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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 Definition, 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.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

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. It is a preliminary answer to your question that helps guide the research process.

Consider a study designed to examine the relationship between sleep deprivation and test performance. The hypothesis might be: "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."

At a Glance

A hypothesis is crucial to scientific research because it offers a clear direction for what the researchers are looking to find. This allows them to design experiments to test their predictions and add to our scientific knowledge about the world. 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. At this point, researchers then 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 numerous 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 adage 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.

How to Formulate a Good Hypothesis

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.

The Importance of Operational Definitions

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.

Operational definitions are specific definitions for all relevant factors in a study. This process helps make vague or ambiguous concepts detailed and measurable.

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 various ways. Clearly defining these variables and how they are measured helps ensure that other researchers can replicate your results.

Replicability

One of the basic principles of any type of scientific research is that the results must be replicable.

Replication means repeating an experiment in the same way to produce the same results. 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. For example, 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.

To measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming others. The researcher might utilize a simulated task to measure aggressiveness in this situation.

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 there is a relationship between one independent variable and one dependent variable.
• Complex hypothesis : This type suggests a relationship between three or more variables, such as two independent and dependent variables.
• 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 population sample 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."
• "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."
• "Children who receive a new reading intervention will have higher reading scores than students who do not receive the intervention."

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:

• "There is no difference in anxiety levels between people who take St. John's wort supplements and those who do not."
• "There is no difference in scores on a memory recall task between children and adults."
• "There is no difference in aggression levels between children who play first-person shooter games and those who do not."

Examples of an alternative hypothesis:

• "People who take St. John's wort supplements will have less anxiety than those who do not."
• "Adults will perform better on a memory task than children."
• "Children who play first-person shooter games will show higher levels of aggression than children who do not." 

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  conducting an experiment is difficult or impossible. 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 examine how the variables are related. This 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.

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.

Thompson WH, Skau S. On the scope of scientific hypotheses .  R Soc Open Sci . 2023;10(8):230607. doi:10.1098/rsos.230607

Taran S, Adhikari NKJ, Fan E. Falsifiability in medicine: what clinicians can learn from Karl Popper [published correction appears in Intensive Care Med. 2021 Jun 17;:].  Intensive Care Med . 2021;47(9):1054-1056. doi:10.1007/s00134-021-06432-z

Eyler AA. Research Methods for Public Health . 1st ed. Springer Publishing Company; 2020. doi:10.1891/9780826182067.0004

Nosek BA, Errington TM. What is replication ?  PLoS Biol . 2020;18(3):e3000691. doi:10.1371/journal.pbio.3000691

Aggarwal R, Ranganathan P. Study designs: Part 2 - Descriptive studies .  Perspect Clin Res . 2019;10(1):34-36. doi:10.4103/picr.PICR_154_18

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."

Educational resources and simple solutions for your research journey

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

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

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

Table of Contents

What is a hypothesis ?  

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

What is a research hypothesis ?  

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

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

Characteristics of a good hypothesis  

Here are the characteristics of a good hypothesis :  

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

How to create an effective research hypothesis  

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

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

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

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

How to write a research hypothesis  

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

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

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

Population: athletes  

Independent variable: daily cold water showers  

Dependent variable: endurance  

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

Research hypothesis checklist  

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

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

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

Types of research hypothesis  

Different types of research hypothesis are used in scientific research:  

1. Null hypothesis:

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

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

2. Alternative hypothesis:

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

Example: “ The newly identified virus is zoonotic .”  

3. Directional hypothesis :

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

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

4. Non-directional hypothesis:

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

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

5. Simple hypothesis :

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

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

6 . Complex hypothesis :

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

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

7. Associative hypothesis:  

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

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

8 . Causal hypothesis:

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

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

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

Research hypothesis examples  

Here are some good research hypothesis examples :  

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

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

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

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

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

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

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

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

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

Importance of testable hypothesis  

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

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

Frequently Asked Questions (FAQs) on research hypothesis  

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

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

2. When to reject null hypothesis ?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

9. Can research hypotheses be used in qualitative research?

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

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15 Hypothesis Examples

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A hypothesis is defined as a testable prediction , and is used primarily in scientific experiments as a potential or predicted outcome that scientists attempt to prove or disprove (Atkinson et al., 2021; Tan, 2022).

In my types of hypothesis article, I outlined 13 different hypotheses, including the directional hypothesis (which makes a prediction about an effect of a treatment will be positive or negative) and the associative hypothesis (which makes a prediction about the association between two variables).

This article will dive into some interesting examples of hypotheses and examine potential ways you might test each one.

Hypothesis Examples

1. “inadequate sleep decreases memory retention”.

Field: Psychology

Type: Causal Hypothesis A causal hypothesis explores the effect of one variable on another. This example posits that a lack of adequate sleep causes decreased memory retention. In other words, if you are not getting enough sleep, your ability to remember and recall information may suffer.

How to Test:

To test this hypothesis, you might devise an experiment whereby your participants are divided into two groups: one receives an average of 8 hours of sleep per night for a week, while the other gets less than the recommended sleep amount.

During this time, all participants would daily study and recall new, specific information. You’d then measure memory retention of this information for both groups using standard memory tests and compare the results.

Should the group with less sleep have statistically significant poorer memory scores, the hypothesis would be supported.

Ensuring the integrity of the experiment requires taking into account factors such as individual health differences, stress levels, and daily nutrition.

Relevant Study: Sleep loss, learning capacity and academic performance (Curcio, Ferrara & De Gennaro, 2006)

2. “Increase in Temperature Leads to Increase in Kinetic Energy”

Field: Physics

Type: Deductive Hypothesis The deductive hypothesis applies the logic of deductive reasoning – it moves from a general premise to a more specific conclusion. This specific hypothesis assumes that as temperature increases, the kinetic energy of particles also increases – that is, when you heat something up, its particles move around more rapidly.

This hypothesis could be examined by heating a gas in a controlled environment and capturing the movement of its particles as a function of temperature.

You’d gradually increase the temperature and measure the kinetic energy of the gas particles with each increment. If the kinetic energy consistently rises with the temperature, your hypothesis gets supporting evidence.

Variables such as pressure and volume of the gas would need to be held constant to ensure validity of results.

3. “Children Raised in Bilingual Homes Develop Better Cognitive Skills”

Field: Psychology/Linguistics

Type: Comparative Hypothesis The comparative hypothesis posits a difference between two or more groups based on certain variables. In this context, you might propose that children raised in bilingual homes have superior cognitive skills compared to those raised in monolingual homes.

Testing this hypothesis could involve identifying two groups of children: those raised in bilingual homes, and those raised in monolingual homes.

Cognitive skills in both groups would be evaluated using a standard cognitive ability test at different stages of development. The examination would be repeated over a significant time period for consistency.

If the group raised in bilingual homes persistently scores higher than the other, the hypothesis would thereby be supported.

The challenge for the researcher would be controlling for other variables that could impact cognitive development, such as socio-economic status, education level of parents, and parenting styles.

Relevant Study: The cognitive benefits of being bilingual (Marian & Shook, 2012)

4. “High-Fiber Diet Leads to Lower Incidences of Cardiovascular Diseases”

Field: Medicine/Nutrition

Type: Alternative Hypothesis The alternative hypothesis suggests an alternative to a null hypothesis. In this context, the implied null hypothesis could be that diet has no effect on cardiovascular health, which the alternative hypothesis contradicts by suggesting that a high-fiber diet leads to fewer instances of cardiovascular diseases.

To test this hypothesis, a longitudinal study could be conducted on two groups of participants; one adheres to a high-fiber diet, while the other follows a diet low in fiber.

After a fixed period, the cardiovascular health of participants in both groups could be analyzed and compared. If the group following a high-fiber diet has a lower number of recorded cases of cardiovascular diseases, it would provide evidence supporting the hypothesis.

Control measures should be implemented to exclude the influence of other lifestyle and genetic factors that contribute to cardiovascular health.

Relevant Study: Dietary fiber, inflammation, and cardiovascular disease (King, 2005)

5. “Gravity Influences the Directional Growth of Plants”

Field: Agronomy / Botany

Type: Explanatory Hypothesis An explanatory hypothesis attempts to explain a phenomenon. In this case, the hypothesis proposes that gravity affects how plants direct their growth – both above-ground (toward sunlight) and below-ground (towards water and other resources).

The testing could be conducted by growing plants in a rotating cylinder to create artificial gravity.

Observations on the direction of growth, over a specified period, can provide insights into the influencing factors. If plants consistently direct their growth in a manner that indicates the influence of gravitational pull, the hypothesis is substantiated.

It is crucial to ensure that other growth-influencing factors, such as light and water, are uniformly distributed so that only gravity influences the directional growth.

6. “The Implementation of Gamified Learning Improves Students’ Motivation”

Field: Education

Type: Relational Hypothesis The relational hypothesis describes the relation between two variables. Here, the hypothesis is that the implementation of gamified learning has a positive effect on the motivation of students.

To validate this proposition, two sets of classes could be compared: one that implements a learning approach with game-based elements, and another that follows a traditional learning approach.

The students’ motivation levels could be gauged by monitoring their engagement, performance, and feedback over a considerable timeframe.

If the students engaged in the gamified learning context present higher levels of motivation and achievement, the hypothesis would be supported.

Control measures ought to be put into place to account for individual differences, including prior knowledge and attitudes towards learning.

Relevant Study: Does educational gamification improve students’ motivation? (Chapman & Rich, 2018)

7. “Mathematics Anxiety Negatively Affects Performance”

Field: Educational Psychology

Type: Research Hypothesis The research hypothesis involves making a prediction that will be tested. In this case, the hypothesis proposes that a student’s anxiety about math can negatively influence their performance in math-related tasks.

To assess this hypothesis, researchers must first measure the mathematics anxiety levels of a sample of students using a validated instrument, such as the Mathematics Anxiety Rating Scale.

Then, the students’ performance in mathematics would be evaluated through standard testing. If there’s a negative correlation between the levels of math anxiety and math performance (meaning as anxiety increases, performance decreases), the hypothesis would be supported.

It would be crucial to control for relevant factors such as overall academic performance and previous mathematical achievement.

8. “Disruption of Natural Sleep Cycle Impairs Worker Productivity”

Field: Organizational Psychology

Type: Operational Hypothesis The operational hypothesis involves defining the variables in measurable terms. In this example, the hypothesis posits that disrupting the natural sleep cycle, for instance through shift work or irregular working hours, can lessen productivity among workers.

To test this hypothesis, you could collect data from workers who maintain regular working hours and those with irregular schedules.

Measuring productivity could involve examining the worker’s ability to complete tasks, the quality of their work, and their efficiency.

If workers with interrupted sleep cycles demonstrate lower productivity compared to those with regular sleep patterns, it would lend support to the hypothesis.

Consideration should be given to potential confounding variables such as job type, worker age, and overall health.

9. “Regular Physical Activity Reduces the Risk of Depression”

Field: Health Psychology

Type: Predictive Hypothesis A predictive hypothesis involves making a prediction about the outcome of a study based on the observed relationship between variables. In this case, it is hypothesized that individuals who engage in regular physical activity are less likely to suffer from depression.

Longitudinal studies would suit to test this hypothesis, tracking participants’ levels of physical activity and their mental health status over time.

The level of physical activity could be self-reported or monitored, while mental health status could be assessed using standard diagnostic tools or surveys.

If data analysis shows that participants maintaining regular physical activity have a lower incidence of depression, this would endorse the hypothesis.

However, care should be taken to control other lifestyle and behavioral factors that could intervene with the results.

Relevant Study: Regular physical exercise and its association with depression (Kim, 2022)

10. “Regular Meditation Enhances Emotional Stability”

Type: Empirical Hypothesis In the empirical hypothesis, predictions are based on amassed empirical evidence . This particular hypothesis theorizes that frequent meditation leads to improved emotional stability, resonating with numerous studies linking meditation to a variety of psychological benefits.

Earlier studies reported some correlations, but to test this hypothesis directly, you’d organize an experiment where one group meditates regularly over a set period while a control group doesn’t.

Both groups’ emotional stability levels would be measured at the start and end of the experiment using a validated emotional stability assessment.

If regular meditators display noticeable improvements in emotional stability compared to the control group, the hypothesis gains credit.

You’d have to ensure a similar emotional baseline for all participants at the start to avoid skewed results.

11. “Children Exposed to Reading at an Early Age Show Superior Academic Progress”

Type: Directional Hypothesis The directional hypothesis predicts the direction of an expected relationship between variables. Here, the hypothesis anticipates that early exposure to reading positively affects a child’s academic advancement.

A longitudinal study tracking children’s reading habits from an early age and their consequent academic performance could validate this hypothesis.

Parents could report their children’s exposure to reading at home, while standardized school exam results would provide a measure of academic achievement.

If the children exposed to early reading consistently perform better acadically, it gives weight to the hypothesis.

However, it would be important to control for variables that might impact academic performance, such as socioeconomic background, parental education level, and school quality.

12. “Adopting Energy-efficient Technologies Reduces Carbon Footprint of Industries”

Field: Environmental Science

Type: Descriptive Hypothesis A descriptive hypothesis predicts the existence of an association or pattern related to variables. In this scenario, the hypothesis suggests that industries adopting energy-efficient technologies will resultantly show a reduced carbon footprint.

Global industries making use of energy-efficient technologies could track their carbon emissions over time. At the same time, others not implementing such technologies continue their regular tracking.

After a defined time, the carbon emission data of both groups could be compared. If industries that adopted energy-efficient technologies demonstrate a notable reduction in their carbon footprints, the hypothesis would hold strong.

In the experiment, you would exclude variations brought by factors such as industry type, size, and location.

13. “Reduced Screen Time Improves Sleep Quality”

Type: Simple Hypothesis The simple hypothesis is a prediction about the relationship between two variables, excluding any other variables from consideration. This example posits that by reducing time spent on devices like smartphones and computers, an individual should experience improved sleep quality.

A sample group would need to reduce their daily screen time for a pre-determined period. Sleep quality before and after the reduction could be measured using self-report sleep diaries and objective measures like actigraphy, monitoring movement and wakefulness during sleep.

If the data shows that sleep quality improved post the screen time reduction, the hypothesis would be validated.

Other aspects affecting sleep quality, like caffeine intake, should be controlled during the experiment.

Relevant Study: Screen time use impacts low‐income preschool children’s sleep quality, tiredness, and ability to fall asleep (Waller et al., 2021)

14. Engaging in Brain-Training Games Improves Cognitive Functioning in Elderly

Field: Gerontology

Type: Inductive Hypothesis Inductive hypotheses are based on observations leading to broader generalizations and theories. In this context, the hypothesis deduces from observed instances that engaging in brain-training games can help improve cognitive functioning in the elderly.

A longitudinal study could be conducted where an experimental group of elderly people partakes in regular brain-training games.

Their cognitive functioning could be assessed at the start of the study and at regular intervals using standard neuropsychological tests.

If the group engaging in brain-training games shows better cognitive functioning scores over time compared to a control group not playing these games, the hypothesis would be supported.

15. Farming Practices Influence Soil Erosion Rates

Type: Null Hypothesis A null hypothesis is a negative statement assuming no relationship or difference between variables. The hypothesis in this context asserts there’s no effect of different farming practices on the rates of soil erosion.

Comparing soil erosion rates in areas with different farming practices over a considerable timeframe could help test this hypothesis.

If, statistically, the farming practices do not lead to differences in soil erosion rates, the null hypothesis is accepted.

However, if marked variation appears, the null hypothesis is rejected, meaning farming practices do influence soil erosion rates. It would be crucial to control for external factors like weather, soil type, and natural vegetation.

The variety of hypotheses mentioned above underscores the diversity of research constructs inherent in different fields, each with its unique purpose and way of testing.

While researchers may develop hypotheses primarily as tools to define and narrow the focus of the study, these hypotheses also serve as valuable guiding forces for the data collection and analysis procedures, making the research process more efficient and direction-focused.

Hypotheses serve as a compass for any form of academic research. The diverse examples provided, from Psychology to Educational Studies, Environmental Science to Gerontology, clearly demonstrate how certain hypotheses suit specific fields more aptly than others.

It is important to underline that although these varied hypotheses differ in their structure and methods of testing, each endorses the fundamental value of empiricism in research. Evidence-based decision making remains at the heart of scholarly inquiry, regardless of the research field, thus aligning all hypotheses to the core purpose of scientific investigation.

Testing hypotheses is an essential part of the scientific method . By doing so, researchers can either confirm their predictions, giving further validity to an existing theory, or they might uncover new insights that could potentially shift the field’s understanding of a particular phenomenon. In either case, hypotheses serve as the stepping stones for scientific exploration and discovery.

Atkinson, P., Delamont, S., Cernat, A., Sakshaug, J. W., & Williams, R. A. (2021).  SAGE research methods foundations . SAGE Publications Ltd.

Curcio, G., Ferrara, M., & De Gennaro, L. (2006). Sleep loss, learning capacity and academic performance.  Sleep medicine reviews ,  10 (5), 323-337.

Kim, J. H. (2022). Regular physical exercise and its association with depression: A population-based study short title: Exercise and depression.  Psychiatry Research ,  309 , 114406.

King, D. E. (2005). Dietary fiber, inflammation, and cardiovascular disease.  Molecular nutrition & food research ,  49 (6), 594-600.

Marian, V., & Shook, A. (2012, September). The cognitive benefits of being bilingual. In Cerebrum: the Dana forum on brain science (Vol. 2012). Dana Foundation.

Tan, W. C. K. (2022). Research Methods: A Practical Guide For Students And Researchers (Second Edition) . World Scientific Publishing Company.

Waller, N. A., Zhang, N., Cocci, A. H., D’Agostino, C., Wesolek‐Greenson, S., Wheelock, K., … & Resnicow, K. (2021). Screen time use impacts low‐income preschool children’s sleep quality, tiredness, and ability to fall asleep. Child: care, health and development, 47 (5), 618-626.

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 19 Top Cognitive Psychology Theories (Explained)
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 119 Bloom’s Taxonomy Examples
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ All 6 Levels of Understanding (on Bloom’s Taxonomy)
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 15 Self-Actualization Examples (Maslow's Hierarchy)

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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 .

Prevent plagiarism, run a free check.

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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Home » What is a Hypothesis – Types, Examples and Writing Guide

What is a Hypothesis – Types, Examples and Writing Guide

Table of Contents

Definition:

Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation.

Hypothesis is often used in scientific research to guide the design of experiments and the collection and analysis of data. It is an essential element of the scientific method, as it allows researchers to make predictions about the outcome of their experiments and to test those predictions to determine their accuracy.

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

A research hypothesis is a statement that predicts a relationship between variables. It is usually formulated as a specific statement that can be tested through research, and it is often used in scientific research to guide the design of experiments.

Null Hypothesis

The null hypothesis is a statement that assumes there is no significant difference or relationship between variables. It is often used as a starting point for testing the research hypothesis, and if the results of the study reject the null hypothesis, it suggests that there is a significant difference or relationship between variables.

Alternative Hypothesis

An alternative hypothesis is a statement that assumes there is a significant difference or relationship between variables. It is often used as an alternative to the null hypothesis and is tested against the null hypothesis to determine which statement is more accurate.

Directional Hypothesis

A directional hypothesis is a statement that predicts the direction of the relationship between variables. For example, a researcher might predict that increasing the amount of exercise will result in a decrease in body weight.

Non-directional Hypothesis

A non-directional hypothesis is a statement that predicts the relationship between variables but does not specify the direction. For example, a researcher might predict that there is a relationship between the amount of exercise and body weight, but they do not specify whether increasing or decreasing exercise will affect body weight.

Statistical Hypothesis

A statistical hypothesis is a statement that assumes a particular statistical model or distribution for the data. It is often used in statistical analysis to test the significance of a particular result.

Composite Hypothesis

A composite hypothesis is a statement that assumes more than one condition or outcome. It can be divided into several sub-hypotheses, each of which represents a different possible outcome.

Empirical Hypothesis

An empirical hypothesis is a statement that is based on observed phenomena or data. It is often used in scientific research to develop theories or models that explain the observed phenomena.

Simple Hypothesis

A simple hypothesis is a statement that assumes only one outcome or condition. It is often used in scientific research to test a single variable or factor.

Complex Hypothesis

A complex hypothesis is a statement that assumes multiple outcomes or conditions. It is often used in scientific research to test the effects of multiple variables or factors on a particular outcome.

