations here. Although gridded datasets have some coverage before 1900, ship-derived temperature data in the region for that period are too sparse to be reliable for calibrating our reconstruction (Supplementary Information section 1.2 ). The regional mean for the GBR is computed using the seven grid-cell locations used by the Australian Bureau of Meteorology (Supplementary Information section 1.1 ). We define the Coral Sea region as the ocean areas inside 4° S–26° S, 142° E–174° E.

Coral-derived temperature proxy data

We use a network of 22 published and publicly available sub-annual and annual resolution temperature-sensitive coral geochemical series (proxies; Fig. 2d , Supplementary Tables 1 and 2 , and Supplementary Fig. 5a–v ) from the western tropical Pacific in our source data region (4° N–27° S, 134° E–184° E) that cover at least the period from 1900 to 1995. Of these 22 series, 16 are δ 18 O, which are in per mil (‰) notation relative to Vienna PeeDee Belemnite (VPDB) 51 ; the remaining six are Sr/Ca series. The coral data are used as predictors in the reconstruction of January–March mean SSTAs in the Coral Sea region. We apply the inverse Rosenblatt transformation 52 , 53 to the coral data to ensure that our reconstruction predictors are normally distributed. Sub-annually resolved series are converted to the annual time step by averaging across the November–April window. This maximizes the detection of the summer peak values, allowing for some inaccuracy in sub-annual dating and the timing of coral skeleton deposition 54 , 55 . A small fraction (less than 0.8%) of missing data is infilled using the regularized expectation maximization (RegEM) algorithm 56 (Supplementary Information section 2.3 ), after which the proxy series are standardized such that each has a mean of zero and a standard deviation of one over their common 1900–1995 period.

Reconstruction method

To produce our Coral Sea reconstruction, we use nested principal component regression 57 (PCR), in which the principal components of the network of 22 coral proxies are used as regressors against the target-region January–March SSTA relative to the 1961–90 average. We perform the reconstructions separately for each nest of proxies, where a nest is a set of proxies that cover the same time period. The longest nest dates back to 1618, when at least two series are available. The nests allow for the use of all coral proxies over the full time period of their coverage. The 96-year portion of the instrumental period (1900–1995) that overlaps with the reconstruction period is used for calibration and evaluation (or equivalently, verification) against observations. We reconstruct regional SSTAs from the principal components of the coral network of δ 18 O and Sr/Ca data, rather than their local SST calibrations, to minimize the number of computational steps and to aid in representing the full reconstruction uncertainty.

Principal component analysis (PCA) is used to reduce the dimensionality of the proxy matrix, as follows. Let P ( t , r ) denote the palaeoclimate-data matrix during the time period t = 1,..., n at an annual time step for proxy series r = 1,..., p . PCA is undertaken on this matrix during the calibration period, P cal . We obtain the principal component coefficients matrix P coeff ( r , e ) for principal components e = 1,..., n PC and principal component scores P score ( t , e ), which are representations of the input matrix P cal in the principal component space. P score is truncated to include n PC,use principal components to form ${P}_{{\rm{score}}}^{{\prime} }$ such that the variance of the proxy network explained by the n PC,use principal components is greater than ${\sigma }_{{\rm{expl}}}^{2}$ (which we set to 95%). Reconstruction tests in which ${\sigma }_{{\rm{expl}}}^{2}$ is varied from 70% to 95% show that our results are not strongly sensitive to this choice, and tests based on lag-one autoregressive noise for ${\sigma }_{{\rm{expl}}}^{2}$ from 50% to 99% further support this choice (Supplementary Information section 3.2 ). These principal components are used as predictors against which the Coral Sea January–March instrumental SSTAs are regressed. We regress the standardized SSTA target data during the calibration period, I cal , against the retained principal components of the predictor data, ${P}_{{\rm{score}}}^{{\prime} }$ :

Thus, we obtain n PC,use estimates of the regression coefficients γ e with gaussian error term ε t ~ N (0, ${\sigma }_{N}^{2}$ ). The principal components are extended back into the pre-instrumental period by multiplying the entire proxy matrix P ( t , p ) with the truncated principal component coefficient matrix ${P}_{{\rm{coeff}}}^{{\prime} }$ ( t , e ) to obtain ${Q}_{{\rm{coeff}}}^{{\prime} }$ :

The reconstruction proceeds with the fitted regression coefficients γ e and extended coefficient matrix ${Q}_{{\rm{coeff}}}^{{\prime} }$ to obtain a reconstruction time series R m ( t ) for a given nest of proxy series

