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• How to Write a Strong Hypothesis | Steps & Examples

How to Write a Strong Hypothesis | Steps & Examples

Published on May 6, 2022 by Shona McCombes . Revised on November 20, 2023.

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection .

Example: Hypothesis

Daily apple consumption leads to fewer doctor’s visits.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, other interesting articles, frequently asked questions about writing hypotheses.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess – it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Variables in hypotheses

Hypotheses propose a relationship between two or more types of variables .

• An independent variable is something the researcher changes or controls.
• A dependent variable is something the researcher observes and measures.

If there are any control variables , extraneous variables , or confounding variables , be sure to jot those down as you go to minimize the chances that research bias  will affect your results.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

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Step 1. ask a question.

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2. Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to ensure that you’re embarking on a relevant topic . This can also help you identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalize more complex constructs.

Step 3. Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

4. Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

• The relevant variables
• The specific group being studied
• The predicted outcome of the experiment or analysis

5. Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in  if…then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis . The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

• H 0 : The number of lectures attended by first-year students has no effect on their final exam scores.
• H 1 : The number of lectures attended by first-year students has a positive effect on their final exam scores.

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.

• 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

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A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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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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How to Write a Great Hypothesis

Hypothesis Format, Examples, and Tips

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Amy Morin, LCSW, is a psychotherapist and international bestselling author. Her books, including "13 Things Mentally Strong People Don't Do," have been translated into more than 40 languages. Her TEDx talk,  "The Secret of Becoming Mentally Strong," is one of the most viewed talks of all time.

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis, operational definitions, types of hypotheses, hypotheses examples.

• Collecting Data

Frequently Asked Questions

A hypothesis is a tentative statement about the relationship between two or more  variables. It is a specific, testable prediction about what you expect to happen in a study.

One hypothesis example would be a study designed to look at the relationship between sleep deprivation and test performance might have a hypothesis that states: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."

This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.

The Hypothesis in the Scientific Method

In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:

• Forming a question
• Performing background research
• Creating a hypothesis
• Designing an experiment
• Collecting data
• Analyzing the results
• Drawing conclusions
• Communicating the results

The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. It is only at this point that researchers begin to develop a testable hypothesis. Unless you are creating an exploratory study, your hypothesis should always explain what you  expect  to happen.

In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.

Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore a number of factors to determine which ones might contribute to the ultimate outcome.

In many cases, researchers may find that the results of an experiment  do not  support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.

In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."

In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk wisdom that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."

Elements of a Good Hypothesis

So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:

• Is your hypothesis based on your research on a topic?
• Can your hypothesis be tested?
• Does your hypothesis include independent and dependent variables?

Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the  journal articles you read . Many authors will suggest questions that still need to be explored.

To form a hypothesis, you should take these steps:

• Collect as many observations about a topic or problem as you can.
• Evaluate these observations and look for possible causes of the problem.
• Create a list of possible explanations that you might want to explore.
• After you have developed some possible hypotheses, think of ways that you could confirm or disprove each hypothesis through experimentation. This is known as falsifiability.

In the scientific method ,  falsifiability is an important part of any valid hypothesis.   In order to test a claim scientifically, it must be possible that the claim could be proven false.

Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that  if  something was false, then it is possible to demonstrate that it is false.

One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.

A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.

For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.

These precise descriptions are important because many things can be measured in a number of different ways. One of the basic principles of any type of scientific research is that the results must be replicable.   By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.

Some variables are more difficult than others to define. How would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.

In order to measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming other people. In this situation, the researcher might utilize a simulated task to measure aggressiveness.

Hypothesis Checklist

• Does your hypothesis focus on something that you can actually test?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate the variables?
• Can your hypothesis be tested without violating ethical standards?

The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:

• Simple hypothesis : This type of hypothesis suggests that there is a relationship between one independent variable and one dependent variable.
• Complex hypothesis : This type of hypothesis suggests a relationship between three or more variables, such as two independent variables and a dependent variable.
• Null hypothesis : This hypothesis suggests no relationship exists between two or more variables.
• Alternative hypothesis : This hypothesis states the opposite of the null hypothesis.
• Statistical hypothesis : This hypothesis uses statistical analysis to evaluate a representative sample of the population and then generalizes the findings to the larger group.
• Logical hypothesis : This hypothesis assumes a relationship between variables without collecting data or evidence.

A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the  dependent variable  if you change the  independent variable .

The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."

A few examples of simple hypotheses:

• "Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."
• Complex hypothesis: "Students who experience test anxiety before an English exam will get lower scores than students who do not experience test anxiety."​
• "Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."

Examples of a complex hypothesis include:

• "People with high-sugar diets and sedentary activity levels are more likely to develop depression."
• "Younger people who are regularly exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces."

Examples of a null hypothesis include:

• "Children who receive a new reading intervention will have scores different than students who do not receive the intervention."
• "There will be no difference in scores on a memory recall task between children and adults."

Examples of an alternative hypothesis:

• "Children who receive a new reading intervention will perform better than students who did not receive the intervention."
• "Adults will perform better on a memory task than children." 

Collecting Data on Your Hypothesis

Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.

Descriptive Research Methods

Descriptive research such as  case studies ,  naturalistic observations , and surveys are often used when it would be impossible or difficult to  conduct an experiment . These methods are best used to describe different aspects of a behavior or psychological phenomenon.

Once a researcher has collected data using descriptive methods, a correlational study can then be used to look at how the variables are related. This type of research method might be used to investigate a hypothesis that is difficult to test experimentally.

Experimental Research Methods

Experimental methods  are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).

Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually  cause  another to change.

A Word From Verywell

The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.

Some examples of how to write a hypothesis include:

• "Staying up late will lead to worse test performance the next day."
• "People who consume one apple each day will visit the doctor fewer times each year."
• "Breaking study sessions up into three 20-minute sessions will lead to better test results than a single 60-minute study session."

The four parts of a hypothesis are:

• The research question
• The independent variable (IV)
• The dependent variable (DV)
• The proposed relationship between the IV and DV

Castillo M. The scientific method: a need for something better? . AJNR Am J Neuroradiol. 2013;34(9):1669-71. doi:10.3174/ajnr.A3401

Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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

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

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

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

Research Hypothesis 101

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

What is a hypothesis?

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

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

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

Hypothesis: sleep impacts academic performance.

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

But that’s not good enough…

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

What is a research hypothesis?

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

Let’s take a look at these more closely.

Need a helping hand?

Hypothesis Essential #1: Specificity & Clarity

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

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

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

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

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

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.

Psst… there’s more (for free)

This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

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16 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

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• How to Write a Strong Hypothesis | Guide & Examples

How to Write a Strong Hypothesis | Guide & Examples

Published on 6 May 2022 by Shona McCombes .

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, frequently asked questions about writing hypotheses.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess – it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).

Variables in hypotheses

Hypotheses propose a relationship between two or more variables . An independent variable is something the researcher changes or controls. A dependent variable is something the researcher observes and measures.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

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Step 1: ask a question.

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2: Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalise more complex constructs.

Step 3: Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

Step 4: Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

• The relevant variables
• The specific group being studied
• The predicted outcome of the experiment or analysis

Step 5: Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in if … then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

Step 6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis. The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

A hypothesis is not just a guess. It should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).

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.

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Research Hypothesis In Psychology: Types, & Examples

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

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A research hypothesis, in its plural form “hypotheses,” is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method .

Hypotheses connect theory to data and guide the research process towards expanding scientific understanding

Some key points about hypotheses:

• A hypothesis expresses an expected pattern or relationship. It connects the variables under investigation.
• It is stated in clear, precise terms before any data collection or analysis occurs. This makes the hypothesis testable.
• A hypothesis must be falsifiable. It should be possible, even if unlikely in practice, to collect data that disconfirms rather than supports the hypothesis.
• Hypotheses guide research. Scientists design studies to explicitly evaluate hypotheses about how nature works.
• For a hypothesis to be valid, it must be testable against empirical evidence. The evidence can then confirm or disprove the testable predictions.
• Hypotheses are informed by background knowledge and observation, but go beyond what is already known to propose an explanation of how or why something occurs.

Predictions typically arise from a thorough knowledge of the research literature, curiosity about real-world problems or implications, and integrating this to advance theory. They build on existing literature while providing new insight.

Types of Research Hypotheses

Alternative hypothesis.

The research hypothesis is often called the alternative or experimental hypothesis in experimental research.

It typically suggests a potential relationship between two key variables: the independent variable, which the researcher manipulates, and the dependent variable, which is measured based on those changes.

The alternative hypothesis states a relationship exists between the two variables being studied (one variable affects the other).

A hypothesis is a testable statement or prediction about the relationship between two or more variables. It is a key component of the scientific method. Some key points about hypotheses:

• Important hypotheses lead to predictions that can be tested empirically. The evidence can then confirm or disprove the testable predictions.

In summary, a hypothesis is a precise, testable statement of what researchers expect to happen in a study and why. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.

An experimental hypothesis predicts what change(s) will occur in the dependent variable when the independent variable is manipulated.

It states that the results are not due to chance and are significant in supporting the theory being investigated.

The alternative hypothesis can be directional, indicating a specific direction of the effect, or non-directional, suggesting a difference without specifying its nature. It’s what researchers aim to support or demonstrate through their study.

Null Hypothesis

The null hypothesis states no relationship exists between the two variables being studied (one variable does not affect the other). There will be no changes in the dependent variable due to manipulating the independent variable.

It states results are due to chance and are not significant in supporting the idea being investigated.

The null hypothesis, positing no effect or relationship, is a foundational contrast to the research hypothesis in scientific inquiry. It establishes a baseline for statistical testing, promoting objectivity by initiating research from a neutral stance.

Many statistical methods are tailored to test the null hypothesis, determining the likelihood of observed results if no true effect exists.

This dual-hypothesis approach provides clarity, ensuring that research intentions are explicit, and fosters consistency across scientific studies, enhancing the standardization and interpretability of research outcomes.

Nondirectional Hypothesis

A non-directional hypothesis, also known as a two-tailed hypothesis, predicts that there is a difference or relationship between two variables but does not specify the direction of this relationship.

It merely indicates that a change or effect will occur without predicting which group will have higher or lower values.

For example, “There is a difference in performance between Group A and Group B” is a non-directional hypothesis.

Directional Hypothesis

A directional (one-tailed) hypothesis predicts the nature of the effect of the independent variable on the dependent variable. It predicts in which direction the change will take place. (i.e., greater, smaller, less, more)

It specifies whether one variable is greater, lesser, or different from another, rather than just indicating that there’s a difference without specifying its nature.

For example, “Exercise increases weight loss” is a directional hypothesis.

Falsifiability

The Falsification Principle, proposed by Karl Popper , is a way of demarcating science from non-science. It suggests that for a theory or hypothesis to be considered scientific, it must be testable and irrefutable.

Falsifiability emphasizes that scientific claims shouldn’t just be confirmable but should also have the potential to be proven wrong.

It means that there should exist some potential evidence or experiment that could prove the proposition false.

However many confirming instances exist for a theory, it only takes one counter observation to falsify it. For example, the hypothesis that “all swans are white,” can be falsified by observing a black swan.

For Popper, science should attempt to disprove a theory rather than attempt to continually provide evidence to support a research hypothesis.

Can a Hypothesis be Proven?

Hypotheses make probabilistic predictions. They state the expected outcome if a particular relationship exists. However, a study result supporting a hypothesis does not definitively prove it is true.

All studies have limitations. There may be unknown confounding factors or issues that limit the certainty of conclusions. Additional studies may yield different results.

In science, hypotheses can realistically only be supported with some degree of confidence, not proven. The process of science is to incrementally accumulate evidence for and against hypothesized relationships in an ongoing pursuit of better models and explanations that best fit the empirical data. But hypotheses remain open to revision and rejection if that is where the evidence leads.

• Disproving a hypothesis is definitive. Solid disconfirmatory evidence will falsify a hypothesis and require altering or discarding it based on the evidence.
• However, confirming evidence is always open to revision. Other explanations may account for the same results, and additional or contradictory evidence may emerge over time.

We can never 100% prove the alternative hypothesis. Instead, we see if we can disprove, or reject the null hypothesis.

If we reject the null hypothesis, this doesn’t mean that our alternative hypothesis is correct but does support the alternative/experimental hypothesis.

Upon analysis of the results, an alternative hypothesis can be rejected or supported, but it can never be proven to be correct. We must avoid any reference to results proving a theory as this implies 100% certainty, and there is always a chance that evidence may exist which could refute a theory.

How to Write a Hypothesis

• Identify variables . The researcher manipulates the independent variable and the dependent variable is the measured outcome.
• Operationalized the variables being investigated . Operationalization of a hypothesis refers to the process of making the variables physically measurable or testable, e.g. if you are about to study aggression, you might count the number of punches given by participants.
• Decide on a direction for your prediction . If there is evidence in the literature to support a specific effect of the independent variable on the dependent variable, write a directional (one-tailed) hypothesis. If there are limited or ambiguous findings in the literature regarding the effect of the independent variable on the dependent variable, write a non-directional (two-tailed) hypothesis.
• Make it Testable : Ensure your hypothesis can be tested through experimentation or observation. It should be possible to prove it false (principle of falsifiability).
• Clear & concise language . A strong hypothesis is concise (typically one to two sentences long), and formulated using clear and straightforward language, ensuring it’s easily understood and testable.

Consider a hypothesis many teachers might subscribe to: students work better on Monday morning than on Friday afternoon (IV=Day, DV= Standard of work).

Now, if we decide to study this by giving the same group of students a lesson on a Monday morning and a Friday afternoon and then measuring their immediate recall of the material covered in each session, we would end up with the following:

• The alternative hypothesis states that students will recall significantly more information on a Monday morning than on a Friday afternoon.
• The null hypothesis states that there will be no significant difference in the amount recalled on a Monday morning compared to a Friday afternoon. Any difference will be due to chance or confounding factors.

More Examples

• Memory : Participants exposed to classical music during study sessions will recall more items from a list than those who studied in silence.
• Social Psychology : Individuals who frequently engage in social media use will report higher levels of perceived social isolation compared to those who use it infrequently.
• Developmental Psychology : Children who engage in regular imaginative play have better problem-solving skills than those who don’t.
• Clinical Psychology : Cognitive-behavioral therapy will be more effective in reducing symptoms of anxiety over a 6-month period compared to traditional talk therapy.
• Cognitive Psychology : Individuals who multitask between various electronic devices will have shorter attention spans on focused tasks than those who single-task.
• Health Psychology : Patients who practice mindfulness meditation will experience lower levels of chronic pain compared to those who don’t meditate.
• Organizational Psychology : Employees in open-plan offices will report higher levels of stress than those in private offices.
• Behavioral Psychology : Rats rewarded with food after pressing a lever will press it more frequently than rats who receive no reward.

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Research Hypothesis: What It Is, Types + How to Develop?

A research study starts with a question. Researchers worldwide ask questions and create research hypotheses. The effectiveness of research relies on developing a good research hypothesis. Examples of research hypotheses can guide researchers in writing effective ones.

In this blog, we’ll learn what a research hypothesis is, why it’s important in research, and the different types used in science. We’ll also guide you through creating your research hypothesis and discussing ways to test and evaluate it.

What is a Research Hypothesis?

A hypothesis is like a guess or idea that you suggest to check if it’s true. A research hypothesis is a statement that brings up a question and predicts what might happen.

It’s really important in the scientific method and is used in experiments to figure things out. Essentially, it’s an educated guess about how things are connected in the research.

A research hypothesis usually includes pointing out the independent variable (the thing they’re changing or studying) and the dependent variable (the result they’re measuring or watching). It helps plan how to gather and analyze data to see if there’s evidence to support or deny the expected connection between these variables.

Importance of Hypothesis in Research

Hypotheses are really important in research. They help design studies, allow for practical testing, and add to our scientific knowledge. Their main role is to organize research projects, making them purposeful, focused, and valuable to the scientific community. Let’s look at some key reasons why they matter:

• A research hypothesis helps test theories.

A hypothesis plays a pivotal role in the scientific method by providing a basis for testing existing theories. For example, a hypothesis might test the predictive power of a psychological theory on human behavior.

• It serves as a great platform for investigation activities.