Applications of Hypothesis

Hypotheses are used in various fields to guide research and make predictions about the outcomes of experiments or observations. Here are some examples of how hypotheses are applied in different fields:

• Science : In scientific research, hypotheses are used to test the validity of theories and models that explain natural phenomena. For example, a hypothesis might be formulated to test the effects of a particular variable on a natural system, such as the effects of climate change on an ecosystem.
• Medicine : In medical research, hypotheses are used to test the effectiveness of treatments and therapies for specific conditions. For example, a hypothesis might be formulated to test the effects of a new drug on a particular disease.
• Psychology : In psychology, hypotheses are used to test theories and models of human behavior and cognition. For example, a hypothesis might be formulated to test the effects of a particular stimulus on the brain or behavior.
• Sociology : In sociology, hypotheses are used to test theories and models of social phenomena, such as the effects of social structures or institutions on human behavior. For example, a hypothesis might be formulated to test the effects of income inequality on crime rates.
• Business : In business research, hypotheses are used to test the validity of theories and models that explain business phenomena, such as consumer behavior or market trends. For example, a hypothesis might be formulated to test the effects of a new marketing campaign on consumer buying behavior.
• Engineering : In engineering, hypotheses are used to test the effectiveness of new technologies or designs. For example, a hypothesis might be formulated to test the efficiency of a new solar panel design.

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

The first step is to identify the research question that you want to answer through your study. This question should be clear, specific, and focused. It should be something that can be investigated empirically and that has some relevance or significance in the field.

Conduct a Literature Review

Before writing your hypothesis, it’s essential to conduct a thorough literature review to understand what is already known about the topic. This will help you to identify the research gap and formulate a hypothesis that builds on existing knowledge.

Determine the Variables

The next step is to identify the variables involved in the research question. A variable is any characteristic or factor that can vary or change. There are two types of variables: independent and dependent. The independent variable is the one that is manipulated or changed by the researcher, while the dependent variable is the one that is measured or observed as a result of the independent variable.

Formulate the Hypothesis

Based on the research question and the variables involved, you can now formulate your hypothesis. A hypothesis should be a clear and concise statement that predicts the relationship between the variables. It should be testable through empirical research and based on existing theory or evidence.

Write the Null Hypothesis

The null hypothesis is the opposite of the alternative hypothesis, which is the hypothesis that you are testing. The null hypothesis states that there is no significant difference or relationship between the variables. It is important to write the null hypothesis because it allows you to compare your results with what would be expected by chance.

Refine the Hypothesis

After formulating the hypothesis, it’s important to refine it and make it more precise. This may involve clarifying the variables, specifying the direction of the relationship, or making the hypothesis more testable.

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

• Psychology : “Increased exposure to violent video games leads to increased aggressive behavior in adolescents.”
• Biology : “Higher levels of carbon dioxide in the atmosphere will lead to increased plant growth.”
• Sociology : “Individuals who grow up in households with higher socioeconomic status will have higher levels of education and income as adults.”
• Education : “Implementing a new teaching method will result in higher student achievement scores.”
• Marketing : “Customers who receive a personalized email will be more likely to make a purchase than those who receive a generic email.”
• Physics : “An increase in temperature will cause an increase in the volume of a gas, assuming all other variables remain constant.”
• Medicine : “Consuming a diet high in saturated fats will increase the risk of developing heart disease.”

Purpose of Hypothesis

The purpose of a hypothesis is to provide a testable explanation for an observed phenomenon or a prediction of a future outcome based on existing knowledge or theories. A hypothesis is an essential part of the scientific method and helps to guide the research process by providing a clear focus for investigation. It enables scientists to design experiments or studies to gather evidence and data that can support or refute the proposed explanation or prediction.

The formulation of a hypothesis is based on existing knowledge, observations, and theories, and it should be specific, testable, and falsifiable. A specific hypothesis helps to define the research question, which is important in the research process as it guides the selection of an appropriate research design and methodology. Testability of the hypothesis means that it can be proven or disproven through empirical data collection and analysis. Falsifiability means that the hypothesis should be formulated in such a way that it can be proven wrong if it is incorrect.

In addition to guiding the research process, the testing of hypotheses can lead to new discoveries and advancements in scientific knowledge. When a hypothesis is supported by the data, it can be used to develop new theories or models to explain the observed phenomenon. When a hypothesis is not supported by the data, it can help to refine existing theories or prompt the development of new hypotheses to explain the phenomenon.

When to use Hypothesis

Here are some common situations in which hypotheses are used:

• In scientific research , hypotheses are used to guide the design of experiments and to help researchers make predictions about the outcomes of those experiments.
• In social science research , hypotheses are used to test theories about human behavior, social relationships, and other phenomena.
• I n business , hypotheses can be used to guide decisions about marketing, product development, and other areas. For example, a hypothesis might be that a new product will sell well in a particular market, and this hypothesis can be tested through market research.

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

• Testable : A hypothesis must be able to be tested through observation or experimentation. This means that it must be possible to collect data that will either support or refute the hypothesis.
• Falsifiable : A hypothesis must be able to be proven false if it is not supported by the data. If a hypothesis cannot be falsified, then it is not a scientific hypothesis.
• Clear and concise : A hypothesis should be stated in a clear and concise manner so that it can be easily understood and tested.
• Based on existing knowledge : A hypothesis should be based on existing knowledge and research in the field. It should not be based on personal beliefs or opinions.
• Specific : A hypothesis should be specific in terms of the variables being tested and the predicted outcome. This will help to ensure that the research is focused and well-designed.
• Tentative: A hypothesis is a tentative statement or assumption that requires further testing and evidence to be confirmed or refuted. It is not a final conclusion or assertion.
• Relevant : A hypothesis should be relevant to the research question or problem being studied. It should address a gap in knowledge or provide a new perspective on the issue.

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

• Guides research: A hypothesis provides a clear and specific direction for research. It helps to focus the research question, select appropriate methods and variables, and interpret the results.
• Predictive powe r: A hypothesis makes predictions about the outcome of research, which can be tested through experimentation. This allows researchers to evaluate the validity of the hypothesis and make new discoveries.
• Facilitates communication: A hypothesis provides a common language and framework for scientists to communicate with one another about their research. This helps to facilitate the exchange of ideas and promotes collaboration.
• Efficient use of resources: A hypothesis helps researchers to use their time, resources, and funding efficiently by directing them towards specific research questions and methods that are most likely to yield results.
• Provides a basis for further research: A hypothesis that is supported by data provides a basis for further research and exploration. It can lead to new hypotheses, theories, and discoveries.
• Increases objectivity: A hypothesis can help to increase objectivity in research by providing a clear and specific framework for testing and interpreting results. This can reduce bias and increase the reliability of research findings.

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

• Limited to observable phenomena: Hypotheses are limited to observable phenomena and cannot account for unobservable or intangible factors. This means that some research questions may not be amenable to hypothesis testing.
• May be inaccurate or incomplete: Hypotheses are based on existing knowledge and research, which may be incomplete or inaccurate. This can lead to flawed hypotheses and erroneous conclusions.
• May be biased: Hypotheses may be biased by the researcher’s own beliefs, values, or assumptions. This can lead to selective interpretation of data and a lack of objectivity in research.
• Cannot prove causation: A hypothesis can only show a correlation between variables, but it cannot prove causation. This requires further experimentation and analysis.
• Limited to specific contexts: Hypotheses are limited to specific contexts and may not be generalizable to other situations or populations. This means that results may not be applicable in other contexts or may require further testing.
• May be affected by chance : Hypotheses may be affected by chance or random variation, which can obscure or distort the true relationship between variables.

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How to Write a Research Hypothesis: Good & Bad Examples

What is a research hypothesis?

A research hypothesis is an attempt at explaining a phenomenon or the relationships between phenomena/variables in the real world. Hypotheses are sometimes called “educated guesses”, but they are in fact (or let’s say they should be) based on previous observations, existing theories, scientific evidence, and logic. A research hypothesis is also not a prediction—rather, predictions are ( should be) based on clearly formulated hypotheses. For example, “We tested the hypothesis that KLF2 knockout mice would show deficiencies in heart development” is an assumption or prediction, not a hypothesis. 

The research hypothesis at the basis of this prediction is “the product of the KLF2 gene is involved in the development of the cardiovascular system in mice”—and this hypothesis is probably (hopefully) based on a clear observation, such as that mice with low levels of Kruppel-like factor 2 (which KLF2 codes for) seem to have heart problems. From this hypothesis, you can derive the idea that a mouse in which this particular gene does not function cannot develop a normal cardiovascular system, and then make the prediction that we started with. 

What is the difference between a hypothesis and a prediction?

You might think that these are very subtle differences, and you will certainly come across many publications that do not contain an actual hypothesis or do not make these distinctions correctly. But considering that the formulation and testing of hypotheses is an integral part of the scientific method, it is good to be aware of the concepts underlying this approach. The two hallmarks of a scientific hypothesis are falsifiability (an evaluation standard that was introduced by the philosopher of science Karl Popper in 1934) and testability —if you cannot use experiments or data to decide whether an idea is true or false, then it is not a hypothesis (or at least a very bad one).

So, in a nutshell, you (1) look at existing evidence/theories, (2) come up with a hypothesis, (3) make a prediction that allows you to (4) design an experiment or data analysis to test it, and (5) come to a conclusion. Of course, not all studies have hypotheses (there is also exploratory or hypothesis-generating research), and you do not necessarily have to state your hypothesis as such in your paper. 

But for the sake of understanding the principles of the scientific method, let’s first take a closer look at the different types of hypotheses that research articles refer to and then give you a step-by-step guide for how to formulate a strong hypothesis for your own paper.

Types of Research Hypotheses

Hypotheses can be simple , which means they describe the relationship between one single independent variable (the one you observe variations in or plan to manipulate) and one single dependent variable (the one you expect to be affected by the variations/manipulation). If there are more variables on either side, you are dealing with a complex hypothesis. You can also distinguish hypotheses according to the kind of relationship between the variables you are interested in (e.g., causal or associative ). But apart from these variations, we are usually interested in what is called the “alternative hypothesis” and, in contrast to that, the “null hypothesis”. If you think these two should be listed the other way round, then you are right, logically speaking—the alternative should surely come second. However, since this is the hypothesis we (as researchers) are usually interested in, let’s start from there.

Alternative Hypothesis

If you predict a relationship between two variables in your study, then the research hypothesis that you formulate to describe that relationship is your alternative hypothesis (usually H1 in statistical terms). The goal of your hypothesis testing is thus to demonstrate that there is sufficient evidence that supports the alternative hypothesis, rather than evidence for the possibility that there is no such relationship. The alternative hypothesis is usually the research hypothesis of a study and is based on the literature, previous observations, and widely known theories. 

Null Hypothesis

The hypothesis that describes the other possible outcome, that is, that your variables are not related, is the null hypothesis ( H0 ). Based on your findings, you choose between the two hypotheses—usually that means that if your prediction was correct, you reject the null hypothesis and accept the alternative. Make sure, however, that you are not getting lost at this step of the thinking process: If your prediction is that there will be no difference or change, then you are trying to find support for the null hypothesis and reject H1. 

Directional Hypothesis

While the null hypothesis is obviously “static”, the alternative hypothesis can specify a direction for the observed relationship between variables—for example, that mice with higher expression levels of a certain protein are more active than those with lower levels. This is then called a one-tailed hypothesis. 

Another example for a directional one-tailed alternative hypothesis would be that 

H1: Attending private classes before important exams has a positive effect on performance. 

Your null hypothesis would then be that

H0: Attending private classes before important exams has no/a negative effect on performance.

Nondirectional Hypothesis

A nondirectional hypothesis does not specify the direction of the potentially observed effect, only that there is a relationship between the studied variables—this is called a two-tailed hypothesis. For instance, if you are studying a new drug that has shown some effects on pathways involved in a certain condition (e.g., anxiety) in vitro in the lab, but you can’t say for sure whether it will have the same effects in an animal model or maybe induce other/side effects that you can’t predict and potentially increase anxiety levels instead, you could state the two hypotheses like this:

H1: The only lab-tested drug (somehow) affects anxiety levels in an anxiety mouse model.

You then test this nondirectional alternative hypothesis against the null hypothesis:

H0: The only lab-tested drug has no effect on anxiety levels in an anxiety mouse model.

How to Write a Hypothesis for a Research Paper

Now that we understand the important distinctions between different kinds of research hypotheses, let’s look at a simple process of how to write a hypothesis.

Writing a Hypothesis Step:1

Ask a question, based on earlier research. Research always starts with a question, but one that takes into account what is already known about a topic or phenomenon. For example, if you are interested in whether people who have pets are happier than those who don’t, do a literature search and find out what has already been demonstrated. You will probably realize that yes, there is quite a bit of research that shows a relationship between happiness and owning a pet—and even studies that show that owning a dog is more beneficial than owning a cat ! Let’s say you are so intrigued by this finding that you wonder: 

What is it that makes dog owners even happier than cat owners? 

Let’s move on to Step 2 and find an answer to that question.

Writing a Hypothesis Step 2:

Formulate a strong hypothesis by answering your own question. Again, you don’t want to make things up, take unicorns into account, or repeat/ignore what has already been done. Looking at the dog-vs-cat papers your literature search returned, you see that most studies are based on self-report questionnaires on personality traits, mental health, and life satisfaction. What you don’t find is any data on actual (mental or physical) health measures, and no experiments. You therefore decide to make a bold claim come up with the carefully thought-through hypothesis that it’s maybe the lifestyle of the dog owners, which includes walking their dog several times per day, engaging in fun and healthy activities such as agility competitions, and taking them on trips, that gives them that extra boost in happiness. You could therefore answer your question in the following way:

Dog owners are happier than cat owners because of the dog-related activities they engage in.

Now you have to verify that your hypothesis fulfills the two requirements we introduced at the beginning of this resource article: falsifiability and testability . If it can’t be wrong and can’t be tested, it’s not a hypothesis. We are lucky, however, because yes, we can test whether owning a dog but not engaging in any of those activities leads to lower levels of happiness or well-being than owning a dog and playing and running around with them or taking them on trips.  

Writing a Hypothesis Step 3:

Make your predictions and define your variables. We have verified that we can test our hypothesis, but now we have to define all the relevant variables, design our experiment or data analysis, and make precise predictions. You could, for example, decide to study dog owners (not surprising at this point), let them fill in questionnaires about their lifestyle as well as their life satisfaction (as other studies did), and then compare two groups of active and inactive dog owners. Alternatively, if you want to go beyond the data that earlier studies produced and analyzed and directly manipulate the activity level of your dog owners to study the effect of that manipulation, you could invite them to your lab, select groups of participants with similar lifestyles, make them change their lifestyle (e.g., couch potato dog owners start agility classes, very active ones have to refrain from any fun activities for a certain period of time) and assess their happiness levels before and after the intervention. In both cases, your independent variable would be “ level of engagement in fun activities with dog” and your dependent variable would be happiness or well-being . 

Examples of a Good and Bad Hypothesis

Let’s look at a few examples of good and bad hypotheses to get you started.

Good Hypothesis Examples

Bad Hypothesis Examples

Tips for Writing a Research Hypothesis

If you understood the distinction between a hypothesis and a prediction we made at the beginning of this article, then you will have no problem formulating your hypotheses and predictions correctly. To refresh your memory: We have to (1) look at existing evidence, (2) come up with a hypothesis, (3) make a prediction, and (4) design an experiment. For example, you could summarize your dog/happiness study like this:

(1) While research suggests that dog owners are happier than cat owners, there are no reports on what factors drive this difference. (2) We hypothesized that it is the fun activities that many dog owners (but very few cat owners) engage in with their pets that increases their happiness levels. (3) We thus predicted that preventing very active dog owners from engaging in such activities for some time and making very inactive dog owners take up such activities would lead to an increase and decrease in their overall self-ratings of happiness, respectively. (4) To test this, we invited dog owners into our lab, assessed their mental and emotional well-being through questionnaires, and then assigned them to an “active” and an “inactive” group, depending on… 

Note that you use “we hypothesize” only for your hypothesis, not for your experimental prediction, and “would” or “if – then” only for your prediction, not your hypothesis. A hypothesis that states that something “would” affect something else sounds as if you don’t have enough confidence to make a clear statement—in which case you can’t expect your readers to believe in your research either. Write in the present tense, don’t use modal verbs that express varying degrees of certainty (such as may, might, or could ), and remember that you are not drawing a conclusion while trying not to exaggerate but making a clear statement that you then, in a way, try to disprove . And if that happens, that is not something to fear but an important part of the scientific process.

Similarly, don’t use “we hypothesize” when you explain the implications of your research or make predictions in the conclusion section of your manuscript, since these are clearly not hypotheses in the true sense of the word. As we said earlier, you will find that many authors of academic articles do not seem to care too much about these rather subtle distinctions, but thinking very clearly about your own research will not only help you write better but also ensure that even that infamous Reviewer 2 will find fewer reasons to nitpick about your manuscript. 

Perfect Your Manuscript With Professional Editing

Now that you know how to write a strong research hypothesis for your research paper, you might be interested in our free AI Proofreader , Wordvice AI, which finds and fixes errors in grammar, punctuation, and word choice in academic texts. Or if you are interested in human proofreading , check out our English editing services , including research paper editing and manuscript editing .

On the Wordvice academic resources website , you can also find many more articles and other resources that can help you with writing the other parts of your research paper , with making a research paper outline before you put everything together, or with writing an effective cover letter once you are ready to submit.

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!

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

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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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Hypothesis Examples

A hypothesis is a prediction of the outcome of a test. It forms the basis for designing an experiment in the scientific method . A good hypothesis is testable, meaning it makes a prediction you can check with observation or experimentation. Here are different hypothesis examples.

Null Hypothesis Examples

The null hypothesis (H 0 ) is also known as the zero-difference or no-difference hypothesis. It predicts that changing one variable ( independent variable ) will have no effect on the variable being measured ( dependent variable ). Here are null hypothesis examples:

• Plant growth is unaffected by temperature.
• If you increase temperature, then solubility of salt will increase.
• Incidence of skin cancer is unrelated to ultraviolet light exposure.
• All brands of light bulb last equally long.
• Cats have no preference for the color of cat food.
• All daisies have the same number of petals.

Sometimes the null hypothesis shows there is a suspected correlation between two variables. For example, if you think plant growth is affected by temperature, you state the null hypothesis: “Plant growth is not affected by temperature.” Why do you do this, rather than say “If you change temperature, plant growth will be affected”? The answer is because it’s easier applying a statistical test that shows, with a high level of confidence, a null hypothesis is correct or incorrect.

Research Hypothesis Examples

A research hypothesis (H 1 ) is a type of hypothesis used to design an experiment. This type of hypothesis is often written as an if-then statement because it’s easy identifying the independent and dependent variables and seeing how one affects the other. If-then statements explore cause and effect. In other cases, the hypothesis shows a correlation between two variables. Here are some research hypothesis examples:

• If you leave the lights on, then it takes longer for people to fall asleep.
• If you refrigerate apples, they last longer before going bad.
• If you keep the curtains closed, then you need less electricity to heat or cool the house (the electric bill is lower).
• If you leave a bucket of water uncovered, then it evaporates more quickly.
• Goldfish lose their color if they are not exposed to light.
• Workers who take vacations are more productive than those who never take time off.

Is It Okay to Disprove a Hypothesis?

Yes! You may even choose to write your hypothesis in such a way that it can be disproved because it’s easier to prove a statement is wrong than to prove it is right. In other cases, if your prediction is incorrect, that doesn’t mean the science is bad. Revising a hypothesis is common. It demonstrates you learned something you did not know before you conducted the experiment.

Test yourself with a Scientific Method Quiz .

• Mellenbergh, G.J. (2008). Chapter 8: Research designs: Testing of research hypotheses. In H.J. Adèr & G.J. Mellenbergh (eds.), Advising on Research Methods: A Consultant’s Companion . Huizen, The Netherlands: Johannes van Kessel Publishing.
• Popper, Karl R. (1959). The Logic of Scientific Discovery . Hutchinson & Co. ISBN 3-1614-8410-X.
• Schick, Theodore; Vaughn, Lewis (2002). How to think about weird things: critical thinking for a New Age . Boston: McGraw-Hill Higher Education. ISBN 0-7674-2048-9.
• Tobi, Hilde; Kampen, Jarl K. (2018). “Research design: the methodology for interdisciplinary research framework”. Quality & Quantity . 52 (3): 1209–1225. doi: 10.1007/s11135-017-0513-8

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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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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

It awesome. It has really positioned me in my research project

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How to Write a Hypothesis: A Step-by-Step Guide

Introduction

An overview of the research hypothesis, different types of hypotheses, variables in a hypothesis, how to formulate an effective research hypothesis, designing a study around your hypothesis.