The standardized reconstruction R m ( t ) is then calibrated to the instrumental data such that the standard deviation and mean of the reconstruction and target during the calibration interval are equal. As well as obtaining reconstructions for each nest of available proxies, we compute stitched reconstructions S c ( t ) for each calibration period c , which include at each time step the reconstructed data for the proxy nest with maximum coefficient of efficiency 58 , 59 (Supplementary Information section 3.1 ). This procedure is performed for contiguous calibration intervals between 60 and 80 years duration between 1900 and 1995, with interval width and location increments of two years, reserving the remaining data in the overlapping period for independent evaluation, and for all proxy nests. The reconstruction error is modelled with a lag-one autoregressive process fitted to the residuals. We evaluate the capacity of our reconstruction method to achieve spurious skill from overfitting by performing a test in which we replace the coral data with synthetic noise (Supplementary Information section 3.2i ). We find that reconstructions based on synthetic noise achieve extremely low or zero skill and as more noise principal components are included in the regression, the evaluation metrics indicate declining skill. Our reconstruction and evaluation methods therefore guard against the potential for spurious skill.

Pseudo-proxy reconstructions

Our reconstruction method is further evaluated by using a pseudo-proxy modelling approach based on the Community Earth System Model (CESM) Last Millennium Experiment (LME) 60 , for which there are 13 full-forcing ensemble members covering the period 850–2005. We use the pseudo-proxy reconstructions to evaluate our reconstruction method and coral network in a fully coupled climate-model environment. We form pseudo-proxies by extracting from each LME ensemble member the SST and sea surface salinity (SSS) from the 1.5° × 1.5° grid cell located nearest to our coral data. We then apply proxy system models in the form of linear regression models, basing δ 18 O on both SST and SSS, and Sr/Ca on SST only (Supplementary Information section 3.3 ). We set the spatial and temporal availability of the pseudo-coral network to match that of the coral network. We then apply our PCR reconstruction and evaluation procedure to the pseudo-proxy network, taking advantage of the availability of the modelled Coral Sea SSTA data across the multi-century period of 1618–2005, which allows for the evaluation of the pseudo-proxy reconstruction over this entire time period. We first test our method using a ‘perfect proxy’ approach (with no proxy measurement error) before superimposing synthetic noise on the pseudo-proxy time series, evaluating our methodology at two separate levels of measurement error, quantified by signal-to-noise ratios of 1.0 and 4.0. The evaluation metrics for these tests indicate that our coral network and reconstruction method obtain skilful reconstructions of Coral Sea SSTAs in the climate-model environment (Supplementary Figs. 17b , 18 , 20b , 21 , 22b and 23 ).

Comparison with independent coral datasets

We use two multi-century five-year-resolution coral series from the central GBR 23 , 24 (Fig. 2b and Supplementary Fig. 24 ) and a network of sub-annual and annual resolution modern coral series (dated from 1900 onwards but not covering the full 1900–1995 period) from 44 sites in the GBR (Supplementary Information section 4.2 ) for independent evaluation of coral-derived evidence for warming in the region. We estimate five-year GBR SSTAs (Fig. 2b ) by aligning the post-1900 mean and variance of the proxy and instrumental (ERSSTv5) data.

Reconstruction sensitivity to non-SST influences

Of the 22 available coral series, 16 are records of δ 18 O, a widely used measure of the ratio of the stable isotopes 18 O and 16 O. In the tropical Pacific Ocean, δ 18 O is significantly correlated with SST 61 , 62 , 63 , 64 . Coral δ 18 O is also sensitive to the δ 18 O of seawater 65 , which can reflect advection of different water masses and/or changes in freshwater input, such as from riverine sources or precipitation, which in turn co-vary with SSS. Thus, it is generally considered that the main non-SST contributions to coral δ 18 O are processes that co-vary with SSS 62 , 66 . Our methodology minimizes the influence of non-temperature impacts on the reconstruction by exploiting the contrast in spatial heterogeneity between SST and SSS in January–March (Supplementary Information section 5.1 ). SSS is spatially inhomogeneous in the tropical Pacific 66 , 67 , leading to low coherence in SSS signals across our coral network. By contrast, the strong and coherent SST signal across our coral network locations and the Coral Sea region leads to principal components that are strongly representative of SST variations. This produces a skilful reconstruction of SST, as determined by evaluation against independent observations, and low correlations with SSS across the Coral Sea region (Supplementary Fig. 31 ).

