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17 research quotes to inspire and amuse you

Being a researcher requires dedication, hard work and more than a little inspiration. Here’s something to boost the last item on that list.

We’ve sourced some of the most interesting and thought-provoking research quotes we can find. We hope they’ll leave you feeling inspired and motivated to start – or complete – your best ever research project.

As these quotes show, research is a common thread running through all kinds of professions and pursuits, from Ancient Rome right up to the present day. If you practice research, you’re part of a long list of people throughout history, all dedicated to finding new knowledge and ideas that ultimately make the world a better place.

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1. “No research without action, no action without research”

- Kurt Lewin

Lewin (1890-1947) was a German-American social psychologist. He’s known for his theory that human behavior is a function of our psychological environment.

2. “Research is seeing what everybody else has seen and thinking what nobody else has thought.”

- Albert Szent-Györgyi 

Szent-Györgyi (1893-1986)  was a Hungarian pharmacologist known for his work on vitamins and oxidation. He was awarded the Nobel Prize in Physiology or Medicine in 1937.

3. "Bad news sells papers. It also sells market research."

- Byron Sharp 

Sharp is Professor of Marketing Science and Director of the Ehrenberg-Bass Institute, the world’s largest centre for research into marketing.

4. "In fact, the world needs more nerds."

- Ben Bernanke

Bernanke is an American economist and former chair of the board of governors at the United Stares Federal Reserve.

5. "Research is what I'm doing when I don't know what I'm doing."

- Wernher von Braun

Von Braun (1912-1977) was a German-American physicist and rocket engineer whose team launched the first US satellite into space.

6. "Research is formalized curiosity. It is poking and prying with a purpose."

- Zora Neale Hurston

Hurston (1891-1960) was an American anthropologist and writer known for her research and writing on slavery, race, folklore and the African-American experience.

7. "Research is creating new knowledge."

- Neil Armstrong

Armstrong (1930-2012) was an American astronaut famed for being the first man to walk on the Moon.

8. "I believe in innovation and that the way you get innovation is you fund research and you learn the basic facts."

- Bill Gates

Gates needs little introduction – he’s an entrepreneur, philanthropist and the founder of Microsoft.

9. “The best research you can do is talk to people”

- Terry Pratchett

Pratchett is an award-winning British science fiction and fantasy author. He was knighted in 2009. He is known for The Hitch Hiker’s Guide to the Galaxy and the Discworld series.

10. “Research means that you don’t know, but are willing to find out”

- Charles F. Kettering

Kettering (1876-1958) was an American engineer, known for inventing the electric starter used in combustion engines, as well as other automobile technologies.

11. “Nothing has such power to broaden the mind as the ability to investigate systematically and truly all that comes under thy observation in life.”

- Marcus Aurelius

Marcus Aurelius (121-180) was a Roman Emperor and Stoic philosopher.

12. “It is a good thing for a research scientist to discard a pet hypothesis every day before breakfast.“

- Konrad Lorenz

Lorenz (1903-1989) was an Austrian biologist known for his game-changing research on animal behavior. He was jointly awarded the Nobel Prize in Physiology or Medicine in 1973.

13. “Research is something that everyone can do, and everyone ought to do. It is simply collecting information and thinking systematically about it.”

- Raewyn Connell

Connell is an Australian sociologist. She is a former professor of at the University of Sydney and is known for her work on gender and transgender studies.

14. “As for the future, your task is not to foresee it, but to enable it.”

- Antoine de Saint Exupery

De Saint Exupery (1900-1944) was a French aviator, author and poet, best known for his story The Little Prince, one of the best-selling books of all time.

15. “It is a capital mistake to theorize before one has data.”

- Arthur Conan Doyle (writing as Sherlock Holmes)

Conan Doyle (1859-1930) was a British crime writer and creator of the legendary Sherlock Holmes, master of deduction.

16. “If we knew what we were doing, it would not be called research, would it?”

- Albert Einstein

Maybe the most famous scientist of all time, Albert Einstein (1879-1955) was a German physicist who came up with the theory of relativity. However, it was his description of the photoelectric effect, the interplay between light and electrically charged atoms, that won him the Nobel Prize for Physics in 1921.

17. “The power of statistics and the clean lines of quantitative research appealed to me, but I fell in love with the richness and depth of qualitative research.”

- Brené Brown

Brown is a researcher and storyteller studying courage, shame, empathy and vulnerability. She is a best-selling author and inspirational speaker. She is a research professor at the University of Houston.

Sarah Fisher

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50 Research Quotes To Inspire The Academic In You

Research is the process of collecting data, saving critical information, then analyzing and interpreting the data.

There are three types of research: exploratory, casual, and descriptive. Each of them is used for a different purpose and in a certain way.