The scientific method can derive and test predictions as hypotheses. Empirical research can then provide support (or lack thereof) for the hypotheses. Even failure to find support for a hypothesis still represents a valuable contribution to scientific knowledge. Let's look more closely at the idea of the hypothesis and the role it plays in research.

As much as the term exists in everyday language, there is a detailed development that informs the word "hypothesis" when applied to research. A good research hypothesis is informed by prior research and guides research design and data analysis , so it is important to understand how a hypothesis is defined and understood by researchers.

What is the simple definition of a hypothesis?

A hypothesis is a testable prediction about an outcome between two or more variables . It functions as a navigational tool in the research process, directing what you aim to predict and how.

What is the hypothesis for in research?

In research, a hypothesis serves as the cornerstone for your empirical study. It not only lays out what you aim to investigate but also provides a structured approach for your data collection and analysis.

Essentially, it bridges the gap between the theoretical and the empirical, guiding your investigation throughout its course.

What is an example of a hypothesis?

If you are studying the relationship between physical exercise and mental health, a suitable hypothesis could be: "Regular physical exercise leads to improved mental well-being among adults."

This statement constitutes a specific and testable hypothesis that directly relates to the variables you are investigating.

What makes a good hypothesis?

A good hypothesis possesses several key characteristics. Firstly, it must be testable, allowing you to analyze data through empirical means, such as observation or experimentation, to assess if there is significant support for the hypothesis. Secondly, a hypothesis should be specific and unambiguous, giving a clear understanding of the expected relationship between variables. Lastly, it should be grounded in existing research or theoretical frameworks , ensuring its relevance and applicability.

Understanding the types of hypotheses can greatly enhance how you construct and work with hypotheses. While all hypotheses serve the essential function of guiding your study, there are varying purposes among the types of hypotheses. In addition, all hypotheses stand in contrast to the null hypothesis, or the assumption that there is no significant relationship between the variables .

Here, we explore various kinds of hypotheses to provide you with the tools needed to craft effective hypotheses for your specific research needs. Bear in mind that many of these hypothesis types may overlap with one another, and the specific type that is typically used will likely depend on the area of research and methodology you are following.

Null hypothesis

The null hypothesis is a statement that there is no effect or relationship between the variables being studied. In statistical terms, it serves as the default assumption that any observed differences are due to random chance.

For example, if you're studying the effect of a drug on blood pressure, the null hypothesis might state that the drug has no effect.

Alternative hypothesis

Contrary to the null hypothesis, the alternative hypothesis suggests that there is a significant relationship or effect between variables.

Using the drug example, the alternative hypothesis would posit that the drug does indeed affect blood pressure. This is what researchers aim to prove.

Simple hypothesis

A simple hypothesis makes a prediction about the relationship between two variables, and only two variables.

For example, "Increased study time results in better exam scores." Here, "study time" and "exam scores" are the only variables involved.

Complex hypothesis

A complex hypothesis, as the name suggests, involves more than two variables. For instance, "Increased study time and access to resources result in better exam scores." Here, "study time," "access to resources," and "exam scores" are all variables.

This hypothesis refers to multiple potential mediating variables. Other hypotheses could also include predictions about variables that moderate the relationship between the independent variable and dependent variable .

Directional hypothesis

A directional hypothesis specifies the direction of the expected relationship between variables. For example, "Eating more fruits and vegetables leads to a decrease in heart disease."

Here, the direction of heart disease is explicitly predicted to decrease, due to effects from eating more fruits and vegetables. All hypotheses typically specify the expected direction of the relationship between the independent and dependent variable, such that researchers can test if this prediction holds in their data analysis .

Statistical hypothesis

A statistical hypothesis is one that is testable through statistical methods, providing a numerical value that can be analyzed. This is commonly seen in quantitative research .

For example, "There is a statistically significant difference in test scores between students who study for one hour and those who study for two."

Empirical hypothesis

An empirical hypothesis is derived from observations and is tested through empirical methods, often through experimentation or survey data . Empirical hypotheses may also be assessed with statistical analyses.

For example, "Regular exercise is correlated with a lower incidence of depression," could be tested through surveys that measure exercise frequency and depression levels.

Causal hypothesis

A causal hypothesis proposes that one variable causes a change in another. This type of hypothesis is often tested through controlled experiments.

For example, "Smoking causes lung cancer," assumes a direct causal relationship.

Associative hypothesis

Unlike causal hypotheses, associative hypotheses suggest a relationship between variables but do not imply causation.

For instance, "People who smoke are more likely to get lung cancer," notes an association but doesn't claim that smoking causes lung cancer directly.

Relational hypothesis

A relational hypothesis explores the relationship between two or more variables but doesn't specify the nature of the relationship.

For example, "There is a relationship between diet and heart health," leaves the nature of the relationship (causal, associative, etc.) open to interpretation.

Logical hypothesis

A logical hypothesis is based on sound reasoning and logical principles. It's often used in theoretical research to explore abstract concepts, rather than being based on empirical data.

For example, "If all men are mortal and Socrates is a man, then Socrates is mortal," employs logical reasoning to make its point.

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In any research hypothesis, variables play a critical role. These are the elements or factors that the researcher manipulates, controls, or measures. Understanding variables is essential for crafting a clear, testable hypothesis and for the stages of research that follow, such as data collection and analysis.

In the realm of hypotheses, there are generally two types of variables to consider: independent and dependent. Independent variables are what you, as the researcher, manipulate or change in your study. It's considered the cause in the relationship you're investigating. For instance, in a study examining the impact of sleep duration on academic performance, the independent variable would be the amount of sleep participants get.

Conversely, the dependent variable is the outcome you measure to gauge the effect of your manipulation. It's the effect in the cause-and-effect relationship. The dependent variable thus refers to the main outcome of interest in your study. In the same sleep study example, the academic performance, perhaps measured by exam scores or GPA, would be the dependent variable.

Beyond these two primary types, you might also encounter control variables. These are variables that could potentially influence the outcome and are therefore kept constant to isolate the relationship between the independent and dependent variables . For example, in the sleep and academic performance study, control variables could include age, diet, or even the subject of study.

By clearly identifying and understanding the roles of these variables in your hypothesis, you set the stage for a methodologically sound research project. It helps you develop focused research questions, design appropriate experiments or observations, and carry out meaningful data analysis . It's a step that lays the groundwork for the success of your entire study.

Crafting a strong, testable hypothesis is crucial for the success of any research project. It sets the stage for everything from your study design to data collection and analysis . Below are some key considerations to keep in mind when formulating your hypothesis:

• Be specific : A vague hypothesis can lead to ambiguous results and interpretations . Clearly define your variables and the expected relationship between them.
• Ensure testability : A good hypothesis should be testable through empirical means, whether by observation , experimentation, or other forms of data analysis.
• Ground in literature : Before creating your hypothesis, consult existing research and theories. This not only helps you identify gaps in current knowledge but also gives you valuable context and credibility for crafting your hypothesis.
• Use simple language : While your hypothesis should be conceptually sound, it doesn't have to be complicated. Aim for clarity and simplicity in your wording.
• State direction, if applicable : If your hypothesis involves a directional outcome (e.g., "increase" or "decrease"), make sure to specify this. You also need to think about how you will measure whether or not the outcome moved in the direction you predicted.
• Keep it focused : One of the common pitfalls in hypothesis formulation is trying to answer too many questions at once. Keep your hypothesis focused on a specific issue or relationship.
• Account for control variables : Identify any variables that could potentially impact the outcome and consider how you will control for them in your study.
• Be ethical : Make sure your hypothesis and the methods for testing it comply with ethical standards , particularly if your research involves human or animal subjects.

Designing your study involves multiple key phases that help ensure the rigor and validity of your research. Here we discuss these crucial components in more detail.

Literature review

Starting with a comprehensive literature review is essential. This step allows you to understand the existing body of knowledge related to your hypothesis and helps you identify gaps that your research could fill. Your research should aim to contribute some novel understanding to existing literature, and your hypotheses can reflect this. A literature review also provides valuable insights into how similar research projects were executed, thereby helping you fine-tune your own approach.

Research methods

Choosing the right research methods is critical. Whether it's a survey, an experiment, or observational study, the methodology should be the most appropriate for testing your hypothesis. Your choice of methods will also depend on whether your research is quantitative, qualitative, or mixed-methods. Make sure the chosen methods align well with the variables you are studying and the type of data you need.

Preliminary research

Before diving into a full-scale study, it’s often beneficial to conduct preliminary research or a pilot study . This allows you to test your research methods on a smaller scale, refine your tools, and identify any potential issues. For instance, a pilot survey can help you determine if your questions are clear and if the survey effectively captures the data you need. This step can save you both time and resources in the long run.

Data analysis

Finally, planning your data analysis in advance is crucial for a successful study. Decide which statistical or analytical tools are most suited for your data type and research questions . For quantitative research, you might opt for t-tests, ANOVA, or regression analyses. For qualitative research , thematic analysis or grounded theory may be more appropriate. This phase is integral for interpreting your results and drawing meaningful conclusions in relation to your research question.

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How to Write a Hypothesis – Steps & Tips

Published by Alaxendra Bets at August 14th, 2021 , Revised On October 26, 2023

What is a Research Hypothesis?

You can test a research statement with the help of experimental or theoretical research, known as a hypothesis.

If you want to find out the similarities, differences, and relationships between variables, you must write a testable hypothesis before compiling the data, performing analysis, and generating results to complete.

The data analysis and findings will help you test the hypothesis and see whether it is true or false. Here is all you need to know about how to write a hypothesis for a  dissertation .

Research Hypothesis Definition

Not sure what the meaning of the research hypothesis is?

A research hypothesis predicts an answer to the research question  based on existing theoretical knowledge or experimental data.

Some studies may have multiple hypothesis statements depending on the research question(s).  A research hypothesis must be based on formulas, facts, and theories. It should be testable by data analysis, observations, experiments, or other scientific methodologies that can refute or support the statement.

Variables in Hypothesis

Developing a hypothesis is easy. Most research studies have two or more variables in the hypothesis, particularly studies involving correlational and experimental research. The researcher can control or change the independent variable(s) while measuring and observing the independent variable(s).

“How long a student sleeps affects test scores.”

In the above statement, the dependent variable is the test score, while the independent variable is the length of time spent in sleep. Developing a hypothesis will be easy if you know your research’s dependent and independent variables.

Once you have developed a thesis statement, questions such as how to write a hypothesis for the dissertation and how to test a research hypothesis become pretty straightforward.

Looking for dissertation help?

Researchprospect to the rescue then.

We have expert writers on our team who are skilled at helping students with quantitative dissertations across a variety of STEM disciplines. Guaranteeing 100% satisfaction!

Step-by-Step Guide on How to Write a Hypothesis

Here are the steps involved in how to write a hypothesis for a dissertation.

Step 1: Start with a Research Question

• Begin by asking a specific question about a topic of interest.
• This question should be clear, concise, and researchable.

Example: Does exposure to sunlight affect plant growth?

Step 2: Do Preliminary Research

• Before formulating a hypothesis, conduct background research to understand existing knowledge on the topic.
• Familiarise yourself with prior studies, theories, or observations related to the research question.

Step 3: Define Variables

• Independent Variable (IV): The factor that you change or manipulate in an experiment.
• Dependent Variable (DV): The factor that you measure.

Example: IV: Amount of sunlight exposure (e.g., 2 hours/day, 4 hours/day, 8 hours/day) DV: Plant growth (e.g., height in centimetres)

Step 4: Formulate the Hypothesis

• A hypothesis is a statement that predicts the relationship between variables.
• It is often written as an “if-then” statement.

Example: If plants receive more sunlight, then they will grow taller.

Step 5: Ensure it is Testable

A good hypothesis is empirically testable. This means you should be able to design an experiment or observation to test its validity.

Example: You can set up an experiment where plants are exposed to varying amounts of sunlight and then measure their growth over a period of time.

Step 6: Consider Potential Confounding Variables

• Confounding variables are factors other than the independent variable that might affect the outcome.
• It is important to identify these to ensure that they do not skew your results.

Example: Soil quality, water frequency, or type of plant can all affect growth. Consider keeping these constant in your experiment.

Step 7: Write the Null Hypothesis

• The null hypothesis is a statement that there is no effect or no relationship between the variables.
• It is what you aim to disprove or reject through your research.

Example: There is no difference in plant growth regardless of the amount of sunlight exposure.

Step 8: Test your Hypothesis

Design an experiment or conduct observations to test your hypothesis.

Example: Grow three sets of plants: one set exposed to 2 hours of sunlight daily, another exposed to 4 hours, and a third exposed to 8 hours. Measure and compare their growth after a set period.

Step 9: Analyse the Results

After testing, review your data to determine if it supports your hypothesis.

Step 10: Draw Conclusions

• Based on your findings, determine whether you can accept or reject the hypothesis.
• Remember, even if you reject your hypothesis, it’s a valuable result. It can guide future research and refine questions.

Three Ways to Phrase a Hypothesis

Try to use “if”… and “then”… to identify the variables. The independent variable should be present in the first part of the hypothesis, while the dependent variable will form the second part of the statement. Consider understanding the below research hypothesis example to create a specific, clear, and concise research hypothesis;

If an obese lady starts attending Zomba fitness classes, her health will improve.

In academic research, you can write the predicted variable relationship directly because most research studies correlate terms.

The number of Zomba fitness classes attended by the obese lady has a positive effect on health.

If your research compares two groups, then you can develop a hypothesis statement on their differences.

An obese lady who attended most Zumba fitness classes will have better health than those who attended a few.

How to Write a Null Hypothesis

If a statistical analysis is involved in your research, then you must create a null hypothesis. If you find any relationship between the variables, then the null hypothesis will be the default position that there is no relationship between them. H0 is the symbol for the null hypothesis, while the hypothesis is represented as H1. The null hypothesis will also answer your question, “How to test the research hypothesis in the dissertation.”

H0: The number of Zumba fitness classes attended by the obese lady does not affect her health.

H1: The number of Zumba fitness classes attended by obese lady positively affects health.

Also see:  Your Dissertation in Education

Hypothesis Examples

Research Question: Does the amount of sunlight a plant receives affect its growth? Hypothesis: Plants that receive more sunlight will grow taller than plants that receive less sunlight.

Research Question: Do students who eat breakfast perform better in school exams than those who don’t? Hypothesis: Students who eat a morning breakfast will score higher on school exams compared to students who skip breakfast.

Research Question: Does listening to music while studying impact a student’s ability to retain information? Hypothesis 1 (Directional): Students who listen to music while studying will retain less information than those who study in silence. Hypothesis 2 (Non-directional): There will be a difference in information retention between students who listen to music while studying and those who study in silence.

How can ResearchProspect Help?

If you are unsure about how to rest a research hypothesis in a dissertation or simply unsure about how to develop a hypothesis for your research, then you can take advantage of our dissertation services which cover every tiny aspect of a dissertation project you might need help with including but not limited to setting up a hypothesis and research questions,  help with individual chapters ,  full dissertation writing ,  statistical analysis , and much more.

Frequently Asked Questions

What are the 5 rules for writing a good hypothesis.

• Clear Statement: State a clear relationship between variables.
• Testable: Ensure it can be investigated and measured.
• Specific: Avoid vague terms, be precise in predictions.
• Falsifiable: Design to allow potential disproof.
• Relevant: Address research question and align with existing knowledge.

What is a hypothesis in simple words?

A hypothesis is an educated guess or prediction about something that can be tested. It is a statement that suggests a possible explanation for an event or phenomenon based on prior knowledge or observation. Scientists use hypotheses as a starting point for experiments to discover if they are true or false.

What is the hypothesis and examples?

A hypothesis is a testable prediction or explanation for an observation or phenomenon. For example, if plants are given sunlight, then they will grow. In this case, the hypothesis suggests that sunlight has a positive effect on plant growth. It can be tested by experimenting with plants in varying light conditions.

What is the hypothesis in research definition?

A hypothesis in research is a clear, testable statement predicting the possible outcome of a study based on prior knowledge and observation. It serves as the foundation for conducting experiments or investigations. Researchers test the validity of the hypothesis to draw conclusions and advance knowledge in a particular field.

Why is it called a hypothesis?

The term “hypothesis” originates from the Greek word “hypothesis,” which means “base” or “foundation.” It’s used to describe a foundational statement or proposition that can be tested. In scientific contexts, it denotes a tentative explanation for a phenomenon, serving as a starting point for investigation or experimentation.

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What is and How to Write a Good Hypothesis in Research?

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Table of Contents

One of the most important aspects of conducting research is constructing a strong hypothesis. But what makes a hypothesis in research effective? In this article, we’ll look at the difference between a hypothesis and a research question, as well as the elements of a good hypothesis in research. We’ll also include some examples of effective hypotheses, and what pitfalls to avoid.

What is a Hypothesis in Research?

Simply put, a hypothesis is a research question that also includes the predicted or expected result of the research. Without a hypothesis, there can be no basis for a scientific or research experiment. As such, it is critical that you carefully construct your hypothesis by being deliberate and thorough, even before you set pen to paper. Unless your hypothesis is clearly and carefully constructed, any flaw can have an adverse, and even grave, effect on the quality of your experiment and its subsequent results.

Research Question vs Hypothesis

It’s easy to confuse research questions with hypotheses, and vice versa. While they’re both critical to the Scientific Method, they have very specific differences. Primarily, a research question, just like a hypothesis, is focused and concise. But a hypothesis includes a prediction based on the proposed research, and is designed to forecast the relationship of and between two (or more) variables. Research questions are open-ended, and invite debate and discussion, while hypotheses are closed, e.g. “The relationship between A and B will be C.”

A hypothesis is generally used if your research topic is fairly well established, and you are relatively certain about the relationship between the variables that will be presented in your research. Since a hypothesis is ideally suited for experimental studies, it will, by its very existence, affect the design of your experiment. The research question is typically used for new topics that have not yet been researched extensively. Here, the relationship between different variables is less known. There is no prediction made, but there may be variables explored. The research question can be casual in nature, simply trying to understand if a relationship even exists, descriptive or comparative.

How to Write Hypothesis in Research

Writing an effective hypothesis starts before you even begin to type. Like any task, preparation is key, so you start first by conducting research yourself, and reading all you can about the topic that you plan to research. From there, you’ll gain the knowledge you need to understand where your focus within the topic will lie.

Remember that a hypothesis is a prediction of the relationship that exists between two or more variables. Your job is to write a hypothesis, and design the research, to “prove” whether or not your prediction is correct. A common pitfall is to use judgments that are subjective and inappropriate for the construction of a hypothesis. It’s important to keep the focus and language of your hypothesis objective.

An effective hypothesis in research is clearly and concisely written, and any terms or definitions clarified and defined. Specific language must also be used to avoid any generalities or assumptions.

Use the following points as a checklist to evaluate the effectiveness of your research hypothesis:

• Predicts the relationship and outcome
• Simple and concise – avoid wordiness
• Clear with no ambiguity or assumptions about the readers’ knowledge
• Observable and testable results
• Relevant and specific to the research question or problem

Research Hypothesis Example

Perhaps the best way to evaluate whether or not your hypothesis is effective is to compare it to those of your colleagues in the field. There is no need to reinvent the wheel when it comes to writing a powerful research hypothesis. As you’re reading and preparing your hypothesis, you’ll also read other hypotheses. These can help guide you on what works, and what doesn’t, when it comes to writing a strong research hypothesis.

Here are a few generic examples to get you started.

Eating an apple each day, after the age of 60, will result in a reduction of frequency of physician visits.

Budget airlines are more likely to receive more customer complaints. A budget airline is defined as an airline that offers lower fares and fewer amenities than a traditional full-service airline. (Note that the term “budget airline” is included in the hypothesis.

Workplaces that offer flexible working hours report higher levels of employee job satisfaction than workplaces with fixed hours.

Each of the above examples are specific, observable and measurable, and the statement of prediction can be verified or shown to be false by utilizing standard experimental practices. It should be noted, however, that often your hypothesis will change as your research progresses.

Language Editing Plus

Elsevier’s Language Editing Plus service can help ensure that your research hypothesis is well-designed, and articulates your research and conclusions. Our most comprehensive editing package, you can count on a thorough language review by native-English speakers who are PhDs or PhD candidates. We’ll check for effective logic and flow of your manuscript, as well as document formatting for your chosen journal, reference checks, and much more.

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Statistics By Jim

Making statistics intuitive

Hypothesis Testing: Uses, Steps & Example

By Jim Frost 4 Comments

What is Hypothesis Testing?

Hypothesis testing in statistics uses sample data to infer the properties of a whole population . These tests determine whether a random sample provides sufficient evidence to conclude an effect or relationship exists in the population. Researchers use them to help separate genuine population-level effects from false effects that random chance can create in samples. These methods are also known as significance testing.

