what are research questions and hypothesis

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Research Questions vs Hypothesis: What’s The Difference?

by  Antony W

August 1, 2024

You’ll need to come up with a research question or a hypothesis to guide your next research project. But what is a hypothesis in the first place? What is the perfect definition for a research question? And, what’s the difference between the two?

In this guide to research questions vs hypothesis, we’ll look at the definition of each component and the difference between the two.

We’ll also look at when a research question and a hypothesis may be useful and provide you with some tips that you can use to come up with hypothesis and research questions that will suit your research topic . 

Let’s get to it.

What’s a Research Question?

We define a research question as the exact question you want to answer on a given topic or research project. Good research questions should be clear and easy to understand, allow for the collection of necessary data, and be specific and relevant to your field of study.

Research questions are part of heuristic research methods, where researchers use personal experiences and observations to understand a research subject. By using such approaches to explore the question, you should be able to provide an analytical justification of why and how you should respond to the question. 

While it’s common for researchers to focus on one question at a time, more complex topics may require two or more questions to cover in-depth.

When is a Research Question Useful? 

A research question may be useful when and if: 

• There isn’t enough previous research on the topic
• You want to report a wider range out of outcome when doing your research project
• You want to conduct a more open ended inquiries 

Perhaps the biggest drawback with research questions is that they tend to researchers in a position to “fish expectations” or excessively manipulate their findings.

Again, research questions sometimes tend to be less specific, and the reason is that there often no sufficient previous research on the questions.

What’s a Hypothesis? 

A hypothesis is a statement you can approve or disapprove. You develop a hypothesis from a research question by changing the question into a statement.

Primarily applied in deductive research, it involves the use of scientific, mathematical, and sociological findings to agree to or write off an assumption.

Researchers use the null approach for statements they can disapprove. They take a hypothesis and add a “not” to it to make it a working null hypothesis.

A null hypothesis is quite common in scientific methods. In this case, you have to formulate a hypothesis, and then conduct an investigation to disapprove the statement.

If you can disapprove the statement, you develop another hypothesis and then repeat the process until you can’t disapprove the statement.

In other words, if a hypothesis is true, then it must have been repeatedly tested and verified.

The consensus among researchers is that, like research questions, a hypothesis should not only be clear and easy to understand but also have a definite focus, answerable, and relevant to your field of study. 

When is a Hypothesis Useful?

A hypothesis may be useful when or if:

• There’s enough previous research on the topic
• You want to test a specific model or a particular theory
• You anticipate a likely outcome in advance 

The drawback to hypothesis as a scientific method is that it can hinder flexibility, or possibly blind a researcher not to see unanticipated results.

Research Question vs Hypothesis: Which One Should Come First 

Researchers use scientific methods to hone on different theories. So if the purpose of the research project were to analyze a concept, a scientific method would be necessary.

Such a case requires coming up with a research question first, followed by a scientific method.

Since a hypothesis is part of a research method, it will come after the research question.

Research Question vs Hypothesis: What’s the Difference? 

The following are the differences between a research question and a hypothesis.

We look at the differences in purpose and structure, writing, as well as conclusion. 

Research Questions vs Hypothesis: Some Useful Advice 

As much as there are differences between hypothesis and research questions, you have to state either one in the introduction and then repeat the same in the conclusion of your research paper.

Whichever element you opt to use, you should clearly demonstrate that you understand your topic, have achieved the goal of your research project, and not swayed a bit in your research process.

If it helps, start and conclude every chapter of your research project by providing additional information on how you’ve or will address the hypothesis or research question.

You should also include the aims and objectives of coming up with the research question or formulating the hypothesis. Doing so will go a long way to demonstrate that you have a strong focus on the research issue at hand. 

Research Questions vs Hypothesis: Conclusion 

If you need help with coming up with research questions, formulating a hypothesis, and completing your research paper writing , feel free to talk to us. 

About the author 

Antony W is a professional writer and coach at Help for Assessment. He spends countless hours every day researching and writing great content filled with expert advice on how to write engaging essays, research papers, and assignments.

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Research Questions & Hypotheses

Generally, in quantitative studies, reviewers expect hypotheses rather than research questions. However, both research questions and hypotheses serve different purposes and can be beneficial when used together.

Research Questions

Clarify the research’s aim (farrugia et al., 2010).

• Research often begins with an interest in a topic, but a deep understanding of the subject is crucial to formulate an appropriate research question.
• Descriptive: “What factors most influence the academic achievement of senior high school students?”
• Comparative: “What is the performance difference between teaching methods A and B?”
• Relationship-based: “What is the relationship between self-efficacy and academic achievement?”
• Increasing knowledge about a subject can be achieved through systematic literature reviews, in-depth interviews with patients (and proxies), focus groups, and consultations with field experts.
• Some funding bodies, like the Canadian Institute for Health Research, recommend conducting a systematic review or a pilot study before seeking grants for full trials.
• The presence of multiple research questions in a study can complicate the design, statistical analysis, and feasibility.
• It’s advisable to focus on a single primary research question for the study.
• The primary question, clearly stated at the end of a grant proposal’s introduction, usually specifies the study population, intervention, and other relevant factors.
• The FINER criteria underscore aspects that can enhance the chances of a successful research project, including specifying the population of interest, aligning with scientific and public interest, clinical relevance, and contribution to the field, while complying with ethical and national research standards.

• The P ICOT approach is crucial in developing the study’s framework and protocol, influencing inclusion and exclusion criteria and identifying patient groups for inclusion.

• Defining the specific population, intervention, comparator, and outcome helps in selecting the right outcome measurement tool.
• The more precise the population definition and stricter the inclusion and exclusion criteria, the more significant the impact on the interpretation, applicability, and generalizability of the research findings.
• A restricted study population enhances internal validity but may limit the study’s external validity and generalizability to clinical practice.
• A broadly defined study population may better reflect clinical practice but could increase bias and reduce internal validity.
• An inadequately formulated research question can negatively impact study design, potentially leading to ineffective outcomes and affecting publication prospects.

Checklist: Good research questions for social science projects (Panke, 2018)

Research Hypotheses

Present the researcher’s predictions based on specific statements.

• These statements define the research problem or issue and indicate the direction of the researcher’s predictions.
• Formulating the research question and hypothesis from existing data (e.g., a database) can lead to multiple statistical comparisons and potentially spurious findings due to chance.
• The research or clinical hypothesis, derived from the research question, shapes the study’s key elements: sampling strategy, intervention, comparison, and outcome variables.
• Hypotheses can express a single outcome or multiple outcomes.
• After statistical testing, the null hypothesis is either rejected or not rejected based on whether the study’s findings are statistically significant.
• Hypothesis testing helps determine if observed findings are due to true differences and not chance.
• Hypotheses can be 1-sided (specific direction of difference) or 2-sided (presence of a difference without specifying direction).
• 2-sided hypotheses are generally preferred unless there’s a strong justification for a 1-sided hypothesis.
• A solid research hypothesis, informed by a good research question, influences the research design and paves the way for defining clear research objectives.

Types of Research Hypothesis

• In a Y-centered research design, the focus is on the dependent variable (DV) which is specified in the research question. Theories are then used to identify independent variables (IV) and explain their causal relationship with the DV.
• Example: “An increase in teacher-led instructional time (IV) is likely to improve student reading comprehension scores (DV), because extensive guided practice under expert supervision enhances learning retention and skill mastery.”
• Hypothesis Explanation: The dependent variable (student reading comprehension scores) is the focus, and the hypothesis explores how changes in the independent variable (teacher-led instructional time) affect it.
• In X-centered research designs, the independent variable is specified in the research question. Theories are used to determine potential dependent variables and the causal mechanisms at play.
• Example: “Implementing technology-based learning tools (IV) is likely to enhance student engagement in the classroom (DV), because interactive and multimedia content increases student interest and participation.”
• Hypothesis Explanation: The independent variable (technology-based learning tools) is the focus, with the hypothesis exploring its impact on a potential dependent variable (student engagement).
• Probabilistic hypotheses suggest that changes in the independent variable are likely to lead to changes in the dependent variable in a predictable manner, but not with absolute certainty.
• Example: “The more teachers engage in professional development programs (IV), the more their teaching effectiveness (DV) is likely to improve, because continuous training updates pedagogical skills and knowledge.”
• Hypothesis Explanation: This hypothesis implies a probable relationship between the extent of professional development (IV) and teaching effectiveness (DV).
• Deterministic hypotheses state that a specific change in the independent variable will lead to a specific change in the dependent variable, implying a more direct and certain relationship.
• Example: “If the school curriculum changes from traditional lecture-based methods to project-based learning (IV), then student collaboration skills (DV) are expected to improve because project-based learning inherently requires teamwork and peer interaction.”
• Hypothesis Explanation: This hypothesis presumes a direct and definite outcome (improvement in collaboration skills) resulting from a specific change in the teaching method.
• Example : “Students who identify as visual learners will score higher on tests that are presented in a visually rich format compared to tests presented in a text-only format.”
• Explanation : This hypothesis aims to describe the potential difference in test scores between visual learners taking visually rich tests and text-only tests, without implying a direct cause-and-effect relationship.
• Example : “Teaching method A will improve student performance more than method B.”
• Explanation : This hypothesis compares the effectiveness of two different teaching methods, suggesting that one will lead to better student performance than the other. It implies a direct comparison but does not necessarily establish a causal mechanism.
• Example : “Students with higher self-efficacy will show higher levels of academic achievement.”
• Explanation : This hypothesis predicts a relationship between the variable of self-efficacy and academic achievement. Unlike a causal hypothesis, it does not necessarily suggest that one variable causes changes in the other, but rather that they are related in some way.

Tips for developing research questions and hypotheses for research studies

• Perform a systematic literature review (if one has not been done) to increase knowledge and familiarity with the topic and to assist with research development.
• Learn about current trends and technological advances on the topic.
• Seek careful input from experts, mentors, colleagues, and collaborators to refine your research question as this will aid in developing the research question and guide the research study.
• Use the FINER criteria in the development of the research question.
• Ensure that the research question follows PICOT format.
• Develop a research hypothesis from the research question.
• Ensure that the research question and objectives are answerable, feasible, and clinically relevant.

If your research hypotheses are derived from your research questions, particularly when multiple hypotheses address a single question, it’s recommended to use both research questions and hypotheses. However, if this isn’t the case, using hypotheses over research questions is advised. It’s important to note these are general guidelines, not strict rules. If you opt not to use hypotheses, consult with your supervisor for the best approach.

Farrugia, P., Petrisor, B. A., Farrokhyar, F., & Bhandari, M. (2010). Practical tips for surgical research: Research questions, hypotheses and objectives.  Canadian journal of surgery. Journal canadien de chirurgie ,  53 (4), 278–281.

Hulley, S. B., Cummings, S. R., Browner, W. S., Grady, D., & Newman, T. B. (2007). Designing clinical research. Philadelphia.

Panke, D. (2018). Research design & method selection: Making good choices in the social sciences.  Research Design & Method Selection , 1-368.

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The Craft of Writing a Strong Hypothesis

Table of Contents

Writing a hypothesis is one of the essential elements of a scientific research paper. It needs to be to the point, clearly communicating what your research is trying to accomplish. A blurry, drawn-out, or complexly-structured hypothesis can confuse your readers. Or worse, the editor and peer reviewers.

A captivating hypothesis is not too intricate. This blog will take you through the process so that, by the end of it, you have a better idea of how to convey your research paper's intent in just one sentence.

What is a Hypothesis?

The first step in your scientific endeavor, a hypothesis, is a strong, concise statement that forms the basis of your research. It is not the same as a thesis statement , which is a brief summary of your research paper .

The sole purpose of a hypothesis is to predict your paper's findings, data, and conclusion. It comes from a place of curiosity and intuition . When you write a hypothesis, you're essentially making an educated guess based on scientific prejudices and evidence, which is further proven or disproven through the scientific method.

