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The Craft of Writing a Strong Hypothesis

Table of Contents

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

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

What is a Hypothesis?

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

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

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

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

Different Types of Hypotheses‌

Types of hypotheses

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

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

1. Null hypothesis

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

2. Alternative hypothesis

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

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

3. Simple hypothesis

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

4. Complex hypothesis

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

5. Associative and casual hypothesis

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

6. Empirical hypothesis

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

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

7. Statistical hypothesis

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

Characteristics of a Good Hypothesis

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

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

Separating a Hypothesis from a Prediction

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

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

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

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

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

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1.  Be clear about your research question

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

2. Carry out a recce

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

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

3. Create a 3-dimensional hypothesis

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

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

4. Write the first draft

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

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

5. Proof your hypothesis

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

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

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

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

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

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

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

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

2. What is an example of hypothesis?

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

3. What is an example of null hypothesis?

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

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

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

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

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

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

7. Difference between research question and research hypothesis?

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

8. What is plural for hypothesis?

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

9. What is the red queen hypothesis?

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

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

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

11. When to reject null hypothesis?

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

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

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

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

Table of Contents

What is a hypothesis ?  

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

What is a research hypothesis ?  

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

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

Characteristics of a good hypothesis  

Here are the characteristics of a good hypothesis :  

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

How to create an effective research hypothesis  

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

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

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

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

How to write a research hypothesis  

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

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

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

Population: athletes  

Independent variable: daily cold water showers  

Dependent variable: endurance  

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

Research hypothesis checklist  

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

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

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

Types of research hypothesis  

Different types of research hypothesis are used in scientific research:  

1. Null hypothesis:

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

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

2. Alternative hypothesis:

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

Example: “ The newly identified virus is zoonotic .”  

3. Directional hypothesis :

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

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

4. Non-directional hypothesis:

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

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

5. Simple hypothesis :

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

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

6 . Complex hypothesis :

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

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

7. Associative hypothesis:  

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

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

8 . Causal hypothesis:

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

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

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

Research hypothesis examples  

Here are some good research hypothesis examples :  

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

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

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

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

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

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

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

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

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

Importance of testable hypothesis  

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

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

Frequently Asked Questions (FAQs) on research hypothesis  

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

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

2. When to reject null hypothesis ?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

9. Can research hypotheses be used in qualitative research?

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

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Table of Contents

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

What is a Hypothesis in Research?

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

Research Question vs Hypothesis

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

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

How to Write Hypothesis in Research

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

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

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

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

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

Research Hypothesis Example

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

Here are a few generic examples to get you started.

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

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

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

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

Language Editing Plus

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

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How to Write a Research Hypothesis

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Since grade school, we've all been familiar with hypotheses. The hypothesis is an essential step of the scientific method. But what makes an effective research hypothesis, how do you create one, and what types of hypotheses are there? We answer these questions and more.

Updated on April 27, 2022

What is a research hypothesis?

General hypothesis.

Since grade school, we've all been familiar with the term “hypothesis.” A hypothesis is a fact-based guess or prediction that has not been proven. It is an essential step of the scientific method. The hypothesis of a study is a drive for experimentation to either prove the hypothesis or dispute it.

Research Hypothesis

A research hypothesis is more specific than a general hypothesis. It is an educated, expected prediction of the outcome of a study that is testable.

What makes an effective research hypothesis?

A good research hypothesis is a clear statement of the relationship between a dependent variable(s) and independent variable(s) relevant to the study that can be disproven.

Research hypothesis checklist

Once you've written a possible hypothesis, make sure it checks the following boxes:

• It must be testable: You need a means to prove your hypothesis. If you can't test it, it's not a hypothesis.
• It must include a dependent and independent variable: At least one independent variable ( cause ) and one dependent variable ( effect ) must be included.
• The language must be easy to understand: Be as clear and concise as possible. Nothing should be left to interpretation.
• It must be relevant to your research topic: You probably shouldn't be talking about cats and dogs if your research topic is outer space. Stay relevant to your topic.

How to create an effective research hypothesis

Pose it as a question first.

Start your research hypothesis from a journalistic approach. Ask one of the five W's: Who, what, when, where, or why.

A possible initial question could be: Why is the sky blue?

Do the preliminary research

Once you have a question in mind, read research around your topic. Collect research from academic journals.

If you're looking for information about the sky and why it is blue, research information about the atmosphere, weather, space, the sun, etc.

Write a draft hypothesis

Once you're comfortable with your subject and have preliminary knowledge, create a working hypothesis. Don't stress much over this. Your first hypothesis is not permanent. Look at it as a draft.

Your first draft of a hypothesis could be: Certain molecules in the Earth's atmosphere are responsive to the sky being the color blue.

Make your working draft perfect

Take your working hypothesis and make it perfect. Narrow it down to include only the information listed in the “Research hypothesis checklist” above.

Now that you've written your working hypothesis, narrow it down. Your new hypothesis could be: Light from the sun hitting oxygen molecules in the sky makes the color of the sky appear blue.

Write a null hypothesis

Your null hypothesis should be the opposite of your research hypothesis. It should be able to be disproven by your research.

In this example, your null hypothesis would be: Light from the sun hitting oxygen molecules in the sky does not make the color of the sky appear blue.

Why is it important to have a clear, testable hypothesis?

One of the main reasons a manuscript can be rejected from a journal is because of a weak hypothesis. “Poor hypothesis, study design, methodology, and improper use of statistics are other reasons for rejection of a manuscript,” says Dr. Ish Kumar Dhammi and Dr. Rehan-Ul-Haq in Indian Journal of Orthopaedics.

According to Dr. James M. Provenzale in American Journal of Roentgenology , “The clear declaration of a research question (or hypothesis) in the Introduction is critical for reviewers to understand the intent of the research study. It is best to clearly state the study goal in plain language (for example, “We set out to determine whether condition x produces condition y.”) An insufficient problem statement is one of the more common reasons for manuscript rejection.”

Characteristics that make a hypothesis weak include:

• Unclear variables
• Unoriginality
• Too general
• Too specific

A weak hypothesis leads to weak research and methods . The goal of a paper is to prove or disprove a hypothesis - or to prove or disprove a null hypothesis. If the hypothesis is not a dependent variable of what is being studied, the paper's methods should come into question.

A strong hypothesis is essential to the scientific method. A hypothesis states an assumed relationship between at least two variables and the experiment then proves or disproves that relationship with statistical significance. Without a proven and reproducible relationship, the paper feeds into the reproducibility crisis. Learn more about writing for reproducibility .

In a study published in The Journal of Obstetrics and Gynecology of India by Dr. Suvarna Satish Khadilkar, she reviewed 400 rejected manuscripts to see why they were rejected. Her studies revealed that poor methodology was a top reason for the submission having a final disposition of rejection.

Aside from publication chances, Dr. Gareth Dyke believes a clear hypothesis helps efficiency.

“Developing a clear and testable hypothesis for your research project means that you will not waste time, energy, and money with your work,” said Dyke. “Refining a hypothesis that is both meaningful, interesting, attainable, and testable is the goal of all effective research.”

Types of research hypotheses

There can be overlap in these types of hypotheses.

Simple hypothesis

A simple hypothesis is a hypothesis at its most basic form. It shows the relationship of one independent and one independent variable.

Example: Drinking soda (independent variable) every day leads to obesity (dependent variable).

Complex hypothesis

A complex hypothesis shows the relationship of two or more independent and dependent variables.

Example: Drinking soda (independent variable) every day leads to obesity (dependent variable) and heart disease (dependent variable).

Directional hypothesis

A directional hypothesis guesses which way the results of an experiment will go. It uses words like increase, decrease, higher, lower, positive, negative, more, or less. It is also frequently used in statistics.

Example: Humans exposed to radiation have a higher risk of cancer than humans not exposed to radiation.

Non-directional hypothesis

A non-directional hypothesis says there will be an effect on the dependent variable, but it does not say which direction.

Associative hypothesis

An associative hypothesis says that when one variable changes, so does the other variable.

Alternative hypothesis

An alternative hypothesis states that the variables have a relationship.

• The opposite of a null hypothesis

Example: An apple a day keeps the doctor away.

Null hypothesis

A null hypothesis states that there is no relationship between the two variables. It is posed as the opposite of what the alternative hypothesis states.

Researchers use a null hypothesis to work to be able to reject it. A null hypothesis:

• Can never be proven
• Can only be rejected
• Is the opposite of an alternative hypothesis

Example: An apple a day does not keep the doctor away.

Logical hypothesis

A logical hypothesis is a suggested explanation while using limited evidence.

Example: Bats can navigate in the dark better than tigers.

In this hypothesis, the researcher knows that tigers cannot see in the dark, and bats mostly live in darkness.