For example, researchers are testing a new medication to see if it lowers blood pressure. They compare a group taking the drug to a control group taking a placebo. If their hypothesis test results are statistically significant, the medication’s effect of lowering blood pressure likely exists in the broader population, not just the sample studied.

Using Hypothesis Tests

A hypothesis test evaluates two mutually exclusive statements about a population to determine which statement the sample data best supports. These two statements are called the null hypothesis and the alternative hypothesis . The following are typical examples:

• Null Hypothesis : The effect does not exist in the population.
• Alternative Hypothesis : The effect does exist in the population.

Hypothesis testing accounts for the inherent uncertainty of using a sample to draw conclusions about a population, which reduces the chances of false discoveries. These procedures determine whether the sample data are sufficiently inconsistent with the null hypothesis that you can reject it. If you can reject the null, your data favor the alternative statement that an effect exists in the population.

Statistical significance in hypothesis testing indicates that an effect you see in sample data also likely exists in the population after accounting for random sampling error , variability, and sample size. Your results are statistically significant when the p-value is less than your significance level or, equivalently, when your confidence interval excludes the null hypothesis value.

Conversely, non-significant results indicate that despite an apparent sample effect, you can’t be sure it exists in the population. It could be chance variation in the sample and not a genuine effect.

Learn more about Failing to Reject the Null .

5 Steps of Significance Testing

Hypothesis testing involves five key steps, each critical to validating a research hypothesis using statistical methods:

• Formulate the Hypotheses : Write your research hypotheses as a null hypothesis (H 0 ) and an alternative hypothesis (H A ).
• Data Collection : Gather data specifically aimed at testing the hypothesis.
• Conduct A Test : Use a suitable statistical test to analyze your data.
• Make a Decision : Based on the statistical test results, decide whether to reject the null hypothesis or fail to reject it.
• Report the Results : Summarize and present the outcomes in your report’s results and discussion sections.

While the specifics of these steps can vary depending on the research context and the data type, the fundamental process of hypothesis testing remains consistent across different studies.

Let’s work through these steps in an example!

Hypothesis Testing Example

Researchers want to determine if a new educational program improves student performance on standardized tests. They randomly assign 30 students to a control group , which follows the standard curriculum, and another 30 students to a treatment group, which participates in the new educational program. After a semester, they compare the test scores of both groups.

Download the CSV data file to perform the hypothesis testing yourself: Hypothesis_Testing .

The researchers write their hypotheses. These statements apply to the population, so they use the mu (μ) symbol for the population mean parameter .

• Null Hypothesis (H 0 ) : The population means of the test scores for the two groups are equal (μ 1 = μ 2 ).
• Alternative Hypothesis (H A ) : The population means of the test scores for the two groups are unequal (μ 1 ≠ μ 2 ).

Choosing the correct hypothesis test depends on attributes such as data type and number of groups. Because they’re using continuous data and comparing two means, the researchers use a 2-sample t-test .

Here are the results.

The treatment group’s mean is 58.70, compared to the control group’s mean of 48.12. The mean difference is 10.67 points. Use the test’s p-value and significance level to determine whether this difference is likely a product of random fluctuation in the sample or a genuine population effect.

Because the p-value (0.000) is less than the standard significance level of 0.05, the results are statistically significant, and we can reject the null hypothesis. The sample data provides sufficient evidence to conclude that the new program’s effect exists in the population.

Limitations

Hypothesis testing improves your effectiveness in making data-driven decisions. However, it is not 100% accurate because random samples occasionally produce fluky results. Hypothesis tests have two types of errors, both relating to drawing incorrect conclusions.

• Type I error: The test rejects a true null hypothesis—a false positive.
• Type II error: The test fails to reject a false null hypothesis—a false negative.

Learn more about Type I and Type II Errors .

Our exploration of hypothesis testing using a practical example of an educational program reveals its powerful ability to guide decisions based on statistical evidence. Whether you’re a student, researcher, or professional, understanding and applying these procedures can open new doors to discovering insights and making informed decisions. Let this tool empower your analytical endeavors as you navigate through the vast seas of data.

Learn more about the Hypothesis Tests for Various Data Types .

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Reader Interactions

June 10, 2024 at 10:51 am

Thank you, Jim, for another helpful article; timely too since I have started reading your new book on hypothesis testing and, now that we are at the end of the school year, my district is asking me to perform a number of evaluations on instructional programs. This is where my question/concern comes in. You mention that hypothesis testing is all about testing samples. However, I use all the students in my district when I make these comparisons. Since I am using the entire “population” in my evaluations (I don’t select a sample of third grade students, for example, but I use all 700 third graders), am I somehow misusing the tests? Or can I rest assured that my district’s student population is only a sample of the universal population of students?

June 10, 2024 at 1:50 pm

I hope you are finding the book helpful!

Yes, the purpose of hypothesis testing is to infer the properties of a population while accounting for random sampling error.

In your case, it comes down to how you want to use the results. Who do you want the results to apply to?

If you’re summarizing the sample, looking for trends and patterns, or evaluating those students and don’t plan to apply those results to other students, you don’t need hypothesis testing because there is no sampling error. They are the population and you can just use descriptive statistics. In this case, you’d only need to focus on the practical significance of the effect sizes.

On the other hand, if you want to apply the results from this group to other students, you’ll need hypothesis testing. However, there is the complicating issue of what population your sample of students represent. I’m sure your district has its own unique characteristics, demographics, etc. Your district’s students probably don’t adequately represent a universal population. At the very least, you’d need to recognize any special attributes of your district and how they could bias the results when trying to apply them outside the district. Or they might apply to similar districts in your region.

However, I’d imagine your 3rd graders probably adequately represent future classes of 3rd graders in your district. You need to be alert to changing demographics. At least in the short run I’d imagine they’d be representative of future classes.

Think about how these results will be used. Do they just apply to the students you measured? Then you don’t need hypothesis tests. However, if the results are being used to infer things about other students outside of the sample, you’ll need hypothesis testing along with considering how well your students represent the other students and how they differ.

I hope that helps!

June 10, 2024 at 3:21 pm

Thank you so much, Jim, for the suggestions in terms of what I need to think about and consider! You are always so clear in your explanations!!!!

June 10, 2024 at 3:22 pm

You’re very welcome! Best of luck with your evaluations!

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

Home » Senior High School » Practical Research » How to Write a Research Hypothesis: Guide and Examples

How to Write a Research Hypothesis: Guide and Examples

A research hypothesis is an important part of any research project of a Senior High School student in the Philippines. It helps to focus the research and ensures that the results are relevant to the question being asked. Without a hypothesis, it can be challenging to know what to look for in the data, or how to interpret the results.

What is a Hypothesis in Research?

A hypothesis is a tentative explanation or answer to a research question regarding variables, their relationships, and other relevant facts. There is always an outcome to research. You may, however, hypothesize, infer, propose, or speculate regarding research-related facts. It is inferential reasoning that leads you to guess something based not only on your experience or factual knowledge but also on the logical conclusions drawn from other research studies. To determine whether a hypothesis is true or false, it must be subjected to analytic examination.

What is the Importance of Hypothesis in Research

Some researchers find the hypothesis essential because of the following reasons:

• A research hypothesis guides you on which aspect of the research to focus on. It provides clarity to the research problem and research objectives.
• It provides opportunities to prove the relationship between variables.
• A research hypothesis gives the right direction for the research. It identifies the population of the research study that is to be investigated or examined. It facilitates data collection, data analysis, and data interpretation.
• It outlines your thoughts on your manner of summarizing the results and of explaining the conclusions.
• It pushes for an empirical study to prove the existence of the relationship between variables and the effects of the independent variable on the dependent variable

Difference Between Hypothesis and Research Questions

In the scientific method, one of the experimental research’s first steps is to develop research questions and hypotheses. Both are related to your study but are not precisely the same. It is very easy to mix up research questions and hypotheses. A research question is a question you are trying to answer when you research a topic or write a research report. A hypothesis, on the other hand, is a statement that can be proved or disproved. You can make a hypothesis out of a research question by changing it into a statement.

Their difference lies in the amount of stock knowledge you have about the focus of your study. You can make hypotheses if you have read a lot about the research topic and know a lot about the variables and other facts that are important to the research—otherwise, form research questions. In any case, these two types of inferential thinking aim to come up with possible or likely answers or solutions to the problem the study is trying to solve. Your research or investigation results show whether the hypotheses are right, wrong, or partly right.

Categories of Hypotheses

A hypothesis is a rough explanation of a set of facts that can be tested by doing more research. There are two types, namely, the null hypothesis and the alternative hypothesis. The two premises exemplify the opposing statements in a statistical test.

A null hypothesis, symbolized by H 0 , states the absence of a relationship between the independent and dependent variables. It is, therefore, a statement to disprove that the independent variable (treatment, intervention, or condition) affects the dependent variable. Null hypothesis pertains to the claim that the means of the variables under study are equal or the values are zero. It is an assumption that the values observed have no change.

A. There is no significant relationship between the brain-boosting supplements and the student’s scores in examinations.

B. The brain-boosting supplements have nothing to do with the student’s score in the examination.

The opposite of the null hypothesis is the alternative hypothesis. Symbolized by H a  or  H 1 , the alternative hypothesis states the relationship between the independent and dependent variables and that the first affects the second one. It is also called affirmative hypothesis as it claims that there is a significant change, effect, or relationship, not equal. not the same, and not equivalent to zero in the value of the means being studied.

A. There is a significant relationship between brain-boosting supplements and the student’s score in the examination. B. The brain-boosting supplements significantly affect the student’s score in the examination.

What are the Different Types of Hypotheses?

The type of hypothesis to use is entirely up to you, the researcher. It does not, however, imply that selecting one of the following categories of the hypothesis will result in the best hypothesis because no hypothesis is superior to any other.

1. Theory-driven vs Data-driven hypothesis

A theory-driven hypothesis is one that is based on current theory to explain the relationship of variables and the impact of one variable on the other variables. However, if it is founded on previous study findings, it is a data-driven hypothesis.

2. Directional (one-tailed) vs. Non-directional (two-tailed) hypothesis

The relationship between two variables as well as the link between these variables is stated in directional hypotheses. It also specifies the direction of the variable relationship.

Example: Children from low-income families, for example, have a greater inferiority complex than children from high-income ones.

In contrast, non-directional hypotheses state the relationship of variables but not the direction of the relationship.

Example: There is a difference in the inferiority complex level of children from low-income families and those from high-income families.

3. Descriptive vs Causal hypotheses

A descriptive hypothesis is a statement that specifies the relationship between two variables as a result of the influence of something; a causal hypothesis is one that specifies the cause-effect relationship. Causal hypotheses are used in true experimental or quasi-experimental research, such as a correlation study, whereas descriptive hypotheses are used in non-experimental research.

How to Write a Hypothesis in Research?

The first step in writing a successful hypothesis is planning. As with any other endeavor, the first step is to do some preliminary study on your own and learn as much as possible about the subject you intend to investigate. From then on, you’ll have all the information you need to determine precisely where you should direct your attention.

Remember that a hypothesis is a statement about the expected relationship between a set of variables. To determine whether or not your prediction is correct, it is up to you to draft a hypothesis and plan the investigation. Subjective and unsuitable judgments are frequently made during the hypothesis-building process. Your hypothesis must maintain an objective focus and tone throughout.

If you want to be skilled in formulating hypotheses, you should consider the following guidelines.

• Write a phrase that explicitly states your hypothesis.
• You can back up your hypotheses with ideas based on other theories, established facts, previous research, or your own accumulated knowledge and experience.
• Ensure that your hypotheses make logical sense in light of the research problem.
• Use your hypotheses to make inferences about the connections between and among the variables.
• Try to find out whether there is a way to put your theories to the test, analyze them, and dig more into the topic.
• Use concise wording that gets to the point while formulating hypotheses.

Research Hypothesis Examples

If you want to see the effectiveness of your hypothesis, you should compare it to the work of others in the same field. When it comes to developing a solid hypothesis for your research, there’s no need to start from scratch. As you do your research and get ready to formulate your hypothesis, you’ll inevitably come across competing theories. These examples can serve as guidelines for what makes a good research hypothesis and what doesn’t.

Some common examples are provided below to get you started.

It is hypothesized that the recognition of the right to physician-assisted suicide is a protection of individual civil liberties.

Eating fruits and vegetables will affect one’s human physical health.

Eating an apple each day, after the age of 60, will result in a reduction in the frequency of physician visits.

Budget airlines are more likely to receive more customer complaints.

Workplaces that offer flexible working hours report higher levels of employee job satisfaction than workplaces with fixed hours.

Increasing levels of stress among undergraduate students will result in decreasing academic performance.

Employees who have a better work-life balance will demonstrate higher productivity compared to those employees who do not have a good work-life balance.

There is a negative dependence between the frequency of social media usage and the attention span of users under 16 years of age.

Video games can have a negative impact on a person’s brain, vision, and memory.

The increase in mental health awareness in schools will result in a better understanding of mental health issues and possible ways to combat them among pupils and teachers.

Each of the above examples is concrete, observable, and quantifiable, and the prediction statement may be tested or proven to be untrue using ordinary scientific procedures. However, it should be remembered that your hypothesis will usually change as your research develops.

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Definition of hypothesis

Did you know.

The Difference Between Hypothesis and Theory

A hypothesis is an assumption, an idea that is proposed for the sake of argument so that it can be tested to see if it might be true.

In the scientific method, the hypothesis is constructed before any applicable research has been done, apart from a basic background review. You ask a question, read up on what has been studied before, and then form a hypothesis.

A hypothesis is usually tentative; it's an assumption or suggestion made strictly for the objective of being tested.

A theory , in contrast, is a principle that has been formed as an attempt to explain things that have already been substantiated by data. It is used in the names of a number of principles accepted in the scientific community, such as the Big Bang Theory . Because of the rigors of experimentation and control, it is understood to be more likely to be true than a hypothesis is.

In non-scientific use, however, hypothesis and theory are often used interchangeably to mean simply an idea, speculation, or hunch, with theory being the more common choice.

Since this casual use does away with the distinctions upheld by the scientific community, hypothesis and theory are prone to being wrongly interpreted even when they are encountered in scientific contexts—or at least, contexts that allude to scientific study without making the critical distinction that scientists employ when weighing hypotheses and theories.

The most common occurrence is when theory is interpreted—and sometimes even gleefully seized upon—to mean something having less truth value than other scientific principles. (The word law applies to principles so firmly established that they are almost never questioned, such as the law of gravity.)

This mistake is one of projection: since we use theory in general to mean something lightly speculated, then it's implied that scientists must be talking about the same level of uncertainty when they use theory to refer to their well-tested and reasoned principles.

The distinction has come to the forefront particularly on occasions when the content of science curricula in schools has been challenged—notably, when a school board in Georgia put stickers on textbooks stating that evolution was "a theory, not a fact, regarding the origin of living things." As Kenneth R. Miller, a cell biologist at Brown University, has said , a theory "doesn’t mean a hunch or a guess. A theory is a system of explanations that ties together a whole bunch of facts. It not only explains those facts, but predicts what you ought to find from other observations and experiments.”

While theories are never completely infallible, they form the basis of scientific reasoning because, as Miller said "to the best of our ability, we’ve tested them, and they’ve held up."

• proposition
• supposition

hypothesis , theory , law mean a formula derived by inference from scientific data that explains a principle operating in nature.

hypothesis implies insufficient evidence to provide more than a tentative explanation.

theory implies a greater range of evidence and greater likelihood of truth.

law implies a statement of order and relation in nature that has been found to be invariable under the same conditions.

Examples of hypothesis in a Sentence

These examples are programmatically compiled from various online sources to illustrate current usage of the word 'hypothesis.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.

Word History

Greek, from hypotithenai to put under, suppose, from hypo- + tithenai to put — more at do

1641, in the meaning defined at sense 1a

Phrases Containing hypothesis

• counter - hypothesis
• nebular hypothesis
• null hypothesis
• planetesimal hypothesis
• Whorfian hypothesis

Articles Related to hypothesis

This is the Difference Between a...

This is the Difference Between a Hypothesis and a Theory

In scientific reasoning, they're two completely different things

Dictionary Entries Near hypothesis

hypothermia

hypothesize

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“Hypothesis.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/hypothesis. Accessed 23 Jun. 2024.

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• 10 Research Question Examples to Guide Your Research Project

10 Research Question Examples to Guide your Research Project

Published on October 30, 2022 by Shona McCombes . Revised on October 19, 2023.

The research question is one of the most important parts of your research paper , thesis or dissertation . It’s important to spend some time assessing and refining your question before you get started.

The exact form of your question will depend on a few things, such as the length of your project, the type of research you’re conducting, the topic , and the research problem . However, all research questions should be focused, specific, and relevant to a timely social or scholarly issue.

Once you’ve read our guide on how to write a research question , you can use these examples to craft your own.

Note that the design of your research question can depend on what method you are pursuing. Here are a few options for qualitative, quantitative, and statistical research questions.

Other interesting articles

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

Methodology

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

 Statistics

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

Research bias

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

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• Published: 18 June 2024

Can digital transformation curtail carbon emissions? Evidence from a quasi-natural experiment

• Zihao Lin   ORCID: orcid.org/0000-0002-1787-0251 1  

Humanities and Social Sciences Communications volume  11 , Article number:  782 ( 2024 ) Cite this article

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• Environmental studies
• Science, technology and society

Digital infrastructure construction (DIC) is a crucial component of digital transformation, yet its impact on the sustainable development of cities remains uncertain. To address this question, this study employs the Difference-in-Differences (DID) model to evaluate the effects of a typical DIC policy, the Broadband China Pilot (BBCP) policy. The research sample covers data from Chinese cities from 2009 to 2019. The DID estimation demonstrates that the BBCP policy effectively decreased carbon emissions (COE). Robustness tests, such as parallel trend tests, placebo tests, propensity score matching DID, and local projection DID, all confirm the negative impact of the BBCP policy on COE. Heterogeneity analysis reveals that the BBCP policy has a greater mitigation effect on COE in non-resource cities. The BBCP policy has a stronger effect on reducing the COE in cities with a higher proportion of green funds or lower fiscal expenditures. Further analysis indicates that the BBCP policy significantly improves low-carbon ecological efficiency and reduces COE intensity. The results of the mechanism analysis reveal that the BBCP policy lowers COE by enhancing the industrial structure and promoting green technological progress. The study provides policy recommendations in the realm of DIC for developing countries to achieve digital transformation and low COE development concurrently. This research can assist governments in better considering how to implement DIC policies to construct ecologically sustainable cities with low COE.

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Introduction.

China’s economic development has led to remarkable environmental consequences, with carbon emissions (COE) surpassing one-third of global emissions (International Energy Agency 2021 ). China is facing tremendous pressure to minimize its COE and has set a target to peak its COE by 2030 (Bai et al. 2023a ). The pressure to reduce COE in China is a microcosm of many developing countries striving to lower their carbon emissions. In fact, due to the relocation of high-carbon-emitting industries to developing countries, these nations commonly face exacerbated carbon emission issues (Wang et al. 2022a ). It is imperative to take innovative measures to reverse the environmental degradation caused by carbon emissions in developing countries.

While reducing carbon emissions is crucial, implementing environmental regulations may have negative economic impacts. Studies suggest that environmental regulations may decrease GDP (Si et al. 2021 ). Furthermore, as developing countries, such as China, rely overly on coal and other fossil fuels, environmental regulations may not achieve their intended goals (Li et al. 2019 ). Governments and populations in developing countries may struggle to accept environmental regulations that can lead to economic downturns. Therefore, it is necessary to explore policies that simultaneously achieve economic development and improve the environment.

The digital economy (DE) is increasingly being seen as a new force to curb environmental degradation (Ghobakhloo 2020 ). Policies supporting digital infrastructure construction (DIC) may offer a pathway toward achieving sustainable development through digital transformation. However, the current body of work predominantly focuses on the implications of traditional infrastructure on COE (Sun and Li 2021 ), with inadequate assessment of the COE of information and communication technology (ICT) infrastructure. Some studies have questioned the COE reduction effects of digital transformation, as ICT infrastructure is often energy-intensive, and digital technology is high-energy-consuming (Salahuddin and Alam 2016 ; Ji et al. 2022 ). The “Broadband China” pilot (BBCP) policy is a Chinese initiative to promote DIC, yet there is limited literature on its impact on low-carbon city construction. The question is whether BBCP policies can reduce the COE. A thorough exploration of the real effects of DIC on COE could potentially achieve a “win-win” scenario by reducing COE and fostering economic development. Exploring the carbon emission reduction effects of DIC policies could provide valuable insights for other developing countries aiming to achieve economic development and COE reduction through digitization. Therefore, the paper aims to evaluate how BBCP policies affect COE.