Although the likelihood of non-SST influences on our SST reconstruction is low, we nonetheless test the sensitivity of our reconstruction and its associated interpretations to the possibility of these influences on the coral data. The tests compute the correlations between our best-estimate SSTA reconstruction (highest coefficient of efficiency) and observations of SSS, along with a series of additional reconstructions based on subsets of our coral network. The correlations between our highest coefficient of efficiency January–March Coral Sea SSTA reconstruction and January–March SSS are mapped for the Coral Sea and its neighbouring domain using three instrumental SSS datasets (Supplementary Fig. 31 ). Correlations are not statistically significant over most of the domain. Noting differing spatial correlation patterns between the instrumental SSS datasets 68 , which also cover different time periods (Supplementary Information section 5.1 ), we undertake six sensitivity tests using subsets of the coral network (Supplementary Information section 5.2 ). We use the following combinations of coral series: (1) the full network of 22 δ 18 O and Sr/Ca series (Figs. 2a and 3 ); (2) a subset of the six available Sr/Ca series (Supplementary Figs. 32 – 33 ), to test how the reconstruction is influenced by the inclusion of coral δ 18 O records; (3) a fixed nest subset of the five longest coral series, extending back to at least 1700 (Supplementary Figs. 34 – 35 ), to test for the potential influence of combining series of differing lengths (from our splicing of portions of the best reconstructions from each nest); (4) a subset of the ten coral series that are most strongly correlated with the target (Supplementary Figs. 36 and 37 ), to test how our reconstruction is influenced by the inclusion of coral series that are less strongly correlated with our target; (5) a subset of coral series that excludes the six records that are reported to potentially include biological mediation or non-climatic effects, or have low correlation with the target (Supplementary Figs. 38 and 39 ), to test their influence on the reconstruction; and (6) a network perturbation test comprising 22 separate subsets of proxies, in which proxy records are added incrementally in order of highest to lowest correlation with the target, starting with a single coral series and increasing the number of included proxies to all 22 series in our network (Supplementary Information section 5.2.5 ), to systematically quantify the influence of gradually including more coral datasets on our reconstruction and its interpretations.

The evaluation metrics (Fig. 2c and Supplementary Figs. 32b , 34b , 36b and 38b ) indicate a skilful reconstruction back to 1618 for the reconstructions based on the Full, Sr/Ca only, Long, Best-10 and OmitBioMed networks. These reconstructions explain 82.7%, 80.6%, 77.6%, 79.8% and 80.4% (R-squared values) of the variance in January–March SSTAs, respectively, in the independent evaluation periods (using ERSSTv5b). All coral subsets in the network perturbation test produce skilful reconstructions (Supplementary Fig. 40 ). The highest-skill reconstructions for all subsets in the network perturbation test align with our key interpretations (Supplementary Figs. 41 and 42 ). Together, our sensitivity tests show that the coral network, observational data and reconstruction methodology are a sound basis for reconstructing Coral Sea January–March SSTAs in past centuries and contextualizing recent high-SST events ( Supplementary Information ).

Climate-model attribution ensembles and experiments

The multi-model attribution analysis used here is based on simulations from CMIP6. We analyse simulations from the historical experiment (including natural and anthropogenic influences for 1850–2014) and the historical-natural experiment (natural-only forcings for 1850–2014). We select climate models for which monthly surface temperature is available in at least three historical and historical-natural simulations (Supplementary Table 5 ). All model simulations are interpolated to a common regular 1.5° × 1.5° latitude–longitude grid. January–March SSTAs relative to 1961–90 are calculated for each simulation. The full historical all-forcings ensemble is composed of 14 models with 268 simulations for 1850–2014. The natural-only ensemble is composed of the same 14 models with 95 individual simulations. A subset of climate models in the CMIP6 ensemble are considered by the science community to be ‘too hot’, simulating warming in response to increased atmospheric carbon dioxide concentrations that is larger than that supported by independent evidence 31 . We omit these models from our analysis by including only models with a transient climate response in the ‘likely’ range 31 of 1.4–2.2 °C. Our results are not strongly sensitive to this selection (Supplementary Information section 6.3 ). The ten remaining models yield a total of 25,410 years from 154 historical ensemble members and 7,095 years from 43 historical-natural ensemble members. We weight the models equally in our analysis using bootstrap sampling. We report linear trends based on simple linear regression models fitted with ordinary least squares. The statistical significance of linear trends is assessed using the Spearman’s rank correlation test 69 .

Time of emergence of the anthropogenic impact

We assess the anthropogenic influence on SSTAs in the Coral Sea region by starting with the assumption that any anthropogenic influence on SSTAs in the Coral Sea is indistinguishable from natural variability at the commencement of the model experiments. We measure the impact of anthropogenic influence on the climate in the region using a signal-to-noise approach 32 , 70 . We calculate the anthropogenic ‘signal’ as the mean of the difference between the smoothed (using a 41-year Lowess filter) modelled historical Coral Sea SSTA and the mean smoothed modelled historical-natural SSTA. Our ‘noise’ is the standard deviation of the difference between the modelled historical SSTA and its smoothed time series (Supplementary Information section 6 ).