Research is important in all fields of work. For example, clinical research is what permits doctors to determine the way to treat patients best.

It is what makes the event of the latest medicines, new procedures, and new tools doable. If it weren't for clinical analysis, we wouldn't be ready to decide if new treatments are more efficient than the current treatments.

Here on our page, you can find 50 inspiring and funny quotes about research. Let's take a look at these quotes. If you like these quotes, do also read our physics quotes and classic literature quotes .  

Deep Quotes About Research

Here are some famous research quotes in all their glory.

1. "No research without action, no action without research."

- Kurt Lewin.

2. "Research has formalized curiosity. It is poking and prying with a purpose."

- Zora Neale Hurston .

3. "I believe in innovation and that the way you get innovation is you fund research, and you learn the basic facts."

- Bill Gates.

4. "Research means that you don’t know, but are willing to find out."

- Charles F. Kettering.

5. "It is a good thing for a research scientist to discard a pet hypothesis every day before breakfast."

- Konrad Lorenz .

6. "You'd be amazed how much research you can get done when you have no life whatsoever."

- Ernest Cline.

7. "Highly organized research is guaranteed to produce nothing new."

- Frank Herbert.

8. "With a library, it is easier to hope for serendipity than to look for a precise answer."

- Lemony Snicket.

9. "The measure of greatness in a scientific idea is the extent to which it stimulates thought and opens up new lines of research."

- Paul Dirac.

10. "What we find changes who we become."

- Peter Morville.

Select Quotes About Scientific Research

Here are some scientific research quotes (Einstein said a few as well) for our readers.

11. "Research is to see what everybody else has seen and to think what nobody else has thought."

- Albert Szent-Gyorgyi.

12. "If we knew what it was we were doing, it would not be called research, would it?"

- Albert Einstein.

13. "The important thing in science is not so much to obtain new facts as to discover new ways of thinking about them."

- William Lawrence Bragg.

14. "The whole of science is nothing more than a refinement of everyday thinking."

15. "The more thoroughly I conduct scientific research, the more I believe that science excludes atheism."

- Lord Kelvin.

16. "Scientific research is one of the most exciting and rewarding of occupations."

- Frederick Sanger.

17. "If we choose to ignore science and refuse to fund important scientific research, we voluntarily cede our place as a world leader in innovation."

- Bill Foster.

18. "We need to have much clearer regulations on things like corporate funding of scientific research. Things need to be made explicit which are implicit."

- Noreena Hertz.

19. "I think, however, that so long as our present economic and national systems continue, scientific research has little to fear."

- John B. S. Haldane.

20. "We need to celebrate and reward people who cure diseases, expand our understanding of humanity, and work to improve people's lives."

- Mark Zuckerberg.

In-Depth Market Research Quotes

Here are some business research quotes - inspirational to many. You'll also find market research quotes that could help your business assess the market.

21. "Without data, you're just another person with an opinion."

- W. Edwards Deming.

22. "The aim of marketing is to know and understand the customer so well, the product or service sells itself."

- Peter Drucker.

23. "Marketing without data is like driving with your eyes closed."

- Dan Zarrella.

24. "When research walks on the field, the judgment does not walk off."

- Dick Kampe.

25. "If you want to understand today, you have to search yesterday."

- Pearl Buck.

26. "Understanding human needs is half the job of meeting them."

- Adlai E Jr Stevenson.

Funny Quotes About Research

Enjoy these funny quotes that will tickle your funny bone.

27. "What is research but a blind date with knowledge?"

- Will Harvey.

28. "Research is what I'm doing when I don't know what I'm doing."

- Werner von Braun.

Scholars Quotes About Academia

Here is some research academic quote for our readers.

29. "You have brains in your head. You have feet in your shoes. You can steer yourself in any direction you choose. You’re on your own. And you know what you know. You are the guy who’ll decide where to go."

- Dr. Seuss.

30. "Don’t say you don’t have enough time. You have exactly the same number of hours per day that were given to Helen Keller, Pasteur, Michelangelo, Mother Teresa, Leonardo da Vinci, Thomas Jefferson, and Albert Einstein."

- H. Jackson Brown Jr.

31. "In much of society, research means to investigate something you do not know or understand."

- Neil Armstrong.

32. "What is the matter with universities is that the students are school children, whereas it is of the very essence of university education that they should be adults."

- George Bernard Shaw.

33. "That afternoon, I came to understand that one of the deepest purposes of intellectual sophistication is to provide distance between us and our most disturbing personal truths and gnawing fears."

- Richard Russo.

34. "What I learned on my own I still remember."

- Nassim Nicholas Taleb.

35. "There are times when wisdom cannot be found in the chambers of parliament or the halls of academia but at the unpretentious setting of the kitchen table."