In theory, the BBCP policy can reduce urban COE. DIC offers technological solutions for intelligent energy management in cities and industries, reducing reliance on fossil fuels, and thereby decreasing COE (Wu et al. 2021 ; Lyu and Liu 2021 ; Zhao et al. 2022a ; Wang et al. 2023a ; Zheng and Wong 2024 ). DIC can shape low-carbon work and lifestyles (Ozcan and Apergis, 2018 ; Ma and Zhu, 2022 ; Wu et al. 2023a ). Digital transformation also provides technical support for the government to implement environmental monitoring and COE management (Bai et al. 2023a ; Zheng et al. 2023 ). Furthermore, the BBCP policy reduces COE by promoting the industrial structure (IS) upgrading and displacing the high-carbon-emitting secondary industry (Zhou et al. 2022 ; Zhang and Ran 2023 ; Bai et al. 2023b ). Lastly, the BBCP policy reduces COE by stimulating green technological innovation (GTI) advancement (Tang et al. 2021 ; Shao et al. 2021 ; Wang et al. 2022b ).

Drawing upon data from urban areas in China from 2009 to 2019, this study discovered a significant decrease in COE due to the BBCP policy. Robustness tests confirm the negative impact of DIC on COE. Further analysis reveals that the BBCP policy has a more pronounced negative effect on COE in cities that are not rich in resources. Cities with lower fiscal spending or a higher proportion of green funds show better COE reduction effects from BBCP policy. Additionally, the BBCP policy enhances low-carbon ecological efficiency and reduces COE intensity. The mechanism analysis suggests that the BBCP policy lowers COE by enhancing IS and fostering GTI.

This research enriches the body of knowledge in various aspects. First, it identifies the COE diminishment effect of the BBCP policy, highlighting the potential of digital transformation policies to curb urban COE (Ji et al. 2022 ). DIC is a feasible approach to achieving low-carbon cities. Additionally, this study furnishes fresh perspectives on the potential of digital transformation policies to decrease urban COE intensity, offering new solutions for decarbonizing the economy (Bai et al. 2023a ). This study has identified that DIC contributes to enhancing the low-carbon ecological efficiency of cities. Furthermore, by estimating the moderating effect of green funds, this study provides the first evidence of the effect of green finance on the COE diminishment effects of ICT infrastructure (Kou and Xu 2022 ). Finally, this study finds that the BBCP policy suppresses urban COE by improving the IS and promoting GTI, offering new evidence for the COE diminishment mechanisms of DE (Wang et al. 2022b ; Bai et al. 2023b ).

The remainder of this study is organized as follows. In Section “Literature review, theoretical analysis, and hypothesis development”, a literature review, theoretical examination, and hypothesis formation are carried out. Section “Methods” outlines the origins of the data and the model used. Section “Empirical results” showcases the practical findings. Section “Possible mechanism” conducts a mechanism analysis. Lastly, Section “Discussion, implications, and conclusion” engages in a brief discussion, offers policy recommendations, and concludes the paper.

Literature review, theoretical analysis, and hypothesis development

Literature review.

Research indicates that population growth and energy consumption are significant factors that contribute to the increase in COE (Ali et al. 2022 ; Nurgazina et al. 2022 ). Several studies have focused on the relationship between DE and COE (Wang et al. 2023b ). Recent research has identified an inverted U-shaped relationship between DE and COE, indicating that DE might enhance COE in the early phases but diminish it in subsequent stages (Li and Wang 2022 ; Wang et al. 2023b ; Sun et al. 2024 ; Xie et al. 2024 ). Additionally, research has found that DE can lead to carbon rebound effects (Zhu and Lan, 2023 ). Some scholars argue that DE aggregation can reduce the COE (Wang and Chen 2023 ). Previous studies have further analyzed the impact of DE on the carbon emissions of industrial chains and exports (Li et al. 2023a ). Analyses at the multi-country level have shown that DE may reduce carbon intensity, but increase per capita COE (Dong et al. 2022 ). Prior studies have primarily focused on the impact of transportation infrastructure on COE (Sun and Li 2021 ). Currently, there is limited attention paid to the impact of DIC on COE. Some analyses suggest that DIC itself may contribute to COE, questioning its role in long-term carbon neutrality (Salahuddin and Alam 2016 ; Ji et al. 2022 ). The complexity in the relationship between DE and COE identified in previous studies may be due to capturing the COE generated by DE growth itself. However, these studies overlooked the potential spillover effects of DIC on other industries. DIC can penetrate the energy sector, leading to energy savings and emissions reductions. These effects of reducing COE may require the use of DIC policies in quasi-natural experiments to capture them. The paper aims to examine the impact of DIC policies on COE.

Research on DIC suggests that it can enhance industrial ecology and encourage businesses to transition to greener practices (Guo et al. 2024 ; Zhou et al. 2024 ). DIC policies can promote regional innovation (Wang and Wang 2024 ). DIC has the potential to drive digital transformation in businesses (Wu et al. 2023b ). DIC can also improve public health (Liu et al. 2024 ). Although there has been considerable focus on the macro- and micro-level impacts of DIC, a scarcity of studies regarding its role in developing low-carbon cities. Prior research has not fully recognized the influence of DIC on improving low-carbon ecological efficiency. Therefore, this study examines the role of DIC in the development of eco-friendly cities and in enhancing low-carbon ecological efficiency.

Theoretical analysis and hypothesis development

Bbcp policy and carbon emissions.

As a digital transformation policy, the BBCP policy is theoretically expected to reduce COE. First, DIC can provide digital solutions for energy management and COE mitigation in industries and cities. DIC supports the development of energy-saving facilities, such as smart grids. The development of the Internet can lead to energy savings and a reduction in COE (Lin and Zhou 2021 ; Wu et al. 2021 ). DE can reduce energy consumption intensity by attracting talent and promoting financial development, thus suppressing COE intensity (Ren et al. 2021 ). Technological progress brought about by DE advancement can help reduce energy usage and achieve eco-friendly urban growth. Studies have revealed that the coordinated advancement of “digital-energy” coupling optimizes the energy consumption structure (Shahbaz et al. 2022 ; Shi et al. 2022 ). Artificial intelligence technology can be utilized in urban energy supply systems to achieve efficient energy scheduling and deployment (Lyu and Liu 2021 ). DIC and artificial intelligence are essential for smart grid development. Energy monitoring and dispatching in urban smart energy systems rely on DIC. DIC policies facilitate the construction of smart energy networks in cities. The development of DE can transform the energy structure and reduce dependence on nonrenewable fossil fuels (Zheng and Wong 2024 ). Fossil fuels are often the primary source of COE (Ali et al. 2022 ; Nurgazina et al. 2022 ). Therefore, DE can lower the COE by promoting the utilization of renewable energy sources. Digital technology can play a crucial role in energy utilization and pollution management (Kovacikova et al. 2021 ). The technological progress brought about by the evolution of DE can diminish energy waste through real-time monitoring of energy usage in urban production activities (Bunse et al. 2011 ). In addition to COE reduction at the urban level, digital technology also offers innovative solutions for COE reduction across various sectors, helping to enhance energy allocation efficiency and reduce energy wastage (Wang et al. 2023a ). For example, smart transportation can enhance the efficiency of the transportation system, alleviate urban congestion, and reduce oil consumption and COE (Zhao et al. 2022a ). Manufacturing enterprises can efficiently improve their energy utilization procedures and enhance their energy stewardship through digital technology (Xu et al. 2023 ). Enterprises can utilize digital technology for the precise computation and prognosis of COE (Ma et al. 2021 ).

DIC has a dematerialization effect. The BBCP policy enables individuals to rely on ICT facilities to lead a predominantly online lifestyle and work, thereby reducing the need for physical materials. The dematerialization effect of DE can facilitate economic decarbonization. Advancements in the digital economy have facilitated a reduction in material consumption and carbon-intensive production. For example, progress in information and communication technology has made online shopping and video conferences more popular, reducing the need for physical travel, alleviating traffic congestion, and saving energy (Ozcan and Apergis 2018 ; Wu et al. 2023a ). Firms can utilize digital platforms to collect user information and guide consumers toward purchasing eco-friendly and low-COE products effectively (Sun and Zhang 2021 ). The construction of digital infrastructure has facilitated non-material consumption, leading to effective energy conservation and pollution reduction (Wu et al. 2023a ). DE has integrated and substituted traditional work and lifestyle patterns. For instance, paperless online meetings can decrease resource usage. Furthermore, digital technologies can facilitate consumer access to green products and promote low-carbon consumption (Ma and Zhu 2022 ; Bai et al. 2023b ).

DIC can offer digital solutions to address past challenges in environmental regulation and enhance environmental monitoring. The application of digital technology has emerged as a breakthrough in addressing environmental regulation challenges as effective COE control policies rely on overseeing and confirming technologies. In the past, even though the government wanted to implement environmental regulatory policies, there were insufficient detection technologies to monitor corporate pollution emissions. However, with the evolution of ICT infrastructure, the integration of digital technology and environmental protection has advanced digitalization, intellect, and enhancement of pollution governance and regulation, thereby enhancing the effectiveness of local governments in executing COE reduction-related governance tasks (Zheng et al. 2023 ). Digital technology enhances the precision and efficacy of governmental eco-guidelines, facilitating the optimization of government environmental regulation models and strengthening government eco-management capacities (Bai et al. 2023a ). Digital technology may help reduce redundant processes and materialization, and enhance environmental supervision and governance capabilities (Yang et al. 2020 ). Governments can utilize digital technology to adjust the overall energy provision to minimize COE (Bhattacharya et al. 2015 ).

In summary, as a policy for digital transformation and digital infrastructure construction, the BBCP policy can achieve carbon reduction by providing COE-reducing solutions for industries and cities, promoting decarbonization in residents’ work and lifestyle, and providing technical support for environmental regulation. Consequently, the study puts forward the first research hypothesis: H1.

H1: The BBCP policy reduces urban COE.

BBCP policy, IS, and COE

The BBCP policy has the potential to decrease COE by improving IS. The traditional secondary industry relies more on natural resources and fossil energy, whereas emerging industries in the tertiary sector have a lower demand for fossil energy. Digital economic transformation can promote the evolution of the tertiary sector and alter a city’s energy demand structure. Specifically, the DE may lead to IS upgrading (Wu et al. 2021 ), significantly reducing the proportion of the traditional secondary industry’s reliance on natural resources and fossil energy through Intelligent transformation of industry and ICT industry advancement (Cheng et al. 2019 ; Zhang and Ran 2023 ). The role of IS upgrading in mitigating COE has been supported in many studies (Chang et al. 2023 ). The transition from a low-tech industrial structure to a high-tech one will undoubtedly improve efficiency and help decrease the carbon emissions associated with production (Zhao et al. 2022b ). Empirical evidence indicates that the DE has achieved COE reductions through scaling up the tertiary sector (Wang et al. 2022b ). DIC redirects resources towards the ICT industry, thereby creating a displacement effect on the high-carbon-emitting secondary industry. DE has also mitigated haze pollution by promoting the transformation of IS (Zhou et al. 2021 ). Wang et al. ( 2021 ) also argue that innovation in the ICT industry is an important way to optimize the ratio of the secondary sector to the service sector and reduce energy usage. The BBCP policy can improve IS (Zhou et al. 2022 ) and facilitate local industrial transformation and upgrading through digital transformation, ultimately reducing COE (Bai et al. 2023b ). Ultimately, DE can stimulate the growth of emerging environmental conservation sectors through digitally enabled environmental surveillance, catalyzing the transformation and advancement of traditional secondary industries (Yang et al. 2021 ). The study puts forward the second research hypothesis, H2:

H2: The BBCP policy reduces urban COE by upgrading industrial structures.

BBCP policy, GTI, and COE

BBCP policy can lessen COE by boosting GTI. Advancements in the DE have the potential to decrease pollution by fostering technological breakthroughs, enhancing energy productivity, and transforming energy frameworks (May et al. 2017 ). For example, in the field of energy, the advancement of DE can facilitate the expansion of eco-friendly power production technologies, thereby reducing the reliance on high-carbon-emission fossil fuel-based power generation (Zhang et al. 2023 ). The shift towards digital transformation in manufacturing firms has been shown to encourage eco-friendly production through technological advancements (Zhang et al. 2023 ). Digital technology eliminates communication and knowledge flow barriers, facilitates cross-domain knowledge dissemination, improves knowledge absorption efficiency, and promotes GTI (Ning et al. 2023 ). In the phases of information scrutiny, data manipulation, and data empowerment, digital transformation enhances the assimilation prowess, inventive aptitude, and adaptive abilities of corporations, thereby boosting the R&D capabilities of GTI (Yang et al. 2023 ). DE can improve the development efficiency of GTI (Li et al. 2023b ). The BBCP policy promotes the diffusion of GTI by enhancing the level of digitalization, thereby facilitating GTI in enterprises (Tang et al. 2021 ). GTI is committed to reducing pollution in production and achieving environmental protection and carbon neutrality goals (Liu et al. 2022 ). GTI promotes the advancement of process technology, reduces the consumption of coal resources, and reduces pollution emissions, thereby inhibiting COE (Zhao et al. 2024 ). Numerous studies have confirmed the connection between GTI and COE reduction (Gu et al. 2021 ). An analysis of the Next-11 countries indicates that the development of GTI can reduce COE, thus alleviating environmental degradation (Shao et al. 2021 ). Ample evidence suggests that the DE can indirectly lessen COE by fostering the GTI (Wang et al. 2022b ). The study puts forward the third research hypothesis, H3.

H3. The BBCP policy reduces urban COE by advancing GTI.

The theoretical underpinning of the paper is depicted in Fig. 1 , elucidating the underlying theoretical construct upon which this research was predicated. In terms of direct effects, DIC offers energy-saving solutions from a digital perspective. DIC helps move work and life online, reducing the physical level of consumption. In other words, DIC has a “dematerialization” effect. Finally, DIC can provide digital tools for environmental regulation. Infrastructure such as broadband, 5 G, and IoT can all help governments implement remote environmental monitoring. The direct effects of DIC help to reduce CE. At the indirect effect level, DIC stimulates green innovation activities and promotes industrial transformation and upgrading. Green innovation and industrial upgrading can both reduce pollution emissions in economic activities and curb CE. Therefore, DIC can indirectly reduce COE through two channels: green innovation and industrial structure.

This figure shows the direct and indirect effects of digital infrastructure on carbon emissions.

To examine the impact of the BBCP policy on COE, this study conducted an empirical analysis of Chinese cities from 2009 to 2019. The data comes from the “China City Statistical Yearbook,” “China Urban Construction Statistical Yearbook,” China National Development and Reform Commission, and the China National Intellectual Property Office. Table 1 provides an overview and explanations of the variables.

Measuring variables

Measuring coe.

The paper measures the energy consumption of cities using the annual consumption of coal gas, methane, LPG, power, and heat energy from each city. This study utilized the conversion factors for energy consumption and COE provided by the “2006 IPCC Guidelines” to calculate the total COE (in kilotons) by aggregating the consumption of coal, natural gas, liquefied petroleum gas, electricity, and heat. The natural logarithm of the urban COE was used as the dependent variable in this study. The maps depicting the COE of Chinese cities in 2009, 2014, and 2019 are displayed in Fig. 2 .

From left to right, the figure shows the spatial distribution of carbon emissions in 2009, 2014 and 2019.

Measuring the “Broadband China” pilot policy

To foster the advancement of ICT infrastructure, the Chinese State Council unveiled the “Broadband China Strategy and Implementation Plan” in 2013. The “Broadband China” strategy includes four main aspects: (1) broadening the user base of high-speed connectivity; (2) boosting the high-speed connectivity penetration rate; (3) improving high-speed connectivity access capacity; (4) advocating the utilization of high-speed connectivity in industrial production and daily life. Subsequently, three batches of BBCP policy pilot city lists were published in 2014, 2015, and 2016, respectively, totaling 117 cities. To gauge the impact of the BBCP policy, this study utilizes a dummy variable called BCPP. When a city is designated as a pilot city for the BBCP policy, it is allocated a value of one for the current year and all following years. Otherwise, a value of 0 is designated.

Measuring other variables

This study incorporates control variables that account for urban economic development (lnGDP), population (lnPOP), foreign direct investment (FDI), fiscal expenditure (FinExp), and infrastructure level (Infra). These control variables were selected based on empirical evidence from previous studies (Ma et al. 2023 ; Zheng et al. 2023 ).

Model specification

The study employed the Difference-in-Differences (DID) approach to assess the BBCP policy (Liu et al. 2024 ; Wang and Wang 2024 ). Empirical research often uses the DID method to evaluate policy effects. The basic idea of the DID method is to first observe the changes in cities affected by BBCP policies before and after the policies, and then observe the changes in cities not affected by BBCP policies before and after the policies, and the difference between the two changes is the impact of BBCP policy intervention on urban COE. Equation ( 1 ) was adopted for DID estimation:

COE is the dependent variable, which represents carbon emissions. BCPP is an independent variable that reflects the impact of BBCP policies. Coefficient b is the core coefficient concerned in this paper, reflecting the marginal effect of BBCP policy on COE. The coefficient a represents the intercept of the model. Controls represent a set of control variables. Year and City represent year-fixed effects and city-fixed effects. Error is the error term of the model.

Empirical results

Baseline did results.

The baseline results of DID regression are depicted in Table 2 . The finding highlights that the BBCP policy significantly reduced carbon emissions by 9.4%. The finding supports the first hypothesis of this study, indicating that the BBCP policy has a significant negative effect on urban COE. The BBCP policy facilitates urban digital transformation, paving the way for sustainable development in cities. The advancement of ICT infrastructure and policies for digital transformation have significantly reduced COE, providing a solution that balances economic development and mitigating growing carbon pressure.

Robustness test

Parallel trend test.

This study employed an event study methodology to evaluate the parallel trend assumption. \({P}_{i,t+j}\) is a series of dummy variables with a value of 1 when city i at time t is j years away from being designated as a pilot city. Otherwise, \({P}_{i,t+j}\) was set to 0. Equation ( 2 ) represents the event study model.

The parallel trend analysis is depicted in Fig. 3 . The estimated coefficients before the BBCP policy was enforced are not statistically significant. Both pilot and non-pilot cities showed similar trends in COE prior to the policy intervention. Prior to the enforcement of the BBCP policy, both the treatment and control cohorts revealed converging trends in carbon emissions, suggesting that no identifiable factors led to significant changes in COE. However, after the implementation of the BBCP policy, the carbon emissions in the pilot cities gradually began to decrease and rapidly declined in the third and fourth stages. The results of the parallel trend test indicate that the differential trends between the experimental and control groups can be attributed to the impact of the BBCP policy intervention. The DID analysis results in Table 2 are supported by these findings. The DIC policy contributes to the reduction of urban CO2 emissions, with its decarbonization effects gradually strengthening over time.

After the implementation of the BCP policy, carbon emissions showed a downward trend year by year.

Placebo test

To ensure the robustness of the estimated effect of the BBCP policy on the COE, it is necessary to understand the potential confounding factors or random events that could lead to COE reductions. To confirm that this possibility did not exist, a placebo trial was conducted. To assess the potential impact of random events on the estimated results, 500 iterations of simulations were performed using randomly generated policy-event shocks. As Fig. 4 shows, the results of the placebo test indicate that the estimated coefficients of these pseudo-policy event shocks are significantly higher than the baseline DID estimation coefficient (represented by the blue line). If the exogenous shocks of BBCP policy events did indeed lead to a decrease in carbon emissions, theoretically, randomly generated events cannot produce the same magnitude of decrease as observed in the actual sample, let alone a larger decrease. From a statistical perspective, if the results of the placebo test can effectively eliminate the impact of random factors, the estimated results for at least 90% of the samples should be higher than the actual estimation. However, in the placebo test in this study, 100% of the samples were significantly higher than the actual estimation results, indicating that the COE reduction effect of the BBCP policy is highly unlikely to be attributable to other random factors with a probability exceeding 99%. The estimated COE mitigation effect shown in Table 2 can be attributed to the impact of the BBCP policy, as no other identifiable random factors affecting the results were observed. Therefore, the negative impact of the BBCP policy on COE cannot be attributed to other random events or policy shocks.

The red dots represent the estimated coefficients from the simulation results, and the blue line on the far left of each figure represents the actual sample estimates from Table 2 .

