hypothesis research role

• Resources Home 🏠
• Try SciSpace Copilot
• Search research papers
• Add Copilot Extension
• Try AI Detector
• Try Paraphraser
• Try Citation Generator
• April Papers
• June Papers
• July Papers

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.

You might also like

Consensus GPT vs. SciSpace GPT: Choose the Best GPT for Research

Literature Review and Theoretical Framework: Understanding the Differences

Types of Essays in Academic Writing - Quick Guide (2024)

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.

Editage All Access is a subscription-based platform that unifies the best AI tools and services designed to speed up, simplify, and streamline every step of a researcher’s journey. The Editage All Access Pack is a one-of-a-kind subscription that unlocks full access to an AI writing assistant, literature recommender, journal finder, scientific illustration tool, and exclusive discounts on professional publication services from Editage.  

Based on 22+ years of experience in academia, Editage All Access empowers researchers to put their best research forward and move closer to success. Explore our top AI Tools pack, AI Tools + Publication Services pack, or Build Your Own Plan. Find everything a researcher needs to succeed, all in one place –  Get All Access now starting at just $14 a month !    

Related Posts

Back to School – Lock-in All Access Pack for a Year at the Best Price

Research Paper Appendix: Format and Examples

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 ...

17 Comments

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

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

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

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

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

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

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

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

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

could you please elaborate it more

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

This is very helpful

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

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

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

this is very important note help me much more

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

Trackbacks/Pingbacks

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

Submit a Comment Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

• Print Friendly

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

• How to Write a Strong Hypothesis | Steps & Examples

How to Write a Strong Hypothesis | Steps & Examples

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

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

Example: Hypothesis

Daily apple consumption leads to fewer doctor’s visits.

Table of contents

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

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

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

Variables in hypotheses

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

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

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

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

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

• Academic style
• Vague sentences
• Style consistency

See an example

Step 1. Ask a question

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

Step 2. Do some preliminary research

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

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

Step 3. Formulate your hypothesis

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

4. Refine your hypothesis

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

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

5. Phrase your hypothesis in three ways

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

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

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

6. Write a null hypothesis

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

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

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

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

 Statistics

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

Research bias

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

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

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

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

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

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

McCombes, S. (2023, November 20). How to Write a Strong Hypothesis | Steps & Examples. Scribbr. Retrieved August 28, 2024, from https://www.scribbr.com/methodology/hypothesis/

Is this article helpful?

Shona McCombes

Other students also liked, construct validity | definition, types, & examples, what is a conceptual framework | tips & examples, operationalization | a guide with examples, pros & cons, "i thought ai proofreading was useless but..".

I've been using Scribbr for years now and I know it's a service that won't disappoint. It does a good job spotting mistakes”

• Skip to main content
• Skip to primary sidebar
• Skip to footer
• QuestionPro

• Solutions Industries Gaming Automotive Sports and events Education Government Travel & Hospitality Financial Services Healthcare Cannabis Technology Use Case AskWhy Communities Audience Contactless surveys Mobile LivePolls Member Experience GDPR Positive People Science 360 Feedback Surveys
• Resources Blog eBooks Survey Templates Case Studies Training Help center

Home Market Research

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.

LEARN MORE         FREE TRIAL

MORE LIKE THIS

Microsoft customer voice vs questionpro: choosing the best.

Aug 29, 2024

Statistical Methods: What It Is, Process, Analyze & Present

Aug 28, 2024

Velodu and QuestionPro: Connecting Data with a Human Touch

Google Forms vs QuestionPro: Which is Best for Your Needs?

Other categories.

• Academic Research
• Artificial Intelligence
• Assessments
• Brand Awareness
• Case Studies
• Communities
• Consumer Insights
• Customer effort score
• Customer Engagement
• Customer Experience
• Customer Loyalty
• Customer Research
• Customer Satisfaction
• Employee Benefits
• Employee Engagement
• Employee Retention
• Friday Five
• General Data Protection Regulation
• Insights Hub
• Life@QuestionPro
• Market Research
• Mobile diaries
• Mobile Surveys
• New Features
• Online Communities
• Question Types
• Questionnaire
• QuestionPro Products
• Release Notes
• Research Tools and Apps
• Revenue at Risk
• Survey Templates
• Training Tips
• Tuesday CX Thoughts (TCXT)
• Uncategorized
• What’s Coming Up
• Workforce Intelligence

• Privacy Policy

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.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Research Questions – Types, Examples and Writing...

Research Methods – Types, Examples and Guide

Context of the Study – Writing Guide and Examples

Research Recommendations – Examples and Writing...

Research Project – Definition, Writing Guide and...

Research Paper – Structure, Examples and Writing...

• Bipolar Disorder
• Therapy Center
• When To See a Therapist
• Types of Therapy
• Best Online Therapy
• Best Couples Therapy
• Managing Stress
• Sleep and Dreaming
• Understanding Emotions
• Self-Improvement
• Healthy Relationships
• Student Resources
• Personality Types
• Sweepstakes
• Guided Meditations
• Verywell Mind Insights
• 2024 Verywell Mind 25
• Mental Health in the Classroom
• Editorial Process
• Meet Our Review Board
• Crisis Support

How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

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."

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.

Let ATLAS.ti take you from research question to key insights

Get started with a free trial and see how ATLAS.ti can make the most of your data.

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.

Turn data into evidence for insights with ATLAS.ti

Powerful analysis for your research paper or presentation is at your fingertips starting with a free trial.

Research Hypothesis In Psychology: Types, & Examples

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

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

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

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

On This Page:

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

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

Some key points about hypotheses:

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

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

Types of Research Hypotheses

Alternative hypothesis.

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

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

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

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

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

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

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

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

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

Null Hypothesis

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

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

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

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

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

Nondirectional Hypothesis

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

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

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

Directional Hypothesis

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

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

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

Falsifiability

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

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

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

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

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

Can a Hypothesis be Proven?

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

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

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

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

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

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

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

How to Write a Hypothesis

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

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

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

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

More Examples

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

The Research Hypothesis: Role and Construction

• First Online: 01 January 2012

Cite this chapter

• Phyllis G. Supino EdD 3  

6104 Accesses

A hypothesis is a logical construct, interposed between a problem and its solution, which represents a proposed answer to a research question. It gives direction to the investigator’s thinking about the problem and, therefore, facilitates a solution. There are three primary modes of inference by which hypotheses are developed: deduction (reasoning from a general propositions to specific instances), induction (reasoning from specific instances to a general proposition), and abduction (formulation/acceptance on probation of a hypothesis to explain a surprising observation).

A research hypothesis should reflect an inference about variables; be stated as a grammatically complete, declarative sentence; be expressed simply and unambiguously; provide an adequate answer to the research problem; and be testable. Hypotheses can be classified as conceptual versus operational, single versus bi- or multivariable, causal or not causal, mechanistic versus nonmechanistic, and null or alternative. Hypotheses most commonly entail statements about “variables” which, in turn, can be classified according to their level of measurement (scaling characteristics) or according to their role in the hypothesis (independent, dependent, moderator, control, or intervening).

A hypothesis is rendered operational when its broadly (conceptually) stated variables are replaced by operational definitions of those variables. Hypotheses stated in this manner are called operational hypotheses, specific hypotheses, or predictions and facilitate testing.

Wrong hypotheses, rightly worked from, have produced more results than unguided observation

—Augustus De Morgan, 1872[ 1 ]—

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime
• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info
• Durable hardcover edition

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

The Nature and Logic of Science: Testing Hypotheses

Abductive Research Methods in Psychological Science

Abductive Research Methods in Psychological Science

De Morgan A, De Morgan S. A budget of paradoxes. London: Longmans Green; 1872.

Google Scholar  

Leedy Paul D. Practical research. Planning and design. 2nd ed. New York: Macmillan; 1960.

Bernard C. Introduction to the study of experimental medicine. New York: Dover; 1957.

Erren TC. The quest for questions—on the logical force of science. Med Hypotheses. 2004;62:635–40.

Article   PubMed   Google Scholar  

Peirce CS. Collected papers of Charles Sanders Peirce, vol. 7. In: Hartshorne C, Weiss P, editors. Boston: The Belknap Press of Harvard University Press; 1966.

Aristotle. The complete works of Aristotle: the revised Oxford Translation. In: Barnes J, editor. vol. 2. Princeton/New Jersey: Princeton University Press; 1984.

Polit D, Beck CT. Conceptualizing a study to generate evidence for nursing. In: Polit D, Beck CT, editors. Nursing research: generating and assessing evidence for nursing practice. 8th ed. Philadelphia: Wolters Kluwer/Lippincott Williams and Wilkins; 2008. Chapter 4.

Jenicek M, Hitchcock DL. Evidence-based practice. Logic and critical thinking in medicine. Chicago: AMA Press; 2005.

Bacon F. The novum organon or a true guide to the interpretation of nature. A new translation by the Rev G.W. Kitchin. Oxford: The University Press; 1855.

Popper KR. Objective knowledge: an evolutionary approach (revised edition). New York: Oxford University Press; 1979.

Morgan AJ, Parker S. Translational mini-review series on vaccines: the Edward Jenner Museum and the history of vaccination. Clin Exp Immunol. 2007;147:389–94.

Article   PubMed   CAS   Google Scholar  

Pead PJ. Benjamin Jesty: new light in the dawn of vaccination. Lancet. 2003;362:2104–9.

Lee JA. The scientific endeavor: a primer on scientific principles and practice. San Francisco: Addison-Wesley Longman; 2000.

Allchin D. Lawson’s shoehorn, or should the philosophy of science be rated, ‘X’? Science and Education. 2003;12:315–29.

Article   Google Scholar  

Lawson AE. What is the role of induction and deduction in reasoning and scientific inquiry? J Res Sci Teach. 2005;42:716–40.

Peirce CS. Collected papers of Charles Sanders Peirce, vol. 2. In: Hartshorne C, Weiss P, editors. Boston: The Belknap Press of Harvard University Press; 1965.

Bonfantini MA, Proni G. To guess or not to guess? In: Eco U, Sebeok T, editors. The sign of three: Dupin, Holmes, Peirce. Bloomington: Indiana University Press; 1983. Chapter 5.

Peirce CS. Collected papers of Charles Sanders Peirce, vol. 5. In: Hartshorne C, Weiss P, editors. Boston: The Belknap Press of Harvard University Press; 1965.

Flach PA, Kakas AC. Abductive and inductive reasoning: background issues. In: Flach PA, Kakas AC, ­editors. Abduction and induction. Essays on their relation and integration. The Netherlands: Klewer; 2000. Chapter 1.

Murray JF. Voltaire, Walpole and Pasteur: variations on the theme of discovery. Am J Respir Crit Care Med. 2005;172:423–6.

Danemark B, Ekstrom M, Jakobsen L, Karlsson JC. Methodological implications, generalization, scientific inference, models (Part II) In: explaining society. Critical realism in the social sciences. New York: Routledge; 2002.

Pasteur L. Inaugural lecture as professor and dean of the faculty of sciences. In: Peterson H, editor. A treasury of the world’s greatest speeches. Douai, France: University of Lille 7 Dec 1954.

Swineburne R. Simplicity as evidence for truth. Milwaukee: Marquette University Press; 1997.

Sakar S, editor. Logical empiricism at its peak: Schlick, Carnap and Neurath. New York: Garland; 1996.

Popper K. The logic of scientific discovery. New York: Basic Books; 1959. 1934, trans. 1959.

Caws P. The philosophy of science. Princeton: D. Van Nostrand Company; 1965.

Popper K. Conjectures and refutations. The growth of scientific knowledge. 4th ed. London: Routledge and Keegan Paul; 1972.

Feyerabend PK. Against method, outline of an anarchistic theory of knowledge. London, UK: Verso; 1978.

Smith PG. Popper: conjectures and refutations (Chapter IV). In: Theory and reality: an introduction to the philosophy of science. Chicago: University of Chicago Press; 2003.

Blystone RV, Blodgett K. WWW: the scientific method. CBE Life Sci Educ. 2006;5:7–11.

Kleinbaum DG, Kupper LL, Morgenstern H. Epidemiological research. Principles and quantitative methods. New York: Van Nostrand Reinhold; 1982.

Fortune AE, Reid WJ. Research in social work. 3rd ed. New York: Columbia University Press; 1999.

Kerlinger FN. Foundations of behavioral research. 1st ed. New York: Hold, Reinhart and Winston; 1970.

Hoskins CN, Mariano C. Research in nursing and health. Understanding and using quantitative and qualitative methods. New York: Springer; 2004.

Tuckman BW. Conducting educational research. New York: Harcourt, Brace, Jovanovich; 1972.

Wang C, Chiari PC, Weihrauch D, Krolikowski JG, Warltier DC, Kersten JR, Pratt Jr PF, Pagel PS. Gender-specificity of delayed preconditioning by isoflurane in rabbits: potential role of endothelial nitric oxide synthase. Anesth Analg. 2006;103:274–80.

Beyer ME, Slesak G, Nerz S, Kazmaier S, Hoffmeister HM. Effects of endothelin-1 and IRL 1620 on myocardial contractility and myocardial energy metabolism. J Cardiovasc Pharmacol. 1995;26(Suppl 3):S150–2.

PubMed   CAS   Google Scholar  

Stone J, Sharpe M. Amnesia for childhood in patients with unexplained neurological symptoms. J Neurol Neurosurg Psychiatry. 2002;72:416–7.

Naughton BJ, Moran M, Ghaly Y, Michalakes C. Computer tomography scanning and delirium in elder patients. Acad Emerg Med. 1997;4:1107–10.

Easterbrook PJ, Berlin JA, Gopalan R, Matthews DR. Publication bias in clinical research. Lancet. 1991;337:867–72.

Stern JM, Simes RJ. Publication bias: evidence of delayed publication in a cohort study of clinical research projects. BMJ. 1997;315:640–5.

Stevens SS. On the theory of scales and measurement. Science. 1946;103:677–80.

Knapp TR. Treating ordinal scales as interval scales: an attempt to resolve the controversy. Nurs Res. 1990;39:121–3.

The Cochrane Collaboration. Open Learning Material. www.cochrane-net.org/openlearning/html/mod14-3.htm . Accessed 12 Oct 2009.

MacCorquodale K, Meehl PE. On a distinction between hypothetical constructs and intervening ­variables. Psychol Rev. 1948;55:95–107.

Baron RM, Kenny DA. The moderator-mediator variable distinction in social psychological research: ­conceptual, strategic and statistical considerations. J Pers Soc Psychol. 1986;51:1173–82.

Williamson GM, Schultz R. Activity restriction mediates the association between pain and depressed affect: a study of younger and older adult cancer patients. Psychol Aging. 1995;10:369–78.

Song M, Lee EO. Development of a functional capacity model for the elderly. Res Nurs Health. 1998;21:189–98.

MacKinnon DP. Introduction to statistical mediation analysis. New York: Routledge; 2008.

Download references

Author information

Authors and affiliations.

Department of Medicine, College of Medicine, SUNY Downstate Medical Center, 450 Clarkson Avenue, 1199, Brooklyn, NY, 11203, USA

Phyllis G. Supino EdD

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Phyllis G. Supino EdD .

Editor information

Editors and affiliations.

, Cardiovascular Medicine, SUNY Downstate Medical Center, Clarkson Avenue, box 1199 450, Brooklyn, 11203, USA

Phyllis G. Supino

, Cardiovascualr Medicine, SUNY Downstate Medical Center, Clarkson Avenue 450, Brooklyn, 11203, USA

Jeffrey S. Borer

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer Science+Business Media, LLC

About this chapter

Supino, P.G. (2012). The Research Hypothesis: Role and Construction. In: Supino, P., Borer, J. (eds) Principles of Research Methodology. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-3360-6_3

Download citation

DOI : https://doi.org/10.1007/978-1-4614-3360-6_3

Published : 18 April 2012

Publisher Name : Springer, New York, NY

Print ISBN : 978-1-4614-3359-0

Online ISBN : 978-1-4614-3360-6

eBook Packages : Medicine Medicine (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

7 Types of Research Hypothesis: Examples, Significance and Step-By-Step Guide

Introduction.

In any research study, a research hypothesis plays a crucial role in guiding the investigation and providing a clear direction for the research. It is an essential component of a thesis as it helps to frame the research question and determine the methodology to be used.

This article will provide a detailed analysis of research hypotheses in a thesis, highlighting their significance and qualities. It will also explore different types of research hypotheses and provide illustrative examples. Additionally, a step-by-step guide to developing research hypotheses and methods for testing and validating them will be discussed. By the end of this article, readers will have a comprehensive understanding of research hypotheses and their role in a thesis.

Understanding Research Hypotheses in a Thesis

A research hypothesis is a statement of expectation or prediction that will be tested by research. In a thesis, a research hypothesis is formulated to address the research question or problem statement . It serves as a tentative answer or explanation to the research question. The research hypothesis guides the direction of the study and helps in determining the research design and methodology.

The research hypothesis is typically based on existing theories, previous research findings, or observations. It is formulated after a thorough review of the literature and understanding of the research area. A well-defined research hypothesis provides a clear focus for the study and helps in generating testable predictions. By testing the research hypothesis, researchers aim to gather evidence to support or reject the hypothesis. This process contributes to the advancement of knowledge in the field and helps in drawing meaningful conclusions.

Significance of Research Hypotheses in a Thesis

One of the key significance of research hypotheses is that they help in organizing and structuring the research study. By formulating a hypothesis, the researcher defines the specific research question and identifies the variables that will be investigated. This helps in narrowing down the scope of the study and ensures that the research is focused and targeted.