Methods additionally rely on Supplementary Information and refs. 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 .

Data availability

The ERSSTv5 instrumental SST data are available from the US National Oceanic and Atmospheric Administration at https://psl.noaa.gov/data/gridded/data.noaa.ersst.v5.html . The HadISST1.1 data are available from the UK Met Office at https://www.metoffice.gov.uk/hadobs/hadisst/ . The original coral palaeoclimate data are available at the links provided in Supplementary Table 2 . Land areas for maps are obtained from the Mapping Toolbox v.23.2 in Matlab v.2023b and the Global Self-consistent, Hierarchical, High-resolution Geography (GSHHS) Database at https://www.soest.hawaii.edu/pwessel/gshhg/ through the m_map toolbox by R. Pawlowicz, available at https://www.eoas.ubc.ca/%7Erich/map.html . Prepared data from the coral geochemical series, reconstructions and climate models that support the findings of this study are available at: https://doi.org/10.24433/CO.4883292.v1 .

Code availability

The code that supports the findings of this study is available and can be run at : https://doi.org/10.24433/CO.4883292.v1 .

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Acknowledgements

We acknowledge the originators of the coral data cited in Supplementary Tables 1 and 2 ; S. E. Perkins-Kirkpatrick and the deceased G. J. van Oldenborgh 105 for contributions to an earlier version of this manuscript; E. P. Dassié and J. Zinke for discussions and data; R. Neukom for advice on an earlier version of the reconstruction code; and B. Trewin and K. Braganza for advice about the Bureau of Meteorology GBR SST time series. B.J.H. and H.V.M. acknowledge support from an Australian Research Council (ARC) SRIEAS grant, Securing Antarctica’s Environmental Future (SR200100005), and ARC Discovery Project DP200100206. A.D.K. acknowledges support from an ARC DECRA (DE180100638) and the Australian government’s National Environmental Science Program. B.J.H. and A.D.K. acknowledge an affiliation with the ARC Centre of Excellence for Climate Extremes (CE170100023). H.V.M. acknowledges support from an ARC Future Fellowship (FT140100286). A.K.A. acknowledges support from an Australian government research training program scholarship and an AINSE postgraduate research award. Funding was provided to B.K.L. by the Vetlesen Foundation through a gift to the Lamont-Doherty Earth Observatory. Grants to B.K.L. enabled the generation of coral oxygen isotope and Sr/Ca data from Fiji that were used in our reconstruction (US National Science Foundation OCE-0318296 and ATM-9901649 and US National Oceanic and Atmospheric Administration NA96GP0406). We acknowledge the support of the NCI facility in Australia and the World Climate Research Programme’s working group on coupled modelling, which is responsible for CMIP. We thank the climate-modelling groups for producing and making available their model output. For CMIP, the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provided coordinating support and led the development of software infrastructure in partnership with the Global Organisation for Earth System Science Portals.

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Benjamin J. Henley, Helen V. McGregor & Ariella K. Arzey

Securing Antarctica’s Environmental Future, University of Wollongong, Wollongong, New South Wales, Australia

School of Agriculture, Food and Ecosystem Sciences, University of Melbourne, Parkville, Victoria, Australia

Benjamin J. Henley

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Andrew D. King & David J. Karoly

ARC Centre of Excellence for Climate Extremes, University of Melbourne, Parkville, Victoria, Australia

Andrew D. King

School of the Environment, The University of Queensland, Brisbane, Queensland, Australia

Ove Hoegh-Guldberg

Australian Institute of Marine Science, Townsville, Queensland, Australia

Janice M. Lough

ARC Centre of Excellence for Coral Reef Studies and School of Earth Sciences, University of Western Australia, Crawley, Western Australia, Australia

Thomas M. DeCarlo

Department of Earth and Environmental Sciences, Tulane University, New Orleans, LA, USA

Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY, USA

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Contributions

B.J.H., H.V.M. and A.D.K. conceived the study and developed the methodology. B.J.H. did most of the analysis. A.K.A. contributed analysis of modern coral data (Supplementary Information section 4.2 ). T.M.D. contributed analysis of instrumental data coverage (Supplementary Information section 1.2 ). B.K.L. contributed sub-annual coral data. B.J.H. and H.V.M. led the preparation of the manuscript, with contributions from A.D.K., O.H.-G., A.K.A., D.J.K., J.M.L., T.M.D. and B.K.L. Generative artificial intelligence was not used in any aspect of this study or manuscript.

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Henley, B.J., McGregor, H.V., King, A.D. et al. Highest ocean heat in four centuries places Great Barrier Reef in danger. Nature 632 , 320–326 (2024). https://doi.org/10.1038/s41586-024-07672-x

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