Empirical hypothesis

An empirical hypothesis is also called a “working hypothesis.” It uses the trial and error method and changes around the independent variables.

• An apple a day keeps the doctor away.
• Two apples a day keep the doctor away.
• Three apples a day keep the doctor away.

In this case, the research changes the hypothesis as the researcher learns more about his/her research.

Statistical hypothesis

A statistical hypothesis is a look of a part of a population or statistical model. This type of hypothesis is especially useful if you are making a statement about a large population. Instead of having to test the entire population of Illinois, you could just use a smaller sample of people who live there.

Example: 70% of people who live in Illinois are iron deficient.

Causal hypothesis

A causal hypothesis states that the independent variable will have an effect on the dependent variable.

Example: Using tobacco products causes cancer.

Final thoughts

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Research Hypothesis In Psychology: Types, & Examples

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

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

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

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.

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• Methodology

Research Design | Step-by-Step Guide with Examples

Published on 5 May 2022 by Shona McCombes . Revised on 20 March 2023.

A research design is a strategy for answering your research question  using empirical data. Creating a research design means making decisions about:

• Your overall aims and approach
• The type of research design you’ll use
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research aims and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, frequently asked questions.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities – start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

Prevent plagiarism, run a free check.

Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types. Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships, while descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends, and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analysing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study – plants, animals, organisations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region, or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalise your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study, your aim is to deeply understand a specific context, not to generalise to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question.

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviours, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews.

Observation methods

Observations allow you to collect data unobtrusively, observing characteristics, behaviours, or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected – for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are reliable and valid.

Operationalisation

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalisation means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in – for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced , while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method, you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample – by mail, online, by phone, or in person?

If you’re using a probability sampling method, it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method, how will you avoid bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organising and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymise and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well organised will save time when it comes to analysing them. It can also help other researchers validate and add to your findings.

On their own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyse the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarise your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarise your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analysing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

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

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

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How to Develop a Good Research Hypothesis

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

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

Table of Contents

What is Hypothesis?

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

What is a Research Hypothesis?

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

Characteristics of a Good Research Hypothesis

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

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

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

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

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

Source: Educational Hub

How to formulate a research hypothesis.

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

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

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

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

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

3. Define the variables

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

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

4. Scrutinize the hypothesis

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

Types of Research Hypothesis

The types of research hypothesis are stated below:

1. Simple Hypothesis

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

2. Complex Hypothesis

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

3. Directional Hypothesis

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

4. Non-directional Hypothesis

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

5. Associative and Causal Hypothesis

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

6. Null Hypothesis

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

7. Alternative Hypothesis

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

Research Hypothesis Examples of Independent and Dependent Variables

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

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

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

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

Importance of a Testable Hypothesis

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

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

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

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

Frequently Asked Questions

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

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

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

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

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

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

Thanks a lot for your valuable guidance.

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

Useful piece!

This is awesome.Wow.

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

Nicely explained

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

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

clear and concise. thanks.

So Good so Amazing

Good to learn

Thanks a lot for explaining to my level of understanding

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

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

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Writing a Research Strategy

This page is focused on providing practical tips and suggestions for preparing The Research Strategy, the primary component of an application's Research Plan along with the Specific Aims. The guidance on this page is primarily geared towards an R01-style application, however, much of it is useful for other grant types as well.

Developing the Research Strategy

The primary audience for your application is your peer review group. When writing your Research Strategy, your goal is to present a well-organized, visually appealing, and readable description of your proposed project and the rationale for pursuing it. Your writing should be streamlined and organized so your reviewers can readily grasp the information. If it's a key point, repeat it, then repeat it again. Add more emphasis by putting the text in bold , or bold italics . If writing is not your forte, get help.  For more information, please visit  W riting For Reviewers .

How to Organize the Research Strategy Section

How to organize a Research Strategy is largely up to the applicant. Start by following the NIH application instructions and guidelines for formatting attachments such as the research plan section.

It is generally structured as follows:

Significance

For  Preliminary Studies (for new applications) or a Progress Report (for renewal and revision applications).

• You can either include preliminary studies or progress report information as a subsection of Approach or integrate it into any or all of the three main sections.
• If you do the latter, be sure to mark the information clearly, for example, with a bold subhead.

 Helpful tips to consider when formatting:

• Organize using bold headers or an outline or numbering system—or both—that are used consistently throughout.
• Start each section with the appropriate header: Significance, Innovation, or Approach.
• Organize the Approach section around the Specific Aims.

For most applications, you need to address Rigor ous Study Design  by describing the experimental design and methods you propose and how they will achieve robust and unbiased results. See the NIH guidance for elaboration on the 4 major areas of rigor and transparency emphasized in grant review.  These requirements apply to research grant, career development, fellowship, and training applications.

Tips for Drafting Sections of the Research Strategy

Although you will emphasize your project's significance throughout the application, the Significance section should give the most details. The farther removed your reviewers are from your field, the more information you'll need to provide on basic biology, importance of the area, research opportunities, and new findings. Reviewing the potentially relevant study section rosters may give you some ideas as to general reviewer expertise. You will also need to describe the prior and preliminary studies that provide a strong scientific rationale for pursuing the proposed studies, emphasizing the strengths and weaknesses in the rigor and transparency of these key studies.

This section gives you the chance to explain how your application is conceptually and/or technically innovative. Some examples as to how you might do this could include but not limited to:

• Demonstrate the proposed research is new and unique, e.g., explores new scientific avenues, has a novel hypothesis, will create new knowledge.
• Explain how the proposed work can refine, improve, or propose a new application of an existing concept or method.

If your proposal is paradigm-shifting or challenges commonly held beliefs, be sure that you include sufficient evidence in your preliminary data to convince reviewers, including strong rationale, data supporting the approach, and clear feasibility. Your job is to make the reviewers feel confident that the risk is worth taking.

For projects predominantly focused on innovation and outside-the-box research, investigators may wish to consider mechanisms other than R01s for example (e.g., exploratory/developmental research (R21) grants, NIH Director's Pioneer Award Program (DP1), and NIH Director's New Innovator Award Program (DP2).

The Approach section is where the experimental design is described. Expect your assigned reviewers to scrutinize your approach: they will want to know what you plan to do, how you plan to do it, and whether you can do it. NIH data show that of the peer review criteria, approach has the highest correlation with the overall impact score. Importantly, elements of rigorous study design should be addressed in this section, such as plans for minimization of bias (e.g. methods for blinding and treatment randomization) and consideration of relevant biological variables. Likewise, be sure to lay out a plan for alternative experiments and approaches in case you get uninterpretable or surprising results, and also consider limitations of the study and alternative interpretations. Point out any procedures, situations, or materials that may be hazardous to personnel and precautions to be exercised. A full discussion on the use of select agents should appear in the Select Agent Research attachment.  Consider including a timeline demonstrating anticipated completion of the Aims. 

Here are some pointers to consider when organizing your Approach section:

• Enter a bold header for each Specific Aim.
• Under each aim, describe the experiments.
• If you get result X, you will follow pathway X; if you get result Y, you will follow pathway Y.
• Consider illustrating this with a flowchart.

Preliminary Studies

If submitting a new application to a NOFO that allows preliminary data, it is strongly encouraged to include preliminary studies.  Preliminary studies demonstrate competency in the methods and interpretation. Well-designed and robust preliminary studies also serve to provide a strong scientific rationale for the proposed follow-up experiments. Reviewers also use preliminary studies together with the biosketches to assess the investigator review criterion, which reflects the competence of the research team. Provide alternative interpretations to your data to show reviewers you've thought through problems in-depth and are prepared to meet future challenges. As noted above, preliminary data can be put anywhere in the Research Strategy, but just make sure reviewers will be able to distinguish it from the proposed studies. Alternatively, it can be a separate section with its own header.

Progress Reports

If applying for a renewal or a revision (a competing supplement to an existing grant), include a progress report for reviewers.

Create a header so reviewers can easily find it and include the following information:

• Project period beginning and end dates.
• Summary of the importance and robustness of the completed findings in relation to the Specific Aims.
• Account of published and unpublished results, highlighting progress toward achieving your Specific Aims.  

Other Helpful Tips

Referencing publications.

References show breadth of knowledge of the field and provide a scientific foundation for your application. If a critical work is omitted, reviewers may assume the applicant is not aware of it or deliberately ignoring it.

Throughout the application, reference all relevant publications for the concepts underlying your research and your methods. Remember the strengths and weaknesses in the rigor of the key studies you cite for justifying your proposal will need to be discussed in the Significance and/or Approach sections.

Read more about Bibliography and References Cited at Additional Application Elements .