The reason for undertaking research is to observe a specific phenomenon. A hypothesis, therefore, lays out what the said phenomenon is. And it does so through two variables, an independent and dependent variable.

The independent variable is the cause behind the observation, while the dependent variable is the effect of the cause. A good example of this is “mixing red and blue forms purple.” In this hypothesis, mixing red and blue is the independent variable as you're combining the two colors at your own will. The formation of purple is the dependent variable as, in this case, it is conditional to the independent variable.

Different Types of Hypotheses‌

Types of hypotheses

Some would stand by the notion that there are only two types of hypotheses: a Null hypothesis and an Alternative hypothesis. While that may have some truth to it, it would be better to fully distinguish the most common forms as these terms come up so often, which might leave you out of context.

Apart from Null and Alternative, there are Complex, Simple, Directional, Non-Directional, Statistical, and Associative and casual hypotheses. They don't necessarily have to be exclusive, as one hypothesis can tick many boxes, but knowing the distinctions between them will make it easier for you to construct your own.

1. Null hypothesis

A null hypothesis proposes no relationship between two variables. Denoted by H 0 , it is a negative statement like “Attending physiotherapy sessions does not affect athletes' on-field performance.” Here, the author claims physiotherapy sessions have no effect on on-field performances. Even if there is, it's only a coincidence.

2. Alternative hypothesis

Considered to be the opposite of a null hypothesis, an alternative hypothesis is donated as H1 or Ha. It explicitly states that the dependent variable affects the independent variable. A good  alternative hypothesis example is “Attending physiotherapy sessions improves athletes' on-field performance.” or “Water evaporates at 100 °C. ” The alternative hypothesis further branches into directional and non-directional.

• Directional hypothesis: A hypothesis that states the result would be either positive or negative is called directional hypothesis. It accompanies H1 with either the ‘<' or ‘>' sign.
• Non-directional hypothesis: A non-directional hypothesis only claims an effect on the dependent variable. It does not clarify whether the result would be positive or negative. The sign for a non-directional hypothesis is ‘≠.'

3. Simple hypothesis

A simple hypothesis is a statement made to reflect the relation between exactly two variables. One independent and one dependent. Consider the example, “Smoking is a prominent cause of lung cancer." The dependent variable, lung cancer, is dependent on the independent variable, smoking.

4. Complex hypothesis

In contrast to a simple hypothesis, a complex hypothesis implies the relationship between multiple independent and dependent variables. For instance, “Individuals who eat more fruits tend to have higher immunity, lesser cholesterol, and high metabolism.” The independent variable is eating more fruits, while the dependent variables are higher immunity, lesser cholesterol, and high metabolism.

5. Associative and casual hypothesis

Associative and casual hypotheses don't exhibit how many variables there will be. They define the relationship between the variables. In an associative hypothesis, changing any one variable, dependent or independent, affects others. In a casual hypothesis, the independent variable directly affects the dependent.

6. Empirical hypothesis

Also referred to as the working hypothesis, an empirical hypothesis claims a theory's validation via experiments and observation. This way, the statement appears justifiable and different from a wild guess.

Say, the hypothesis is “Women who take iron tablets face a lesser risk of anemia than those who take vitamin B12.” This is an example of an empirical hypothesis where the researcher  the statement after assessing a group of women who take iron tablets and charting the findings.

7. Statistical hypothesis

The point of a statistical hypothesis is to test an already existing hypothesis by studying a population sample. Hypothesis like “44% of the Indian population belong in the age group of 22-27.” leverage evidence to prove or disprove a particular statement.

Characteristics of a Good Hypothesis

Writing a hypothesis is essential as it can make or break your research for you. That includes your chances of getting published in a journal. So when you're designing one, keep an eye out for these pointers:

• A research hypothesis has to be simple yet clear to look justifiable enough.
• It has to be testable — your research would be rendered pointless if too far-fetched into reality or limited by technology.
• It has to be precise about the results —what you are trying to do and achieve through it should come out in your hypothesis.
• A research hypothesis should be self-explanatory, leaving no doubt in the reader's mind.
• If you are developing a relational hypothesis, you need to include the variables and establish an appropriate relationship among them.
• A hypothesis must keep and reflect the scope for further investigations and experiments.

Separating a Hypothesis from a Prediction

Outside of academia, hypothesis and prediction are often used interchangeably. In research writing, this is not only confusing but also incorrect. And although a hypothesis and prediction are guesses at their core, there are many differences between them.

A hypothesis is an educated guess or even a testable prediction validated through research. It aims to analyze the gathered evidence and facts to define a relationship between variables and put forth a logical explanation behind the nature of events.

Predictions are assumptions or expected outcomes made without any backing evidence. They are more fictionally inclined regardless of where they originate from.

For this reason, a hypothesis holds much more weight than a prediction. It sticks to the scientific method rather than pure guesswork. "Planets revolve around the Sun." is an example of a hypothesis as it is previous knowledge and observed trends. Additionally, we can test it through the scientific method.

Whereas "COVID-19 will be eradicated by 2030." is a prediction. Even though it results from past trends, we can't prove or disprove it. So, the only way this gets validated is to wait and watch if COVID-19 cases end by 2030.

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1.  Be clear about your research question

A hypothesis should instantly address the research question or the problem statement. To do so, you need to ask a question. Understand the constraints of your undertaken research topic and then formulate a simple and topic-centric problem. Only after that can you develop a hypothesis and further test for evidence.

2. Carry out a recce

Once you have your research's foundation laid out, it would be best to conduct preliminary research. Go through previous theories, academic papers, data, and experiments before you start curating your research hypothesis. It will give you an idea of your hypothesis's viability or originality.

Making use of references from relevant research papers helps draft a good research hypothesis. SciSpace Discover offers a repository of over 270 million research papers to browse through and gain a deeper understanding of related studies on a particular topic. Additionally, you can use SciSpace Copilot , your AI research assistant, for reading any lengthy research paper and getting a more summarized context of it. A hypothesis can be formed after evaluating many such summarized research papers. Copilot also offers explanations for theories and equations, explains paper in simplified version, allows you to highlight any text in the paper or clip math equations and tables and provides a deeper, clear understanding of what is being said. This can improve the hypothesis by helping you identify potential research gaps.

3. Create a 3-dimensional hypothesis

Variables are an essential part of any reasonable hypothesis. So, identify your independent and dependent variable(s) and form a correlation between them. The ideal way to do this is to write the hypothetical assumption in the ‘if-then' form. If you use this form, make sure that you state the predefined relationship between the variables.

In another way, you can choose to present your hypothesis as a comparison between two variables. Here, you must specify the difference you expect to observe in the results.

4. Write the first draft

Now that everything is in place, it's time to write your hypothesis. For starters, create the first draft. In this version, write what you expect to find from your research.

Clearly separate your independent and dependent variables and the link between them. Don't fixate on syntax at this stage. The goal is to ensure your hypothesis addresses the issue.

5. Proof your hypothesis

After preparing the first draft of your hypothesis, you need to inspect it thoroughly. It should tick all the boxes, like being concise, straightforward, relevant, and accurate. Your final hypothesis has to be well-structured as well.

Research projects are an exciting and crucial part of being a scholar. And once you have your research question, you need a great hypothesis to begin conducting research. Thus, knowing how to write a hypothesis is very important.

Now that you have a firmer grasp on what a good hypothesis constitutes, the different kinds there are, and what process to follow, you will find it much easier to write your hypothesis, which ultimately helps your research.

Now it's easier than ever to streamline your research workflow with SciSpace Discover . Its integrated, comprehensive end-to-end platform for research allows scholars to easily discover, write and publish their research and fosters collaboration.

It includes everything you need, including a repository of over 270 million research papers across disciplines, SEO-optimized summaries and public profiles to show your expertise and experience.

If you found these tips on writing a research hypothesis useful, head over to our blog on Statistical Hypothesis Testing to learn about the top researchers, papers, and institutions in this domain.

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

According to the Oxford dictionary, a hypothesis is defined as “An idea or explanation of something that is based on a few known facts, but that has not yet been proved to be true or correct”.

2. What is an example of hypothesis?

The hypothesis is a statement that proposes a relationship between two or more variables. An example: "If we increase the number of new users who join our platform by 25%, then we will see an increase in revenue."

3. What is an example of null hypothesis?

A null hypothesis is a statement that there is no relationship between two variables. The null hypothesis is written as H0. The null hypothesis states that there is no effect. For example, if you're studying whether or not a particular type of exercise increases strength, your null hypothesis will be "there is no difference in strength between people who exercise and people who don't."

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

• Your hypothesis should be able to predict the relationship and outcome.

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

• Null hypotheses are used to test the claim that "there is no difference between two groups of data".

• Alternative hypotheses test the claim that "there is a difference between two data groups".

7. Difference between research question and research hypothesis?

A research question is a broad, open-ended question you will try to answer through your research. A hypothesis is a statement based on prior research or theory that you expect to be true due to your study. Example - Research question: What are the factors that influence the adoption of the new technology? Research hypothesis: There is a positive relationship between age, education and income level with the adoption of the new technology.

8. What is plural for hypothesis?

The plural of hypothesis is hypotheses. Here's an example of how it would be used in a statement, "Numerous well-considered hypotheses are presented in this part, and they are supported by tables and figures that are well-illustrated."

9. What is the red queen hypothesis?

The red queen hypothesis in evolutionary biology states that species must constantly evolve to avoid extinction because if they don't, they will be outcompeted by other species that are evolving. Leigh Van Valen first proposed it in 1973; since then, it has been tested and substantiated many times.

10. Who is known as the father of null hypothesis?

The father of the null hypothesis is Sir Ronald Fisher. He published a paper in 1925 that introduced the concept of null hypothesis testing, and he was also the first to use the term itself.

11. When to reject null hypothesis?

You need to find a significant difference between your two populations to reject the null hypothesis. You can determine that by running statistical tests such as an independent sample t-test or a dependent sample t-test. You should reject the null hypothesis if the p-value is less than 0.05.

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What is a Research Hypothesis: How to Write it, Types, and Examples

Any research begins with a research question and a research hypothesis . A research question alone may not suffice to design the experiment(s) needed to answer it. A hypothesis is central to the scientific method. But what is a hypothesis ? A hypothesis is a testable statement that proposes a possible explanation to a phenomenon, and it may include a prediction. Next, you may ask what is a research hypothesis ? Simply put, a research hypothesis is a prediction or educated guess about the relationship between the variables that you want to investigate.  

It is important to be thorough when developing your research hypothesis. Shortcomings in the framing of a hypothesis can affect the study design and the results. A better understanding of the research hypothesis definition and characteristics of a good hypothesis will make it easier for you to develop your own hypothesis for your research. Let’s dive in to know more about the types of research hypothesis , how to write a research hypothesis , and some research hypothesis examples .  

Table of Contents

What is a hypothesis ?  

A hypothesis is based on the existing body of knowledge in a study area. Framed before the data are collected, a hypothesis states the tentative relationship between independent and dependent variables, along with a prediction of the outcome.  

What is a research hypothesis ?  

Young researchers starting out their journey are usually brimming with questions like “ What is a hypothesis ?” “ What is a research hypothesis ?” “How can I write a good research hypothesis ?”   

A research hypothesis is a statement that proposes a possible explanation for an observable phenomenon or pattern. It guides the direction of a study and predicts the outcome of the investigation. A research hypothesis is testable, i.e., it can be supported or disproven through experimentation or observation.     

Characteristics of a good hypothesis  

Here are the characteristics of a good hypothesis :  

• Clearly formulated and free of language errors and ambiguity  
• Concise and not unnecessarily verbose  
• Has clearly defined variables  
• Testable and stated in a way that allows for it to be disproven  
• Can be tested using a research design that is feasible, ethical, and practical   
• Specific and relevant to the research problem  
• Rooted in a thorough literature search  
• Can generate new knowledge or understanding.  