It serves as a launching pad for investigation activities, which offers researchers a clear starting point. A research hypothesis can explore the relationship between exercise and stress reduction.

• Hypothesis guides the research work or study.

A well-formulated hypothesis guides the entire research process. It ensures that the study remains focused and purposeful. For instance, a hypothesis about the impact of social media on interpersonal relationships provides clear guidance for a study.

• Hypothesis sometimes suggests theories.

In some cases, a hypothesis can suggest new theories or modifications to existing ones. For example, a hypothesis testing the effectiveness of a new drug might prompt a reconsideration of current medical theories.

• It helps in knowing the data needs.

A hypothesis clarifies the data requirements for a study, ensuring that researchers collect the necessary information—a hypothesis guiding the collection of demographic data to analyze the influence of age on a particular phenomenon.

• The hypothesis explains social phenomena.

Hypotheses are instrumental in explaining complex social phenomena. For instance, a hypothesis might explore the relationship between economic factors and crime rates in a given community.

• Hypothesis provides a relationship between phenomena for empirical Testing.

Hypotheses establish clear relationships between phenomena, paving the way for empirical testing. An example could be a hypothesis exploring the correlation between sleep patterns and academic performance.

• It helps in knowing the most suitable analysis technique.

A hypothesis guides researchers in selecting the most appropriate analysis techniques for their data. For example, a hypothesis focusing on the effectiveness of a teaching method may lead to the choice of statistical analyses best suited for educational research.

Characteristics of a Good Research Hypothesis

A hypothesis is a specific idea that you can test in a study. It often comes from looking at past research and theories. A good hypothesis usually starts with a research question that you can explore through background research. For it to be effective, consider these key characteristics:

• Clear and Focused Language: A good hypothesis uses clear and focused language to avoid confusion and ensure everyone understands it.
• Related to the Research Topic: The hypothesis should directly relate to the research topic, acting as a bridge between the specific question and the broader study.
• Testable: An effective hypothesis can be tested, meaning its prediction can be checked with real data to support or challenge the proposed relationship.
• Potential for Exploration: A good hypothesis often comes from a research question that invites further exploration. Doing background research helps find gaps and potential areas to investigate.
• Includes Variables: The hypothesis should clearly state both the independent and dependent variables, specifying the factors being studied and the expected outcomes.
• Ethical Considerations: Check if variables can be manipulated without breaking ethical standards. It’s crucial to maintain ethical research practices.
• Predicts Outcomes: The hypothesis should predict the expected relationship and outcome, acting as a roadmap for the study and guiding data collection and analysis.
• Simple and Concise: A good hypothesis avoids unnecessary complexity and is simple and concise, expressing the essence of the proposed relationship clearly.
• Clear and Assumption-Free: The hypothesis should be clear and free from assumptions about the reader’s prior knowledge, ensuring universal understanding.
• Observable and Testable Results: A strong hypothesis implies research that produces observable and testable results, making sure the study’s outcomes can be effectively measured and analyzed.

When you use these characteristics as a checklist, it can help you create a good research hypothesis. It’ll guide improving and strengthening the hypothesis, identifying any weaknesses, and making necessary changes. Crafting a hypothesis with these features helps you conduct a thorough and insightful research study.

Types of Research Hypotheses

The research hypothesis comes in various types, each serving a specific purpose in guiding the scientific investigation. Knowing the differences will make it easier for you to create your own hypothesis. Here’s an overview of the common types:

01. Null Hypothesis

The null hypothesis states that there is no connection between two considered variables or that two groups are unrelated. As discussed earlier, a hypothesis is an unproven assumption lacking sufficient supporting data. It serves as the statement researchers aim to disprove. It is testable, verifiable, and can be rejected.

For example, if you’re studying the relationship between Project A and Project B, assuming both projects are of equal standard is your null hypothesis. It needs to be specific for your study.

02. Alternative Hypothesis

The alternative hypothesis is basically another option to the null hypothesis. It involves looking for a significant change or alternative that could lead you to reject the null hypothesis. It’s a different idea compared to the null hypothesis.

When you create a null hypothesis, you’re making an educated guess about whether something is true or if there’s a connection between that thing and another variable. If the null view suggests something is correct, the alternative hypothesis says it’s incorrect. 

For instance, if your null hypothesis is “I’m going to be $1000 richer,” the alternative hypothesis would be “I’m not going to get $1000 or be richer.”

03. Directional Hypothesis

The directional hypothesis predicts the direction of the relationship between independent and dependent variables. They specify whether the effect will be positive or negative.

If you increase your study hours, you will experience a positive association with your exam scores. This hypothesis suggests that as you increase the independent variable (study hours), there will also be an increase in the dependent variable (exam scores).

04. Non-directional Hypothesis

The non-directional hypothesis predicts the existence of a relationship between variables but does not specify the direction of the effect. It suggests that there will be a significant difference or relationship, but it does not predict the nature of that difference.

For example, you will find no notable difference in test scores between students who receive the educational intervention and those who do not. However, once you compare the test scores of the two groups, you will notice an important difference.

05. Simple Hypothesis

A simple hypothesis predicts a relationship between one dependent variable and one independent variable without specifying the nature of that relationship. It’s simple and usually used when we don’t know much about how the two things are connected.

For example, if you adopt effective study habits, you will achieve higher exam scores than those with poor study habits.

06. Complex Hypothesis

A complex hypothesis is an idea that specifies a relationship between multiple independent and dependent variables. It is a more detailed idea than a simple hypothesis.

While a simple view suggests a straightforward cause-and-effect relationship between two things, a complex hypothesis involves many factors and how they’re connected to each other.

For example, when you increase your study time, you tend to achieve higher exam scores. The connection between your study time and exam performance is affected by various factors, including the quality of your sleep, your motivation levels, and the effectiveness of your study techniques.

If you sleep well, stay highly motivated, and use effective study strategies, you may observe a more robust positive correlation between the time you spend studying and your exam scores, unlike those who may lack these factors.

07. Associative Hypothesis

An associative hypothesis proposes a connection between two things without saying that one causes the other. Basically, it suggests that when one thing changes, the other changes too, but it doesn’t claim that one thing is causing the change in the other.

For example, you will likely notice higher exam scores when you increase your study time. You can recognize an association between your study time and exam scores in this scenario.

Your hypothesis acknowledges a relationship between the two variables—your study time and exam scores—without asserting that increased study time directly causes higher exam scores. You need to consider that other factors, like motivation or learning style, could affect the observed association.

08. Causal Hypothesis

A causal hypothesis proposes a cause-and-effect relationship between two variables. It suggests that changes in one variable directly cause changes in another variable.

For example, when you increase your study time, you experience higher exam scores. This hypothesis suggests a direct cause-and-effect relationship, indicating that the more time you spend studying, the higher your exam scores. It assumes that changes in your study time directly influence changes in your exam performance.

09. Empirical Hypothesis

An empirical hypothesis is a statement based on things we can see and measure. It comes from direct observation or experiments and can be tested with real-world evidence. If an experiment proves a theory, it supports the idea and shows it’s not just a guess. This makes the statement more reliable than a wild guess.

For example, if you increase the dosage of a certain medication, you might observe a quicker recovery time for patients. Imagine you’re in charge of a clinical trial. In this trial, patients are given varying dosages of the medication, and you measure and compare their recovery times. This allows you to directly see the effects of different dosages on how fast patients recover.

This way, you can create a research hypothesis: “Increasing the dosage of a certain medication will lead to a faster recovery time for patients.”

10. Statistical Hypothesis

A statistical hypothesis is a statement or assumption about a population parameter that is the subject of an investigation. It serves as the basis for statistical analysis and testing. It is often tested using statistical methods to draw inferences about the larger population.

In a hypothesis test, statistical evidence is collected to either reject the null hypothesis in favor of the alternative hypothesis or fail to reject the null hypothesis due to insufficient evidence.

For example, let’s say you’re testing a new medicine. Your hypothesis could be that the medicine doesn’t really help patients get better. So, you collect data and use statistics to see if your guess is right or if the medicine actually makes a difference.

If the data strongly shows that the medicine does help, you say your guess was wrong, and the medicine does make a difference. But if the proof isn’t strong enough, you can stick with your original guess because you didn’t get enough evidence to change your mind.

How to Develop a Research Hypotheses?

Step 1: identify your research problem or topic..

Define the area of interest or the problem you want to investigate. Make sure it’s clear and well-defined.

Start by asking a question about your chosen topic. Consider the limitations of your research and create a straightforward problem related to your topic. Once you’ve done that, you can develop and test a hypothesis with evidence.

Step 2: Conduct a literature review

Review existing literature related to your research problem. This will help you understand the current state of knowledge in the field, identify gaps, and build a foundation for your hypothesis. Consider the following questions:

• What existing research has been conducted on your chosen topic?
• Are there any gaps or unanswered questions in the current literature?
• How will the existing literature contribute to the foundation of your research?

Step 3: Formulate your research question

Based on your literature review, create a specific and concise research question that addresses your identified problem. Your research question should be clear, focused, and relevant to your field of study.

Step 4: Identify variables

Determine the key variables involved in your research question. Variables are the factors or phenomena that you will study and manipulate to test your hypothesis.

• Independent Variable: The variable you manipulate or control.
• Dependent Variable: The variable you measure to observe the effect of the independent variable.

Step 5: State the Null hypothesis

The null hypothesis is a statement that there is no significant difference or effect. It serves as a baseline for comparison with the alternative hypothesis.

Step 6: Select appropriate methods for testing the hypothesis

Choose research methods that align with your study objectives, such as experiments, surveys, or observational studies. The selected methods enable you to test your research hypothesis effectively.

Creating a research hypothesis usually takes more than one try. Expect to make changes as you collect data. It’s normal to test and say no to a few hypotheses before you find the right answer to your research question.

Testing and Evaluating Hypotheses

Testing hypotheses is a really important part of research. It’s like the practical side of things. Here, real-world evidence will help you determine how different things are connected. Let’s explore the main steps in hypothesis testing:

• State your research hypothesis.

Before testing, clearly articulate your research hypothesis. This involves framing both a null hypothesis, suggesting no significant effect or relationship, and an alternative hypothesis, proposing the expected outcome.

• Collect data strategically.

Plan how you will gather information in a way that fits your study. Make sure your data collection method matches the things you’re studying.

Whether through surveys, observations, or experiments, this step demands precision and adherence to the established methodology. The quality of data collected directly influences the credibility of study outcomes.

• Perform an appropriate statistical test.

Choose a statistical test that aligns with the nature of your data and the hypotheses being tested. Whether it’s a t-test, chi-square test, ANOVA, or regression analysis, selecting the right statistical tool is paramount for accurate and reliable results.

• Decide if your idea was right or wrong.

Following the statistical analysis, evaluate the results in the context of your null hypothesis. You need to decide if you should reject your null hypothesis or not.

• Share what you found.

When discussing what you found in your research, be clear and organized. Say whether your idea was supported or not, and talk about what your results mean. Also, mention any limits to your study and suggest ideas for future research.

The Role of QuestionPro to Develop a Good Research Hypothesis

QuestionPro is a survey and research platform that provides tools for creating, distributing, and analyzing surveys. It plays a crucial role in the research process, especially when you’re in the initial stages of hypothesis development. Here’s how QuestionPro can help you to develop a good research hypothesis:

• Survey design and data collection: You can use the platform to create targeted questions that help you gather relevant data.
• Exploratory research: Through surveys and feedback mechanisms on QuestionPro, you can conduct exploratory research to understand the landscape of a particular subject.
• Literature review and background research: QuestionPro surveys can collect sample population opinions, experiences, and preferences. This data and a thorough literature evaluation can help you generate a well-grounded hypothesis by improving your research knowledge.
• Identifying variables: Using targeted survey questions, you can identify relevant variables related to their research topic.
• Testing assumptions: You can use surveys to informally test certain assumptions or hypotheses before formalizing a research hypothesis.
• Data analysis tools: QuestionPro provides tools for analyzing survey data. You can use these tools to identify the collected data’s patterns, correlations, or trends.
• Refining your hypotheses: As you collect data through QuestionPro, you can adjust your hypotheses based on the real-world responses you receive.

A research hypothesis is like a guide for researchers in science. It’s a well-thought-out idea that has been thoroughly tested. This idea is crucial as researchers can explore different fields, such as medicine, social sciences, and natural sciences. The research hypothesis links theories to real-world evidence and gives researchers a clear path to explore and make discoveries.

QuestionPro Research Suite is a helpful tool for researchers. It makes creating surveys, collecting data, and analyzing information easily. It supports all kinds of research, from exploring new ideas to forming hypotheses. With a focus on using data, it helps researchers do their best work.

Are you interested in learning more about QuestionPro Research Suite? Take advantage of QuestionPro’s free trial to get an initial look at its capabilities and realize the full potential of your research efforts.

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Home » Research Findings – Types Examples and Writing Guide

Research Findings – Types Examples and Writing Guide

Table of Contents

Research Findings

Definition:

Research findings refer to the results obtained from a study or investigation conducted through a systematic and scientific approach. These findings are the outcomes of the data analysis, interpretation, and evaluation carried out during the research process.

Types of Research Findings

There are two main types of research findings:

Qualitative Findings

Qualitative research is an exploratory research method used to understand the complexities of human behavior and experiences. Qualitative findings are non-numerical and descriptive data that describe the meaning and interpretation of the data collected. Examples of qualitative findings include quotes from participants, themes that emerge from the data, and descriptions of experiences and phenomena.

Quantitative Findings

Quantitative research is a research method that uses numerical data and statistical analysis to measure and quantify a phenomenon or behavior. Quantitative findings include numerical data such as mean, median, and mode, as well as statistical analyses such as t-tests, ANOVA, and regression analysis. These findings are often presented in tables, graphs, or charts.

Both qualitative and quantitative findings are important in research and can provide different insights into a research question or problem. Combining both types of findings can provide a more comprehensive understanding of a phenomenon and improve the validity and reliability of research results.

Parts of Research Findings

Research findings typically consist of several parts, including:

• Introduction: This section provides an overview of the research topic and the purpose of the study.
• Literature Review: This section summarizes previous research studies and findings that are relevant to the current study.
• Methodology : This section describes the research design, methods, and procedures used in the study, including details on the sample, data collection, and data analysis.
• Results : This section presents the findings of the study, including statistical analyses and data visualizations.
• Discussion : This section interprets the results and explains what they mean in relation to the research question(s) and hypotheses. It may also compare and contrast the current findings with previous research studies and explore any implications or limitations of the study.
• Conclusion : This section provides a summary of the key findings and the main conclusions of the study.
• Recommendations: This section suggests areas for further research and potential applications or implications of the study’s findings.

How to Write Research Findings

Writing research findings requires careful planning and attention to detail. Here are some general steps to follow when writing research findings:

• Organize your findings: Before you begin writing, it’s essential to organize your findings logically. Consider creating an outline or a flowchart that outlines the main points you want to make and how they relate to one another.
• Use clear and concise language : When presenting your findings, be sure to use clear and concise language that is easy to understand. Avoid using jargon or technical terms unless they are necessary to convey your meaning.
• Use visual aids : Visual aids such as tables, charts, and graphs can be helpful in presenting your findings. Be sure to label and title your visual aids clearly, and make sure they are easy to read.
• Use headings and subheadings: Using headings and subheadings can help organize your findings and make them easier to read. Make sure your headings and subheadings are clear and descriptive.
• Interpret your findings : When presenting your findings, it’s important to provide some interpretation of what the results mean. This can include discussing how your findings relate to the existing literature, identifying any limitations of your study, and suggesting areas for future research.
• Be precise and accurate : When presenting your findings, be sure to use precise and accurate language. Avoid making generalizations or overstatements and be careful not to misrepresent your data.
• Edit and revise: Once you have written your research findings, be sure to edit and revise them carefully. Check for grammar and spelling errors, make sure your formatting is consistent, and ensure that your writing is clear and concise.

Research Findings Example

Following is a Research Findings Example sample for students:

Title: The Effects of Exercise on Mental Health

Sample : 500 participants, both men and women, between the ages of 18-45.

Methodology : Participants were divided into two groups. The first group engaged in 30 minutes of moderate intensity exercise five times a week for eight weeks. The second group did not exercise during the study period. Participants in both groups completed a questionnaire that assessed their mental health before and after the study period.