Graphics can illustrate complex information in a small space and add visual interest to your application. Including schematics, tables, illustrations, graphs, and other types of graphics can enhance applications. Consider adding a timetable or flowchart to illustrate your experimental plan, including decision trees with alternative experimental pathways to help your reviewers understand your plans.

Video may enhance your application beyond what graphics alone can achieve. If you plan to send one or more videos, you'll need to meet certain requirements and include key information in your Research Strategy. State in your cover letter that a video will be included in your application (don't attach your files to the application). After you apply and get assignment information from the Commons, ask your assigned Scientific Review Officer (SRO) how your business official should send the files. Your video files are due at least one month before the peer review meeting.

However, you can't count on all reviewers being able to see or hear video, so you'll want to be strategic in how you incorporate it into your application by taking the following steps:

• Caption any narration in the video.
• Include key images from the video
• Write a description of the video, so the text would make sense even without the video.

Tracking for Your Budget

As you design your experiments, keep a running tab of the following essential data:

• Who. A list of people who will help (for the Key Personnel section later).
• What. A list of equipment and supplies for the experiments
• Time. Notes on how long each step takes. Timing directly affects the budget as well as how many Specific Aims can realistically be achieved.

Jotting this information down will help when Creating a Budget  and complete other sections later.

Review and Finalize Your Research Plan

Critically review the research plan through the lens of a reviewer to identify potential questions or weak spots.

Enlist others to review your application with a fresh eye. Include people who aren't familiar with the research to make sure the proposed work is clear to someone outside the field.

When finalizing the details of the Research Strategy, revisit and revise the Specific Aims as needed. Please see Writing Specific Aims . 

comments Want to contact NINDS staff? Please visit our Find Your NINDS Program Officer page to learn more about contacting Program Officer, Grants Management Specialists, Scientific Review Officers, and Health Program Specialists.

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• Knowledge Base

Methodology

Research Methods | Definitions, Types, Examples

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

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

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

Second, decide how you will analyze the data .

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

Table of contents

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

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

Qualitative vs. quantitative data

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

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

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

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

Primary vs. secondary research

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

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

Descriptive vs. experimental data

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

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

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

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

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

Qualitative analysis methods

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

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

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

Quantitative analysis methods

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

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

• During an experiment .
• Using probability sampling methods .

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

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

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

Research bias

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

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

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

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

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

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

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

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

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

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

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

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

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Deductive Approach (Deductive Reasoning)

A deductive approach is concerned with “developing a hypothesis (or hypotheses) based on existing theory, and then designing a research strategy to test the hypothesis” [1]

It has been stated that “deductive means reasoning from the particular to the general. If a causal relationship or link seems to be implied by a particular theory or case example, it might be true in many cases. A deductive design might test to see if this relationship or link did obtain on more general circumstances” [2] .

Deductive approach can be explained by the means of hypotheses, which can be derived from the propositions of the theory. In other words, deductive approach is concerned with deducting conclusions from premises or propositions.

Deduction begins with an expected pattern “that is tested against observations, whereas induction begins with observations and seeks to find a pattern within them” [3] .

Advantages of Deductive Approach

Deductive approach offers the following advantages:

• Possibility to explain causal relationships between concepts and variables
• Possibility to measure concepts quantitatively
• Possibility to generalize research findings to a certain extent

Alternative to deductive approach is  inductive approach.  The table below guides the choice of specific approach depending on circumstances:

Choice between deductive and inductive approaches

Deductive research approach explores a known theory or phenomenon and tests if that theory is valid in given circumstances. It has been noted that “the deductive approach follows the path of logic most closely. The reasoning starts with a theory and leads to a new hypothesis. This hypothesis is put to the test by confronting it with observations that either lead to a confirmation or a rejection of the hypothesis” [4] .

Moreover, deductive reasoning can be explained as “reasoning from the general to the particular” [5] , whereas inductive reasoning is the opposite. In other words, deductive approach involves formulation of hypotheses and their subjection to testing during the research process, while inductive studies do not deal with hypotheses in any ways.

Application of Deductive Approach (Deductive Reasoning) in Business Research

In studies with deductive approach, the researcher formulates a set of hypotheses at the start of the research. Then, relevant research methods are chosen and applied to test the hypotheses to prove them right or wrong.

Generally, studies using deductive approach follow the following stages:

• Deducing  hypothesis from theory.
• Formulating  hypothesis in operational terms and proposing relationships between two specific variables
• Testing  hypothesis with the application of relevant method(s). These are quantitative methods such as regression and correlation analysis, mean, mode and median and others.
• Examining  the outcome of the test, and thus confirming or rejecting the theory. When analysing the outcome of tests, it is important to compare research findings with the literature review findings.
• Modifying  theory in instances when hypothesis is not confirmed.

My e-book,  The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step assistance  contains discussions of theory and application of research approaches. The e-book also explains all stages of the  research process  starting from the  selection of the research area  to writing personal reflection. Important elements of dissertations such as  research philosophy ,   research design ,  methods of data collection ,   data analysis  and   sampling   are explained in this e-book in simple words.

John Dudovskiy

[1] Wilson, J. (2010) “Essentials of Business Research: A Guide to Doing Your Research Project” SAGE Publications, p.7

[2] Gulati, PM, 2009, Research Management: Fundamental and Applied Research, Global India Publications, p.42

[3] Babbie, E. R. (2010) “The Practice of Social Research” Cengage Learning, p.52

[4] Snieder, R. & Larner, K. (2009) “The Art of Being a Scientist: A Guide for Graduate Students and their Mentors”, Cambridge University Press, p.16

[5] Pelissier, R. (2008) “Business Research Made Easy” Juta & Co., p.3

Saunders’ Research Onion: Explained Simply

Peeling the onion, layer by layer (with examples).

By: David Phair (PhD) and Kerryn Warren (PhD) | January 2021

If you’re learning about research skills and methodologies, you may have heard the term “ research onion ”. Specifically, the research onion developed by Saunders et al in 2007 . But what exactly is this elusive onion? In this post, we’ll break Saunders’ research onion down into bite-sized chunks to make it a little more digestible.

Saunders’ (2007) Research Onion – What is it?

At the simplest level, Saunders’ research onion describes the different decisions you’ll need to make when developing a  research methodology   – whether that’s for your dissertation, thesis or any other formal research project. As you work from the outside of the onion inwards , you’ll face a range of choices that progress from high-level and philosophical to tactical and practical in nature. This also mimics the general structure for the methodology chapter .

While Saunders’ research onion is certainly not perfect, it’s a useful tool for thinking holistically about methodology. At a minimum, it helps you understand what decisions you need to make in terms of your research design and methodology.

The layers of Saunders’ research onion

The onion is made up of 6 layers, which you’ll need to peel back one at a time as you develop your research methodology:

• Research philosophy
• Research approach
• Research strategy
• Time horizon
• Techniques & procedures

Onion Layer 1: Research Philosophy

The very first layer of the onion is the research philosophy . But what does that mean? Well, the research philosophy is the foundation of any study as it describes the set of beliefs the research is built upon . Research philosophy can be described from either an  ontological  or  epistemological  point of view. “A what?!”, you ask?

In simple terms,  ontology  is the “what” and “how” of what we know – in other words, what is the nature of reality and what are we really able to know and understand. For example, does reality exist as a single objective thing, or is it different for each person? Think about the simulated reality in the film The Matrix.

Epistemology , on the other hand, is about “how” we can obtain knowledge and come to understand things – in other words, how can we figure out what reality is, and what the limits of this knowledge are. This is a gross oversimplification, but it’s a useful starting point (we’ll cover ontology and epistemology another post).

With that fluffy stuff out the way, let’s look at three of the main research philosophies that operate on different ontological and epistemological assumptions:

• Interpretivism

These certainly aren’t the only research philosophies, but they are very common and provide a good starting point for understanding the spectrum of philosophies.

Research Philosophy 1:  Positivism

Positivist research takes the view that knowledge exists outside of what’s being studied . In other words, what is being studied can only be done so objectively , and it cannot include opinions or personal viewpoints – the researcher doesn’t interpret, they only observe. Positivism states that there is only one reality  and that all meaning is consistent between subjects.

In the positivist’s view, knowledge can only be acquired through empirical research , which is based on measurement and observation. In other words, all knowledge is viewed as a posteriori knowledge – knowledge that is not reliant on human reasoning but instead is gained from research.

For the positivist, knowledge can only be true, false, or meaningless . Basically, if something is not found to be true or false, it no longer holds any ground and is thus dismissed.

Let’s look at an example, based on the question of whether God exists or not. Since positivism takes the stance that knowledge has to be empirically vigorous, the knowledge of whether God exists or not is irrelevant. This topic cannot be proven to be true or false, and thus this knowledge is seen as meaningless.