How to create an effective research hypothesis  

A study begins with the formulation of a research question. A researcher then performs background research. This background information forms the basis for building a good research hypothesis . The researcher then performs experiments, collects, and analyzes the data, interprets the findings, and ultimately, determines if the findings support or negate the original hypothesis.  

Let’s look at each step for creating an effective, testable, and good research hypothesis :  

• Identify a research problem or question: Start by identifying a specific research problem.   
• Review the literature: Conduct an in-depth review of the existing literature related to the research problem to grasp the current knowledge and gaps in the field.   
• Formulate a clear and testable hypothesis : Based on the research question, use existing knowledge to form a clear and testable hypothesis . The hypothesis should state a predicted relationship between two or more variables that can be measured and manipulated. Improve the original draft till it is clear and meaningful.  
• State the null hypothesis: The null hypothesis is a statement that there is no relationship between the variables you are studying.   
• Define the population and sample: Clearly define the population you are studying and the sample you will be using for your research.  
• Select appropriate methods for testing the hypothesis: Select appropriate research methods, such as experiments, surveys, or observational studies, which will allow you to test your research hypothesis .  

Remember that creating a research hypothesis is an iterative process, i.e., you might have to revise it based on the data you collect. You may need to test and reject several hypotheses before answering the research problem.  

How to write a research hypothesis  

When you start writing a research hypothesis , you use an “if–then” statement format, which states the predicted relationship between two or more variables. Clearly identify the independent variables (the variables being changed) and the dependent variables (the variables being measured), as well as the population you are studying. Review and revise your hypothesis as needed.  

An example of a research hypothesis in this format is as follows:  

“ If [athletes] follow [cold water showers daily], then their [endurance] increases.”  

Population: athletes  

Independent variable: daily cold water showers  

Dependent variable: endurance  

You may have understood the characteristics of a good hypothesis . But note that a research hypothesis is not always confirmed; a researcher should be prepared to accept or reject the hypothesis based on the study findings.  

Research hypothesis checklist  

Following from above, here is a 10-point checklist for a good research hypothesis :  

• Testable: A research hypothesis should be able to be tested via experimentation or observation.  
• Specific: A research hypothesis should clearly state the relationship between the variables being studied.  
• Based on prior research: A research hypothesis should be based on existing knowledge and previous research in the field.  
• Falsifiable: A research hypothesis should be able to be disproven through testing.  
• Clear and concise: A research hypothesis should be stated in a clear and concise manner.  
• Logical: A research hypothesis should be logical and consistent with current understanding of the subject.  
• Relevant: A research hypothesis should be relevant to the research question and objectives.  
• Feasible: A research hypothesis should be feasible to test within the scope of the study.  
• Reflects the population: A research hypothesis should consider the population or sample being studied.  
• Uncomplicated: A good research hypothesis is written in a way that is easy for the target audience to understand.  

By following this research hypothesis checklist , you will be able to create a research hypothesis that is strong, well-constructed, and more likely to yield meaningful results.  

Types of research hypothesis  

Different types of research hypothesis are used in scientific research:  

1. Null hypothesis:

A null hypothesis states that there is no change in the dependent variable due to changes to the independent variable. This means that the results are due to chance and are not significant. A null hypothesis is denoted as H0 and is stated as the opposite of what the alternative hypothesis states.   

Example: “ The newly identified virus is not zoonotic .”  

2. Alternative hypothesis:

This states that there is a significant difference or relationship between the variables being studied. It is denoted as H1 or Ha and is usually accepted or rejected in favor of the null hypothesis.  

Example: “ The newly identified virus is zoonotic .”  

3. Directional hypothesis :

This specifies the direction of the relationship or difference between variables; therefore, it tends to use terms like increase, decrease, positive, negative, more, or less.   

Example: “ The inclusion of intervention X decreases infant mortality compared to the original treatment .”   

4. Non-directional hypothesis:

While it does not predict the exact direction or nature of the relationship between the two variables, a non-directional hypothesis states the existence of a relationship or difference between variables but not the direction, nature, or magnitude of the relationship. A non-directional hypothesis may be used when there is no underlying theory or when findings contradict previous research.  

Example, “ Cats and dogs differ in the amount of affection they express .”  

5. Simple hypothesis :

A simple hypothesis only predicts the relationship between one independent and another independent variable.  

Example: “ Applying sunscreen every day slows skin aging .”  

6 . Complex hypothesis :

A complex hypothesis states the relationship or difference between two or more independent and dependent variables.   

Example: “ Applying sunscreen every day slows skin aging, reduces sun burn, and reduces the chances of skin cancer .” (Here, the three dependent variables are slowing skin aging, reducing sun burn, and reducing the chances of skin cancer.)  

7. Associative hypothesis:  

An associative hypothesis states that a change in one variable results in the change of the other variable. The associative hypothesis defines interdependency between variables.  

Example: “ There is a positive association between physical activity levels and overall health .”  

8 . Causal hypothesis:

A causal hypothesis proposes a cause-and-effect interaction between variables.  

Example: “ Long-term alcohol use causes liver damage .”  

Note that some of the types of research hypothesis mentioned above might overlap. The types of hypothesis chosen will depend on the research question and the objective of the study.  

Research hypothesis examples  

Here are some good research hypothesis examples :  

“The use of a specific type of therapy will lead to a reduction in symptoms of depression in individuals with a history of major depressive disorder.”  

“Providing educational interventions on healthy eating habits will result in weight loss in overweight individuals.”  

“Plants that are exposed to certain types of music will grow taller than those that are not exposed to music.”  

“The use of the plant growth regulator X will lead to an increase in the number of flowers produced by plants.”  

Characteristics that make a research hypothesis weak are unclear variables, unoriginality, being too general or too vague, and being untestable. A weak hypothesis leads to weak research and improper methods.   

Some bad research hypothesis examples (and the reasons why they are “bad”) are as follows:  

“This study will show that treatment X is better than any other treatment . ” (This statement is not testable, too broad, and does not consider other treatments that may be effective.)  

“This study will prove that this type of therapy is effective for all mental disorders . ” (This statement is too broad and not testable as mental disorders are complex and different disorders may respond differently to different types of therapy.)  

“Plants can communicate with each other through telepathy . ” (This statement is not testable and lacks a scientific basis.)  

Importance of testable hypothesis  

If a research hypothesis is not testable, the results will not prove or disprove anything meaningful. The conclusions will be vague at best. A testable hypothesis helps a researcher focus on the study outcome and understand the implication of the question and the different variables involved. A testable hypothesis helps a researcher make precise predictions based on prior research.  

To be considered testable, there must be a way to prove that the hypothesis is true or false; further, the results of the hypothesis must be reproducible.  

Frequently Asked Questions (FAQs) on research hypothesis  

1. What is the difference between research question and research hypothesis ?  

A research question defines the problem and helps outline the study objective(s). It is an open-ended statement that is exploratory or probing in nature. Therefore, it does not make predictions or assumptions. It helps a researcher identify what information to collect. A research hypothesis , however, is a specific, testable prediction about the relationship between variables. Accordingly, it guides the study design and data analysis approach.

2. When to reject null hypothesis ?

A null hypothesis should be rejected when the evidence from a statistical test shows that it is unlikely to be true. This happens when the test statistic (e.g., p -value) is less than the defined significance level (e.g., 0.05). Rejecting the null hypothesis does not necessarily mean that the alternative hypothesis is true; it simply means that the evidence found is not compatible with the null hypothesis.  

3. How can I be sure my hypothesis is testable?  

A testable hypothesis should be specific and measurable, and it should state a clear relationship between variables that can be tested with data. To ensure that your hypothesis is testable, consider the following:  

• Clearly define the key variables in your hypothesis. You should be able to measure and manipulate these variables in a way that allows you to test the hypothesis.  
• The hypothesis should predict a specific outcome or relationship between variables that can be measured or quantified.   
• You should be able to collect the necessary data within the constraints of your study.  
• It should be possible for other researchers to replicate your study, using the same methods and variables.   
• Your hypothesis should be testable by using appropriate statistical analysis techniques, so you can draw conclusions, and make inferences about the population from the sample data.  
• The hypothesis should be able to be disproven or rejected through the collection of data.  

4. How do I revise my research hypothesis if my data does not support it?  

If your data does not support your research hypothesis , you will need to revise it or develop a new one. You should examine your data carefully and identify any patterns or anomalies, re-examine your research question, and/or revisit your theory to look for any alternative explanations for your results. Based on your review of the data, literature, and theories, modify your research hypothesis to better align it with the results you obtained. Use your revised hypothesis to guide your research design and data collection. It is important to remain objective throughout the process.  

5. I am performing exploratory research. Do I need to formulate a research hypothesis?  

As opposed to “confirmatory” research, where a researcher has some idea about the relationship between the variables under investigation, exploratory research (or hypothesis-generating research) looks into a completely new topic about which limited information is available. Therefore, the researcher will not have any prior hypotheses. In such cases, a researcher will need to develop a post-hoc hypothesis. A post-hoc research hypothesis is generated after these results are known.  

6. How is a research hypothesis different from a research question?

A research question is an inquiry about a specific topic or phenomenon, typically expressed as a question. It seeks to explore and understand a particular aspect of the research subject. In contrast, a research hypothesis is a specific statement or prediction that suggests an expected relationship between variables. It is formulated based on existing knowledge or theories and guides the research design and data analysis.

7. Can a research hypothesis change during the research process?

Yes, research hypotheses can change during the research process. As researchers collect and analyze data, new insights and information may emerge that require modification or refinement of the initial hypotheses. This can be due to unexpected findings, limitations in the original hypotheses, or the need to explore additional dimensions of the research topic. Flexibility is crucial in research, allowing for adaptation and adjustment of hypotheses to align with the evolving understanding of the subject matter.

8. How many hypotheses should be included in a research study?

The number of research hypotheses in a research study varies depending on the nature and scope of the research. It is not necessary to have multiple hypotheses in every study. Some studies may have only one primary hypothesis, while others may have several related hypotheses. The number of hypotheses should be determined based on the research objectives, research questions, and the complexity of the research topic. It is important to ensure that the hypotheses are focused, testable, and directly related to the research aims.

9. Can research hypotheses be used in qualitative research?

Yes, research hypotheses can be used in qualitative research, although they are more commonly associated with quantitative research. In qualitative research, hypotheses may be formulated as tentative or exploratory statements that guide the investigation. Instead of testing hypotheses through statistical analysis, qualitative researchers may use the hypotheses to guide data collection and analysis, seeking to uncover patterns, themes, or relationships within the qualitative data. The emphasis in qualitative research is often on generating insights and understanding rather than confirming or rejecting specific research hypotheses through statistical testing.

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• Writing Strong Research Questions | Criteria & Examples

Writing Strong Research Questions | Criteria & Examples

Published on October 26, 2022 by Shona McCombes . Revised on November 21, 2023.

A research question pinpoints exactly what you want to find out in your work. A good research question is essential to guide your research paper , dissertation , or thesis .

All research questions should be:

• Focused on a single problem or issue
• Researchable using primary and/or secondary sources
• Feasible to answer within the timeframe and practical constraints
• Specific enough to answer thoroughly
• Complex enough to develop the answer over the space of a paper or thesis
• Relevant to your field of study and/or society more broadly

Table of contents

How to write a research question, what makes a strong research question, using sub-questions to strengthen your main research question, research questions quiz, other interesting articles, frequently asked questions about research questions.

You can follow these steps to develop a strong research question:

• Choose your topic
• Do some preliminary reading about the current state of the field
• Narrow your focus to a specific niche
• Identify the research problem that you will address

The way you frame your question depends on what your research aims to achieve. The table below shows some examples of how you might formulate questions for different purposes.

Using your research problem to develop your research question

Note that while most research questions can be answered with various types of research , the way you frame your question should help determine your choices.

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Research questions anchor your whole project, so it’s important to spend some time refining them. The criteria below can help you evaluate the strength of your research question.

Focused and researchable

Feasible and specific

Complex and arguable

Relevant and original

Chances are that your main research question likely can’t be answered all at once. That’s why sub-questions are important: they allow you to answer your main question in a step-by-step manner.