Propensity score matching DID method

To minimize differences between the treatment and control cohorts, a propensity score matching approach was utilized. The propensity score matching method can address selection bias in the DID method and enhance the accuracy of estimation in quasi-experimental research. It determines whether a city will be influenced by policies or other activities through methods such as logit estimation (Cao et al. 2021 ; Ghimire et al. 2024 ). The role of propensity score matching is to identify similar cities in the intervention and control groups so that they have comparable characteristics. The propensity score matching DID method resolves the selection bias problem caused by both observable and unobservable variables. This method estimates matching values to help eliminate unmatched samples, ensuring that pretreatment control variables are balanced among groups, reducing the likelihood of extraneous factors distorting treatment outcomes. Propensity score matching eliminates unmatched samples through 1:1 nearest-neighbor matching, using all control variables as matching indicators. The results of the propensity score matching DID regression, depicted in Table 3 , reveal a significant negative impact of BBCP policy on COE.

Estimation results of the local projection DID approach

The estimation of Multistage DID may be affected by confounding effects (De Chaisemartin and D’Haultfoeuille 2020 ). Therefore, this study employed the local projection DID approach to obtain more accurate treatment effect estimates. The local projection DID method is better equipped to address treatment effects following the expansion of pilot cities, resulting in more reliable and robust estimates of the effects. As depicted in Fig. 5 , the treatment effect estimate of the BCPP is significantly negative. The findings from the local projection DID analysis reveal that the inhibitory effect of BBCP policy events on COE does not diminish to an insignificant level.

Event analysis results from local projection DID. It reflects that after the implementation of the BCP policy, carbon emissions show a downward trend year by year.

Further analysis

Heterogeneity analysis: green fund.

Carbon reduction relies on green finance. In urban areas with a higher proportion of green funds, companies are more likely to receive support for integrating digital technology and carbon reduction techniques. As a result, BBCP policies may have a stronger impact on carbon reduction in cities with a higher proportion of green funds. To test this hypothesis, the study divided the sample into two groups based on the median ratio of annual green funds to total funds: high green fund proportion (High GreenFund = 1) and low green fund proportion (High GreenFund = 0). The heterogeneity analysis results in Table 4 indicate that the interaction term between High GreenFund and BCPP is significantly negative. This suggests that the effect of the BBCP policy on COE reduction is stronger in areas with a higher proportion of green funds. Therefore, combining digital transformation and green finance policies will lead to better COE reduction effects.

Heterogeneity analysis: degree of resource dependence

The resource curse may pose obstacles for industries in accessing the necessary elements for development. BBCP policies may encounter difficulties in reducing carbon emissions in cities dependent on natural resources. In 2013, the Chinese State Council published a list of cities abundant in resources. Based on this list, the study categorized the sample into resource-dependent and non-resource-based cities. The results of the subsample regressions are presented in columns (1) and (2) of Table 5 . BBCP policies have a significant impact on reducing carbon emissions in non-resource-rich cities. Therefore, the resource curse can hinder access to funds for the ICT industry. DIC faces challenges in securing adequate financial support in resource-rich cities, thereby weakening the effectiveness of BBCP policies in reducing carbon emissions

Heterogeneity analysis: fiscal expenditure

Some countries and governments may face constraints in implementing large-scale fiscal expenditures due to fiscal institutions, cultural factors, or low fiscal revenues. DIC policies are typically government-led and require substantial public funds. Can governments with limited capacity for fiscal expenditure effectively implement DIC policies given the financial support required? The study categorized the sample into cities with high and low levels of fiscal expenditure based on the median annual fiscal expenditure. Columns (3) and (4) of Table 5 present the results of the subsample regressions. The findings suggest that BBCP policies have a more pronounced impact on carbon emissions mitigation in cities with lower levels of fiscal expenditure. These cities demonstrate a greater willingness to promote social and private capital investments in DIC initiatives. Private capital dynamism can drive sustainable development more effectively than reliance solely on public funds. Cities with lower levels of fiscal expenditure may have less developed traditional infrastructure, making DIC initiatives more impactful as they can partially replace traditional infrastructure. Conversely, cities with higher levels of fiscal expenditure may exhibit inertia in their dependence on public funds, potentially crowding out private capital investment. Therefore, DIC policies should offer tax incentives to incentivize private enterprises to invest to reduce reliance on fiscal expenditures.

Estimation results of low-carbon eco-efficiency

The study aimed to assess the impact of BBCP policies on enhancing low-carbon ecological efficiency. It developed an indicator for measuring the integration of low-carbon development and ecology within economic growth. The paper utilized the super-efficiency slacks-based measure model to calculate low-carbon ecological efficiency, considering energy usage, capital, and labor as inputs, while GDP and per capita green space area were expected outputs. Undesirable outputs such as COE, sulfur dioxide, dust, and wastewater were also taken into account. The regression results revealed in column (1) of Table 6 demonstrate that BBCP policies significantly improve cities’ low-carbon green efficiency. This indicates that DIC can effectively promote coordinated economic and ecological development.

Estimation results of COE intensity

The study examined the impact of BBCP policies on COE intensity. COE intensity is measured using the ratio of COE to GDP. The results in column (2) of Table 6 demonstrate that BBCP policies significantly reduce carbon intensity in the pilot cities. Therefore, this study concludes that digital infrastructure development policies effectively decrease urban COE intensity. The evolution of the DE can contribute to economic decarbonization.

Possible mechanism

Upgrading of is.

The paper sought to explore the mechanisms through which BBCP policies have a negative impact on COE. Hypothesis 2 suggests that BBCP policies have a carbon reduction effect by improving the IS. The ratio of the tertiary sector to the secondary sector (Structure) is used as an indicator of urban industrial structure, and Eqs. ( 3 ) and ( 4 ) were used for the mechanism analysis. If BBCP policies generate carbon reduction effects by improving the industrial structure, the interaction term between BCPP and Structure should be significant and negative. The findings of the mechanism analysis are showcased in columns (1) and (2) of Table 7 . The BBCP policy has led to an upgrade in IS. The estimated coefficient of the interaction term between BCPP and Structure is significantly negative. Thus, the results of the mechanism analysis confirmed the validity of Hypothesis 2. BBCP policies suppress urban COE by improving the IS. The digitization of the economy promotes the advancement of the ICT sector, controls the high-carbon-emitting secondary sector, improves the industrial structure, and achieves carbon reduction.

Green innovation

Hypothesis 3 suggests that BBCP policy suppresses urban COE by promoting green technological advancements. This study utilized GreenPat as an indicator of urban green technological progress. Equations ( 5 ) and ( 6 ) were used to analyze the mechanism. If BBCP policy effectively suppresses COE by promoting GTI, the interaction term between BCPP and GreenPat should be notably negative. The findings of the mechanism analysis are unveiled in columns (3) and (4) of Table 7 . The BBCP policy has stimulated the progress of the GTI. The coefficient of the interaction term between BCPP and GreenPat was significantly negative. Thus, the results of the mechanism analysis confirmed the validity of Hypothesis 3. BBCP policy reduces COE by enhancing GTI in cities. The digital transformation policy can foster eco-friendly development by promoting green technological advancement.

Discussion, implications, and conclusion

Discussion and policy implications.

This study utilized city-level data in China from 2009 to 2019 and found that BBCP policies significantly reduced urban COE. This research revealed that DIC policies are beneficial for the development of low-carbon cities, consistent with prior studies on DE and low-carbon development (Zhang et al. 2022 ; Dong et al. 2023 ). BBCP policies restrained COE intensity, consistent with previous research on DE and COE intensity (Dong et al. 2022 ; Chang et al. 2023 ). Furthermore, the study indicated that BBCP policies can enhance low-carbon ecological efficiency, consistent with research on DE and the efficiency of green low-carbon economies (Luo et al. 2022 ; Wang et al. 2022c ). This study reveals that the synergistic effect of DIC policy and GTI may be beneficial for COE reduction. The study shows that the organic combination of DIC and GTI can reduce COE, providing more evidence for previous studies on GTI and COE (Shao et al. 2021 ). Finally, the study found that BBCP policies may not be able to overcome the resource curse at the environmental level, showing some differences from prior research (Sun et al. 2023 ).

This study has several policy implications. First, high-carbon emitting countries can implement digital transformation policies to promote economic development while achieving a certain level of COE reduction. DIC can facilitate the construction of low-carbon cities. Governments should provide tax incentives for DIC to attract investments in digital infrastructure. DIC is beneficial for enhancing the efficiency of green- and low-carbon economies. Stimulating DIC can provide economic and environmental benefits to cities.

Second, for cities facing financial constraints, encouraging DIC from a policy perspective can lead to significant carbon emission reductions. Even if the government cannot financially support DIC, tax incentives can be provided to companies or employees involved in DIC processes. Governments with abundant land resources can offer free or low-cost land for DIC management. The World Bank should allocate a portion of its green funds to support DIC, promote the ICT industry in developing countries, and displace high carbon-emitting industries. Governments should also leverage green finance policies to encourage NGOs or green funds to support green development and invest in DIC. These measures help financially constrained countries and cities promote DIC in reducing carbon emissions.

Finally, DIC enhances GTI. Given the long-term role of GTI in COE reduction (Shao et al. 2021 ), government-led DIC should actively consider support for GTI. Governments should encourage the application of digital technology in GTI. DIC policies can benefit GTI which requires digital infrastructure. In government-led DIC projects, access fees for GTI can be reduced appropriately. This not only stimulates GTI research and development but also provides a platform for GTI to reduce COE. Governments should strongly support the ICT industry’s participation in DIC and its benefits. Tax incentives should be provided to ICT industries willing to invest in DIC, thereby curbing the development of high-carbon-emitting industries.

The DID estimation results of this study show that the BBCP policy mitigates carbon emissions. Robustness checks confirm the negative impact of the BBCP policy on COE. Heterogeneity analysis reveals that the effect of BBCP policies on reducing COE is stronger in non-resource-dependent cities, cities with a higher proportion of green funds, and cities with lower fiscal expenditure. Further analysis also indicates that BBCP policies significantly enhance low-carbon ecological efficiency. The BBCP reduced the COE intensity. The findings of the mechanism analysis demonstrate that the BBCP policy lowers COE by improving the IS and fostering GTI.

This study makes a theoretical contribution by showing that digital transformation generates significant green effects, which can mitigate urban carbon emissions. By enhancing DIC and developing the ICT industry, the DIC policy offers cities a pathway to low-carbon development. DIC not only drives the growth of the DE and improves low-carbon ecological efficiency but also reduces carbon intensity. DIC policy is poised as a green development approach that can achieve economic and ecological benefits. Advancing the DE can promote IS upgrading and shift the IS from high-carbon-emitting industries to low-carbon-emission digital services, thereby curbing carbon emissions. DIC policy can incentivize cities to engage in more green innovation activities, ultimately leading to a reduction in COE. Owing to the resource curse, the DIC policy is better suited for cities that are not resource-dependent.

This study had some limitations. First, owing to the lack of data on industrial and household COE, the impact of BBCP policies on industrial and household COE was not analyzed in this study. Considering that the manufacturing sector may benefit more from DIC, its impact on industrial COE could be more significant. Future research could further analyze the impact of DIC on industrial and household COE. Additionally, this study does not consider the impact of DIC on the integration of DE and the real economy. As DE matures, it is likely to reduce the COE (Wang et al. 2023a ). The coupling of DE with the real economy may also affect the COE. Future research could further explore the role of DIC in the integration of DE and the real economy.

Data availability

The datasets used in Table 1 , baseline DID results (Table 2 ) and possible mechanism (Table 7 ) are provided as supplementary files. Other datasets generated during the current study are available from the corresponding author on reasonable request.

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Measurement properties of the Iranian version of the breast cancer perception scale (BCPS) according to the COSMIN checklist

• Sepideh Mashayekh-Amiri 1 ,
• Mohammad Asghari Jafarabadi 2 , 3 , 4 ,
• Mina Hosseinzadeh 5 ,
• Elham seyed Kanani 6 &
• Mojgan Mirghafourvand 7 , 8  

BMC Cancer volume  24 , Article number:  743 ( 2024 ) Cite this article

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Metrics details

Breast cancer is a prevalent cancer characterized by its aggressive nature and potential to cause mortality among women. The rising mortality rates and women’s inadequate perception of the disease’s severity in developing countries highlight the importance of screening using conventional methods and reliable scales. Since the validity and reliability of the breast cancer perception scale (BCPS) have not been established in the Iranian context. Therefore, this study aimed to determine the measurement properties of the BCPS in women residing in Tabriz, Iran.

The present study comprised a cross-sectional design, encompassing a sample of 372 Iranian women. The participants were selected through a multi-stage cluster random sampling technique conducted over a period spanning from November 2022 to February 2023. The measurement properties of the Iranian version of BCPS were assessed following the guidelines outlined in the COSMIN checklist. This involved conducting various steps, including the translation process, reliability testing (internal consistency, test-retest reliability, and measurement error), and methodological tests for validity (content validity, face validity, construct validity, and hypothesis testing). The study also investigated the factors of responsiveness and interpretability. The presence of floor and ceiling effects was assessed.

The internal consistency of the scale was assessed using Cronbach’s alpha, yielding a satisfactory value of 0.68. Additionally, McDonald’s omega (95% CI) was computed, resulting in a value of 0.70 (0.66 to 0.74). Furthermore, the test-retest reliability was evaluated, revealing a high intraclass correlation coefficient (ICC) of 0.97 (95% CI: 0.94 to 0.99). The CVI, CVR, and impact scores of the BCPS were determined to be 0.98, 0.95, and 3.70, respectively, indicating favorable levels of content and face validity. To assess construct validity, an examination of the Exploratory Factor Analysis (EFA) was conducted on a set of 24 items. This analysis revealed the presence of six distinct factors, which collectively accounted for 52% of the cumulative variance. The fit indices of the validity model (CFI = 0.91, NFI = 0.96, RFI = 0.94, TLI = 0.90, χ 2 /df = 2.03, RMSEA = 0.055 and SRMR = 0.055) were confirmed during the confirmatory factor analysis (CFA). The overall score of BCPS exhibited a ceiling effect of 0.3%. The floor effect observed in the overall score (BCPS) was found to be 0.5%. Concerning the validation of the hypothesis, Spearman’s correlation coefficient of 0.55 was obtained between the BCPS and the QLICP-BR V2.0. This correlation value signifies a statistically significant association. Furthermore, it is worth noting that the minimum important change (MIC) of 3.92 exhibited a higher value compared to the smallest detectable change (SDC) of 3.70, thus suggesting a satisfactory level of response.

Conclusions

The obtained findings suggest that the Iranian version of the BCPS demonstrates satisfactory psychometric properties for assessing the perception of breast cancer among Iranian women. Furthermore, it exhibits favorable responsiveness to clinical variations. Consequently, it can serve as a screening instrument for healthcare professionals to comprehend breast cancer and as a reliable tool in research endeavors.

Peer Review reports

Breast cancer is a significant global health issue [ 1 ], accounting for approximately 30% of cancer cases among women [ 2 ]. It is recognized as the second-leading cause of mortality in developed nations and the third-leading cause of mortality in less developed nations [ 3 ]. Based on the findings of the Global Cancer Incidence, Mortality and Prevalence (GLOBOCAN) report in 2020, it was determined that there were an estimated 2,261,419 million new cases (1 in 4 new cancer cases) (11.7%), and 684,996 (1 in 6 deaths) (6.9%) fatalities [ 4 ]. It is also predicted that these figures will reach 2,964,197 in 2040 (31% rise from 2018) [ 5 ], and 4.4 million in 2070 (110% rise from 2018) [ 6 ]. According to the report of the American Cancer Society in 2024, the number of new cases of female breast cancer in the United States was 310,720 and the number of deaths was 42,250 [ 7 ]. Approximately two-thirds of these fatalities are documented in regions with lower levels of development [ 4 ]. Alternatively, based on the projection provided by the World Health Organization (WHO), it is anticipated that by 2050, approximately 2.3 million women will receive a diagnosis of breast cancer [ 8 ]. Breast cancer is recognized as a highly costly form of cancer on a global scale, with an estimated annual expenditure of approximately 88 billion dollars. Failing to promptly diagnose and conduct screening examinations, coupled with the consequential impact on the entire family unit, imposes substantial financial burdens on society [ 9 ].

Breast cancer exhibits the highest prevalence and mortality rates among women in the Eastern Mediterranean region (EMR), encompassing Iran when compared to other forms of cancer. Breast cancer is widely recognized as the predominant form of cancer in Iran, ranking as the fifth-highest cause of mortality among women in the country [ 10 ]. The Age-standardized rate (ASR) incidence rate is approximately 28 per 100,000 individuals, exhibiting a recent upward trend [ 11 ]. Based on a systematic review, it has been documented that the incidence rate of breast cancer in Iran stands at 23.6% [ 12 ]. The reported prevalence of this cancer in the United States (US) is approximately 13%, indicating that one out of every eight individuals is affected [ 13 ].

Breast cancer is correlated with numerous risk factors, a significant proportion of which remain unidentified. The findings of a systematic review conducted in 2020 in Iran reveal various risk factors associated with breast cancer. These factors include family history, hormone replacement therapy (HRT), exposure to passive smoking, advanced maternal age during pregnancy, history of abortion, consumption of sweets, and possession of the Arg/Arg genotype. These factors have been found to potentially elevate the risk of developing breast cancer. Conversely, certain factors such as the late onset of menstruation, nulliparity, breastfeeding for a duration of 13 to 24 months, regular physical exercise, and consumption of vegetables have been observed to have a protective effect against the incidence of breast cancer [ 14 ].

It is noteworthy that the incidence of breast cancer among Iranian women occurs at an age approximately 10 years earlier compared to women in other developed nations. According to various studies, there has been a documented increase in the prevalence of breast cancer among women under the age of 40 in recent years [ 15 ]. The rise in its occurrence in developing nations is primarily attributed to alterations in lifestyle and reproductive behaviors [ 16 ]. The majority of female individuals afflicted with breast cancer receive a diagnosis during the later stages of the ailment, thereby correlating with an elevated mortality rate. Hence, it has been observed that early detection of breast cancer leads to a significant improvement in both survival rates and treatment outcomes, with a reported increase of 90% [ 17 ].

The United States Office of Disease Prevention and Health Promotion’s Healthy People 2020 initiative has as its goals the improvement of breast cancer diagnostic procedures for women, a decrease in the prevalence of cases of end-stage cancer, and a reduction in breast cancer mortality rates. Conversely, in the case of cancers that exhibit both genetic and environmental risk factors, it is imperative to adopt strategies that prioritize modifiable risk factors and early detection. Hence, the implementation of a preventive strategy aimed at early detection, which incorporates the evaluation of knowledge on breast cancer and its associated risks, assumes paramount significance [ 18 ].

The perception of breast cancer is a crucial subjective psychological phenomenon that is associated with the evaluation of potential threats. This evaluation is linked to an individual’s assessment of their susceptibility to the disease and the probability of gaining advantages from engaging in preventive measures [ 15 ]. Various studies have indicated that risk perception is a significant determinant of preventive health-related behaviors, such as screening. The motivation to undergo screening tests can be influenced by individuals’ perceptions of the risk associated with breast cancer [ 16 ]. According to the literature, screening tests play a crucial role in mitigating complications and mortality associated with breast cancer [ 17 ].

Various studies have documented divergent findings regarding the correlation between the perception of breast cancer risk and the utilization of screening tests, such as mammography [ 18 ]. Research findings indicate that the implementation of mammography screening during the age range of 40 to 49 years has been associated with a reduction in mortality rates of approximately 15 to 20% [ 19 ].

According to a study conducted on families to assess their perception of breast cancer risk, the rate of adherence to screening tests in Germany was found to be 83% [ 20 ]. Conversely, a research study conducted in Iran examined the adherence rate of women aged 35 to 69 years to mammography, as recommended by screening programs. The findings revealed that in urban areas, the adherence rate was 8.3%, while in rural areas, it was 3.16% [ 1 ]. Therefore, the perception level that women possess regarding breast cancer has the potential to influence their subsequent actions, such as seeking medical evaluation and undergoing screening procedures like breast self-examination (BSE), clinical breast examination (CBE), and mammography. Therefore, it is imperative to assess the perception of breast cancer in women using a multidimensional approach [ 20 ].