Kinda harsh, right? Well, that’s the one end of the spectrum – let’s look at the other end.

Research Philosophy 2: Interpretivism

On the other side of the spectrum, interpretivism emphasises the influence that social and cultural factors can have on an individual. This view focuses on  people’s thoughts and ideas , in light of the socio-cultural backdrop. With the interpretivist philosophy, the researcher plays an active role in the study, as it’s necessary to draw a holistic view of the participant and their actions, thoughts and meanings.

Let’s look at an example. If you were studying psychology, you may make use of a case study in your research which investigates an individual with a proposed diagnosis of schizophrenia. The interpretivist view would come into play here as social and cultural factors may influence the outcome of this diagnosis.

Through your research, you may find that the individual originates from India, where schizophrenic symptoms like hallucinations are viewed positively, as they are thought to indicate that the person is a spirit medium. This example illustrates an interpretivist approach since you, as a researcher, would make use of the patient’s point of view, as well as your own interpretation when assessing the case study.

Research Philosophy 3: Pragmatism

Pragmatism highlights the importance of using the best tools possible to investigate phenomena. The main aim of pragmatism is to approach research from a practical point of view , where knowledge is not fixed, but instead is constantly questioned and interpreted. For this reason, pragmatism consists of an element of researcher involvement and subjectivity, specifically when drawing conclusions based on participants’ responses and decisions. In other words, pragmatism is not committed to (or limited by) one specific philosophy.

Let’s look at an example in the form of the trolley problem, which is a set of ethical and psychological thought experiments. In these, participants have to decide on either killing one person to save multiple people or allowing multiple people to die to avoid killing one person. 

This experiment can be altered, including details such as the one person or the group of people being family members or loved ones. The fact that the experiment can be altered to suit the researcher’s needs is an example of pragmatism – in other words, the outcome of the person doing the thought experiment is more important than the philosophical ideas behind the experiment.

To recap, research philosophy is the foundation of any research project and reflects the ontological and epistemological assumptions of the researcher. So, when you’re designing your research methodology , the first thing you need to think about is which philosophy you’ll adopt, given the nature of your research.

Onion Layer 2: Research Approach

Let’s peel off another layer and take a look at the research approach . Your research approach is the broader method you’ll use for your research –  inductive  or  deductive . It’s important to clearly identify your research approach as it will inform the decisions you take in terms of data collection and analysis in your study (we’ll get to that layer soon).

Inductive approaches entail generating theories from research , rather than starting a project with a theory as a foundation.  Deductive approaches, on the other hand, begin with a theory and aim to build on it (or test it) through research.

Sounds a bit fluffy? Let’s look at two examples:

An  inductive approach  could be used in the study of an otherwise unknown isolated community. There is very little knowledge about this community, and therefore, research would have to be conducted to gain information on the community, thus leading to the formation of theories.

On the other hand, a  deductive approach  would be taken when investigating changes in the physical properties of animals over time, as this would likely be rooted in the theory of evolution. In other words, the starting point is a well-established pre-existing body of research.

Closely linked to research approaches are  qualitative and  quantitative  research. Simply put, qualitative research focuses on textual , visual or audio-based data, while quantitative research focuses on numerical data. To learn more about qualitative and quantitative research, check out our dedicated post here .

What’s the relevance of qualitative and quantitative data to research approaches? Well, inductive approaches are usually used within qualitative research, while quantitative research tends to reflect a deductive approach, usually informed by positivist philosophy. The reason for using a deductive approach here is that quantitative research typically begins with theory as a foundation, where progress is made through hypothesis testing. In other words, a wider theory is applied to a particular context, event, or observation to see whether these fit in with the theory, as with our example of evolution above.

So, to recap, the two research approaches are  inductive  and  deductive . To decide on the right approach for your study, you need to assess the type of research you aim to conduct. Ask yourself whether your research will build on something that exists, or whether you’ll be investigating something that cannot necessarily be rooted in previous research. The former suggests a deductive approach while the latter suggests an inductive approach.

Need a helping hand?

Onion Layer 3: Research Strategy

So far, we’ve looked at pretty conceptual and intangible aspects of the onion. Now, it’s time to peel another layer off that onion and get a little more practical – introducing research strategy . This layer of the research onion details how, based on the aims of the study, research can be conducted. Note that outside of the onion, these strategies are referred to as research designs.

There are several strategies  you can take, so let’s have a look at some of them.

• Experimental research
• Action research
• Case study research
• Grounded theory
• Ethnography
• Archival research

Strategy 1: Experimental research

Experimental research involves manipulating one variable (the independent variable ) to observe a change in another variable (the dependent variable ) – in other words, to assess the relationship between variables. The purpose of experimental research is to support, refute or validate a  research hypothesis . This research strategy follows the principles of the  scientific method  and is conducted within a controlled environment or setting (for example, a laboratory).

Experimental research aims to test existing theories rather than create new ones, and as such, is deductive in nature. Experimental research aligns with the positivist research philosophy, as it assumes that knowledge can only be studied objectively and in isolation from external factors such as context or culture.

Let’s look at an example of experimental research. If you had a hypothesis that a certain brand of dog food can raise a dogs’ protein levels, you could make use of experimental research to compare the effects of the specific brand to a “regular” diet. In other words, you could test your hypothesis.

In this example, you would have two groups, where one group consists of dogs with no changes to their diet (this is called  the control group) and the other group consists of dogs being fed the specific brand that you aim to investigate (this is called the experimental/treatment group). You would then test your hypothesis by comparing the protein levels in both groups.

Strategy 2: Action research

Next, we have action research . The simplest way of describing action research is by saying that it involves learning through… wait for it… action. Action research is conducted in practical settings such as a classroom, a hospital, a workspace, etc – as opposed to controlled environments like a lab. Action research helps to inform researchers of problems or weaknesses related to interactions within the real-world . With action research, there’s a strong focus on the participants (the people involved in the issue being studied, which is why it’s sometimes referred to as “participant action research” or PAR.

An example of PAR is a community intervention (for therapy, farming, education, whatever). The researcher comes with an idea and it is implemented with the help of the community (i.e. the participants). The findings are then discussed with the community to see how to better the intervention. The process is repeated until the intervention works just right for the community. In this way, a practical solution is given to a problem and it is generated by the combination of researcher and community (participant) feedback.

This kind of research is generally applied in the social sciences , specifically in professions where individuals aim to improve on themselves and the work that they are doing. Action research is most commonly adopted in qualitative studies and is rarely seen in quantitative studies. This is because, as you can see in the above examples, action research makes use of language and interactions rather than statistics and numbers.

Strategy 3: Case study research

A case study is a detailed, in-depth study of a single subject – for example, a person, a group or an institution, or an event, phenomenon or issue. In this type of research, the subject is analysed to gain an in-depth understanding of issues in a real-life setting. The objective here is to gain an in-depth understanding within the context of the study – not (necessarily) to generalise the findings.

It is vital that, when conducting case study research, you take the social context and culture into account, which means that this type of research is (more often than not) qualitative in nature and tends to be inductive. Also, since the researcher’s assumptions and understanding play a role in case study research, it is typically informed by an interpretivist philosophy.

For example, a study on political views of a specific group of people needs to take into account the current political situation within a country and factors that could contribute towards participants taking a certain view.

Strategy 4: Grounded theory

Next up, grounded theory. Grounded theory is all about “letting the data speak for itself”. In other words, in grounded theory, you let the data inform the development of a new theory, model or framework. True to the name, the theory you develop is “ grounded ” in the data. Ground theory is therefore very useful for research into issues that are completely new or under-researched.

Grounded theory research is typically qualitative (although it can also use quantitative data) and takes an inductive approach. Typically, this form of research involves identifying commonalities between sets of data, and results are then drawn from completed research without the aim of fitting the findings in with a pre-existing theory or framework.

For example, if you were to study the mythology of an unknown culture through artefacts, you’d enter your research without any hypotheses or theories, and rather work from the knowledge you gain from your study to develop these.

Strategy 5: Ethnography

Ethnography involves observing people in their natural environments and drawing meaning from their cultural interactions. The objective with ethnography is to capture the subjective experiences of participants, to see the world through their eyes. Creswell (2013) says it best: “Ethnographers study the meaning of the behaviour, the language, and the interaction among members of the culture-sharing group.”

For example, if you were interested in studying interactions on a mental health discussion board, you could use ethnography to analyse interactions and draw an understanding of the participants’ subjective experiences.

For example, if you wanted to explore the behaviour, language, and beliefs of an isolated Amazonian tribe, ethnography could allow you to develop a complex, complete description of the social behaviours of the group by immersing yourself into the community, rather than just observing from the outside.  