Moreover, research hypotheses provide a framework for data collection and analysis. They guide the researcher in selecting appropriate research methods , tools, and techniques to gather relevant data. The hypotheses also help in determining the statistical tests and analysis techniques that will be used to analyze the collected data.

Another significance of research hypotheses is that they contribute to the advancement of knowledge in a particular field. By formulating hypotheses and conducting research to test them, researchers are able to generate new insights, theories, and explanations. This contributes to the existing body of knowledge and helps in expanding the understanding of a specific phenomenon or topic.

Furthermore, research hypotheses are important for establishing the validity and reliability of the research findings. By formulating clear and testable hypotheses, researchers can ensure that their study is based on sound scientific principles. The hypotheses provide a basis for evaluating the accuracy and generalizability of the research results.

In addition, research hypotheses are essential for making informed decisions and recommendations based on the research findings. They help in drawing conclusions and making predictions about the relationship between variables. This information can be used to inform policy decisions, develop interventions, or guide future research in the field.

Qualities of an Effective Research Hypothesis in a Thesis

An effective research hypothesis in a thesis possesses several key qualities that contribute to its strength and validity. These qualities are essential for ensuring that the hypothesis can be tested and validated through empirical research. The following are some of the qualities that make a research hypothesis effective:

1. Specificity: A good research hypothesis is specific and clearly defines the variables and the relationship between them. It provides a clear direction for the research and allows for precise testing of the hypothesis.

2. Testability: An effective hypothesis in research is testable, meaning that it can be empirically examined and either supported or refuted through data analysis. It should be possible to design experiments or collect data that can provide evidence for or against the hypothesis.

3. Clarity: A research hypothesis should be written in clear and concise language. It should avoid ambiguity and ensure that the intended meaning is easily understood by the readers. Clear language helps in communicating the hypothesis effectively and facilitates its evaluation.

4. Falsifiability: A strong research hypothesis is falsifiable, which means that it is possible to prove it wrong. It should be formulated in a way that allows for the possibility of obtaining evidence that contradicts the hypothesis. This is important for the scientific process as it encourages critical thinking and the exploration of alternative explanations.

5. Relevance: An effective research hypothesis is relevant to the research question and the overall objectives of the study. It should address a significant gap in knowledge or contribute to the existing body of literature. A relevant hypothesis adds value to the research and increases its significance.

6. Novelty: A good research hypothesis is original and innovative. It should propose a new idea or approach that has not been extensively explored before. Novelty in the hypothesis increases the potential for new discoveries and contributes to the advancement of knowledge in the field.

7. Coherence: An effective research hypothesis should be coherent and consistent with existing theories, concepts, and empirical evidence. It should align with the current understanding of the topic and build upon previous research. Coherence ensures that the hypothesis is grounded in a solid foundation and enhances its credibility.

8. Measurability: A research hypothesis should be measurable, meaning that it can be quantitatively or qualitatively assessed. It should be possible to collect data or evidence that can be used to evaluate the hypothesis. Measurability allows for objective testing and increases the reliability of the research findings.

By incorporating these qualities into the formulation of a research hypothesis, researchers can enhance the validity and reliability of their study.

Different Types of Research Hypotheses in a Thesis

In a thesis, there are several different types of research hypotheses that can be used to test the relationship between variables. These hypotheses provide a framework for the research and guide the direction of the study. Understanding the different types of research hypotheses is essential for conducting a comprehensive and effective thesis.

Null Hypothesis

The null hypothesis is a statement that suggests there is no significant relationship between the variables being studied. It assumes that any observed differences or relationships are due to chance or random variation. The null hypothesis is denoted as H0 and is often used as a starting point for hypothesis testing.

Alternative Hypothesis

The alternative hypothesis, also known as the research hypothesis, is a statement that suggests there is a significant relationship between the variables being studied. It contradicts the null hypothesis and proposes that the observed differences or relationships are not due to chance.

Directional Hypothesis

A directional hypothesis is a specific type of alternative hypothesis that predicts the direction of the relationship between variables. It states that there is a positive or negative relationship between the variables, indicating the direction of the effect.

Non-Directional Hypothesis

In contrast to a directional hypothesis, a non-directional hypothesis does not predict the direction of the relationship between variables. It simply states that there is a relationship between the variables without specifying the direction of the effect.

Statistical Hypothesis

A statistical hypothesis is a hypothesis that is formulated based on statistical analysis. It involves using statistical tests to determine the likelihood of the observed data occurring under the null hypothesis.

Associative Hypothesis

An associative hypothesis suggests that there is a relationship between variables, but it does not imply causation. It indicates that changes in one variable are associated with changes in another variable.

Causal Hypothesis

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

These different types of research hypotheses provide researchers with various options to explore and test the relationships between variables in a thesis. The choice of hypothesis depends on the research question, the nature of the variables, and the available data.

Illustrative Examples of Research Hypotheses in a Thesis

To better understand research hypotheses in a thesis, let’s explore some illustrative examples. These examples will demonstrate how hypotheses are formulated and tested in different research studies.

Example 1: Hypothesis for a study on the effects of exercise on weight loss:

Null Hypothesis (H0): There is no significant difference in weight loss between individuals who engage in regular exercise and those who do not.

Alternative Hypothesis (H1): Individuals who engage in regular exercise will experience greater weight loss compared to those who do not exercise.

Example 2: Hypothesis for a study on the impact of social media on self-esteem:

Null Hypothesis (H0): There is no significant relationship between social media usage and self-esteem levels.

Alternative Hypothesis (H1): Increased social media usage is associated with lower self-esteem levels.

Example 3: Hypothesis for a study on the effectiveness of a new teaching method in improving student performance:

Null Hypothesis (H0): There is no significant difference in student performance between the traditional teaching method and the new teaching method.

Alternative Hypothesis (H1): The new teaching method leads to improved student performance compared to the traditional teaching method.

These examples highlight the structure of research hypotheses, where the null hypothesis represents no effect or relationship, while the alternative hypothesis suggests the presence of an effect or relationship. It is important to note that these hypotheses are testable and can be analyzed using appropriate statistical methods.

Step-by-Step Guide to Developing Research Hypotheses in a Thesis

Developing a research hypothesis is a crucial step in the process of conducting a thesis. In this section, we will provide a step-by-step guide to developing research hypotheses in a thesis.

Step 1: Identify the Research Topic

The first step in developing a research hypothesis is to clearly identify the research topic. This involves understanding the research problem and determining the specific area of study.

Step 2: Conduct Preliminary Research

Once the research topic is identified, it is important to conduct preliminary research to gather relevant information. This helps in understanding the existing knowledge and identifying any gaps or areas that need further investigation.

Step 3: Formulate the Research Question

Based on the preliminary research, formulate a clear and concise research question. The research question should be specific and focused, addressing the research problem identified in step 1.

Step 4: Define the Variables

Identify the variables that will be studied in the research. Variables are the factors or concepts that are being measured or manipulated in the study. It is important to clearly define the variables to ensure the research hypothesis is specific and testable.

Step 5: Predict the Relationship and Outcome

The research hypothesis should propose a link between the variables and predict the expected outcome. It should clearly state the expected relationship between the variables and the anticipated result.

Step 6: Ensure Clarity and Conciseness

A good research hypothesis should be simple and concise, avoiding wordiness. It should be clear and free from ambiguity or assumptions about the readers’ knowledge. The hypothesis should also be observable and measurable.

Step 7: Validate the Hypothesis

Before finalizing the research hypothesis, it is important to validate it. This can be done through further research, literature review , or consultation with experts in the field. Validating the hypothesis ensures its relevance and novelty.

By following these step-by-step guidelines, researchers can develop effective research hypotheses for their theses. A well-developed hypothesis provides a solid foundation for the research and helps in generating meaningful results.

Methods for Testing and Validating Research Hypotheses in a Thesis

Hypothesis testing is a formal procedure for investigating our ideas about the world. It allows you to statistically test your predictions. The usual process is to make a hypothesis, create an experiment to test it, run the experiment, draw a conclusion, and then allow other researchers to replicate the study to validate the findings. There are several methods for testing and validating research hypotheses in a thesis.

Experimental Research

One common method is experimental research, where researchers manipulate variables and measure their effects on the dependent variable.

Observational Research

Another method is observational research, where researchers observe and record data without manipulating variables. This method is often used when it is not feasible or ethical to conduct experiments.

Survey Research

Survey research is another method that involves collecting data from a sample of individuals using questionnaires or interviews . This method is useful for studying attitudes, opinions, and behaviors.

Conducting Meta-analysis

In addition to these methods, researchers can also use existing data or conduct meta-analyses to test and validate research hypotheses. Existing data can be obtained from sources such as government databases, previous studies, or publicly available datasets. Meta-analysis involves combining the results of multiple studies to determine the overall effect size and to test the generalizability of findings across different populations and contexts. Once the data is collected, researchers can use statistical analysis techniques to analyze the data and test the research hypotheses. Common statistical tests include t-tests, analysis of variance (ANOVA), regression analysis, and chi-square tests.

The choice of statistical test depends on the research design, the type of data collected, and the specific research hypotheses being tested. It is important to note that testing and validating research hypotheses is an iterative process. Researchers may need to refine their hypotheses, modify their research design, or collect additional data based on the initial findings. By using rigorous methods for testing and validating research hypotheses, researchers can ensure the reliability and validity of their findings, contributing to the advancement of knowledge in their field.

In conclusion, research hypotheses are essential components of a thesis that guide the research process and contribute to the advancement of knowledge in a particular field. By formulating clear and testable hypotheses, researchers can make meaningful contributions to their field and address important research questions. It is important for researchers to carefully develop and validate their hypotheses to ensure the credibility and reliability of their findings.

Leave a Comment Cancel reply

Save my name, email, and website in this browser for the next time I comment.

Related articles

7 Easy Step-By-Step Guide of Using ChatGPT: The Best Literature Review Generator for Time-Saving Academic Research

Writing Engaging Introduction in Research Papers : Tips and Tricks!

Understanding Comparative Frameworks: Their Importance, Components, Examples and 8 Best Practices

Revolutionizing Effective Thesis Writing for PhD Students Using Artificial Intelligence!

3 Types of Interviews in Qualitative Research: An Essential Research Instrument and Handy Tips to Conduct Them

Highlight Abstracts: An Ultimate Guide For Researchers!

Crafting Critical Abstracts: 11 Expert Strategies for Summarizing Research

Crafting the Perfect Informative Abstract: Definition, Importance and 8 Expert Writing Tips

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• J Korean Med Sci
• v.34(45); 2019 Nov 25

Scientific Hypotheses: Writing, Promoting, and Predicting Implications

Armen yuri gasparyan.

1 Departments of Rheumatology and Research and Development, Dudley Group NHS Foundation Trust (Teaching Trust of the University of Birmingham, UK), Russells Hall Hospital, Dudley, West Midlands, UK.

Lilit Ayvazyan

2 Department of Medical Chemistry, Yerevan State Medical University, Yerevan, Armenia.

Ulzhan Mukanova

3 Department of Surgical Disciplines, South Kazakhstan Medical Academy, Shymkent, Kazakhstan.

Marlen Yessirkepov

4 Department of Biology and Biochemistry, South Kazakhstan Medical Academy, Shymkent, Kazakhstan.

George D. Kitas

5 Arthritis Research UK Epidemiology Unit, University of Manchester, Manchester, UK.

Scientific hypotheses are essential for progress in rapidly developing academic disciplines. Proposing new ideas and hypotheses require thorough analyses of evidence-based data and predictions of the implications. One of the main concerns relates to the ethical implications of the generated hypotheses. The authors may need to outline potential benefits and limitations of their suggestions and target widely visible publication outlets to ignite discussion by experts and start testing the hypotheses. Not many publication outlets are currently welcoming hypotheses and unconventional ideas that may open gates to criticism and conservative remarks. A few scholarly journals guide the authors on how to structure hypotheses. Reflecting on general and specific issues around the subject matter is often recommended for drafting a well-structured hypothesis article. An analysis of influential hypotheses, presented in this article, particularly Strachan's hygiene hypothesis with global implications in the field of immunology and allergy, points to the need for properly interpreting and testing new suggestions. Envisaging the ethical implications of the hypotheses should be considered both by authors and journal editors during the writing and publishing process.

INTRODUCTION

We live in times of digitization that radically changes scientific research, reporting, and publishing strategies. Researchers all over the world are overwhelmed with processing large volumes of information and searching through numerous online platforms, all of which make the whole process of scholarly analysis and synthesis complex and sophisticated.

Current research activities are diversifying to combine scientific observations with analysis of facts recorded by scholars from various professional backgrounds. 1 Citation analyses and networking on social media are also becoming essential for shaping research and publishing strategies globally. 2 Learning specifics of increasingly interdisciplinary research studies and acquiring information facilitation skills aid researchers in formulating innovative ideas and predicting developments in interrelated scientific fields.

Arguably, researchers are currently offered more opportunities than in the past for generating new ideas by performing their routine laboratory activities, observing individual cases and unusual developments, and critically analyzing published scientific facts. What they need at the start of their research is to formulate a scientific hypothesis that revisits conventional theories, real-world processes, and related evidence to propose new studies and test ideas in an ethical way. 3 Such a hypothesis can be of most benefit if published in an ethical journal with wide visibility and exposure to relevant online databases and promotion platforms.

Although hypotheses are crucially important for the scientific progress, only few highly skilled researchers formulate and eventually publish their innovative ideas per se . Understandably, in an increasingly competitive research environment, most authors would prefer to prioritize their ideas by discussing and conducting tests in their own laboratories or clinical departments, and publishing research reports afterwards. However, there are instances when simple observations and research studies in a single center are not capable of explaining and testing new groundbreaking ideas. Formulating hypothesis articles first and calling for multicenter and interdisciplinary research can be a solution in such instances, potentially launching influential scientific directions, if not academic disciplines.

The aim of this article is to overview the importance and implications of infrequently published scientific hypotheses that may open new avenues of thinking and research.

Despite the seemingly established views on innovative ideas and hypotheses as essential research tools, no structured definition exists to tag the term and systematically track related articles. In 1973, the Medical Subject Heading (MeSH) of the U.S. National Library of Medicine introduced “Research Design” as a structured keyword that referred to the importance of collecting data and properly testing hypotheses, and indirectly linked the term to ethics, methods and standards, among many other subheadings.

One of the experts in the field defines “hypothesis” as a well-argued analysis of available evidence to provide a realistic (scientific) explanation of existing facts, fill gaps in public understanding of sophisticated processes, and propose a new theory or a test. 4 A hypothesis can be proven wrong partially or entirely. However, even such an erroneous hypothesis may influence progress in science by initiating professional debates that help generate more realistic ideas. The main ethical requirement for hypothesis authors is to be honest about the limitations of their suggestions. 5

EXAMPLES OF INFLUENTIAL SCIENTIFIC HYPOTHESES

Daily routine in a research laboratory may lead to groundbreaking discoveries provided the daily accounts are comprehensively analyzed and reproduced by peers. The discovery of penicillin by Sir Alexander Fleming (1928) can be viewed as a prime example of such discoveries that introduced therapies to treat staphylococcal and streptococcal infections and modulate blood coagulation. 6 , 7 Penicillin got worldwide recognition due to the inventor's seminal works published by highly prestigious and widely visible British journals, effective ‘real-world’ antibiotic therapy of pneumonia and wounds during World War II, and euphoric media coverage. 8 In 1945, Fleming, Florey and Chain got a much deserved Nobel Prize in Physiology or Medicine for the discovery that led to the mass production of the wonder drug in the U.S. and ‘real-world practice’ that tested the use of penicillin. What remained globally unnoticed is that Zinaida Yermolyeva, the outstanding Soviet microbiologist, created the Soviet penicillin, which turned out to be more effective than the Anglo-American penicillin and entered mass production in 1943; that year marked the turning of the tide of the Great Patriotic War. 9 One of the reasons of the widely unnoticed discovery of Zinaida Yermolyeva is that her works were published exclusively by local Russian (Soviet) journals.

The past decades have been marked by an unprecedented growth of multicenter and global research studies involving hundreds and thousands of human subjects. This trend is shaped by an increasing number of reports on clinical trials and large cohort studies that create a strong evidence base for practice recommendations. Mega-studies may help generate and test large-scale hypotheses aiming to solve health issues globally. Properly designed epidemiological studies, for example, may introduce clarity to the hygiene hypothesis that was originally proposed by David Strachan in 1989. 10 David Strachan studied the epidemiology of hay fever in a cohort of 17,414 British children and concluded that declining family size and improved personal hygiene had reduced the chances of cross infections in families, resulting in epidemics of atopic disease in post-industrial Britain. Over the past four decades, several related hypotheses have been proposed to expand the potential role of symbiotic microorganisms and parasites in the development of human physiological immune responses early in life and protection from allergic and autoimmune diseases later on. 11 , 12 Given the popularity and the scientific importance of the hygiene hypothesis, it was introduced as a MeSH term in 2012. 13

Hypotheses can be proposed based on an analysis of recorded historic events that resulted in mass migrations and spreading of certain genetic diseases. As a prime example, familial Mediterranean fever (FMF), the prototype periodic fever syndrome, is believed to spread from Mesopotamia to the Mediterranean region and all over Europe due to migrations and religious prosecutions millennia ago. 14 Genetic mutations spearing mild clinical forms of FMF are hypothesized to emerge and persist in the Mediterranean region as protective factors against more serious infectious diseases, particularly tuberculosis, historically common in that part of the world. 15 The speculations over the advantages of carrying the MEditerranean FeVer (MEFV) gene are further strengthened by recorded low mortality rates from tuberculosis among FMF patients of different nationalities living in Tunisia in the first half of the 20th century. 16

Diagnostic hypotheses shedding light on peculiarities of diseases throughout the history of mankind can be formulated using artefacts, particularly historic paintings. 17 Such paintings may reveal joint deformities and disfigurements due to rheumatic diseases in individual subjects. A series of paintings with similar signs of pathological conditions interpreted in a historic context may uncover mysteries of epidemics of certain diseases, which is the case with Ruben's paintings depicting signs of rheumatic hands and making some doctors to believe that rheumatoid arthritis was common in Europe in the 16th and 17th century. 18

WRITING SCIENTIFIC HYPOTHESES

There are author instructions of a few journals that specifically guide how to structure, format, and make submissions categorized as hypotheses attractive. One of the examples is presented by Med Hypotheses , the flagship journal in its field with more than four decades of publishing and influencing hypothesis authors globally. However, such guidance is not based on widely discussed, implemented, and approved reporting standards, which are becoming mandatory for all scholarly journals.