Numerous methodologies have been suggested for assessing the perception of breast cancer risk, which can be categorized into two distinct types: evaluation of the objective perception of risk (i.e., actual risk) and evaluation of the subjective perception of risk [ 21 ]. Currently, Gill’s model predominantly serves as a tool for conducting quantitative risk assessments. This approach aims to objectively evaluate the actual risk by considering the attributes of risk factors [ 22 ]. The second method entails the assessment of self-perceived risk, which can be anticipated by gauging individuals’ mental perceptions using a visual analog scale (VAS). Despite the presence of a multitude of tools within this domain, their practicality appears to be limited as they do not provide comprehensive coverage of all the factors that influence behaviors related to the diagnosis of breast cancer [ 23 ].

Taylan et al. (2021) developed the BCPS in Turkey, considering the health belief model for the first time. This scale encompasses various domains, including Perceived knowledge, Perceived treatment belief, Perceived need for health check, Perceived stigma, Perceived fear and Perceived risk. The utilization of this scale offers several benefits in assessing women’s perceptions regarding the factors influencing breast cancer diagnostic behavior comprehensively. Furthermore, it is worth noting that the extent of women’s perceived knowledge of breast cancer has not been quantitatively evaluated thus far. Consequently, this tool serves as a distinctive scale specifically designed to measure women’s knowledge regarding cancer. Additionally, it quantifies the dimensions of the breast [ 23 ].

The Health Belief Model (HBM) was initially formulated by Becker et al., in 1974 to comprehend health-related protective behaviors [ 24 ]. The evaluation of perceived risk, employing the HBM, has been validated in various studies examining screening behaviors, such as those related to breast cancer diagnostics [ 25 , 26 , 27 ]. The model encompasses various dimensions, namely perceived sensitivity, perceived severity, perceived benefits, perceived barriers, self-efficacy, and guidance for action. Based on the presented model, individuals’ healthcare behaviors can be subject to influence from factors such as perception, beliefs, values, and attitudes. By identifying and modifying an individual’s perceptions, beliefs, and attitudes, the effectiveness of healthcare education or treatment can be enhanced [ 26 ].

However, it is crucial to assess the methodological rigor of studies that evaluate the measurement properties of instruments used to measure health-related patient-reported outcomes (HR-PROs) [ 28 ]. The Consensus-Based Standards for the Selection of Health Status Measurement Instruments (COSMIN) checklist was developed by Mokkink et al. in 2010 through a consensus-based approach utilizing the Delphi method [ 28 ]. The COSMIN list is widely regarded as a highly comprehensive set of criteria for selecting an appropriate tool. It serves as a valuable guide for researchers, offering a range of logical indicators that aid in the process of tool selection [ 29 ].

Given the rising incidence of breast cancer, the significance of early screening, and the potential influence of risk perception on women’s adoption of preventive behaviors, such as breast screening methods, it is pertinent to assess the level of knowledge regarding breast cancer when devising interventions aimed at modifying health behaviors. It is worth noting that the validity and reliability of the aforementioned assessment tool have not been established in Iran. The present study was undertaken to conduct a measurement propertice of the BCPS in women residing in Tabriz city-Iran, by using the COSMIN checklist.

The present study was conducted with the aim of determining the measurement properties of the breast cancer risk perception scale (BCPS) in according to COSMIN checklist in women in Tabriz, Iran.

Validity procedure

The measurement properties of the Iranian version of BCPS were assessed following the guidelines outlined in the COSMIN checklist [ 29 ]. This involved conducting various steps, including the translation process, reliability testing (internal consistency, test-retest reliability, and measurement error), and methodological tests for validity (content validity, face validity, construct validity, and hypothesis testing). The study also investigated the factors of responsiveness and interpretability. The presence of floor and ceiling effects was assessed.

Translation process

Initially, permission to use the BCPS was obtained by sending an email from the original designer of the instrument (Taylan et al.) [ 23 ]. Efforts were made to maintain the integrity of the original intent during the translation process. Following the recommendations made by the WHO, EORTC Quality of Life Group Translation Procedure Guidelines and expert panel review, this was performed [ 30 ]. The translation process involves the utilization of two distinct methods. The two methods utilized in this study are the Forward-Backward method (FB) and the Dual Panel method (DP), which were implemented throughout four distinct stages. The process consisted of four stages: forward translation, backward translation, pre-testing and cognitive interviewing, and the final version.

During the initial phase of translation, the original English version of the instrument was administered to two individuals who were native Persian speakers, proficient in English, and possessed expertise in the development of the instrument as well as knowledge in the field of breast cancer. Translators were subsequently instructed to translate the tool in a fully autonomous and individual manner, with a focus on conceptual rather than literal translations. Additionally, they were encouraged to use language that would be comprehensible to the majority of the target audience. Ultimately, two translators looked into the discrepancies between the two translated versions, which led to a reconciled translation. Subsequently, the identified issues were addressed, leading to the presentation of a unified version [ 31 ]. Subsequently, the backward translation method was employed to guarantee a comprehensive correspondence between the Persian translation and the original version. The translated questionnaire from the preceding stage was administered to two individuals who are native English speakers. These individuals were not involved in the forward translation process and had no prior exposure to the original version of the questionnaire. They were instructed to retranslate the questionnaire back into English. The concluding report at the culmination of this phase encompassed the following components: two forward translations from the English language to Farsi, a reconciled translation, two backward translations from Farsi to English, and the incorporation of any supplementary remarks regarding the translations provided by the panel of experts. Ultimately, before implementing the tool in the intended population, it is imperative to conduct a pilot study. To achieve the intended objective, a questionnaire was administered to a sample of ten qualified female participants. Based on the feedback received from these participants regarding the ease of completing the instrument, grammar, comprehensibility, and writing style, modifications were made to the Persian version, and the revised version was finalized and presented [ 31 ].

Validation study

This cross-sectional study aimed to assess the measurement properties of the Iranian version of the BCPS among a sample of 372 Iranian women who sought healthcare services at Tabriz health centers affiliated with Tabriz University of Medical Sciences in two separate secondary samples (172 participants via exploratory factor analysis and 200 participants via confirmatory factor analysis). The study was conducted following the approval of the Ethics Committee of Tabriz University of Medical Sciences (ref: IR.TBZMED.REC.1401.390) from November 2022 to February 2023. It is important to acknowledge that informed consent was obtained from all participants. The study was conducted in adherence to the applicable regulations of the Ethics Committee of the University of Medical Sciences and the Declaration of Helsinki.

Among the 410 women, it was found that 16 participants did not satisfy the predetermined eligibility criteria, leading to their exclusion from the research investigation. Out of the remaining 394 participants who met the eligibility criteria, a total of 22 participants expressed their unwillingness to participate in the study. Ultimately, a total of 372 participants were incorporated into the research investigation. The response rate for the study was 94%, (372/394). In the sampling procedure, the cluster method was employed to randomly select a quarter of the 92 health centers in Tabriz City. The selection process was facilitated through the utilization of the website ( www.random.org ). In addition to their contact information, which was obtained from the SIB system (integrated health system), women were selected at random from the compiled list. To clarify, the selection of women from each center was determined based on the proportional sampling method, and the process of randomly selecting women was carried out utilizing the aforementioned website. Following a telephone conversation with the participants, wherein the researcher offered a concise overview of the research, the researcher proceeded to invite the women to attend the designated health center within the specified timeframe. The purpose of this visit was to provide additional explanations and administer the questionnaires. Upon conducting a visit and assessing the fundamental aspects such as basic information and inclusion and exclusion criteria, the individual proceeded to furnish the concerned parties with extensive details related to the research, its advantages, outcomes, and the confidentiality of the data. It is important to acknowledge that the random selection of participants was conducted before the assessment of their eligibility criteria. Following their visit to the health center, participants underwent a comprehensive evaluation to gather baseline data and determine their eligibility. Full information regarding the research objectives and methodology was exclusively provided to individuals who satisfied the predetermined eligibility criteria, and only they were extended an invitation to participatein the study. After agreeing to participate in the study, the participants proceeded to complete the informed consent form, the questionnaire of socio-demographic characteristics, and the BCPS.

The study’s inclusion criteria encompassed women who were at least 20 years old, exhibited no indications of abnormal breast lesions during clinical examination, and possessed the necessary literacy skills to complete the questionnaire. The study excluded individuals who met the following criteria: a confirmed diagnosis of breast cancer as documented in medical records, a history of cosmetic breast surgery, and impairment in communication skills related to hearing and speaking, and an inability to physically, cognitively, or mentally respond to questions.

Socio-demographic questionnaire

The questionnaire included questions regarding socio-demographic factors such as age, spouse age, marital status, educational level, job, income, breast cancer history, history of hormone therapy, family history of breast cancer and menopause status.

Breast cancer perception scale

Taylan et al. (2021) in Turkey [ 23 ] developed the BCPS. The present tool is grounded in the theoretical framework of the HBM and comprises a set of 24 items designed to assess women’s perceptions of breast cancer. The construct comprises six sub-dimensions, including Perceived knowledge, Perceived treatment belief, Perceived need for health check, Perceived stigma, Perceived fear and Perceived risk, which are assessed using a five-point Likert scale. The responses span a spectrum from strongly disagree (1) to strongly agree (5). The scale’s validity and reliability have been empirically established within the specific demographic of Turkish women in 2021. The lower bound of the scoring range for this questionnaire is 24, while the upper bound is set at 120. A positive correlation exists between higher scores and a greater level of women’s perception of breast cancer [ 23 ].

Sample size determination

It is imperative to ascertain the appropriate sample size to conduct the factor analysis procedure. According to a rule of thumb, the classification of sample size for EFA is as follows: a sample size of 50 is considered very poor, 100 is poor, 200 is fair, 300 is good, 500 is very good, and 1000 is excellent [ 32 ]. To ensure the reliability and validity of the results, it is necessary to have an appropriate sample size when conducting factor analysis. This study incorporated the guidelines proposed by Nunnally [ 33 ], which recommend a sample size of 5 to 10 samples for each instrument question to facilitate the generalizability of the findings to the broader community. Under these guidelines, it was deemed appropriate to utilize a sample size of 10 samples for each case, taking into account that the tool consisted of 24 items. Therefore, initially, a minimum of 240 samples were considered necessary. Nevertheless, it is imperative to take into account the effectof the cluster sampling technique employed in the research. The cluster sampling method introduces a factor of intra-cluster correlation that necessitates its inclusion in the calculation of the sample size [ 33 ]. To address this issue, a design effect of 1.5 was employed to modify the sample size. Due to a 10% attrition rate, the sample size has consequently expanded to encompass a total of 372 participants.

Statistical analysis

The statistical analysis was conducted using the SPSS software package (version 16, IBM Corp., Armonk, NY, USA), STATA14 (Statcorp, College Station, Texas, USA), and R software 4.2 (Psych package). To examine socio-demographic data, descriptive statistics were employed, including frequency (percentage) for qualitative variables, minimum and maximum values, and mean ± standard deviation (SD) for quantitative variables. The evaluation encompassed methodological testing, which involved the assessment of reliability, validity, and responsiveness. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) techniques were employed to assess construct validity on a larger scale. The direct oblimin method was employed in the EFA. Bartlett’s test for sphericity and KMO’s test for assessing the adequacy of scale content and sample size were conducted. The CFA methodology was employed to assess the factor structure and factor loadings of the scale. In conclusion, an assessment was conducted to evaluate the reliability of the study, specifically focusing on internal consistency, test-retest reliability, and measurement error. Finally, the presence of ceiling and floor effects was assessed.

Methodological testing according to the COSMIN checklist

• Reliability

Reliability refers to the extent to which a measurement is devoid of any errors that may arise during the measurement process. The evaluation of reliability primarily involves the assessment of three key characteristics: internal consistency, test-retest reliability, and measurement error [ 29 ].

Internal consistency

Internal consistency refers to the extent of interconnectedness among items. It serves as an estimation of the correlation level between the variables that constitute the intended structure or instrument [ 29 ]. The internal consistency of the instrument as a whole and its six subscales was assessed using both Cronbach’s alpha coefficient and McDonald’s omega coefficient. A minimum threshold of 0.7 was deemed necessary for both Cronbach’s alpha and MacDonald’s Omega coefficients to establish satisfactory internal consistency [ 34 ].

Test–retest reliability

Test-retest reliability refers to the extent to which the outcomes of a patient with identical health conditions remain consistent over a while [ 29 ]. Following the guidelines outlined in the COSMIN manual, a test-retest procedure was conducted with a minimum interval of two weeks. This time frame was chosen to prevent participants from recalling their previous responses and to account for any potential changes in their health status [ 29 ]. To achieve the intended objective, a survey was administered to a cohort of 30 female participants on two separate occasions, with a 14-day gap between each administration. The resulting scores were subsequently utilized to assess the reliability of the survey instrument through the application of the intraclass correlation coeficient (ICC). A reliability coefficient greater than 0.7 was deemed advantageous [ 34 ].

Measurement error

Measurement error is considered one of the key indicators of measurement and test reliability. In essence, it refers to the presence of both systematic and random errors in the patient’s score, which cannot be attributed to genuine variations in the construct under consideration. The calculation of the standard error of measurement (SEM) involves the use of the formula (SEM = SD√1-ICC), where SD represents the standard deviation [ 34 ]. The concept of the smallest detectable change (SDC) pertains to the minimum magnitude of an individual score change that can be accurately interpreted as a genuine change. The calculation of the SDC is determined by employing the formula (SDC = SEM*1.96*√2). A reduced level of the SDC corresponds to an increased level of measurement sensitivity [ 34 ].

Validity refers to the extent to which a given instrument accurately measures the specific characteristic it is designed to assess [ 29 ].

Face validity

Face validity is a concept that pertains to the extent to which the items within an instrument, specifically the HR-PRO, accurately represent the underlying construct that is intended to be measured [ 29 ]. The researchers conducted an assessment of face validity using both qualitative and quantitative methods. To conduct a qualitative assessment of face validity, a sample of 10 women from health centers in Tabriz City was selected using a convenience sampling method. This sample then examined the initial questionnaire. The participants assessed the quality, level of difficulty, lack of relevance, and degree of ambiguity of the items. To evaluate the face validity, item impact scores were quantitatively computed. During this phase, the aforementioned participants assessed each item on a 5-point Likert scale, ranging from “completely important” to “not at all important,” with scores ranging from 5 to 1 (representing “completely important,” “important,” “moderately important,” “slightly important,” and “not important,” respectively). The impact score is calculated by multiplying the Frequency (expressed as a percentage) by the Importance (Impact Score = Frequency (%) × Importance). Items with an impact score exceeding 1.5 were deemed appropriate and were subsequently retained for further stages of analysis [ 35 ].

Content validity

The degree to which the content of an HR-PRO instrument effectively represents the construct that is intended to be assessed [ 29 ]. Both qualitative and quantitative methods were used to examine the validity of the content of the questionnaire. To assess the credibility of the qualitative content, a group of ten experts, including three experts in reproductive health, two specialists in midwifery, three specialists in medical-surgical nursing, and two specialists in community health nursing, were invited to provide their insights and opinions on topics considering grammar, vocabulary choice, item arrangement, and scoring.

The inclusion criteria of the experts to determine content validity include voluntary participation, faculty members with the rank of assistant professor and above, PhDs of midwifery/nursing and individuals with clinical experience in breast cancer. The process of assessing quantitative content validity involves the calculation of two measures: the content validity ratio (CVR) and the content validity index (CVI) [ 36 ]. To fulfill the objective, a questionnaire comprising questions organized into two overarching categories was distributed to each expert. In the initial phase, the participants assessed the items using a 3-point Likert scale (necessary, useful but not necessary, not necessary) to ascertain the CVR, which was computed using the following mathematical expression:

CVR= (Ne-N/2)/ (N/2).

Where, “Ne” represents the count of experts who have chosen the “necessary” option, and N denotes the total number of experts. Regarding Lawshe table, a CVR > 0.62 for a sample size of 10 individuals, confirms the essentiality of the items under investigation [ 37 ].

Subsequently, the CVI review underwent evaluation by an identical group of 10 experts. Concerning this matter, questions have been raised regarding the three criteria of relevance, clarity, and simplicity for each item. These criteria have been assessed using a four-point Likert scale, which includes options such as irrelevant, somewhat relevant, relevant, and completely relevant. The assessment is based on the content validity index [ 38 ] developed by Waltz and Basel. The level of relevance, clarity, and simplicity was assessed by experts based on their subjective evaluation, and then the CVI was computed using the following formula:

CVI = number of experts giving a rating of 3 and 4 / total number of experts.

CVIs higher than 0.79, between 0.70 and 0.79, and less than 0.70 were considered acceptable, in need of correction, and unacceptable, respectively [ 39 ].

Construct validity

The concept of construct validity pertains to the extent to which the scores obtained from an HR-PRO instrument align with the anticipated hypotheses. This alignment can be observed in terms of internal relationships, relationships with scores obtained from other instruments, or differences between relevant groups. This assessment is contingent upon the assumption that the HR-PRO instrument possesses validity. The concept of validity pertains to the extent to which a given measure accurately assesses the construct it is intended to measure. The three aspects encompassed in this study are as follows: structural validity, which pertains to the internal relationships within the construct; hypothesis testing; and cross-cultural validity, which focuses on the relationships with scores on other instruments or differences between relevant groups [ 29 ].

Structural validity

The suitability of the data for EFA was assessed by employing the Kaiser-Meyer Olkin (KMO) criterion and Bartlett’s test of sphericity. The KMO test is a statistical measure that quantifies the proportion of variance in the questions that can be attributed to the primary factors. Typically, values falling within the range of 0.8–1 are indicative of adequate data sampling to conduct factor analysis. However, when the value of the statistic falls below 0.7, it indicates that the sample size is insufficient, necessitating the implementation of corrective actions [ 40 ].

Bartlett’s test of sphericity is a frequently employed statistical test to assess the appropriateness of data for factor analysis. The significance of this test serves as an indicator of the suitability of the data for factor analysis [ 40 ]. The process of extracting factors from the 24 items of the questionnaire was conducted using the principal component analysis method, employing varimax rotation (direct oblimin). The determination of the number of factors was based on the criterion of an Eigenvalue greater than 1 and the examination of the Scree plot. In this analysis, a minimum factor loading threshold of 0.3 was utilized for the extraction of factors. In contrast, CFA employs the maximum likelihood method to estimate the model’s fit indices, and a range of indices are utilized to assess the appropriateness of the model. This study assessed the adequacy of the model by employing the indicators outlined below [ 41 ]:

Root mean score error of approximation (RMSEA < 0.08), standardized root mean square residual (SRMR < 0.10), normed Chi 2 (x 2 / df) < 5, comparative fit indices including comparative fit index (CFI > 0.90), Bentler-Bonett Normed Fit Index (NFI) > 0.90, Relative fit index (RFI) > 0.90 and Tucker-Lewis Index (TLI) > 0.90.

Hypothesis testing

The process of hypothesis testing is characterized by its continuous and iterative nature. Hypotheses serve as a means to express the anticipated direction and magnitude of correlations or differences between the construct under investigation and other constructs. As the number of hypotheses tested regarding the alignment between the data and pre-existing hypotheses increases, a greater amount of evidence supporting construct validity is accumulated [ 29 ]. To assess construct validity, an analysis of the hypotheses that were previously formulated was conducted. In this study, it was postulated that the BCPS would exhibit a strong correlation with other subjective scales, such as quality-of-life instruments for cancer patients (QLICP-BR V2.0). Hence, confirmation of the desired hypothesis can be achieved when the Pearson correlation coefficient exceeds 0.5. Furthermore, the study computed the floor and ceiling effect (F/C) as well as the proportion of women who achieved the minimum and maximum scores. F/C effects refer to the percentage of individuals who achieve the highest (ceiling) or lowest (floor) possible scores within a specific domain. These effects serve as indicators of a questionnaire’s sensitivity and coverage at the extreme ends of the scale. In the context of this study, a problematic scenario is defined as a situation where 15% or more of the respondents fall into either the ceiling or floor category [ 42 ].

Responsiveness

Measurement instruments should possess a high degree of sensitivity to detect and accurately capture changes, while also demonstrating a responsive nature to promptly reflect these changes. According to the COSMIN checklist, responsiveness refers to the capacity of an HR-PRO instrument to accurately identify alterations in the construct being assessed over a while [ 29 ]. Terwee et al. [ 34 ] argue that responsiveness can be assessed by examining the relationship between the smallest detectable change (SDC) and the minimally important change (MIC). If the value of SDC is less than MIC, then the responsiveness is confirmed.

Interpretability

Interpretability refers to the extent of qualitative significance, specifically the minimally important changes (MIC) within the instrument. The extent to which quantitative instrument scores or changes in scores can be attributed to qualitative meaning, such as clinical or commonly understood meanings, has been discussed [ 29 ]. The estimation of the minimum important change (MIC) was conducted by dividing the standard deviation (SD) by two, as outlined in the study conducted by Norman et al. [ 43 ].