Findings : The group that engaged in regular exercise reported a significant improvement in mental health compared to the control group. Specifically, they reported lower levels of anxiety and depression, improved mood, and increased self-esteem.

Conclusion : Regular exercise can have a positive impact on mental health and may be an effective intervention for individuals experiencing symptoms of anxiety or depression.

Applications of Research Findings

Research findings can be applied in various fields to improve processes, products, services, and outcomes. Here are some examples:

• Healthcare : Research findings in medicine and healthcare can be applied to improve patient outcomes, reduce morbidity and mortality rates, and develop new treatments for various diseases.
• Education : Research findings in education can be used to develop effective teaching methods, improve learning outcomes, and design new educational programs.
• Technology : Research findings in technology can be applied to develop new products, improve existing products, and enhance user experiences.
• Business : Research findings in business can be applied to develop new strategies, improve operations, and increase profitability.
• Public Policy: Research findings can be used to inform public policy decisions on issues such as environmental protection, social welfare, and economic development.
• Social Sciences: Research findings in social sciences can be used to improve understanding of human behavior and social phenomena, inform public policy decisions, and develop interventions to address social issues.
• Agriculture: Research findings in agriculture can be applied to improve crop yields, develop new farming techniques, and enhance food security.
• Sports : Research findings in sports can be applied to improve athlete performance, reduce injuries, and develop new training programs.

When to use Research Findings

Research findings can be used in a variety of situations, depending on the context and the purpose. Here are some examples of when research findings may be useful:

• Decision-making : Research findings can be used to inform decisions in various fields, such as business, education, healthcare, and public policy. For example, a business may use market research findings to make decisions about new product development or marketing strategies.
• Problem-solving : Research findings can be used to solve problems or challenges in various fields, such as healthcare, engineering, and social sciences. For example, medical researchers may use findings from clinical trials to develop new treatments for diseases.
• Policy development : Research findings can be used to inform the development of policies in various fields, such as environmental protection, social welfare, and economic development. For example, policymakers may use research findings to develop policies aimed at reducing greenhouse gas emissions.
• Program evaluation: Research findings can be used to evaluate the effectiveness of programs or interventions in various fields, such as education, healthcare, and social services. For example, educational researchers may use findings from evaluations of educational programs to improve teaching and learning outcomes.
• Innovation: Research findings can be used to inspire or guide innovation in various fields, such as technology and engineering. For example, engineers may use research findings on materials science to develop new and innovative products.

Purpose of Research Findings

The purpose of research findings is to contribute to the knowledge and understanding of a particular topic or issue. Research findings are the result of a systematic and rigorous investigation of a research question or hypothesis, using appropriate research methods and techniques.

The main purposes of research findings are:

• To generate new knowledge : Research findings contribute to the body of knowledge on a particular topic, by adding new information, insights, and understanding to the existing knowledge base.
• To test hypotheses or theories : Research findings can be used to test hypotheses or theories that have been proposed in a particular field or discipline. This helps to determine the validity and reliability of the hypotheses or theories, and to refine or develop new ones.
• To inform practice: Research findings can be used to inform practice in various fields, such as healthcare, education, and business. By identifying best practices and evidence-based interventions, research findings can help practitioners to make informed decisions and improve outcomes.
• To identify gaps in knowledge: Research findings can help to identify gaps in knowledge and understanding of a particular topic, which can then be addressed by further research.
• To contribute to policy development: Research findings can be used to inform policy development in various fields, such as environmental protection, social welfare, and economic development. By providing evidence-based recommendations, research findings can help policymakers to develop effective policies that address societal challenges.

Characteristics of Research Findings

Research findings have several key characteristics that distinguish them from other types of information or knowledge. Here are some of the main characteristics of research findings:

• Objective : Research findings are based on a systematic and rigorous investigation of a research question or hypothesis, using appropriate research methods and techniques. As such, they are generally considered to be more objective and reliable than other types of information.
• Empirical : Research findings are based on empirical evidence, which means that they are derived from observations or measurements of the real world. This gives them a high degree of credibility and validity.
• Generalizable : Research findings are often intended to be generalizable to a larger population or context beyond the specific study. This means that the findings can be applied to other situations or populations with similar characteristics.
• Transparent : Research findings are typically reported in a transparent manner, with a clear description of the research methods and data analysis techniques used. This allows others to assess the credibility and reliability of the findings.
• Peer-reviewed: Research findings are often subject to a rigorous peer-review process, in which experts in the field review the research methods, data analysis, and conclusions of the study. This helps to ensure the validity and reliability of the findings.
• Reproducible : Research findings are often designed to be reproducible, meaning that other researchers can replicate the study using the same methods and obtain similar results. This helps to ensure the validity and reliability of the findings.

Advantages of Research Findings

Research findings have many advantages, which make them valuable sources of knowledge and information. Here are some of the main advantages of research findings:

• Evidence-based: Research findings are based on empirical evidence, which means that they are grounded in data and observations from the real world. This makes them a reliable and credible source of information.
• Inform decision-making: Research findings can be used to inform decision-making in various fields, such as healthcare, education, and business. By identifying best practices and evidence-based interventions, research findings can help practitioners and policymakers to make informed decisions and improve outcomes.
• Identify gaps in knowledge: Research findings can help to identify gaps in knowledge and understanding of a particular topic, which can then be addressed by further research. This contributes to the ongoing development of knowledge in various fields.
• Improve outcomes : Research findings can be used to develop and implement evidence-based practices and interventions, which have been shown to improve outcomes in various fields, such as healthcare, education, and social services.
• Foster innovation: Research findings can inspire or guide innovation in various fields, such as technology and engineering. By providing new information and understanding of a particular topic, research findings can stimulate new ideas and approaches to problem-solving.
• Enhance credibility: Research findings are generally considered to be more credible and reliable than other types of information, as they are based on rigorous research methods and are subject to peer-review processes.

Limitations of Research Findings

While research findings have many advantages, they also have some limitations. Here are some of the main limitations of research findings:

• Limited scope: Research findings are typically based on a particular study or set of studies, which may have a limited scope or focus. This means that they may not be applicable to other contexts or populations.
• Potential for bias : Research findings can be influenced by various sources of bias, such as researcher bias, selection bias, or measurement bias. This can affect the validity and reliability of the findings.
• Ethical considerations: Research findings can raise ethical considerations, particularly in studies involving human subjects. Researchers must ensure that their studies are conducted in an ethical and responsible manner, with appropriate measures to protect the welfare and privacy of participants.
• Time and resource constraints : Research studies can be time-consuming and require significant resources, which can limit the number and scope of studies that are conducted. This can lead to gaps in knowledge or a lack of research on certain topics.
• Complexity: Some research findings can be complex and difficult to interpret, particularly in fields such as science or medicine. This can make it challenging for practitioners and policymakers to apply the findings to their work.
• Lack of generalizability : While research findings are intended to be generalizable to larger populations or contexts, there may be factors that limit their generalizability. For example, cultural or environmental factors may influence how a particular intervention or treatment works in different populations or contexts.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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How to Develop a Good Research Hypothesis

The story of a research study begins by asking a question. Researchers all around the globe are asking curious questions and formulating research hypothesis. However, whether the research study provides an effective conclusion depends on how well one develops a good research hypothesis. Research hypothesis examples could help researchers get an idea as to how to write a good research hypothesis.

This blog will help you understand what is a research hypothesis, its characteristics and, how to formulate a research hypothesis

Table of Contents

What is Hypothesis?

Hypothesis is an assumption or an idea proposed for the sake of argument so that it can be tested. It is a precise, testable statement of what the researchers predict will be outcome of the study.  Hypothesis usually involves proposing a relationship between two variables: the independent variable (what the researchers change) and the dependent variable (what the research measures).

What is a Research Hypothesis?

Research hypothesis is a statement that introduces a research question and proposes an expected result. It is an integral part of the scientific method that forms the basis of scientific experiments. Therefore, you need to be careful and thorough when building your research hypothesis. A minor flaw in the construction of your hypothesis could have an adverse effect on your experiment. In research, there is a convention that the hypothesis is written in two forms, the null hypothesis, and the alternative hypothesis (called the experimental hypothesis when the method of investigation is an experiment).

Characteristics of a Good Research Hypothesis

As the hypothesis is specific, there is a testable prediction about what you expect to happen in a study. You may consider drawing hypothesis from previously published research based on the theory.

A good research hypothesis involves more effort than just a guess. In particular, your hypothesis may begin with a question that could be further explored through background research.

To help you formulate a promising research hypothesis, you should ask yourself the following questions:

• Is the language clear and focused?
• What is the relationship between your hypothesis and your research topic?
• Is your hypothesis testable? If yes, then how?
• What are the possible explanations that you might want to explore?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate your variables without hampering the ethical standards?
• Does your research predict the relationship and outcome?
• Is your research simple and concise (avoids wordiness)?
• Is it clear with no ambiguity or assumptions about the readers’ knowledge
• Is your research observable and testable results?
• Is it relevant and specific to the research question or problem?

The questions listed above can be used as a checklist to make sure your hypothesis is based on a solid foundation. Furthermore, it can help you identify weaknesses in your hypothesis and revise it if necessary.

Source: Educational Hub

How to formulate a research hypothesis.

A testable hypothesis is not a simple statement. It is rather an intricate statement that needs to offer a clear introduction to a scientific experiment, its intentions, and the possible outcomes. However, there are some important things to consider when building a compelling hypothesis.

1. State the problem that you are trying to solve.

Make sure that the hypothesis clearly defines the topic and the focus of the experiment.

2. Try to write the hypothesis as an if-then statement.

Follow this template: If a specific action is taken, then a certain outcome is expected.

3. Define the variables

Independent variables are the ones that are manipulated, controlled, or changed. Independent variables are isolated from other factors of the study.

Dependent variables , as the name suggests are dependent on other factors of the study. They are influenced by the change in independent variable.

4. Scrutinize the hypothesis

Evaluate assumptions, predictions, and evidence rigorously to refine your understanding.

Types of Research Hypothesis

The types of research hypothesis are stated below:

1. Simple Hypothesis

It predicts the relationship between a single dependent variable and a single independent variable.

2. Complex Hypothesis

It predicts the relationship between two or more independent and dependent variables.

3. Directional Hypothesis

It specifies the expected direction to be followed to determine the relationship between variables and is derived from theory. Furthermore, it implies the researcher’s intellectual commitment to a particular outcome.

4. Non-directional Hypothesis

It does not predict the exact direction or nature of the relationship between the two variables. The non-directional hypothesis is used when there is no theory involved or when findings contradict previous research.

5. Associative and Causal Hypothesis

The associative hypothesis defines interdependency between variables. A change in one variable results in the change of the other variable. On the other hand, the causal hypothesis proposes an effect on the dependent due to manipulation of the independent variable.

6. Null Hypothesis

Null hypothesis states a negative statement to support the researcher’s findings that there is no relationship between two variables. There will be no changes in the dependent variable due the manipulation of the independent variable. Furthermore, it states results are due to chance and are not significant in terms of supporting the idea being investigated.

7. Alternative Hypothesis

It states that there is a relationship between the two variables of the study and that the results are significant to the research topic. An experimental hypothesis predicts what changes will take place in the dependent variable when the independent variable is manipulated. Also, it states that the results are not due to chance and that they are significant in terms of supporting the theory being investigated.

Research Hypothesis Examples of Independent and Dependent Variables

Research Hypothesis Example 1 The greater number of coal plants in a region (independent variable) increases water pollution (dependent variable). If you change the independent variable (building more coal factories), it will change the dependent variable (amount of water pollution).

Research Hypothesis Example 2 What is the effect of diet or regular soda (independent variable) on blood sugar levels (dependent variable)? If you change the independent variable (the type of soda you consume), it will change the dependent variable (blood sugar levels)

You should not ignore the importance of the above steps. The validity of your experiment and its results rely on a robust testable hypothesis. Developing a strong testable hypothesis has few advantages, it compels us to think intensely and specifically about the outcomes of a study. Consequently, it enables us to understand the implication of the question and the different variables involved in the study. Furthermore, it helps us to make precise predictions based on prior research. Hence, forming a hypothesis would be of great value to the research. Here are some good examples of testable hypotheses.

More importantly, you need to build a robust testable research hypothesis for your scientific experiments. A testable hypothesis is a hypothesis that can be proved or disproved as a result of experimentation.

Importance of a Testable Hypothesis

To devise and perform an experiment using scientific method, you need to make sure that your hypothesis is testable. To be considered testable, some essential criteria must be met:

• There must be a possibility to prove that the hypothesis is true.
• There must be a possibility to prove that the hypothesis is false.
• The results of the hypothesis must be reproducible.

Without these criteria, the hypothesis and the results will be vague. As a result, the experiment will not prove or disprove anything significant.

What are your experiences with building hypotheses for scientific experiments? What challenges did you face? How did you overcome these challenges? Please share your thoughts with us in the comments section.

Frequently Asked Questions

The steps to write a research hypothesis are: 1. Stating the problem: Ensure that the hypothesis defines the research problem 2. Writing a hypothesis as an 'if-then' statement: Include the action and the expected outcome of your study by following a ‘if-then’ structure. 3. Defining the variables: Define the variables as Dependent or Independent based on their dependency to other factors. 4. Scrutinizing the hypothesis: Identify the type of your hypothesis

Hypothesis testing is a statistical tool which is used to make inferences about a population data to draw conclusions for a particular hypothesis.

Hypothesis in statistics is a formal statement about the nature of a population within a structured framework of a statistical model. It is used to test an existing hypothesis by studying a population.

Research hypothesis is a statement that introduces a research question and proposes an expected result. It forms the basis of scientific experiments.

The different types of hypothesis in research are: • Null hypothesis: Null hypothesis is a negative statement to support the researcher’s findings that there is no relationship between two variables. • Alternate hypothesis: Alternate hypothesis predicts the relationship between the two variables of the study. • Directional hypothesis: Directional hypothesis specifies the expected direction to be followed to determine the relationship between variables. • Non-directional hypothesis: Non-directional hypothesis does not predict the exact direction or nature of the relationship between the two variables. • Simple hypothesis: Simple hypothesis predicts the relationship between a single dependent variable and a single independent variable. • Complex hypothesis: Complex hypothesis predicts the relationship between two or more independent and dependent variables. • Associative and casual hypothesis: Associative and casual hypothesis predicts the relationship between two or more independent and dependent variables. • Empirical hypothesis: Empirical hypothesis can be tested via experiments and observation. • Statistical hypothesis: A statistical hypothesis utilizes statistical models to draw conclusions about broader populations.

Wow! You really simplified your explanation that even dummies would find it easy to comprehend. Thank you so much.

Thanks a lot for your valuable guidance.

I enjoy reading the post. Hypotheses are actually an intrinsic part in a study. It bridges the research question and the methodology of the study.

Useful piece!

This is awesome.Wow.

It very interesting to read the topic, can you guide me any specific example of hypothesis process establish throw the Demand and supply of the specific product in market

Nicely explained

It is really a useful for me Kindly give some examples of hypothesis

It was a well explained content ,can you please give me an example with the null and alternative hypothesis illustrated

clear and concise. thanks.

So Good so Amazing

Good to learn

Thanks a lot for explaining to my level of understanding

Explained well and in simple terms. Quick read! Thank you

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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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Biology library

Course: biology library   >   unit 1, the scientific method.

• Controlled experiments
• The scientific method and experimental design

Introduction

• Make an observation.
• Ask a question.
• Form a hypothesis , or testable explanation.
• Make a prediction based on the hypothesis.
• Test the prediction.
• Iterate: use the results to make new hypotheses or predictions.

Scientific method example: Failure to toast

1. make an observation..

• Observation: the toaster won't toast.

2. Ask a question.

• Question: Why won't my toaster toast?

3. Propose a hypothesis.

• Hypothesis: Maybe the outlet is broken.

4. Make predictions.

• Prediction: If I plug the toaster into a different outlet, then it will toast the bread.

5. Test the predictions.

• Test of prediction: Plug the toaster into a different outlet and try again.
• If the toaster does toast, then the hypothesis is supported—likely correct.
• If the toaster doesn't toast, then the hypothesis is not supported—likely wrong.

Logical possibility

Practical possibility, building a body of evidence, 6. iterate..