- E.A. Bucchianeri.

36. "We do not need magic to change the world, we carry all the power we need inside ourselves already: we have the power to imagine better."

- J.K. Rowling.

37. "If you don’t go after what you want, you’ll never have it. If you don’t ask, the answer is always no. If you don’t step forward, you’re always in the same place."

- Nora Roberts.

38. " Trust the process and it will bring out the hidden subject as the results.

- David Harris.

Medical Research Quotes

Here are some science research quotes and cancer research quotes. There are also a few stem cell research quotes.

39. "Advances in medicine and agriculture have saved vastly more lives than have been lost in all the wars in history."

- Carl Sagan.

40. "America's doctors, nurses, and medical researchers are the best in the world, but our health care system is broken."

- Mike Ferguson.

41. "Prior to penicillin and medical research, death was an everyday occurrence. It was intimate."

- Katherine Dunn.

42. "Stem cell research can revolutionize medicine, more than anything since antibiotics."

- Ron Reagan.

43. "Medical research in the twentieth century mostly takes place in the lab; in the Renaissance, though, researchers went first and foremost to the library to see what the ancients had said."

- Peter Lewis Allen.

44. "It is certainly important to be looking for cures to medical disorders, but it is equally important to conduct research on human health and well-being."

- Stephen LaBerge.

45. "A wise physician skilled our wounds to heal, is more than armies to the public weal."

- Alexander Pope.

46. "It is false to suggest that medical breakthroughs come only through government research."

- Roger Wicker.

47. "The realities are that it's difficult to find funding for research for a medical cure. I believe in developing technology as opposed to medical research."

- Steve Gleason.

48. "A doctor is a man who writes prescriptions till the patient either dies or is cured by nature."

- William Broome.

49. "A fool will not only pay for a 'cure' that does him no good but will write a testimonial to the effect that he was cured."

- E. W. Howe.

50. "I decided to take two years between finishing undergraduate and beginning medical school to devote fully to medical research. I knew that I wanted to go to medical school during undergraduate, but I was also eager to get a significant amount of research experience."

- Eva Vertes.

Here at Kidadl , we have carefully created lots of interesting family-friendly quotes for everyone to enjoy! If you liked our suggestions for research quotes, then why not take a look at funny science quotes , or poetry quotes .

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Writvik Gupta

A professional content writer hailing from Kolkata, India, with extensive experience in the corporate sector, Writvik Gupta has worked with several reputed companies, including ITC WelcomHotel Jodhpur, Bharti AXA Life Insurance, Aryan Imaging, and Eduquity. He also serves as a consultant for a startup based in Bangalore. With a passion for the outdoors, Writvik enjoys trekking and traveling to remote destinations. He also has a keen interest in exploring various cuisines and regularly volunteers for social causes.

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31 Inspiring Market Research Quotes

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One of the most fascinating aspects of research is its omnipresence. The practice of gathering data, analysing it and generating insight be applied in a staggering variety of setting – from science to social studies, business to politics and beyond. As well as its multitude of applications, research can also draw on a range of subject matter to improve and better the practice.

This means there is a lot of inspiration out there for researchers, and also a broad range of ways it can be applied. It is important to remember that research does not exist in a vacuum. It is surrounded by business operations, drawing on ideas that improve insight, process, communications and more. However, it can be easy to forget this – to fall into ‘research blinkers’ that narrow our field of view. These quotes have been collated to remind us of the unique, privileged position market research occupies and provide a taste of fields it can lean on.

If you find a quote you want to share, click on the Twitter icon beside the quote to automatically tweet it.

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Generating Insight from Research

Blending research and innovation, integrating voice of customer data.

What are your favourite market research quotes - did they make our list? Leave a comment below to let us know which research & insight quotes you think are best and join the conversation.

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How To Write The Results/Findings Chapter

For quantitative studies (dissertations & theses).

By: Derek Jansen (MBA) | Expert Reviewed By: Kerryn Warren (PhD) | July 2021

So, you’ve completed your quantitative data analysis and it’s time to report on your findings. But where do you start? In this post, we’ll walk you through the results chapter (also called the findings or analysis chapter), step by step, so that you can craft this section of your dissertation or thesis with confidence. If you’re looking for information regarding the results chapter for qualitative studies, you can find that here .

Overview: Quantitative Results Chapter

• What exactly the results chapter is
• What you need to include in your chapter
• How to structure the chapter
• Tips and tricks for writing a top-notch chapter
• Free results chapter template

What exactly is the results chapter?

The results chapter (also referred to as the findings or analysis chapter) is one of the most important chapters of your dissertation or thesis because it shows the reader what you’ve found in terms of the quantitative data you’ve collected. It presents the data using a clear text narrative, supported by tables, graphs and charts. In doing so, it also highlights any potential issues (such as outliers or unusual findings) you’ve come across.