Good sub-questions should be:

• Less complex than the main question
• Focused only on 1 type of research
• Presented in a logical order

Here are a few examples of descriptive and framing questions:

• Descriptive: According to current government arguments, how should a European bank tax be implemented?
• Descriptive: Which countries have a bank tax/levy on financial transactions?
• Framing: How should a bank tax/levy on financial transactions look at a European level?

Keep in mind that sub-questions are by no means mandatory. They should only be asked if you need the findings to answer your main question. If your main question is simple enough to stand on its own, it’s okay to skip the sub-question part. As a rule of thumb, the more complex your subject, the more sub-questions you’ll need.

Try to limit yourself to 4 or 5 sub-questions, maximum. If you feel you need more than this, it may be indication that your main research question is not sufficiently specific. In this case, it’s is better to revisit your problem statement and try to tighten your main question up.

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

Methodology

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

 Statistics

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

Research bias

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

The way you present your research problem in your introduction varies depending on the nature of your research paper . A research paper that presents a sustained argument will usually encapsulate this argument in a thesis statement .

A research paper designed to present the results of empirical research tends to present a research question that it seeks to answer. It may also include a hypothesis —a prediction that will be confirmed or disproved by your research.

As you cannot possibly read every source related to your topic, it’s important to evaluate sources to assess their relevance. Use preliminary evaluation to determine whether a source is worth examining in more depth.

This involves:

• Reading abstracts , prefaces, introductions , and conclusions
• Looking at the table of contents to determine the scope of the work
• Consulting the index for key terms or the names of important scholars

A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (“ x affects y because …”).

A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses . In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.

Formulating a main research question can be a difficult task. Overall, your question should contribute to solving the problem that you have defined in your problem statement .

However, it should also fulfill criteria in three main areas:

• Researchability
• Feasibility and specificity
• Relevance and originality

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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.

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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.

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 🙂

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.

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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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The Difference Between Research Questions & Hypothesis

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Research questions and hypothesis are tools used in similar ways for different research methods. Both hypothesis and research questions are written before research begins and are used to help guide the research. Hypothesis are used in deductive research, where researchers use logic and scientific findings to either prove or disprove assumptions. Heuristic research is based on experience, where researchers use observations to learn about the research subject.

Definitions

A hypothesis is defined as an educated guess, while a research question is simply the researcher wondering about the world. Hypothesis are part of the scientific research method. They are employed in research in science, sociology, mathematics and more. Research questions are part of heuristic research methods, and are also used in many fields including literature, and sociology.

As its name suggests, research questions are always written as questions. Hypothesis are written as statements preceded with the words "I predict." For example, a research question would ask, "What is the effect of heat on the effectiveness of bleach?" A hypothesis would state, "I predict heat will diminish the effectiveness of bleach."

Before Writing

Before writing a hypothesis, the researcher must determine what others have discovered about this subject. On the other hand, a research question requires less preparation, but focus and structure is critical.

For example, a researcher using a hypothesis would look up studies about bleach, information on the chemical properties of the chemical when heated and data about its effectiveness before writing the hypothesis. When using a research question, the researcher would think about how to phrase the question to ensure its scope is not too broad, too narrow or impossible to answer.

Writing Conclusions

When writing the conclusion for research conducted using a hypothesis, the researcher will write whether the hypothesis was correct or incorrect, followed by an explanation of the results of the research. The researcher using only a research question will write the answer to the question, followed by the findings of the research.

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About the Author

Alane Michaelson began writing professionally in 2002. Her work has appeared in Michigan publications such as the "Detroit Free Press" and the "Flint Journal." Michaelson graduated from Oakland University in 2006, earning a Bachelor of Arts in journalism.

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Dissertations & projects: Research questions

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“The central question that you ask or hypothesis you frame drives your research: it defines your purpose.” Bryan Greetham, How to Write Your Undergraduate Dissertation

This page gives some help and guidance in developing a realistic research question. It also considers the role of sub-questions and how these can influence your methodological choices. 

Choosing your research topic

You may have been provided with a list of potential topics or even specific questions to choose from. It is more common for you to have to come up with your own ideas and then refine them with the help of your tutor. This is a crucial decision as you will be immersing yourself in it for a long time.

Some students struggle to find a topic that is sufficiently significant and yet researchable within the limitations of an undergraduate project. You may feel overwhelmed by the freedom to choose your own topic but you could get ideas by considering the following:

Choose a topic that you find interesting . This may seem obvious but a lot of students go for what they think will be easy over what they think will be interesting - and regret it when they realise nothing is particularly easy and they are bored by the work. Think back over your lectures or talks from visiting speakers - was there anything you really enjoyed? Was there anything that left you with questions?

Choose something distinct . Whilst at undergraduate level you do not have to find something completely unique, if you find something a bit different you have more opportunity to come to some interesting conclusions. Have you some unique experiences that you can bring: personal biography, placements, study abroad etc?

Don't make your topic too wide . If your topic is too wide, it will be harder to develop research questions that you can actually answer in the context of a small research project.

Don't make your work too narrow . If your topic is too narrow, you will not be able to expand on the ideas sufficiently and make useful conclusions. You may also struggle to find enough literature to support it.

Scope out the field before deciding your topic . This is especially important if you have a few different options and are not sure which to pick. Spend a little time researching each one to get a feel for the amount of literature that exists and any particular avenues that could be worth exploring.

Think about your future . Some topics may fit better than others with your future plans, be they for further study or employment. Becoming more expert in something that you may have to be interviewed about is never a bad thing!

Once you have an idea (or even a few), speak to your tutor. They will advise on whether it is the right sort of topic for a dissertation or independent study. They have a lot of experience and will know if it is too much to take on, has enough material to build on etc.

Developing a research question or hypothesis

Research question vs hypothesis.

First, it may be useful to explain the difference between a research question and a hypothesis. A research question is simply a question that your research will address and hopefully answer (or give an explanation of why you couldn't answer it). A hypothesis is a statement that suggests how you expect something to function or behave (and which you would test to see if it actually happens or not).

Research question examples

• How significant is league table position when students choose their university?
• What impact can a diagnosis of depression have on physical health?

Note that these are open questions - i.e. they cannot be answered with a simple 'yes' or 'no'. This is the best form of question.

Hypotheses examples

• Students primarily choose their university based on league table position.
• A diagnosis of depression can impact physical health.

Note that these are things that you can test to see if they are true or false. This makes them more definite then research questions - but you can still answer them more fully than 'no they don't' or 'yes it does'. For example, in the above examples you would look to see how relevant other factors were when choosing universities and in what ways physical health may be impacted.

For more examples of the same topic formulated as hypotheses, research questions and paper titles see those given at the bottom of this document from Oakland University: Formulation of Research Hypothesis

Which do you need?

Generally, research questions are more common in the humanities, social sciences and business, whereas hypotheses are more common in the sciences. This is not a hard rule though, talk things through with your supervisor to see which they are expecting or which they think fits best with your topic.

What makes a good research question or hypothesis?

Unless you are undertaking a systematic review as your research method, you will develop your research question  as a result of reviewing the literature on your broader topic. After all, it is only by seeing what research has already been done (or not) that you can justify the need for your question or your approach to answering it. At the end of that process, you should be able to come up with a question or hypothesis that is:

• Clear (easily understandable)
• Focused (specific not vague or huge)
• Answerable (the data is available and analysable in the time frame)
• Relevant (to your area of study)
• Significant (it is worth answering)

You can try a few out, using a table like this (yours would all be in the same discipline):

A similar, though different table is available from the University of California: What makes a good research topic?   The completed table has some supervisor comments which may also be helpful.

Ultimately, your final research question will be mutually agreed between yourself and your supervisor - but you should always bring your own ideas to the conversation.

The role of sub-questions

Your main research question will probably still be too big to answer easily. This is where sub-questions come in. They are specific, narrower questions that you can answer directly from your data.

So, looking at the question " How much do online users know and care about how their self-images can be used by Apple, Google, Microsoft and Facebook? " from the table above, the sub-questions could be:

• What rights do the terms and conditions of signing up for Apple, Google, Microsoft and Facebook accounts give those companies regarding the use of self-images?
• What proportion of users read the terms and conditions when creating accounts with these companies?
• How aware are users of the rights they are giving away regarding their self-images when creating accounts with these companies?
• How comfortable are users with giving away these rights?

Together, the answers to your sub-questions should enable you to answer the overarching research question.

How do you answer your sub-questions?

Depending on the type of dissertation/project your are undertaking, some (or all) the questions may be answered with information collected from the literature and some (or none) may be answered by analysing data directly collected as part of your primary empirical research .

In the above example, the first question would be answered by documentary analysis of the relevant terms and conditions, the second by a mixture of reviewing the literature and analysing survey responses from participants and the last two also by analysing survey responses. Different projects will require different approaches.

Some sub-questions could be answered by reviewing the literature and others from empirical study.

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Home » Education » Difference Between Hypothesis and Research Question

Difference Between Hypothesis and Research Question

Main difference – hypothesis vs research question.

Research question and hypothesis are the foundations of a research study. Formulating the research question or developing the hypothesis can help you to decide on the approach of the research. A research question is the question the research study sets out to answer. Hypothesis is the statement the research study sets out to prove or disprove. The main difference between hypothesis and research question is that hypothesis is predictive in nature whereas research question is inquisitive in nature.

In this article, we’ll discuss,

1. What is a Hypothesis? – Meaning, Features, Characteristics, and Usage

2. What is a Research Question? – Meaning, Features, Characteristics, and Usage

What is a Hypothesis

A hypothesis is a prediction about the relationship between two or more variables. It can be described as an educated guess about what happens in an experiment. Researchers usually tend to use hypotheses when significant knowledge is already available on the subject. The hypothesis is based on this existing knowledge. After the hypothesis is developed, the researcher can develop data, analyze and use them to support or negate the hypothesis.

Not all studies have hypotheses. They are usually used in experimental quantitative research studies. They are useful in testing a specific theory or model.  A complete hypothesis always includes the variables, population and the predicted relationship between the variables. The main disadvantage of hypotheses is that their tendency to blind a researcher to unexpected results. 

What is a Research Question

A research question is the question a research study sets to answer. However, a research study can have more than one research question. The research methodologies , tools used to collect data, etc. all depend on the research question.

Research questions are often used in qualitative research, which seek to answer open-ended questions . But they can also be used in quantitative studies. Research questions can be used instead of hypotheses when there is little previous research on the subject. Research questions allow the researcher to conduct more open-ended queries, and a wide range of results can be reported.

A properly constructed research question should always be clear and concise. It should include the variables, population and the topic being studied.

Hypothesis is a tentative prediction about the relationship between two or more variables.

Research Question is the question a research study sets to answer.

Hypothesis is predictive in nature.

Research Question is inquisitive in nature.

Existing Research

Hypothesis can be used if there is significant knowledge or previous research on this subject.

Research Question can be used if there is little previous research on the subject.

Quantitative vs Qualitative

Hypothesis is mainly used in experimental quantitative studies.

Research Question can be used in both quantitative and qualitative studies.

Hypothesis doesn’t allow a wide range of outcomes.

Research Question allows a wide range of outcomes.

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How Does a Hypothesis Differ From a Research Question?

To understand the difference between a hypothesis and a research question , we must first define the exact nature of scientific inquiry . Essentially, scientific inquiry represents a structured and systematic approach to exploration and discovery, grounded in empirical evidence and guided by the principles of logical reasoning and critical analysis. At the heart of scientific inquiry lies a fundamental commitment to unbiased observation and the rigorous assessment of information, a process that seeks to generate verifiable knowledge based on well-founded theories and methodological robustness.

A pivotal facet of successful scientific investigation is the appropriate framing of research, which serves to delineate the scope and direction of the scholarly endeavor. The meticulous articulation of research parameters not only guides investigators in the methodical exploration of a particular phenomenon but also ensures the reliability and validity of the findings derived from it. Correctly framing a research endeavor equips scholars with a clear framework, thereby preventing research ambiguities and facilitating a coherent and purposeful investigative journey.