• Iteration time!
• If the hypothesis was supported, we might do additional tests to confirm it, or revise it to be more specific. For instance, we might investigate why the outlet is broken.
• If the hypothesis was not supported, we would come up with a new hypothesis. For instance, the next hypothesis might be that there's a broken wire in the toaster.

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Open Access

Peer-reviewed

Research Article

Liking music with and without sadness: Testing the direct effect hypothesis of pleasurable negative emotion

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Empirical Musicology Laboratory, School of the Arts and Media, UNSW Australia, Sydney, NSW, Australia

• Emery Schubert

• Published: April 10, 2024
• https://doi.org/10.1371/journal.pone.0299115
• Reader Comments

Negative emotion evoked in listeners of music can produce intense pleasure, but we do not fully understand why. The present study addressed the question by asking participants (n = 50) to self-select a piece of sadness-evoking music that was loved. The key part of the study asked participants to imagine that the felt sadness could be removed. Overall participants reported performing the task successfully. They also indicated that the removal of the sadness reduced their liking of the music, and 82% of participants reported that the evoked sadness also adds to the enjoyment of the music. The study provided evidence for a “Direct effect hypothesis”, which draws on the multicomponent model of emotion, where a component of the negative emotion is experienced as positive during music (and other aesthetic) experiences. Earlier evidence of a mediator, such as ‘being moved’, as the source of enjoyment was reinterpreted in light of the new findings. Instead, the present study applied a semantic overlap explanation, arguing that sadness primes emotions that share meaning with sadness, such as being-moved. The priming occurs if the overlap in meaning is sufficient. The degree of semantic overlap was defined empirically. The present study therefore suggests that mediator-based explanations need to be treated with caution both as a finding of the study, and because of analytic limitations in earlier research that are discussed in the paper.

Citation: Schubert E (2024) Liking music with and without sadness: Testing the direct effect hypothesis of pleasurable negative emotion. PLoS ONE 19(4): e0299115. https://doi.org/10.1371/journal.pone.0299115

Editor: Maja Vukadinovic, Novi Sad School of Business, SERBIA

Received: December 5, 2023; Accepted: February 5, 2024; Published: April 10, 2024

Copyright: © 2024 Emery Schubert. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Data contain potentially identifying or sensitive participant information because open ended responses about personal experiences to music could have been reported. The decision to restrict data sharing was part of the approval given by the institutional ethics committee. The email contact for the institutional ethics advisory committee that granted approval for this design is [email protected] .

Funding: Initials of the authors who received each award: ES Grant numbers awarded to each author: FT120100053 (ES) The full name of each funder: Australian Research Council URL of each funder website: https://www.arc.gov.au/ Did the sponsors or funders play any role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript?: No.

Competing interests: The authors have declared that no competing interests exist.

Introduction

A considerable portion of the population (estimates ranging from around 25% to 50%) will report that music they love can also make them feel negative emotions such as sadness [ 1 – 6 ]. This finding has mystified researchers. How can a loved activity simultaneously produce a negative feeling, and yet lead the same individual to eagerly seek out the experience?

The Indirect effect hypothesis

Much theorising has been proposed to explain the conundrum as it applies to music listening and the contemplation of the arts in general. A dominating approach argues that the ‘sadness’ (the negative emotion that is the focus of the current investigation, and one that has received much attention) evoked by the music serves some non-negative purpose. The negative emotion is not in and of itself enjoyed. We will refer to such explanations as part of the ‘Indirect effect hypothesis’, meaning that a negative emotion such as sadness itself cannot or should not directly play a role in the generation of pleasure. The Indirect effect hypothesis is old, with written origins in Aristotle’s concept of catharsis from 4 th century BCE–where certain negative emotions in response to the arts act as a psychic cleanser, which removes bad or negative emotions from the soul [ 7 , 8 ]. The enduring concept of catharsis suggests an Indirect effect hypothesis because the negative emotion itself is not enjoyed directly. Rather, it is the cleansing, or the product of the cleansing that feels good. (Please note that in this article, the terms enjoyment, pleasure, feels-good, preferred, loved and liked are treated, more or less, as substitutable synonyms; see [ 9 ]) The negative impact of the emotion is thus compensated for by the positive effect on the soul or, in early 21 st century parlance, the mind.

A more recent version of the Indirect effect hypothesis is that sadness produces pleasure indirectly by triggering an intermediary step, sometimes referred to as a ‘mediator’. ‘Being moved’, for example, has been reported as the underlying reason for listening to otherwise sad music. Being moved can be seen as consisting of positive aspects, in addition to negative aspects [ 10 – 13 ]. It is the positive aspects of being moved that are responsible for the pleasure of the otherwise sadness-inducing music. Such explanations argue that the negative emotion occurs alongside a mediator, and so itself is not the direct cause of the positive aspects of the experience, thus eradicating the paradoxical aspect of the phenomenon.

A common technique to test the Indirect effect hypothesis is to ask participants to listen to a piece of music and rate the felt sadness and enjoyment experienced, in addition to rating the alleged mediator. If the enjoyment ratings are correlated with the mediator, and provided this correlation is overall stronger than is sadness with enjoyment, we have evidence, albeit correlational, that the mediator is the direct cause of the liking, not the sadness, supporting the Indirect effect hypothesis. To date, being moved has produced the strongest evidence of mediating sadness [ 3 , 14 – 16 ]. But other contenders that have been proposed, including beauty, wonder and nostalgia [for an overview, see 3 , 17 ].

Limitations of the Indirect effect hypothesis

An inherent weakness of Indirect effect hypothesis, and in particular the mediator-based explanation, is that it does not consider the phenomenal experience of the individual who claims that they both experience sadness, and that the sadness itself, for them, forms at least part of the pleasure [e.g., 6 ]. There are also limitations with research methods that are used to test the mediator explanation in the extant literature, as elucidated in the Method section.

Another limitation specifically concerns the mediator driven approach because it does not explain why the negative emotion would be present at all if it is the mediator that is driving the pleasure. If music is pleasurable because it is moving, and not because it evokes sadness, why would the listener not just seek the music that is moving but not sadness evoking? Is it because the mediator generates the negative (sad) emotion, as a by-product? But this would suggest that the occurrence of enjoyed negative emotion experiences such as sadness in response to music should be nothing more than an outlier, and be rarely reported as an enjoyed part of the experience (presumably well under the 25% of reports that are typical of published research, as indicated at the Introduction). Mediation theory therefore only explains why listeners claim to enjoy felt negative emotions to a limited extent. An alternative explanation is worth considering, and here the Direct effect hypothesis is proposed.

The Direct effect hypothesis

The Direct effect hypothesis argues that there is something intrinsic about felt negative emotion evoked by music that attracts the listener, without mandating a mediator or some factor outside the negative emotion itself. The presence of accompanying affects (such as being moved) are not excluded, but they are not essential. One line of research that supports this hypothesis is the link between individual differences and enjoyment of sad music. Such research does not exclude the Indirect effect account, but it does suggest that individual factors attract the listener to sadness in music, raising the possibility that there is something peculiar about some negative emotions that allow them to be enjoyed in their own right.

Strong contenders for the disposition of people who enjoy the sadness evoked by music are empathisers, fantasisers, ruminators, those who demonstrate an openness to experience, and those with a high propensity to fall into states of absorption [ 2 , 3 , 16 , 18 – 22 ]. Current thinking is that these personal characteristics, especially empathising, absorption and openness to experience, allow the individual to connect with fictional narratives while suspending disbelief, and so exhibit a good capacity to “make-believe” [ 23 , 24 ], a capacity which generalises to emotions in music listening [e.g., see 16 , 25 – 27 ]. This explanation also presents an alternative theoretical perspective to the above cited literature, because rather than presenting sadness as a mere by-product of mediation or as a means to some beneficial end, the sadness can be ‘enjoyed’ for its own sake (directly). It is not real-sadness, but a make-believe, or aesthetic, kind of sadness, still experienced as sadness, but with some real-life negative aspect of the sadness not triggered [ 28 ].

The Direct effect hypothesis has a theoretical foundation. Emotion researchers such as Frijda [ 29 ] and Scherer [ 30 ] have conceptualised emotion as consisting of multiple phases or components operating in synchrony. This view is both reflective of contemporary understandings of emotion, and defined networks in the brain. In one instantiation of a componential model, Sander, Grandjean and Scherer [ 31 ] proposed five components/networks of emotion building on Scherer’s model: ‘Expression’ (e.g., a facial expression that communicates the emotion), ‘Action Tendency’ (e.g., motivation to approach toward, or flee from the cause of the emotion), ‘Autonomic Reaction’ (e.g., changed heart rate), ‘Feeling’ (what the emotion feels-like, such as ‘I feel sadness’) and ‘Elicitation’ (the internally triggered cause of the emotion through interpretation of environmental situation, association and instinct) such as prolonged loneliness eliciting sadness.

In the case of the enjoyment of negative emotions Schubert [ 32 ] proposed that when contemplating aesthetic stimuli the Action tendency component of an emotion is experienced as positive (motivation to approach) while other components remain as they would for real-life, non-aesthetic experiences of such emotions. The individual is not compelled to act in a withdrawn or aversive manner to the stimulus or event under contemplation because the perceiver has an implicit awareness that it is presented in an aesthetic or make-believe context. This dissociated response occurs because the individual has an intrinsic understanding of the safe, make-believe context in which the causal stimulus/event is perceived [ 33 – 35 ].

Limitations of the direct effect hypothesis

The Direct effect hypothesis of enjoyment of negative emotion has arguably been difficult to test. If emotions happen to be correlated (such as sadness and being moved), researchers typically take this as an indication in favour of the Indirect effect hypothesis. But such interpretations do not exclude the possibility that the enjoyment directly stems from the sadness. While there is some evidence that those who enjoy negative emotion in music are indeed enjoying the negative emotion, there has been little systematic investigation of the experiential aspect of enjoyment of negative emotion in music. Other approaches to falsifying the Direct effect hypothesis are needed.

The approach taken in the present research is in the form of an ‘empirical thought experiment’, which has origins in so-called experimental philosophy [ 36 ]. Thought experiments, also referred to as mental simulation or ‘prefactual thinking’, rely on the participant’s capacity to imagine a situation and provide a response to that situation. The method can be particularly useful when a real-life stimulus-effect manipulation of interest is not possible or ethically compromising [e.g., 37 ]. It has been applied successfully to the empirical investigation of a range or research questions [ 38 ] and, of relevance here, to scenarios involving mental simulation of emotions [ 39 – 42 ].

Probing listeners to mentally simulate manipulating aspects of sadness induced by music is a simple approach to address both the Direct and Indirect effect hypotheses of enjoyment of experienced negative emotion in music. In brief, if a listener reports experiencing the sadness induced by a piece of music as pleasurable, the thought experiment to address the question of interest (to test if the sadness is the cause of the pleasure) is to ask the participant to imagine that the felt sadness, and only the felt sadness, can somehow be removed. If enjoyment is consequently diminished (as a result of the mentally simulated, excised sadness), the Direct effect hypothesis will be supported. Assurances would need to be set in place that the sadness was experienced (felt) and not just expressed by the music [ 43 ], and that the music was responsible for triggering the sadness, not some (extramusical) association (as discussed in the Method section).

The aim of this study was to investigate whether negative emotion in music, in this case sadness, can be both experienced and enjoyed. Two competing hypotheses were tested:

H1 –the Indirect effect hypothesis, which predicts that: Sadness removed from a liked piece of music will increase or not change enjoyment. This is because it is not the sadness that is enjoyed, but something external to the sadness, such as being moved or some other mediator.

H2 –the Direct effect hypothesis predicts that: Sadness removed from a liked piece of sadness will decrease enjoyment. This is because the sadness itself is somehow enjoyed, regardless of the impact of correlated variables (such as being moved, etc.).

Methodological and data analysis issues

This preamble to the method examines four key issues encountered in extant methods and data-analysis conventions stemming from controversy about use of experimenter- versus participant-selected stimuli. These issues are: Confounding extramusical association, Phenomenon of interest, Demand characteristics and Prospective mediators. This is followed by a discussion of problems that have emerged in experimenter-selected stimulus, and, as a result, a justification for the use of participant-selected music is then presented.

Confounding extramusical association.

There has been growing consensus that investigations of enjoyed sadness in music should be assessed through experimenter-selected music. Participant- or ‘self’-selected music has the disadvantage that the music can have personal or other non-musical associations, meaning that it is not the music that is directly responsible for triggering sadness, but previously formed, ‘extramusical’ associations with the music. Self-selected music could therefore lead to confounding extramusical associations that evoke sadness: the music acting as a mere go-between with the external cause of the sadness and the experience of sadness, and therefore potentially lead to false conclusion of negative emotion being caused by the music. Furthermore, self-selected music does not assure that findings would be generalisable to other participants who did not self-select the same piece. Self-selected music is inevitably music that is familiar. Personal meanings and associations with familiar music could well lead to idiosyncratic responses, peculiar to one or a small number of individuals [for a detailed discussion on limitations in use of familiar music, see 44 ].

Although one of the main drivers for using experimenter-selected music is to avoid confounding extramusical associations , it is possible that even for unfamiliar (experimenter-selected) music a participant will have an emotional response to music because it triggers an external factor, rather than emanating from the music itself [ 45 ]. For example, while Day and Thompson [ 46 ] found that familiar music is more successful at evoking visual imagery (and hence increasing the likelihood of extramusical emotional associations), they also observed the important role of fluency, where music that is complex (low in fluency) is more likely to trigger visual imagery than music that is less complex (high in fluency), regardless of familiarity. Furthermore, autobiographical memories have been reported to be triggered by unfamiliar music, although to a lesser extent than familiar music [ 47 , 48 , see also 49 ]. Thus experimenter-selected music can help to diminish the likelihood of data pollution through confounding extramusical associations , even if not eliminate it.

Phenomenon of interest.

Use of unfamiliar music that is rated by an independent panel, or some other means, as evoking sadness and being pleasurable has been proposed to remedy the problem of confounding extramusical association [e.g., 14 , 16 ]. However, this approach also has its shortcomings. Others deciding what music is likely to evoke sadness will not necessarily evoke sadness to a sufficient degree in a randomly sampled participant to address the phenomenon of interest (enjoyment of evoked negative emotion in music). It is well documented that familiar music can evoke stronger emotions than unfamiliar music, with self-selected music being a particularly effective way to elicit the strong emotions [e.g., 43 , 50 – 56 ]. Similarly, others deciding what music someone likes is riddled with problems. Music preference calls into play several factors such as familiarity [ 57 ], making the assumption of an absolute, objective rating of pleasure in response to a given piece of music problematic. This constitutes a considerable drawback of experimenter-selected design because additional precautions need to be taken to assure that participant experiences capture the phenomenon of interest (both strong liking and experiencing of sadness), as discussed below.

Demand characteristics.

Another problem with self-selected music is that it may attract demand characteristics bias. This bias can occur when the participant infers the research question [ 58 , 59 ]. For self-selected music the research objective can be inferred by the participant, in particular if they are asked to select music that they love that also evokes sadness. In this situation, the participant may guess that the study is concerned with enjoyment and experiencing sadness. If consciously or subconsciously they wish to please the experimenter, they may inflate their assessment of the amount of enjoyment the music generates or the amount of sadness it evokes or both. Furthermore, during participant recruiting, if mention is made that people are sought who experience sadness in response to loved music, it is self-evident that the participant pool will be biased, because only those who have the targeted experience are likely to participate, overlooking the opportunity to estimate how common the phenomenon is in a general population.

Prospective mediators.

Overall, the studies adopting experimenter-selected designs have used interval rating scale measurements of the variables of interest (enjoyment, sadness, and the prospective mediator variables, such as being moved). In addition, other variables are rated to help reduce the likelihood that the participant will successfully intuit the aim of the study, and to capture information about alternative, prospective mediators. Interval rating scales have the advantage of being convenient for correlation based data processing procedures, such as statistical mediation analysis [ 60 ].

Problems with experiment-selected designs.

Although research using experimenter-selected music designs have claimed to manage several methodological problems identified in self-selected music designs to address the current research question, as summarised above, experimenter-selected stimuli based approaches nevertheless have their own limitations (some overlapping with self-selected music approaches).