But how’s that different from the discussion chapter?

Well, in the results chapter, you only present your statistical findings. Only the numbers, so to speak – no more, no less. Contrasted to this, in the discussion chapter , you interpret your findings and link them to prior research (i.e. your literature review), as well as your research objectives and research questions . In other words, the results chapter presents and describes the data, while the discussion chapter interprets the data.

Let’s look at an example.

In your results chapter, you may have a plot that shows how respondents to a survey  responded: the numbers of respondents per category, for instance. You may also state whether this supports a hypothesis by using a p-value from a statistical test. But it is only in the discussion chapter where you will say why this is relevant or how it compares with the literature or the broader picture. So, in your results chapter, make sure that you don’t present anything other than the hard facts – this is not the place for subjectivity.

It’s worth mentioning that some universities prefer you to combine the results and discussion chapters. Even so, it is good practice to separate the results and discussion elements within the chapter, as this ensures your findings are fully described. Typically, though, the results and discussion chapters are split up in quantitative studies. If you’re unsure, chat with your research supervisor or chair to find out what their preference is.

What should you include in the results chapter?

Following your analysis, it’s likely you’ll have far more data than are necessary to include in your chapter. In all likelihood, you’ll have a mountain of SPSS or R output data, and it’s your job to decide what’s most relevant. You’ll need to cut through the noise and focus on the data that matters.

This doesn’t mean that those analyses were a waste of time – on the contrary, those analyses ensure that you have a good understanding of your dataset and how to interpret it. However, that doesn’t mean your reader or examiner needs to see the 165 histograms you created! Relevance is key.

How do I decide what’s relevant?

At this point, it can be difficult to strike a balance between what is and isn’t important. But the most important thing is to ensure your results reflect and align with the purpose of your study .  So, you need to revisit your research aims, objectives and research questions and use these as a litmus test for relevance. Make sure that you refer back to these constantly when writing up your chapter so that you stay on track.

As a general guide, your results chapter will typically include the following:

• Some demographic data about your sample
• Reliability tests (if you used measurement scales)
• Descriptive statistics
• Inferential statistics (if your research objectives and questions require these)
• Hypothesis tests (again, if your research objectives and questions require these)

We’ll discuss each of these points in more detail in the next section.

Importantly, your results chapter needs to lay the foundation for your discussion chapter . This means that, in your results chapter, you need to include all the data that you will use as the basis for your interpretation in the discussion chapter.

For example, if you plan to highlight the strong relationship between Variable X and Variable Y in your discussion chapter, you need to present the respective analysis in your results chapter – perhaps a correlation or regression analysis.

Need a helping hand?

How do I write the results chapter?

There are multiple steps involved in writing up the results chapter for your quantitative research. The exact number of steps applicable to you will vary from study to study and will depend on the nature of the research aims, objectives and research questions . However, we’ll outline the generic steps below.

Step 1 – Revisit your research questions

The first step in writing your results chapter is to revisit your research objectives and research questions . These will be (or at least, should be!) the driving force behind your results and discussion chapters, so you need to review them and then ask yourself which statistical analyses and tests (from your mountain of data) would specifically help you address these . For each research objective and research question, list the specific piece (or pieces) of analysis that address it.

At this stage, it’s also useful to think about the key points that you want to raise in your discussion chapter and note these down so that you have a clear reminder of which data points and analyses you want to highlight in the results chapter. Again, list your points and then list the specific piece of analysis that addresses each point. 

Next, you should draw up a rough outline of how you plan to structure your chapter . Which analyses and statistical tests will you present and in what order? We’ll discuss the “standard structure” in more detail later, but it’s worth mentioning now that it’s always useful to draw up a rough outline before you start writing (this advice applies to any chapter).

Step 2 – Craft an overview introduction

As with all chapters in your dissertation or thesis, you should start your quantitative results chapter by providing a brief overview of what you’ll do in the chapter and why . For example, you’d explain that you will start by presenting demographic data to understand the representativeness of the sample, before moving onto X, Y and Z.

This section shouldn’t be lengthy – a paragraph or two maximum. Also, it’s a good idea to weave the research questions into this section so that there’s a golden thread that runs through the document.

Step 3 – Present the sample demographic data

The first set of data that you’ll present is an overview of the sample demographics – in other words, the demographics of your respondents.

For example:

• What age range are they?
• How is gender distributed?
• How is ethnicity distributed?
• What areas do the participants live in?

The purpose of this is to assess how representative the sample is of the broader population. This is important for the sake of the generalisability of the results. If your sample is not representative of the population, you will not be able to generalise your findings. This is not necessarily the end of the world, but it is a limitation you’ll need to acknowledge.