Given the nature of ethnography, it generally reflects an interpretivist research philosophy and involves an inductive , qualitative research approach. However, there are exceptions to this – for example, quantitative ethnography as proposed by David Shafer.

Strategy 6: Archival research

Last but not least is archival research. An archival research strategy draws from materials that already exist, and meaning is then established through a review of this existing data. This method is particularly well-suited to historical research and can make use of materials such as manuscripts and records.

For example, if you were interested in people’s beliefs about so-called supernatural phenomena in the medieval period, you could consult manuscripts and records from the time, and use those as your core data set.

Onion Layer 4: Choices

The next layer of the research onion is simply called “choices” – they could have been a little more specific, right? In any case, this layer is simply about deciding how many data types (qualitative or quantitative) you’ll use in your research. There are three options – mono , mixed , and multi-method .

Let’s take a look at them.

Choosing to use a  mono method  means that you’ll only make use of one data type – either qualitative or quantitative. For example, if you were to conduct a study investigating a community’s opinions on a specific pizza restaurant, you could make use of a qualitative approach only, so that you can analyse participants’ views and opinions of the restaurant.

If you were to make use of both quantitative and qualitative data, you’d be taking a  mixed-methods approach. Keeping with the previous example, you may also want to assess how many people in a community eat specific types of pizza. For this, you could make use of a survey to collect quantitative data and then analyse the results statistically, producing quantitative results in addition to your qualitative ones.

Lastly, there’s  multi-method . With a multi-method approach, you’d make use of a wider range of approaches, with more than just a one quantitative and one qualitative approach. For example, if you conduct a study looking at archives from a specific culture, you could make use of two qualitative methods (such as thematic analysis and content analysis ), and then additionally make use of quantitative methods to analyse numerical data.

As with all the layers of the research onion, the right choice here depends on the nature of your research, as well as your research aims and objectives . There’s also the practical consideration of viability – in other words, what kind of data will you be able to access, given your constraints.

Onion Layer 5: Time horizon

What’s that far in the distance? It’s the time horizon. But what exactly is it? Thankfully, this one’s pretty straightforward. The time horizon simply describes how many points in time you plan to collect your data at . Two options exist – the  cross-sectional  and  longitudinal  time horizon.

Imagine that you’re wasting time on social media and think, “Ooh! I want to study the language of memes and how this language evolves over time”. For this study, you’d need to collect data over multiple points in time – perhaps over a few weeks, months, or even years. Therefore, you’d make use of a  longitudinal time horizon. This option is highly beneficial when studying changes and progressions over time.

If instead, you wanted to study the language used in memes at a certain point in time (for example, in 2020), you’d make use of a  cross-sectional  time horizon. This is where data is collected at one point in time, so you wouldn’t be gathering data to see how language changes, but rather what language exists at a snapshot point in time. The type of data collected could be qualitative, quantitative or a mix of both, as the focus is on the time of collection, not the data type.

As with all the other choices, the nature of your research and your research aims and objectives are the key determining factors when deciding on the time horizon. You’ll also need to consider practical constraints , such as the amount of time you have available to complete your research (especially in the case of a dissertation or thesis).

Onion Layer 6: Techniques and Procedures

Finally, we reach the centre of the onion – this is where you get down to the real practicalities of your research to make choices regarding specific techniques and procedures .

Specifically, this is where you’ll:

• Decide on what data you’ll collect and what data collection methods you’ll use (for example, will you use a survey? Or perhaps one-on-one interviews?)
• Decide how you’ll go about sampling the population (for example, snowball sampling, random sampling, convenience sampling, etc).
• Determine the type of data analysis you’ll use to answer your research questions (such as content analysis or a statistical analysis like correlation).
• Set up the materials you’ll be using for your study (such as writing up questions for a survey or interview)

What’s important to note here is that these techniques and procedures need to align with all the other layers of the research onion – i.e., research philosophy, research approaches, research strategy, choices, and time horizon.

For example, you if you’re adopting a deductive, quantitative research approach, it’s unlikely that you’ll use interviews to collect your data, as you’ll want high-volume, numerical data (which surveys are far better suited to). So, you need to ensure that the decisions at each layer of your onion align with the rest, and most importantly, that they align with your research aims and objectives.

Let’s Recap: Research Onion 101

The research onion details the many interrelated choices you’ll need to make when you’re crafting your research methodology. These include:

• Research philosophy – the set of beliefs your research is based on (positivism, interpretivism, pragmatism)
• Research approaches – the broader method you’ll use (inductive, deductive, qualitative and quantitative)
• Research strategies – how you’ll conduct the research (e.g., experimental, action, case study, etc.)
• Choices – how many methods you’ll use (mono method, mixed-method or multi-method)
• Time horizons – the number of points in time at which you’ll collect your data (cross-sectional or longitudinal)
• Techniques and procedures (data collection methods, data analysis techniques, sampling strategies, etc.)

Psst... there’s more!

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59 Comments

This is good

Wow this was sooo helpful. I don’t feel so blank about my research anymore. With this information I can conquer my research. Going ‘write’ into it. Get it write not right hahahaha

I am doing research with Bolton University so i would like to empower myself.

Really thoughtful presentation and preparation. I learnt too much to teach my students in a very simple and understandable way

Very useful, thank you.

You’re most welcome. Good luck with your research!

thanks alot for your brief and brilliant notes

I am a Student at Malawi Institute of Management, pursuing a Masters’ degree in Business Administration. I find this to be very helpful

Extremely useful, well explained. Thank you so much

I would like to download this file… I can’t find the attachment file. Thanks

Thank you so much for explaining it in the most simple and precise manner!

Very thoughtful and well expained, thanks.

This is good for upgrade my research knowledge

I have enjoying your videos on YouTube, they are very educative and useful. I have learned a lot. Thanks

Thank you this has really helped me with writing my dissertation methodology !

Thanks so much for this piece. Just to be clear, which layer do interviews fit in?

well explained i found it to be very engaging. now i’m going to pass my research methods course. thank you.

Thank you so much this has really helped as I can’t get this insight from uni due to covid

well explained with more clarity!

this is an excellent piece i find it super helpful

Beautiful, thank you!

Beautiful and helpful. Thank you!

This is well done!

A complex but useful approach to research simplified! I would like to learn more from the team.

A very simplified version of a complex topic. I found it really helpful. I would like to know if this publication can be cited for academic research. Thank you

You’re welcome to cite this page, but it would be better to cite the original work of Saunders.

Thirteen odd years since my MSc in HRM & HRD at UoL. I’d like to say thank you for the effort to produce such an insightful discussion of a rather complex topic.

I am a PhD in Media Studies student. I found this enormously helpful when stringing together the methodology chapter, especially the research philosophy section.

Hello there. Thank you for summarising the work on the onion. A more recent version of the onion (Saunders et al., 2019) refers to ‘methodological choices’ rather than choices. This can be downloaded, along with the chapter dealing with research philosophies at: https://www.researchgate.net/publication/330760964_Research_Methods_for_Business_Students_Chapter_4_Understanding_research_philosophy_and_approaches_to_theory_development or https://www.academia.edu/42304065/Research_Methods_for_Business_Students_Chapter_4_Understanding_research_philosophy_and_approaches_to_theory_development_8th_edition

Thank you Mark Saunders. Your work is very insightful

Thank you for the update and additional reading Mark, very helpful indeed.

THROUGHLY AND SIMPLY BRIEFED TO MAKE SENSE AND A CLEAR INSIGHT. THANK YOU, VERY MUCH.

Thank you for the sharing the recent version of the Onion!

I want to keep it in my reference of my assignment. May I??

Great summary, thank you taking the time to put this together. I’m sure it’s been a big help to lots of people. It definitely was to me.

I love the analysis… some people do not recognize qualitative or quantitative as an approach but rather have inductive, abductive, and deductive.

This has been helpful in the understanding of research . Thank you for this valuable information.

Great summary. Well explained. Thank you, guys.

This makes my fears on methodology go away. I confidently look forward to working on my methodology now. Thank you so much I ma doing a PhD with UNIMA, School of Education

simple and clear

Simple guide to crafting a research methodology. Quite impactful. Thank you

Thank you for this, this makes things very clear. Now I’m off to conquer my research proposal. Thanks again.

Thank you for this very informative and valuable information. What would the best approach be to take if you are using secondary data to form a qualitative study and relying on industry reports and peer journals to distinguish what factors influence the use of say cryptocurrency ?

Thanks for providing the whole idea/knowledge in the simplest way with essential factors which made my entire research process more efficient as well as valuable.

what is about research design such as descriptive, causal-comparative, correlation, developmental where these fall in the research onion?