As mentioned above, experimenter-selected music is less likely to evoke strong emotions compared with self-selected music, and so it is possible that a person who is capable of experiencing intense sadness in response to loved music will not have that experience for music selected by the best-intentioned experimenter. Even with self-selected music, some studies have shown that only about one quarter to one third of participants report experiencing negative emotions such as sadness in response to music they love (see Introduction ). Schubert (6) used the self-selection approach while considerably circumventing the problem of demand characteristics. He asked participants to select a piece of music that they love, but not revealing the research interest in negative emotions. As it turned out, about one third (25/73) of the participants spontaneously reported experiencing negative emotions, with specific mention made of sadness in 12/72 (i.e., one sixth of) cases (p. 17). In that study it was not clear, however, whether the sadness emanated from the music itself, or through some confounding extramusical association . Nevertheless the method mitigated demand characteristics bias, and above all, it ensured that the piece selected was highly liked, something which experimenter-selected approaches rarely guarantee. Konečni [ 61 ] also argued that fully-fledged aesthetic experiences in response to music are rare even under regular listening circumstances. Therefore, the phenomenon of interest would occur in an even smaller proportion of cases in studies applying experimenter-selected music, even if the stimuli have been previously screened for sadness evocation and enjoyment by individuals other than the participant them/her/himself.

Another related limitation of studies using experimenter-selected pieces concerns the response format itself, which commonly employs an integer-based rating scale for each of the affective variables of interest. The problem is not the use of rating scales per se , but the tradition of publishing rating scale results. Studies typically report scale (i.e., item) mean (X) and standard deviation (SD) scores, and/or the correlation coefficient (usually the Pearson product moment coefficient, r) for pairs of variables. The chief problem with such reporting is they imply assumptions about the distribution of the responses. Providing these descriptive statistics, and in particular when the data are then applied to parametric statistical analysis procedures, infers that the distribution of the data are normal, have homogenous variance and are linear [ 62 , p. 311]. If these assumptions are taken at face value, it means that the density of responses diminish as data points are located further away from the mean, with the diminution per scale step being more rapid when the standard deviation is small. Consequently, when there is no explicit information provided about the nature of the distribution, the number of responses that meet the criterion for the phenomenon of interest could be relatively small, and risk not providing statistically sufficient power for meaningful analysis. A simple visual diagnosis can be made through scatterplots of felt sadness versus liking ratings. The decision needs to be made as to where the cut off mark is for sadness and liking scores above which count as satisfying the phenomenon of interest .

This weakness in extant research constitutes the most serious problem of the mediation-based explanation, which, to the author’s knowledge, has exclusively employed experimenter-selected stimuli and use of interval rating scales with X/SD/r reporting, assuming that any amount of sadness evoked by a piece of music should be proportionally implicated in its enjoyment. The assumption is incorrect because it asserts that a linear relationship is evidence of the phenomenon of interest . In fact, the phenomenon of interest is not concerned with enjoyed that accompanies low levels of sadness because when sadness levels are low, other reasons for enjoying the music are still perfectly viable. Evidence of this problem is reflected to some extent by the generally low correlations reported between sadness and liking scores, usually with a small effect size [r < .3, see 63 ]. When the correlation coefficient is small, no conclusion can be drawn about the phenomenon of interest because low correlation only reveals a lack of (non-zero) linearity, rather than information about the modality of the bivariate distribution. That is, a small correlation coefficient provides no information regarding the location of the mode of the distribution, or whether a desirable mode (also) exists in the high sadness, high liking region of the distribution.

In short, by not diagnosing the nature of the bivariate response distribution, the analytic approaches adopted for currently available experimenter-selected designs potentially exclude cases of high evoked sadness that accompany high liking, meaning that they have not captured the phenomenon of interest and so cannot make conclusions about it, or should do so with caution. One solution for future research employing ratings for all variables of interest while maintaining the advantages of the experimenter-selected stimuli approach is to recruit a sufficiently large random sample so that enough cases happen to fall in the desired range spontaneously. However, using self-selected music is more efficient because the phenomenon of interest is achieved by categorical self-selection.

Using self-selected stimuli–justification.

With the above arguments, the stimulus self-selection approach can be justified provided some modifications are made to the way the approach has been applied in the past. These are itemised here in six points. Based on the above overview, the main innovations to note are points 2, 3c, 3d and 4. Square bracketed text following each point indicates the main methodological issue(s) discussed above that are addressed by each of the proposed actions.

• Correspondence used for recruiting participants is not to indicate that the study is concerned with experiencing sadness in music, its enjoyment, or both [as per recommendations by 58 , 59 ]. [Demand characteristics]
• During the study, request that the participant selects music that is loved, not just liked, to ensure that the desired (high) liking category of music is attained [ 64 ]. [Phenomenon of interest]
• that the music is highly liked,
• the sadness is indeed felt,
• the sadness emanates directly from the music, and not through extramusical association, and
• the experienced sadness is implicated in the enjoyment of the music. [Confounding extramusical association; Phenomenon of interest]
• A control condition is employed, for example where instead of requesting sadness-evoking music, music evoking another emotion that is not paradoxical is requested, such as a mediator proposed in previous research. An obvious choice is moving music (that is loved). [Demand characteristics; Phenomenon of interest]
• A number of affect terms, including sadness and the control condition emotion should be added to a list of emotions rated in both test and control conditions to allow for comparison, and help identify prospective mediators. [Prospective mediators]
• Since participants are explicitly asked to have potentially powerfully sad emotions evoked, towards the end of the study an additional stimulus is rated that requires evocation of a positive emotion. This satisfies potential ethical concerns where sadness experience could influence mood negatively, and allows the option of further comparisons with affects in the test condition that were prospective mediators. [Prospective mediators]

Participants

103 participants, recruited from an English speaking tertiary institution, consisting mostly of undergraduate music students, completed the study. They were randomly assigned to one of the two conditions. Fifty participants were randomly assigned to the Sadness condition and 53 to the Moving condition in a between-subjects design. The research received ethics approval from the UNSW Australia institutional review board Human Research Advisory Panel B: Arts, Architecture, Design and Law. Participants were recruited from June 4, 2021 until June 9, 2021. Consent to participate was provided at the opening of the online survey, with a checkbox selected if the participant agreed to participate. No minors participated in the study.

The Qualtrics survey platform ( https://www.qualtrics.com ) was used for human data collection. Self-selected music was identified through online links searched for and reported within the survey by the participant. The participant used an electronic device, such as a laptop, iPad or tablet. They were encouraged to wear earphones to listen to music, but this was not enforced. Affect terms consisted of a list of terms that are drawn from Schindler, Hosoya [ 65 ] and Schubert [ 66 ], as presented in the Procedures.

Prior to commencing the study, informed consent was requested verbally through the online interface, with all participants being asked to read an online participant information sheet, which included information about being free to withdraw from the study at any time. They were informed that their data would be treated confidentially, and were encouraged to ask questions if needed, and then to indicate if they wished to commence the study. Participants were randomly assigned into a Sadness (test) or Moving (control) condition. We describe the sadness condition here, but the moving condition is identical, except that ‘sad’ and ‘sadness’ is replaced with ‘moved’/’being moved’ and ‘movingness’ (respectively). Otherwise, where grammatically straight-forward ‘[CONDITION]’ is shown, which was replaced by ‘sadness’ or ‘moved’/’being moved’, depending on the assigned condition. After the tasks for the test or control condition were completed, all participants were invited to select another piece, but this time one that made them feel happy. Although this step of the study was completed by all participants, it will be referred to as the Happy ‘condition’ for convenience. The steps of the study are listed below. They followed one another in sequence, and the participant could not return to a step once they had answered the questions in that step and progressed.

• Participants were asked to self-select a piece that they both loved and that evoked sadness. They were encouraged to think about this for a few minutes if necessary. For those who could not come up with a piece that met these criteria, some alternative pieces were proposed, from which they could select, or, have further opportunity to select another piece. Details of the piece were collected.
• Enjoyment of the piece was rated: "How much do you like this piece?” (anchors: 0 = dislike it a lot; 100 = like it a lot)
• Open-ended felt emotions requested: “Please indicate in as much detail as possible any emotions that you feel in response to this piece. Be sure to include [CONDITION], of course.” (Free text response.)
• Affects felt . 26 felt affect terms were rated on a 3-point scale (A lot, A little, Not felt) on the extent to which each terms was felt. The wording of each terms was presented to the participant as—1: Being absorbed/completely immersed in the music; 2. Anger; 3. A sense of awe; 4. Feeling of beauty; 5. Calm; 6. Chills; 7. Compassion; 8. Empathy; 9. Euphoria; 10. Fear; 11. A feeling that is sublime; 12. Goosebumps; 13. Grief; 14. Happiness; 15. Joy; 16. Being moved; 17. Nostalgia; 18. Peacefulness; 19. Powerful feelings; 20. Release or relief (sometimes referred to as ’Catharsis’); 21. Sadness; 22. Tears/wanting to cry/feeling like crying/actually crying; 23. Tenderness; 24. Transcendence; 25. Tragedy; 26. Wonder.
• Confirm felt and direct . Confirm that: Affect terms marked as present in the previous step (‘A lot’ or ‘A little’) were (a) felt and (b) that they were triggered directly by the music, not by thoughts, memories, images, etc. (Yes/No for each of (a) and (b)).
• I would like the piece a LITTLE LESS;
• It would make NO DIFFERENCE;
• I would like the piece a LITTLE MORE;
• I would like the piece a LOT MORE.
• Affects that add to liking . The same 26 Affect terms listed in step iv were rated on a 3-point scale (Adds to the pleasure, Does not add to the pleasure, Don’t know/not relevant) to assess whether the “the felt emotions add to the liking, pleasure, attraction or enjoyment”.
• Cooling down. The above procedure was repeated for a self-selected happy piece, but without any ratings of the 26 Affect terms requested (i.e. steps iv, v & vii excluded).
• Background (age, gender, music background) data were collected after which the participant was thanked and farewelled.

Some researchers, such as [ 67 , 68 ], treat the concepts of affect and emotion as distinct. In the present study the distinction is partly made for the convenience of distinguishing between participant open-ended response in step iii (emotion) versus their selection from a predetermined list of terms in steps iv, v & vii (affect). The term ‘emotion’ rather than ‘affect’ was used in all of these instruction steps because the former term was considered better understood by participants, regardless of whether referred to as emotion or affect in this article.

Data validation

Participant profile by condition..

Inferential tests demonstrated that the Sadness and Moving groups were statistically identical in terms of gender, age and years of music lessons ( Table 1 ). Also comparable across the groups was the overall rating of liking, averaging over 90 on a 0–100 scale, with upper quartiles (Q3) demonstrating a ceiling effect in both conditions which supports the use of self-selected music for generating high levels of pleasure.

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https://doi.org/10.1371/journal.pone.0299115.t001

Check that the emotion was felt and evoked emotion was directly due to the music.

There was overall high confirmation that the emotions were felt (over 96% of participants) and over 90% of participants in both conditions confirmed that the sadness was triggered intrinsically by the music (not triggered by something outside the music). See Table 1 for breakdown by condition. Overall, participants from both conditions were successful at experiencing the target emotion (Sadness or Being moved) and confirmed that, as requested, the music was directly responsible for triggering the emotion, rather than due to some extramusical factor. All participants were retained for further analysis.

Most frequently reported music excerpts.

All participants selected a piece that met the music selection criteria. Although researcher-suggested pieces were prepared in case a participant could not identify a self-selected piece meeting the criteria, none of the participants requested the researcher-suggested option, and so the research-suggested options were never used in the study. A selection of the self-selected items is presented in Table 2 , showing composers/artists reported by at least three participants across the cohort, and listing the works reported at least twice across the cohort. Interesting similarities can be observed across conditions, with composers Beethoven, Chopin and Debussy, and artists Taylor Swift and Bon Iver appearing in the Moving and Sad conditions. Furthermore, for the Beethoven, two pieces were mentioned in both of these conditions: Für Elise and Moonlight Sonata (1st Movement). These selections reflect the shared tastes across the groups, and at the high proportion of musicians, in particular pianists, who participated (all of the more frequently selected Beethoven, Chopin and Debussy pieces were for piano). Table 1 reveals the overall high average years of music lessons reported across the cohort [ 69 ]. These selections also indicate the capacity for the same piece of music to evoke different emotions (being moving and sadness).

https://doi.org/10.1371/journal.pone.0299115.t002

Emotion profile of sad music: Open-ended

After selection of a piece in their assigned condition, participants were asked to provide free descriptions of the emotions they felt in response to the selected piece (self-selected sad or self-selected moving music). The reported terms were pre-processed by identifying all reported emotion terms (participants could report more than one), correcting spelling mistakes, checking context and lemmatizing terms. This was followed by a frequency count of these terms for each condition. The target emotion was expected to be reported frequently in each condition.

Table 3 lists the emotion terms in descending order of frequency for each condition (including the Happy condition, where the same task was requested of participants in both conditions, but for a happy piece), with the most frequent words shown (down to a count of five). The selection of most frequent terms shown with an asterisk in the top rows of the table (above the horizontal cell divider) was determined by the ‘Power Fitted Elbow’ (PFE) technique that builds on word frequency distribution characteristics [ 70 – 73 ]. The expected target emotion (shown in italics font in the table) is reported most frequently in all conditions. Noteworthy is that sad was reported frequently in the Moved condition, while negative emotions were reported exclusively among the most frequently reported Sad condition emotions. Nostalgia was frequently reported in all conditions. In the Sad condition, the lemma Moved (not shown in the table) was mentioned 4 times, but was not reported frequently, according to the PFE criterion. Another interesting finding is that none of the frequently investigated mediator emotions (Being moved, in particular), appear in the most frequently reported items of the Sad condition list (sad, nostalgia, loss, melancholy and lonely). In contrast, the Moved condition did lead to frequent open-ended reporting of sadness.

https://doi.org/10.1371/journal.pone.0299115.t003

Emotion profile of sad music: Felt Affect term ratings

After open-ended responses were reported, participants were asked to indicate the extent to which each of 26 affect terms were felt when listening to the music. Again, the target affect terms were expected to be rated highest. The ratings for each affect term within and between conditions were examined.

Means for each affect term by condition are summarised in Fig 1 . Ratings of the same affect term between conditions were analysed using Bonferroni adjusted independent samples t-tests. Felt sadness was rated higher in the Sad condition, but (non-significantly) higher ratings were given to felt Power, Moved and Absorption ratings in the Sad condition. For the Moved condition the affect term Being moved was rated as the second highest scale (second to Absorption), and the rating was statistically the same as for the rating of Being Moved in the Sad condition. Other differences within and across the two conditions can be observed in Fig 1 . Differences for within conditions are not shown because of the large number that were significantly different at p = .05. The highest scoring (with mean rating in at least one condition > 1.5) affect terms were Absorption, Awe, Beauty, Moved, Power, and Sadness.

https://doi.org/10.1371/journal.pone.0299115.g001

In these data, a relatively high rating of Being moved can be observed in the Sad condition, and it received a higher rating than the target emotion (Sadness) by M = .122, though non-significantly (p = 1.0), which could be taken to support the action of a mediator, being moved, as responsible for the pleasure generated by the music, despite the accompanying rating of sadness.

Affects that add to enjoyment

The above results indicate the presence of emotion during the enjoyable music experience. However this does not necessarily confirm that the emotion itself is implicated in the enjoyment of the music. The next step of the study addressed this with an explicit question about the contribution of each affect term to the enjoyment of the music. The 26 Affect terms were presented again this time to be classified as contributing, not contributing, or being irrelevant to the enjoyment of the music. Table 4 lists the counts across each of the three possibilities for each Affect term, by Condition. Chi-Square tests identified whether the Affect words add to enjoyment of the music by chance or not.

https://doi.org/10.1371/journal.pone.0299115.t004

Significant Chi-square test statistics (at p = .05 with Bonferroni correction) ranged from 15.500 (Fear) to 83.400 (Absorption) for the Moving condition and 14.596 (Fear) to 82.383 (Being moved) for the Sad condition (at p = .05). Chi-Squared tests for Sad and Moving conditions pooled produced statistically significant results for all emotions at p = .05 with Bonferroni correction, ranging from χ 2 = 13.273 (Tragedy) to 158.606 (Being Moved), with second highest χ 2 = 156.85 (for Absorption) and third highest χ 2 = 84.061 (for Sadness).