Generating new ideas and scientific hypotheses is a sophisticated task since not all researchers and authors are skilled to plan, conduct, and interpret various research studies. Some experience with formulating focused research questions and strong working hypotheses of original research studies is definitely helpful for advancing critical appraisal skills. However, aspiring authors of scientific hypotheses may need something different, which is more related to discerning scientific facts, pooling homogenous data from primary research works, and synthesizing new information in a systematic way by analyzing similar sets of articles. To some extent, this activity is reminiscent of writing narrative and systematic reviews. As in the case of reviews, scientific hypotheses need to be formulated on the basis of comprehensive search strategies to retrieve all available studies on the topics of interest and then synthesize new information selectively referring to the most relevant items. One of the main differences between scientific hypothesis and review articles relates to the volume of supportive literature sources ( Table 1 ). In fact, hypothesis is usually formulated by referring to a few scientific facts or compelling evidence derived from a handful of literature sources. 19 By contrast, reviews require analyses of a large number of published documents retrieved from several well-organized and evidence-based databases in accordance with predefined search strategies. 20 , 21 , 22

The format of hypotheses, especially the implications part, may vary widely across disciplines. Clinicians may limit their suggestions to the clinical manifestations of diseases, outcomes, and management strategies. Basic and laboratory scientists analysing genetic, molecular, and biochemical mechanisms may need to view beyond the frames of their narrow fields and predict social and population-based implications of the proposed ideas. 23

Advanced writing skills are essential for presenting an interesting theoretical article which appeals to the global readership. Merely listing opposing facts and ideas, without proper interpretation and analysis, may distract the experienced readers. The essence of a great hypothesis is a story behind the scientific facts and evidence-based data.

ETHICAL IMPLICATIONS

The authors of hypotheses substantiate their arguments by referring to and discerning rational points from published articles that might be overlooked by others. Their arguments may contradict the established theories and practices, and pose global ethical issues, particularly when more or less efficient medical technologies and public health interventions are devalued. The ethical issues may arise primarily because of the careless references to articles with low priorities, inadequate and apparently unethical methodologies, and concealed reporting of negative results. 24 , 25

Misinterpretation and misunderstanding of the published ideas and scientific hypotheses may complicate the issue further. For example, Alexander Fleming, whose innovative ideas of penicillin use to kill susceptible bacteria saved millions of lives, warned of the consequences of uncontrolled prescription of the drug. The issue of antibiotic resistance had emerged within the first ten years of penicillin use on a global scale due to the overprescription that affected the efficacy of antibiotic therapies, with undesirable consequences for millions. 26

The misunderstanding of the hygiene hypothesis that primarily aimed to shed light on the role of the microbiome in allergic and autoimmune diseases resulted in decline of public confidence in hygiene with dire societal implications, forcing some experts to abandon the original idea. 27 , 28 Although that hypothesis is unrelated to the issue of vaccinations, the public misunderstanding has resulted in decline of vaccinations at a time of upsurge of old and new infections.

A number of ethical issues are posed by the denial of the viral (human immunodeficiency viruses; HIV) hypothesis of acquired Immune deficiency Syndrome (AIDS) by Peter Duesberg, who overviewed the links between illicit recreational drugs and antiretroviral therapies with AIDS and refuted the etiological role of HIV. 29 That controversial hypothesis was rejected by several journals, but was eventually published without external peer review at Med Hypotheses in 2010. The publication itself raised concerns of the unconventional editorial policy of the journal, causing major perturbations and more scrutinized publishing policies by journals processing hypotheses.

WHERE TO PUBLISH HYPOTHESES

Although scientific authors are currently well informed and equipped with search tools to draft evidence-based hypotheses, there are still limited quality publication outlets calling for related articles. The journal editors may be hesitant to publish articles that do not adhere to any research reporting guidelines and open gates for harsh criticism of unconventional and untested ideas. Occasionally, the editors opting for open-access publishing and upgrading their ethics regulations launch a section to selectively publish scientific hypotheses attractive to the experienced readers. 30 However, the absence of approved standards for this article type, particularly no mandate for outlining potential ethical implications, may lead to publication of potentially harmful ideas in an attractive format.

A suggestion of simultaneously publishing multiple or alternative hypotheses to balance the reader views and feedback is a potential solution for the mainstream scholarly journals. 31 However, that option alone is hardly applicable to emerging journals with unconventional quality checks and peer review, accumulating papers with multiple rejections by established journals.

A large group of experts view hypotheses with improbable and controversial ideas publishable after formal editorial (in-house) checks to preserve the authors' genuine ideas and avoid conservative amendments imposed by external peer reviewers. 32 That approach may be acceptable for established publishers with large teams of experienced editors. However, the same approach can lead to dire consequences if employed by nonselective start-up, open-access journals processing all types of articles and primarily accepting those with charged publication fees. 33 In fact, pseudoscientific ideas arguing Newton's and Einstein's seminal works or those denying climate change that are hardly testable have already found their niche in substandard electronic journals with soft or nonexistent peer review. 34

CITATIONS AND SOCIAL MEDIA ATTENTION

The available preliminary evidence points to the attractiveness of hypothesis articles for readers, particularly those from research-intensive countries who actively download related documents. 35 However, citations of such articles are disproportionately low. Only a small proportion of top-downloaded hypotheses (13%) in the highly prestigious Med Hypotheses receive on average 5 citations per article within a two-year window. 36

With the exception of a few historic papers, the vast majority of hypotheses attract relatively small number of citations in a long term. 36 Plausible explanations are that these articles often contain a single or only a few citable points and that suggested research studies to test hypotheses are rarely conducted and reported, limiting chances of citing and crediting authors of genuine research ideas.

A snapshot analysis of citation activity of hypothesis articles may reveal interest of the global scientific community towards their implications across various disciplines and countries. As a prime example, Strachan's hygiene hypothesis, published in 1989, 10 is still attracting numerous citations on Scopus, the largest bibliographic database. As of August 28, 2019, the number of the linked citations in the database is 3,201. Of the citing articles, 160 are cited at least 160 times ( h -index of this research topic = 160). The first three citations are recorded in 1992 and followed by a rapid annual increase in citation activity and a peak of 212 in 2015 ( Fig. 1 ). The top 5 sources of the citations are Clin Exp Allergy (n = 136), J Allergy Clin Immunol (n = 119), Allergy (n = 81), Pediatr Allergy Immunol (n = 69), and PLOS One (n = 44). The top 5 citing authors are leading experts in pediatrics and allergology Erika von Mutius (Munich, Germany, number of publications with the index citation = 30), Erika Isolauri (Turku, Finland, n = 27), Patrick G Holt (Subiaco, Australia, n = 25), David P. Strachan (London, UK, n = 23), and Bengt Björksten (Stockholm, Sweden, n = 22). The U.S. is the leading country in terms of citation activity with 809 related documents, followed by the UK (n = 494), Germany (n = 314), Australia (n = 211), and the Netherlands (n = 177). The largest proportion of citing documents are articles (n = 1,726, 54%), followed by reviews (n = 950, 29.7%), and book chapters (n = 213, 6.7%). The main subject areas of the citing items are medicine (n = 2,581, 51.7%), immunology and microbiology (n = 1,179, 23.6%), and biochemistry, genetics and molecular biology (n = 415, 8.3%).

Interestingly, a recent analysis of 111 publications related to Strachan's hygiene hypothesis, stating that the lack of exposure to infections in early life increases the risk of rhinitis, revealed a selection bias of 5,551 citations on Web of Science. 37 The articles supportive of the hypothesis were cited more than nonsupportive ones (odds ratio adjusted for study design, 2.2; 95% confidence interval, 1.6–3.1). A similar conclusion pointing to a citation bias distorting bibliometrics of hypotheses was reached by an earlier analysis of a citation network linked to the idea that β-amyloid, which is involved in the pathogenesis of Alzheimer disease, is produced by skeletal muscle of patients with inclusion body myositis. 38 The results of both studies are in line with the notion that ‘positive’ citations are more frequent in the field of biomedicine than ‘negative’ ones, and that citations to articles with proven hypotheses are too common. 39

Social media channels are playing an increasingly active role in the generation and evaluation of scientific hypotheses. In fact, publicly discussing research questions on platforms of news outlets, such as Reddit, may shape hypotheses on health-related issues of global importance, such as obesity. 40 Analyzing Twitter comments, researchers may reveal both potentially valuable ideas and unfounded claims that surround groundbreaking research ideas. 41 Social media activities, however, are unevenly distributed across different research topics, journals and countries, and these are not always objective professional reflections of the breakthroughs in science. 2 , 42

Scientific hypotheses are essential for progress in science and advances in healthcare. Innovative ideas should be based on a critical overview of related scientific facts and evidence-based data, often overlooked by others. To generate realistic hypothetical theories, the authors should comprehensively analyze the literature and suggest relevant and ethically sound design for future studies. They should also consider their hypotheses in the context of research and publication ethics norms acceptable for their target journals. The journal editors aiming to diversify their portfolio by maintaining and introducing hypotheses section are in a position to upgrade guidelines for related articles by pointing to general and specific analyses of the subject, preferred study designs to test hypotheses, and ethical implications. The latter is closely related to specifics of hypotheses. For example, editorial recommendations to outline benefits and risks of a new laboratory test or therapy may result in a more balanced article and minimize associated risks afterwards.

Not all scientific hypotheses have immediate positive effects. Some, if not most, are never tested in properly designed research studies and never cited in credible and indexed publication outlets. Hypotheses in specialized scientific fields, particularly those hardly understandable for nonexperts, lose their attractiveness for increasingly interdisciplinary audience. The authors' honest analysis of the benefits and limitations of their hypotheses and concerted efforts of all stakeholders in science communication to initiate public discussion on widely visible platforms and social media may reveal rational points and caveats of the new ideas.

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

Author Contributions:

• Conceptualization: Gasparyan AY, Yessirkepov M, Kitas GD.
• Methodology: Gasparyan AY, Mukanova U, Ayvazyan L.
• Writing - original draft: Gasparyan AY, Ayvazyan L, Yessirkepov M.
• Writing - review & editing: Gasparyan AY, Yessirkepov M, Mukanova U, Kitas GD.

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

Brief and easily digested

Rate this article Cancel Reply

Your email address will not be published.

Enago Academy's Most Popular Articles

• Reporting Research

Choosing the Right Analytical Approach: Thematic analysis vs. content analysis for data interpretation

In research, choosing the right approach to understand data is crucial for deriving meaningful insights.…

Comparing Cross Sectional and Longitudinal Studies: 5 steps for choosing the right approach

The process of choosing the right research design can put ourselves at the crossroads of…

• Industry News

COPE Forum Discussion Highlights Challenges and Urges Clarity in Institutional Authorship Standards

The COPE forum discussion held in December 2023 initiated with a fundamental question — is…

• Career Corner

Unlocking the Power of Networking in Academic Conferences

Embarking on your first academic conference experience? Fear not, we got you covered! Academic conferences…

Research Recommendations – Guiding policy-makers for evidence-based decision making

Research recommendations play a crucial role in guiding scholars and researchers toward fruitful avenues of…

Choosing the Right Analytical Approach: Thematic analysis vs. content analysis for…

Comparing Cross Sectional and Longitudinal Studies: 5 steps for choosing the right…

How to Design Effective Research Questionnaires for Robust Findings

Sign-up to read more

Subscribe for free to get unrestricted access to all our resources on research writing and academic publishing including:

• 2000+ blog articles
• 50+ Webinars
• 10+ Expert podcasts
• 50+ Infographics
• 10+ Checklists
• Research Guides

We hate spam too. We promise to protect your privacy and never spam you.

• Publishing Research
• AI in Academia
• Promoting Research
• Diversity and Inclusion
• Infographics
• Expert Video Library
• Other Resources
• Enago Learn
• Upcoming & On-Demand Webinars
• Peer Review Week 2024
• Open Access Week 2023
• Conference Videos
• Enago Report
• Journal Finder
• Enago Plagiarism & AI Grammar Check
• Editing Services
• Publication Support Services
• Research Impact
• Translation Services
• Publication solutions
• AI-Based Solutions
• Thought Leadership
• Call for Articles
• Call for Speakers
• Author Training
• Edit Profile

I am looking for Editing/ Proofreading services for my manuscript Tentative date of next journal submission:

In your opinion, what is the most effective way to improve integrity in the peer review process?

Definition of a Hypothesis

What it is and how it's used in sociology

• Key Concepts
• Major Sociologists
• News & Issues
• Research, Samples, and Statistics
• Recommended Reading
• Archaeology

A hypothesis is a prediction of what will be found at the outcome of a research project and is typically focused on the relationship between two different variables studied in the research. It is usually based on both theoretical expectations about how things work and already existing scientific evidence.

Within social science, a hypothesis can take two forms. It can predict that there is no relationship between two variables, in which case it is a null hypothesis . Or, it can predict the existence of a relationship between variables, which is known as an alternative hypothesis.

In either case, the variable that is thought to either affect or not affect the outcome is known as the independent variable, and the variable that is thought to either be affected or not is the dependent variable.

Researchers seek to determine whether or not their hypothesis, or hypotheses if they have more than one, will prove true. Sometimes they do, and sometimes they do not. Either way, the research is considered successful if one can conclude whether or not a hypothesis is true. 

Null Hypothesis

A researcher has a null hypothesis when she or he believes, based on theory and existing scientific evidence, that there will not be a relationship between two variables. For example, when examining what factors influence a person's highest level of education within the U.S., a researcher might expect that place of birth, number of siblings, and religion would not have an impact on the level of education. This would mean the researcher has stated three null hypotheses.

Alternative Hypothesis

Taking the same example, a researcher might expect that the economic class and educational attainment of one's parents, and the race of the person in question are likely to have an effect on one's educational attainment. Existing evidence and social theories that recognize the connections between wealth and cultural resources , and how race affects access to rights and resources in the U.S. , would suggest that both economic class and educational attainment of the one's parents would have a positive effect on educational attainment. In this case, economic class and educational attainment of one's parents are independent variables, and one's educational attainment is the dependent variable—it is hypothesized to be dependent on the other two.

Conversely, an informed researcher would expect that being a race other than white in the U.S. is likely to have a negative impact on a person's educational attainment. This would be characterized as a negative relationship, wherein being a person of color has a negative effect on one's educational attainment. In reality, this hypothesis proves true, with the exception of Asian Americans , who go to college at a higher rate than whites do. However, Blacks and Hispanics and Latinos are far less likely than whites and Asian Americans to go to college.

Formulating a Hypothesis

Formulating a hypothesis can take place at the very beginning of a research project , or after a bit of research has already been done. Sometimes a researcher knows right from the start which variables she is interested in studying, and she may already have a hunch about their relationships. Other times, a researcher may have an interest in ​a particular topic, trend, or phenomenon, but he may not know enough about it to identify variables or formulate a hypothesis.

Whenever a hypothesis is formulated, the most important thing is to be precise about what one's variables are, what the nature of the relationship between them might be, and how one can go about conducting a study of them.

Updated by Nicki Lisa Cole, Ph.D

• What It Means When a Variable Is Spurious
• Understanding Path Analysis
• Pilot Study in Research
• Simple Random Sampling
• Exploitation
• What Is Multiculturalism? Definition, Theories, and Examples
• Convenience Samples for Research
• What Is Cultural Capital? Do I Have It?
• What Does Consumerism Mean?
• Visualizing Social Stratification in the U.S.
• What Is Symbolic Interactionism?
• What Is Cultural Hegemony?
• Understanding Stratified Samples and How to Make Them
• What Is Groupthink? Definition and Examples
• What Is Ethnography?
• What Is a Reference Group?

Public Health Notes

Your partner for better health, hypothesis in research: definition, types and importance .

April 21, 2020 Kusum Wagle Epidemiology 0

Table of Contents

What is Hypothesis?

• Hypothesis is a logical prediction of certain occurrences without the support of empirical confirmation or evidence.
• In scientific terms, it is a tentative theory or testable statement about the relationship between two or more variables i.e. independent and dependent variable.

Different Types of Hypothesis:

1. Simple Hypothesis:

• A Simple hypothesis is also known as composite hypothesis.
• In simple hypothesis all parameters of the distribution are specified.
• It predicts relationship between two variables i.e. the dependent and the independent variable

2. Complex Hypothesis:

• A Complex hypothesis examines relationship between two or more independent variables and two or more dependent variables.

3. Working or Research Hypothesis:

• A research hypothesis is a specific, clear prediction about the possible outcome of a scientific research study based on specific factors of the population.