This is very helpful. Thank you for this wonderful piece. However, it would be nicer to have References to the knowledge provided here. My suggestion

This material is very important for researchers, particularly for PhD scholars to conduct further study.

This was insightful. Thank you for the knowledge.

Thank you for the wonderful knowledge !Easy to understand and grasp.

thanks very much very simple. will need a coach

Hi this is a great article giving much help to my research. I just wanted to mention here that the example where you mentioned that ” schizophrenic symptoms like hallucinations are viewed positively, as they are thought to indicate the person is a spirit medium” is completely false as those are different cases and a bit out of context here. We are medically and psychologically well versed and obviously understand the difference between the two. As much as I am grateful to this article I would like to suggest you to give proper examples.

Thank you very much, sincerely I appreciate your efforts, it is insightful information. Once again I’m grateful .

In short, a complete insight of and for writing research methodology.

This information was very helpful, I was having difficulties in writing my methodology now I can say I have the full knowledge to write a more informative research methodology.

Thank you so much for this amazing explanation. As a person who hasn’t ever done a research project, this video helped me to clear my doubts and approach my research in a clear and concise manner. Great work

very well explained , after going through this there is no need any material to study . a very concise and to the point.

I have one small query. If I choose mixed -methods (quantitative and qualitative techniques), Then, my research Philosophy will also change to both Positivists and Interpretivist. Isn’t?

well explained and thank you

Thanks for this presentation. Quite simple and easy to understand, and to teach others.

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“A fact is a simple statement that everyone believes. It is innocent, unless found guilty. A hypothesis is a novel suggestion that no one wants to believe. It is guilty until found effective.”

– Edward Teller, Nuclear Physicist

During my first brainstorming meeting on my first project at McKinsey, this very serious partner, who had a PhD in Physics, looked at me and said, “So, Joe, what are your main hypotheses.” I looked back at him, perplexed, and said, “Ummm, my what?” I was used to people simply asking, “what are your best ideas, opinions, thoughts, etc.” Over time, I began to understand the importance of hypotheses and how it plays an important role in McKinsey’s problem solving of separating ideas and opinions from facts.

What is a Hypothesis?

“Hypothesis” is probably one of the top 5 words used by McKinsey consultants. And, being hypothesis-driven was required to have any success at McKinsey. A hypothesis is an idea or theory, often based on limited data, which is typically the beginning of a thread of further investigation to prove, disprove or improve the hypothesis through facts and empirical data.

The first step in being hypothesis-driven is to focus on the highest potential ideas and theories of how to solve a problem or realize an opportunity.

Let’s go over an example of being hypothesis-driven.

Let’s say you own a website, and you brainstorm ten ideas to improve web traffic, but you don’t have the budget to execute all ten ideas. The first step in being hypothesis-driven is to prioritize the ten ideas based on how much impact you hypothesize they will create.

The second step in being hypothesis-driven is to apply the scientific method to your hypotheses by creating the fact base to prove or disprove your hypothesis, which then allows you to turn your hypothesis into fact and knowledge. Running with our example, you could prove or disprove your hypothesis on the ideas you think will drive the most impact by executing:

1. An analysis of previous research and the performance of the different ideas 2. A survey where customers rank order the ideas 3. An actual test of the ten ideas to create a fact base on click-through rates and cost

While there are many other ways to validate the hypothesis on your prioritization , I find most people do not take this critical step in validating a hypothesis. Instead, they apply bad logic to many important decisions . An idea pops into their head, and then somehow it just becomes a fact.

One of my favorite lousy logic moments was a CEO who stated,

“I’ve never heard our customers talk about price, so the price doesn’t matter with our products , and I’ve decided we’re going to raise prices.”

Luckily, his management team was able to do a survey to dig deeper into the hypothesis that customers weren’t price-sensitive. Well, of course, they were and through the survey, they built a fantastic fact base that proved and disproved many other important hypotheses.

Why is being hypothesis-driven so important?

Imagine if medicine never actually used the scientific method. We would probably still be living in a world of lobotomies and bleeding people. Many organizations are still stuck in the dark ages, having built a house of cards on opinions disguised as facts, because they don’t prove or disprove their hypotheses. Decisions made on top of decisions, made on top of opinions, steer organizations clear of reality and the facts necessary to objectively evolve their strategic understanding and knowledge. I’ve seen too many leadership teams led solely by gut and opinion. The problem with intuition and gut is if you don’t ever prove or disprove if your gut is right or wrong, you’re never going to improve your intuition. There is a reason why being hypothesis-driven is the cornerstone of problem solving at McKinsey and every other top strategy consulting firm.

How do you become hypothesis-driven?

Most people are idea-driven, and constantly have hypotheses on how the world works and what they or their organization should do to improve. Though, there is often a fatal flaw in that many people turn their hypotheses into false facts, without actually finding or creating the facts to prove or disprove their hypotheses. These people aren’t hypothesis-driven; they are gut-driven.

The conversation typically goes something like “doing this discount promotion will increase our profits” or “our customers need to have this feature” or “morale is in the toilet because we don’t pay well, so we need to increase pay.” These should all be hypotheses that need the appropriate fact base, but instead, they become false facts, often leading to unintended results and consequences. In each of these cases, to become hypothesis-driven necessitates a different framing.

• Instead of “doing this discount promotion will increase our profits,” a hypothesis-driven approach is to ask “what are the best marketing ideas to increase our profits?” and then conduct a marketing experiment to see which ideas increase profits the most.

• Instead of “our customers need to have this feature,” ask the question, “what features would our customers value most?” And, then conduct a simple survey having customers rank order the features based on value to them.

• Instead of “morale is in the toilet because we don’t pay well, so we need to increase pay,” conduct a survey asking, “what is the level of morale?” what are potential issues affecting morale?” and what are the best ideas to improve morale?”

Beyond, watching out for just following your gut, here are some of the other best practices in being hypothesis-driven:

Listen to Your Intuition

Your mind has taken the collision of your experiences and everything you’ve learned over the years to create your intuition, which are those ideas that pop into your head and those hunches that come from your gut. Your intuition is your wellspring of hypotheses. So listen to your intuition, build hypotheses from it, and then prove or disprove those hypotheses, which will, in turn, improve your intuition. Intuition without feedback will over time typically evolve into poor intuition, which leads to poor judgment, thinking, and decisions.

Constantly Be Curious

I’m always curious about cause and effect. At Sports Authority, I had a hypothesis that customers that received service and assistance as they shopped, were worth more than customers who didn’t receive assistance from an associate. We figured out how to prove or disprove this hypothesis by tying surveys to transactional data of customers, and we found the hypothesis was true, which led us to a broad initiative around improving service. The key is you have to be always curious about what you think does or will drive value, create hypotheses and then prove or disprove those hypotheses.

Validate Hypotheses

You need to validate and prove or disprove hypotheses. Don’t just chalk up an idea as fact. In most cases, you’re going to have to create a fact base utilizing logic, observation, testing (see the section on Experimentation ), surveys, and analysis.

Be a Learning Organization

The foundation of learning organizations is the testing of and learning from hypotheses. I remember my first strategy internship at Mercer Management Consulting when I spent a good part of the summer combing through the results, findings, and insights of thousands of experiments that a banking client had conducted. It was fascinating to see the vastness and depth of their collective knowledge base. And, in today’s world of knowledge portals, it is so easy to disseminate, learn from, and build upon the knowledge created by companies.

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UB researcher awarded Hypothesis Fund to explore if RNA droplets helped originate life on Earth

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UB faculty member Priya Banerjee has received a Hypothesis Fund seed grant to explore RNA molecules and their role in the origin of life on Earth. Photo: Douglas Levere

By TOM DINKI

Published April 17, 2024

The RNA world theory suggests that life on Earth began with RNA molecules that copied themselves. It’s believed this self-replication eventually gave rise over millions of years to DNA and protein, which then formed with RNA to create cells. 

Yet RNA would seem ill-suited to serve such an important role in the harsh environment of the prebiotic world — it’s known to destabilize under high temperature and pressure.

UB faculty member Priya R. Banerjee believes the key to solving this puzzle may be RNA’s intrinsic ability to form liquid-like droplets at high temperatures, which may have protected it from harsh conditions and compartmentalized its functions.

Banerjee, associate professor of physics, has now received a seed grant from the Hypothesis Fund to better study these RNA droplets and their potential role in the origin of life on Earth. 

The project, “Liquid RNA Condensates as Programmable Scaffolds for Compartmentalization and Catalysis,” was selected for the boldness of the science, as well as Banerjee’s willingness to take risks and go after a big idea, according to the Hypothesis Fund, which announced the award this week. 