Self-selected sad music was associated with good likelihood of reporting felt sadness as adding to the pleasure of the experience (83% of response in the Sad condition versus 71% in the Moving condition). The same applies for the affect term rating of Being moved in the Moving condition.

All emotions contributed to the enjoyment of the self-selected music, with the exception of Anger, Fear, Tragedy (both conditions for each, though Tragedy was approaching significance), Grief (Moving condition), Euphoria, Sublime, Happiness, Joy, Peacefulness and Wonder (Sad condition for each). Absorption and Being Moved made the most consistently positive contribution to enjoyment of music, with each being reported as contributing to enjoyment by 90% or more of participants regardless of condition ( Table 4 ).

Fewer nominally negative emotions add to enjoyment in the Moving condition, whereas fewer positive emotions add to enjoyment in the Sad condition. Sadness and crying are emotions with nominally negative connotations, but were reported as adding to the pleasure, regardless of the condition.

Additional emotions that add to liking

The 26 Affect terms might not have exhaustively covered all the emotions that could be experienced, or enjoyed. Therefore, a final question invited participants to list any other emotions that added to the enjoyment of the music.

Only one expression was reported by different participants more than once—Hopelessness (3 independent mentions, one in the Moving condition). 72 participants indicated that no additional emotions contributed to enjoyment (36 in the Moving condition and 36 in the Sad condition). A higher proportion of participants who did report additional emotions mentioned ones that could be interpreted as negative in the Sad condition compared to the Moving condition, but because of the heterogeneity of the responses, which included some words that were already among the 26 Affect terms, no strong conclusion can be drawn, except that the set of Affect terms was effective in identifying the feelings implicated in pleasurable musical experiences.

Hypothesis test–Sadness is liked because the music is sad

For the responses to the Sadness removed step, the following scoring was applied to responses: -2 for ‘I would like the piece a LOT LESS’, -1 for ‘I would like the piece a LITTLE LESS’, 1 for ‘I would like the piece a LITTLE MORE’, 2 for ‘I would like the piece a LOT MORE’, and 0 for NO DIFFERENCE. If the Direct effect hypothesis is supported, we would expect liking to reduce when sadness is removed from the experience. The Indirect effect hypothesis, on the other hand, predicts that removal of sadness would not change liking (change of 0) or increase liking. A single sample t-test supported the Direct effect hypothesis, with an overall reduction of .83 (SD = .916) in liking on the scale of -2 to +2 (t(46) = -.6.207, p < .001, Cohen’s-d = .916). For comparison, in the control condition, removal of movingness also led to a reduction in liking (M = -.77, SD = .807, t(51) = -.6872, p < .001, Cohen’s-d = .807). Taken together the data from this step of the study supports the Direct effect hypothesis.

Based on an overall interpretation of the data, the Direct effect hypothesis is supported. In the specific part of the study that tested the hypothesis, the Sadness removed step, participants reported overall significant reduction in pleasure if the felt sadness, and only the felt sadness evoked by the music, were excised. If sadness were not in itself enjoyed, we may have expected participants to attribute non-sad emotions to the enjoyment, or be unable to perform the task. As it turned out, we can confirm that 83% of participants could perform the task and verify that the sadness was specifically enjoyed, suggesting that the phenomenon of interest is empirically demonstrable. To further ascertain if this is a plausible interpretation, the results are interpreted through the alternative, Indirect effect hypothesis, lens by examining whether mediators still play a commensurate or dominant role in the effect.

Mediation explanation

In the results where affect terms were all rated, a term can be viewed as a mediator if its score or count is statistically equal to or higher than the score or count of the target emotion. Based on this criterion, several steps of the study could be interpreted as supporting the presence of a mediator. In the Open-ended felt emotions step Nostalgia, a prospective mediator of sadness-enjoyment, was spontaneously reported ( Table 3 ). However, Being moved was not, despite previous evidence that Being moved is the stronger candidate of the two [ 15 ]. Nostalgia appeared frequently in the Moved condition as well, but in the Moved condition no mediator was expected because the target emotion (being moved) itself already contained an implicitly positive component. Furthermore, Sadness was also frequently reported in the Moved condition, but, again, there is no reason that being moved would require a mediator. The Indirect effect hypothesis does not predict a mediator that is itself negatively valenced. Thus a mediator based explanation for these results is not straight forward.

In the Affects felt step a more credible impact of prospective mediators can be observed. In the Sad condition, Absorbed (rated highest, with M = 1.796), Being moved (rated higher than Sadness by M = .122, though non-significantly [NS], p = 1.0) and Powerful feelings (rated higher than Sadness by M = 0.020, NS p = 1.0) are all rated as high or higher than the target emotion (Sadness). In the Moved condition only Absorbed (M = 1.942) is rated higher than Being moved (by M = .135, NS p = .074). If we set aside the finding for the Moved condition, the mediator-based explanation is supported, triangulating extant evidence that two of these affects (absorbed and moved) are mediators of sadness.

So it is possible to find support for the Indirect-effect hypothesis, and the mediator-based explanation in particular. However, the findings refer to the presence of emotions. There is no assurance that any of the emotions identified are adding to the pleasure, with the exception of the target emotion, since that requirement was made explicit in the procedure.

The Affects that add to liking step addressed the matter. Being moved, Absorption, and Powerful feelings (but not Nostalgia) all had the same or higher counts than the target (Sadness) emotion, indicating that they add to enjoyment in the Sad condition ( Table 4 ). For example, the affect term Being moved was voted as ’adding to pleasure’ by 96% of participants in the Sad condition, compared to the affect term Sadness ’adding to pleasure’ according to 83% of participants. This supports the Indirect effect hypothesis ( Table 4 ).

Here we have the strongest evidence of mediators in explaining enjoyment of sadness, and this aligns with evidence from previous research [as discussed in the introduction, see 17 ]. But Absorption (adds to pleasure according to 92% of participants) also has a higher count than the target emotion (90%) in the Moved condition. Does that mean that Absorption also mediates Being moved? As pointed out above, that seems unlikely because Being moved already contains a positive aspect, and so should not need a mediator. Using the mediator-based explanation, Absorption adding to enjoyment votes should have (at least) been fewer than the votes for Being moved in the Moving condition (which was not the case). Furthermore, in the Sadness condition, the target emotion itself received statistically significant votes as adding to pleasure, meaning that the alleged mediators may not have served any essential purpose in contributing to the enjoyment. The mediation explanation is only able to partially explain the results. An alternative explanation is proposed by applying the concept of ‘semantic overlap’.

Semantic overlap explanation

Semantic overlap is a phenomenon concerned with the mental organisation of concepts and word meanings. Words with similar meanings (synonyms) are more linked with one another in a mental space than words with unrelated meanings. This is often characterised in network inspired models of the mind, foundationally proposed by Quillian and the notion of the semantic network [ 74 , 75 ]. Word meanings are organised in a complex yet systematic manner according to network principles, of particular interest here being through similarities in the meaning of words, where expressions that are more similar in meaning appear ‘closer together’ in the mental network. This means that when a word is triggered (e.g., heard or read), the semantically more closely related words are more primed (ready to be raised to conscious attention) in the mental network than less closely related words. Cognitive linguists by and large agree that words are pointers or approximate representations of concepts and experiences stored in memory [ 76 , 77 ]. The implication is that words can be mapped onto points in multidimensional semantic space, with distance between words reflecting (of interest here) degree of conceptual dissimilarity between the words. Considerable effort has been devoted to organising emotions by similarity [e.g., 78 – 83 ]. Semantic distance may therefore explain why Being moved frequently appears for sad evoking music (a frequently reported result), and the novel findings identified in the present study.

It is possible to estimate the relative semantic distance between the two words moving and sadness by looking up the terms in a published list of words with quantified point estimates of locations in theoretical semantic space. A large such database was developed by Mohammad [ 82 ], where estimates of location in semantic space of some 20,000 English words were produced. The semantic space in that research adopts a conventional representation of the space, particularly relevant for emotions, referred to as ‘VAD’ space. Emotions can be reasonably well expressed in terms of two dimensions, labelled valence (V) and arousal (A), where the former refers to the positive or negative aspect of the word’s meaning (e.g., happy and calm exhibit positive valence, while sad and angry negative) and the degree of activity associated with the word’s meaning (e.g., joyous and furious are high arousal, while calm and sad are low arousal). Some have argued that two dimensions are only partially sufficient for describing the meaning of an emotion [ 81 , 84 – 87 ], and a frequently proposed third dimension is dominance (D) (where words such as angry and energetic exhibit high dominance, while fear and innocuous are low in dominance), leading to the VAD (Valence, Arousal, Dominance) abbreviation for this three dimensional configuration [other examples: 85 , 88 , for a review, see 89 , 90 ]. Mohammad (82) provided numerical VAD scores for each term scaled to a score between 0 and 1 (negative to positive for valence, low to high for arousal and for dominance) based on human ratings. From these data it is possible to estimate the semantic distance between emotions.

Through calculations using the VAD word list published by Mohammad (82), Moved and Sadness have a semantic distance in VAD space of 0.607 units (numbers closer to 0 indicating greater similarity). With Sadness as the reference, positive emotions appearing in the Affect term list have distances that range from 0.852 for Calm to 1.243 for Joy (all greater than the distance between Sadness and Moving), while negative emotions have scores ranging from 0.469 for Grief (closest negative emotion to Sadness from the Affect terms presented) to 0.768 (Anger), which apart from Anger are all closer to Sadness than Moving is to Sadness. That is, Moving has more semantic overlap with Sadness than does Anger and the positive emotions Joy and Happiness, suggesting semantic overlap as a viable alternative to mediation as to why being moved appears in tandem with sadness. The VAD data also suggest that Moving is semantically more closely related to Sadness than Catharsis, since Catharsis has a distance of 0.633 from Sadness (slightly more distant than Moving). High ratings of Moving for a Sad-evoking context can therefore be explained by semantic overlap. Such an interpretation strengthens the case for supporting the Direct effect hypothesis, because being moved need not be treated as surrogate for sadness.

The Direct effect hypothesis proposes that pleasure is experienced by contextualised re-appraisal or ‘dissociation’ of the Action tendency component of an otherwise negative emotion. The consequent positive experience (enjoyment, pleasure, preference) provides another clue for the remaining Affect terms that were rated the same or higher than the target emotion in each condition. The mediation account fails to explain why Sadness was voted (by 71% of participant) as adding to enjoyment in the Moving condition. The mediator based explanation is also poor at explaining why Absorption was reported as adding to enjoyment, and for doing so in both conditions.

The semantic overlap approach can better explain these results, too. Affect terms such as Absorption and Powerful feelings are affects related to enjoyment when experiencing art. Consider the Absorption in Music scale developed by Sandstrom and Russo [ 91 ]. The 34 item scale contains several items related to the pleasure of being engaged with music in different ways [see also 2 , 18 , 92 , 93 ]. Powerful experiences are reported during special, personal experiences that occur during strong positive aesthetic experiences [ 94 – 96 , p. xiv]. That is, the task itself, of identifying a loved piece of music, also produces semantic overlap of these terms. Furthermore, in the Sad condition several positive emotions were reported more frequently as having no relevance to enjoyment, in comparison to the Moved condition: Euphoria (57% in the Sad condition versus 15% in the Moving condition), Happiness (43% vs 8%) and Joy (49% vs 12%). Mediation struggles to explain why purely positive affect terms are not voted as adding to enjoyment. Semantic overlap, on the other hand, suggests that the activation of sadness is more likely to be associated with other negative emotions, while being moved would be more associated with emotions of both positive and negative valence. In addition to the possibly misleading interpretations of enjoyed-sadness in music research employing a mediator-based approach to explaining the phenomenon, discussed in the Method section, semantic overlap offers an explanation of the results that is superior to the mediator-based explanation.

Conclusions

This study investigated whether the experience of sadness, evoked by music, can itself be highly enjoyable. A novel method was applied where participants were asked to imagine how enjoyment would be impacted should the felt sadness somehow be removed. The results demonstrated that sadness is directly implicated in the enjoyment of such music, providing support for the ‘Direct effect hypothesis’. This hypothesis states that when sad music is enjoyed, the sadness itself directly contributes to the enjoyment. A theoretical position has been presumed by the hypothesis–that the experience of sadness contains a component that can be dissociated from regular experience of the negative emotion when contemplating music or any aesthetic event. The presence of emotions such as being moved were explained by the concept of semantic overlap, where an emotion concept is not activated as a lexical singular, but rather as the meaning that the emotion encompasses, or that is spread to other related emotions, according to how similar they are (in this case to the concept of sadness). Being moved is sufficiently close in meaning to sadness to allow it to be activated during a sadness evoking music experience, regardless of the extent to which it is enjoyed, meaning that the presence of an emotion such as being moved does not necessarily explain (and is not needed to explain) why felt sadness can be enjoyed. Absorption is another affect that accompanied loved, sadness-inducing music. This, too, was explained by semantic overlap, with the positive component of the sadness activating other, reasonably nearby, positive affects, including Absorption. The state of absorption may also play a causal role in attraction to music [ 20 , 97 ], and so there could well be some feedback loop between absorption and other aspects of the experience, including evoked emotions. Suggestions were made for further research to test whether the semantic overlap account and the Direct effect hypothesis better characterise enjoyment of negative emotion in music than mediators (such as being moved and absorption) that themselves have a positive component, through which enjoyment is indirectly generated.

The results of the present study were enhanced by applying a modified version of research using self-selected stimuli that minimised demand characteristics, while ensuring that the phenomenon of interest was investigated. Methodologically, the study took the critical step of ensuring that the impact of particular affects on enjoyment of the music were investigated, not just their presence. Future research is likely to continue the more popular method of using experimenter-selected stimuli which are then rated along various affect terms. This paper made recommendations on how such research could be more successful at identifying the phenomenon of interest, and in so doing better address the debate on the enjoyment of felt sadness and other felt negative emotions in music.

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Are We Becoming More Ethical Consumers During the Global Pandemic? The Moderating Role of Negotiable Fate Across Cultures

• Original Paper
• Published: 11 April 2024

Cite this article

• Junjun Cheng 1 , 2 ,
• Yimin Huang 3 &
• Bo Chen 4  

The COVID-19 pandemic is a global crisis which has witnessed consumers experiencing significant anxiety provoked by the threats to their health and even lives. Meanwhile, consumers have been observed to make more ethical purchases since the start of the pandemic. Drawing on literature on terror management and negotiable fate, this research employs a moderated moderating model to investigate how consumers’ perception of the pandemic severity leads to ethical consumption as a defensive mechanism against death-related anxiety, as well as the differential role of consumers’ belief in negotiable fate in moderating this pandemic impact across tight and loose cultures. We conducted two cross-cultural studies with 592 and 423 respondents, respectively, at different times during the pandemic. Results consistently show that perceived pandemic severity increases consumers’ intention to consume ethically. Consumers’ belief in negotiable fate directly enhances ethical consumption, but it alleviates the effect of pandemic severity on ethical consumption among consumers who live in a tight culture. Our findings reveal the existential meaning of ethical consumption as a buffer against pandemic-triggered mortality salience, identify the positive psychological functions of negotiable fate in promoting ethical consumption but mitigating consumers’ need to buffer death-related concerns, and advances the importance of investigating the cultural variances in the terror management process toward ethical consumption. The findings offer insights for marketers and policy makers to develop effective strategies to support consumers’ ethical coping during societal crises.

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Appendix A: Item description and factor loadings of EFA for Study 1 and Study 2

• Loading values greater than 0.5 are shown in bold

Appendix B: Results of multigroup analysis for Study 1 and Study 2

Appendix c: correlation results and descriptive statistics for study 2.

• N Total  = 423; * p  < 0.05, ** p  < 0.01, *** p  < 0.001; two tailed
• The square roots of AVE are displayed in brackets along the diagonal

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Cheng, J., Huang, Y. & Chen, B. Are We Becoming More Ethical Consumers During the Global Pandemic? The Moderating Role of Negotiable Fate Across Cultures. J Bus Ethics (2024). https://doi.org/10.1007/s10551-024-05660-9

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Research questions, hypotheses and objectives

Patricia farrugia.