Of course, to make this representativeness assessment, you’ll need to have a clear view of the demographics of the population. So, make sure that you design your survey to capture the correct demographic information that you will compare your sample to.

But what if I’m not interested in generalisability?

Well, even if your purpose is not necessarily to extrapolate your findings to the broader population, understanding your sample will allow you to interpret your findings appropriately, considering who responded. In other words, it will help you contextualise your findings . For example, if 80% of your sample was aged over 65, this may be a significant contextual factor to consider when interpreting the data. Therefore, it’s important to understand and present the demographic data.

 Step 4 – Review composite measures and the data “shape”.

Before you undertake any statistical analysis, you’ll need to do some checks to ensure that your data are suitable for the analysis methods and techniques you plan to use. If you try to analyse data that doesn’t meet the assumptions of a specific statistical technique, your results will be largely meaningless. Therefore, you may need to show that the methods and techniques you’ll use are “allowed”.

Most commonly, there are two areas you need to pay attention to:

#1: Composite measures

The first is when you have multiple scale-based measures that combine to capture one construct – this is called a composite measure .  For example, you may have four Likert scale-based measures that (should) all measure the same thing, but in different ways. In other words, in a survey, these four scales should all receive similar ratings. This is called “ internal consistency ”.

Internal consistency is not guaranteed though (especially if you developed the measures yourself), so you need to assess the reliability of each composite measure using a test. Typically, Cronbach’s Alpha is a common test used to assess internal consistency – i.e., to show that the items you’re combining are more or less saying the same thing. A high alpha score means that your measure is internally consistent. A low alpha score means you may need to consider scrapping one or more of the measures.

#2: Data shape

The second matter that you should address early on in your results chapter is data shape. In other words, you need to assess whether the data in your set are symmetrical (i.e. normally distributed) or not, as this will directly impact what type of analyses you can use. For many common inferential tests such as T-tests or ANOVAs (we’ll discuss these a bit later), your data needs to be normally distributed. If it’s not, you’ll need to adjust your strategy and use alternative tests.

To assess the shape of the data, you’ll usually assess a variety of descriptive statistics (such as the mean, median and skewness), which is what we’ll look at next.

Step 5 – Present the descriptive statistics

Now that you’ve laid the foundation by discussing the representativeness of your sample, as well as the reliability of your measures and the shape of your data, you can get started with the actual statistical analysis. The first step is to present the descriptive statistics for your variables.

For scaled data, this usually includes statistics such as:

• The mean – this is simply the mathematical average of a range of numbers.
• The median – this is the midpoint in a range of numbers when the numbers are arranged in order.
• The mode – this is the most commonly repeated number in the data set.
• Standard deviation – this metric indicates how dispersed a range of numbers is. In other words, how close all the numbers are to the mean (the average).
• Skewness – this indicates how symmetrical a range of numbers is. In other words, do they tend to cluster into a smooth bell curve shape in the middle of the graph (this is called a normal or parametric distribution), or do they lean to the left or right (this is called a non-normal or non-parametric distribution).
• Kurtosis – this metric indicates whether the data are heavily or lightly-tailed, relative to the normal distribution. In other words, how peaked or flat the distribution is.

A large table that indicates all the above for multiple variables can be a very effective way to present your data economically. You can also use colour coding to help make the data more easily digestible.

For categorical data, where you show the percentage of people who chose or fit into a category, for instance, you can either just plain describe the percentages or numbers of people who responded to something or use graphs and charts (such as bar graphs and pie charts) to present your data in this section of the chapter.

When using figures, make sure that you label them simply and clearly , so that your reader can easily understand them. There’s nothing more frustrating than a graph that’s missing axis labels! Keep in mind that although you’ll be presenting charts and graphs, your text content needs to present a clear narrative that can stand on its own. In other words, don’t rely purely on your figures and tables to convey your key points: highlight the crucial trends and values in the text. Figures and tables should complement the writing, not carry it .

Depending on your research aims, objectives and research questions, you may stop your analysis at this point (i.e. descriptive statistics). However, if your study requires inferential statistics, then it’s time to deep dive into those .

Step 6 – Present the inferential statistics

Inferential statistics are used to make generalisations about a population , whereas descriptive statistics focus purely on the sample . Inferential statistical techniques, broadly speaking, can be broken down into two groups .

First, there are those that compare measurements between groups , such as t-tests (which measure differences between two groups) and ANOVAs (which measure differences between multiple groups). Second, there are techniques that assess the relationships between variables , such as correlation analysis and regression analysis. Within each of these, some tests can be used for normally distributed (parametric) data and some tests are designed specifically for use on non-parametric data.