4. Null Hypothesis:

• A null hypothesis is a general statement which states no relationship between two variables or two phenomena. It is usually denoted by H 0 .

5. Alternative Hypothesis:

• An alternative hypothesis is a statement which states some statistical significance between two phenomena. It is usually denoted by H 1 or H A .

6. Logical Hypothesis:

• A logical hypothesis is a planned explanation holding limited evidence.

7. Statistical Hypothesis:

• A statistical hypothesis, sometimes called confirmatory data analysis, is an assumption about a population parameter.

Although there are different types of hypothesis, the most commonly and used hypothesis are Null hypothesis and alternate hypothesis . So, what is the difference between null hypothesis and alternate hypothesis? Let’s have a look:

Major Differences Between Null Hypothesis and Alternative Hypothesis:

Importance of Hypothesis:

• It ensures the entire research methodologies are scientific and valid.
• It helps to assume the probability of research failure and progress.
• It helps to provide link to the underlying theory and specific research question.
• It helps in data analysis and measure the validity and reliability of the research.
• It provides a basis or evidence to prove the validity of the research.
• It helps to describe research study in concrete terms rather than theoretical terms.

Characteristics of Good Hypothesis:

• Should be simple.
• Should be specific.
• Should be stated in advance.

References and For More Information:

https://ocw.jhsph.edu/courses/StatisticalReasoning1/PDFs/2009/BiostatisticsLecture4.pdf

https://keydifferences.com/difference-between-type-i-and-type-ii-errors.html

https://www.khanacademy.org/math/ap-statistics/tests-significance-ap/error-probabilities-power/a/consequences-errors-significance

https://stattrek.com/hypothesis-test/hypothesis-testing.aspx

http://davidmlane.com/hyperstat/A2917.html

https://study.com/academy/lesson/what-is-a-hypothesis-definition-lesson-quiz.html

https://keydifferences.com/difference-between-null-and-alternative-hypothesis.html

https://blog.minitab.com/blog/adventures-in-statistics-2/understanding-hypothesis-tests-why-we-need-to-use-hypothesis-tests-in-statistics

• Characteristics of Good Hypothesis
• complex hypothesis
• example of alternative hypothesis
• example of null hypothesis
• how is null hypothesis different to alternative hypothesis
• Importance of Hypothesis
• null hypothesis vs alternate hypothesis
• simple hypothesis
• Types of Hypotheses
• what is alternate hypothesis
• what is alternative hypothesis
• what is hypothesis?
• what is logical hypothesis
• what is null hypothesis
• what is research hypothesis
• what is statistical hypothesis
• why is hypothesis necessary

Copyright © 2024 | WordPress Theme by MH Themes

• Translators
• Graphic Designers

Please enter the email address you used for your account. Your sign in information will be sent to your email address after it has been verified.

How Does a Hypothesis Differ From a Research Question?

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

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

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

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

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

Understanding hypotheses

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

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

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

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

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

Understanding research questions

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

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

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

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

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

Key differences between a hypothesis and a research question

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

When to use which

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

Situations where a hypothesis is more appropriate

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

Situations where a research question is more appropriate

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

The interrelation between hypotheses and research questions

Understanding how a research question can give rise to hypotheses.

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

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

Can a hypothesis transform into a research question?

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

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

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

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

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

Header image by Luke Tanis .

Talk to Our counsellor: 9916082261

• Book your demo
• GS Foundation Classroom Program
• Current Affairs Monthly Magazine
• Our Toppers

Practice  Questions  – Write short note on Importance and Sources of Hypothesis in Sociological Research. [ UPSC 2008]

Approach –  Introduction, What makes Hypothesis relevant in a sociological research?, What are the sources which aids us to derive hypothesis?, Conclusion

INTRODUCTION

A hypothesis is a prediction of what will be found at the outcome of a research project and is typically focused on the relationship between two different variables studied in the research. It is usually based on both theoretical expectations about how things work and already existing scientific evidence.

We know that research begins with a problem or a felt need or difficulty. The purpose of research is to find a solution to the difficulty. It is desirable that the researcher should propose a set of suggested solutions or explanations of the  difficulty which the research proposes to solve. Such tentative solutions formulated as a proposition are called hypotheses. The suggested solutions formulated as hypotheses may or may not be the real solutions to the problem. Whether they are or not is the task of research to test and establish.

DEFINTITIONS

• Lundberg- A Hypothesis is a tentative generalisation, the validity of which remains to be tested. In its most elementary stages, the hypothesis may be any hunch, guess imaginative idea or Intuition whatsoever which becomes the basis of action or Investigation.
• Bogardus- A Hypothesis is a proposition to be tested.
• Goode and Hatt- It is a proposition which can be put to test to determinants validity.
• P. V. Yaung- The idea of ​a temporary but central importance that becomes the basis of useful research is called a working hypothesis.

TYPES OF HYPOTHESIS

i)  Explanatory Hypothesis : The purpose of this hypothesis is to explain a certain fact. All hypotheses are in a way explanatory for a hypothesis is advanced only when we try to explain the observed fact. A large number of hypotheses are advanced to explain the individual facts in life. A theft, a murder, an accident are examples.

ii) Descriptive Hypothesis:  Some times a researcher comes across a complex phenomenon. He/ she does not understand the relations among the observed facts. But how to account for these facts? The answer is a descriptive hypothesis. A hypothesis is descriptive when it is based upon the points of resemblance of some thing. It describes the cause and effect relationship of a phenomenon e.g., the current unemployment rate of a state exceeds 25% of the work force. Similarly, the consumers of local made products constitute asignificant market segment.

iii) Analogical Hypothesis : When we formulate a hypothesis on the basis of similarities (analogy), it is called an analogical hypothesis e.g., families with higher earnings invest more surplus income on long term investments.

iv) Working hypothesis : Some times certain facts cannot be explained adequately by existing hypotheses, and no new hypothesis comes up. Thus, the investigation is held up. In this situation, a researcher formulates a hypothesis which enables to continue investigation. Such a hypothesis, though inadequate and formulated for the purpose of further investigation only, is called a working hypothesis. It is simply accepted as a starting point in the process of investigation.

v) Null Hypothesis:  It is an important concept that is used widely in the sampling theory. It forms the basis of many tests of significance. Under this type, the hypothesis is stated negatively. It is null because it may be nullified, if the evidence of a random sample is unfavourable to the hypothesis. It is a hypothesis being tested (H0). If the calculated value of the test is less than the permissible value, Null hypothesis is accepted, otherwise it is rejected. The rejection of a null hypothesis implies that the difference could not have arisen due to chance or sampling fluctuations.

USES OF HYPOTHESIS

i) It is a starting point for many a research work. ii) It helps in deciding the direction in which to proceed. iii) It helps in selecting and collecting pertinent facts. iv) It is an aid to explanation. v) It helps in drawing specific conclusions. vi) It helps in testing theories. vii) It works as a basis for future knowledge.

ROLE  OF HYPOTHESIS

In any scientific investigation, the role of hypothesis is indispensable as it always guides and gives direction to scientific research. Research remains unfocused without a hypothesis. Without it, the scientist is not in position to decide as to what to observe and how to observe. He may at best beat around the bush. In the words of Northrop, “The function of hypothesis is to direct our search for order among facts, the suggestions formulated in any hypothesis may be solution to the problem, whether they are, is the task of the enquiry”.

First ,  it is an operating tool of theory. It can be deduced from other hypotheses and theories. If it is correctly drawn and scientifically formulated, it enables the researcher to proceed on correct line of study. Due to this progress, the investigator becomes capable of drawing proper conclusions. In the words of Goode and Hatt, “without hypothesis the research is unfocussed, a random empirical wandering. The results cannot be studied as facts with clear meaning. Hypothesis is a necessary link between theory and investigation which leads to discovery and addition to knowledge.

Secondly,  the hypothesis acts as a pointer to enquiry. Scientific research has to proceed in certain definite lines and through hypothesis the researcher becomes capable of knowing specifically what he has to find out by determining the direction provided by the hypothesis. Hypotheses acts like a pole star or a compass to a sailor with the help of which he is able to head in the proper direction.

Thirdly , the hypothesis enables us to select relevant and pertinent facts and makes our task easier. Once, the direction and points are identified, the researcher is in a position to eliminate the irrelevant facts and concentrate only on the relevant facts. Highlighting the role of hypothesis in providing pertinent facts, P.V. Young has stated, “The use of hypothesis prevents a blind research and indiscriminate gathering of masses of data which may later prove irrelevant to the problem under study”. For example, if the researcher is interested in examining the relationship between broken home and juvenile delinquency, he can easily proceed in the proper direction and collect pertinent information succeeded only when he has succeed in formulating a useful hypothesis.

Fourthly , the hypothesis provides guidance by way of providing the direction, pointing to enquiry, enabling to select pertinent facts and helping to draw specific conclusions. It saves the researcher from the botheration of ‘trial and error’ which causes loss of money, energy and time.

Finally,  the hypothesis plays a significant role in facilitating advancement of knowledge beyond one’s value and opinions. In real terms, the science is incomplete without hypotheses.

STAGES OF HYPOTHESIS TESTING

• EXPERIMENTATION   : Research study focuses its study which is manageable and approachable to it and where it can test its hypothesis. The study gradually becomes more focused on its variables and influences on variables so that hypothesis may be tested. In this process, hypothesis can be disproved.
• REHEARSAL TESTING :   The researcher should conduct a pre testing or rehearsal before going for field work or data collection.
• FIELD RESEARCH :  To test and investigate hypothesis, field work with predetermined research methodology tools is conducted in which interviews, observations with stakeholders, questionnaires, surveys etc are used to follow. The documentation study may also happens at this stage.
• PRIMARY & SECONDARY DATA/INFORMATION ANALYSIS :  The primary or secondary data and information’s available prior to hypothesis testing may be used to ascertain validity of hypothesis itself.

Formulating a hypothesis can take place at the very beginning of a research project, or after a bit of research has already been done. Sometimes a researcher knows right from the start which variables she is interested in studying, and she may already have a hunch about their relationships. Other times, a researcher may have an interest in ​a particular topic, trend, or phenomenon, but he may not know enough about it to identify variables or formulate a hypothesis. Whenever a hypothesis is formulated, the most important thing is to be precise about what one’s variables are, what the nature of the relationship between them might be, and how one can go about conducting a study of them.

Request a call back.

Let us help you guide towards your career path We will give you a call between 9 AM to 9 PM

Join us to give your preparation a new direction and ultimately crack the Civil service examination with top rank.

• #1360, 2nd floor,above Philips showroom, Marenhalli, 100ft road, Jayanagar 9th Block, Bangalore
• [email protected]
• +91 9916082261
• Terms & Conditions
• Privacy Policy

© 2022 Achievers IAS Classes

Click Here To Download Brochure

• Health & Social Care
• Public Health General
• Public Health Behavior & Education

Health Behavior: Theory, Research, and Practice, 6th Edition

ISBN: 978-1-394-21130-2

August 2024

Jossey-Bass

Digital Evaluation Copy

Karen Glanz , Barbara K. Rimer , K. Viswanath

The essential health behavior text, updated with the latest theories, research, and issues

Health Behavior: Theory, Research and Practice provides a thorough introduction to understanding and changing health behavior—important facets of the public health role. Since the publication of the first edition, this comprehensive book has become the gold standard of health behavior texts. This new sixth edition has been updated to reflect the most recent changes in the public health field, including findings from real-world interventions based on the theories described in the book. Offering perspective applicable at the individual, interpersonal, group, and community levels, this essential guide gives public health students and practitioners an authoritative reference for both the theoretical and practical aspects of health behavior.

• Explore the link between culture, health, and the importance of community
• Get up to date on emerging theories of health behavior and their applications
• Examine the push toward evidence-based interventions and focus on diverse populations
• Learn how e-health and social media factor into health communication

Written and edited by leading theorists and researchers in the field, Health Behavior builds a solid understanding of how to analyze and improve health behaviors and health.

KAREN GLANZ, PhD, MPH, is George A. Weiss University Professor and Director of the UPenn Prevention Research Center and the Center for Health Behavior Research at the University of Pennsylvania.

BARBARA K. RIMER, DrPH, is Dean Emerita and Alumni Distinguished Professor Emerita of Health Behavior at the University of North Carolina at Chapel Hill Gillings School of Global Public Health.

K. VISWANATH, PhD, is Lee Kum Kee Professor of Health Communication in the Department of Social and Behavioral Sciences at the Harvard T. H. Chan School of Public Health and Professor of Population Sciences at the Dana-Farber Cancer Institute.

COLUMBIA UNIVERSITY IN THE CITY OF NEW YORK

Student Research Worker

• Division of Scholarship & Rsch
• Columbia University Medical Center
• Opening on: Aug 28 2024
• Job Type: Short Term Casual
• Bargaining Unit:
• Regular/Temporary: Temporary
• End Date if Temporary:
• Hours Per Week: 10
• Standard Work Schedule:
• Salary Range: 22.50-22.50

Position Summary

The School of Nursing is looking to hire one Student Research Worker to work in a research assistant capacity on an NIH-funded study testing the impact of an asthma intervention with teenagers in NYC high schools. Most of the duties will occur in the schools between October and June, with some office work during this time as well. Targeted high schools are located in southern Brooklyn, and northern and downtown Manhattan. The incumbent must be able to travel to these schools.

Responsibilities

Responsibilities will include:

• Photocopying study materials;
• Distributing informational letters to students regarding the study, which may include answering students' questions about the study;
• Proctoring a brief, 15-minute survey completed by high school students in class;
• Describing the study to eligible teenagers and obtaining study consent;
• Interviewing students in person and/or by phone or Zoom, and their caregivers by phone or Zoom;
• Entering data; and
• Other tasks as needed.

This is a grant-funded position.  Continued employment is based on the availability of funding.

This is a temporary position with a flexible work schedule.

Minimum Qualifications

Requires at least a bachelor’s degree and current enrollment at Columbia University.

Preferred Qualifications

• At least one year of related experience or equivalent in education, training, and experience.
• Currently enrolled as a student at Columbia University
• Computer fluency in Microsoft Word, Excel, and PowerPoint.
• Excellent interpersonal, communication, and organizational skills.
• Ability to work well in a team. 
• Spanish fluency is strongly preferred.
• Previous research experience is desired.
• Must possess excellent interpersonal and organizational skills
• Ability to present information to groups of teenagers (i.e., make classroom presentations)
• Must be able to travel between Manhattan and Brooklyn currently, which may expand to other boroughs
• Ability to work in the schools a few days a week during the high school day, which is typically 8 – 3
• Ability to work some nights/weekends to follow up with teenagers and/or interview parents.

Equal Opportunity Employer / Disability / Veteran

Columbia University is committed to the hiring of qualified local residents.

Commitment to Diversity 

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

Thank you - we'll send an email shortly.

Other Recently Posted Jobs

Clinical Coordinator II - RN

Clinical coordinator ii - nephrology, technician b.

Refer someone to this job

• ©2022 Columbia University
• Accessibility
• Administrator Log in

Wait! Before you go, are you interested in a career at Columbia University? Sign up here! 

Thank you, for sharing your information. A member of our team will reach out to you soon!

This website uses cookies as well as similar tools and technologies to understand visitors' experiences. By continuing to use this website, you consent to Columbia University's usage of cookies and similar technologies, in accordance with the Columbia University Website Cookie Notice .

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 28 August 2024

The role of brand identity, brand lifestyle congruence, and brand satisfaction on repurchase intention: a multi-group structural equation model

• Ayşegül Acar 1 ,
• Naci Büyükdağ 2 ,
• Burak Türten 1 ,
• Ersin Diker 3 &
• Gülsüm Çalışır 3  

Humanities and Social Sciences Communications volume  11 , Article number:  1102 ( 2024 ) Cite this article

Metrics details

• Business and management
• Operational research

This study investigated the relationship between brand identity, brand lifestyle congruence, brand satisfaction, and repurchase intention. In addition, this study examined how the primary reference group’s family and peer/friend affected individuals’ perceptions of brand identity, brand-lifestyle congruence, brand satisfaction, and purchase intention through a multi-group structural equation model. A total of 610 valid and useable responses, collected from a social media channel, were analyzed. Grounded in social identity theory and self-congruity theory, a set of hypotheses was examined within a research model. The findings show that brand identity significantly affects brand lifestyle congruence, brand satisfaction, and repurchase intentions. In addition, brand-lifestyle congruence significantly affects brand satisfaction and repurchase intentions, with brand satisfaction also significantly affecting purchase intentions. Also, high-income and elderly consumers tend to ignore the family and peer effects. Middle-aged, middle-income men who value product origin show a strong brand perception, and are less influenced by family. In contrast, women, typically lower-income and price-focused, are more receptive to family and peer effects and generally indifferent to product origin. This research advances brand identity literature by examining the effects of brand brand-lifestyle congruence, brand satisfaction, and purchase intention. It suggests that the synergy between brand identity, brand lifestyle congruence, and brand satisfaction significantly enhances repurchase intentions. Besides, examining profiles in the context of brands, consumers, and reference groups contributes additional value to the field.

Similar content being viewed by others

COBRAs and virality: viral campaign values on consumer behaviour

Culturally mixed co-branding product framing in China: the role of cultural sensitivity, product quality, and purchase probability

Exploring the effects of audience and strategies used by beauty vloggers on behavioural intention towards endorsed brands

Introduction.