* Michael G. DeGroote School of Medicine, the

Bradley A. Petrisor

† Division of Orthopaedic Surgery and the

Forough Farrokhyar

‡ Departments of Surgery and

§ Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ont

Mohit Bhandari

There is an increasing familiarity with the principles of evidence-based medicine in the surgical community. As surgeons become more aware of the hierarchy of evidence, grades of recommendations and the principles of critical appraisal, they develop an increasing familiarity with research design. Surgeons and clinicians are looking more and more to the literature and clinical trials to guide their practice; as such, it is becoming a responsibility of the clinical research community to attempt to answer questions that are not only well thought out but also clinically relevant. The development of the research question, including a supportive hypothesis and objectives, is a necessary key step in producing clinically relevant results to be used in evidence-based practice. A well-defined and specific research question is more likely to help guide us in making decisions about study design and population and subsequently what data will be collected and analyzed. 1

Objectives of this article

In this article, we discuss important considerations in the development of a research question and hypothesis and in defining objectives for research. By the end of this article, the reader will be able to appreciate the significance of constructing a good research question and developing hypotheses and research objectives for the successful design of a research study. The following article is divided into 3 sections: research question, research hypothesis and research objectives.

Research question

Interest in a particular topic usually begins the research process, but it is the familiarity with the subject that helps define an appropriate research question for a study. 1 Questions then arise out of a perceived knowledge deficit within a subject area or field of study. 2 Indeed, Haynes suggests that it is important to know “where the boundary between current knowledge and ignorance lies.” 1 The challenge in developing an appropriate research question is in determining which clinical uncertainties could or should be studied and also rationalizing the need for their investigation.

Increasing one’s knowledge about the subject of interest can be accomplished in many ways. Appropriate methods include systematically searching the literature, in-depth interviews and focus groups with patients (and proxies) and interviews with experts in the field. In addition, awareness of current trends and technological advances can assist with the development of research questions. 2 It is imperative to understand what has been studied about a topic to date in order to further the knowledge that has been previously gathered on a topic. Indeed, some granting institutions (e.g., Canadian Institute for Health Research) encourage applicants to conduct a systematic review of the available evidence if a recent review does not already exist and preferably a pilot or feasibility study before applying for a grant for a full trial.

In-depth knowledge about a subject may generate a number of questions. It then becomes necessary to ask whether these questions can be answered through one study or if more than one study needed. 1 Additional research questions can be developed, but several basic principles should be taken into consideration. 1 All questions, primary and secondary, should be developed at the beginning and planning stages of a study. Any additional questions should never compromise the primary question because it is the primary research question that forms the basis of the hypothesis and study objectives. It must be kept in mind that within the scope of one study, the presence of a number of research questions will affect and potentially increase the complexity of both the study design and subsequent statistical analyses, not to mention the actual feasibility of answering every question. 1 A sensible strategy is to establish a single primary research question around which to focus the study plan. 3 In a study, the primary research question should be clearly stated at the end of the introduction of the grant proposal, and it usually specifies the population to be studied, the intervention to be implemented and other circumstantial factors. 4

Hulley and colleagues 2 have suggested the use of the FINER criteria in the development of a good research question ( Box 1 ). The FINER criteria highlight useful points that may increase the chances of developing a successful research project. A good research question should specify the population of interest, be of interest to the scientific community and potentially to the public, have clinical relevance and further current knowledge in the field (and of course be compliant with the standards of ethical boards and national research standards).

FINER criteria for a good research question

Adapted with permission from Wolters Kluwer Health. 2

Whereas the FINER criteria outline the important aspects of the question in general, a useful format to use in the development of a specific research question is the PICO format — consider the population (P) of interest, the intervention (I) being studied, the comparison (C) group (or to what is the intervention being compared) and the outcome of interest (O). 3 , 5 , 6 Often timing (T) is added to PICO ( Box 2 ) — that is, “Over what time frame will the study take place?” 1 The PICOT approach helps generate a question that aids in constructing the framework of the study and subsequently in protocol development by alluding to the inclusion and exclusion criteria and identifying the groups of patients to be included. Knowing the specific population of interest, intervention (and comparator) and outcome of interest may also help the researcher identify an appropriate outcome measurement tool. 7 The more defined the population of interest, and thus the more stringent the inclusion and exclusion criteria, the greater the effect on the interpretation and subsequent applicability and generalizability of the research findings. 1 , 2 A restricted study population (and exclusion criteria) may limit bias and increase the internal validity of the study; however, this approach will limit external validity of the study and, thus, the generalizability of the findings to the practical clinical setting. Conversely, a broadly defined study population and inclusion criteria may be representative of practical clinical practice but may increase bias and reduce the internal validity of the study.

PICOT criteria 1

A poorly devised research question may affect the choice of study design, potentially lead to futile situations and, thus, hamper the chance of determining anything of clinical significance, which will then affect the potential for publication. Without devoting appropriate resources to developing the research question, the quality of the study and subsequent results may be compromised. During the initial stages of any research study, it is therefore imperative to formulate a research question that is both clinically relevant and answerable.

Research hypothesis

The primary research question should be driven by the hypothesis rather than the data. 1 , 2 That is, the research question and hypothesis should be developed before the start of the study. This sounds intuitive; however, if we take, for example, a database of information, it is potentially possible to perform multiple statistical comparisons of groups within the database to find a statistically significant association. This could then lead one to work backward from the data and develop the “question.” This is counterintuitive to the process because the question is asked specifically to then find the answer, thus collecting data along the way (i.e., in a prospective manner). Multiple statistical testing of associations from data previously collected could potentially lead to spuriously positive findings of association through chance alone. 2 Therefore, a good hypothesis must be based on a good research question at the start of a trial and, indeed, drive data collection for the study.

The research or clinical hypothesis is developed from the research question and then the main elements of the study — sampling strategy, intervention (if applicable), comparison and outcome variables — are summarized in a form that establishes the basis for testing, statistical and ultimately clinical significance. 3 For example, in a research study comparing computer-assisted acetabular component insertion versus freehand acetabular component placement in patients in need of total hip arthroplasty, the experimental group would be computer-assisted insertion and the control/conventional group would be free-hand placement. The investigative team would first state a research hypothesis. This could be expressed as a single outcome (e.g., computer-assisted acetabular component placement leads to improved functional outcome) or potentially as a complex/composite outcome; that is, more than one outcome (e.g., computer-assisted acetabular component placement leads to both improved radiographic cup placement and improved functional outcome).

However, when formally testing statistical significance, the hypothesis should be stated as a “null” hypothesis. 2 The purpose of hypothesis testing is to make an inference about the population of interest on the basis of a random sample taken from that population. The null hypothesis for the preceding research hypothesis then would be that there is no difference in mean functional outcome between the computer-assisted insertion and free-hand placement techniques. After forming the null hypothesis, the researchers would form an alternate hypothesis stating the nature of the difference, if it should appear. The alternate hypothesis would be that there is a difference in mean functional outcome between these techniques. At the end of the study, the null hypothesis is then tested statistically. If the findings of the study are not statistically significant (i.e., there is no difference in functional outcome between the groups in a statistical sense), we cannot reject the null hypothesis, whereas if the findings were significant, we can reject the null hypothesis and accept the alternate hypothesis (i.e., there is a difference in mean functional outcome between the study groups), errors in testing notwithstanding. In other words, hypothesis testing confirms or refutes the statement that the observed findings did not occur by chance alone but rather occurred because there was a true difference in outcomes between these surgical procedures. The concept of statistical hypothesis testing is complex, and the details are beyond the scope of this article.

Another important concept inherent in hypothesis testing is whether the hypotheses will be 1-sided or 2-sided. A 2-sided hypothesis states that there is a difference between the experimental group and the control group, but it does not specify in advance the expected direction of the difference. For example, we asked whether there is there an improvement in outcomes with computer-assisted surgery or whether the outcomes worse with computer-assisted surgery. We presented a 2-sided test in the above example because we did not specify the direction of the difference. A 1-sided hypothesis states a specific direction (e.g., there is an improvement in outcomes with computer-assisted surgery). A 2-sided hypothesis should be used unless there is a good justification for using a 1-sided hypothesis. As Bland and Atlman 8 stated, “One-sided hypothesis testing should never be used as a device to make a conventionally nonsignificant difference significant.”

The research hypothesis should be stated at the beginning of the study to guide the objectives for research. Whereas the investigators may state the hypothesis as being 1-sided (there is an improvement with treatment), the study and investigators must adhere to the concept of clinical equipoise. According to this principle, a clinical (or surgical) trial is ethical only if the expert community is uncertain about the relative therapeutic merits of the experimental and control groups being evaluated. 9 It means there must exist an honest and professional disagreement among expert clinicians about the preferred treatment. 9

Designing a research hypothesis is supported by a good research question and will influence the type of research design for the study. Acting on the principles of appropriate hypothesis development, the study can then confidently proceed to the development of the research objective.

Research objective

The primary objective should be coupled with the hypothesis of the study. Study objectives define the specific aims of the study and should be clearly stated in the introduction of the research protocol. 7 From our previous example and using the investigative hypothesis that there is a difference in functional outcomes between computer-assisted acetabular component placement and free-hand placement, the primary objective can be stated as follows: this study will compare the functional outcomes of computer-assisted acetabular component insertion versus free-hand placement in patients undergoing total hip arthroplasty. Note that the study objective is an active statement about how the study is going to answer the specific research question. Objectives can (and often do) state exactly which outcome measures are going to be used within their statements. They are important because they not only help guide the development of the protocol and design of study but also play a role in sample size calculations and determining the power of the study. 7 These concepts will be discussed in other articles in this series.

From the surgeon’s point of view, it is important for the study objectives to be focused on outcomes that are important to patients and clinically relevant. For example, the most methodologically sound randomized controlled trial comparing 2 techniques of distal radial fixation would have little or no clinical impact if the primary objective was to determine the effect of treatment A as compared to treatment B on intraoperative fluoroscopy time. However, if the objective was to determine the effect of treatment A as compared to treatment B on patient functional outcome at 1 year, this would have a much more significant impact on clinical decision-making. Second, more meaningful surgeon–patient discussions could ensue, incorporating patient values and preferences with the results from this study. 6 , 7 It is the precise objective and what the investigator is trying to measure that is of clinical relevance in the practical setting.

The following is an example from the literature about the relation between the research question, hypothesis and study objectives:

Study: Warden SJ, Metcalf BR, Kiss ZS, et al. Low-intensity pulsed ultrasound for chronic patellar tendinopathy: a randomized, double-blind, placebo-controlled trial. Rheumatology 2008;47:467–71.

Research question: How does low-intensity pulsed ultrasound (LIPUS) compare with a placebo device in managing the symptoms of skeletally mature patients with patellar tendinopathy?

Research hypothesis: Pain levels are reduced in patients who receive daily active-LIPUS (treatment) for 12 weeks compared with individuals who receive inactive-LIPUS (placebo).

Objective: To investigate the clinical efficacy of LIPUS in the management of patellar tendinopathy symptoms.

The development of the research question is the most important aspect of a research project. A research project can fail if the objectives and hypothesis are poorly focused and underdeveloped. Useful tips for surgical researchers are provided in Box 3 . Designing and developing an appropriate and relevant research question, hypothesis and objectives can be a difficult task. The critical appraisal of the research question used in a study is vital to the application of the findings to clinical practice. Focusing resources, time and dedication to these 3 very important tasks will help to guide a successful research project, influence interpretation of the results and affect future publication efforts.

Tips for developing research questions, hypotheses and objectives 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.
• Develop clear and well-defined primary and secondary (if needed) objectives.
• Ensure that the research question and objectives are answerable, feasible and clinically relevant.

FINER = feasible, interesting, novel, ethical, relevant; PICOT = population (patients), intervention (for intervention studies only), comparison group, outcome of interest, time.

Competing interests: No funding was received in preparation of this paper. Dr. Bhandari was funded, in part, by a Canada Research Chair, McMaster University.

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Accelerated Aging May Increase the Risk of Early-onset Cancers in Younger Generations

SAN DIEGO – Accelerated aging was more common in recent birth cohorts and was associated with increased incidence of early-onset solid tumors, according to research presented at the American Association for Cancer Research (AACR) Annual Meeting 2024 , held April 5-10.

“Multiple cancer types are becoming increasingly common among younger adults in the United States and globally,” said Ruiyi Tian, MPH , a graduate student in the lab of Yin Cao, ScD, MPH at Washington University School of Medicine in St. Louis. “Understanding the factors driving this increase will be key to improve the prevention or early detection of cancers in younger and future generations.”

Tian and colleagues hypothesized that increased biological age, indicative of accelerated aging, may contribute to the development of early-onset cancers, often defined as cancers diagnosed in adults younger than 55 years. In contrast to chronological age—which measures how long a person has been alive—biological age refers to the condition of a person’s body and physiological processes and is considered modifiable, Tian explained.

“Unlike chronological age, biological age may be influenced by factors such as diet, physical activity, mental health, and environmental stressors,” she added. “Accumulating evidence suggests that the younger generations may be aging more swiftly than anticipated, likely due to earlier exposure to various risk factors and environmental insults. However, the impact of accelerated aging on early-onset cancer development remains unclear.”

To examine the association between biological age and cancer risk in younger individuals, Tian and colleagues examined data of 148,724 individuals housed in the U.K. Biobank database. They calculated each participant’s biological age using nine biomarkers found in blood: albumin, alkaline phosphatase, creatinine, C-reactive protein, glucose, mean corpuscular volume, red cell distribution width, white blood cell count, and lymphocyte proportion. Individuals whose biological age was higher than their chronological age were defined as having accelerated aging.

Tian and colleagues first evaluated accelerated aging across birth cohorts and found that individuals born in or after 1965 had a 17% higher likelihood of accelerated aging than those born between 1950 and 1954. They then evaluated the association between accelerated aging and the risk of early-onset cancers. They found that each standard deviation increase in accelerated aging was associated with a 42% increased risk of early-onset lung cancer, a 22% increased risk of early-onset gastrointestinal cancer, and a 36% increased risk of early-onset uterine cancer. Accelerated aging did not significantly impact the risk of late-onset lung cancer (defined here as cancer diagnosed after age 55), but it was associated with a 16% and 23% increased risk of late-onset gastrointestinal and uterine cancers, respectively.

“By examining the relationship between accelerating aging and the risk of early-onset cancers, we provide a fresh perspective on the shared etiology of early-onset cancers,” Tian said. “If validated, our findings suggest that interventions to slow biological aging could be a new avenue for cancer prevention, and screening efforts tailored to younger individuals with signs of accelerated aging could help detect cancers early.”

Future research from Tian and colleagues will aim to uncover the mechanisms driving accelerated aging and early-onset cancers to develop precision cancer prevention strategies.

A limitation of the study is that all participants were from the United Kingdom, which may limit the generalizability of the findings to populations with different genetic backgrounds, lifestyles, and environmental exposures. Tian noted that validation in diverse populations is needed.

The study was supported by the National Institutes of Health. Tian declares no conflicts of interest.

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What are the key findings of the NHS gender identity review?

Report by Dr Hilary Cass finds young people being let down by lack of research and evidence on medical interventions

• Thousands of children ‘let down by NHS’
• Review has major implications for mental health services

A review into the NHS’s gender identity services has found that children and young people have been let down by a lack of research and evidence on medical interventions in a debate that has become exceptionally toxic.

Dr Hilary Cass said her report was not about defining “what it means to be trans” or “undermining the validity of trans identities”, but about “how best to help the growing number of children and young people who are looking for support from the NHS in relation to their gender identity”. Here are the review’s key findings.

The evidence

“This is an area of remarkably weak evidence,” Cass writes in the foreword to her 398-page report.

Despite that, she adds: “Results of studies are exaggerated or misrepresented by people on all sides of the debate to support their viewpoint. The reality is that we have no good evidence on the long-term outcomes of interventions to manage gender-related distress.”

When Cass began her inquiry in 2020, the evidence base, especially about puberty blockers and masculinising and feminising cross-sex hormones was “weak”. That was exacerbated by the existence of “a lot of misinformation, easily accessible online, with opposing sides of the debate pointing to research to justify a position, regardless of the quality of the studies.”