Hypothesis Fund seed grants fund innovation research at its earliest stages, typically before there is any preliminary data, with the goal of supporting high-risk, high-reward ideas that may not be funded or pursued otherwise. 

“Dr. Banerjee’s project brings unique insights into the origin of life by understanding the biophysical properties and self-organization principles encoded into RNA molecules. His hypothesis is bold and innovative, and has the potential to answer conundrums in how life may have arisen with RNA, while also bringing insight to the development of more effective RNA-based interventions,” says Hypothesis Fund Scout Taekjip Ha, professor of pediatrics at Harvard Medical School and cellular and molecular medicine at Boston Children’s Hospital. 

According to RNA world theory, RNA served functions in the primordial soup later done by DNA and protein — encoding genetic material and catalyzing chemical reactions. 

However, the theory is hotly debated. Key objections include thermal instability of RNAs and a lack of mechanistic understanding of how RNA-driven compartmentalization was achieved in the prebiotic world. 

Banerjee, who is also director of graduate studies in the Department of Physics, has recently reported an unexpected discovery of  RNA phase separation into droplets, or condensates, when exposed to high temperatures . He is now studying how these droplets, which are also formed by DNA and protein, impact cellular function and disease processes. 

“We posit that temperature-controlled reversible and dynamic droplet formation by RNA molecules can address this key knowledge gap,” Banerjee says. “Our working hypothesis is that tiny RNA liquid droplets are programmable microscale compartments for RNA biology.”

By shedding light on the molecular origin of RNAs’ thermo-responsive droplet formation, the project could determine the role of the droplet state of RNAs in diverse biological functions, Banerjee says. 

The project is expected to last for 18 months.

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Research questions, hypotheses and objectives

Patricia farrugia.

* Michael G. DeGroote School of Medicine, the

Bradley A. Petrisor

† Division of Orthopaedic Surgery and the

Forough Farrokhyar

‡ Departments of Surgery and

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

Mohit Bhandari

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

Objectives of this article

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

Research question

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

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

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

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

FINER criteria for a good research question

Adapted with permission from Wolters Kluwer Health. 2

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

PICOT criteria 1

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

Research hypothesis

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

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

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

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

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

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

Research objective

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

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

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

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

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

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

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

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

Tips for developing research questions, hypotheses and objectives for research studies

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

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

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

Innovative growers: A view from the top

In this current era of competing priorities and endless disruption and uncertainty, we know that innovation remains a must-have , not just a nice-to-have, when capital is readily available. 1 Matt Banholzer, Michael Birshan, Rebecca Doherty, and Laura LaBerge, “ Innovation: Your solution for weathering uncertainty ,” McKinsey, January 10, 2023. We also know that making a conscious choice to grow  and supporting that choice with the right mindsets, development pathways, and capabilities can yield superior shareholder returns. 2 “ Choosing to grow: The leader’s blueprint ,” McKinsey, July 7, 2022. But what is the role of innovation in growth and vice versa?

Where do innovative growers come from?

To find out, we identified and analyzed about 650 of the largest public companies that achieved profitable growth relative to their industry between 2016 and 2021 while also excelling in the essential capabilities associated with innovation . 3 Our assessment is based on McKinsey’s proprietary database of about 12,000 companies and their relative mastery of capabilities along four innovation categories: aspire/choose, discover/evolve, accelerate/scale, and extend/mobilize. Using machine learning, natural-language processing, and sentiment analysis of employee reviews, we created a score that served as a reliable proxy for innovation capabilities across these categories. We then reviewed companies that grew faster than their industry while delivering positive economic profit between 2016 and 2021. Some of these companies outgrew their peers, others were more innovative than competitors, but 53 companies managed to do both. The 50-plus “innovative growers,” as we call them, are a diverse group, spread across four continents and ten industries. They include renowned brands with a trillion-dollar market capitalization as well as smaller companies that are just starting to make a name for themselves, some as young as three years old (see sidebar, “Where do innovative growers come from?”).

For all their diversity, these companies consistently excel in both growth and innovation—and they share a number of best practices that other companies can learn from.

Do innovative growers perform better than others?

In a word, yes.

Most of our innovative growers achieved total shareholder returns (TSR) above their industry median between 2012 and 2022 (Exhibit 1). The median excess annual shareholder return among these 50-plus companies was 11 points higher than that for Global 2000 companies. What’s more, two-thirds of the innovative growers were in the top quintile of the economic-profit power curve , which represents the distribution of economic profit among Global 2000 companies. 4 Chris Bradley, Martin Hirt, and Sven Smit, “ Strategy to beat the odds ,” McKinsey Quarterly , February 13, 2018. Their presence on the high end of the curve is not surprising: McKinsey research on the power curve points to the importance of making big innovative moves to beat the market, including programmatic M&A , dynamic reallocation of resources, and differentiating product and process improvements. In fact, the research suggests making no moves is a dangerous strategy—one that brings stagnation and underperformance.

What sets innovative growers apart?

The numbers speak for themselves, but when we examined how innovative growers were achieving such a high level of performance, we observed that all demonstrate a mastery of the eight essentials of innovation , which our past research  shows are correlated with strong financial performance.

Specifically, they build innovation into their overall strategy aspirations. They activate critical growth pathways within their core businesses and enter only those adjacent markets where they have the strongest competitive advantage . They pursue excellence in execution and invest in key innovation capabilities. And they use M&A, particularly programmatic M&A, to extend their innovation reach.

Aspire: Link innovation to growth aspirations

According to our research, innovative growers unfailingly put innovation at the center of strategic and financial discussions, thereby signaling its importance to the growth and health of the organization. For instance, our review of the innovative growers’ earnings calls reveals that they talk about innovation twice as much as their peers 5 We analyzed a lexicon of innovation keywords across earnings calls across our sample set and determined the relative frequency of usage and discussion of innovation topics versus the overall management discussion. and, in those conversations, emphasize innovation as a means to create profitable and sustainable growth . This is consistent with our previous research on “ courageous growers ” and the importance of cultivating an innovation mindset among employees. 6 “ Courageous growth: Six strategies for continuous growth outperformance ,” McKinsey, October 23, 2023. Innovative growers communicate to employees achievable aspirations and clear targets to reduce fears of failure, criticism, and negative career impact  that often hold back innovation. Innovative growers share frequent progress updates and success stories to inspire and motivate teams and investors. What’s more, innovative growers frequently voice their commitment to investing more resources in talent and digital capabilities, and they are almost three times more likely than their fast-growing but not innovative peers to frame their efforts as a “transformation.”

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High digital aspirations. Digital transformation was the impetus for innovation at one leading retailer among our innovative growers: the company sought to increase its online sales by introducing new features such as faster mobile checkout and an app with augmented reality built into it so customers could visualize how the retailer’s products might look in their homes. The CEO and other C-suite executives reinforced the importance of “transformation through innovation” in town hall meetings with employees, during earnings calls, in public interviews, and in press releases. The leaders’ words and investments sent a clear message to customers, employees, and other stakeholders about the importance of innovation in the retailer’s ability to transform and grow. And the efforts paid off: over time, as the new features were launched, the company’s online sales grew 80 percent, with digital sales accounting for 60 percent of overall revenue.

Activate: Pursue multiple pathways to growth

Our research shows that innovative growers deliver market-leading revenue growth in both their core businesses and when entering adjacent customer segments, industries, or geographies. In their core businesses, for instance, innovative growers tend to generate twice as much excess growth, even relative to other companies that outperform on growth. And when diversifying into adjacent segments, innovative growers achieve at least double the revenue growth compared with other firms (Exhibit 2).

They do this by entering adjacent business areas  where they can connect to one or more clear opportunities to create value, such as customer-driven growth, capability-driven growth, value chain–driven growth, or business model innovation in areas such as digital and sustainability. For instance, a recent McKinsey analysis  shows that chemical players with low-carbon product portfolios or high exposure to end markets supporting sustainability grew their shareholder returns at more than double the rate of sustainability laggards between 2016 and 2021. 7 “ The triple play: Growth, profit, and sustainability ,” McKinsey, August 9, 2023.

In fact, our data indicate that innovative growers combine two or more of the previously mentioned value propositions in more than 70 percent of the adjacencies they enter (compared with less than 25 percent among peers). They seem to prioritize growth in those adjacencies where there is some similarity among portfolios and an obvious “right to win.” And make no mistake, portfolio similarity matters: consider General Mills’ purchase of Pillsbury, a company that shared many of the same competencies and assets. This move allowed General Mills to reduce its purchasing, manufacturing, and distribution costs and raise its operating profit by about 70 percent. 8 Chris Bradley, Rebecca Doherty, Nicholas Northcote, and Tido Röder, “ The ten rules of growth ,” McKinsey, August 12, 2022.