Central to the framing of research are two interrelated yet distinct elements: the research question and the hypothesis. While the research question generally articulates the primary inquiry or set of inquiries to be addressed in a study, offering a focal point for the exploration, a hypothesis presents a tentative, testable prediction regarding the expected outcomes of the research. It is grounded in the existing literature and theoretical frameworks, serving as a provisional answer to the research question that is subject to empirical verification.

In essence, a research question seeks to identify and explore potential relationships, patterns, or trends, fostering a deep understanding of the underlying phenomena. In contrast, a hypothesis endeavors to affirm or refute predetermined assumptions through methodical testing and validation, aiming to substantiate or discredit specific theoretical postulates.

To correctly formulate and differentiate between research questions and hypotheses, let us investigate each one in further detail.

Understanding hypotheses

Crafting a well-defined hypothesis is a pivotal step in scholarly research. This task necessitates a profound grasp of the subject matter alongside a comprehensive awareness of existing scholarly dialogues and theories relevant to the topic. The hypothesis acts as a foundational pillar that directs the analytical pathways of the investigation, anchoring the exploration with grounded expectations based on existing knowledge.

In the formulation of a hypothesis, researchers must adhere to vital principles to ensure the creation of a substantial and verifiable statement. A robust hypothesis is delineated by several attributes, including precision, testability, and a congruent alignment with established research and theories. Moreover, it is formulated to facilitate empirical substantiation, aiming to either confirm or refute the established propositions through systematic investigation.

To deepen our comprehension of a hypothesis, let us examine some examples in different research contexts, illustrating how a hypothesis can shape and steer a study:

• Individuals between the ages of 40 and 60 who engage in regular physical activity are less likely to develop heart diseases than those who do not.
• Adolescents who experience traumatic events during the COVID-19 pandemic have a higher prevalence of mental health issues than those who do not.
• Remote learning hampers the development of social skills in elementary school students more than traditional classroom learning does.
• Implementing multicultural education strategies diminishes the achievement gap in multicultural classrooms.
• Marine ecosystems that experience high levels of plastic pollution exhibit a substantial reduction in biodiversity.
• Urbanization leads to a significant decrease in biodiversity in metropolitan areas due to habitat loss.
• Voting behavior in urban communities is significantly influenced by the socioeconomic status of the individuals.
• The prevalent use of social media significantly influences the formation of societal norms and behaviors in contemporary society.
• The integration of artificial intelligence in manufacturing elevates efficiency and productivity.
• An increased dependence on digital platforms compromises personal privacy and heightens the risk of data security breaches.

Each of these hypothesis examples is constructed to offer focused and testable propositions, rooted in contemporary concerns, creating a pathway for empirical verification and the generation of data-driven insights.

Understanding research questions

A critical first step in any research endeavor is the formulation of a research question, a task that requires a deep understanding of both the topic at hand and the existing scholarly landscape surrounding it. The research question serves as the beacon that guides the trajectory of the investigation, providing a focal point that centers the research activities and objectives.

In constructing a research question, scholars must be guided by certain key principles to ensure that their inquiry is both meaningful and fruitful. A well-framed research question is characterized by clarity, specificity, and a sensible alignment with existing research, which aids in building upon established foundations to foster novel insights within its scholarly domain.

To further understand the concept of research questions, let us consider some concrete examples from various fields that illustrate how a well-articulated research question can guide a research project:

• How does lifestyle affect the risk of heart disease in adults aged 40-60?
• What impact has the COVID-19 pandemic had on mental health outcomes in adolescents?
• How does remote learning impact the academic performance and social skills of elementary school students?
• What strategies can be employed to reduce the achievement gap in multicultural classrooms?
• What are the effects of plastic waste on marine ecosystems?
• How does urbanization impact biodiversity in metropolitan regions?
• How do socioeconomic factors influence voting behavior in urban communities?
• What role does social media play in shaping contemporary societal norms and behaviors?
• How does the implementation of artificial intelligence in manufacturing enhance efficiency and productivity?
• What are the implications of increasing reliance on digital platforms for personal privacy and data security?

Each of these research question examples not only maintains a clear focus on a specific topic but also stands grounded in current concerns, thereby paving the way for empirical exploration and data-driven conclusions.

Key differences between a hypothesis and a research question

In scholarly research, it is imperative to differentiate clearly between a hypothesis and a research question. The following table delineates the comparative aspects of both concepts:

When to use which

The decision to use a hypothesis or a research question largely hinges on the nature and objectives of the study. Essentially, researchers delineate between exploratory and confirmatory research . The former seeks to explore new phenomena and generate new insights, while the latter aims to verify existing theories and hypotheses. Understanding the correct circumstance for employing either a research question or a hypothesis can significantly streamline the research process, directing it towards more targeted conclusions. Let's delve into the specific situations where one may be more appropriate over the other.

Situations where a hypothesis is more appropriate

• Confirmatory Research: When the research is grounded in existing theories and seeks to validate or invalidate a specific claim or relationship.
• Quantitative Studies: In research designs that predominantly involve statistical analysis of numerical data to address the research problem.
• Experimental Research: Where controlled experiments are conducted to explore the causal relationships between different variables.
• Deductive Approaches: When the research follows a deductive approach , deriving a specific prediction from a general theory.

Situations where a research question is more appropriate

• Exploratory Research: In studies aiming to explore a new field or topic without much existing literature or established theories.
• Qualitative Research: When the study involves analyzing non-numerical data such as texts, interviews, or observational data to garner insights.
• Pilot Studies: Preliminary studies that aim to identify potential issues and refine research tools before a large-scale study.
• Inductive Approaches: Research approaches that work from specific observations to broader generalizations, aiming to develop new theories.

The interrelation between hypotheses and research questions

Understanding how a research question can give rise to hypotheses.

In scholarly inquiries, the formation of a hypothesis often finds its genesis in a well-articulated research question. This dynamic represents a pivotal juncture in research methodology, facilitating a transition from questioning to hypothesizing and setting the stage for focused analytical scrutiny. Leveraging the exploratory nature of research questions can foster the formulation of grounded hypotheses, guiding the investigative trajectory towards evidence-based conclusions.

Indeed, a well-structured research question can give rise to a series of hypotheses, each presenting a plausible answer to the research question and serving as a focal point for systematic investigation. This correlation facilitates a scaffolded approach to exploration, where researchers can build a layered understanding through a structured inquiry process.

Can a hypothesis transform into a research question?

This iterative process we have described can be envisioned as a cyclic pathway rather than a linear trajectory, wherein hypotheses, once tested and analyzed, can refine or even reformulate the initial research questions. This reflexive relationship fosters a deepened understanding and a more nuanced exploration of the research topic at hand.

To illustrate, consider a research question in the field of healthcare: "What are the primary factors influencing sleep quality in adults?" From this question, a researcher might derive several hypotheses, such as "Adults who engage in regular physical activity experience better sleep quality than those who do not." Once this hypothesis is tested, the findings could lead to further questions, fine-tuning the initial research query to delve into specific age groups, lifestyle factors, or physiological aspects, thereby perpetuating a cycle of inquiry that propels the research into deeper and more focused directions.

Research questions serve as the launchpad for scientific exploration, fostering a direction and scope that steer investigations towards relevant and focused pathways. Conversely, hypotheses act as tentative answers to these research questions, laying a grounded foundation for systematic investigations and guiding the trajectory towards evidence-based conclusions.

Selecting the right approach—whether formulating a hypothesis or crafting a research question—is not merely a procedural choice; it is a strategic decision that significantly influences the outcome of the investigation. Recognizing the interdependent and reflexive relationship between the two can foster a more robust and nuanced approach to scientific inquiry.

By embracing the cyclic pathway that intertwines questioning with hypothesizing, researchers can unlock deeper levels of understanding, paving the way for profound discoveries enriched with insight. Remember, the quality of the answers we obtain is invariably linked to the quality of the questions we ask and the hypotheses we formulate.

Header image by Luke Tanis .

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educational research techniques

Research techniques and education.

Research Purpose, Hypotheses, and Questions

Four key components to a research project are the purpose statement, research questions, hypotheses, and research objectives. In this post, we will define each of these.

Definitions

The purpose of this study is to examine the relationship between college completion and organizational commitment of undergraduate students in Thailand. 

Here is an example of a qualitative purpose statement.

The purpose of this study is to explore student experiences at a university in Thailand about completing their tertiary degree.

Both of these examples are short one-sentence responses to what the study will attempt to do. This is a critical first step in shaping the study.

Research Question

The research question(s) in a quantitative or qualitative study narrows the purpose down to a specific question(s) for the researcher to find answers. Below are examples from both the quantitative and qualitative perspective. We are continuing the research themes from the previous section on the purpose statement.

Quantitative

Does organizational commitment affect college completion of students?

Qualitative

What kinds of experiences have students had while completing their degree?

On closer examination, you may have noticed that the research questions sound a lot like the purpose statement. Research questions often split a part a long complex purpose statement into several questions. This is why questions sound so redundant when compared to the purpose statement. Despite this apparent problem, this thought process helps researchers to organize their thinking and proceed in a manner that is much more efficient.

The next two components only relate to quantitative research and they are the hypotheses and research objective(s). For this reason our illustration of qualitative concepts will stop at this point.

Hypotheses are statements a researcher makes about the potential outcome(s) of a study based on the examination of literature. Below is an example from the same theme as before.

Students who have a higher perception of organizational commitment will also have a higher likelihood of completing college.

Again, the wording of the research questions, hypotheses and purpose statement are similarly. The difference is only slightly and is due to context. Seeing these similarities quickly will help you to move faster in finishing a study. The difference between these elements is a matter of perspective rather than a strong difference, as they do sound awfully similar.

Research Objectives

Research objectives are the goals a researcher has for a study. This component is not always included in a study. Below is an example.

To examine the correlation between organizational commitment and the rate of college completion

Share this:

10 thoughts on “ research purpose, hypotheses, and questions ”.

This is the wrong use of the word, it should be “their” not “there”.

The purpose of this study is to explore student experiences at a university in Thailand about completing there tertiary degree.

Whoops, thanks for catching that

Thank you for this, very helpful 🙂

This has been helpful.

This was helpful. Thank you

Pingback: Developing a Data Analysis Plan | educational research techniques

This was helpful.

Glad to be of service

As an emerging researcher, my worry is that I have six objectives but five research questions and hypotheses. Am I correct or they must all be the same in times of numbers? Thank you

Thank you, this information helped me so much.

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Getting Started With Library Research: Research Questions & Hypothesis

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Scribbr: How to Develop a STRONG Research Question

In this video, we will go through how to develop a strong research question in 5 steps.

Identifying Research Questions

Elements of a research question from Academic Research Foundations: Quantitative by Rolin Moe

Constructing Research Questions

How to write a research question from Academic Research Foundations: Quantitative by Rolin Moe

Scribbr: 6 Steps to Formulate a STRONG Hypothesis

This video will guide you through the 6 steps you need to write a strong hypothesis for your research.

Research frameworks and hypotheses from Academic Research Foundations: Quantitative by Rolin Moe

Null Hypothesis

Understand the null hypothesis from Academic Research Foundations: Quantitative by Rolin Moe

Research Books\Ebooks

Research Question & Hypothesis Links

• George Mason University - How to Write a Research Question Explains research questions and steps to develop them. Gives sample research questions (bad versus how it could be improved)
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• Science Notes: Hypothesis & Null Hypothesis Examples
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Formulation of Research Question – Stepwise Approach

Simmi k. ratan.