Consumption in sociology is influenced by fundamental sources such as identity formation and group communication among members. These factors are closely related to the development of different lifestyles, personal identity, and self-concept (Haanpää, 2007 ). An essential element of identity influencing consumption and group membership is national identity (Black and Veloutsou, 2017 ). Moreover, brands act as critical cultural symbols, and the meanings they convey are pivotal in establishing harmony between consumers and brands (Hollenbeck et al. 2008 ). Haanpää ( 2007 ) stated that post-modern consumer society’s pursuit of diversity and freedom of choice creates various social identities and lifestyles. Although the impact of lifestyle on purchasing is often underrecognized by consumers, it is crucial in shaping consumption practices (Suyanto et al. 2019 ). The dynamic nature of changing lifestyles supports the development of individual purchasing styles and decision-making (Haanpää, 2007 ), which are further influenced by factors such as personalities, past purchasing experiences, and ages (Adnan et al. 2017 ). This interaction between individual traits and societal influences culminates in a consumer behavior pattern where individuals purchase identities rather than mere products. They seek to express their lifestyles through these choices, prioritizing expression over functional needs (Arunyanart and Utiswannakul, 2019 ). Therefore, every purchase behavior related to consumption is intertwined with identity and lifestyle (Suyanto et al. 2019 ).

Brand identity communicates to consumers what a brand provides or stands for, focuses on meeting consumers’ symbolic needs more than their functional needs, and communicates uniqueness and the status and prestige offered by the brand compared to competitors through brand distinctiveness and brand prestige. (Alnawas and Altarifi, 2016 : 114). So, brand identity is an important element of differentiation in a crowded market by conveying to consumers what a brand stands for and focusing on their symbolic rather than functional needs (Alnawas and Altarifi, 2016 ). It not only meets consumer needs but also communicates the uniqueness and status offered by the brand, which is essential given the extensive array of brand options available (Da Silveira et al. 2013a ). Patagonia, for example, targets environmentally conscious consumers with its strong commitment to sustainability and its “Worn Wear” campaign, encouraging the purchase of used products to support eco-friendly lifestyles. Similarly, Apple’s aligns with tech-savvy individuals through its emphasis on innovation and simplicity, while Whole Foods Market attracts health-conscious shoppers with its emphasis on organic and natural foods (Konuk, 2023 ). Moreover, Red Bull creates a brand identity around extreme sports and energy, appealing to adventure-seekers and athletes. These examples show how brand identity not only reflects but also shapes consumer behavior and preferences. This strategic alignment of brand identities with consumer lifestyles not only segments the market effectively but also significantly influences consumer behavior and brand loyalty (Holt, 2002 ; Nam et al. 2011a ). Furthermore, brand identity is deeply influenced by cultural factors, which further shape and define the relationship between consumers and brands (Coleman et al. 2011 ). These interactions highlight how cultural contexts and consumer lifestyle choices interplay to mold brand perception and consumer engagement.

Despite extensive studies on consumer behavior and brand identity, a significant gap persists in understanding the role of social identities and reference groups in shaping interactions between brand identity and lifestyle choices, particularly within culturally diverse and developing markets like Türkiye. The influences of internal and external reference groups, such as family and friends, on consumer choices have not been sufficiently explored, especially regarding how these groups mediate the relationship between consumer self-concept, lifestyle alignment, and brand preferences. This research delves into these understudied dynamics and addresses a critical gap in the literature. It proposes a comprehensive approach to understanding the nuanced factors influencing consumer decisions in non-Western contexts.

This study investigated the relationship between brand identity, brand lifestyle congruence, brand satisfaction, and repurchase intention, with a particular focus on how reference groups affect these dynamics within the Turkish consumer market (Le-Hoang, 2020 ; Mi et al. 2019 ; Ozdemir et al. 2020 ; Veloutsou and Moutinho, 2009 ; Wang et al. 2012 ; Wei and Yu, 2012 ; Yang et al. 2007 ). By exploring these relationships, the study offers empirical evidence that enhances the understanding of brand loyalty and consumer engagement, considering the social systems that consumers operate within. The findings enrich theoretical discussions and provide practical implications for marketers, helping them to tailor their strategies to align more closely with the social identities and lifestyles of their target demographics. This comprehensive approach both addresses a critical gap in the literature and enhances the academic and practical knowledge of brand management and consumer psychology in a culturally rich setting.

Literature review

Brand identity.

Brand identity comprises a set of strategic tools used by organizations to enhance visibility, differentiate from competitors, and build brand value and consumer loyalty (Keller, 1993 : Wheeler, 2014 ). It serves to connect customers with a brand, offering a mix of tangible and intangible benefits that foster a strong brand-customer relationship (Aaker and Keller, 1996 ). Within branding literature, brand identity is seen as an internal, enduring framework within a corporation (Koporcic and Halinen, 2018 ), shaping customer perceptions and interactions with the brand (Törmala and Gyrd-Jones, 2017 ). Iglesias et al. ( 2020 ) defined it as the amalgamation of a firm’s intended image and the commitments it makes to its clients, which are crucial for conveying the brand’s identity and values to both external and internal stakeholders (Essamri et al. 2019 ). The strategic approach of a brand identity is vital, influencing the brand’s market success by fostering trust, ensuring distinctiveness, and enhancing customer commitment (Malaska Saraniemi and Tahtinen, 2011 ; Muhonen et al. 2017 ). It includes developing a value proposition that offers practical, emotional, and self-expressive advantages (Gustafson and Pomirleanu, 2021 ), thus establishing significant connections with clients and reinforcing long-term loyalty. Overall, a well-defined brand identity not only supports a business’s strategic goals but also plays a crucial role in building lasting bonds between the brand and its customers, encompassing more than mere visual features or logos (Essamri et al. 2019 ; Iglesias et al. 2020 ).

Brand-lifestyle congurence

Brand-lifestyle congruence is a pivotal concept that captures the alignment between a brand’s ethos and the individual lifestyles of its customers. Lifestyle, encompassing individual psychological preferences, values, beliefs, and consumption patterns, significantly influences consumer behavior (Coursaris and Van Osch, 2015 ; Díaz et al. 2017 ; Holt, 2002 ; Li et al. 2018 ). It serves as a powerful means for individuals to convey their identities and preferences, often through the brands they choose (Li et al. 2018 ). This expression is not limited to any single domain but is evident across various aspects of life, including food preferences, which can reflect broader lifestyle choices (Jang et al. 2011 ).

Importantly, lifestyle is a more significant predictor of consumer behavior than demographic factors, making it crucial for brands to understand and align with the lifestyles of their target markets (Tangsupwattana and Liu, 2017 ). Brands that successfully resonate with the lifestyles of their consumers are more likely to foster strong loyalty, as customers tend to gravitate towards brands that reflect their personal values and lifestyles (Alnawas and Altarifi, 2015 ; Catalin and Andreea, 2014 ; Nam et al. 2011b ; Solomon, 2015 ). Such alignment offers symbolic benefits that are as important as the functional aspects of a product, thereby enhancing consumer satisfaction and promoting repeat purchases (Nam et al. 2011a ; Sharma et al. 2018 ). Moreover, understanding and monitoring the evolving lifestyles of consumers can help brands stay relevant and maintain a positive relationship with their audience. This connection leads to increased brand loyalty and purchasing behavior driven by emotional connections with the brand (Çifci et al. 2016 ; Ekinci et al. 2013 ).

Brand satisfaction

Brand satisfaction is a critical measure of a consumer’s appraisal of a product or service post-purchase, determined by comparing their initial expectations against the actual performance (Tse and Wilton, 1988 ). Tu and Chang ( 2012 ) stated that satisfaction can manifest in two forms: transaction-specific, which focuses on individual purchase experiences, and accumulative, which considers the overall experience with a product over time (Wardani and Gustia, 2016 ; Zboja and Voorhees, 2006 ). A positive brand experience triggers a favorable attitude towards the brand, significantly influencing consumer loyalty (Chen-Yu et al. 2017 ; Cheng et al. 2019 ).

Grisaffe and Nguyen ( 2011 ) defined brand satisfaction as the comprehensive assessment of a brand by consumers, reflecting the depth of their contentment with the product or service. This satisfaction fosters trust in the brand, enhancing the likelihood of repeat purchases (Chinomona, 2013 ; Cuong, 2020 ). Further, empirical studies have shown that satisfaction not only predicts brand trust but also increases brand preference. Consumers who are satisfied are more likely to choose the same brand repeatedly (Shin, et al. 2019 ). This preference develops because satisfied customers experience a deeper emotional connection with the brand, which reflects the alignment of the brand’s performance with their expectations. (Chinomona et al. 2013 ; Grisaffe and Nguyen, 2011 ).

Repurchase intention

Ismail and Ismail ( 2022 ) characterized repurchase intention as a critical metric in consumer behavior. It reflects the likelihood that a customer will buy a product again after an initial purchase (Taylor and Baker, 1994 ). This intention is often gauged by how consumers evaluate the performance of a product relative to their expectations (Ismail and Ismail, 2022 ). Han et al. ( 2020 ) described it as the culmination of the purchasing decision-making process, where consumers assess the service and product quality they received. This evaluation is crucial as it influences overall purchasing behavior and can accurately predict future buying behavior. Satisfaction plays an important role here; consumers compare their initial expectations with the actual product performance, and this comparison dictates their future purchasing decisions (Oliver, 1980 ). Han et al. ( 2019 ) further emphasized that positive reactions to product performance or quality foster strong repurchase intentions. Achieving high customer repurchase intentions is crucial for brands aiming to improve their market reputation and worth (Yin et al. 2022 ).

Influence of reference groups and theoretical integration

Reference groups significantly influence consumer behavior by shaping their perceptions, attitudes, and brand preferences, as explained by social identity theory. According to Tajfel and Turner ( 1986 ), this theory posits that individuals derive aspects of their identity from the groups to which they belong, which in turn guides their behavior and preferences. This is particularly relevant in culturally rich markets like Türkiye, where consumer decisions are heavily influenced by both internal and external reference groups, such as family and social networks. Ekinci et al. ( 2013 ) emphasized that lifestyle serves as a tangible reference point influenced by social identities, while brand identity is supported by more abstract aspects of self-concept. Further studies by Carrillo Barbosa and Guzmán Rincón ( 2022 ) and Castillo-Abdul et al. ( 2022 ) illustrated how companies create social systems and relationships tailored to specific demographic and cultural realities, thus applying theoretical insights to practical marketing strategies. These dynamics between social identity and consumer behavior highlight the importance of understanding reference groups in developing effective marketing strategies that resonate well with target demographics, which enhances brand loyalty and consumer engagement. This comprehensive approach not only strengthens the paper’s theoretical framework but also provides a solid foundation for practical applications in non-Western markets.

Hypothesis development

Relationships between the study variables.

Self-concept is a useful theoretical framework for understanding consumer decision-making. Research has indicated that customers tend to choose brands that align with their self-perception or desired self-image (Landon, 1974 ; Malhotra, 1988 ; Sirgy, 2018 ). Several studies have explored how consumers express their identities through interactions with brand personalities (Belk, 1988 ; Dolich, 1969 ; Malhotra, 1988 ). In psychology, self-concept (also called self-image) is described as “the totality of the individual’s thoughts and feelings having reference to himself/herself as an object” (Wang et al. 2021 , p. 177).

Self-congruity is a logical extension of self-concept. It plays a critical role in predicting consumer behavior, a concept widely accepted in psychology, marketing, and other disciplines (Sop and Kozak, 2019 ). The self-congruity theory posits that people choose brands or products that match their self-concept (Kumar, 2016 ). Research based on this theory contends that customer perception of brand self-congruence influences their choices and purchasing decisions (Kumar, 2022 ; Sirgy et al. 2016 ). Greater brand self-congruence fosters customer satisfaction, belief, and loyalty toward the brand (Li and Peng, 2021 ; Sirgy et al. 2016 ; Wang et al. 2019 ).

The relationship between brand self-congruity and brand identity is centered on how customers assess the extent to which a brand’s identity aligns with their own when choosing or forming a connection with it (Kumar, 2016 ; Usakli and Baloglu, 2011 ). Consumers who perceive a strong alignment between their identity and a brand’s identity are more likely to have a favorable attitude and preference toward that brand (Lee and Jeong, 2014 ). This alignment strengthens the connection between brand perception and customer loyalty. A strong brand identity helps consumers develop an affinity for the brand and develop strong relationships. Moreover, a clear brand identity satisfies consumer’s desire for uniqueness while simultaneously enhancing self-esteem through brand prestige (Alnawas and Altarifi, 2016 ).

Lifestyle is another crucial elements of common consumption culture that reflects time, money, and consumer identity (Manthiou et al. 2018 ). Aro et al. ( 2018 ) found that consumers prefer brands that reflect their identity and lifestyle over those that do not. Companies such as Patagonia, Apple, Whole Foods Market, and Red Bull exemplify this relationship in their marketing strategies. Therefore, brand identity establishes a relationship between the brand and the customer (Mao et al. 2020 ). Examining these real-world relationships offers valuable insights and expands the academic literature.

H 1 : Brand identity positively affects brand lifestyle congruence .

Brand identity significantly affects the way consumers perceive and value a brand (He et al. 2012 ). A strong brand identity meets both a consumer’s functional needs and their symbolic ones, which strengthens their bonds with the brand and increasing loyalty (Coleman et al. 2011 ). The stronger the brand identity, the greater is the consumer identification and satisfaction with that brand.

According to brand relationship theory, a strong consumer–brand relationship reduces the likelihood of consumers switching to competing brands (Casidy et al. 2018 ). This theory assumes that consumers personify brands and consider them partners. When a strong brand relationship is established, consumers feel satisfied with the brand. In this context, brand identity increases the quality and depth of consumer–brand relationships, thus positively affecting brand satisfaction. A strong brand identity leads consumers to perceive the brand as more valuable and feel more satisfied with their relationship with it.

H 2 : Brand identity positively affects brand satisfaction .

When consumers believe that a brand’s identity aligns with their own, it enhances their connection to the business and increases their likelihood of making repeat purchases (Dennis et al. 2016 ). For instance, Patagonia emphasizes its eco-friendly practices and reducing its carbon footprint. This aligns with environmentally conscious customers, who are thus more inclined to buy and remain loyal to Patagonia. The perceived alignment between brand and consumer identity positively influences the consumer–brand relationship and increases repurchase intentions, as consumers are likely to revisit a brand that reflects their identity (He et al. 2012 ).

Mao et al. ( 2020 ) found a significant positive effect between brand identity and purchase intention. Consumers were more inclined to repurchase and remain loyal to a brand when they perceive its identity aligns with their own values, attitudes, or lifestyles (Prentice et al. 2019 ).

H 3 : Brand identity positively affects repurchase intention .

Grzeskowiak et al. ( 2016 ) noted that brand satisfaction depends on the alignment between a consumer’s identity and the brand. Lifestyle, which refers to the daily wishes and needs of individuals, is also a reflection of the consumer identity (Mathieu et al. 2018 ). Brands that align their identities with customer lifestyles better meet consumer needs (Augusto and Torres, 2018 ). Compared to brands that focus solely on products, those that cater to lifestyle provide consumers with richer experiences, amusement, enjoyment, and satisfaction (Sina and Kim, 2019 ). According to Nam et al. ( 2011a ), lifestyle congruence significantly affects customer satisfaction with the brand.

H 4 : Brand-lifestyle congruence positively affects brand satisfaction .

Brand-lifestyle congruence strengthens brand loyalty as individuals form emotional bonds with brands that align with their lifestyles (Haanpää, 2007 ). Consumers establish these bonds when they perceive that a brand understands their lifestyles and aligns with their values (Li et al. 2012 ). Therefore, individuals are inclined to exhibit loyalty and consistently opt for that brand over its competitors (Tangsupwattana and Liu, 2017 ). The sense of loyalty and emotional bond leads consumers to consider repurchasing the brand owing to the favorable emotions linked to it (Pan et al. 2018 ).

Moreover, satisfaction positively affects purchase intention (Alnawas and Aburub, 2016 ; Carlson et al. 2019 ; Li and Fang, 2019 ; Park et al. 2019 ). Satisfied customers trust a brand more, and when consistently satisfied with their experiences, they may view the brand as trustworthy. Trust leads to a stronger desire to repurchase (Trivedi and Yadav, 2020 ).

H 5 : Brand-lifestyle congruence positively affects repurchase intention .

H 6 : Brand satisfaction positively affects repurchase intention .

The role of reference group

The concept of social identity refers to the aspect of self-image that emerges from an individual’s sense of belonging to a social group and the emotional value attached to this affiliation (Tajfel, 1974 ). According to social identity theory, people form a unique personal identity and a social identity based on their group affiliation (Bao et al. 2017 ). In addition, the theory states that shared attitudes and personality traits activate cultural patterns, which prompts individuals to achieve a positive social identity by associating with desirable groups (Pentina et al. 2013 ). People exhibit similar behaviors within their group and differing behaviors outside of it (Jiang et al. 2016 ). Group members also tend to prioritize maintaining the group’s image and identity (Shimul and Phau, 2023 ). Hence, social identity theory is expected to clarify customer brand self-congruence, emotional attachment to brand identity, and inter-group interactions influencing repurchase experiences.

Scholars employ social identity theory to analyze the impact of reference groups on brand identity (Escalas and Bettman, 2003 ; White and Dahl, 2007 ). Reference groups directly affect the consumer–brand relationship (Veloutsou and Moutinho, 2009 ). White and Dahl ( 2006 ) categorized them into in-groups (family, peers, etc.) and out-groups (aspirational groups, dissociative groups, etc.). “In-group” or “member group” refers to a group that the consumer belongs to. Aligning brand identity with the in-group might increase the consumer–brand relationship (Escalas and Bettman, 2003 ). Family and peers impact consumer–brand relationships or brand loyalty at different levels. Family advice is often trusted due to familiarity, while peer influence supports brand choice through shared experiences and a sense of belonging, though in a less authoritarian manner (Girard, 2010 ; Nolan et al. 2008 ).