Additionally, innovative growers are using advanced analytics and other digital tools to identify hidden growth opportunities, and then they are going through a rigorous process of selecting the just-right operating model and governance structure for the new business and appointing senior leaders with the competencies most needed in the new business. 9 Chris Bradley, Rebecca Doherty, Anna Koivuniemi, and Nicholas Northcote, “ Igniting your next growth business ,” McKinsey, July 23, 2021.

Game, set, and match. A leading technology company with deep expertise in hardware design, artificial intelligence, and cloud computing acquired a gaming company with the goal of using its own capabilities to improve the gaming company’s offerings. The pathway to growth here was relatively clear and unencumbered; although they were in slightly different segments of the technology market, the companies boasted similar product portfolios, and once the technical capabilities were integrated, the joint venture was able to go to market with several special releases of legacy games and one-off “special event” gaming offerings, all of which were well received.

Execute: Invest productively in all innovation capabilities

Our research shows that innovative growers invest productively in a range of critical innovation capabilities—including R&D, resourcing, and operational agility—leading to strong business outcomes. In fact, they delivered more than five points of additional excess gross margin versus other Global 2000 firms, which is a key indicator of product differentiation . 10 “ Strategy to beat the odds ,” February 2018.

R&D. Innovative growers tend to deliver more tangible outcomes from their R&D investments than their peers. In our research, they generated, on average, 100-plus more patents than their peers but also delivered more strong patents—or patents with broad applicability and lots of citations to other patents. In fact, over the past two decades, innovative growers were awarded three times as many strong patents compared with industry peers (Exhibit 3). And the presence of strong patents often indicates higher value creation potential.

Resourcing and operations. Innovative growers are also more likely than peers to have adopted agile operating models and implemented rigorous and dynamic resource allocation processes. They also tend to invest more in digital and analytics and other new technologies compared with peers: our research shows innovative growers have 30 percent more digital and analytics personnel on staff compared with industry peers. And in McKinsey’s recent digital strategy survey of more than 1,000 companies, there was a clear link between organizations with strong innovation cultures and operating models and their ability to increase value through new technologies, including generative AI. 11 Matt Banholzer, Ben Fletcher, Laura LaBerge, and Jon McClain, “ Companies with innovative cultures have a big edge with generative AI ,” McKinsey, August 31, 2023. Even in the current uncertain business climate, almost 90 percent of the survey respondents said they are still looking for new growth. Over the past two years, they have been allocating resources to a range of growth pathways—expanding the core, innovating in adjacent areas, or igniting breakout businesses (Exhibit 4). 12 “ Companies with innovative cultures have a big edge with generative AI ,” August 31, 2023.

Smart resourcing, smart growth. Combining strong innovation capabilities with appropriate levels of resourcing can result in significant value creation opportunities. Senior management at one medical-technology company wanted to build a new line of surgical robotics offerings and, to that end, increased the amount of resources allocated to the company’s R&D function. Over time, that R&D team generated a flood of new patents, averaging about 750 more patents than its medtech peers and delivering one and a half times the total shareholder return. Similarly, a global technology company invested upward of $3 billion to adapt its existing hardware products to support applications in the fast-growing AI and data-processing spaces, more than tripling its annual capital expenditure between 2017 and 2022. This bold move has resulted in 20 percent annual revenue growth at the company over the past five years.

Extend: Cultivate a strong M&A capability

In our experience, innovative growers also distinguish themselves through their dealmaking—and specifically, in their ability to cultivate a strong M&A capability (alongside strong capabilities in R&D, finance, operations). To be clear, there are many “nondigital” technologies (new molecules, for instance). However, looking at digital M&A provides one illustrative lens. For instance, our research shows that innovative growers complete three times more digital M&A deals 13 Digital M&A deals are those that target assets or capabilities in the digital, analytics, or technology spaces. compared with peers, demonstrating a desire to acquire promising technical capabilities and intellectual property (IP) and a willingness to embrace new technologies and methods to stay ahead of the competition. 14 “ Are you chasing the right digital assets? ,” McKinsey, December 22, 2021. Additionally, innovative growers routinely define their growth and M&A objectives up front , and leaders come to a shared understanding of the types of deals they want to target, which allows innovative growers to act with speed and purpose when M&A opportunities come up. What’s more, innovative growers are 50 percent more likely than peers to follow a programmatic approach to M&A, 15 A programmatic approach to M&A involves creating value by choreographing a series of deals (two or more) around a specific business case or M&A theme rather than pursuing singular “big bang” transactions. which McKinsey has repeatedly reaffirmed is far more likely than other M&A approaches to lead to stronger performance and less risk. 16 “ How one approach to M&A is more likely to create value than all others ,” McKinsey Quarterly , October 13, 2021.

Forging an ecosystem through programmatic M&A. One technology company pursued a series of midsize acquisitions to bolster its product offerings and exploit cross-product synergies to create an ecosystem for “home security” products. Over a two-year period, the company acquired a wireless security camera player, a home security company, and a DIY home security system provider. These acquisitions came with associated patents, such as the smart doorbell, and allowed the company to expand its reach and to innovate new products (combining the acquired IP with the company’s own hardware and software products).

Innovative growers are delivering profitable growth relative to their industry while also excelling in the essential capabilities associated with innovation. Our research reveals the degree to which their focus on both is helping these organizations create lasting value. It also suggests that other companies, too, can join this small but diverse set of outperformers by putting innovation at the center of all decision making and supporting it with the right mindsets, pursuing multiple pathways to growth and innovation, and establishing the right capabilities across R&D, digital, analytics, and M&A.

The path may be steep, and the transformation will likely take time and dedicated management attention, but the companies that seek to emulate the innovative growers may eventually achieve a profitable balance between today’s growth objectives and tomorrow’s innovation potential.

Matt Banholzer is a partner in McKinsey’s Chicago office, Rebecca Doherty is a partner in the Bay Area office, Alex Morris is a partner in the Toronto office, and Scott Schwaitzberg is an associate partner in the New York office.

The authors wish to thank Guillermo Domínguez, Gopal Galgali, Brooke Harvey, Tim Koller, Laura LaBerge, Karin Löffler, Karthik Ramesh, Werner Rehm, Tido Röder, Erik Roth, Eshita Sangal, and Jill Zucker for their contributions to this article.

This article was edited by Roberta Fusaro, an editorial director in the Waltham, Massachusetts, office.

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6 Common Leadership Styles — and How to Decide Which to Use When

• Rebecca Knight

Being a great leader means recognizing that different circumstances call for different approaches.

Research suggests that the most effective leaders adapt their style to different circumstances — be it a change in setting, a shift in organizational dynamics, or a turn in the business cycle. But what if you feel like you’re not equipped to take on a new and different leadership style — let alone more than one? In this article, the author outlines the six leadership styles Daniel Goleman first introduced in his 2000 HBR article, “Leadership That Gets Results,” and explains when to use each one. The good news is that personality is not destiny. Even if you’re naturally introverted or you tend to be driven by data and analysis rather than emotion, you can still learn how to adapt different leadership styles to organize, motivate, and direct your team.

Much has been written about common leadership styles and how to identify the right style for you, whether it’s transactional or transformational, bureaucratic or laissez-faire. But according to Daniel Goleman, a psychologist best known for his work on emotional intelligence, “Being a great leader means recognizing that different circumstances may call for different approaches.”

• RK Rebecca Knight is a journalist who writes about all things related to the changing nature of careers and the workplace. Her essays and reported stories have been featured in The Boston Globe, Business Insider, The New York Times, BBC, and The Christian Science Monitor. She was shortlisted as a Reuters Institute Fellow at Oxford University in 2023. Earlier in her career, she spent a decade as an editor and reporter at the Financial Times in New York, London, and Boston.

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Research Strategies and Methods

• First Online: 01 January 2014

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• Paul Johannesson 3 &
• Erik Perjons 3  

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Researchers have since centuries used research methods for supporting the creation of reliable knowledge based on empirical evidence and logical arguments. This chapter offers an overview of established research strategies and methods with a focus on empirical research in the social sciences. The chapter discusses research strategies, such as experiment, survey, case study, ethnography, grounded theory, action research, and phenomenology. Research methods for data collection are also described, including questionnaires, interviews, focus groups, observations, and documents. Qualitative and quantitative methods for data analysis are discussed. Finally, the use of research strategies and methods in design science is investigated.

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Paul Johannesson & Erik Perjons

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Johannesson, P., Perjons, E. (2014). Research Strategies and Methods. In: An Introduction to Design Science. Springer, Cham. https://doi.org/10.1007/978-3-319-10632-8_3

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DOI : https://doi.org/10.1007/978-3-319-10632-8_3

Published : 12 September 2014

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