Department of Pediatric Surgery, Maulana Azad Medical College, New Delhi, India

1 Department of Community Medicine, North Delhi Municipal Corporation Medical College, New Delhi, India

2 Department of Pediatric Surgery, Batra Hospital and Research Centre, New Delhi, India

Formulation of research question (RQ) is an essentiality before starting any research. It aims to explore an existing uncertainty in an area of concern and points to a need for deliberate investigation. It is, therefore, pertinent to formulate a good RQ. The present paper aims to discuss the process of formulation of RQ with stepwise approach. The characteristics of good RQ are expressed by acronym “FINERMAPS” expanded as feasible, interesting, novel, ethical, relevant, manageable, appropriate, potential value, publishability, and systematic. A RQ can address different formats depending on the aspect to be evaluated. Based on this, there can be different types of RQ such as based on the existence of the phenomenon, description and classification, composition, relationship, comparative, and causality. To develop a RQ, one needs to begin by identifying the subject of interest and then do preliminary research on that subject. The researcher then defines what still needs to be known in that particular subject and assesses the implied questions. After narrowing the focus and scope of the research subject, researcher frames a RQ and then evaluates it. Thus, conception to formulation of RQ is very systematic process and has to be performed meticulously as research guided by such question can have wider impact in the field of social and health research by leading to formulation of policies for the benefit of larger population.

I NTRODUCTION

A good research question (RQ) forms backbone of a good research, which in turn is vital in unraveling mysteries of nature and giving insight into a problem.[ 1 , 2 , 3 , 4 ] RQ identifies the problem to be studied and guides to the methodology. It leads to building up of an appropriate hypothesis (Hs). Hence, RQ aims to explore an existing uncertainty in an area of concern and points to a need for deliberate investigation. A good RQ helps support a focused arguable thesis and construction of a logical argument. Hence, formulation of a good RQ is undoubtedly one of the first critical steps in the research process, especially in the field of social and health research, where the systematic generation of knowledge that can be used to promote, restore, maintain, and/or protect health of individuals and populations.[ 1 , 3 , 4 ] Basically, the research can be classified as action, applied, basic, clinical, empirical, administrative, theoretical, or qualitative or quantitative research, depending on its purpose.[ 2 ]

Research plays an important role in developing clinical practices and instituting new health policies. Hence, there is a need for a logical scientific approach as research has an important goal of generating new claims.[ 1 ]

C HARACTERISTICS OF G OOD R ESEARCH Q UESTION

“The most successful research topics are narrowly focused and carefully defined but are important parts of a broad-ranging, complex problem.”

A good RQ is an asset as it:

• Details the problem statement
• Further describes and refines the issue under study
• Adds focus to the problem statement
• Guides data collection and analysis
• Sets context of research.

Hence, while writing RQ, it is important to see if it is relevant to the existing time frame and conditions. For example, the impact of “odd-even” vehicle formula in decreasing the level of air particulate pollution in various districts of Delhi.

A good research is represented by acronym FINERMAPS[ 5 ]

Interesting.

• Appropriate
• Potential value and publishability
• Systematic.

Feasibility means that it is within the ability of the investigator to carry out. It should be backed by an appropriate number of subjects and methodology as well as time and funds to reach the conclusions. One needs to be realistic about the scope and scale of the project. One has to have access to the people, gadgets, documents, statistics, etc. One should be able to relate the concepts of the RQ to the observations, phenomena, indicators, or variables that one can access. One should be clear that the collection of data and the proceedings of project can be completed within the limited time and resources available to the investigator. Sometimes, a RQ appears feasible, but when fieldwork or study gets started, it proves otherwise. In this situation, it is important to write up the problems honestly and to reflect on what has been learned. One should try to discuss with more experienced colleagues or the supervisor so as to develop a contingency plan to anticipate possible problems while working on a RQ and find possible solutions in such situations.

This is essential that one has a real grounded interest in one's RQ and one can explore this and back it up with academic and intellectual debate. This interest will motivate one to keep going with RQ.

The question should not simply copy questions investigated by other workers but should have scope to be investigated. It may aim at confirming or refuting the already established findings, establish new facts, or find new aspects of the established facts. It should show imagination of the researcher. Above all, the question has to be simple and clear. The complexity of a question can frequently hide unclear thoughts and lead to a confused research process. A very elaborate RQ, or a question which is not differentiated into different parts, may hide concepts that are contradictory or not relevant. This needs to be clear and thought-through. Having one key question with several subcomponents will guide your research.

This is the foremost requirement of any RQ and is mandatory to get clearance from appropriate authorities before stating research on the question. Further, the RQ should be such that it minimizes the risk of harm to the participants in the research, protect the privacy and maintain their confidentiality, and provide the participants right to withdraw from research. It should also guide in avoiding deceptive practices in research.

The question should of academic and intellectual interest to people in the field you have chosen to study. The question preferably should arise from issues raised in the current situation, literature, or in practice. It should establish a clear purpose for the research in relation to the chosen field. For example, filling a gap in knowledge, analyzing academic assumptions or professional practice, monitoring a development in practice, comparing different approaches, or testing theories within a specific population are some of the relevant RQs.

Manageable (M): It has the similar essence as of feasibility but mainly means that the following research can be managed by the researcher.

Appropriate (A): RQ should be appropriate logically and scientifically for the community and institution.

Potential value and publishability (P): The study can make significant health impact in clinical and community practices. Therefore, research should aim for significant economic impact to reduce unnecessary or excessive costs. Furthermore, the proposed study should exist within a clinical, consumer, or policy-making context that is amenable to evidence-based change. Above all, a good RQ must address a topic that has clear implications for resolving important dilemmas in health and health-care decisions made by one or more stakeholder groups.

Systematic (S): Research is structured with specified steps to be taken in a specified sequence in accordance with the well-defined set of rules though it does not rule out creative thinking.

Example of RQ: Would the topical skin application of oil as a skin barrier reduces hypothermia in preterm infants? This question fulfills the criteria of a good RQ, that is, feasible, interesting, novel, ethical, and relevant.

Types of research question

A RQ can address different formats depending on the aspect to be evaluated.[ 6 ] For example:

• Existence: This is designed to uphold the existence of a particular phenomenon or to rule out rival explanation, for example, can neonates perceive pain?
• Description and classification: This type of question encompasses statement of uniqueness, for example, what are characteristics and types of neuropathic bladders?
• Composition: It calls for breakdown of whole into components, for example, what are stages of reflux nephropathy?
• Relationship: Evaluate relation between variables, for example, association between tumor rupture and recurrence rates in Wilm's tumor
• Descriptive—comparative: Expected that researcher will ensure that all is same between groups except issue in question, for example, Are germ cell tumors occurring in gonads more aggressive than those occurring in extragonadal sites?
• Causality: Does deletion of p53 leads to worse outcome in patients with neuroblastoma?
• Causality—comparative: Such questions frequently aim to see effect of two rival treatments, for example, does adding surgical resection improves survival rate outcome in children with neuroblastoma than with chemotherapy alone?
• Causality–Comparative interactions: Does immunotherapy leads to better survival outcome in neuroblastoma Stage IV S than with chemotherapy in the setting of adverse genetic profile than without it? (Does X cause more changes in Y than those caused by Z under certain condition and not under other conditions).

How to develop a research question

• Begin by identifying a broader subject of interest that lends itself to investigate, for example, hormone levels among hypospadias
• Do preliminary research on the general topic to find out what research has already been done and what literature already exists.[ 7 ] Therefore, one should begin with “information gaps” (What do you already know about the problem? For example, studies with results on testosterone levels among hypospadias
• What do you still need to know? (e.g., levels of other reproductive hormones among hypospadias)
• What are the implied questions: The need to know about a problem will lead to few implied questions. Each general question should lead to more specific questions (e.g., how hormone levels differ among isolated hypospadias with respect to that in normal population)
• Narrow the scope and focus of research (e.g., assessment of reproductive hormone levels among isolated hypospadias and hypospadias those with associated anomalies)
• Is RQ clear? With so much research available on any given topic, RQs must be as clear as possible in order to be effective in helping the writer direct his or her research
• Is the RQ focused? RQs must be specific enough to be well covered in the space available
• Is the RQ complex? RQs should not be answerable with a simple “yes” or “no” or by easily found facts. They should, instead, require both research and analysis on the part of the writer
• Is the RQ one that is of interest to the researcher and potentially useful to others? Is it a new issue or problem that needs to be solved or is it attempting to shed light on previously researched topic
• Is the RQ researchable? Consider the available time frame and the required resources. Is the methodology to conduct the research feasible?
• Is the RQ measurable and will the process produce data that can be supported or contradicted?
• Is the RQ too broad or too narrow?
• Create Hs: After formulating RQ, think where research is likely to be progressing? What kind of argument is likely to be made/supported? What would it mean if the research disputed the planned argument? At this step, one can well be on the way to have a focus for the research and construction of a thesis. Hs consists of more specific predictions about the nature and direction of the relationship between two variables. It is a predictive statement about the outcome of the research, dictate the method, and design of the research[ 1 ]
• Understand implications of your research: This is important for application: whether one achieves to fill gap in knowledge and how the results of the research have practical implications, for example, to develop health policies or improve educational policies.[ 1 , 8 ]

Brainstorm/Concept map for formulating research question

• First, identify what types of studies have been done in the past?
• Is there a unique area that is yet to be investigated or is there a particular question that may be worth replicating?
• Begin to narrow the topic by asking open-ended “how” and “why” questions
• Evaluate the question
• Develop a Hypothesis (Hs)
• Write down the RQ.

Writing down the research question

• State the question in your own words
• Write down the RQ as completely as possible.

For example, Evaluation of reproductive hormonal profile in children presenting with isolated hypospadias)

• Divide your question into concepts. Narrow to two or three concepts (reproductive hormonal profile, isolated hypospadias, compare with normal/not isolated hypospadias–implied)
• Specify the population to be studied (children with isolated hypospadias)
• Refer to the exposure or intervention to be investigated, if any
• Reflect the outcome of interest (hormonal profile).

Another example of a research question

Would the topical skin application of oil as a skin barrier reduces hypothermia in preterm infants? Apart from fulfilling the criteria of a good RQ, that is, feasible, interesting, novel, ethical, and relevant, it also details about the intervention done (topical skin application of oil), rationale of intervention (as a skin barrier), population to be studied (preterm infants), and outcome (reduces hypothermia).

Other important points to be heeded to while framing research question

• Make reference to a population when a relationship is expected among a certain type of subjects
• RQs and Hs should be made as specific as possible
• Avoid words or terms that do not add to the meaning of RQs and Hs
• Stick to what will be studied, not implications
• Name the variables in the order in which they occur/will be measured
• Avoid the words significant/”prove”
• Avoid using two different terms to refer to the same variable.

Some of the other problems and their possible solutions have been discussed in Table 1 .

Potential problems and solutions while making research question

G OING B EYOND F ORMULATION OF R ESEARCH Q UESTION–THE P ATH A HEAD

Once RQ is formulated, a Hs can be developed. Hs means transformation of a RQ into an operational analog.[ 1 ] It means a statement as to what prediction one makes about the phenomenon to be examined.[ 4 ] More often, for case–control trial, null Hs is generated which is later accepted or refuted.

A strong Hs should have following characteristics:

• Give insight into a RQ
• Are testable and measurable by the proposed experiments
• Have logical basis
• Follows the most likely outcome, not the exceptional outcome.

E XAMPLES OF R ESEARCH Q UESTION AND H YPOTHESIS

Research question-1.

• Does reduced gap between the two segments of the esophagus in patients of esophageal atresia reduces the mortality and morbidity of such patients?

Hypothesis-1

• Reduced gap between the two segments of the esophagus in patients of esophageal atresia reduces the mortality and morbidity of such patients
• In pediatric patients with esophageal atresia, gap of <2 cm between two segments of the esophagus and proper mobilization of proximal pouch reduces the morbidity and mortality among such patients.

Research question-2

• Does application of mitomycin C improves the outcome in patient of corrosive esophageal strictures?

Hypothesis-2

In patients aged 2–9 years with corrosive esophageal strictures, 34 applications of mitomycin C in dosage of 0.4 mg/ml for 5 min over a period of 6 months improve the outcome in terms of symptomatic and radiological relief. Some other examples of good and bad RQs have been shown in Table 2 .