There are a seemingly endless number of tests that you can use to crunch your data, so it’s easy to run down a rabbit hole and end up with piles of test data. Ultimately, the most important thing is to make sure that you adopt the tests and techniques that allow you to achieve your research objectives and answer your research questions .

In this section of the results chapter, you should try to make use of figures and visual components as effectively as possible. For example, if you present a correlation table, use colour coding to highlight the significance of the correlation values, or scatterplots to visually demonstrate what the trend is. The easier you make it for your reader to digest your findings, the more effectively you’ll be able to make your arguments in the next chapter.

Step 7 – Test your hypotheses

If your study requires it, the next stage is hypothesis testing. A hypothesis is a statement , often indicating a difference between groups or relationship between variables, that can be supported or rejected by a statistical test. However, not all studies will involve hypotheses (again, it depends on the research objectives), so don’t feel like you “must” present and test hypotheses just because you’re undertaking quantitative research.

The basic process for hypothesis testing is as follows:

• Specify your null hypothesis (for example, “The chemical psilocybin has no effect on time perception).
• Specify your alternative hypothesis (e.g., “The chemical psilocybin has an effect on time perception)
• Set your significance level (this is usually 0.05)
• Calculate your statistics and find your p-value (e.g., p=0.01)
• Draw your conclusions (e.g., “The chemical psilocybin does have an effect on time perception”)

Finally, if the aim of your study is to develop and test a conceptual framework , this is the time to present it, following the testing of your hypotheses. While you don’t need to develop or discuss these findings further in the results chapter, indicating whether the tests (and their p-values) support or reject the hypotheses is crucial.

Step 8 – Provide a chapter summary

To wrap up your results chapter and transition to the discussion chapter, you should provide a brief summary of the key findings . “Brief” is the keyword here – much like the chapter introduction, this shouldn’t be lengthy – a paragraph or two maximum. Highlight the findings most relevant to your research objectives and research questions, and wrap it up.

Some final thoughts, tips and tricks

Now that you’ve got the essentials down, here are a few tips and tricks to make your quantitative results chapter shine:

• When writing your results chapter, report your findings in the past tense . You’re talking about what you’ve found in your data, not what you are currently looking for or trying to find.
• Structure your results chapter systematically and sequentially . If you had two experiments where findings from the one generated inputs into the other, report on them in order.
• Make your own tables and graphs rather than copying and pasting them from statistical analysis programmes like SPSS. Check out the DataIsBeautiful reddit for some inspiration.
• Once you’re done writing, review your work to make sure that you have provided enough information to answer your research questions , but also that you didn’t include superfluous information.

If you’ve got any questions about writing up the quantitative results chapter, please leave a comment below. If you’d like 1-on-1 assistance with your quantitative analysis and discussion, check out our hands-on coaching service , or book a free consultation with a friendly coach.

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Home • Knowledge hub • What is quantitative research?

What is quantitative research?

Quantitative research is an integral part of market research that relies on hard facts and numerical data to gain an objective picture of people’s opinions as possible.

Quantitative research differs from  qualitative research  in several important ways and is a highly useful tool for researchers.

In this article, we’ll take a deep dive into quantitative research, why it’s important, and how to use it effectively.

How is it different from qualitative research?

Although they’re both beneficial, there are a number of key differences between quantitative and qualitative market research strategies. A solid market research strategy will use both qualitative and quantitative research.

• Quantitative research relies on gathering numerical data points. Qualitative research, on the other hand, as the name suggests, seeks to gather qualitative data by speaking to people in individual or group settings. 
• Quantitative research typically uses closed questions, while qualitative research uses open questions more frequently.
• Quantitative research is excellent for establishing trends and patterns of behavior, whereas qualitative methods are great for explaining the “why” behind them.

Why is quantitative research useful?

Quantitative research has a crucial role to play in any market research strategy for a range of reasons:

• It enables you to conduct research at scale.
• When conducting quantitative research in a representative way, it can reveal insights about broader groups of people or the population as a whole.
• It enables us to compare different groups easily (e.g., by age, gender, or market) to understand similarities or differences. 
• It can help businesses understand the size of a new opportunity. 
• It can help reduce a complex problem or topic to a limited number of variables.

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Quantitative research data collection methods

When collecting the data you need for quantitative research, you have several possibilities available. Each has pros and cons, and it might be best to use a mix. Here are some of the main ones:

Survey research

Survey research involves sending out surveys to your target audience to collect information before statistically analyzing the results to draw conclusions and insights. It’s a great way to understand your target customers better or explore a new market, and it can be turned around quickly. 