H7: The family effect within the reference group moderates the entire model .

H8: The peer/friend effect within the reference group moderates the entire model .

As a result, the research model shown in Fig. 1 was established. In Study 2, the effect of the reference group on consumer profiling was also assessed using multiple correspondence analysis.

Research model.

Methodology

Preparing the data set.

The questionnaires were translated from English to Turkish by three academicians fluent in English and familiar with marketing literature. They ensured the face validity of the scales based on their expertise. Two additional academicians conducted back-translations to verify accuracy. Before beginning the data collection process, a pilot test was conducted to evaluate the clarity of the survey with 75 participants.

Data collection

The target audience was consumers aged 18 and older who regularly use brands. Respondents were asked to choose their favorite brand and fill out a questionnaire based on their choice. A convenience sampling method was used for cost and time efficiency. The survey was administered online via Google Drive, and social media channels (Facebook, Twitter, LinkedIn) were used to distribute the surveys. Data collection started and ended in early 2020. After excluding incomplete responses, 610 valid questionnaires remained. Participation was voluntary, and no incentives were provided.

Among the participants, 53.9% were women, and 46.1% were men. In terms of income, 39.8% earned 3000₺ (Turkish lira) or less (around the minimum wage), while 48.4% had an income between 3001₺ and 7000₺, placing them in the middle-income group. The remaining 11.8% earned 7000₺ or more, classifying them as high-income. Regarding education, 12.9% had an associate degree or lower, 54.3% had an undergraduate degree, and 32.8% had postgraduate education, indicating a well-educated sample.

Participants were from various regions of Türkiye. Most participants (46.4%) were from the Mediterranean region, while the Black Sea region had the lowest representation (2.8%). This broad representation provides a general trend across Türkiye.

In terms of industry preferences, the most liked sector was clothing (32.8%), followed by automotive (17%), technology (14.1%), and sportswear (9.8%). Other sectors ranged between 0.2 and 3.4%. Most participants (92.8%) reported that their friends and peers also use the same brand, and 79.5% said their family members did, too. For 42.3% of respondents, product pricing was a primary consideration, while 57.7% prioritized brand. In addition, 48.4% followed the brand on social media, but only 27% were members of brand communities both online and offline.

All constructs were measured using seven-point Likert-type scales, anchored from “strongly disagree” (1) to “strongly agree” (7). The brand identity scale, adapted from Torres et al. ( 2017 ), consisted of five items, and the repurchase intention scale, adapted from the same source, had three items. The brand-lifestyle congruence scale, adapted from Nam et al. ( 2011a ), consisted of three items. Brand satisfaction, measured using three items, was adapted from Davvetas and Diamantopoulos ( 2017 ).

To identify the reference group, participants were asked, “Do your family members use or have used the brand you chose?” and “Do your friends or peers use or have used the brand you chose?” Responses were used to classify participants into the appropriate reference groups.

Data analysis and results

Structural equation modeling (SEM), a statistical method based on covariance, was used, and the maximum likelihood method, which assumes multivariate normality of the variables, was applied. The first stage involved confirmatory factor analysis (CFA) to determine how well the theoretical model aligns with reality, testing measurement theory by matching theoretical constructs to measured variables (Hair et al. 2014a ). Once confirmed by CFA, the relationships between these constructs were then examined. This approach is preferred because it accurately examines simultaneous relationships and aims to reveal causal relationships between variables.

The structural equation model represents the theory with sets of structural equations, depicted with a visual diagram (Hair et al. 2014b ). To address the difficulty of testing multivariate normality, univariate normality was provided for each variable and its corresponding items (Hair et al. 2014a ). The Kolmogorov–Smirnov and Shapiro-Wilk tests revealed that the variables were not normally distributed ( p value = 0.00 for all variables). However, skewness and kurtosis values ranged between −1 and +1, and the Q-Q plot provided in Appendix 1 illustrates the distribution. Also, the central limit theorem assumes that for sufficiently large sample sizes, the distribution of sample means will approximate normality regardless of the variables’ distributions (Tabachnick and Fidell, 2007 ).

Measurement model

Four constructs were measured using 14 items. Hu and Bentler ( 1999 ) indicated that the d indices exceed the threshold values. Table 1 presents the results of the CFA.

The reliability and convergent validity were confirmed as the composite reliability exceeds 0.7 and the AVE is above 0.5 (Hair et al. 2014a ). Table 2 also shows that discriminant validity was achieved.

Common method bias (CMB) occurs when a single factor explains most of the variance (Gaskin and Lim, 2016 ; Podsakoff et al. 2003 ). The single-factor Harman test was performed to determine whether one factor captured most of the variance. The explained variance rate was 45%. However, given the weakness of this test, the CMB was re-examined using the directly measured latent methods factor approach suggested by Podsakoff et al. ( 2003 ). With an equally restricted regression path of 0.58, the variance explained was 34%. Since the CMB does not exceed 50%, it is either absent or insignificant.

Structural model

According to Hu and Bentler ( 1999 ), all indexes exceed the threshold values, indicating alignment between theory and reality. Table 3 and Fig. 2 present these results.

The results of the structural equation model.

Table 3 shows that brand identity significantly affects brand-lifestyle congruence, brand satisfaction, and repurchase intention. Similarly, brand-lifestyle congruence significantly affects brand satisfaction and repurchase intention, while brand satisfaction significantly affects purchase intention. The model accounts for 14% of the variance in brand-lifestyle congruence, 51% in brand satisfaction, and 56% in repurchase intention.

Multi-group structural equation modeling and analysis results

Multi-group structural equation modeling (SEM) was applied to compare the regression paths between variables. Previous research demonstrated the value of multi-group SEM for examining variables like culture (Babin et al. 2016 ), demographics (Huang and Ge, 2019 ), store design (Murray et al. 2017 ), and marital status (Aka and Buyukdag, 2021 ). This method provides a detailed analysis of consumer behavior. Multi-group SEM examines whether causal relationships between simultaneous events vary based on different moderating variables, allowing inferences regarding different demographic or psychographic variables. This study examined how family and peer/friend reference groups affect perceptions of brand identity, brand-lifestyle congruence, brand satisfaction, and purchase intention.

The chi-square/df value for multi-group SEM was 2.772, comparative fit index (CFI) was 0.961, root mean square error of approximation (RMSEA) value was 0.051, goodness-of-fit index (GFI) was 0.924, and adjusted goodness-of-fit index (AGFI) was 0.887, indicating a strong fit. The chi-square difference for the family effect was 34.53, with a df difference of 16, indicating no significant difference between models ( p  = 0.78). There is a significant difference between constrained and unconstrained models for the family effect ( p  = 0.005).

Regarding the peer effect, the chi-square difference was 24.545, and the df difference was 16. There was no significant difference between constrained and unconstrained models for peer influence ( p  = 0.078). Peer/friend effects on each path were analyzed to identify any significant differences in local paths, as shown in Table 4 and Fig. 3 .

The results of the multi-group SEM.

The relationship between brand identity and brand-lifestyle congruence was significant when participants were influenced by family, but insignificant without that influence. This result shows the moderating effect of family. The peer/friend effect did not moderate this relationship, as it was significant regardless.

Brand identity significantly influenced brand satisfaction with and without family effect. This result indicates that family did not moderate this relationship. The peer effect, however, moderated brand satisfaction, showing influence in its presence, but not without it. Also, peer effect did not moderate the relationship between brand identity and brand satisfaction.

The effect of brand identity on repurchase intention was significant with both family and peer effects, but not without them, and neither effect moderated the relationship. The effect of brand-lifestyle congruence on brand satisfaction was statistically significant with and without family effect, and family moderated this relationship. The peer effect did not moderate this relationship, despite being significant in both cases.

The relationship between brand-lifestyle congruence and repurchase intention was significant with and without peer effects, but the peer effect moderated this relationship. For consumers without the peer effect, brand-lifestyle congruence had a higher effect on repurchase intention than those with the peer effect. Therefore, even without peer effect, a strong alignment between brand and lifestyle causes individuals to adopt the brand and show higher repurchase intentions.

The relationship between brand satisfaction and repurchase intention was significant regardless of the family effect, which moderated the relationship. Although this relationship was significant with and without peer effect, the peer effect did not moderate this relationship. Satisfaction had a lower impact on repurchase intentions with family involvement, while consumers with no family effects were more satisfaction-oriented. This relationship indicates that consumers continue their past habits and make purchases even if less satisfied. However, those without family effects are more satisfaction-oriented.

Brand-lifestyle congruence accounted for 22.4% of the variance in the family effect, 0% in the non-family effect, 15.1% in the peer effect, and 21.7% in the non-peer effect. Satisfaction variance was 55.1% in the family effect, 32.5% in the non-family effect, 54.3% in the peer effect, and 29.9% in the non-peer effect. Repurchase intention variance was 55.9% in the family effect, 63% in the non-family effect, 56.1% in the peer effect, and 65.3% in the non-peer effect.

Correspondence analysis

Correspondence analysis (CA) is a multivariate method used to represent a set of objects in a multidimensional space by identifying relationships among nominal data through consumers’ similarities and preferences (Hair et al. 2014a ). Unlike principal component analysis, it examines the forms of relationships between variables, making the positioning map vital (Galiano-Coronil et al. 2023 ). This study examined family and peer effects in the primary reference group based on different demographic variables. This analysis identified differences between the reference groups through multi-group analysis, learned how they aligned with the sector, demographic structures, and psychographic variables, and offered deep insights. Multiple correspondence analysis (MCA) includes peer/friend effect, family effect, gender, income, product origin, brand sector, perceived product importance, and age.

The MCA results reveal that consumers with low brand identity, brand-lifestyle congruence, and repurchase intention were mainly in the logistics sector. Consumers with medium levels were middle-aged, have medium incomes, prefer the clothing sector, and are price-oriented. High brand identity and repurchase intention are linked to brand-oriented consumers in the alcohol, technology, and food sectors. Younger, low-income women generally prefer clothing, are price-oriented, and are more open to family and peer influence. Men value product origin and brands while being less influenced by family. Figure 4 indicates that high-income and older consumers less influenced by peers are found mainly in the energy, automotive, and luxury products sectors.

The results of the plot of category points in MCA.

This study confirms the substantial positive impact of brand identity on brand-lifestyle congruence (β = 0.373, p  = 0.001), brand satisfaction (β = 0.472, p  = 0.001), and repurchase intention (β = 0.362, p  = 0.001). These findings are consistent with existing literature suggesting that consumers often transfer their personality traits and symbolic features of their reference groups to brands (Da Silveira et al. 2013b ). This behavior affects their identity and purchase behaviors. Haanpää ( 2007 ) stated that consumption lifestyle creation are a means of shaping personal identity. As a result, brand identity may affect brand lifestyle congruence. A strong brand identity contributes to brand satisfaction (Alnawas and Altarifi, 2015 ; Coelho et al. 2018 ; He et al. 2012 ; Stokburger-Sauer et al. 2012 ). The literature also revealed that brand identity significantly affects brand identification and brand satisfaction (He et al. 2012 ; Shirazi et al. 2013 ).

Brand-lifestyle congruence positively affected both brand satisfaction (β = 0.389, p  = 0.001) and repurchase intention (β = 0.176, p  = 0.001). However, Çifci et al. ( 2016 ) found no significant effect between brand-lifestyle congruence and brand satisfaction, differing from this study. However, Suyanto et al. ( 2019 ) reported that lifestyle congruence impacts product preferences and that social class lifestyles are effective in influencing product preferences, and that social class lifestyles shape these preferences as consumers assimilate into global culture. Cătălin and Andreea ( 2014 ) found that consumer compliance with brands was analyzed through brand identity and lifestyle. They concluded that consumers tend to trust brands that express their identity and tend to prefer brands that present a unique brand image regarding their lifestyle. Brand satisfaction positively and significantly impacted repurchase intention (β = 0.351, p  = 0.001). These findings were consistent with studies by Alnawas and Aburub ( 2016 ), Carlson et al. ( 2019 ), Park et al. ( 2019 ), and Li and Fang ( 2019 ).

The family effect moderates the entire model, and introduces significant differences within the structural framework. The relations between variables differ according to specific local pathways. For consumers influenced by their family’s previous brand choices, brand identity significantly affects brand-lifestyle congruence, brand satisfaction, and repurchase intention. However, brand identity only affects satisfaction for consumers without this familial influence. As a result, if consumers are used to products through family useage and lifestyles, they are likely to continue purchasing them. In this context, we argue that long-term memory appears to be more effective than peer influence, particularly in cases involving family impact.

Brand-lifestyle congruence affects both brand satisfaction and repurchase intention regarding the family effect. However, without this influence, only the relationship between brand-lifestyle congruence and brand satisfaction remains significant and positive. Ozdemir et al. ( 2020 ) stated that consumer experiences with brands are social and influenced by peers. Da Silva et al. ( 2016 ) stated that lifestyle is formed from past experiences and personal characteristics, which affects the consumption habits within families. Thus, lifestyle influenced by familial factors will affect family members’ perceptions of brands. Chen ( 2018 ) also stated that families and reference groups could affect consumer lifestyles. While these findings are consistent with the literature, they differ from the results of Çifci et al. ( 2016 ).

The effect of satisfaction on repurchase intention is significant both with and without the family effect, but the regression coefficient is higher without it. So, consumer behavior influenced by family turns into repurchasing through lifestyle, while consumer behavior without family influence is driven more by satisfaction. Therefore, brands should emphasize active family usage and carry out awareness and promotional activities to reinforce this behavior. If not possible, maximizing brand satisfaction remains crucial.

Regarding peers or friends, no moderating effect was detected across the whole model However, the peer effect did show a moderating effect in local pathways. Brand identity positively affected brand lifestyle congruence, brand satisfaction, and repurchase intention when considering the peer effect. However, this relationship was only significant between brand identity and brand lifestyle congruence without peer effect. Brand-lifestyle congruence significantly and positively impacted brand satisfaction and repurchase intention regardless of peer effect. Still, the relationship between brand lifestyle congruence and brand satisfaction was stronger without the peer effect and showed a significant moderator effect. While these findings differ from Çifci et al. ( 2016 ), they are consistent with Zhang ( 2010 ), as the peer effect significantly impacted brand satisfaction and repurchase intention. This study is consistent with existing literature.

Chernev et al. ( 2011 ) also stated that individuals use brands according to the social groups they want to enter or leave. This finding supports the literature. Brand reinforcement and repitition occurred less frequently with peer influence than with family influence, since social and professional circles often change. However, when social circles shift, peer influence has a shorter-lasting effect compared to family, since repetition and reinforcement of the brands are less consistent.

For individuals influenced by their peers (those who choose brands used by their peers), the link between brand-lifestyle congruence and repurchase intention is weaker than for those who are not influenced by their peers. Thus, those easily influenced by their friends are more likely to change their lifestyle or preferred brand. Conversely, individuals less influenced by their circle of friends develop stronger bonds with brands that fit their lifestyle. Gomez and Spielmann ( 2019 ) reported that the reference group effect depends on how closely individuals identify with the group, and brands associated with the in-group will associate more strongly with the self than out-group brands. Wei and Yu ( 2012 ) also found that exposure to the reference group effect varies by age, gender, ethnicity, and social relationships, with Hispanic consumers showing differing perceptions based on ethnic identification. The findings indicate that individuals form weaker identification bonds with brands under peer effect but develop stronger bonds with brands within the family context.

Wei and Yu ( 2012 ) stated that the family effect is stronger than the peer effect in Thailand compared to the United States. In Türkiye, a different cultural region, the variance rate explained by lifestyle regarding the family effect was 22.4%, while the peer effect accounted for 15.1%, indicating a 7.3% difference. Therefore, family has more influence than peers regarding lifestyle. However, this situation does not significantly differ in brand satisfaction and repurchase intention because the differences ranged from 0.2% to 0.9%.

In contrast, individuals not affected by the family effect cared about product brands, had a high brand perception, and concentrated on the fast-moving consumer goods, technology, and sports products sectors. Studies indicate that peer effects are particularly influential in high-income and elderly groups, especially in sectors like automotive, energy, and luxury products. Consumers swayed by both family and peers tend to be price-sensitive and are primarily women. They focus on fast-moving consumer goods, electronics, and sports products, emphasize product origin, and act in a price-oriented manner.

Makgosa and Mohube ( 2007 ) noted that peer influence varies significantly across product categories, affecting both normative and informational influences, especially in luxury goods compared to necessities. The absence of the family effect seems to heighten consumer sensitivity to brand image, particularly in fast-moving consumer goods, technology, and sports products. This shift suggests more personal or lifestyle-driven purchasing behaviors when family influence is minimal (Bearden and Etzel, 1982 ).

Greco ( 2015 ) emphasized that family and peer influences distinctively mold consumer preferences across product lines, reflecting how social dynamics shape purchasing habits. Kovitcharoenkul and Anantachart ( 2018 ) also highlighted the nuanced interplay of social influences on consumer behavior in a more multifaceted manner compared to individual family or peer effects.