Examples of few bad (left-hand side column) and few good (right-hand side) research questions

R ESEARCH Q UESTION AND S TUDY D ESIGN

RQ determines study design, for example, the question aimed to find the incidence of a disease in population will lead to conducting a survey; to find risk factors for a disease will need case–control study or a cohort study. RQ may also culminate into clinical trial.[ 9 , 10 ] For example, effect of administration of folic acid tablet in the perinatal period in decreasing incidence of neural tube defect. Accordingly, Hs is framed.

Appropriate statistical calculations are instituted to generate sample size. The subject inclusion, exclusion criteria and time frame of research are carefully defined. The detailed subject information sheet and pro forma are carefully defined. Moreover, research is set off few examples of research methodology guided by RQ:

• Incidence of anorectal malformations among adolescent females (hospital-based survey)
• Risk factors for the development of spontaneous pneumoperitoneum in pediatric patients (case–control design and cohort study)
• Effect of technique of extramucosal ureteric reimplantation without the creation of submucosal tunnel for the preservation of upper tract in bladder exstrophy (clinical trial).

The results of the research are then be available for wider applications for health and social life

C ONCLUSION

A good RQ needs thorough literature search and deep insight into the specific area/problem to be investigated. A RQ has to be focused yet simple. Research guided by such question can have wider impact in the field of social and health research by leading to formulation of policies for the benefit of larger population.

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Conflicts of interest.

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R EFERENCES

Text Analysis

• Constellate

What is text analysis?

Text analysis, also known as text mining or text data mining (TDM), is a research method where large amounts of text are compiled, organized, and quantitatively analyzed in order to derive new information. Researchers can examine text from a variety of sources such as books, journal and magazine articles, or social media posts in order to answer research questions using various analytical tools. General research questions that text analysis can answer include: How are these texts connected? What types of language and emotion are contained within these texts? How are these texts similar or different? and How has the use of this word or phrase changed over time within these texts?

Methods of text analysis

Text analysis can help you identify topics and themes in your text-based dataset. The following list includes common methods used in text analysis:

• Word frequency  - Used to determine the frequency of a word, or set of words, in your text dataset. 
• Concordance - Delivers the frequency of a word with the context in which it is used.   
• Collocation  - Finds and determines where words are used in conjunction with one another. 
• Topic modeling   - Discovers topics that emerge from a group of texts
• Significant terms - Measures important terms within your text database 
• Sentiment Analysis - Measures the sentiment within text, often on a scale of positive to negative, to determine the affect or emotion. 
• Text summarization  - Develops a simple summary from long-form text. This method often uses large language models to write summaries.

How to perform text analysis

Not all text analysis projects will be alike, but it is important that some basic guidelines are followed to ensure a replicable and defendable product. Below is a basic outline for how your text analysis project may operate. 

1. Develop a corpus - In text analysis, a corpus is your text dataset. It can include journal articles, books, social media posts, speeches, etc. You the researcher must determine the criteria you will use for adding items to your corpus. 

2. Prepare your corpus for analysis (pre-processing)  - Here you preform all pre-analysis to ensure your results are accurate. This includes filtering your dataset to include only the relevant documents needed for analysis, tokenizing the texts, removing stop words, lemmatize, and stem words.  

3. Explore your data - Understand the basics of your corpus with methods like word frequencies, significant terms, etc. Here you can develop testable hypotheses to answer with more sophisticated analyses. 

4. Analyze your data - Use a large language model do test your hypotheses. Further refine your data with pre-processing methods, if needed.

5. Visualize and summarize your findings - Distill important information with visual representations. Develop simplified findings from your corpus. 

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• Last Updated: Aug 1, 2024 2:24 PM
• URL: https://libguides.library.ncat.edu/textanalysis

Research on the Relationship Between Higher Education, Technological Innovation, and Green Economy—Analysis Based on Chinese Data from 2011 to 2020

• Published: 02 August 2024

Cite this article

• Jiahui Sun   ORCID: orcid.org/0000-0001-8702-5232 1 ,
• Hechun Wu   ORCID: orcid.org/0009-0005-1933-155X 2 &
• Song Shi   ORCID: orcid.org/0000-0003-0560-6004 3  

Higher education (HE) and technological innovation (TI) are increasingly crucial for green economic (GE) development. The provincial panel data of China from 2011 to 2020 were sorted out to explore the relationship between higher education, technological innovation, and the green economy. The coupling coordination and PVAR models were used to empirically analyze the coupling coordination and impulse response status of higher education, technological innovation, and green economy. The current research found that, firstly, the comprehensive development level of higher education, technological innovation, and green economy in Eastern China, Inner China, and Western China is on an upward trend as time passes. Secondly, in terms of time, the coupling coordination level among higher education, technological innovation, and green economy in Eastern China, Inner China, and Western China is on an upward trend as time passes. Thirdly, regarding space, the development of coupling coordination levels among higher education, technological innovation, and green economy in Eastern China, Inner China, and Western China is uneven. Among the regions, it shows the spatial distribution of “high in Eastern China, low in Inner, and Western China.” Within the regions, there is a spatial distribution pattern centering on “Beijing-Tianjin-Guangdong,” “Jiangsu-Zhejiang-Shanghai,” and “Shaanxi-Hubei-Chongqing,” and spreading to the surrounding areas. Finally, there are interactive and dynamic effects among higher education, technological innovation, and green economy in Eastern China, Inner China, and Western China. However, the interactive effects among the three have short-term effects, and the effects are minor, and most effects are not significant.

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Department of Education, Jeonju University, Jeonju, 55069, Korea

Department of English Education, Jeonbuk National University, Jeonju, 55069, Korea

Engineering Research Center of Integration and Application of Digital Learning Technology, Ministry of Education, Beijing, 100081, China

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Sun, J., Wu, H. & Shi, S. Research on the Relationship Between Higher Education, Technological Innovation, and Green Economy—Analysis Based on Chinese Data from 2011 to 2020. J Knowl Econ (2024). https://doi.org/10.1007/s13132-024-02226-y

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How Culture Shapes What People Want from AI

Stanford researchers explore how to build culturally inclusive and equitable AI by offering initial empirical evidence on cultural variations in people’s ideal preferences about AI. 

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If AI agents could play an active role in our human social life rather than simply operating in the background, would that be desirable? The answer may depend on your cultural perspective. When a team of Stanford researchers applied cultural psychology theory to study what people want from AI, they found clear associations between the cultural models of agency that are common in cultural contexts and the type of AI that is considered ideal. 

Today, the prevailing view in AI development often assumes people desire control over the technology, treating AI as a tool in service of individual goals and concerns. It is an impersonal, hierarchical relationship. But Stanford psychology researcher  Xiao Ge and postdoc researcher  Chunchen Xu say this view is not the way people everywhere think about AI, but instead it reflects the cultural model of agency that is prevalent in many European American middle-class cultural contexts. 

According to their research, a broader look at cultures across diverse groups suggests that many people have a different view of the role that AI could play. Some envision AI with a higher capacity to influence its surroundings (e.g., some people want AI to have feelings, emotions, and autonomy). In this interpretation, for example, some imagine intelligent machines that can act spontaneously and participate in their social situations.

“There is an urgent need to incorporate the perceptions, imaginings, concerns, and creativity of diverse groups in future AI developments. We want to enable AI stakeholders to increase representation of different worldviews in the design and use of AI, so that it can fulfill the needs of wider segments of the population,” Ge explains.

Drawing on independent and interdependent cultural models, and the results of two online surveys, the team has developed a theoretical framework for understanding people’s ideal relationship with AI, which they presented in a new paper, “ How Culture Shapes What People Want from AI .” Supported by the Stanford Institute for Human-Centered AI’s  Seed Grant Program , this work is spurring important conversations about the role of culture in defining mainstream conceptions of AI. 

The Foundations of Culture in HCI 

Many human-computer interaction (HCI) studies have investigated the impact of technology on people in different countries; however, few researchers to date have tried to “flip the conversation” to look at how culture can affect AI design or how AI products reflect cultural ideas.

“When HCI researchers consider culture, it tends to be at the later stages of development—for example, in terms of useability or user interface design. But our findings suggest that cultural factors may even shape the initial creation and design of technology as well as what designers imagine its potential benefits and outcomes to be,” says Jeanne Tsai, professor of psychology, director of the Stanford  Culture and Emotion Lab , and one of the paper’s co-authors.

Read the full study,  How Culture Shapes What People Want from AI

To arrive at a deeper understanding of culture and AI, the researchers applied a well-established cultural psychology framework for depicting variations in how different cultures tend to view “the self” and its relation to surrounding environments. In the independent model, individuals view themselves as unique and separate from others and the socio-physical context. By contrast, the interdependent model holds that everyone is connected fundamentally to other humans, as well as to their physical and social environments.

This framework also implicates whether individuals want and expect the environment to influence them. Researchers refer to this factor as having “capacities to influence.” People in some cultures tend to see the environment as a source of influence to guide their thoughts, feelings, and behaviors, while others are less inclined to view their surrounding environments as an active source of agency.

“Using these two dimensions can help us discover ideal human-AI interactions according to different cultures, and they can be quite different than those that immediately come to mind in our predominantly individualist, middle-class contexts,” says co-author Hazel Rose Markus, professor of psychology and faculty director of the  Stanford SPARQ behavioral science center.

Through the Lens of Two Cultural Models 

Applying this cultural psychology framework, prior studies in behavioral science have established:

• People in  European American cultural contexts   tend to embrace an independent model of agency and see the person as a bigger source of influence than the environment. This cultural model represents people as more active, alive, capable, and in control than their environments. Furthermore, people will aim to change their environments to be more consistent with their preferences, desires, and beliefs.
• People in  Chinese cultural contexts   tend to favor an interdependent model of agency. Accordingly, they may view the boundaries between people and their surrounding environments as permeable and malleable. In this context, people may conceptualize the social and physical environment as encompassing them and prefer that the environment be more active, alive, and capable of exerting influence on people.
• People in African American cultural contexts adopt elements of both cultural models, and their preferences may be influenced by the experience with switching between predominantly independent contexts and predominantly Black contexts, which are often more interdependent. 

Against this backdrop, the Stanford researchers hypothesized that European Americans would seek control over AI more than Chinese respondents, while Chinese participants would seek connection with AI more than European Americans. And, if prior patterns held, African American preferences to control and connect with AI would lie between those of European American and Chinese cultures.

Similarly, on the topic of the environment influencing individuals, the team expected to find European Americans less likely to want AI to have influencing characteristics and Chinese more likely to prefer these characteristics, while African Americans’ preference would fall between the two.

Testing Theoretical Assumptions

To test the hypotheses, the team first created a survey to confirm that these three cultural groups do in fact adopt different models of the self and its relationship to the environment. 373 participants looked at seven variations of a graphic representing the relationship between the self and the environment, ranging from “The environment strongly influences the person” to “The person strongly influences the environment” and selected the picture that best described their ideal balance between the two. 

The pictorial 7-pt. scale (only 1, 4 and 7 are presented for illustration purposes) used to measure the ideal level and direction of influence between the self and the environment in the pilot study. 1 = “The environment strongly influences the person,” 7 = “The person strongly influences the environment.”

As expected, results of this study revealed cultural differences in the ideal level and direction of influence between the self and the environment.

Based on these initial findings, the team examined how these cultural models affected people’s preferences about AI. They fielded a study in which 348 participants read a short description of AI and then saw one of six randomly assigned scenarios of different AI applications in home management, well-being, teamwork, education, wildfire conservation, and manufacturing contexts. (For example, one scenario read: “Imagine that in the future a well-being management AI is developed to gather information about people’s physical and mental health conditions. It makes customized predictions and decisions to improve people’s well-being management.”) 

In the last step of the study, participants answered a list of questions about their preferences for AI in an ideal situation. The questions were designed to align with the independent/interdependent cultural models, as well as core HCI characteristics such as AI’s autonomy and its perceived emotionality.