There are several different ways of conducting services, such as:

• Email  — is a quick way of reaching a large number of people and can be more affordable than the other methods described below.
• Phone  — not everyone has access to the internet, so if you’re looking to reach a particular demographic that may struggle to engage in this way (e.g., older consumers), telephone surveys can be a better approach. That said, it can be expensive and time-consuming.
• Post  — as with the phone, you can reach a broad segment of the population, but it’s expensive and takes a long time. As organizations look to identify and react to changes in consumer behavior quickly, postal surveys have become somewhat outdated. 
• In-person  — in some instances, it makes sense to conduct quantitative research in person. Examples include intercepts, where you need to collect quantitative data about the customer experience in the moment, taste tests, or  central location tests , where you need consumers to interact physically with a product to provide useful feedback. Conducting research in this way can be expensive and logistically challenging to organize and carry out.

Survey questions for quantitative research usually include closed questions rather than the open questions used in qualitative research. For example, instead of asking

“How do you feel about our delivery policy?”

You might ask

“How satisfied are you with our delivery policy? “Very satisfied / Satisfied / Don’t Know / Dissatisfied / Very Dissatisfied.” 

This way, you’ll gain data that can be categorized and analyzed in a quantitative, or numbers-based way.

Analyzing results

Once you have your results, the next step — and one of the most important overall — is to categorize and analyze them.

There are many ways to do this. One powerful method is cross-tabulation, where you separate your results into categories based on demographic subgroups. For example, of the people who answered ‘yes’ to a question, how many were business leaders, and how many were entry-level employees?

You’ll also need to take time to clean the data (for example, removing people who sped through the survey) to make sure you can confidently draw conclusions. This can all be taken care of by the right team of experts.

The importance of quantitative research

Quantitative research is a powerful tool for anyone looking to learn more about their market and customers. It allows you to gain reliable, objective insights from data and clearly understand trends and patterns.

Where quantitative research falls short is in explaining the ‘why’. This is where you need to turn to other methods, like qualitative research, where you’ll talk to your audience and delve into the more subjective factors driving their decision-making.

At Kadence, it’s our job to help you with every aspect of your research strategy. We’ve done this with countless businesses, and we’d love to do it with you. To find out more,  get in touch with us .

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Quantitative Research

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Quantitative research methods are concerned with the planning, design, and implementation of strategies to collect and analyze data. Descartes, the seventeenth-century philosopher, suggested that how the results are achieved is often more important than the results themselves, as the journey taken along the research path is a journey of discovery. High-quality quantitative research is characterized by the attention given to the methods and the reliability of the tools used to collect the data. The ability to critique research in a systematic way is an essential component of a health professional’s role in order to deliver high quality, evidence-based healthcare. This chapter is intended to provide a simple overview of the way new researchers and health practitioners can understand and employ quantitative methods. The chapter offers practical, realistic guidance in a learner-friendly way and uses a logical sequence to understand the process of hypothesis development, study design, data collection and handling, and finally data analysis and interpretation.

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Writing Quantitative Research Studies

Qualitative Research Methods

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Wilson, L.A. (2019). Quantitative Research. In: Liamputtong, P. (eds) Handbook of Research Methods in Health Social Sciences. Springer, Singapore. https://doi.org/10.1007/978-981-10-5251-4_54

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Importance of quantitative research

In physical and anthropological sciences or other distinct fields, quantitative research is methodical experimental research of noticeable events via analytical, numerical, or computational methods. The purpose of quantitative analysis is to improve and apply numerical principles, methods, and theories about happenings. The method of analysis is necessary for quantitative analysis because it presents the underlying reciprocity between experimental research and substantiated interpretation of quantitative correlations.

Quantitative analysis is generally utilized in science, commerce, demography, synecology, retailing, population fitness, wellness & individual improvement, gender education, and administrative ability; and few often in history and anthropology. Analysis in analytical sociology, like science, is more “quantitative” by interpretation, though the aforementioned usage of the course varies in connection. In human physiology, the course links to experimental systems starting in both rational annals of statistics and positivism in disagreement with qualitative analysis techniques.

Objective purpose and further reason for quantitative analysis is to create awareness and generate knowledge regarding civil society. Quantitative analysis is utilized by civil specialists, consisting of information researchers, to recognize aspects or circumstances concerning people. Civil specialists are involved in the research of personages. Quantitative analysis is a method to determine an appropriate organization of individuals, perceived as a unified community. Utilizing experimental analysis, the quantitative analysis depends on data which are measured or observed to explore inquisitions concerning the example group. Some importance of Quantitative research are:

• It can be utilized in statistics to conclude a result.
• It usually decreases and restructures and decreases a complicated puzzle to a restricted amount of variables.
• Views at associations linking variables and may authenticate purpose and result in profoundly controlled conditions.
• Tests hypotheses or data.
• Assumes example is a delegate of the community.
• The subjectivity of the researcher in the process is identified less.
• Few accuracies than qualitative data and can abstain from a solicited reply of the member.