These findings reveal the complexity of consumer decisions, showing how social influences shape behavior across demographics. They help identify segments and their vulnerabilities to social influences, guiding targeted marketing strategies. In summary, this analysis enriches existing knowledge by detailing how peer and family effects distinctly and collectively influence consumer choices across different sectors and demographics. This comprehensive view of consumer behavior dynamics illustrates how social influences distinctly or jointly shape behavior and offers insight into specific consumer segment susceptibilities—informing targeted marketing strategies.

The brand identity was vital regarding consumer brand-lifestyle congruence, brand satisfaction, and repurchase intentions. Brand-lifestyle congruence affected brand satisfaction and repurchase intention, while brand satisfaction itself significantly affected repurchase intention. Family and peer effects also shape consumer behavior. The family effect moderated the whole model, whereas the peer effect was effective only in local pathways. The family effect was essential for aligning the brand with consumer lifestyles, and both family and peer effects were critical for brand satisfaction and repurchase intention. In the absence of family or peer influence, brands still reflected consumers’ lifestyles, and high brand satisfaction led to strong repurchase intentions. Finally, high-income and elderly consumers were susceptible to peer and family influence, while consumers influenced by family tended to be middle-aged, low-income men.

Soininen and Merisuo-Storm ( 2010 ) said that peers or friends played an essential role for young people, consistent with the MCA analysis findings. Older individuals were less affected by peers, while middle-aged and younger individuals were more aligned with clusters influenced by peers and family. These consumers prioritized brand and product origin when making purchases. In terms of both family and peer influence, women were more affected, less concerned with product origin, and more price-oriented. Thus, women were more open to family and peer effects than men.

Theoretical contribution

This study highlighted a significant relationship between brand identity and brand lifestyle. It marked an important theoretical contribution to the literature. It also revealed the impactful role of reference groups (family and peer/friend) in this relationship. According to social identity theory, the fit between brand lifestyle and consumer identity serves as an important moderator in consumer–brand identification. Additionally, this research noted that family influence tends to have a stronger impact on the sense of belonging than peer influence. This insight supports the notion from socialization theory that consumers develop consumption-related attitudes and behaviors through interaction with social agents.

Wang et al. ( 2012 ) discussed peers as significant agents of socialization, particularly in how they shape brand preferences through communication. However, their analysis did not deeply explore family influence in brand selection. This study fills that gap by providing empirical evidence that family influence more profoundly shapes consumer–brand identification than peer influence. This insight further enhances our understanding of the roles different socialization agents play in enhancing brand loyalty and engagement.

This research extends self-congruity theory by linking brand identity with lifestyle congruity and examining their collective impact on brand satisfaction and repurchase intentions. This composite model illustrates how different reference groups can moderate these relationships, which leads to different consumer behaviors. This complexity provides a comprehensive framework for understanding consumer–brand relationships, which only enriches understanding of the theory.

Relationship marketing theory states that strong brand relationships reduce inter-brand switching. This study reinforces this by showing that family influence strengthens consumer–brand relationships more than peer influence, which enhances trust and reduces the likelihood of switching brands. Petina et al. ( 2013 ) emphasized trust as a cornerstone of relationship marketing, but the literature has not fully explored how reference groups influence trust dynamics within brand relationships. This study addressed this oversight and demonstrated that family groups tend to generate deeper trust and stronger brand relationships. This insight shows marketers the importance of designing brand strategies that cultivate trust in targeted groups to enhance effectiveness and loyalty.

Cultural branding theory states that brands derive power from consumers’ efforts to communicate their identities through brand choices (Smith and Speed, 2011 ). Previous studies often failed to address how different reference groups—specifically family and peers—affect branding efforts. This study addresses this gap by demonstrating that these groups distinctly influence consumer–brand relationships. The findings suggest that brand strategies must account for prevailing reference group dynamics to effectively negotiate brand identity within these social contexts. This insight enhances our understanding of cultural branding by emphasizing the need to adapt branding strategies to specific social influences impacting consumer behavior.

In summary, the findings of this study support relationship marketing and cultural branding theories by showing that strong, trust-based relationships, influenced by family dynamics, lead to reduced inter-brand switching. Additionally, the effectiveness of reference groups in shaping brand identity and consumer behavior offers new insights into how leveraging cultural elements to strengthen consumer connections.

Managerial implication

This study provides essential insights for managers and marketers seeking to optimize their strategies around brand identity, lifestyle congruence, and their influence on brand satisfaction and repurchase intention. A strong brand identity that resonates with the lifestyles of consumers not only enhances brand satisfaction and repurchase intentions but also positions the brand competitively in the market. Investing strategically in brand identity helps resonate with and shape target demographics’ lifestyles, as exemplified by Apple’s success. Brands with clear identities are better equipped to dominate their market sectors.

Family and peer groups significantly shape consumer behavior. Family ties, in particular, profoundly impact brand satisfaction and lifestyle congruence. Brands should leverage these insights through promotional messages that tap into nostalgic connections, especially for consumers with strong family ties. Family-oriented marketing strategies can embed the brand into consumers’ lifestyles and create long-term loyalty, with customers more likely to repurchase the products.

Different strategies may be required for consumers influenced primarily by peers versus family. Peer-influenced lifestyle changes are less stable, so businesses should invest in branding that targets permanent and widely appealing lifestyles to ensure long-term profitability. For consumers heavily influenced by family, nostalgia-based marketing can be particularly effective.

Businesses should focus on increasing brand quality perception and satisfaction, especially for consumers not strongly influenced by family or peers. Effective communication and brand awareness campaigns are essential to improve the brand’s position in consumers’ minds. By understanding and addressing these consumers specific needs and preferences, companies can achieve higher brand satisfaction and loyalty.

Marketing strategies should also consider gender differences in response to peer and family influence. For women, price-sensitive strategies that maintain quality capture significant market share, particularly in clothing and media. For men, emphasizing brand origin and offering high-quality items can be more effective due to stronger peer influence. Monitoring social media channels for real-time feedback and addressing negative perceptions is crucial to maintain consumer trust and loyalty.

Businesses should incorporate these insights into their long-term strategies. Building a brand identity that aligns with evolving lifestyles and effectively using reference group dynamics will help companies stay competitive and achieve sustained success.

Future research and limitation

The first limitation was in sample collection; since the data were not collected randomly, the study cannot be generalized. Therefore, similar studies should use random sampling methods to increase the validity of the findings. The second limitation was the study’s focus on a specific geographic region and a collectivist culture. Similar research conducted in different geographies, cultures (individualist or collectivist), or comparative analyses of different cultures would improve the research’s validity. The third limitation was the use of a limited number of psychographic variables. The human factor is crucial in the social sciences and is often influenced by many variables simultaneously. Lastly, the study focused solely on primary reference groups. Future research should include secondary reference groups and compare their effects with primary groups to add valuable insights to the literature.

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Aaker DA, Keller KL (1996) Building strong brands. The Free Press

Google Scholar  

Adnan A, Ahmad A, Khan MN (2017) Examining the role of consumer lifestyles on ecological behavior among young Indian consumers. Young Consum 18:348–377

Article   Google Scholar  

Aka S, Buyukdag N (2021) How to prevent food waste behaviour? A deep empirical research. J Retail Consum Serv 61:102560

Alnawas I, Altarifi S (2015) Exploring the role of brand identification and brand love in generating higher levels of brand loyalty. J Vacation Mark 22:111–128. https://doi.org/10.1177/1356766715604663

Alnawas I, Altarifi S (2016) Exploring the role of brand identification and brand love in generating higher levels of brand loyalty. J Vacation Mark 22(2):111–128. https://doi.org/10.1177/1356766715604663

Alnawas I, Aburub F (2016) The effect of benefits generated from interacting with branded mobile apps on consumer satisfaction and purchase intentions. J Retail Consum Serv 31:313–322

Alnawas I, Altarifi S (2016) Exploring the role of brand identification and brand love in generating higher levels of brand loyalty. J Vacat Mark 22:111–128

Aro K, Suomi K, Saraniemi S (2018) Antecedents and consequences of destination brand love—A case study from Finnish Lapland. Tour Manag 67:71–81

Arunyanart S, Utiswannakul P (2019) A model for generating a lifestyle fashion brand from cultural capital for generation asia in Bangkok. Thailand. Des J 22:125–142

Augusto M, Torres P (2018) Effects of brand attitude and eWOM on consumers’ willingness to pay in the banking industry: mediating role of consumer-brand identification and brand equity. J Retail Consum Serv 42:1–10

Babin BJ, Borges A, James K (2016) The role of retail price image in a multi-country context: France and the USA. J Bus Res 69:1074–1081

Bao H, Deng S, Xu S, Peng Y (2017) Conspicuous consumption behavior of land-lost farmers: a perspective of social identity. Cities 66:81–90

Bearden W, Etzel M (1982) Reference group influence on product and brand purchase decisions. J Consum Res 9:183–194

Belk RW (1988) Possessions and the extended self. J Consum Res 15:139–168

Black I, Veloutsou C (2017) Working consumers: co-creation of brand identity, consumer identity and brand community identity. J Bus Res 70:416–429

Carlson J, Rahman MM, Taylor A, Voola R (2019) Feel the VIBE: examining value-in-the-brand-page-experience and its impact on satisfaction and customer engagement behaviours in mobile social media. J Retail Consum Serv 46:149–162

Carrillo Barbosa RL, Guzmán Rincón, A (2022). Art workers in Colombia: characteristics, symbolic consumption and social identity. Eur Res Manag Bus Econ. https://doi.org/10.1016/j.iedeen.2021.100180

Casidy R, Wymer W, O’Cass A (2018) Enhancing hotel brand performance through fostering brand relationship orientation in the minds of consumers. Tour Manag 66:72–84

Castillo-Abdul B, Ortega Fernandez E, Romero-Rodriguez LM (2022). Corporate social responsibility communication of male luxury fashion brands: analysis on Instagram, Facebook and TikTok. Manag Decis. https://doi.org/10.1108/MD-09-2022-1316

Catalin MC, Andreea P (2014) Brands as a means of consumer self-expression and desired personal lifestyle. Procedia - Soc Behav Sci 109:103–107. https://doi.org/10.1016/j.sbspro.2013.12.427

Chen HJ (2018) What drives consumers’ mobile shopping? 4Ps or shopping preferences? Asia Pac J Mark Logist 30:797–815

Cheng YY, Tung WF, Yang MH, Chiang CT (2019) Linking relationship equity to brand resonance in a social networking brand community. Electron Commerce Res Appl 35:100849

Chen-Yu J, Cho S, Kincade D (2017) Brand perception and brand repurchase intent in online apparel shopping: An examination of brand experience, image congruence, brand affect, and brand trust. J Glob Fashion Marke 7(1):30–44. https://doi.org/10.1080/20932685.2015.1110042

Chernev A, Hamilton R, Gal D (2011) Competing for consumer identity: limits to self-expression and the perils of lifestyle branding. J Mark 75:66–82

Chinomona R (2013) The influence of brand experience on brand satisfaction, trust and attachment in South Africa. Int Bus Econ Res J 12(10):1303–1316. https://doi.org/10.19030/iber.v12i10.8138

Chinomona R, Mahlangu D, Pooe D (2013) Brand service quality, satisfaction, trust and preference as predictors of consumer brand loyalty in the retailing industry. Mediterranean J Soc Sci 4(14):181

Çifci S, Ekinci Y, Whyatt G, Japutra A, Molinillo S, Siala H (2016) A cross validation of consumer-based brand equity models: driving customer equity in retail brands. J Bus Res 69:3740–3747

Claudiu Cătălin M, Andreea P (2014) Brands as a means of consumer self-expression and desired personal lifestyle. Procedia - Soc Behav Sci 109:103–107

Coelho PS, Rita P, Santos ZP (2018) On the relationship between consumer-brand identification, brand community, and brand loyalty. J Retailing Consum Ser 43(1):101–110. https://doi.org/10.1016/j.jretconser.2018.03.011

Coleman D, de Chernatony L, Christodoulides G (2011) B2B service brand identity: scale development and validation. Ind Mark Manag 40:1063–1071

Coursaris CK, Van Osch W (2015) Social media research: An assessment of the domain’s productivity and intellectual evolution. In Proceedings of the 2015 48th Hawaii International Conference on System Sciences (HICSS) (pp. 3540–3549). IEEE. https://doi.org/10.1109/HICSS.2015.201

Cuong DT (2020) The impact of customer satisfaction, brand image on brand love and brand loyalty. Journal of Advanced Research in Dynamical and Control Systems, 12. https://doi.org/10.5373/JARDCS/V12I6/S20201280

Da Silva IS, Slongo LA, Antunes Rohde L (2016) Empty nest couples: lifestyles and typology. Int J Consum Stud 40:695–700

Da Silveira C, Lages C, Simões C (2013b) Reconceptualizing brand identity in a dynamic environment. J Bus Res 66:28–36

da Silveira C, Lages C, Simões C (2013a) Reconceptualizing brand identity in a dynamic environment. J Bus Res 66:28–36

Davvetas V, Diamantopoulos A (2017) “Regretting your brand-self?” The moderating role of consumer-brand identification on consumer responses to purchase regret. J Bus Res 80:218–227

Dennis C, Papagiannidis S, Alamanos E, Bourlakis M (2016) The role of brand attachment strength in higher education. J Bus Res 69:3049–3057

Díaz A, Gómez M, Molina A (2017) A comparison of online and offline consumer behaviour: an empirical study on a cinema shopping context. J Retail Consum Serv 38:44–50

Dolich IJ (1969) Congruence relationships between self-images and product brands. J Mark Res 6:80e84

Ekinci Y, Sirakaya-Turk E, Preciado S (2013) Symbolic consumption of tourism destination brands. J Bus Res 66:711–718

Escalas JE, Bettman JR (2003) You are what they eat: the influence of reference groups on consumers’ connections to brands. J Consum Psychol 13:339–348

Essamri A, McKechnie S, Winklhofer H (2019) Co-creating corporate brand identity with online brand communities: A managerial perspective. J Bus Res 96:366–375. https://doi.org/10.1016/j.jbusres.2018.07.015

Galiano-Coronil A, Aguirre Montero A, López Sánchez JA & Díaz Ortega, R (2023). Exploring social responsibility, social marketing and happiness using artificial intelligence, automated text analysis and correspondence analysis. Manag Decis. https://doi.org/10.1108/MD-01-2023-0099

Gaskin J, Lim J (2016). Confirmatory factor analysis—StatWiki. Retrieved from http://statwiki.kolobkreations.com/index.php?title=Confirmatory_Factor_Analysis

Girard T (2010) The role of demographics on the susceptibility to social influence: a pretest study. J Mark Dev Compet 5:9–22

Gomez P, Spielmann N (2019) A taste of the elite: the effect of pairing food products with elite groups on taste perceptions. J Bus Res 100:175–183

Greco A (2015). Differences in susceptibility to reference group influence on brand decisions: middle aged and elderly group participants. https://doi.org/10.1007/978-3-319-11101-8_10

Grisaffe DB, Nguyen HP (2011) Antecedents of emotional attachment to brands. J Bus Res 64(10):1052–1059

Grzeskowiak S, Sirgy MJ, Foscht T, Swoboda B (2016) Linking retailing experiences with life satisfaction: the concept of story-type congruity with shopper’s identity. Int J Retail Distrib Manag 44:124–138

Gustafson BM, Pomirleanu N (2021) A discursive framework of B2B brand legitimacy. Ind Mark Manag 93(4):22–31. https://doi.org/10.1016/j.indmarman.2020.12.009

Haanpää L (2007) Consumers’ green commitment: Indication of a postmodern lifestyle? Int J Consum Stud 31:478–486

Han H, Kim W, Hyun SS (2019) Critical role of satisfaction in the formation of customer loyalty and repurchase intention in restaurant services. Int J Hospitality Manage 82:165–175

Han H, Nguyen H, Lee TJ (2020) The importance of product and service quality in the development of brand loyalty and repurchase intentions. J Retailing and Consumer Services 55:102094

Hair JF, Black WC, Babin BJ, Anderson RE (2014a) Multivariate data analysis. Prentice Hall, Pearson, p 816

Hair, JF, Black, WC, Babin, BJ, & Anderson, RE (2014b). Multivariate data analysis. Pearson Education

He H, Li Y, Harris L (2012) Social identity perspective on brand loyalty. J Bus Res 65:648–657

Hollenbeck CR, Peters C, Zinkhan GM (2008) Retail spectacles and brand meaning: Insights from a brand museum case study. J Retail 84:334–353

Holt DB (2002). Poststructuralist lifestyle analysis: conceptualizing the social patterning of consumption in postmodernity. J Consumer Res. https://doi.org/10.1086/209487 , https://interbrand.com/best-brands/

Hu L, Bentler PM (1999) Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct Equ Model Multidiscip J 6(1):1–55

Huang X, Ge J (2019) Electric vehicle development in Beijing: an analysis of consumer purchase intention. J Clean Prod 216:361–372

Iglesias O, Landgraf P, Ind N, Markovic S, Koporcic N (2020) Corporate brand identity co-creation in business-to-business contexts. Industrial Marketing Manage 87:155–171. https://doi.org/10.1016/j.indmarman.2019.10.001

Ismail IJ (2022) I trust friends before I trust companies: The mediation of WOM and brand love on psychological contract fulfilment and repurchase intention. Manage Matters 19(2):167–186. https://doi.org/10.1108/MANM-02-2022-0033

Jang YJ, Kim GW, Bonn MA (2011) Generation Y consumers’ selection attributes and behavioral intentions concerning green restaurants. International J Hospital Manag 95(1):345–361

Jiang C, Zhao W, Sun X, Zhang K, Zheng R, Qu W (2016) The effects of the self and social identity on the intention to microblog: an extension of the theory of planned behavior. Comput Hum Behav 64:754–759

Keller KL (1993) Conceptualizing, measuring, and managing customer-based brand equity. J Mark 57(1):1–22. https://doi.org/10.2307/1252054

Article   ADS   MathSciNet   Google Scholar  

Konuk FA (2023). Trustworthy brand signals, price fairness and organic food restaurant brand loyalty. Manag Decis. https://doi.org/10.1108/MD-07-2022-0889

Koporcic N, Halinen A (2018) Creating corporate identity and reputation through interpersonal interaction. Interactive Network Branding 12(2):392–408

Kovitcharoenkul N, Anantachart S (2018) Examining reference group influences on young consumers’ product and brand decisions. J Public Relat 9:131–148

Kumar V (2016) Examining the role of destination personality and self-congruity in predicting tourist behavior. Tour Manag Perspect 20:217–227

Kumar V (2022) Enhancing participation intentions in online brand communities. Mark Intell Plan 40:898–913

Landon EL (1974) Self-concept, ideal self-concept, and consumer purchase intentions. J Consum Res 1:44–51

Lee S, Jeong M (2014) Enhancing online brand experiences: an application of congruity theory. Int J Hosp Manag 40:49–58

Le-Hoang PV (2020) Factors affecting online purchase intention: the case of e-commerce on lazada. Indep J Manag Prod 11:1018

Li G, Li G, Kambele Z (2012) Luxury fashion brand consumers in China: perceived value, fashion lifestyle, and willingness to pay. J Bus Res 65:1516–1522

Li Y, Peng Y (2021) Influencer marketing: purchase intention and its antecedents. Mark Intell Plan 39:960–978

Li Y, Liu H, Lim ETK, Goh JM, Yang F, Lee MKO (2018) Customer’s reaction to cross-channel integration in omnichannel retailing: the mediating roles of retailer uncertainty, identity attractiveness, and switching costs. Decis Support Syst 109:50–60

Li CY, Fang YH (2019) Predicting continuance intention toward mobile branded apps through satisfaction and attachment. Telemat Inform 43:101248

Makgosa R, Mohube K (2007) Peer influence on young adults’ products purchase decisions. Afr J Bu. Manag 1:064–071

Mäläskä M, Saraniemi S, Tähtinen J (2011) Network actors’ participation in B2B SME branding. Industrial Mark Manag 40(7):1144–1152

Malhotra KN (1988) Self-concept and product choice: An integrated perspective. J Economic Psychology 9(1):1–28. https://doi.org/10.1016/0167-4870(88)90029-3

Manthiou A, Kang J, Hyun SS, Fu XX (2018) The impact of brand authenticity on building brand love: an investigation of impression in memory and lifestyle-congruence. Int J Hosp Manag 75:38–47

Mao Y, Lai Y, Luo Y, Liu S, Du Y, Zhou J, Ma J, Bonaiuto F, Bonaiuto M (2020) Apple or Huawei: understanding flow, brand image, brand identity, brand personality and purchase intention of smartphone. Sustainability 12:1–22

Mathieu WJ, Gallagher PT, Domingo MA, Klock EA (2018) The annual review of organizational psychology and organizational behavior. Ann Rev Organizational Psychology and Organizational Behavior 6(1):17–46

Mi L, Zhu H, Yang J, Gan X, Xu T, Qiao L, Liu Q (2019) A new perspective to promote low-carbon consumption: the influence of reference groups. Ecol Econ 161:100–108

Murray J, Elms J, Teller C (2017) Examining the role of store design on consumers’ cross-sectional perceptions of retail brand loyalty. J Retail Consum Serv 38:147–156

Muhonen T, Hirvonen S, Laukkanen T (2017) SME brand identity: Its components, and performance effects. J Product Brand Manag 26(1):52–67. https://doi.org/10.1108/JPBM-01-2016-1083

Nam J, Ekinci Y, Whyatt G (2011b) Brand equity, brand loyalty and consumer satisfaction. Ann Tour Res 38:1009–1030

Nam J, Ekinci Y, Whyatt G (2011a) Brand equity, brand loyalty and consumer satisfaction. Ann Tour Res 38:1009–1030

Nolan JM, Schultz PW, Cialdini RB, Goldstein NJ, Griskevicius V (2008) Normative social influence is underdetected. Personal Soc Psychol Bull 34:913–923

Oliver RL (1980) A cognitive model of the antecedents and consequences of satisfaction decisions. J Mark Res 17(4):460–469. https://doi.org/10.2307/3150499

Ozdemir S, Zhang SJ, Gupta S, Bebek G (2020). The effects of trust and peer influence on corporate brand—Consumer relationships and consumer loyalty. J Bus Res. https://doi.org/10.1016/j.jbusres.2020.02.027

Pan Y, Wang F, Liu D, Gao L, Yuan Y (2018) How different lifestyles affect value appraisals and purchase of ICT products: a comparative empirical study. J Electron Commer Res 19:280–300

Park JY, Back RM, Bufquin D, Shapoval V (2019) Servicescape, positive affect, satisfaction and behavioral intentions: the moderating role of familiarity. Int J Hosp Manag 78:102–111

Pentina I, Gammoh BS, Zhang L, Mallin M (2013) Drivers and outcomes of brand relationship quality in the context of online social networks. Int J Electron Commer 17:63–86

Petina D, Amine A, Taylor CR (2013) The impact of trust on relationship marketing and brand loyalty. J Consum Mark 30(4):329–338

Podsakoff PM, MacKenzie SB, Lee JY, Podsakoff NP (2003) Common method biases in behavioral research: a critical review of the literature and recommended remedies. J Appl Psychol 88:879–903

Article   PubMed   Google Scholar  

Prentice C, Han XY, Hua LL, Hu L (2019) The influence of identity-driven customer engagement on purchase intention. J Retail Consum Serv 47:339–347

Sharma P, Chen ISN, Luk STK (2018) Tourist shoppers’ evaluation of retail service: a study of cross-border versus international outshoppers. J Hosp Tour Res 42:392–419

Shimul AS, Phau I (2023) The role of brand self-congruence, brand love and brand attachment on brand advocacy: a serial mediation model. Mark Intell Plan 41:649–666

Shin SKS, Amenuvor FE, Basilisco R, Owusu-Antwi K (2019) Brand trust and brand loyalty: A moderation and mediation perspective. Current J Applied Sci Technol 38(4):1–17

Shirazi SM, Imran HM, Akib S, Yusop Z, Harun ZB (2013) Groundwater vulnerability assessment in the Melaka State of Malaysia using DRASTIC and GIS techniques. Environment Earth Sci 70(5):2293–2304. https://doi.org/10.1007/s12665-013-2360-9

Article   ADS   Google Scholar  

Sina AS, Kim HY (2019) Enhancing consumer satisfaction and retail patronage through brand experience, cognitive pleasure, and shopping enjoyment: a comparison between lifestyle and product-centric displays. J Glob Fash Mark 10:129–144

Sirgy MJ (2018) Self-congruity theory in consumer behavior: a little history. J. Glob Scholars Mark Sci 28:197–207

Sirgy MJ, Lee DJ, Grace BY (2016). Revisiting self-congruity theory in consumer behaviour: making sense of the research so far. In: Jansson-Boyd CV, Zawisza MJ (eds) Routledge international handbook of consumer psychology. Routledge, p 203–219

Smith G, Speed R (2011) Cultural branding and political marketing: an exploratory analysis. J Mark Manag 27:1304–1321

Soininen M, Merisuo-Storm T (2010) The life style of the youth, their every day life and relationships in Finland. Procedia Soc Behav Sci 2:1665–1669

Solomon, M. R. (2009). Consumer behavior: Buying, having, and being (8th ed.). Pearson Education

Sop SA, Kozak N (2019) Effects of brand personality, self-congruity, and functional congruity on hotel brand loyalty. J Hosp Mark Manag 28:926–956

Stokburger-Sauer N, Ratneshwar S, Sen S (2012) Drivers of consumer–brand identification. Int J Res Mark 29(4):406–418

Suyanto B, Sugihartati R, Hidayat M, Subiakto H (2019) Global vs. local: lifestyle and consumption behaviour among the urban middle class in East Java, Indonesia. South East Asia Res 27:398–417

Tabachnick BG, Fidell LS (2007). Using multivariate statistics. Pearson Education

Tajfel H (1974) Social identity and intergroup behaviour. Social Sci Inform 13(2):65–93

Tajfel H, Turner JC (1986) The social identity theory of intergroup behavior. In S. Worchel & W. G. Austin (Eds.), Psychology of intergroup relation (pp. 7–24). Hall Publishers

Tangsupwattana W, Liu X (2017) Symbolic consumption and generation Y consumers: evidence from Thailand. Asia Pac J Mark Logist 29:917–932

Taylor SA, Baker TL (1994) An assessment of the relationship between service quality and customer satisfaction in formation of consumers’ purchase intentions. J Retailing 70(2):163–178. https://doi.org/10.1016/0022-4359(94)90013-2

Torres P, Augusto M, Godinho P (2017) Predicting high consumer-brand identification and high repurchase: necessary and sufficient conditions. J Bus Res 79:52–65

Törmala M, Gyrd-Jones R (2017) Development of new B2B venture corporate brand identity: A narrative performance approach. Industrial Mark Manag 65:76–85. https://doi.org/10.1016/j.indmarman.2017.05.002

Trivedi SK, Yadav M (2020) Repurchase intentions in Y generation: mediation of trust and e-satisfaction. Mark Intell Plan 38:401–415

Tse DK, Wilton PC (1988) Models of consumer satisfaction formation: An extension. J Market Res 25(2):204–212. https://doi.org/10.2307/3172652

Tu Y-T, Chang H-C (2012) Corporate brand image and customer satisfaction on loyalty: An empirical study of Starbucks Coffee in Taiwan. J Social Develop Sci 3(1):24–32. https://doi.org/10.22610/jsds.v3i1.682

Usakli A, Baloglu S (2011) Brand personality of tourist destinations: an application of self-congruity theory. Tour Manag 32:114–127

Veloutsou C, Moutinho L (2009) Brand relationships through brand reputation and brand tribalism. J Bus Res 62:314–322

Wang X, Yu C, Wei Y (2012) Social media peer communication and impacts on Ppurchase intentions: a consumer socialization framework. J Interact Mark 26:198–208

Wang YC, Qu H, Yang J (2019) The formation of sub-brand love and corporate brand love in hotel brand portfolios. Int J Hosp Manag 77:375–384

Wang Z, Yuan R, Liu MJ, Luo LJ (2021) Luxury symbolism, self-congruity, self-affirmation and luxury consumption behavior: a comparison study of China and the US. Int Mark Rev 39:166–206

Article   CAS   Google Scholar  

Wardani D, Gustia RR (2016) Analysis of brand experience, brand satisfaction and brand trust relationship to brand attachment. J Ilmu Manajemen & Ekonomika 9(1):59–72

Wei Y, Yu C (2012) How do reference groups influence self-brand connections among chinese consumers? J Advert 41:39–54

Wheeler A (2014) Designing brand identity: An essential guide for the whole branding team (4th ed.). Wiley

White K, Dahl DW (2006) To be or not be? The influence of dissociative reference groups on consumer preferences. J Consum Psychol 16:404–414

White K, Dahl DW (2007) Are all out-groups created equal? Consumer identity and dissociative influence. J Consum Res 34(4):525–536. https://doi.org/10.1086/520077

Yang J, He X, Lee H (2007) Social reference group influence on mobile phone purchasing behaviour: a cross-nation comparative study. Int J Mob Commun 5:319–338

Yin X, Qiao Y, Xing G (2022) Impact of perceived product value on customer-based brand equity: Marx’s theory – Value-based perspective. Frontiers in Psychology 13:931064. https://doi.org/10.3389/fpsyg.2022.931064

Article   PubMed   PubMed Central   Google Scholar  

Zboja JJ, Voorhees CM (2006) The impact of brand trust and satisfaction on retailer repurchase intentions. J Serv Mark 20(6):381–390. https://doi.org/10.1108/08876040610691275

Zhang EM (2010) Understanding the acceptance of mobile SMS advertising among young Chinese consumers. Psychol Mark 30:461–469

Download references

Author information

Authors and affiliations.

Karabuk University, Karabuk, Turkey

Ayşegül Acar & Burak Türten

Akdeniz University, Antalya, Turkey

Naci Büyükdağ

Gümüşhane University, Gümüşhane, Turkey

Ersin Diker & Gülsüm Çalışır

You can also search for this author in PubMed   Google Scholar

Contributions

The authors jointly supervised this work.

Corresponding author

Correspondence to Ayşegül Acar .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Ethical approval

The questionnaire and methodology for this study was approved by the Human Research Ethics committee of the Karabuk University (Ethics approval number: E-78975511-060.01.04-288661).

Informed consent

In compliance with ethical standards, the informed written consent form was obtained from the participant before the data collection.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Acar, A., Büyükdağ, N., Türten, B. et al. The role of brand identity, brand lifestyle congruence, and brand satisfaction on repurchase intention: a multi-group structural equation model. Humanit Soc Sci Commun 11 , 1102 (2024). https://doi.org/10.1057/s41599-024-03618-w

Download citation

Received : 24 January 2024

Accepted : 19 August 2024

Published : 28 August 2024

DOI : https://doi.org/10.1057/s41599-024-03618-w

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Key Success Factors for "Real World" Projects: A Principal-Agency Theory Perspective: ACM SIGMIS Computers and People Research 2023

New citation alert added.

This alert has been successfully added and will be sent to:

You will be notified whenever a record that you have chosen has been cited.

To manage your alert preferences, click on the button below.

New Citation Alert!

Please log in to your account

Information & Contributors

Bibliometrics & citations, view options, index terms.

Social and professional topics

Professional topics

Computing education

Recommendations

Correlation of critical success factors with success of software projects: an empirical investigation.

Software engineering researchers have, over the years, proposed different critical success factors (CSFs) which are believed to be critically correlated with the success of software projects. To conduct an empirical investigation into the correlation of ...

SIGMIS-CPR'18: Proceedings of the 2018 ACM SIGMIS Conference on Computers and People Research

Sigmis-cpr '15: proceedings of the 2015 acm sigmis conference on computers and people research, information, published in.

• SIGMIS: ACM Special Interest Group on Management Information Systems

Association for Computing Machinery

New York, NY, United States

Publication History

Permissions, check for updates, author tags.

• Agency Theory
• Problem-based Learning
• Project Management
• Short-paper
• Refereed limited

Acceptance Rates

Contributors, other metrics, bibliometrics, article metrics.

• 0 Total Citations
• 0 Total Downloads
• Downloads (Last 12 months) 0
• Downloads (Last 6 weeks) 0

View options

View or Download as a PDF file.

View online with eReader .

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Share this publication link.

Copying failed.

Share on social media

Affiliations, export citations.

• Please download or close your previous search result export first before starting a new bulk export. Preview is not available. By clicking download, a status dialog will open to start the export process. The process may take a few minutes but once it finishes a file will be downloadable from your browser. You may continue to browse the DL while the export process is in progress. Download
• Download citation
• Copy citation

We are preparing your search results for download ...

We will inform you here when the file is ready.

Your file of search results citations is now ready.

Your search export query has expired. Please try again.

trending now in Lifestyle

I'm a doctor — these are 3 daily supplements I swear by for...

There's a new form of sexual attraction heating up: Here's what...

This sleep routine can cut your heart disease risk by 20%: new...

Drug gang busted after smuggling cocaine worth millions in...

Remarkable video shows dad with Parkinson's before and after...

I’m a travel expert — avoid this US popular airport if...

Dear Abby: My rude neighbor keeps stealing my vegetables — and...

Flight attendant reveals passengers who are the most 'difficult'...

What do dogs dream about research explores the mystery of canine sleep.

You could be the owner of your dog’s dreams.

If you’ve ever wondered why your dog might kick, twitch or whine while snoozing, experts believe it’s because they’re dreaming just like humans do.

One of the more notable studies about dog dreams came out of MIT more than 20 years ago, in which researchers studied the sleep cycle of rats, finding that the rodents were recalling real-life events and experiencing complex dreams.

Dogs, then, are likely to dream similarly.

“(Dogs) probably dream about their owners and interacting with other dogs and with people that they enjoy,” veterinarian José Arce, former president of the American Veterinary Medical Association, told USA Today . 

A dog’s age and size also plays a role in the frequency of dreaming.

According to the American Kennel Club, smaller dogs are more likely to have increased dream frequency than larger pups, but they also have shorter dreams, whereas larger dogs have fewer dreams but for a longer duration.

“Puppies seem to dream more, and for some reason older dogs seem to dream a little bit more than the middle-aged dogs,” Arce added. 

But, just like humans have nightmares, your Fido might, too. Experts warn, though, not to wake a dog despite it growling or whimpering in its sleep, lest you face the consequences.

The Journal of Veterinary Behavior reports that approximately 6% of dog bites happen while the pooch is asleep.

“When a dog is growling, that’s something I wouldn’t interfere with,” Arce explained. “You have to be very careful around their mouth because sometimes they will bite you, not on purpose, but … they don’t have any control.”

While little else is known about what happens when animals sleep, Arce believes artificial intelligence might factor into future research and help researchers “find more answers.”

“Because a lot of these technologies [are] just developing as we speak and it’s getting more sophisticated,” he said.

Recent research on dogs has yielded more findings about their brains and behaviors , like the fact they can smell humans’ stress , can distinguish languages , how they emote and why little dogs outlive big dogs .

Advertisement