Finding Evidence in Support of the Hypotheses

After analyzing the data, the research team suggested that culture shapes both people’s conceptions of what it means to be human and what people desire in their interactions with AI. Specifically, compared to European Americans, Chinese participants regarded it as less important to control AI but more important to have a sense of connection with AI. Meanwhile, European Americans preferred AI to have less capacity to influence — with less autonomy, spontaneity, and emotion.

African Americans aligned with European Americans in wanting control over AI, but they fell between European Americans and Chinese in terms of their desire to connect with AI. African American preferences for the optimal level of influencing capacity for AI was between European Americans and Chinese, as predicted. Notably, the researchers found that while Chinese participants placed the lowest importance of the three cultures on having control over AI, their average score still reflected a desire to have some control over the technology.

“There is a gold rush underway to optimize every urban function, from education to healthcare to banking, but there’s a serious lack of reflection and understanding of how culture shapes these conceptions,” says Ge. “Our work is filling an important gap in the literature, as well as in the practice of AI development.” 

The team acknowledges several limitations inherent in this preliminary approach that could be explored in future studies: 

• Sample sizes were relatively low for the surveys. 
• The definition of AI was intentionally very broad. Understanding how people feel about specific AI—chatbots or decision algorithms, for example—could provide even more insight.
• The study didn’t examine whether people’s reported preferences align with actual interactions with AI.

As a next step, they would like to focus on establishing the reliability and validity of the new measures they devised to capture people’s ideal models of self in relation to their environments. 

Contributions to the Field of HCI

According to co-authors Markus and Tsai, these findings present exciting new insights for the field of human-computer interaction. With this work, the team shows that it is possible to develop rigorous and systematic empirical approaches to examine culturally shaped preferences about AI purposes, forms, and functions. They also shed light on the importance of recognizing the implicit cultural defaults that are built into current models of human-computer interaction. From the perspective of many Western contexts, it is hard to imagine agency as shared or as outside the person. Outside Western contexts, this is possible, even obvious. 

“If we continue to rely on preexisting cultural models, we are likely to limit creativity and the potential of AI to improve the human condition across the globe,” says Xu. On the other hand, it is easy to imagine that if developers begin to rethink human agency and tap into a wider variety of cultural ideas, a new era of innovation could unfold, broadening AI’s potential societal and environmental benefits. 

Stanford HAI’s mission is to advance AI research, education, policy and practice to improve the human condition.  Learn more . 

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Changes in self-reported alcohol consumption at high and low consumption in the wake of the COVID-19 pandemic: A test of the polarization hypothesis

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Abstract Background The Coronavirus Disease 2019 (COVID-19) pandemic and associated public health measures had an impact on alcohol use. Based on the literature of past crises (health, economic, etc.), it was hypothesized that the COVID-19 pandemic led to a polarization of drinking. That is, heavy drinkers increased their drinking, while light to moderate drinkers decreased their drinking and/or temporarily abstained. The aim of the current study was to test the respective hypothesis. Methods Data from the Reducing Alcohol Related Harm Standard European Alcohol Survey for Lithuania were obtained for 2015 and 2020. Average daily consumption (in grams per day) was decomposed into deciles for each year, and compared pre-COVID to onset-of-COVID pandemic across the highest, second highest, and lowest deciles. A comparison of population-levels of mental health was conducted between pre-COVID and the onset-of-COVID. Results The findings indicated that overall, there was higher consumption in 2015, M2015 = 11.49 (SD = 8.23) vs. M2020 = 10.71 (SD = 12.12), p < .00001. However the opposite was found in the highest decile M2015 = 29.26 (SD = 5.44) vs. M2020 = 39.23 (SD = 20.58), p = .0003. This reversal pattern was not observed in the second highest nor the lowest decile. There was a lower proportion of respondents indicating bad mental health pre- vs.post-COVID (3.4% vs. 6.5%). Conclusion Although COVID was associated with nationwide declines in alcohol consumption, this was not the case for all segments of the population. In Lithuania, it appears that there was an increase in consumption among the heaviest drinkers, supporting the polarization hypothesis.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

The authors would like to acknowledge the National Institute on Alcohol Abuse and Alcoholism (NIAAA) (Award Number 1R01AA028224) of the National Institutes of Health for funding this research.

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I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Ethics Statement All analyses received Centre for Addiction and Mental Health (CAMH) Research Ethics Board (REB) approval as per protocol: #050/2020.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

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All data produced in the present study are available upon reasonable request to the authors

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Taylor & Francis AI Deal Sets ‘Worrying Precedent’ for Academic Publishing

The publisher didn’t give authors any notice before selling access to its data to Microsoft for $10 million. The agreement could improve academic research, but it further entrenches the predatory nature of academic publishing, experts say.

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Legal experts say giving AI access to scholarly research is likely protected under publishers’ current licensing agreements with authors.

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Academic researchers around the world are reeling from news announced in May that Informa, the parent company of academic publisher Taylor & Francis, has signed a $10 million data-access agreement with Microsoft.

The AI partnership agreement gives Microsoft “nonexclusive access to Advanced Learning Content” across Taylor & Francis’s nearly 3,000 academic journals. After the initial access fee of $10 million, Informa said it would receive recurring payments for the next three years.

Scholars across the U.S. and Europe said the news took them by surprise and they’re worried about how their research will be republished and cited by the publisher’s AI tools.

“I was shocked, not that it had happened, but that no one had said anything to us at all,” said Lauren Barbeau, assistant director of learning and technology initiatives at Georgia Institute of Technology, who has also published a book with Routledge, an academic publisher owned by Taylor & Francis.

“I’ve come to terms with the fact, as an author who has published, that at some point my work is going to go into AI, whether that’s through an illegal copy published somewhere on the internet or some other means. I just didn’t expect it to be my publisher.”

Barbeau said what disturbs her the most “is that there’s so little agency given to the authors about how their work is going to be shared and presented to the world,” which underscores the already “predatory” nature of academic publishing.

She admits she’s optimistic that the deal means well-researched, accurate data will be used to train AI models. “Then, there’s the other side of me that wonders, if it’s going into AI, is it going to properly contextualize my work?” she said. “Or is it going to take my words and my thoughts out of context and misrepresent them because it’s not giving the full picture?”

Ruth Alison Clemens, a lecturer in literature at the Leiden University Centre for the Arts in Society in the Netherlands, wrote on X , “The bulk sale of data to big tech to train AI that has as-yet unknown reach & applications sets a new and worrying precedent for data capitalism & academic research. So email your editors!”

But the bulk sale of data to big tech to train AI that has as-yet unknown reach & applications sets a new and worrying precedent for data capitalism & academic research. So email your editors! 4/ — Dr Ruth Alison Clemens (@RuthieClems) July 17, 2024

The deal could also mean the potential loss of citations, which are valuable currency in a scholar’s career.

“What we’ve seen of AI by and large is that it’s not very good at citing or articulating what came from where and why,” Lance Eaton, a higher education consultant who specializes in generative AI, said. “Here, the publisher is selling the content to an AI tool, which we don’t know how it’s going to be used, but we know they want the scholarly information. What happens to those ideas in that scholarship? How does it get used and how do people get credit for it?”

A news release from Informa said it’s considering such concerns, and the agreement “protects intellectual property rights, including limits on verbatim text extracts and alignment on the importance of detailed citation references.”

$75 Million-Plus Profit Expected

Informa’s half-year results published on July 24 said the firm had “secured a second major partnership” with another AI company and expects AI-related revenues to exceed $75 million for the year.

The data-access agreements, it said, “are a source of significant new value for Taylor & Francis and additional royalties for authors.”

Academics, however, are skeptical that they will financially benefit from the agreement. While Barbeau—like most scholars—earns single-digit royalties when publishers sell copies of her book, she said, “It’s still a concern that my work is now out there and publicly accessible and they were reimbursed for that.”

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She wants to see changes in the way research is evaluated and shared. “This is not the beginning of this conversation, but I hope it’s a tipping point.”

Meanwhile, Eben Kirksey a professor of anthropology at the University of Oxford, wrote on X , “Um, pay us please? Taylor and Francis is making millions by selling academic articles to Microsoft AI. At least give scholars and published authors the right of opting out of this money making data grab.”

Um, pay us please? Taylor and Francis is making millions by selling academic articles to Microsoft AI. At least give scholars and published authors the right of opting out of this money making data grab. https://t.co/g0HLBtUSMm — Eben Kirksey (@eben_kirksey) July 20, 2024

Giving scholars the opportunity to opt in—or out—of their data being used to train Microsoft AI is critical to defending their agency, said Heather Joseph, executive director of SPARC, the Scholarly Publishing and Academic Resources Coalition, a nonprofit group whose members include libraries and academic organizations advocating for open-access research.

“It’s an unqualified good thing for generative AI to be trained on the latest and most high-quality information,” she said. “But there are some circumstances under which we don’t want our information to go to a particular use.”

Developing standards on how and when authors can both retain control of their content and allow other entities to benefit from it is what Joseph is focused on in the wake of the Taylor & Francis deal: “It’s a balancing act at this point.”

AI to ‘Improve Research’

Taylor & Francis publishes over 2,700 journals and partners with more than 700 scholarly societies, according to its website. It owns Routledge, a leading publisher in humanities and social science fields, as well as CRC Press, a publisher of medical, science and technology imprints.

Mark Robinson, a spokesperson for the publisher, didn’t answer specific questions about why the company didn’t contact its authors prior to the announcement of the AI partnerships.

However, he said in an email that the deal is intended to accelerate the company’s ongoing exploration of “new applications that will improve research and make it easier to analyze data, generate hypotheses, automate tasks, work across disciplines and research ideas.”

Informa has already been using AI to maximize efficiencies in its academic markets, including with research submissions, authenticity screening, validation and accuracy, customer response, and content summaries.

The contract with Microsoft, which is set to last until 2027, “expands on our existing enterprise technology partnership to explore how the use of AI can drive innovation and improve productivity across the Informa Group,” according to a news release from the company .

The partnership is focused on improving research productivity, developing—and improving the speed and accuracy of—automated citation referencing, testing the development of expert AI agents that can assist authors and librarians with research and the creation and sharing of new knowledge, and improving relevance and performance of AI systems.

Legal Recourse Not Likely

The scholarly publishing industry, even before the proliferation of AI, often disproportionately favors publishers over individual authors.

For many academics, “publishing is the key to their tenure,” said Eaton, the generative AI–focused higher ed consultant who’s also writing a dissertation related to the ethics of scholarly publishing. “It’s really hard to think about what leverage they have when they’re often publishing for free.”

Additionally, top-tier academic research is often only available through costly subscriptions— some worth hundreds of thousands of dollars —that not all academic libraries can afford to buy, pushing scholars to turn to academic piracy networks, such as Sci-hub or Libgen, to access relevant research papers.

Although the Taylor & Francis deal with Microsoft may be among the first of its kind – Wiley  announced a similar deal  with an unnamed "large tech company" last month – Eaton said he expects more scholarly publishers to sign similar data-access deals, which will only worsen the power imbalances in academic publishing.

“Those scholars feel disempowered by the way that the scholarly publishing industry strips them of their ability to do the work they are paying to do through their education, and they have to find ways to pay or break the law in order to access research to continue their work,” he said.

The AI piece is just one more insult, he said.

“Now, when scholars do publish, not only do they have to give all their rights away to these publishers, the [publishers] are going to profit even more.”

While authors say they understand that licensing agreements allow publishers to sell access to their work, some have questioned if those policies also include AI systems.

But legal experts believe that, although it’s dependent on the language of a particular copyright agreement, most publishers who sell data to generative AI tools will likely be insulated from legal consequences.

“The typical copyright agreement major publishers use is almost certainly going to make it impossible for academic authors to sue,” said James Grimmelmann, a professor of digital and information law at Cornell Law School. “The AI training is going to be fairly similar to the ways in which the publishers already sell bulk access to their collections to libraries and other institutions.”

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