Qualitative analysis is also considered as probing and is utilized to reveal inclinations in views and ideas, while quantitative analysis is utilized to quantify the difficulty employing generating statistical records or data that may be converted into valuable statistics.

Quantitative analysis is utilized to quantify actions, ideas, stances, and additional variables and proffer generalizations of a broader community. The quantitative analysis utilizes quantifiable data to explain details and expose models in analysis. This kind of analysis technique requires the usage of analytical, numerical devices to obtain outcomes.

While venturing to quantify a problem, quantitative information decides on its design and the vital process through studying for results that may be driven to wider society. This information gathering process involves several kinds of paper, online, motorized, kiosk reviews; online votes; face-to-face interviews, methodical searches; telephone conferences, etc.

Quantitative analysis is also favored above qualitative analysis as it is further experimental, purpose, active, acceptable and focused. Nevertheless, qualitative analysis is utilized while the researcher has zero meaning what to anticipate. It is utilized to determine the obstacle or progress and plan to the predicament.

• Control-sensitive : The researcher has added authority over the process of the data that is collected and is further abstracted of the investigation. An external viewpoint is obtained utilizing this process.
• Less objective or biased : The research suggests concerning objectivity that is sans prejudice, and is secluded of the data. The specialist has precisely established analysis issues to which real solutions are explored.
• More accurate : A huge amount of information is collected and later examined statistically. This nearly wipes off preferences, and if specialists administered the report on the information, they will eternally close up along with the corresponding amounts at the edge of it.
• Joined : The goal of the study is determined before its effects and analysis are utilized to examine a hypothesis and eventually approve or deny it.
• Repeatable : The analysis research may regularly be duplicated or copied, provided its large security.
• Orders with added large units : The results are established on extra-large individual areas that are indicative of the area. A huge example of mass is used to achieve statistically important results in consumer perception.
• Generalizable : Design may be applied to infer thoughts further widely, prophesy expected outcomes, or examine causal links. Conclusions may be generalized if the selection method is well-developed and the specimen is the agent of a research community.
• Systematized in single scientific purposes : Obtained data happens to be in the structure of quantities and statistics, usually systematized in spreadsheets, graphs, illustrations, or additional distinct non-textual information.
• Relatable : Quantitative analysis intends to obtain prognostications, verify details and analysis principles that ought previously been declared. It strives to ascertain proof that promotes or offers no assistance with an actual reason. It examines and proves already assembled ideas about the process and the reason behind the phenomena.
• Relevant in the next steps of analysis : Quantitative investigation is normally prescribed in the next degrees of study as it provides added positive outcomes.
• Compatible with information : With quantitative analysis, attaining information that is accurate, consistent, reliable, numerical, and quantitative.
• More agreeable : It can have tremendous credibility amongst several influential individuals (for instance administrators, donors, politicians, and sponsors).
• Further structured : The researcher utilizes devices, like inquiries or tools to obtain statistical data.
• Helpful for resolution-making : Information from quantitative analysis like business area, demographics, and client decisions presents valuable data for marketing arrangements.
• Swift : Data acquisition utilizing quantitative techniques is comparatively fast (for instance telephone conferences). Also, the information report is proportionately less time-wasting (utilizing analytical software).

Thus, Quantitative analysis may perform this by applying data recovery techniques and mathematical analysis. Quantitative business analysis is utilized for measuring consumer opinions and practices, segmentation, identifying drivers, and market sizing for label products and recall gain arrangements.

As the designation implies, quantitative business analysis programs emphasize the capacity of analysis as objected to the property. The quantitative retailing analysis is utilized to calculate the effects of quantitative business reviews of the whole marketplace. Well-liked quantitative business review purposes incorporate :

• Online Reviews
• Personal Quantitative Conferences
• E-Mail Conferences
• Intercept Researches
• Telephone Reviews

The market analysis represents a pivotal function in deciding the representatives that supervise to firm profit. Whether to measure the volume of a possible business or know the contest for distinct merchandise, it is extremely relevant to practice techniques that will waive weighable issues in conveying a market examination responsibility. Thus, Quantitative analysis is an essential part of market analysis.

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

Edward barroga.

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

Glafera Janet Matanguihan

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

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

INTRODUCTION

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

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

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

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

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

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

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

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

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

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

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

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

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

Research questions in quantitative research

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

Hypotheses in quantitative research

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

Research questions in qualitative research

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

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

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

Hypotheses in qualitative research

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

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

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

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

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

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

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

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

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

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

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

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

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

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

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

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

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

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

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

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

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

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

Author Contributions:

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