Descriptive characteristics of participants

This study involved the participation of 372 women. The participants were randomly split into two groups, one group of 172 participants for EFA and another group of 200 participants for CFA. The average age was 52.7 and 52.3 years with a standard deviation of 9.5 and 8.5 years in EFA and CFA group, respectively. A significant majority of the individuals surveyed were married (78.5%, 80.5% in EFA and CFA group, respectively), and 73.8%, 61.0% of them identified themselves as housewives in EFA and CFA group, respectively. Table  1 provides a summary of the additional socio-demographic characteristics of the two groups of participants.

In the present study, the mean (SD) of the entire BCPS scale was 61.66 (8.44), with a range of obtainable scores from 24 to 120. The mean (SD) for the six extracted factors, namely Perceived fear, Perceived knowledge, Perceived treatment belief, Perceived risk, Perceived need for a health check, and Perceived stigma, were respectively 8.28 (4.02), 11.63 (3.79), 10.5 (2.41), 9.37 (2.12), 10.19 (3.22), and 12.15 (2.65).

The values of Cronbach’s alpha and McDonald’s omega (95% CI) were found to be 0.68 and 0.70 (0.66 to 0.74), respectively. These results suggest that the questionnaire exhibits satisfactory internal consistency. Also, the ICC (95% CI) gave a value of 0.97 (0.94 to 0.99). Standard error of measurment is a statistical metric utilized to assess the accuracy and consistency of a given measurement. The SEM value in this study was determined to be 1.36. This implies that upon conducting multiple iterations of the measurement, it is anticipated that the recorded values will fall within a range of ± 1.36 units with the actual score. Moreover, the SDC denotes the smallest detectable change that can be consistently detected by the measuring apparatus. Within the given framework, the value of SDC was ascertained to be 3.73 units. This implies that any deviation in the measured quantity that is less than 3.73 units may not be discernible due to measurement errors and can be regarded as insignificant (Table  2 ).

The tool’s content and face validity were assessed using the CVI (CVI range: 0.87–1.00), CVR (CVR range: 0.75–1.00), and impact scores (3.06–4.00), which yielded values of 0.98, 0.95, and 3.70, respectively.

The construct validity investigation involved conducting an EFA on a set of 24 items. The resulting Kaiser-Meyer-Olkin (KMO) value of 0.71 was obtained at a statistically significant level of less than 0.001, indicating that the sample size in the current study was sufficient. Furthermore, the statistical analysis revealed that Bartlett’s test of sphericity yielded a significant result ( p  ≤ 0.001), indicating that factor analysis was appropriately conducted based on the correlation matrix in the sample under investigation.

The scree plot displayed the results of EFA, revealing six factors with eigenvalues > 1. These factors collectively accounted for 52% of the variance (Fig.  1 ). Table  3 presents the extracted components alongside the corresponding items associated with each factor. The initial factor examined in this study was Perceived fear, comprising a set of four questions that contributed to 13.60% of the overall variance. The second factor, referred to as Perceived knowledge, comprises a set of four questions that collectively account for 11.00% of the overall variance. The third and fourth factors were Perceived treatment belief and Perceived risk, respectively. These factors consisted of four and two questions, respectively, and accounted for 8.9% and 6.4% of the variance. The fifth factor, referred to as Perceived need for a health check, consists of four questions with a variance of 6.3%. Additionally, the sixth factor, known as “Perceived stigma,” comprises four questions that account for 5.8% of the total variance (Fig.  2 ). After preliminary psychometric testing, 24 items were factor-analysed providing a 22-item, six-factor scale. It is important to highlight that, in the original instrument, questions 9 and 23, respectively addressed the notions that “Breast cancer treatment does not change the outcome” and “The risk for breast cancer is higher in those with a family history of breast cancer,” were excluded from the EFA in the present study due to their factor loadings being less than 0.3. Consequently, there was a reduction in the number of instrument questions from 24 items in the original instrument to 22 items.

Factor load scree plot of the items for determining the number of extracted factors of the Iranian version of BCPS

Factor structure model of the BCPS based on CFA. All factor-item relationships were significant ( P  < 0.05). Fc1: Perceived fear, Fc2: Perceived knowledge, Fc3: Perceived treatment belief, Fc4: Perceived risk, Fc5: Perceived need for health check, Fc6: Perceived stigma

CFA was employed to examine the six factors that were derived from EFA. The findings indicate that the model has attained a level of fit that is considered optimal, thereby providing support for confirming the factor structure. The indicator \(\raisebox{1ex}{${x}^{2}$}\!\left/ \!\raisebox{-1ex}{$df$}\right.\) is found to be 2.029 (χ2 = 393.781, df = 194, P-value < 0.001). Additionally, the fit indexes TLI, CFI, NFI, and RFI all exceed the threshold of 0.9. Furthermore, the RMSEA and SRMR index values are both equal to 0.055, indicating a valid model.

Hypothesis testing, responsiveness and interpretability

The hypothesis confirmation involved the computation of Spearman’s correlation coefficient between the BCPS and QLICP-BR V2.0, and the resulting coefficient of 0.55 indicated a statistically significant correlation. To assess the feasibility of the tool, the ceiling effect in the overall score of BCPS was found to be 0.3%. In the sub-domains, the ceiling effects for Perceived fear, Perceived knowledge, Perceived treatment belief, Perceived risk, Perceived need for a health check, and Perceived stigma were determined to be 2.2%, 0.4%, 0.3%, 1.1%, 0.3%, and 0.8%, respectively. The floor effect in the overall score of BCPS was observed to be 0.5%, while in the specific subdomains, it was found to be 26.9%, 3.5%, 3.2%, 1.3%, 4.6%, and 0.8%, respectively. It is noteworthy to mention that the MIC refers to a specific threshold value that delineates the smallest alteration in the measured parameter that holds clinical or practical significance. In this particular instance, the MIC was determined to be 3.92 units. Specifically, the study reveals that the MIC value surpasses the SDC value by 3.73 units. This observation indicates that the Iranian version of the measurement tool is sufficiently responsive. Put simply, the measurement tool can accurately identify and assess changes that hold significance or relevance within the given measurement framework. SEM of this study’s findings generally implies that the measuring device used exhibits a satisfactory level of precision. The comparison between the SDC and the MIC values further demonstrates the instrument’s capacity to consistently identify significant variations in the measured variable (Table  2 ).

The aim of this study was to assess the measurment properties of the Breast Cancer Perception Scale (BCPS) in Iranian women, according to the COSMIN checklist for the first time. The findings of the research substantiate the validity, reliability, responsiveness, and interpretability of the BCPS, which is grounded in the health belief model (HBM) when applied to Iranian women.

The HBM has been widely employed in the study of breast cancer diagnostic behaviors for an extended period [ 25 , 26 , 27 ]. The BCPS is a novel screening tool for breast cancer that has been developed utilizing the HBM [ 23 ]. Despite the presence of various tools in this domain, such as “belief in mammography” and “breast self-examination,” “perceived sensitivity toward breast cancer,” “perceived benefits and obstacles of mammography usage,” “fear of breast cancer (FBC)”, and “fatalism regarding cancer “, these instruments appear to lack practicality as they assess factors individually and fail to encompass all relevant domains. The BCPS demonstrates utility in its comprehensive coverage of various domains, particularly in assessing previously unmeasured aspects such as perceived knowledge, and mental measurements, including the perceived need for a health check, perceived stigma, perceived fear, and perceived risk [ 23 ].

Breast cancer perception is one of the most important indicators for preventing breast cancer and adopting protective behaviors against breast cancer. Proper perception of breast cancer serves as a motivator for women to adhere to breast cancer prevention methods. Despite the existence of various preventive and diagnostic methods for breast cancer, none of these methods will be effective until there is a proper perception of breast cancer. Therefore, the BCPS scale, considering important dimensions such as Perceived knowledge, Perceived treatment belief, Perceived need for health check, Perceived stigma, Perceived fear and Perceived risk, can play an important role in creating preventive behaviors against breast cancer [ 23 ].

In the current investigation, EFA was conducted on a set of 24 items of the instrument. The analysis yielded six factors, namely Perceived fear, Perceived knowledge, Perceived treatment belief, Perceived risk, Perceived need for a health check, and Perceived stigma. These factors aligned with the original instrument and collectively accounted for approximately 52% of the variance, while in the original instrument, they accounted for 74.36% of the variance [ 23 ]. To assess the validity of the instrument, the KMO measure was computed, yielding a value of 0.71. Additionally, the adequacy of the model was verified through Bartlett’s test, which yielded a significance level of 0.77 in the original study [ 23 ]. Furthermore, the reliability of the instrument was obtained ranging from 0.64 to 0.94 by using Cronbach’s alpha, and these values align with the original study’s reported range of 0.81 to 0.95 [ 23 ].

In the present study, the initial factor extracted during the exploratory EFA was identified as perceived fear. The influence of perceived fear on women’s adoption of protective behaviors against breast cancer can be observed. The findings of various studies indicate that a significant majority of women encounter fear regarding the potential diagnosis of breast cancer and the subsequent possibility of undergoing a mastectomy, either unilaterally or bilaterally, at some point in their lives [ 44 , 45 ]. In a similar vein, a separate study indicated that women who exhibited a heightened FBC were found to undergo mammograms less frequently within a one-year timeframe in comparison to their counterparts [ 46 ]. In the study conducted by Aguirre et al., it was observed that young Spanish women exhibited a notable level of fear towards breast cancer, despite not expressing a general sense of concern regarding the disease. According to the study [ 47 ], it was found that 25.3% of the participants reported above-average FBC, while 59.7% reported high FBC. This finding implies that breast cancer may elicit a particularly strong sense of fear, even among young women who do not have significant health issues and have a low objective risk. This observation aligns with the findings of previous research [ 48 , 49 ]. Furthermore, when comparing the findings of this study to previous research conducted within the past two decades, it becomes evident that the extent of fear induced by breast cancer has remained relatively stable despite favorable epidemiological advancements such as reduced mortality rates and enhanced treatment options [ 50 ].

The second factor that was extracted in this study related to perceived knowledge. Perceived knowledge encompasses biases, such as unrealistic optimism and implicit confidence [ 51 ]. The concept of perceived knowledge pertains to an individual’s level of knowledge and is not directly associated with one’s knowledge, specifically regarding breast cancer. The WHO has advocated for the adoption of breast cancer knowledge and awareness as a viable medical strategy for the management of breast cancer. This approach is deemed essential and should be universally implemented, irrespective of financial constraints. In this regard, Izanloo et al. demonstrated that a significant majority of the participants, totaling over 84%, exhibited a lack of knowledge regarding breast cancer and screening tests among 14- to 84-year-old Iranian women. The primary factors cited by women as barriers to undergoing screening tests were the absence of discernible symptoms or issues and their perception of the test’s necessity. A significant difference was observed in the level of women’s knowledge of breast cancer and screening tests concerning factors such as employment status, education level, and family history of breast cancer. However, no significant difference was found in the level of knowledge among women based on their marital status or income level [ 52 ]. Moreover, a study conducted by Mehejabin et al. sought to examine the level of knowledge regarding various aspects of breast cancer among women in Bangladesh. The findings revealed that a majority of the participants, exceeding 50%, possessed a limited understanding of the risk factors associated with breast cancer, indicating a significant lack of knowledge [ 53 ].

Perceived treatment beliefs constituted an additional factor. Perceived belief in treatment can be influenced by various factors, including women’s spiritual and religious beliefs, familial history of breast cancer treatment, and prior experiences with breast cancer treatment [ 45 ]. Concerning this matter, individuals’ perceptions of their treatment beliefs have the potential to influence their engagement in protective behaviors. The findings of the study conducted by Mehejabin et al. indicate that a considerable proportion of women hold the belief that breast cancer can be detected at a young age. Furthermore, the participants held the belief that early diagnosis of the disease could lead to its potential cure [ 53 ]. The aforementioned findings align with the results obtained from a research study carried out at Dhaka Medical College Hospital in Bangladesh, wherein 51.43% of female participants indicated that early detection of breast cancer leads to a potential cure [ 54 ]. Suwankhong and Liamputtong have posited that religious belief significantly influences individuals’ decision-making processes concerning treatment options and risk factors associated with breast cancer [ 55 ]. Yew et al., found a significant difference in the perceptions of breast cancer risk with religious affiliations, specifically between the Muslim and Buddhist cohorts. The impact of Islam and Buddhism on individuals’ lifestyles and health-related behaviors has been significant. Muslim women exhibited a profound conviction in the authority of God (Allah), whereas Buddhist women commonly invoked their karma [ 56 ].

Perceived risk, identified as an additional extracted factor, holds a significant influence over breast cancer protective behavior [ 45 ]. Observing the challenges and distress experienced by our beloved individuals throughout breast cancer treatment amplifies both the perceived fear and the perceived risk of breast cancer [ 57 ]. The primary determinant of health behaviors for breast cancer prevention, diagnosis, and control is the perceived risk. Conversely, establishing concordance between the perceived risk and the objective risk of developing breast cancer results in a more accurate and actual perception of the risk. Consequently, it can serve as motivation for fostering suitable health behaviors [ 58 ]. Hajian et al. [ 59 ], examined the perceived risk of breast cancer among 800 Iranian women about the actual risk. The findings of the study revealed that both women with a low and high risk of breast cancer exhibited a significantly higher perceived risk of the disease compared to their actual risk. This finding suggests a significant inclination towards pessimism in the assessment of breast cancer risk, consistent with previous research conducted in this domain [ 60 , 61 ].

The results of this study also identified the perceived need for a health check as an additional extracted factor. One of the main obstacles to breast cancer screening among women is a diminished perception of the necessity for health screening. Women typically do not perceive the necessity of seeking medical attention unless they possess knowledge regarding the specific symptoms associated with a particular ailment [ 62 ]. Research findings indicate that women residing in developing nations often tend to decline the notion of early diagnosis and screening for breast cancer, primarily influenced by their cultural and personal beliefs. The aforementioned factor has a detrimental impact on the implementation of preventive measures aimed at mitigating the risk of breast cancer [ 63 ]. Therefore, variations in the perceived need for health screening can potentially impact individuals’ engagement in breast cancer protective behaviors. The scoping review study conducted by Omidi et al. examined the current status of breast cancer screening strategies and indicators among Iranian women. The study findings revealed that the prevalence rates of screening methods, including BSE, CBE, and mammography, among Iranian women were reported as 0-79.4%, 4.1–41.1%, and 1.3–45%, respectively [ 64 ].

Based on the HBM theory, Darvishpour et al. [ 25 ] posited that the decision of women to engage in breast cancer screening is influenced by factors such as self-efficacy and perceived benefits. Conversely, the presence of perceived barriers diminishes the likelihood of self-examination. According to Khazir et al., individuals who perceive fewer barriers are more likely to engage in breast cancer screening programs [ 65 ]. Abdel-Aziz et al. conducted a study utilizing EFA to examine the perceived barriers faced by women with breast cancer. Their findings indicate that personal fears, specifically fear of doctors/examiners, fear of screening results, and fear of the hospital environment, are the primary obstacles preventing women from utilizing free screening. These fears were identified as the main barriers based on their eigenvalue values, which exceeded 3.335, representing 30.4% of the barriers identified [ 66 ].

The final factor that was extracted pertains to the concept of perceived stigma. The symbolic significance of breasts for women stems from their association with childbirth, breastfeeding, childrearing, and sexual desires. Consequently, the symbolic significance associated with this phenomenon may impede women from accessing necessary healthcare services, interventions, or diagnostic procedures [ 67 ]. Furthermore, the absence of discussion regarding breast cancer and screening behaviors may be associated with societal stigmatization and cultural taboos surrounding the topic of breasts [ 68 ]. It is well known that stigma plays a significant role in the psychological distress that breast cancer-diagnosed women experience. The occurrence of rejection, blame, or devaluation is what defines the social phenomenon known as stigma. This arises from the personal experience, perception, or rational expectation of an unfavorable social evaluation directed toward an individual or a collective entity [ 69 ]. It was found that around 76.7% and 8.7% of breast cancer survivors reported moderate and high levels of stigma, respectively [ 70 ]. Based on prior research, it has been established that the perceived stigma among individuals diagnosed with breast cancer has significant adverse consequences for their overall well-being and health-related outcomes. These repercussions encompass various aspects such as sexual dysfunction, depressive symptoms, compromised sleep quality, reduced inclination to seek medical assistance, and diminished quality of life [ 71 ].

In terms of clinical application, the use of this scale is considered to save time and enable early detection of breast cancer during assessment. Utilizing this screeninig tool by health care providers improves a quick and comprehensive attitude toward breast cancer perception among women. The main advantage of BCPS is that it helps more subjective measurements compared to other scales in this area. In addition, its goal is to evaluate the relationship between breast cancer and diagnostic behaviors for breast cancer (such as maintaining healthy behaviors like diet, physical activity, mammography, breast self-examination, and clinical breast examination), knowledge about breast cancer, and family history of breast cancer [ 23 ].

Strength and limitation

The present study possesses several notable strengths. Firstly, it is the first study to assess BCPS among Iranian women. Secondly, the study adheres to the COSMIN checklist, ensuring methodological rigor. Additionally, the study incorporates both the DP and FB methods for the translation process, effectively addressing the limitations associated with the FB method. Lastly, the study includes a comparative analysis of BCPS with other versions.

However, it is important to acknowledge the limitations of the current study. These limitations include the lack of criterion validity calculations due to the lack of a gold standard, the lack of an assessment of cross-cultural validity and the possibility of bias from participants’ tendency to give socially desirable answers when using self-reported measures. As we conducted this study in Tabriz-Iran, should be cautious about the generalizability of findings. In conclusion, it is recommended that future research endeavors employ a larger sample sizeand assess the measurment properties in diverse contexts.

The obtained findings suggest that the Iranian version of the BCPS demonstrates satisfactory measurment properties for assessing the perception of breast cancer among Iranian women. Furthermore, it exhibits favorable responsiveness to clinical variations. The assessment of women’s perceptions of breast cancer is imperative for the advancement of preventive behaviors against this disease. The present scale can be employed for the assessment of the association between breast cancer and behaviors related to breast cancer diagnosis, including breast self-examination, clinical breast examination, mammography, and the adoption of healthy behaviors such as diet and exercise. Finally, it can be utilized to investigate the correlation between breast cancer knowledge and family history.

Data availability

The datasets generated and/or analyzed during the current study are not publicly available due to the limitations of ethical approval involving the patient data and anonymity, but are available from the corresponding author upon reasonable requests.

Abbreviations

Breast Cancer Perception Scale

Consensus-Based Standards for the Selection of Health Status Measurement Instruments

Breast Cancer

Global Cancer Incidence Mortality and Prevalence

World health organization

Eastern Mediterranean Region

Age-standardized rate

Hormone replacement therapy

Visual Analouge Scale

Health Belief Model

Breast self-examination

Clinical Breast Examination

Health related-patient reported outcomes

Dual pannel

Forward-backward

Exploratory factor analysis

Confirmatory factor analysis

Intra-class Correlation Coefficient

standard deviation

Content validity index

Content validity ratio

Standard error of measurment

Smallest detectable change

Minimal important change

Degree of freedom

Average variance extracted

Kaiser-Meyer-Olkin

Root mean squared error of approximation

Comparative fit index

Tucker–Lewis index

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Acknowledgements

We should thank the Vice-chancellor for Research of Tabriz University of Medical Sciences for their financial support and the invaluable participation of women would be appreciated.

This Study is funded by Tabriz University of Medical Sciences (grant number: 69930). The funding source had no role in the design and conduct of the study, and decision to this manuscript writing and submission.

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MM, SMA, MH contributed to the design of the study. SMA, ESK, MM has written the first draft of this article and MAJ Analyzed and data. All authors have critically read the text and contributed with inputs and revisions, and all authors read and approved the final manuscript.

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The current study was approved by the Ethics Committee of Tabriz University of Medical Sciences [ref: IR.TBZMED.REC.1401.390]. Written Informed consent to participate in the study was obtained from all the participants before enrolment. Permission to use the BCPS was obtained by sending an email from the original designer of the instrument. All methods were carried out in accordance with relevant guidelines and regulations.

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Mashayekh-Amiri, S., Jafarabadi, M.A., Hosseinzadeh, M. et al. Measurement properties of the Iranian version of the breast cancer perception scale (BCPS) according to the COSMIN checklist. BMC Cancer 24 , 743 (2024). https://doi.org/10.1186/s12885-024-12493-2

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Received : 16 July 2023

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DOI : https://doi.org/10.1186/s12885-024-12493-2

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