Cass commissioned the University of York to undertake systematic reviews of the evidence on key issues, such as puberty blockers. It found that “there continues to be a lack of high-quality evidence in this area”. York academics, as part of their research, tried to document the outcomes seen among the 9,000 young people who the Tavistock and Portman NHS trust’s gender identity development service (Gids) treated between 2009-2020. However, it was “thwarted by a lack of cooperation from [six of England’s seven NHS] adult gender services”.

The new NHS services for these young people must routinely collect evidence of what treatments work, and learn from them to improve clinical practice, the report states.

Cass acknowledges that the discussion around how to care for such young people is polarised, both among health professionals and in wider society. For example, some clinicians believe that most people who present to gender services “will go on to have a long-term trans identity and should be supported to access a medical pathway at an early stage”.

“Others feel that we are medicalising children and young people whose multiple other difficulties are manifesting through gender confusion and gender-related distress. The toxicity of the debate is exceptional,” the report says.

Cass has been criticised for talking both to groups who support gender affirmation – the medical approach – and also those who believe greater caution is needed. Some experienced doctors who have offered different viewpoints have been “dismissed and invalidated”, she says.

“There are few other areas of healthcare where professionals are so afraid to openly discuss their views, where people are vilified on social media and where name-calling echoes the worst bullying behaviour. This must stop.”

The toxicity of debate has made some clinicians fearful of working with these young people.

The Tavistock and Portman NHS Trust

When its Gids service was set up in 1989, it saw fewer than 10 children a year, mainly birth-registered males who had not reached puberty. Most received therapy and only a few hormones from the age of 16.

But in 2011 the UK began trialling the use of puberty blockers, as a result of the emergence of “the Dutch protocol”, which involved using them from early puberty. However, a study undertaken in 2015-16, although not published until 2020, shows “a lack of any positive measurable outcomes”.

“Despite this, from 2014 puberty blockers moved from a research-only protocol to being available in routine clinical practice.” This “adoption of a treatment with uncertain benefits without further scrutiny” helped increase the demand among patients for them, the report finds.

An NHS England review in 2019, which examined the evidence on medical intervention and found evidence of its effectiveness to be “weak”, led to Cass being asked to undertake her review.

Changing patient profile

Referral rates to Gids have rocketed since 2014, but there has also been a shift in the profile of those using services. For centuries transgender people have been predominantly trans females who present in adulthood. Now the vast majority are teenagers who were registered as female at birth.

An audit of discharge notes of Gids patients between 1 April 2018 and 31 December 2022 showed the youngest patient was three, the oldest 18, and 73% were birth-registered females, according to the review, which tries to discover why things have changed so dramatically.

One area it explores is the deterioration in mental health among young people, and the links with social media, which have brought pressures to bear on them that no previous generation has experienced.

“The increase in presentations to gender clinics has to some degree paralleled this deterioration in child and adolescent mental health,” the review says. “Mental health problems have risen in both boys and girls, but have been most striking in girls and young women.”

Youngsters who present with gender identity issues to services may also have depression, anxiety, body dysmorphia, tics and eating disorders, as well as autism spectrum disorder (ASD) and/or attention deficit hyperactivity disorder (ADHD). Referrals to Gids are also associated with higher than average rates of adverse childhood experiences, the review says.

“There is no single explanation for the increase in prevalence of gender incongruence or the change in case-mix of those being referred to gender services,” the review says, concluding instead that gender incongruence is a result of “a complex interplay between biological, psychological and social factors”.

Transitioning

Young people’s sense of their identity is not always fixed and can evolve over time, Cass says.

“Whilst some young people may feel an urgency to transition, young adults looking back at their younger selves would often advise slowing down,” the report says.

“For some, the best outcome will be transition, whereas others may resolve their distress in other ways. Some may transition and then de/retransition and/or experience regret. The NHS needs to care for all those seeking support.”

Social transitioning

Social transitioning is the process by which individuals make social changes in order to live as a different gender, such as changing name, pronouns, hair or clothing, and it is something that schools in England have been grappling with in recent years.

According to the Cass review, many children and young people attending Gids have already changed their names by deed-poll and attend school in their chosen gender by the time they are seen.

The review says research on the impact of social transition is generally of a poor quality and the findings are contradictory. Some studies suggest that allowing a child to socially transition may improve mental health and social and educational participation.

Others say a child who is allowed to socially transition is more likely to have an altered trajectory, leading to medical intervention, which will have life-long implications, when they might otherwise have desisted.

“Given the weakness of the research in this area there remain many unknowns about the impact of social transition,” the review concludes. “In particular, it is unclear whether it alters the trajectory of gender development, and what short- and longer-term impact this may have on mental health.”

The review recommends that parents should be involved in decision making, unless there are strong grounds to believe this may put a child at risk, and where children are pre-puberty, families should be seen as early as possible by a clinician with relevant experience. It also suggests avoiding premature decisions and considering partial rather than full transitioning as a way of keeping options open.

Future care

The report says that in the future any young person seeking NHS help with gender-related distress should be screened to see if they have any neurodevelopmental conditions, such as autism spectrum disorder, and also given a mental health assessment.

NHS England has already in effect banned the use of puberty blockers because of limited evidence that they work. Cass found that there is “no evidence that puberty blockers buy time to think”, which their advocates have claimed. There is also “concern that they may change the trajectory of psychosexual and gender identity development” as well as pose long-term risks to users’ bone health, the review says.

There is also a lack of evidence to prove that masculinising and feminising hormones improve a young person’s body satisfaction and psychosocial health, and there is concern over the impact on fertility, growth and bone health. There is also no evidence they reduce the risk of suicide in children, as their proponents have claimed.

Lastly, the evidence base showing whether psychosocial interventions – therapy – work for those who do not undergo hormone treatment is “as weak” as for puberty blockers and cross-sex hormones.

All this means that there is “a major gap in our knowledge about how best to support and help the growing population of young people with gender-related distress in the context of complex presentations”.

• Transgender
• Young people

More on this story

Five thousand children with gender-related distress awaiting NHS care in England

Ban on children’s puberty blockers to be enforced in private sector in England

What Cass review says about surge in children seeking gender services

Adult transgender clinics in England face inquiry into patient care

‘Children are being used as a football’: Hilary Cass on her review of gender identity services

Thousands of children unsure of gender identity ‘let down by NHS’, report finds

Review of gender services has major implications for mental health services

Mother criticises ‘agenda from above’ after release of Cass report

Gender medicine ‘built on shaky foundations’, Cass review finds

Cass review set to confirm shift in NHS care for children with gender dysphoria

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What’s it like to be a teacher in america today, public k-12 teachers are stressed about their jobs and few are optimistic about the future of education; many say poverty, absenteeism and mental health are major problems at their school.

Pew Research Center conducted this study to better understand the views and experiences of public K-12 school teachers. The analysis in this report is based on an online survey of 2,531 U.S. public K-12 teachers conducted from Oct. 17 to Nov. 14, 2023. The teachers surveyed are members of RAND’s American Teacher Panel, a nationally representative panel of public K-12 school teachers recruited through MDR Education. Survey data is weighted to state and national teacher characteristics to account for differences in sampling and response to ensure they are representative of the target population.

Here are the questions used for this report , along with responses, and the survey methodology .

Low-poverty , medium-poverty and high-poverty schools are based on the percentage of students eligible for free and reduced-price lunch, as reported by the National Center for Education Statistics (less than 40%, 40%-59% and 60% or more, respectively).

Secondary schools include both middle schools and high schools.

All references to party affiliation include those who lean toward that party. Republicans include those who identify as Republicans and those who say they lean toward the Republican Party. Democrats include those who identify as Democrats and those who say they lean toward the Democratic Party.

Public K-12 schools in the United States face a host of challenges these days – from teacher shortages to the lingering effects of COVID-19 learning loss to political battles over curriculum .

In the midst of all this, teachers express low levels of satisfaction with their jobs. In fact, they’re much less satisfied than U.S. workers overall.

Here’s how public K-12 teachers are feeling about their jobs:

• 77% say their job is frequently stressful.
• 68% say it’s overwhelming.
• 70% say their school is understaffed.
• 52% say they would not advise a young person starting out today to become a teacher.

When it comes to how their students are doing in school, teachers are relatively downbeat about both academic performance and behavior.

Here’s how public K-12 teachers rate academic performance and behavior at their school:

• 48% say the academic performance of most students at their school is fair or poor. A third say it’s good, and only 17% describe it as excellent or very good.
• 49% say the behavior of most students at their school is fair or poor; 35% say it’s good and 13% say it’s excellent or very good.

The COVID-19 pandemic likely compounded these issues. About eight-in-ten teachers (among those who have been teaching for at least a year) say the lasting impact of the pandemic on students’ behavior, academic performance and emotional well-being has been very or somewhat negative.

Assessments of student performance and behavior differ widely by school poverty level. 1 Teachers in high-poverty schools have a much more negative outlook. But feelings of stress and dissatisfaction among teachers are fairly universal, regardless of where they teach.

Related: What Public K-12 Teachers Want Americans To Know About Teaching

As they navigate these challenges, teachers don’t feel they’re getting the support or reinforcement they need from parents.

Majorities of teachers say parents are doing too little when it comes to holding their children accountable if they misbehave in school, helping them with their schoolwork and ensuring their attendance.

Teachers in high- and medium-poverty schools are more likely than those in low-poverty schools to say parents are doing too little in each of these areas.

These findings are based on a survey of 2,531 U.S. public K-12 teachers conducted Oct. 17-Nov. 14, 2023, using the RAND American Teacher Panel. 2 The survey looks at the following aspects of teachers’ experiences:

• Teachers’ job satisfaction (Chapter 1)
• How teachers manage their workload (Chapter 2)
• Problems students are facing at public K-12 schools (Chapter 3)
• Challenges in the classroom (Chapter 4)
• Teachers’ views of parent involvement (Chapter 5)
• Teachers’ views on the state of public K-12 education (Chapter 6)

Problems students are facing

We asked teachers about some of the challenges students at their school are facing. Three problems topped the list:

• Poverty (53% say this is a major problem among students who attend their school)
• Chronic absenteeism (49%)
• Anxiety and depression (48%)

Chronic absenteeism (that is, students missing a substantial number of school days) is a particular challenge at high schools, with 61% of high school teachers saying this is a major problem where they teach. By comparison, 46% of middle school teachers and 43% of elementary school teachers say the same.

Anxiety and depression are viewed as a more serious problem at the secondary school level: 69% of high school teachers and 57% of middle school teachers say this is a major problem among their students, compared with 29% of elementary school teachers.

Fewer teachers (20%) view bullying as a major problem at their school, though the share is significantly higher among middle school teachers (34%).

A look inside the classroom

We also asked teachers how things are going in their classroom and specifically about some of the issues that may get in the way of teaching.

• 47% of teachers say students showing little or no interest in learning is a major problem in their classroom. The share rises to 58% among high school teachers.
• 33% say students being distracted by their cellphones is a major problem. This is particularly an issue for high school teachers, with 72% saying this is a major problem.
• About one-in-five teachers say students getting up and walking around when they’re not supposed to and being disrespectful toward them (21% each) are major problems. Teachers in elementary and middle schools are more likely than those in high schools to see these as challenges.

A majority of teachers (68%) say they’ve experienced verbal abuse from a student – such as being yelled at or threatened. Some 21% say this happens at least a few times a month.

Physical violence is less common. Even so, 40% of teachers say a student has been violent toward them , with 9% saying this happens at least a few times a month.

About two-thirds of teachers (66%) say that the current discipline practices at their school are very or somewhat mild. Only 2% say the discipline practices at their school are very or somewhat harsh, while 31% say they are neither harsh nor mild. Most teachers (67%) say teachers themselves don’t have enough influence in determining discipline practices at their school.

Behavioral issues and mental health challenges

In addition to their teaching duties, a majority of teachers (58%) say they have to address behavioral issues in their classroom every day. About three-in-ten teachers (28%) say they have to help students with mental health challenges daily.

In each of these areas, elementary and middle school teachers are more likely than those at the high school level to say they do these things on a daily basis.

And teachers in high-poverty schools are more likely than those in medium- and low-poverty schools to say they deal with these issues each day.

Cellphone policies and enforcement

Most teachers (82%) say their school or district has policies regarding cellphone use in the classroom.

Of those, 56% say these policies are at least somewhat easy to enforce, 30% say they’re difficult to enforce, and 14% say they’re neither easy nor difficult to enforce.

Experiences with cellphone policies vary widely across school levels. High school teachers (60%) are much more likely than middle school (30%) and elementary school teachers (12%) to say the policies are difficult to enforce (among those who say their school or district has a cellphone policy).

How teachers are experiencing their jobs

Thinking about the various aspects of their jobs, teachers are most satisfied with their relationship with other teachers at their school (71% are extremely or very satisfied).

They’re least satisfied with how much they’re paid – only 15% are extremely or very satisfied with their pay, while 51% are not too or not at all satisfied.

Among teachers who don’t plan to retire or stop working this year, 29% say it’s at least somewhat likely they will look for a new job in the 2023-24 school year. Within that group, 40% say they would look for a job outside of education, 29% say they’d seek a non-teaching job in education, and only 18% say they’d look for a teaching job at another public K-12 school.

Do teachers find their work fulfilling and enjoyable?

Overall, 56% of teachers say they find their job to be fulfilling extremely often or often; 53% say their job is enjoyable. These are significantly lower than the shares who say their job is frequently stressful (77%) or overwhelming (68%).

Positive experiences are more common among newer teachers. Two-thirds of those who’ve been teaching less than six years say their work is fulfilling extremely often or often, and 62% of this group says their work is frequently enjoyable.

Teachers with longer tenures are somewhat less likely to feel this way. For example, 48% of those who’ve been teaching for six to 10 years say their work is frequently enjoyable.

Balancing the workload

Most teachers (84%) say there’s not enough time during their regular work hours to do tasks like grading, lesson planning, paperwork and answering work emails.

Among those who feel this way, 81% say simply having too much work is a major reason.

Many also point to having to spend time helping students outside the classroom, performing non-teaching duties like lunch duty, and covering other teachers’ classrooms as at least minor reasons they don’t have enough time to get all their work done.

A majority of teachers (54%) say it’s very or somewhat difficult for them to balance work and their personal life. About one-in-four (26%) say it’s very or somewhat easy for them to balance these things, and 20% say it’s neither easy nor difficult.

Among teachers, women are more likely than men to say work-life balance is difficult for them (57% vs. 43%). Women teachers are also more likely to say they often find their job stressful or overwhelming.

How teachers view the education system

A large majority of teachers (82%) say the overall state of public K-12 education has gotten worse in the past five years.

And very few are optimistic about the next five years: Only 20% of teachers say public K-12 education will be a lot or somewhat better five years from now. A narrow majority (53%) say it will be worse.

Among teachers who think things have gotten worse in recent years, majorities say the current political climate (60%) and the lasting effects of the COVID-19 pandemic (57%) are major reasons. A sizable share (46%) also point to changes in the availability of funding and resources.

Related:  About half of Americans say public K-12 education is going in the wrong direction

Which political party do teachers trust more to deal with educational challenges?

On balance, more teachers say they trust the Democratic Party than say they trust the Republican Party to do a better job handling key issues facing the K-12 education system. But three-in-ten or more across the following issues say they don’t trust either party:

• Shaping school curriculum (42% say they trust neither party)
• Ensuring teachers have adequate pay and benefits (35%)
• Making schools safer (35%)
• Ensuring adequate funding for schools (33%)
• Ensuring all students have equal access to high-quality K-12 education (31%)

A majority of public K-12 teachers (58%) identify or lean toward the Democratic Party. This is higher than the share among the general public (47%).

• Poverty levels are based on the percentage of students in the school who are eligible for free and reduced-price lunch. ↩
• For details, refer to the Methodology section of the report. ↩
• Urban, suburban and rural schools are based on the location of the school as reported by the National Center for Education Statistics (rural includes town). Definitions match those used by the U.S. Census Bureau. ↩

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Table of contents, ‘back to school’ means anytime from late july to after labor day, depending on where in the u.s. you live, among many u.s. children, reading for fun has become less common, federal data shows, most european students learn english in school, for u.s. teens today, summer means more schooling and less leisure time than in the past, about one-in-six u.s. teachers work second jobs – and not just in the summer, most popular.

About Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .