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Research Question Examples 🧑🏻‍🏫

25+ Practical Examples & Ideas To Help You Get Started 

By: Derek Jansen (MBA) | October 2023

A well-crafted research question (or set of questions) sets the stage for a robust study and meaningful insights.  But, if you’re new to research, it’s not always clear what exactly constitutes a good research question. In this post, we’ll provide you with clear examples of quality research questions across various disciplines, so that you can approach your research project with confidence!

Research Question Examples

• Psychology research questions
• Business research questions
• Education research questions
• Healthcare research questions
• Computer science research questions

Examples: Psychology

Let’s start by looking at some examples of research questions that you might encounter within the discipline of psychology.

How does sleep quality affect academic performance in university students?

This question is specific to a population (university students) and looks at a direct relationship between sleep and academic performance, both of which are quantifiable and measurable variables.

What factors contribute to the onset of anxiety disorders in adolescents?

The question narrows down the age group and focuses on identifying multiple contributing factors. There are various ways in which it could be approached from a methodological standpoint, including both qualitatively and quantitatively.

Do mindfulness techniques improve emotional well-being?

This is a focused research question aiming to evaluate the effectiveness of a specific intervention.

How does early childhood trauma impact adult relationships?

This research question targets a clear cause-and-effect relationship over a long timescale, making it focused but comprehensive.

Is there a correlation between screen time and depression in teenagers?

This research question focuses on an in-demand current issue and a specific demographic, allowing for a focused investigation. The key variables are clearly stated within the question and can be measured and analysed (i.e., high feasibility).

Examples: Business/Management

Next, let’s look at some examples of well-articulated research questions within the business and management realm.

How do leadership styles impact employee retention?

This is an example of a strong research question because it directly looks at the effect of one variable (leadership styles) on another (employee retention), allowing from a strongly aligned methodological approach.

What role does corporate social responsibility play in consumer choice?

Current and precise, this research question can reveal how social concerns are influencing buying behaviour by way of a qualitative exploration.

Does remote work increase or decrease productivity in tech companies?

Focused on a particular industry and a hot topic, this research question could yield timely, actionable insights that would have high practical value in the real world.

How do economic downturns affect small businesses in the homebuilding industry?

Vital for policy-making, this highly specific research question aims to uncover the challenges faced by small businesses within a certain industry.

Which employee benefits have the greatest impact on job satisfaction?

By being straightforward and specific, answering this research question could provide tangible insights to employers.

Examples: Education

Next, let’s look at some potential research questions within the education, training and development domain.

How does class size affect students’ academic performance in primary schools?

This example research question targets two clearly defined variables, which can be measured and analysed relatively easily.

Do online courses result in better retention of material than traditional courses?

Timely, specific and focused, answering this research question can help inform educational policy and personal choices about learning formats.

What impact do US public school lunches have on student health?

Targeting a specific, well-defined context, the research could lead to direct changes in public health policies.

To what degree does parental involvement improve academic outcomes in secondary education in the Midwest?

This research question focuses on a specific context (secondary education in the Midwest) and has clearly defined constructs.

What are the negative effects of standardised tests on student learning within Oklahoma primary schools?

This research question has a clear focus (negative outcomes) and is narrowed into a very specific context.

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Examples: Healthcare

Shifting to a different field, let’s look at some examples of research questions within the healthcare space.

What are the most effective treatments for chronic back pain amongst UK senior males?

Specific and solution-oriented, this research question focuses on clear variables and a well-defined context (senior males within the UK).

How do different healthcare policies affect patient satisfaction in public hospitals in South Africa?

This question is has clearly defined variables and is narrowly focused in terms of context.

Which factors contribute to obesity rates in urban areas within California?

This question is focused yet broad, aiming to reveal several contributing factors for targeted interventions.

Does telemedicine provide the same perceived quality of care as in-person visits for diabetes patients?

Ideal for a qualitative study, this research question explores a single construct (perceived quality of care) within a well-defined sample (diabetes patients).

Which lifestyle factors have the greatest affect on the risk of heart disease?

This research question aims to uncover modifiable factors, offering preventive health recommendations.

Examples: Computer Science

Last but certainly not least, let’s look at a few examples of research questions within the computer science world.

What are the perceived risks of cloud-based storage systems?

Highly relevant in our digital age, this research question would align well with a qualitative interview approach to better understand what users feel the key risks of cloud storage are.

Which factors affect the energy efficiency of data centres in Ohio?

With a clear focus, this research question lays a firm foundation for a quantitative study.

How do TikTok algorithms impact user behaviour amongst new graduates?

While this research question is more open-ended, it could form the basis for a qualitative investigation.

What are the perceived risk and benefits of open-source software software within the web design industry?

Practical and straightforward, the results could guide both developers and end-users in their choices.

Remember, these are just examples…

In this post, we’ve tried to provide a wide range of research question examples to help you get a feel for what research questions look like in practice. That said, it’s important to remember that these are just examples and don’t necessarily equate to good research topics . If you’re still trying to find a topic, check out our topic megalist for inspiration.

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Sat / act prep online guides and tips, 113 great research paper topics.

General Education

One of the hardest parts of writing a research paper can be just finding a good topic to write about. Fortunately we've done the hard work for you and have compiled a list of 113 interesting research paper topics. They've been organized into ten categories and cover a wide range of subjects so you can easily find the best topic for you.

In addition to the list of good research topics, we've included advice on what makes a good research paper topic and how you can use your topic to start writing a great paper.

What Makes a Good Research Paper Topic?

Not all research paper topics are created equal, and you want to make sure you choose a great topic before you start writing. Below are the three most important factors to consider to make sure you choose the best research paper topics.

#1: It's Something You're Interested In

A paper is always easier to write if you're interested in the topic, and you'll be more motivated to do in-depth research and write a paper that really covers the entire subject. Even if a certain research paper topic is getting a lot of buzz right now or other people seem interested in writing about it, don't feel tempted to make it your topic unless you genuinely have some sort of interest in it as well.

#2: There's Enough Information to Write a Paper

Even if you come up with the absolute best research paper topic and you're so excited to write about it, you won't be able to produce a good paper if there isn't enough research about the topic. This can happen for very specific or specialized topics, as well as topics that are too new to have enough research done on them at the moment. Easy research paper topics will always be topics with enough information to write a full-length paper.

Trying to write a research paper on a topic that doesn't have much research on it is incredibly hard, so before you decide on a topic, do a bit of preliminary searching and make sure you'll have all the information you need to write your paper.

#3: It Fits Your Teacher's Guidelines

Don't get so carried away looking at lists of research paper topics that you forget any requirements or restrictions your teacher may have put on research topic ideas. If you're writing a research paper on a health-related topic, deciding to write about the impact of rap on the music scene probably won't be allowed, but there may be some sort of leeway. For example, if you're really interested in current events but your teacher wants you to write a research paper on a history topic, you may be able to choose a topic that fits both categories, like exploring the relationship between the US and North Korea. No matter what, always get your research paper topic approved by your teacher first before you begin writing.

113 Good Research Paper Topics

Below are 113 good research topics to help you get you started on your paper. We've organized them into ten categories to make it easier to find the type of research paper topics you're looking for.

Arts/Culture

• Discuss the main differences in art from the Italian Renaissance and the Northern Renaissance .
• Analyze the impact a famous artist had on the world.
• How is sexism portrayed in different types of media (music, film, video games, etc.)? Has the amount/type of sexism changed over the years?
• How has the music of slaves brought over from Africa shaped modern American music?
• How has rap music evolved in the past decade?
• How has the portrayal of minorities in the media changed?

Current Events

• What have been the impacts of China's one child policy?
• How have the goals of feminists changed over the decades?
• How has the Trump presidency changed international relations?
• Analyze the history of the relationship between the United States and North Korea.
• What factors contributed to the current decline in the rate of unemployment?
• What have been the impacts of states which have increased their minimum wage?
• How do US immigration laws compare to immigration laws of other countries?
• How have the US's immigration laws changed in the past few years/decades?
• How has the Black Lives Matter movement affected discussions and view about racism in the US?
• What impact has the Affordable Care Act had on healthcare in the US?
• What factors contributed to the UK deciding to leave the EU (Brexit)?
• What factors contributed to China becoming an economic power?
• Discuss the history of Bitcoin or other cryptocurrencies  (some of which tokenize the S&P 500 Index on the blockchain) .
• Do students in schools that eliminate grades do better in college and their careers?
• Do students from wealthier backgrounds score higher on standardized tests?
• Do students who receive free meals at school get higher grades compared to when they weren't receiving a free meal?
• Do students who attend charter schools score higher on standardized tests than students in public schools?
• Do students learn better in same-sex classrooms?
• How does giving each student access to an iPad or laptop affect their studies?
• What are the benefits and drawbacks of the Montessori Method ?
• Do children who attend preschool do better in school later on?
• What was the impact of the No Child Left Behind act?
• How does the US education system compare to education systems in other countries?
• What impact does mandatory physical education classes have on students' health?
• Which methods are most effective at reducing bullying in schools?
• Do homeschoolers who attend college do as well as students who attended traditional schools?
• Does offering tenure increase or decrease quality of teaching?
• How does college debt affect future life choices of students?
• Should graduate students be able to form unions?

• What are different ways to lower gun-related deaths in the US?
• How and why have divorce rates changed over time?
• Is affirmative action still necessary in education and/or the workplace?
• Should physician-assisted suicide be legal?
• How has stem cell research impacted the medical field?
• How can human trafficking be reduced in the United States/world?
• Should people be able to donate organs in exchange for money?
• Which types of juvenile punishment have proven most effective at preventing future crimes?
• Has the increase in US airport security made passengers safer?
• Analyze the immigration policies of certain countries and how they are similar and different from one another.
• Several states have legalized recreational marijuana. What positive and negative impacts have they experienced as a result?
• Do tariffs increase the number of domestic jobs?
• Which prison reforms have proven most effective?
• Should governments be able to censor certain information on the internet?
• Which methods/programs have been most effective at reducing teen pregnancy?
• What are the benefits and drawbacks of the Keto diet?
• How effective are different exercise regimes for losing weight and maintaining weight loss?
• How do the healthcare plans of various countries differ from each other?
• What are the most effective ways to treat depression ?
• What are the pros and cons of genetically modified foods?
• Which methods are most effective for improving memory?
• What can be done to lower healthcare costs in the US?
• What factors contributed to the current opioid crisis?
• Analyze the history and impact of the HIV/AIDS epidemic .
• Are low-carbohydrate or low-fat diets more effective for weight loss?
• How much exercise should the average adult be getting each week?
• Which methods are most effective to get parents to vaccinate their children?
• What are the pros and cons of clean needle programs?
• How does stress affect the body?
• Discuss the history of the conflict between Israel and the Palestinians.
• What were the causes and effects of the Salem Witch Trials?
• Who was responsible for the Iran-Contra situation?
• How has New Orleans and the government's response to natural disasters changed since Hurricane Katrina?
• What events led to the fall of the Roman Empire?
• What were the impacts of British rule in India ?
• Was the atomic bombing of Hiroshima and Nagasaki necessary?
• What were the successes and failures of the women's suffrage movement in the United States?
• What were the causes of the Civil War?
• How did Abraham Lincoln's assassination impact the country and reconstruction after the Civil War?
• Which factors contributed to the colonies winning the American Revolution?
• What caused Hitler's rise to power?
• Discuss how a specific invention impacted history.
• What led to Cleopatra's fall as ruler of Egypt?
• How has Japan changed and evolved over the centuries?
• What were the causes of the Rwandan genocide ?

• Why did Martin Luther decide to split with the Catholic Church?
• Analyze the history and impact of a well-known cult (Jonestown, Manson family, etc.)
• How did the sexual abuse scandal impact how people view the Catholic Church?
• How has the Catholic church's power changed over the past decades/centuries?
• What are the causes behind the rise in atheism/ agnosticism in the United States?
• What were the influences in Siddhartha's life resulted in him becoming the Buddha?
• How has media portrayal of Islam/Muslims changed since September 11th?

Science/Environment

• How has the earth's climate changed in the past few decades?
• How has the use and elimination of DDT affected bird populations in the US?
• Analyze how the number and severity of natural disasters have increased in the past few decades.
• Analyze deforestation rates in a certain area or globally over a period of time.
• How have past oil spills changed regulations and cleanup methods?
• How has the Flint water crisis changed water regulation safety?
• What are the pros and cons of fracking?
• What impact has the Paris Climate Agreement had so far?
• What have NASA's biggest successes and failures been?
• How can we improve access to clean water around the world?
• Does ecotourism actually have a positive impact on the environment?
• Should the US rely on nuclear energy more?
• What can be done to save amphibian species currently at risk of extinction?
• What impact has climate change had on coral reefs?
• How are black holes created?
• Are teens who spend more time on social media more likely to suffer anxiety and/or depression?
• How will the loss of net neutrality affect internet users?
• Analyze the history and progress of self-driving vehicles.
• How has the use of drones changed surveillance and warfare methods?
• Has social media made people more or less connected?
• What progress has currently been made with artificial intelligence ?
• Do smartphones increase or decrease workplace productivity?
• What are the most effective ways to use technology in the classroom?
• How is Google search affecting our intelligence?
• When is the best age for a child to begin owning a smartphone?
• Has frequent texting reduced teen literacy rates?

How to Write a Great Research Paper

Even great research paper topics won't give you a great research paper if you don't hone your topic before and during the writing process. Follow these three tips to turn good research paper topics into great papers.

#1: Figure Out Your Thesis Early

Before you start writing a single word of your paper, you first need to know what your thesis will be. Your thesis is a statement that explains what you intend to prove/show in your paper. Every sentence in your research paper will relate back to your thesis, so you don't want to start writing without it!

As some examples, if you're writing a research paper on if students learn better in same-sex classrooms, your thesis might be "Research has shown that elementary-age students in same-sex classrooms score higher on standardized tests and report feeling more comfortable in the classroom."

If you're writing a paper on the causes of the Civil War, your thesis might be "While the dispute between the North and South over slavery is the most well-known cause of the Civil War, other key causes include differences in the economies of the North and South, states' rights, and territorial expansion."

#2: Back Every Statement Up With Research

Remember, this is a research paper you're writing, so you'll need to use lots of research to make your points. Every statement you give must be backed up with research, properly cited the way your teacher requested. You're allowed to include opinions of your own, but they must also be supported by the research you give.

#3: Do Your Research Before You Begin Writing

You don't want to start writing your research paper and then learn that there isn't enough research to back up the points you're making, or, even worse, that the research contradicts the points you're trying to make!

Get most of your research on your good research topics done before you begin writing. Then use the research you've collected to create a rough outline of what your paper will cover and the key points you're going to make. This will help keep your paper clear and organized, and it'll ensure you have enough research to produce a strong paper.

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Christine graduated from Michigan State University with degrees in Environmental Biology and Geography and received her Master's from Duke University. In high school she scored in the 99th percentile on the SAT and was named a National Merit Finalist. She has taught English and biology in several countries.

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Qualitative Research Questions: Gain Powerful Insights + 25 Examples

We review the basics of qualitative research questions, including their key components, how to craft them effectively, & 25 example questions.

Einstein was many things—a physicist, a philosopher, and, undoubtedly, a mastermind. He also had an incredible way with words. His quote, "Everything that can be counted does not necessarily count; everything that counts cannot necessarily be counted," is particularly poignant when it comes to research. 

Some inquiries call for a quantitative approach, for counting and measuring data in order to arrive at general conclusions. Other investigations, like qualitative research, rely on deep exploration and understanding of individual cases in order to develop a greater understanding of the whole. That’s what we’re going to focus on today.

Qualitative research questions focus on the "how" and "why" of things, rather than the "what". They ask about people's experiences and perceptions , and can be used to explore a wide range of topics.

The following article will discuss the basics of qualitative research questions, including their key components, and how to craft them effectively. You'll also find 25 examples of effective qualitative research questions you can use as inspiration for your own studies.

Let’s get started!

What are qualitative research questions, and when are they used?

When researchers set out to conduct a study on a certain topic, their research is chiefly directed by an overarching question . This question provides focus for the study and helps determine what kind of data will be collected.

By starting with a question, we gain parameters and objectives for our line of research. What are we studying? For what purpose? How will we know when we’ve achieved our goals?

Of course, some of these questions can be described as quantitative in nature. When a research question is quantitative, it usually seeks to measure or calculate something in a systematic way.

For example:

• How many people in our town use the library?
• What is the average income of families in our city?
• How much does the average person weigh?

Other research questions, however—and the ones we will be focusing on in this article—are qualitative in nature. Qualitative research questions are open-ended and seek to explore a given topic in-depth.

According to the Australian & New Zealand Journal of Psychiatry , “Qualitative research aims to address questions concerned with developing an understanding of the meaning and experience dimensions of humans’ lives and social worlds.”

This type of research can be used to gain a better understanding of people’s thoughts, feelings and experiences by “addressing questions beyond ‘what works’, towards ‘what works for whom when, how and why, and focusing on intervention improvement rather than accreditation,” states one paper in Neurological Research and Practice .

Qualitative questions often produce rich data that can help researchers develop hypotheses for further quantitative study.

• What are people’s thoughts on the new library?
• How does it feel to be a first-generation student at our school?
• How do people feel about the changes taking place in our town?

As stated by a paper in Human Reproduction , “...‘qualitative’ methods are used to answer questions about experience, meaning, and perspective, most often from the standpoint of the participant. These data are usually not amenable to counting or measuring.”

Both quantitative and qualitative questions have their uses; in fact, they often complement each other. A well-designed research study will include a mix of both types of questions in order to gain a fuller understanding of the topic at hand.

If you would like to recruit unlimited participants for qualitative research for free and only pay for the interview you conduct, try using Respondent  today. 

Crafting qualitative research questions for powerful insights

Now that we have a basic understanding of what qualitative research questions are and when they are used, let’s take a look at how you can begin crafting your own.

According to a study in the International Journal of Qualitative Studies in Education, there is a certain process researchers should follow when crafting their questions, which we’ll explore in more depth.

1. Beginning the process 

Start with a point of interest or curiosity, and pose a draft question or ‘self-question’. What do you want to know about the topic at hand? What is your specific curiosity? You may find it helpful to begin by writing several questions.

For example, if you’re interested in understanding how your customer base feels about a recent change to your product, you might ask: 

• What made you decide to try the new product?
• How do you feel about the change?
• What do you think of the new design/functionality?
• What benefits do you see in the change?

2. Create one overarching, guiding question 

At this point, narrow down the draft questions into one specific question. “Sometimes, these broader research questions are not stated as questions, but rather as goals for the study.”

As an example of this, you might narrow down these three questions: 

into the following question: 

• What are our customers’ thoughts on the recent change to our product?

3. Theoretical framing 

As you read the relevant literature and apply theory to your research, the question should be altered to achieve better outcomes. Experts agree that pursuing a qualitative line of inquiry should open up the possibility for questioning your original theories and altering the conceptual framework with which the research began.

If we continue with the current example, it’s possible you may uncover new data that informs your research and changes your question. For instance, you may discover that customers’ feelings about the change are not just a reaction to the change itself, but also to how it was implemented. In this case, your question would need to reflect this new information: 

• How did customers react to the process of the change, as well as the change itself?

4. Ethical considerations 

A study in the International Journal of Qualitative Studies in Education stresses that ethics are “a central issue when a researcher proposes to study the lives of others, especially marginalized populations.” Consider how your question or inquiry will affect the people it relates to—their lives and their safety. Shape your question to avoid physical, emotional, or mental upset for the focus group.

In analyzing your question from this perspective, if you feel that it may cause harm, you should consider changing the question or ending your research project. Perhaps you’ve discovered that your question encourages harmful or invasive questioning, in which case you should reformulate it.

5. Writing the question 

The actual process of writing the question comes only after considering the above points. The purpose of crafting your research questions is to delve into what your study is specifically about” Remember that qualitative research questions are not trying to find the cause of an effect, but rather to explore the effect itself.

Your questions should be clear, concise, and understandable to those outside of your field. In addition, they should generate rich data. The questions you choose will also depend on the type of research you are conducting: 

• If you’re doing a phenomenological study, your questions might be open-ended, in order to allow participants to share their experiences in their own words.
• If you’re doing a grounded-theory study, your questions might be focused on generating a list of categories or themes.
• If you’re doing ethnography, your questions might be about understanding the culture you’re studying.

Whenyou have well-written questions, it is much easier to develop your research design and collect data that accurately reflects your inquiry.

In writing your questions, it may help you to refer to this simple flowchart process for constructing questions:

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25 examples of expertly crafted qualitative research questions

It's easy enough to cover the theory of writing a qualitative research question, but sometimes it's best if you can see the process in practice. In this section, we'll list 25 examples of B2B and B2C-related qualitative questions.

Let's begin with five questions. We'll show you the question, explain why it's considered qualitative, and then give you an example of how it can be used in research.

1. What is the customer's perception of our company's brand?

Qualitative research questions are often open-ended and invite respondents to share their thoughts and feelings on a subject. This question is qualitative because it seeks customer feedback on the company's brand. 

This question can be used in research to understand how customers feel about the company's branding, what they like and don't like about it, and whether they would recommend it to others.

2. Why do customers buy our product?

This question is also qualitative because it seeks to understand the customer's motivations for purchasing a product. It can be used in research to identify the reasons  customers buy a certain product, what needs or desires the product fulfills for them, and how they feel about the purchase after using the product.

3. How do our customers interact with our products?

Again, this question is qualitative because it seeks to understand customer behavior. In this case, it can be used in research to see how customers use the product, how they interact with it, and what emotions or thoughts the product evokes in them.

4. What are our customers' biggest frustrations with our products?

By seeking to understand customer frustrations, this question is qualitative and can provide valuable insights. It can be used in research to help identify areas in which the company needs to make improvements with its products.

5. How do our customers feel about our customer service?

Rather than asking why customers like or dislike something, this question asks how they feel. This qualitative question can provide insights into customer satisfaction or dissatisfaction with a company. 

This type of question can be used in research to understand what customers think of the company's customer service and whether they feel it meets their needs.

20 more examples to refer to when writing your question

Now that you’re aware of what makes certain questions qualitative, let's move into 20 more examples of qualitative research questions:

• How do your customers react when updates are made to your app interface?
• How do customers feel when they complete their purchase through your ecommerce site?
• What are your customers' main frustrations with your service?
• How do people feel about the quality of your products compared to those of your competitors?
• What motivates customers to refer their friends and family members to your product or service?
• What are the main benefits your customers receive from using your product or service?
• How do people feel when they finish a purchase on your website?
• What are the main motivations behind customer loyalty to your brand?
• How does your app make people feel emotionally?
• For younger generations using your app, how does it make them feel about themselves?
• What reputation do people associate with your brand?
• How inclusive do people find your app?
• In what ways are your customers' experiences unique to them?
• What are the main areas of improvement your customers would like to see in your product or service?
• How do people feel about their interactions with your tech team?
• What are the top five reasons people use your online marketplace?
• How does using your app make people feel in terms of connectedness?
• What emotions do people experience when they're using your product or service?
• Aside from the features of your product, what else about it attracts customers?
• How does your company culture make people feel?

As you can see, these kinds of questions are completely open-ended. In a way, they allow the research and discoveries made along the way to direct the research. The questions are merely a starting point from which to explore.

This video offers tips on how to write good qualitative research questions, produced by Qualitative Research Expert, Kimberly Baker.

Wrap-up: crafting your own qualitative research questions.

Over the course of this article, we've explored what qualitative research questions are, why they matter, and how they should be written. Hopefully you now have a clear understanding of how to craft your own.

Remember, qualitative research questions should always be designed to explore a certain experience or phenomena in-depth, in order to generate powerful insights. As you write your questions, be sure to keep the following in mind:

• Are you being inclusive of all relevant perspectives?
• Are your questions specific enough to generate clear answers?
• Will your questions allow for an in-depth exploration of the topic at hand?
• Do the questions reflect your research goals and objectives?

If you can answer "yes" to all of the questions above, and you've followed the tips for writing qualitative research questions we shared in this article, then you're well on your way to crafting powerful queries that will yield valuable insights.

Download Free E-Book

Asking the right questions in the right way is the key to research success. That’s true for not just the discussion guide but for every step of a research project. Following are 100+ questions that will take you from defining your research objective through  screening and participant discussions.

Fill out the form below to access free e-book! 

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How to Craft a Strong Research Question (With Research Question Examples)

A sound and effective research question is a key element that must be identified and pinned down before researchers can even begin their research study or work. A strong research question lays the foundation for your entire study, guiding your investigation and shaping your findings. Hence, it is critical that researchers spend considerable time assessing and refining the research question based on in-depth reading and comprehensive literature review. In this article, we will discuss how to write a strong research question and provide you with some good examples of research questions across various disciplines.

Table of Contents

The importance of a research question

A research question plays a crucial role in driving scientific inquiry, setting the direction and purpose of your study, and guiding your entire research process. By formulating a clear and focused research question, you lay the foundation for your investigation, ensuring that your research remains on track and aligned with your objectives so you can make meaningful contribution to the existing body of knowledge. A well-crafted research question also helps you define the scope of your study and identify the appropriate methodologies and data collection techniques to employ.

Key components of a strong research question

A good research question possesses several key components that contribute to the quality and impact of your study. Apart from providing a clear framework to generate meaningful results, a well-defined research question allows other researchers to understand the purpose and significance of your work. So, when working on your research question, incorporate the following elements:

• Specificity : A strong research question should be specific about the main focus of your study, enabling you to gather precise data and draw accurate conclusions. It clearly defines the variables, participants, and context involved, leaving no room for ambiguity.
• Clarity : A good research question is clear and easily understood, so articulate the purpose and intent of your study concisely without being generic or vague. Ensuring clarity in your research question helps both you and your readers grasp the research objective.
• Feasibility : While crafting a research question, consider the practicality of conducting the research and availability of necessary data or access to participants. Think whether your study is realistic and achievable within the constraints of time, resources, and ethical considerations.

How to craft a well-defined research question

A first step that will help save time and effort is knowing what your aims are and thinking about a few problem statements on the area or aspect one wants to study or do research on. Contemplating these statements as one undertakes more progressive reading can help the researcher in reassessing and fine-tuning the research question. This can be done over time as they read and learn more about the research topic, along with a broad literature review and parallel discussions with peer researchers and supervisors. In some cases, a researcher can have more than one research question if the research being undertaken is a PhD thesis or dissertation, but try not to cover multiple concerns on a topic.

A strong research question must be researchable, original, complex, and relevant. Here are five simple steps that can make the entire process easier.

• Identify a broad topic from your areas of interest, something that is relevant, and you are passionate about since you’ll be spending a lot of time conducting your research.
• Do a thorough literature review to weed out potential gaps in research and stay updated on what’s currently being done in your chosen topic and subject area.
• Shortlist possible research questions based on the research gaps or see how you can build on or refute previously published ideas and concepts.
• Assess your chosen research question using the FINER criteria that helps you evaluate whether the research is Feasible, Interesting, Novel, Ethical, and Relevant. 1
• Formulate the final research question, while ensuring it is clear, well-written, and addresses all the key elements of a strong research question.

Examples of research questions

Remember to adapt your research question to suit your purpose, whether it’s exploratory, descriptive, comparative, experimental, qualitative, or quantitative. Embrace the iterative nature of the research process, continually evaluating and refining your question as you progress. Here are some good examples of research questions across various disciplines.

Exploratory research question examples

• How does social media impact interpersonal relationships among teenagers?
• What are the potential benefits of incorporating mindfulness practices in the workplace?

Descriptive research question examples

• What factors influence customer loyalty in the e-commerce industry?
• Is there a relationship between socioeconomic status and academic performance among elementary school students?

Comparative research question examples

• How does the effectiveness of traditional teaching methods compare to online learning platforms in mathematics education?
• What is the impact of different healthcare policies on patient outcomes in various countries?

Experimental research question examples

• What are the effects of a new drug on reducing symptoms of a specific medical condition?
• Does a dietary intervention have an impact on weight loss among individuals with obesity?

Qualitative research question examples

• What are the lived experiences of immigrants adapting to a new culture?
• What factors influence job satisfaction among healthcare professionals?

Quantitative research question examples

• Is there a relationship between sleep duration and academic performance among college students?
• How effective is a specific intervention in reducing anxiety levels among individuals with phobias?

With these simple guidelines and inspiring examples of research questions, you are equipped to embark on your research journey with confidence and purpose. Here’s wishing you all the best for your future endeavors!

References:

• How to write a research question: Steps and examples. Indeed Career Guide. Available online at https://www.indeed.com/career-advice/career-development/how-to-write-research-questions

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How to Write a Good Research Question (w/ Examples)

What is a Research Question?

A research question is the main question that your study sought or is seeking to answer. A clear research question guides your research paper or thesis and states exactly what you want to find out, giving your work a focus and objective. Learning  how to write a hypothesis or research question is the start to composing any thesis, dissertation, or research paper. It is also one of the most important sections of a research proposal . 

A good research question not only clarifies the writing in your study; it provides your readers with a clear focus and facilitates their understanding of your research topic, as well as outlining your study’s objectives. Before drafting the paper and receiving research paper editing (and usually before performing your study), you should write a concise statement of what this study intends to accomplish or reveal.

Research Question Writing Tips

Listed below are the important characteristics of a good research question:

A good research question should:

• Be clear and provide specific information so readers can easily understand the purpose.
• Be focused in its scope and narrow enough to be addressed in the space allowed by your paper
• Be relevant and concise and express your main ideas in as few words as possible, like a hypothesis.
• Be precise and complex enough that it does not simply answer a closed “yes or no” question, but requires an analysis of arguments and literature prior to its being considered acceptable. 
• Be arguable or testable so that answers to the research question are open to scrutiny and specific questions and counterarguments.

Some of these characteristics might be difficult to understand in the form of a list. Let’s go into more detail about what a research question must do and look at some examples of research questions.

The research question should be specific and focused 

Research questions that are too broad are not suitable to be addressed in a single study. One reason for this can be if there are many factors or variables to consider. In addition, a sample data set that is too large or an experimental timeline that is too long may suggest that the research question is not focused enough.

A specific research question means that the collective data and observations come together to either confirm or deny the chosen hypothesis in a clear manner. If a research question is too vague, then the data might end up creating an alternate research problem or hypothesis that you haven’t addressed in your Introduction section .

The research question should be based on the literature 

An effective research question should be answerable and verifiable based on prior research because an effective scientific study must be placed in the context of a wider academic consensus. This means that conspiracy or fringe theories are not good research paper topics.

Instead, a good research question must extend, examine, and verify the context of your research field. It should fit naturally within the literature and be searchable by other research authors.

References to the literature can be in different citation styles and must be properly formatted according to the guidelines set forth by the publishing journal, university, or academic institution. This includes in-text citations as well as the Reference section . 

The research question should be realistic in time, scope, and budget

There are two main constraints to the research process: timeframe and budget.

A proper research question will include study or experimental procedures that can be executed within a feasible time frame, typically by a graduate doctoral or master’s student or lab technician. Research that requires future technology, expensive resources, or follow-up procedures is problematic.

A researcher’s budget is also a major constraint to performing timely research. Research at many large universities or institutions is publicly funded and is thus accountable to funding restrictions. 

The research question should be in-depth

Research papers, dissertations and theses , and academic journal articles are usually dozens if not hundreds of pages in length.

A good research question or thesis statement must be sufficiently complex to warrant such a length, as it must stand up to the scrutiny of peer review and be reproducible by other scientists and researchers.

Research Question Types

Qualitative and quantitative research are the two major types of research, and it is essential to develop research questions for each type of study. 

Quantitative Research Questions

Quantitative research questions are specific. A typical research question involves the population to be studied, dependent and independent variables, and the research design.

In addition, quantitative research questions connect the research question and the research design. In addition, it is not possible to answer these questions definitively with a “yes” or “no” response. For example, scientific fields such as biology, physics, and chemistry often deal with “states,” in which different quantities, amounts, or velocities drastically alter the relevance of the research.

As a consequence, quantitative research questions do not contain qualitative, categorical, or ordinal qualifiers such as “is,” “are,” “does,” or “does not.”

Categories of quantitative research questions

Qualitative Research Questions

In quantitative research, research questions have the potential to relate to broad research areas as well as more specific areas of study. Qualitative research questions are less directional, more flexible, and adaptable compared with their quantitative counterparts. Thus, studies based on these questions tend to focus on “discovering,” “explaining,” “elucidating,” and “exploring.”

Categories of qualitative research questions

Quantitative and Qualitative Research Question Examples

Good and Bad Research Question Examples

Below are some good (and not-so-good) examples of research questions that researchers can use to guide them in crafting their own research questions.

Research Question Example 1

The first research question is too vague in both its independent and dependent variables. There is no specific information on what “exposure” means. Does this refer to comments, likes, engagement, or just how much time is spent on the social media platform?

Second, there is no useful information on what exactly “affected” means. Does the subject’s behavior change in some measurable way? Or does this term refer to another factor such as the user’s emotions?

Research Question Example 2

In this research question, the first example is too simple and not sufficiently complex, making it difficult to assess whether the study answered the question. The author could really only answer this question with a simple “yes” or “no.” Further, the presence of data would not help answer this question more deeply, which is a sure sign of a poorly constructed research topic.

The second research question is specific, complex, and empirically verifiable. One can measure program effectiveness based on metrics such as attendance or grades. Further, “bullying” is made into an empirical, quantitative measurement in the form of recorded disciplinary actions.

Steps for Writing a Research Question

Good research questions are relevant, focused, and meaningful. It can be difficult to come up with a good research question, but there are a few steps you can follow to make it a bit easier.

1. Start with an interesting and relevant topic

Choose a research topic that is interesting but also relevant and aligned with your own country’s culture or your university’s capabilities. Popular academic topics include healthcare and medical-related research. However, if you are attending an engineering school or humanities program, you should obviously choose a research question that pertains to your specific study and major.

Below is an embedded graph of the most popular research fields of study based on publication output according to region. As you can see, healthcare and the basic sciences receive the most funding and earn the highest number of publications. 

2. Do preliminary research  

You can begin doing preliminary research once you have chosen a research topic. Two objectives should be accomplished during this first phase of research. First, you should undertake a preliminary review of related literature to discover issues that scholars and peers are currently discussing. With this method, you show that you are informed about the latest developments in the field.

Secondly, identify knowledge gaps or limitations in your topic by conducting a preliminary literature review . It is possible to later use these gaps to focus your research question after a certain amount of fine-tuning.

3. Narrow your research to determine specific research questions

You can focus on a more specific area of study once you have a good handle on the topic you want to explore. Focusing on recent literature or knowledge gaps is one good option. 

By identifying study limitations in the literature and overlooked areas of study, an author can carve out a good research question. The same is true for choosing research questions that extend or complement existing literature.

4. Evaluate your research question

Make sure you evaluate the research question by asking the following questions:

Is my research question clear?

The resulting data and observations that your study produces should be clear. For quantitative studies, data must be empirical and measurable. For qualitative, the observations should be clearly delineable across categories.

Is my research question focused and specific?

A strong research question should be specific enough that your methodology or testing procedure produces an objective result, not one left to subjective interpretation. Open-ended research questions or those relating to general topics can create ambiguous connections between the results and the aims of the study. 

Is my research question sufficiently complex?

The result of your research should be consequential and substantial (and fall sufficiently within the context of your field) to warrant an academic study. Simply reinforcing or supporting a scientific consensus is superfluous and will likely not be well received by most journal editors.  

Editing Your Research Question

Your research question should be fully formulated well before you begin drafting your research paper. However, you can receive English paper editing and proofreading services at any point in the drafting process. Language editors with expertise in your academic field can assist you with the content and language in your Introduction section or other manuscript sections. And if you need further assistance or information regarding paper compositions, in the meantime, check out our academic resources , which provide dozens of articles and videos on a variety of academic writing and publication topics.

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How To Write a Research Question

Academic writing and research require a distinct focus and direction. A well-designed research question gives purpose and clarity to your research. In addition, it helps your readers understand the issue you are trying to address and explore.

Every time you want to know more about a subject, you will pose a question. The same idea is used in research as well. You must pose a question in order to effectively address a research problem. That's why the research question is an integral part of the research process. Additionally, it offers the author writing and reading guidelines, be it qualitative research or quantitative research.

In your research paper , you must single out just one issue or problem. The specific issue or claim you wish to address should be included in your thesis statement in order to clarify your main argument.

A good research question must have the following characteristics.

• Should include only one problem in the research question
• Should be able to find the answer using primary data and secondary data sources
• Should be possible to resolve within the given time and other constraints
• Detailed and in-depth results should be achievable
• Should be relevant and realistic.
• It should relate to your chosen area of research

While a larger project, like a thesis, might have several research questions to address, each one should be directed at your main area of study. Of course, you can use different research designs and research methods (qualitative research or quantitative research) to address various research questions. However, they must all be pertinent to the study's objectives.

What is a Research Question?

A research question is an inquiry that the research attempts to answer. It is the heart of the systematic investigation. Research questions are the most important step in any research project. In essence, it initiates the research project and establishes the pace for the specific research A research question is:

• Clear : It provides enough detail that the audience understands its purpose without any additional explanation.
• Focused : It is so specific that it can be addressed within the time constraints of the writing task.
• Succinct: It is written in the shortest possible words.
• Complex : It is not possible to answer it with a "yes" or "no", but requires analysis and synthesis of ideas before somebody can create a solution.
• Argumental : Its potential answers are open for debate rather than accepted facts.

A good research question usually focuses on the research and determines the research design, methodology, and hypothesis. It guides all phases of inquiry, data collection, analysis, and reporting. You should gather valuable information by asking the right questions.

Why are Research Questions so important?

Regardless of whether it is a qualitative research or quantitative research project, research questions provide writers and their audience with a way to navigate the writing and research process. Writers can avoid "all-about" papers by asking straightforward and specific research questions that help them focus on their research and support a specific thesis.

Types of Research Questions

There are two types of research: Qualitative research and Quantitative research . There must be research questions for every type of research. Your research question will be based on the type of research you want to conduct and the type of data collection.

The first step in designing research involves identifying a gap and creating a focused research question.

Below is a list of common research questions that can be used in a dissertation. Keep in mind that these are merely illustrations of typical research questions used in dissertation projects. The real research questions themselves might be more difficult.

Example Research Questions

The following are a few examples of research questions and research problems to help you understand how research questions can be created for a particular research problem.

Steps to Write Research Questions

You can focus on the issue or research gaps you're attempting to solve by using the research questions as a direction.

If you're unsure how to go about writing a good research question, these are the steps to follow in the process:

• Select an interesting topic Always choose a topic that interests you. Because if your curiosity isn’t aroused by a subject, you’ll have a hard time conducting research around it. Alos, it’s better that you pick something that’s neither too narrow or too broad.
• Do preliminary research on the topic Search for relevant literature to gauge what problems have already been tackled by scholars. You can do that conveniently through repositories like Scispace , where you’ll find millions of papers in one place. Once you do find the papers you’re looking for, try our reading assistant, SciSpace Copilot to get simple explanations for the paper . You’ll be able to quickly understand the abstract, find the key takeaways, and the main arguments presented in the paper. This will give you a more contextual understanding of your subject and you’ll have an easier time identifying knowledge gaps in your discipline.

     Also: ChatPDF vs. SciSpace Copilot: Unveiling the best tool for your research

• Consider your audience It is essential to understand your audience to develop focused research questions for essays or dissertations. When narrowing down your topic, you can identify aspects that might interest your audience.
• Ask questions Asking questions will give you a deeper understanding of the topic. Evaluate your question through the What, Why, When, How, and other open-ended questions assessment.
• Assess your question Once you have created a research question, assess its effectiveness to determine if it is useful for the purpose. Refine and revise the dissertation research question multiple times.

Additionally, use this list of questions as a guide when formulating your research question.

Are you able to answer a specific research question? After identifying a gap in research, it would be helpful to formulate the research question. And this will allow the research to solve a part of the problem. Is your research question clear and centered on the main topic? It is important that your research question should be specific and related to your central goal. Are you tackling a difficult research question? It is not possible to answer the research question with a simple yes or no. The problem requires in-depth analysis. It is often started with "How" and "Why."

Start your research Once you have completed your dissertation research questions, it is time to review the literature on similar topics to discover different perspectives.

Strong  Research Question Samples

Uncertain: How should social networking sites work on the hatred that flows through their platform?

Certain: What should social media sites like Twitter or Facebook do to address the harm they are causing?

This unclear question does not specify the social networking sites that are being used or what harm they might be causing. In addition, this question assumes that the "harm" has been proven and/or accepted. This version is more specific and identifies the sites (Twitter, Facebook), the type and extent of harm (privacy concerns), and who might be suffering from that harm (users). Effective research questions should not be ambiguous or interpreted.

Unfocused: What are the effects of global warming on the environment?

Focused: What are the most important effects of glacial melting in Antarctica on penguins' lives?

This broad research question cannot be addressed in a book, let alone a college-level paper. Focused research targets a specific effect of global heating (glacial  melting), an area (Antarctica), or a specific animal (penguins). The writer must also decide which effect will have the greatest impact on the animals affected. If in doubt, narrow down your research question to the most specific possible.

Too Simple: What are the U.S. doctors doing to treat diabetes?

Appropriately complex: Which factors, if any, are most likely to predict a person's risk of developing diabetes?

This simple version can be found online. It is easy to answer with a few facts. The second, more complicated version of this question is divided into two parts. It is thought-provoking and requires extensive investigation as well as evaluation by the author. So, ensure that a quick Google search should not answer your research question.

How to write a strong Research Question?

The foundation of all research is the research question. You should therefore spend as much time as necessary to refine your research question based on various data.

You can conduct your research more efficiently and analyze your results better if you have great research questions for your dissertation, research paper , or essay .

The following criteria can help you evaluate the strength and importance of your research question and can be used to determine the strength of your research question:

• Researchable
• It should only cover one issue.
• A subjective judgment should not be included in the question.
• It can be answered with data analysis and research.
• Specific and Practical
• It should not contain a plan of action, policy, or solution.
• It should be clearly defined
• Within research limits
• Complex and Arguable
• It shouldn't be difficult to answer.
• To find the truth, you need in-depth knowledge
• Allows for discussion and deliberation
• Original and Relevant
• It should be in your area of study
• Its results should be measurable
• It should be original

Conclusion - How to write Research Questions?

Research questions provide a clear guideline for research. One research question may be part of a larger project, such as a dissertation. However, each question should only focus on one topic.

Research questions must be answerable, practical, specific, and applicable to your field. The research type that you use to base your research questions on will determine the research topic. You can start by selecting an interesting topic and doing preliminary research. Then, you can begin asking questions, evaluating your questions, and start your research.

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

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How to Write the Research Questions – Tips & Examples

Published by Owen Ingram at August 13th, 2021 , Revised On October 3, 2023

Conducting research and writing an academic paper requires a clear direction and focus.

A good research question provides purpose to your research and clarifies the direction. It further helps your readers to understand what issue your research aims to explore and address.

If you are unsure about how to write research questions, here is a list of the attributes of a good research question;

• The research question should contain only a single problem
• You should be able to find the answer to it using  primary and secondary data sources
• You should be able to address it within the time limit and other constraints
• Can attain in-depth and detailed results
• Relevant and applicable
• Should relate to your chosen field of research

Whenever you want to discover something new about a  topic , you will ask a question about it. Therefore, the research question is important in the overall research process  and provides the author with the reading and writing guidelines.

In a research paper or an essay, you will need to create a single research question that highlights just one problem or issue. The thesis statement should include the specific problem you aim to investigate to establish your argument’s central position or claim.

A larger project such as a  dissertation or thesis , on the other hand, can have multiple research questions, but every question should focus on your main  research problem .  Different types of research will help you answer different research questions, but they should all be relevant to the research scope.

How to Write a Research Question

Steps to develop your research question.

• Choose a topic  with a wide range of published literature
• Read and skim relevant articles to find out different problems and issues
• Specify a theoretical or practical  research problem  that your research question will address
• Narrow down the focus of your selected core niche

Example Research Question (s)

Here are examples of research problems and research questions to help you understand how to create a research question for a given research problem.

Types of Research Questions

There are two main types of research;  quantitative and qualitative research . Both types of research require research questions. What research question you will answer is dependent on the type of research you wish to employ.

The first part of  designing research  is to find a gap and create a fully focused research question.

The following table shows common research questions for a dissertation project. However, it is important to note that these examples of dissertation research questions are straightforward, and the actual research questions may be more complicated than these examples.

What data collection method best suits your research?

• Find out by hiring an expert from ResearchProspect today!
• Despite how challenging the subject may be, we are here to help you.

Steps to Write Research Questions

The research question provides you with a path and focuses on the real problem and the research gap you aim to fill. These are steps you need to take if you are unsure about how to write a research question:

Choose an Interesting Topic

Choose a topic  of research according to your interest. The selected topic should be neither too broad nor too narrow.

Do Preliminary Research on the Topic

Find articles, books, journals, and theses relevant to your chosen topic. Understand what research problem each scholar addressed as part of their research project.

Consider your Audience

It is necessary to know your audience to develop focused research questions for your essay or dissertation. You can find aspects of your topic that could be interesting to your audience when narrowing your topic.

Start Asking Questions

What, why, when, how, and other open-ended questions will provide in-depth knowledge about the topic.

Evaluate your Question

After formulating a research question, evaluate to check its effectiveness and how it can serve the purpose. Revise and refine the dissertation research question.

• Do you have a clear research question? 

It would help if you formed the research question after finding a research gap. This approach will enable the research to solve part of the problem.

• Do you have a focused research question?

It is necessary that the research question is specific and relating to the central aim of your research.

• Do you have a complex research question? 

The research question cannot be answered by yes or no but requires in-depth analysis. It often begins with “How” or “Why.”

Begin your Research

After you have prepared dissertation research questions, you should research the existing literature on similar topics to find various perspectives.

Also See: Formulation of Research Question

If you have been struggling to devise research questions for your dissertation or are unsure about which topic would be suitable for your needs, then you might be interested in taking advantage of our dissertation topic and outline service, which includes several topic ideas in your preferred area of study and a 500/1000 words plan on your chosen topic. Our topic and outline service will help you jump-start your dissertation project.

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Tips on How to Write a Strong Research Question

A research question is the foundation of the entire research. Therefore, you should spend as much time as required to refine the research question.

If you have good research questions for the dissertation, research paper , or essay, you can perform the research and analyse your results more effectively. You can evaluate the strength of the research question with the help of the following criteria. Your research question should be;

Intensive and Researchable

• It should cover a single issue
• The question shouldn’t include a subjective judgment
• It can be answerable with the data analysis or research=

Practical and Specific

• It should not include a course of action, policy, or solution
• It should be well-defined
• Answerable within research limits

Complicated and Arguable

• It should not be simple to answer
• Need in-depth knowledge to find facts
• Provides scope for debate and deliberation

Unique and Relevant

• It should lie in your field of study
• Its results should be contributable
• It should be unique

Conclusion – How to Write Research Questions

A research question provides a clear direction for research work. A bigger project, such as a dissertation, may have more than one research question, but every question should focus on one issue only.

Your research questions should be researchable, feasible to answer, specific to find results, complex (for Masters and PhD projects), and relevant to your field of study. Dissertation research questions depend upon the research type you are basing your paper on.

Start creating a research question by choosing an interesting topic, do some preliminary research, consider your audience, start asking questions, evaluating your question, and begin your research.

At ResearchProspect, we have dissertation experts for all academic subjects. Whether you need help with the individual chapters or the whole dissertation paper, you can be confident that your paper competed to the highest academic standard. There is a reason why our clients keep returning to us over and over. You can also look at our essay services if you are struggling to draft a first-class academic paper.

At ResearchProspect, we have dissertation experts for all academic subjects. Whether you need help with the  individual chapters  or the  whole dissertation paper,  you can be confident that your paper competed to the highest academic standard. There is a reason why our clients keep returning to us over and over.

You can also look at our  essay services  if you are struggling to draft a first-class academic paper.

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Frequently Asked Questions

How are research questions written.

Research questions are written by:

• Identifying your topic.
• Considering what you want to explore.
• Making questions clear and concise.
• Ensuring they’re researchable.
• Avoiding bias or leading language.
• Focusing on one main idea per question.

What are examples of research questions?

• Does regular exercise improve mental well-being in adults over 50?
• How do online courses impact student engagement compared to traditional classes?
• What are the economic effects of prolonged pandemic lockdowns?
• How does early childhood nutrition influence academic performance in later life?
• Does urban green space reduce stress levels?

How to write a research question?

• Identify a specific topic or issue of interest.
• Conduct preliminary research to understand existing knowledge.
• Narrow the focus to address gaps or unresolved issues.
• Phrase the question to be clear, concise, and researchable.
• Ensure it is specific enough for systematic investigation.

How to formulate my research questions for my geography dissertation?

• Identify a geographical topic or phenomenon of interest.
• Review existing literature to find gaps.
• Consider spatial, temporal, environmental, or societal aspects.
• Ensure questions are specific, feasible, and significant.
• Frame questions to guide methodology: quantitative, qualitative, or mixed.
• Seek feedback from peers/advisors.

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• Exploratory Research | Definition, Guide, & Examples

Exploratory Research | Definition, Guide, & Examples

Published on December 6, 2021 by Tegan George . Revised on November 20, 2023.

Exploratory research is a methodology approach that investigates research questions that have not previously been studied in depth.

Exploratory research is often qualitative and primary in nature. However, a study with a large sample conducted in an exploratory manner can be quantitative as well. It is also often referred to as interpretive research or a grounded theory approach due to its flexible and open-ended nature.

Table of contents

When to use exploratory research, exploratory research questions, exploratory research data collection, step-by-step example of exploratory research, exploratory vs. explanatory research, advantages and disadvantages of exploratory research, other interesting articles, frequently asked questions about exploratory research.

Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason.

You can use this type of research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

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Exploratory research questions are designed to help you understand more about a particular topic of interest. They can help you connect ideas to understand the groundwork of your analysis without adding any preconceived notions or assumptions yet.

Here are some examples:

• What effect does using a digital notebook have on the attention span of middle schoolers?
• What factors influence mental health in undergraduates?
• What outcomes are associated with an authoritative parenting style?
• In what ways does the presence of a non-native accent affect intelligibility?
• How can the use of a grocery delivery service reduce food waste in single-person households?

Collecting information on a previously unexplored topic can be challenging. Exploratory research can help you narrow down your topic and formulate a clear hypothesis and problem statement , as well as giving you the “lay of the land” on your topic.

Data collection using exploratory research is often divided into primary and secondary research methods, with data analysis following the same model.

Primary research

In primary research, your data is collected directly from primary sources : your participants. There is a variety of ways to collect primary data.

Some examples include:

• Survey methodology: Sending a survey out to the student body asking them if they would eat vegan meals
• Focus groups: Compiling groups of 8–10 students and discussing what they think of vegan options for dining hall food
• Interviews: Interviewing students entering and exiting the dining hall, asking if they would eat vegan meals

Secondary research

In secondary research, your data is collected from preexisting primary research, such as experiments or surveys.

Some other examples include:

• Case studies : Health of an all-vegan diet
• Literature reviews : Preexisting research about students’ eating habits and how they have changed over time
• Online polls, surveys, blog posts, or interviews; social media: Have other schools done something similar?

For some subjects, it’s possible to use large- n government data, such as the decennial census or yearly American Community Survey (ACS) open-source data.

How you proceed with your exploratory research design depends on the research method you choose to collect your data. In most cases, you will follow five steps.

We’ll walk you through the steps using the following example.

Therefore, you would like to focus on improving intelligibility instead of reducing the learner’s accent.

Step 1: Identify your problem

The first step in conducting exploratory research is identifying what the problem is and whether this type of research is the right avenue for you to pursue. Remember that exploratory research is most advantageous when you are investigating a previously unexplored problem.

Step 2: Hypothesize a solution

The next step is to come up with a solution to the problem you’re investigating. Formulate a hypothetical statement to guide your research.

Step 3. Design your methodology

Next, conceptualize your data collection and data analysis methods and write them up in a research design.

Step 4: Collect and analyze data

Next, you proceed with collecting and analyzing your data so you can determine whether your preliminary results are in line with your hypothesis.

In most types of research, you should formulate your hypotheses a priori and refrain from changing them due to the increased risk of Type I errors and data integrity issues. However, in exploratory research, you are allowed to change your hypothesis based on your findings, since you are exploring a previously unexplained phenomenon that could have many explanations.

Step 5: Avenues for future research

Decide if you would like to continue studying your topic. If so, it is likely that you will need to change to another type of research. As exploratory research is often qualitative in nature, you may need to conduct quantitative research with a larger sample size to achieve more generalizable results.

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It can be easy to confuse exploratory research with explanatory research. To understand the relationship, it can help to remember that exploratory research lays the groundwork for later explanatory research.

Exploratory research investigates research questions that have not been studied in depth. The preliminary results often lay the groundwork for future analysis.

Explanatory research questions tend to start with “why” or “how”, and the goal is to explain why or how a previously studied phenomenon takes place.

Like any other research design , exploratory studies have their trade-offs: they provide a unique set of benefits but also come with downsides.

• It can be very helpful in narrowing down a challenging or nebulous problem that has not been previously studied.
• It can serve as a great guide for future research, whether your own or another researcher’s. With new and challenging research problems, adding to the body of research in the early stages can be very fulfilling.
• It is very flexible, cost-effective, and open-ended. You are free to proceed however you think is best.

Disadvantages

• It usually lacks conclusive results, and results can be biased or subjective due to a lack of preexisting knowledge on your topic.
• It’s typically not externally valid and generalizable, and it suffers from many of the challenges of qualitative research .
• Since you are not operating within an existing research paradigm, this type of research can be very labor-intensive.

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.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way.

Exploratory research aims to explore the main aspects of an under-researched problem, while explanatory research aims to explain the causes and consequences of a well-defined problem.

You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

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.

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How to Write a Research Question: Types and Examples 

The first step in any research project is framing the research question. It can be considered the core of any systematic investigation as the research outcomes are tied to asking the right questions. Thus, this primary interrogation point sets the pace for your research as it helps collect relevant and insightful information that ultimately influences your work.   

Typically, the research question guides the stages of inquiry, analysis, and reporting. Depending on the use of quantifiable or quantitative data, research questions are broadly categorized into quantitative or qualitative research questions. Both types of research questions can be used independently or together, considering the overall focus and objectives of your research.  

What is a research question?

A research question is a clear, focused, concise, and arguable question on which your research and writing are centered. 1 It states various aspects of the study, including the population and variables to be studied and the problem the study addresses. These questions also set the boundaries of the study, ensuring cohesion. 

Designing the research question is a dynamic process where the researcher can change or refine the research question as they review related literature and develop a framework for the study. Depending on the scale of your research, the study can include single or multiple research questions. 

A good research question has the following features: 

• It is relevant to the chosen field of study. 
• The question posed is arguable and open for debate, requiring synthesizing and analysis of ideas. 
• It is focused and concisely framed. 
• A feasible solution is possible within the given practical constraint and timeframe. 

A poorly formulated research question poses several risks. 1   

• Researchers can adopt an erroneous design. 
• It can create confusion and hinder the thought process, including developing a clear protocol.  
• It can jeopardize publication efforts.  
• It causes difficulty in determining the relevance of the study findings.  
• It causes difficulty in whether the study fulfils the inclusion criteria for systematic review and meta-analysis. This creates challenges in determining whether additional studies or data collection is needed to answer the question.  
• Readers may fail to understand the objective of the study. This reduces the likelihood of the study being cited by others. 

Now that you know “What is a research question?”, let’s look at the different types of research questions. 

Types of research questions

Depending on the type of research to be done, research questions can be classified broadly into quantitative, qualitative, or mixed-methods studies. Knowing the type of research helps determine the best type of research question that reflects the direction and epistemological underpinnings of your research. 

The structure and wording of quantitative 2 and qualitative research 3 questions differ significantly. The quantitative study looks at causal relationships, whereas the qualitative study aims at exploring a phenomenon. 

• Quantitative research questions:  
• Seeks to investigate social, familial, or educational experiences or processes in a particular context and/or location.  
• Answers ‘how,’ ‘what,’ or ‘why’ questions. 
• Investigates connections, relations, or comparisons between independent and dependent variables. 

Quantitative research questions can be further categorized into descriptive, comparative, and relationship, as explained in the Table below. 

• Qualitative research questions  

Qualitative research questions are adaptable, non-directional, and more flexible. It concerns broad areas of research or more specific areas of study to discover, explain, or explore a phenomenon. These are further classified as follows: 

• Mixed-methods studies  

Mixed-methods studies use both quantitative and qualitative research questions to answer your research question. Mixed methods provide a complete picture than standalone quantitative or qualitative research, as it integrates the benefits of both methods. Mixed methods research is often used in multidisciplinary settings and complex situational or societal research, especially in the behavioral, health, and social science fields. 

What makes a good research question

A good research question should be clear and focused to guide your research. It should synthesize multiple sources to present your unique argument, and should ideally be something that you are interested in. But avoid questions that can be answered in a few factual statements. The following are the main attributes of a good research question. 

• Specific: The research question should not be a fishing expedition performed in the hopes that some new information will be found that will benefit the researcher. The central research question should work with your research problem to keep your work focused. If using multiple questions, they should all tie back to the central aim. 
• Measurable: The research question must be answerable using quantitative and/or qualitative data or from scholarly sources to develop your research question. If such data is impossible to access, it is better to rethink your question. 
• Attainable: Ensure you have enough time and resources to do all research required to answer your question. If it seems you will not be able to gain access to the data you need, consider narrowing down your question to be more specific. 
• You have the expertise 
• You have the equipment and resources 
• Realistic: Developing your research question should be based on initial reading about your topic. It should focus on addressing a problem or gap in the existing knowledge in your field or discipline. 
• Based on some sort of rational physics 
• Can be done in a reasonable time frame 
• Timely: The research question should contribute to an existing and current debate in your field or in society at large. It should produce knowledge that future researchers or practitioners can later build on. 
• Novel 
• Based on current technologies. 
• Important to answer current problems or concerns. 
• Lead to new directions. 
• Important: Your question should have some aspect of originality. Incremental research is as important as exploring disruptive technologies. For example, you can focus on a specific location or explore a new angle. 
• Meaningful whether the answer is “Yes” or “No.” Closed-ended, yes/no questions are too simple to work as good research questions. Such questions do not provide enough scope for robust investigation and discussion. A good research question requires original data, synthesis of multiple sources, and original interpretation and argumentation before providing an answer. 

Steps for developing a good research question

The importance of research questions cannot be understated. When drafting a research question, use the following frameworks to guide the components of your question to ease the process. 4  

• Determine the requirements: Before constructing a good research question, set your research requirements. What is the purpose? Is it descriptive, comparative, or explorative research? Determining the research aim will help you choose the most appropriate topic and word your question appropriately. 
• Select a broad research topic: Identify a broader subject area of interest that requires investigation. Techniques such as brainstorming or concept mapping can help identify relevant connections and themes within a broad research topic. For example, how to learn and help students learn. 
• Perform preliminary investigation: Preliminary research is needed to obtain up-to-date and relevant knowledge on your topic. It also helps identify issues currently being discussed from which information gaps can be identified. 
• Narrow your focus: Narrow the scope and focus of your research to a specific niche. This involves focusing on gaps in existing knowledge or recent literature or extending or complementing the findings of existing literature. Another approach involves constructing strong research questions that challenge your views or knowledge of the area of study (Example: Is learning consistent with the existing learning theory and research). 
• Identify the research problem: Once the research question has been framed, one should evaluate it. This is to realize the importance of the research questions and if there is a need for more revising (Example: How do your beliefs on learning theory and research impact your instructional practices). 

How to write a research question

Those struggling to understand how to write a research question, these simple steps can help you simplify the process of writing a research question. 

Sample Research Questions

The following are some bad and good research question examples 

• Example 1 

• Example 2 

References:  

• Thabane, L., Thomas, T., Ye, C., & Paul, J. (2009). Posing the research question: not so simple.  Canadian Journal of Anesthesia/Journal canadien d’anesthésie ,  56 (1), 71-79. 
• Rutberg, S., & Bouikidis, C. D. (2018). Focusing on the fundamentals: A simplistic differentiation between qualitative and quantitative research.  Nephrology Nursing Journal ,  45 (2), 209-213. 
• Kyngäs, H. (2020). Qualitative research and content analysis.  The application of content analysis in nursing science research , 3-11. 
• Mattick, K., Johnston, J., & de la Croix, A. (2018). How to… write a good research question.  The clinical teacher ,  15 (2), 104-108. 
• Fandino, W. (2019). Formulating a good research question: Pearls and pitfalls.  Indian Journal of Anaesthesia ,  63 (8), 611. 
• Richardson, W. S., Wilson, M. C., Nishikawa, J., & Hayward, R. S. (1995). The well-built clinical question: a key to evidence-based decisions.  ACP journal club ,  123 (3), A12-A13 

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Research Essentials

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Developing a Research Question

For more information on developing a research question, check out this video from the Laurier Library. 

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Choose an area of interest to explore. 

For you to successfully finish a research project, it is important to choose a research topic that is relevant to your field of study and piques your curiosity. The flip side is that curiosity can take you down long and winding paths, so you also need to consider scope in how to effectively cover the topic in the space that you have available. If there's an idea or concept you've recently learned that's stuck with you, that might be a good place to start !

Gather background information.

You may not know right away what your research question is - that's okay! Start out with a broad topic, and see what information is out there through cursory background research. This will help you explore possibilities and narrow your topic to something manageable.    Do a few quick searches in OneSearch@IU  or in other relevant sources. See what other researchers have already written to help narrow your focus.  

Narrow your topic.

  Once you have a sense of how other researchers are talking about the topics you’re interested, narrow down your topic by asking the 5 Ws

• Who – population or group (e.g., working class, college students, Native Americans)
• What – discipline or focus (e.g., anthropological or art history)
• Where – geographic location (e.g., United States; universities; small towns; Standing Rock)
• When – time period or era (17 th century; contemporary; 2017)
• Why – why is the topic important? (to the class, to the field, or to you)

Broad topic: Native American representations in museums

Narrowed topic: Museum efforts to adhere to NAGPRA

Adapted from: University of Michigan. (2023 Finding and Exploring your topic. Retrieved from  https://guides.lib.umich.edu/c.php?g=283095&p=1886086

So, you have done some background research and narrowed down your topic. Now what? Start to turn that topic into a series of questions that you will attempt to answer the course of your research.  Keep in mind that you will probably end up changing and adjusting the question(s) you have as you gather more information and synthesize it in your writing. However, having a clear line of inquiry can help you maintain a sense of your direction, which will then in turn help you evaluate sources and identify relevant information throughout your research process. 

Exploratory questions.

These are the questions that comes from a genuine curiosity about your topic. When narrowing down your topic, you got a good sense of the Who, What, When, and Where of things. Now it’s time to consider

• Asking open-ended “how” and “why” questions about your general topic, which can lead you to better explanations about a phenomenon or concept
• Consider the “so what?” of your topic. Why does this topic matter to you? Why should it matter to others? What are the implications of the information you’re discovering through the search process to the Who and the What of your topic?

Evaluate your research question.

Use the following to determine if any of the questions you generated would be appropriate and workable for your assignment. 

• Is your question clear ? Do you have a specific aspect of your general topic that you are going to explore further? Will the reader of your research be able to keep it in mind?
• Is your question focused? Will you be able to cover the topic adequately in the space available? Are you able to concisely ask the question?
• Is your question and arguable ? If it can be answered with a simple Yes or No, then dig deeper. Once you get to “it depends on X, Y, and Z” then you might be getting on the right track.

Hypothesize. 

Once you have developed your research question, consider how you will attempt to answer or address it. 

• What connections can you make between the research you’ve read and your research question? Why do those connections matter?
• What other kinds of sources will you need in order to support your argument?
• If someone refutes the answer to your research question, what is your argument to back up your conclusion?
• How might others challenge your argument? Why do those challenges ultimately not hold water?

Adapted from: George Mason University Writing Center. (2018). How to write a research question. Retrieved from  https://writingcenter.gmu.edu/writing-resources/research-based-writing/how-to-write-a-research-question

A good research question is clear, focused, and has an appropriate level of complexity. Developing a strong question is a process, so you will likely refine your question as you continue to research and to develop your ideas.  

Unclear : Why are social networking sites harmful?

Clear:  How are online users experiencing or addressing privacy issues on such social networking sites as Facebook and TikTok?

Unfocused:  What is the effect on the environment from global warming?

Focused:  How is glacial melting affecting penguins in Antarctica?

Simple vs Complex

Too simple:  How are doctors addressing diabetes in the U.S.?

Appropriately Complex:   What are common traits of those suffering from diabetes in America, and how can these commonalities be used to aid the medical community in prevention of the disease?

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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.

55 research questions about mental health

Last updated

11 March 2024

Reviewed by

Brittany Ferri, PhD, OTR/L

Short on time? Get an AI generated summary of this article instead

Research in the mental health space helps fill knowledge gaps and create a fuller picture for patients, healthcare professionals, and policymakers. Over time, these efforts result in better quality care and more accessible treatment options for those who need them.

Use this list of mental health research questions to kickstart your next project or assignment and give yourself the best chance of producing successful and fulfilling research.

• Why does mental health research matter?

Mental health research is an essential area of study. It includes any research that focuses on topics related to people’s mental and emotional well-being.

As a complex health topic that, despite the prevalence of mental health conditions, still has an unending number of unanswered questions, the need for thorough research into causes, triggers, and treatment options is clear.

Research into this heavily stigmatized and often misunderstood topic is needed to find better ways to support people struggling with mental health conditions. Understanding what causes them is another crucial area of study, as it enables individuals, companies, and policymakers to make well-informed choices that can help prevent illnesses like anxiety and depression.

• How to choose a strong mental health research topic

As one of the most important parts of beginning a new research project, picking a topic that is intriguing, unique, and in demand is a great way to get the best results from your efforts.

Mental health is a blanket term with many niches and specific areas to explore. But, no matter which direction you choose, follow the tips below to ensure you pick the right topic.

Prioritize your interests and skills

While a big part of research is exploring a new and exciting topic, this exploration is best done within a topic or niche in which you are interested and experienced.

Research is tough, even at the best of times. To combat fatigue and increase your chances of pushing through to the finish line, we recommend choosing a topic that aligns with your personal interests, training, or skill set.

Consider emerging trends

Topical and current research questions are hot commodities because they offer solutions and insights into culturally and socially relevant problems.

Depending on the scope and level of freedom you have with your upcoming research project, choosing a topic that’s trending in your area of study is one way to get support and funding (if you need it).

Not every study can be based on a cutting-edge topic, but this can be a great way to explore a new space and create baseline research data for future studies.

Assess your resources and timeline

Before choosing a super ambitious and exciting research topic, consider your project restrictions.

You’ll need to think about things like your research timeline, access to resources and funding, and expected project scope when deciding how broad your research topic will be. In most cases, it’s better to start small and focus on a specific area of study.

Broad research projects are expensive and labor and resource-intensive. They can take years or even decades to complete. Before biting off more than you can chew, consider your scope and find a research question that fits within it.

Read up on the latest research

Finally, once you have narrowed in on a specific topic, you need to read up on the latest studies and published research. A thorough research assessment is a great way to gain some background context on your chosen topic and stops you from repeating a study design. Using the existing work as your guide, you can explore more specific and niche questions to provide highly beneficial answers and insights.

• Trending research questions for post-secondary students

As a post-secondary student, finding interesting research questions that fit within the scope of your classes or resources can be challenging. But, with a little bit of effort and pre-planning, you can find unique mental health research topics that will meet your class or project requirements.

Examples of research topics for post-secondary students include the following:

How does school-related stress impact a person’s mental health?

To what extent does burnout impact mental health in medical students?

How does chronic school stress impact a student’s physical health?

How does exam season affect the severity of mental health symptoms?

Is mental health counseling effective for students in an acute mental crisis?

• Research questions about anxiety and depression

Anxiety and depression are two of the most commonly spoken about mental health conditions. You might assume that research about these conditions has already been exhausted or that it’s no longer in demand. That’s not the case at all.

According to a 2022 survey by Centers for Disease Control and Prevention (CDC), 12.5% of American adults struggle with regular feelings of worry, nervousness, and anxiety, and 5% struggle with regular feelings of depression. These percentages amount to millions of lives affected, meaning new research into these conditions is essential.

If either of these topics interests you, here are a few trending research questions you could consider:

Does gender play a role in the early diagnosis of anxiety?

How does untreated anxiety impact quality of life?

What are the most common symptoms of anxiety in working professionals aged 20–29?

To what extent do treatment delays impact quality of life in patients with undiagnosed anxiety?

To what extent does stigma affect the quality of care received by people with anxiety?

Here are some examples of research questions about depression:

Does diet play a role in the severity of depression symptoms?

Can people have a genetic predisposition to developing depression?

How common is depression in work-from-home employees?

Does mood journaling help manage depression symptoms?

What role does exercise play in the management of depression symptoms?

• Research questions about personality disorders

Personality disorders are complex mental health conditions tied to a person’s behaviors, sense of self, and how they interact with the world around them. Without a diagnosis and treatment, people with personality disorders are more likely to develop negative coping strategies during periods of stress and adversity, which can impact their quality of life and relationships.

There’s no shortage of specific research questions in this category. Here are some examples of research questions about personality disorders that you could explore:

What environments are more likely to trigger the development of a personality disorder?

What barriers impact access to care for people with personality disorders?

To what extent does undiagnosed borderline personality disorder impact a person’s ability to build relationships?

How does group therapy impact symptom severity in people with schizotypal personality disorder?

What is the treatment compliance rate of people with paranoid personality disorder?

• Research questions about substance use disorders

“Substance use disorders” is a blanket term for treatable behaviors and patterns within a person’s brain that lead them to become dependent on illicit drugs, alcohol, or prescription medications. It’s one of the most stigmatized mental health categories.

The severity of a person’s symptoms and how they impact their ability to participate in their regular daily life can vary significantly from person to person. But, even in less severe cases, people with a substance use disorder display some level of loss of control due to their need to use the substance they are dependent on.

This is an ever-evolving topic where research is in hot demand. Here are some example research questions:

To what extent do meditation practices help with craving management?

How effective are detox centers in treating acute substance use disorder?

Are there genetic factors that increase a person’s chances of developing a substance use disorder?

How prevalent are substance use disorders in immigrant populations?

To what extent do prescription medications play a role in developing substance use disorders?

• Research questions about mental health treatments

Treatments for mental health, pharmaceutical therapies in particular, are a common topic for research and exploration in this space.

Besides the clinical trials required for a drug to receive FDA approval, studies into the efficacy, risks, and patient experiences are essential to better understand mental health therapies.

These types of studies can easily become large in scope, but it’s possible to conduct small cohort research on mental health therapies that can provide helpful insights into the actual experiences of the people receiving these treatments.

Here are some questions you might consider:

What are the long-term effects of electroconvulsive therapy (ECT) for patients with severe depression?

How common is insomnia as a side effect of oral mental health medications?

What are the most common causes of non-compliance for mental health treatments?

How long does it take for patients to report noticeable changes in symptom severity after starting injectable mental health medications?

What issues are most common when weaning a patient off of an anxiety medication?

• Controversial mental health research questions

If you’re interested in exploring more cutting-edge research topics, you might consider one that’s “controversial.”

Depending on your own personal values, you might not think many of these topics are controversial. In the context of the research environment, this depends on the perspectives of your project lead and the desires of your sponsors. These topics may not align with the preferred subject matter.

That being said, that doesn’t make them any less worth exploring. In many cases, it makes them more worthwhile, as they encourage people to ask questions and think critically.

Here are just a few examples of “controversial” mental health research questions:

To what extent do financial crises impact mental health in young adults?

How have climate concerns impacted anxiety levels in young adults?

To what extent do psychotropic drugs help patients struggling with anxiety and depression?

To what extent does political reform impact the mental health of LGBTQ+ people?

What mental health supports should be available for the families of people who opt for medically assisted dying?

• Research questions about socioeconomic factors & mental health

Socioeconomic factors—like where a person grew up, their annual income, the communities they are exposed to, and the amount, type, and quality of mental health resources they have access to—significantly impact overall health.

This is a complex and multifaceted issue. Choosing a research question that addresses these topics can help researchers, experts, and policymakers provide more equitable and accessible care over time.

Examples of questions that tackle socioeconomic factors and mental health include the following:

How does sliding scale pricing for therapy increase retention rates?

What is the average cost to access acute mental health crisis care in [a specific region]?

To what extent does a person’s environment impact their risk of developing a mental health condition?

How does mental health stigma impact early detection of mental health conditions?

To what extent does discrimination affect the mental health of LGBTQ+ people?

• Research questions about the benefits of therapy

Therapy, whether that’s in groups or one-to-one sessions, is one of the most commonly utilized resources for managing mental health conditions. It can help support long-term healing and the development of coping mechanisms.

Yet, despite its popularity, more research is needed to properly understand its benefits and limitations.

Here are some therapy-based questions you could consider to inspire your own research:

In what instances does group therapy benefit people more than solo sessions?

How effective is cognitive behavioral therapy for patients with severe anxiety?

After how many therapy sessions do people report feeling a better sense of self?

Does including meditation reminders during therapy improve patient outcomes?

To what extent has virtual therapy improved access to mental health resources in rural areas?

• Research questions about mental health trends in teens

Adolescents are a particularly interesting group for mental health research due to the prevalence of early-onset mental health symptoms in this age group.

As a time of self-discovery and change, puberty brings plenty of stress, anxiety, and hardships, all of which can contribute to worsening mental health symptoms.

If you’re looking to learn more about how to support this age group with mental health, here are some examples of questions you could explore:

Does parenting style impact anxiety rates in teens?

How early should teenagers receive mental health treatment?

To what extent does cyberbullying impact adolescent mental health?

What are the most common harmful coping mechanisms explored by teens?

How have smartphones affected teenagers’ self-worth and sense of self?

• Research questions about social media and mental health

Social media platforms like TikTok, Instagram, YouTube, Facebook, and X (formerly Twitter) have significantly impacted day-to-day communication. However, despite their numerous benefits and uses, they have also become a significant source of stress, anxiety, and self-worth issues for those who use them.

These platforms have been around for a while now, but research on their impact is still in its infancy. Are you interested in building knowledge about this ever-changing topic? Here are some examples of social media research questions you could consider:

To what extent does TikTok’s mental health content impact people’s perception of their health?

How much non-professional mental health content is created on social media platforms?

How has social media content increased the likelihood of a teen self-identifying themselves with ADHD or autism?

To what extent do social media photoshopped images impact body image and self-worth?

Has social media access increased feelings of anxiety and dread in young adults?

• Mental health research is incredibly important

As you have seen, there are so many unique mental health research questions worth exploring. Which options are piquing your interest?

Whether you are a university student considering your next paper topic or a professional looking to explore a new area of study, mental health is an exciting and ever-changing area of research to get involved with.

Your research will be valuable, no matter how big or small. As a niche area of healthcare still shrouded in stigma, any insights you gain into new ways to support, treat, or identify mental health triggers and trends are a net positive for millions of people worldwide.

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In-depth Interviews: Definition and how to conduct them

Online surveys, user review sites and focus groups can be great methods for collecting data. However, another method of gathering data that is sometimes overlooked are the in-depth interviews.

All of these methods can be used in your comprehensive customer experience management strategy, but in-depth interviews can help you collect data that can offer rich insights into your target audience’s experience and preferences from a broad sample.

In this article you will discover the main characteristics of in-depth interviews as a great tool for your qualitative research and gather better insights from your objects of study.

LEARN ABOUT: Behavioral Research

What are in-depth interviews?

In-depth interviews are a qualitative data collection method that allows for the collection of a large amount of information about the behavior, attitude and perception of the interviewees.

LEARN ABOUT: Best Data Collection Tools

During in-depth interviews, researchers and participants have the freedom to explore additional points and change the direction of the process when necessary. It is an independent research method that can adopt multiple strategies according to the needs of the research.

Characteristics of in-depth interviews

There are many types of interviews , each with its particularities, in this case the most important characteristics of in-depth interviews are:

• Flexible structure: Although it is not very structured, it covers a few topics based on a guide, which allows the interviewer to cover areas appropriate for the interviewee.
• Interactive: The interviewer processes the material that is produced during the interview. During the interaction the interviewer poses initial questions in a positive manner, so that the respondent is encouraged to answer. The complete process is very human, and so less mundane and dull.
• Deep: Many probing techniques are used in in-depth interviews, so that results are understood through exploration and explanation. The interviewer asks follow-up questions to gain a deeper perspective and understand the participant’s viewpoint.
• Generative: Often interacting with your target audience creates new knowledge. For instance, if you are talking to your customers, you learn more about the purchase behavior. Researchers and participants present ideas for a specific topic and solutions to the problems posed.

To learn more about the characteristics of in-depth interviews, check out our blog on interview questions .

Importance of conducting in-depth interviews

As an in-depth interview is a one-on-one conversation, you get enough opportunities to get to the root causes of likes/dislikes, perceptions, or beliefs. 

Generally, questions are open-ended questions and can be customized as per the particular situation. You can use single ease questions . A single-ease question is a straightforward query that elicits a concise and uncomplicated response. The interviewer gets an opportunity to develop a rapport with the participant, thereby making them feel comfortable. Thus, they can bring out honest feedback and also note their expressions and body language. Such cues can amount to rich qualitative data.

LEARN ABOUT: Selection Bias

With surveys, there are chances that the respondents may select answers in a rush, but in case of in-depth interviews it’s hardly the worry of researchers. 

Conversations can prove to be an excellent method to collect data. In fact, people might be reluctant to answer questions in written format, but given the nature of an interview, participants might agree giving information verbally. You can also discuss with the interviewees if they want to keep their identity confidential.

In-depth interviews are aimed at uncovering the issues in order to obtain detailed results. This method allows you to gain insight into the experiences, feelings and perspectives of the interviewees.

When conducting the initial stage of a large research project, in-depth interviews prove to be useful to narrow down and focus on important research details.

When you want to have the context of a problem, in-depth interviews allow you to evaluate different solutions to manage the research process while assisting in in-depth data analysis .

LEARN ABOUT: Research Process Steps

Steps to conduct in-depth interviews

• Obtain the necessary information about the respondents and the context in which they operate.
• Make a script or a list of topics you want to cover. This will make it easy to add secondary questions.
• Schedule an interview at a time and date of the respondent’s choice.
• Ask questions confidently and let the interviewees feel comfortable, so that they too are confident and can answer difficult questions with ease.
• Set a maximum duration such that it doesn’t feel exhaustive.
• Observe and make notes on the interviewee’s body expressions and gestures.
• It is important to maintain ethics throughout the process.
• Transcribe the recordings and verify them with the interviewee.

Advantages of in-depth interviews

The benefits of conducting an in-depth interview include the following:

• They allow the researcher and participants to have a comfortable relationship to generate more in-depth responses regarding sensitive topics.
• Researchers can ask follow-up questions , obtain additional information, and return to key questions to gain a better understanding of the participants’ attitudes.
• The sampling is more accurate than other data collection methods .
• Researchers can monitor changes in tone and word choice of participants to gain a better understanding of opinions.
• Fewer participants are needed to obtain useful information. 
• In-depth interviews can be very beneficial when a detailed report on a person’s opinion and behavior is needed. In addition, it explores new ideas and contexts that give the researcher a complete picture of the phenomena that occurred.

Disadvantages 

The disadvantages of in-depth interviews are:

• They are time-consuming, as they must be transcribed, organized, analyzed in detail.
• If the interviewer is inexperienced, it affects the complete process.
• It is a costly research method compared to other methods.
• Participants must be chosen carefully to avoid bias, otherwise it can lengthen the process.
• Generally, participants decide to collaborate only when they receive an incentive in return.

LEARN ABOUT: Self-Selection Bias

What is the purpose of in-depth interviews?

The main purpose of in-depth interviews is to understand the consumer behavior and make well-informed decisions. Organizations can formulate their marketing strategies based on the information received from the respondents. They can also gain insights into the probable demand and know consumer pulse.

In the case of B2B businesses, researchers can understand the demand in more detail and can ask questions targeted for the experts. Interviews offer a chance to understand the customer’s thought process and design products that have higher chances of being accepted in the market.

LEARN ABOUT: 12 Best Tools for Researchers

Final words

An in-depth interview should follow all the steps of the process to collect meaningful data. Hope this blog helps you decide whether you should conduct a detailed interview with your target audience, keeping in mind the pros and cons of it.

If you want to get started with conducting research online, we suggest using an online survey software that offers features like designing a questionnaire , customized look and feel, distributing to your contacts and data analytics. Create an account with QuestionPro Surveys and explore the tool. If you need any help with research or data collection, feel free to connect with us.

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How to Write Qualitative Research Questions: Types & Examples

Sameer Bhatia

Founder and CEO - ProProfs

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Sameer Bhatia is the Founder and Chief Executive Officer of ProProfs.com. He believes that software should make you happy and is driven to create a 100-year company that delivers delightfully ... Read more

Sameer Bhatia is the Founder and Chief Executive Officer of ProProfs.com. He believes that software should make you happy and is driven to create a 100-year company that delivers delightfully smart software with awesome support. His favorite word is 'delight,' and he dislikes the term 'customer satisfaction,' as he believes that 'satisfaction' is a low bar and users must get nothing less than a delightful experience at ProProfs. Sameer holds a Masters in Computer Science from the University of Southern California (USC). He lives in Santa Monica with his wife & two daughters. Read less

Market Research Specialist

Emma David, a seasoned market research professional, specializes in employee engagement, survey administration, and data management. Her expertise in leveraging data for informed decisions has positively impacted several brands, enhancing their market position.

Qualitative research questions focus on depth and quality, exploring the “why and how” behind decisions, without relying on statistical tools.

Unlike quantitative research, which aims to collect tangible, measurable data from a broader demographic, qualitative analysis involves smaller, focused datasets, identifying patterns for insights.

The information collected by qualitative surveys can vary from text to images, demanding a deep understanding of the subject, and therefore, crafting precise qualitative research questions is crucial for success.

In this guide, we’ll discuss how to write effective qualitative research questions, explore various types, and highlight characteristics of good qualitative research questions.

Let’s dive in!

What Are Qualitative Research Questions?

Qualitative questions aim to understand the depth and nuances of a phenomenon, focusing on “why” and “how” rather than quantifiable measures.

They explore subjective experiences, perspectives, and behaviors, often using open-ended inquiries to gather rich, descriptive data.

Unlike quantitative questions, which seek numerical data, qualitative questions try to find out meanings, patterns, and underlying processes within a specific context.

These questions are essential for exploring complex issues, generating hypotheses, and gaining deeper insights into human behavior and phenomena.

Here’s an example of a qualitative research question:

“How do you perceive and navigate organizational culture within a tech startup environment?”

This question asks about the respondent’s subjective interpretations and experiences of organizational culture within a specific context, such as a tech startup.

It seeks to uncover insights into the values, norms, and practices that shape workplace dynamics and employee behaviors, providing qualitative data for analysis and understanding.

When Should We Use Qualitative Research Questions?

Qualitative research questions typically aim to open up conversations, encourage detailed narratives, and foster a deep understanding of the subject matter. Here are some scenarios they are best suited for:

• Exploring Complex Phenomena : When the research topic involves understanding complex processes, behaviors, or interactions that cannot be quantified easily, qualitative questions help delve into these intricate details.
• Understanding Contexts and Cultures : To grasp the nuances of different social contexts, cultures, or subcultures, qualitative research questions allow for an in-depth exploration of these environments and how they influence individuals and groups.
• Exploring Perceptions and Experiences : When the aim is to understand people’s perceptions, experiences, or feelings about a particular subject, qualitative questions facilitate capturing the depth and variety of these perspectives.
• Developing Concepts or Theories : In the early stages of research, where concepts or theories are not yet well-developed, qualitative questions can help generate hypotheses, identify variables, and develop theoretical frameworks based on observations and interpretations.
• Investigating Processes : To understand how processes unfold over time and the factors that influence these processes, qualitative questions are useful for capturing the dynamics and complexities involved.
• Seeking to Understand Change : When researching how individuals or groups experience change, adapt to new circumstances, or make decisions, qualitative research questions can provide insights into the motivations, challenges, and strategies involved.
• Studying Phenomena Not Easily Quantified : For phenomena that are not easily captured through quantitative measures, such as emotions, beliefs, or motivations, qualitative questions can probe these abstract concepts more effectively.
• Addressing Sensitive or Taboo Topics : In studies where topics may be sensitive, controversial, or taboo, qualitative research questions allow for a respectful and empathetic exploration of these subjects, providing space for participants to share their experiences in their own words.

How to Write Qualitative Research Questions?

Read this guide to learn how you can craft well-thought-out qualitative research questions:

1. Begin with Your Research Goals

The first step in formulating qualitative research questions is to have a clear understanding of what you aim to discover or understand through your research. There are two types of qualitative questionnaires or research – Ontological and Epistemological.

Finding out the nature of your research influences all aspects of your research design, including the formulation of research questions.

Subsequently:

• Identify your main objective : Consider the broader context of your study. Are you trying to explore a phenomenon, understand a process, or interpret the meanings behind behaviors? Your main objective should guide the formulation of your questions, ensuring they are aligned with what you seek to achieve.
• Focus on the ‘how’ and ‘why’ : Qualitative research is inherently exploratory and aims to understand the nuances of human behavior and experience. Starting your questions with “how” or “why” encourages a deeper investigation into the motivations, processes, and contexts underlying the subject matter. This approach facilitates an open-ended exploration, allowing participants to provide rich, detailed responses that illuminate their perspectives and experiences.

Take a quick look at the following visual for a better understanding:

So, if you are doing Ontological research, ensure that the questions focus on the “what” aspects of reality (the premise of your research) and opt for the nature of the knowledge for Epistemological research.

2. Choose the Right Structure

The structure of your research questions significantly impacts the depth and quality of data you collect. Opting for an open-ended format allows respondents the flexibility to express themselves freely, providing insights that pre-defined answers might miss.

• Open-ended format : These questions do not constrain respondents to a set of predetermined answers, unlike closed-ended questions. By allowing participants to articulate their thoughts in their own words, you can uncover nuances and complexities in their responses that might otherwise be overlooked.
• Avoid yes/no questions : Yes/no questions tend to limit the depth of responses. While they might be useful for gathering straightforward factual information, they are not conducive to exploring the depths and nuances that qualitative research seeks to uncover. Encouraging participants to elaborate on their experiences and perspectives leads to richer, more informative data.

For example, take a look at some qualitative questions examples shown in the following image:

3. Be Clear and Specific

Clarity and specificity in your questions are crucial to ensure that participants understand what is being asked and that their responses are relevant to your research objectives.

• Use clear language : Use straightforward, understandable language in your questions. Avoid jargon, acronyms, or overly technical terms that might confuse participants or lead to misinterpretation. The goal is to make your questions accessible to everyone involved in your study.
• Be specific : While maintaining the open-ended nature of qualitative questions, it’s important to narrow down your focus to specific aspects of the phenomenon you’re studying. This specificity helps guide participants’ responses and ensures that the data you collect directly relates to your research objectives.

4. Ensure Relevance and Feasibility

Each question should be carefully considered for its relevance to your research goals and its feasibility, given the constraints of your study.

• Relevance : Questions should be crafted to address the core objectives of your research directly. They should probe areas that are essential to understanding the phenomenon under investigation and should align with your theoretical framework or literature review findings.
• Feasibility : Consider the practical aspects of your research, including the time available for data collection and analysis, resources, and access to participants. Questions should be designed to elicit meaningful responses within the constraints of your study, ensuring that you can gather and analyze data effectively.

5. Focus on a Single Concept or Theme per Question

To ensure clarity and depth, each question should concentrate on a single idea or theme. However, if your main qualitative research question is tough to understand or has a complex structure, you can create sub-questions in limited numbers and with a “ladder structure”.

This will help your respondents understand the overall research objective in mind, and your research can be executed in a better manner.

For example, suppose your main question is – “What is the current state of illiteracy in your state?”

Then, you can create the following subquestions: 

“How does illiteracy block progress in your state?”

“How would you best describe the feelings you have about illiteracy in your state?”

For an even better understanding, you can see the various qualitative research question examples in the following image:

Types of Qualitative Research Questions With Examples

Qualitative survey questions primarily focus on a specific group of respondents that are participating in case studies, surveys, ethnography studies, etc., rather than numbers or statistics.

As a result, the questions are mostly open-ended and can be subdivided into the following types as discussed below:

1. Descriptive Questions

Descriptive research questions aim to detail the “what” of a phenomenon, providing a comprehensive overview of the context, individuals, or situations under study. These questions are foundational, helping to establish a baseline understanding of the research topic.

• What are the daily experiences of teachers in urban elementary schools?
• What strategies do small businesses employ to adapt to rapid technological changes?
• How do young adults describe their transition from college to the workforce?
• What are the coping mechanisms of families with members suffering from chronic illnesses?
• How do community leaders perceive the impact of gentrification in their neighborhoods?

2. Interpretive Questions

Interpretive questions seek to understand the “how” and “why” behind a phenomenon, focusing on the meanings people attach to their experiences. These questions delve into the subjective interpretations and perceptions of participants.

• How do survivors of natural disasters interpret their experiences of recovery and rebuilding?
• Why do individuals engage in voluntary work within their communities?
• How do parents interpret and navigate the challenges of remote schooling for their children?
• Why do consumers prefer local products over global brands in certain markets?
• How do artists interpret the influence of digital media on traditional art forms?

3. Comparative Questions

Comparative research questions are designed to explore differences and similarities between groups, settings, or time periods. These questions can help to highlight the impact of specific variables on the phenomenon under study.

• How do the strategies for managing work-life balance compare between remote and office workers?
• What are the differences in consumer behavior towards sustainable products in urban versus rural areas?
• How do parenting styles in single-parent households compare to those in dual-parent households?
• What are the similarities and differences in leadership styles across different cultures?
• How has the perception of online privacy changed among teenagers over the past decade?

4. Process-oriented Questions

These questions focus on understanding the processes or sequences of events over time. They aim to uncover the “how” of a phenomenon, tracing the development, changes, or evolution of specific situations or behaviors.

• How do non-profit organizations develop and implement community outreach programs?
• What is the process of decision-making in high-stakes business environments?
• How do individuals navigate the process of career transition after significant industry changes?
• What are the stages of adaptation for immigrants in a new country?
• How do social movements evolve from inception to national recognition?

5. Evaluative Questions

Evaluative questions aim to assess the effectiveness, value, or impact of a program, policy, or phenomenon. These questions are critical for understanding the outcomes and implications of various initiatives or situations.

• How effective are online therapy sessions compared to in-person sessions in treating anxiety?
• What is the impact of community gardening programs on neighborhood cohesion?
• How do participants evaluate the outcomes of leadership training programs in their professional development?
• What are the perceived benefits and drawbacks of telecommuting for employees and employers?
• How do residents evaluate the effectiveness of local government policies on waste management?

6. One-on-One Questions

The one-on-one questions are asked to a single person and can be thought of as individual interviews that you can conduct online via phone and video chat as well.

The main aim of such questions is to ask your customers or people in the focus group a series of questions about their purchase motivations. These questions might also come with follow-ups, and if your customers respond with some interesting fact or detail, dig deeper and explore the findings as much as you want.

• What makes you happy in regard to [your research topic]?
• If I could make a wish of yours come true, what do you desire the most?
• What do you still find hard to come to terms with?
• Have you bought [your product] before?
• If so, what was your initial motivation behind the purchase?

7. Exploratory Questions

These questions are designed to enhance your understanding of a particular topic. However, while asking exploratory questions, you must ensure that there are no preconceived notions or biases to it. The more transparent and bias-free your questions are, the better and fair results you will get.

• What is the effect of personal smart devices on today’s youth?
• Do you feel that smart devices have positively or negatively impacted you?
• How do your kids spend their weekends?
• What do you do on a typical weekend morning?

8. Predictive Questions

The predictive questions are used for qualitative research that is focused on the future outcomes of an action or a series of actions. So, you will be using past information to predict the reactions of respondents to hypothetical events that might or might not happen in the future.

These questions come in extremely handy for identifying your customers’ current brand expectations, pain points, and purchase motivation.

• Are you more likely to buy a product when a celebrity promotes it?
• Would you ever try a new product because one of your favorite celebs claims that it actually worked for them?
• Would people in your neighborhood enjoy a park with rides and exercise options?
• How often would you go to a park with your kids if it had free rides?

9. Focus Groups

These questions are mostly asked in person to the customer or respondent groups. The in-person nature of these surveys or studies ensures that the group members get a safe and comfortable environment to express their thoughts and feelings about your brand or services.

• How would you describe your ease of using our product?
• How well do you think you were able to do this task before you started using our product?
• What do you like about our promotional campaigns?
• How well do you think our ads convey the meaning?

10. In-Home Videos

Collecting video feedback from customers in their comfortable, natural settings offers a unique perspective. At home, customers are more relaxed and less concerned about their mannerisms, posture, and choice of words when responding.

This approach is partly why Vogue’s 73 Questions Series is highly popular among celebrities and viewers alike. In-home videos provide insights into customers in a relaxed environment, encouraging them to be honest and share genuine experiences.

• What was your first reaction when you used our product for the first time?
• How well do you think our product performed compared to your expectations?
• What was your worst experience with our product?
• What made you switch to our brand?

11. Online Focus Groups

Online focus groups mirror the traditional, in-person format but are conducted virtually, offering a more cost-effective and efficient approach to gathering data. This digital format extends your reach and allows a rapid collection of responses from a broader audience through online platforms.

You can utilize social media and other digital forums to create communities of respondents and initiate meaningful discussions. Once you have them started, you can simply observe the exchange of thoughts and gather massive amounts of interesting insights!

• What do you like best about our product?
• How familiar are you with this particular service or product we offer?
• What are your concerns with our product?
• What changes can we make to make our product better?

Ask the Right Qualitative Research Questions for Meaningful Insights From Your Respondents

Watch: How to Create a Survey Using ProProfs Survey Maker

By now, you might have realized that manually creating a list of qualitative research questions is a daunting task. Keeping numerous considerations in mind, it’s easy to run out of ideas while crafting qualitative survey questions .

However, investing in smart survey tools, like ProProfs Survey Maker, can significantly streamline this process, allowing you to create various types of surveys in minutes.

With this survey tool , you can generate forms, NPS surveys , tests, quizzes, and assessments.

It’s also useful for conducting polls, sidebar surveys, and in-app surveys. Offering over 100 templates and more than 1,000,000 ready-to-use examples of phenomenological research questions, this software simplifies the task immensely.

Equipped with the right tools and the professional tips shared here, you’re well-prepared to conduct thorough research studies and obtain valuable insights that drive impactful results.

Frequently Asked Questions on Q ualitative Research Questions

1. how do you choose qualitative research questions.

To choose qualitative research questions, identify your main research goal, focus on exploring ‘how’ and ‘why’ aspects, ensure questions are open-ended, and align them with your theoretical framework and methodology.

2. Why are good qualitative research questions important?

Good qualitative research questions are important because they guide the research focus, enable the exploration of depth and complexity, and facilitate the gathering of rich, detailed insights into human experiences and behaviors.

About the author

Emma David is a seasoned market research professional with 8+ years of experience. Having kick-started her journey in research, she has developed rich expertise in employee engagement, survey creation and administration, and data management. Emma believes in the power of data to shape business performance positively. She continues to help brands and businesses make strategic decisions and improve their market standing through her understanding of research methodologies.

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A comprehensive guide to in-depth interviews (IDIs)

UserTesting

You might have user data. But do you fully understand the why behind the data? Do you know who they are as people? If the answer is no, you're not alone.

A 2019 study found that more than half of researchers would like to use more in-person interviews as a UX research method . The good news? Most people are actually willing to give you their undivided attention to actively improve your app, site, or service user experience. As a UX researcher, you’re sitting on a potential goldmine of information and insights from your customer base. 

To pull that insightful data from participants, you’ll need to conduct in-depth interviews (IDIs) . These interviews require more planning and resources than other data collection methods. You’re also asking for more of your user’s time. 

For those reasons, it's essential to ensure you go into the process with a clear idea of what data you hope to get out of one. 

What is an in-depth interview? 

An in-depth interview is a qualitative research technique that involves conducting multiple individual interviews. They involve one-on-one engagement with participants, usually taking place face-to-face, either remotely or in-person. 

Unlike other research methods, in-depth interviews have a more flexible structure than moderated usability studies .

IDIs are used to get a more detailed and well-rounded perspective of users’ opinions, experiences, and feelings about a product's UX. 

Instead of more general qualitative or quantitative questionnaires that are sent out to a larger group of customers, IDI questions can be tailored to the interviewee and their individual usage. 

In these more intensive interviews, organizations usually ask a smaller number of customers to take part. This means that responses to different ideas, features, services, or future plans are more deeply investigated. 

You could consider using in-depth interviews for the following reasons: 

• To get feedback on a new product or service your business has launched 
• As a way of understanding the needs and expectations of your customers during persona gathering sessions
• For coming up with new ideas on how customers would make improvements to an existing product or service 
• Following a usability study to better understand how users intend to use your platform
• To get insight into how a customer thinks about design elements on certain pages

As you can see, they’re most effective when used in combination with other research methods like online surveys and usability testing .

Why are in-depth interviews important? 

There’s a large gap between the consumer's experience of brands and the marketer's confidence in their own branding. While most marketers are confident they can meet their target market's level of expectation, just under half of consumers say brands fail to meet their expectations.

Most users will switch to a competitor if they have just one bad experience with a brand they typically like.

What does this mean? To put it simply: creating a positive user experience is key for encouraging people to interact with your app, service, or site. 

In-depth interviews are one way of bridging this gap between consumer experience and business confidence. You can get insight into a users’ thoughts and feelings—and use that qualitative data to improve design, product launches, and key messaging.

What are the benefits of performing in-depth interviews? 

IDIs should show you how users feel about specific elements of your UX. They can also help you gain confidence in making future decisions. 

Here are a few of the top benefits of using IDIs in your UX process. 

Smaller sample size

Given the higher quality relevant insights, researchers require fewer participants to take part in in-depth interviews. 

Lower drop-off rates mean that interviewers can conduct fewer IDIs and still collect rich data. For instance, online questionnaires have a higher drop-off rate, so they require a larger sampling. But with IDIs, you can get a lot of data from each individual participant. 

Get honest feedback

One-on-one in-depth interviews are free from possible peer-pressure dynamics or distractions that are sometimes present in larger focus groups . By taking an hour to chat with a participant directly, the two-way conversation leaves zero space for other users’ influence.

Some people may also feel more comfortable providing honest feedback in conversation instead of through a written questionnaire. 

Gain a deeper understanding of user behavior

Face-to-face in-depth interviews, whether remote or in-person, allow researchers to interpret body language . Interviewers can also analyze changes in tone of voice and word choice. 

These nuances help interviewers build a complete picture of user behavior that isn’t possible through other online or offline feedback channels. 

Build a stronger understanding of user expectations and motivations 

It’s easier to ask follow-up questions, request more detailed information, and explore particular topics in more depth with an IDI. They’re suited to asking open-ended questions that encourage longer and more detailed responses from the participant. 

As a researcher, you should take advantage of having participants’ undivided attention. Take the time to explore their opinions more deeply, beyond the surface level, for the most useful qualitative data .

What are the challenges? 

IDIs can give you valuable insight into users’ expectations and actual uses of your site. But, there are some challenges.

They’re time-consuming

Every interview you conduct will need to be transcribed, analyzed, organized, and properly stored. Multiple team members may need to be involved in the process.

While often more informative, IDIs require more time and preparation than other research methods—including simple written surveys.

Interviewers or moderators require thorough training and briefing 

You need skilled interviewers to ask the right research questions and properly engage with participants to obtain valuable insights. 

Successful IDIs depend on an interviewer's ability to ask thoughtful questions at the right time. They need to give the participants space to think and talk—while making them feel comfortable enough to do so. It takes training for an interviewer to hone in on this skill set.

Participants require careful vetting 

To gather valuable and balanced insights, it’s essential to use random sampling to gather a group of participants that accurately represent your organization’s user base. Once you have a random set of participants, you should check that they represent your user base’s different groups. 

Depending on the size of your customer base, it likely won’t be possible to interview all your customers. That being said, it's important to interview a variety of different users. 

For instance, you may want to interview a group of users who are unfamiliar with your site design, those who have been using your site for six months, and another group who left for a competitor. 

The best way to do this would be to  segment your customer base , then randomly generate a sample of participants to invite to an IDI.

How do you structure IDIs? 

When it comes to how structured your IDIs are, you have several options: 

• Structured interviews  are fixed in their methodology. The interviewer would only ask predetermined questions and target specific experiences. A structured approach limits the scope for exploring discussed topics in more depth. 
• Unstructured interviews  aren’t defined and don’t include pre-planned questions. It’s more like a conversation between the researcher and the respondent.
• Semi-structured interviews  follow some protocols to guide the process. While it’s a conversation between two individuals, and the interviewer can ask for more details, most of the questions are scripted. Interviewers will plan some initial questions and themes to cover, but allow the respondents’ answers to guide the interview direction. 

Generally, the most valuable in-depth interviews are semi-structured. 

These IDIs have a loose structure, but remain adaptable to the participant’s issues and ideas. This flexibility enables interviewers to explore each response fully and better engage with the user. 

When to use unstructured interviews: "If you’re at the beginning of a design phase, just trying to path-find for innovation, or trying to really dig into another layer of how your users use your product, that's when you're going to want to be more unstructured. When you've already got the product or the prototype, and you're wanting to validate, and you want to make sure that you're designing it the right way." - Julie Strubel, UserZoom Senior UX Researcher

How do you conduct a good in-depth interview?

Some preparation is key for conducting an insightful in-depth interview. Planning topics and conversation starters in advance will help you use your participants’ time more wisely. If your customers feel that their time was wasted or the IDI was simply too long, they may be reluctant to participate in future drives for customer insights. 

Keeping your IDIs brief and well-structured will also help your participants maintain focus until the end. Longer interviews with less clear objectives run the risk of tiring customers out and reducing the quality of their answers.

In addition to the tips we just mentioned, we've pulled together 9 best practices to keep in mind when you're running IDIs:

• Know your aims
• Define your scope
• Set a time limit
• Ask the right questions
• Remove bias
• Make questions actionable
• Test your questions
• Create an IDI guide
• Put your insights into action

1. Know your aims

Before you plan on conducting any in-depth interviews, it’s important to know what you’re aiming to get out of the process. This helps guide your questions—and ultimately, the conversation.  

Perhaps you’re looking to understand how customers feel about your site’s new design. In that case, you need to ask open-ended questions like, “What do you think of our new site design when compared to our previous version?”

If you want to find out if participants find your checkout page to be intuitive, ask questions like, “How did you feel about the navigation experience of our checkout page?”

When you identify your goals , it’ll be easier to plan questions to help build your understanding of what your end-user is looking to achieve. 

For this reason, Hector Harris-Burton of Imaginaire recommends having some structure.

“Have a loose structure so that you can cover topics that you'd like to hear about. While an IDI is supposed to be a freeform conversation, one of the best ways to get the most information out of the interviewee is to ensure that you have talking points and some kind of structure.  By having a structure, not only are you able to cover the topics you need, but you can also aid the conversation. No matter who you talk to, there is always the potential for the conversation to run dry. But with a structure and talking points, you're able to lead the conversation and move forward, rather than struggling.”

2. Define your scope 

Always clarify the extent of your research before you begin interviewing people. 

Decide how much time you have to spend on conducting IDIs, and the minimum number of respondents you need to consider any themes as standard. That should define the number of users you need to interview.

There’s no magic number when it comes to determining the size of your sample . Always prioritize quality over quantity, and check that you can spend a reasonable amount of time in each interview. 

For instance, there’s not much point in interviewing 120 people for five minutes each. It would be better to spend the same amount of time interviewing 20 people for 30 minutes each. That way, you’d have time to explore topics more deeply with each participant instead of rushing through a list of questions. 

3. Set a time limit

When conducting an IDI, make sure you’re mindful of your participants’ time. Remember: their answer quality may drop off towards the end of the conversation if you’ve been talking for hours. 

Let participants know how much time each interview will take and stick to it. 

Keeping each interview to a maximum of one hour will allow you to ask participants plenty of questions without going off-topic. It’s also short enough for them to commit to the interview. 

4. Ask the right questions 

Asking the right questions will encourage respondents to share their honest points of view. 

In your in-depth interviews, add in a mix of question types, such as:

• General ice-breaking questions.  Ease people into the discussion by asking light-hearted questions first. For example, “Tell me about your biggest challenges right now.”
• More specific detail-oriented questions.  Start to explore subjects that are more closely linked to your research goals. For instance, “What are you hoping to achieve by using our app?”
• Insight-based questions.  Ask more specific questions about existing or new features. Use your last questions to find out how users feel about your future plans. For example, “How useful would this new calendar function be for you?” 

Using the right questions will help you better listen to users and then effectively implement their feedback. You’ll also gather qualitative data that helps you make smarter UX decisions. 

5. Remove bias 

It’s easy to accidentally influence customers’ answers without intending to. Take care with how you phrase each of your questions to make sure you’re not accidentally influencing their responses. 

Consider the following question:  “What do you like about this new service?”  

The phrasing of the question restricts users to only talking about what they like about the service as opposed to providing a more balanced answer. 

To make the question more neutral and bias-free , ask something along the lines of:  “What do you think about our new service?” 

This simple change of phrasing leaves the customer free to provide an honest perspective (as opposed to just listing what they like about the service).

The key is to collect valuable, actionable feedback that isn’t shaped by your organization’s expectations or agenda. So, always ask users open-ended questions and avoid leading questions that influence participants’ answers. 

Read up on other examples of leading questions so you know what to avoid.

Don't ask them leading questions designed to elicit a certain response. You want them to answer truthfully, so it's best to ask questions straight up and don't suggest an answer. For example, avoid questions like “What do you think is helpful in the new package we offered? The new payment methods?” Ask open-ended questions that will lead to expansive responses. Remember, you want to find out what they are trying to do and what their problems are. - Lauri Kinkar, Messente CEO

6. Make questions actionable 

When conducting IDIs, only ask users questions that are actionable. That way, you’ll be able to directly use their answers to improve user experience. 

Here’s an example of an actionable question: “Is there anything you would change about our checkout page?” Any answers you receive to this question will improve your current page based on users’ current sentiment around it. 

If you’re in doubt about asking a particular question, think about whether you could use the answer to improve your UX. If you can’t find a way of using the response to improve your user experience, it’s best to ask another question. 

7. Test your interview questions

Test your questions on teammates and ask for feedback on whether your questions are straightforward. 

Do they give the answers you were expecting? Or bounce the questions back to you, asking what you meant?

Your users’ responses should give you a clear idea of what needs to be changed or improved moving forwards. 

8. Create an IDI guide

IDI guides are an informative document that outlines the interview process from start to finish.

 It should act as a to-do list that you refer to throughout the interview process, as UX Specialist Andreas Johansson explains:

"As part of the user research plan, I also create an interview guide. This is basically a rough structure for me to refer to when I do the interviews. I tend to do semi-structured interviews. That means that I refer to the interview guide if I get stuck, but I don't tend to be too rigid about it. Sometimes it's good if the discussions go off on a tangent, for instance."

First, state your objectives and then outline the general flow of the interview. Include all the topics you want to talk about and in what order—remembering  why  you’re asking them in the first place. 

This interview guide will stop you from getting side-tracked during the conversation, helping you create the best possible experience for your participants. 

Consider giving your colleagues a copy of the IDI guide, too. They can provide you with feedback on what you’re planning to ask. Knowing what insights you’re planning to pull will also help them anticipate the data they’ll later analyze and store.

9. Put your insights into action

It’s all well and good to have a jam-packed day of IDIs. Once you’ve collected that data, though, you need to turn it into actionable insights.

Olga Kimalana, Senior Conversion Strategist at Scandiweb, explains:

“There’s no point in conducting user interviews if you don’t act upon the insights you gathered, so make sure to present your insights and plans of action to stakeholders to kick off the improvements.”

The simplest way to do this is to listen back through each interview. Create a transcription of the conversion, and flag different parts of the conversation that are interesting. This can include snippets you want to re-listen or pay closer attention to.

We recommend using a professional UX platform (instead of Zoom or Google Meet) for this reason. UserTesting, for example, has a note-taking feature. You can annotate transcripts, and add hashtags to certain topics, to spot themes across several IDIs.

Five in-depth interview best practices 

Following a few best practices will ensure you make the most of each interview and collect data for improving your UX. Here are five to start with. 

1. Ensure participants feel comfortable 

In-depth interviews are voluntary, so it’s important to make customers feel comfortable enough to share their honest opinions. 

As an interviewer, you should approach each interview with an approachable, friendly, and open-minded attitude. Avoid making the interview feel too formal–you don’t want users to feel under pressure or stressed. 

"You're setting the stage, you want people to feel relaxed. Even if they're just going through and doing a talk-out-loud for a usability session, you want to put them at ease, and that's not just about reading the script. It's about making that human connection in the first three minutes." - Julie Strubel, UserZoom Senior UX Researcher

If customers have a positive experience during your interview, they’ll be more likely to readily offer feedback if you request it again in the future. You’ll also get better data from respondents who were genuinely interested in the conversation. 

2. Properly engage with your interviewees

Effectively engaging with interviewees is sometimes easier said than done—especially when you have a busy day full of in-depth interviews. 

Taking detailed notes may distract you from what your customer is saying or take you out of the moment, so you may want to use an audio or video recording device instead. That way, you can give your interviewee 100% of your attention and remain responsive to their answers.

Making recordings of each interview will enable you to fully engage with participants without worrying about forgetting critical data or insights later on. Simply refer to your recordings afterward and collect all of the relevant insights. 

3. Follow-up on user responses

It’s vital to understand what your customers mean by their responses and what’s behind their opinion of your app, site, or service. Try clarifying their responses by summarizing their thoughts. If you’re not sure, always ask for clarification. 

Whenever users share an opinion, follow-up on their response by asking why they feel the way they do.

For instance, if a participant says they don’t find your checkout page intuitive, you should follow-up by asking them what it is that they don’t find user-friendly. Is it the layout? Are the payment instructions unclear? Do they have to click on too many buttons?

Avoid putting words into participants’ mouths, but make sure to find out what it is precisely about the page they find unintuitive. 

Alternatively, if a user says they prefer your old site design, make sure to pinpoint why. Was there a specific menu flow they found easy to use? Was the search capability stronger? 

Asking why will help you go beyond surface-level responses and genuinely engage with your customer base. 

4. Provide consent forms

You should always provide your participants with consent forms that outline the purpose of the interview. 

To legally use the participants’ responses and details, you need to make sure that everyone signs an agreement as to how the information gathered from the interview will be used. 

Consent always needs to be:

• Based on clearly explained information.  Participants need to know what exactly is being researched. Provide a detailed information sheet for them to read before signing.  
• Given freely.  Consent can’t be dependent on receiving a benefit. If you plan on offering incentives, make sure you do this before asking for consent. 
• Given separately from other information.  Consent forms need to be given separately to an NDA form, for example. 
• Given for a specific purpose . If you plan on using the research in multiple ways, consent needs to be given for all of them.
• Able to be refused and withdrawn.  The interviewee needs to know how they can withdraw consent at any moment during the process. 

5. Say thank you 

Participants are giving up their time to share their opinions and perspectives on your product or service with you. Make it a priority to thank your customers for their time, so they feel appreciated. 

You could also provide incentives as a way of saying thank you to participants. For example, you could automatically enter participants into an Amazon gift card giveaway. 

Michael Margolis, a UX research partner at Google Ventures, offers different incentives depending on the customer type:

“I typically offer a $100 gift card for customer interviews. [...] Some people may be enticed by a larger “honorarium” or charitable donation made in their names. But experts often respond more to professional incentives, such as sharing a version of the research results, previewing a new or advanced technology, or giving them credit in a public way.”

Conduct more insightful IDIs today

In-depth interviews are a great source of original user insights as part of a broader UX research process. 

When planned and conducted correctly, IDIs make it easier to understand your users’ expectations and pain points. Unlike other qualitative data collection methods, you’ll be able to interact with users directly and dig deeper into how they feel about your UX.

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How AI Revolutionized Protein Science, but Didn’t End It

June 26, 2024

How does a one-dimensional string of molecules fold correctly into its innate three-dimensional shape? This question, known as the protein folding problem, was recently solved by artificial intelligence.

Fran Pulido for  Quanta Magazine

Introduction

In December 2020, when pandemic lockdowns made in-person meetings impossible, hundreds of computational scientists gathered in front of their screens to watch a new era of science unfold.

They were assembled for a conference, a friendly competition some of them had attended in person for almost three decades where they could all get together and obsess over the same question. Known as the protein folding problem, it was simple to state: Could they accurately predict the three-dimensional shape of a protein molecule from the barest of information — its one-dimensional molecular code? Proteins keep our cells and bodies alive and running. Because the shape of a protein determines its behavior, successfully solving this problem would have profound implications for our understanding of diseases, production of new medicines and insight into how life works.

At the conference, held every other year, the scientists put their latest protein-folding tools to the test. But a solution always loomed beyond reach. Some of them had spent their entire careers trying to get just incrementally better at such predictions. These competitions were marked by baby steps, and the researchers had little reason to think that 2020 would be any different.

They were wrong about that.

That week, a relative newcomer to the protein science community named John Jumper had presented a new artificial intelligence tool, AlphaFold2, which had emerged from the offices of Google DeepMind, the tech company’s artificial intelligence arm in London. Over Zoom, he presented data showing that AlphaFold2’s predictive models of 3D protein structures were over 90% accurate — five times better than those of its closest competitor.

In an instant, the protein folding problem had gone from impossible to painless. The success of artificial intelligence where the human mind had floundered rocked the community of biologists. “I was in shock,” said Mohammed AlQuraishi , a systems biologist at Columbia University’s Program for Mathematical Genomics, who attended the meeting. “A lot of people were in denial.”

But in the conference’s concluding remarks, its organizer John Moult left little room for doubt: AlphaFold2 had “largely solved” the protein folding problem — and shifted protein science forever. Sitting in front of a bookshelf in his home office in a black turtleneck, clicking through his slides on Zoom, Moult spoke in tones that were excited but also ominous. “This is not an end but a beginning,” he said.

Quanta Magazine ; source: RCSB PDB

Proteins are molecules that come in hundreds of millions of different shapes. Each one serves a particular biological function, from carrying oxygen through the blood to sparking chemical reactions. The function is typically defined by its shape or structure.

When Google’s public relations machine churned the news out to the world, the media went wild. Headlines claimed that AlphaFold2 “ will change everything .” Protein biologists who had spent their entire careers investigating the structures of single proteins feared that they would lose their jobs. Some claimed that AlphaFold2 would revolutionize drug development; now that biologists could quickly learn proteins’ shapes, they could create new medicines that could target them. Others pushed back, arguing that the results were mostly hype and little would change.

Moult could barely comprehend the news himself. He ended the conference with the question on everyone’s mind: “What now?”

That was three and a half years ago. It’s finally possible to start answering his question.

AlphaFold2 has undeniably shifted the way biologists study proteins. However, while AlphaFold2 is a powerful prediction tool, it’s not an omniscient machine. It has solved one part of the protein folding problem very cleverly, but not the way a scientist would. It has not replaced biological experiments but rather emphasized the need for them.

Perhaps AlphaFold2’s biggest impact has been drawing biologists’ attention to the power of artificial intelligence. It has already inspired new algorithms, including ones that design new proteins not found in nature; new biotech companies; and new ways to practice science. And its successor, AlphaFold3, which was announced in May 2024 , has moved to the next phase of biological prediction by modeling the structures of proteins in combination with other molecules like DNA or RNA.

“It’s the biggest ‘machine learning in science’ story that there has been,” AlQuraishi said.

However, there are still massive gaps that artificial intelligence hasn’t filled. These tools can’t simulate how proteins change through time or model them in the context in which they exist: within cells. “AlphaFold changed everything and nothing,” said Paul Adams , a structural biologist who develops algorithms to model the structures of biomolecules at Lawrence Berkeley National Laboratory.

This is the story of how Jumper’s team at Google DeepMind pulled off their coup in protein science, and what it means for the future of artificial intelligence in biology.

Formulating the Problem

A piece of origami paper is little more than pressed wood pulp until it’s folded in specific ways; then it becomes something new. A few precise crimps and flips, and it’s a fortune teller, a paper device that can predict your future. Take the same piece of paper, change a few of the folding steps, and now it’s a winged crane, granting good fortune to its recipient.

Similarly, a long string of amino acid molecules has no function until it spontaneously folds into its innate shape, which biologists call its structure. A protein’s structure determines how it binds to or otherwise interacts with other molecules, and therefore defines its role in a cell.

Mark Belan for  Quanta Magazine

There are a couple hundred million known proteins on the planet and many more unknown ones. They do it all: Hemoglobin and myoglobin ferry oxygen around the muscles and body. Keratin gives structure to hair, nails and skin. Insulin enables glucose to move into cells to be converted into energy. Proteins can take on a seemingly infinite number of shapes to match the seemingly infinite number of jobs they do in life.

“Right from the atom all the way to ecosystems, [protein structure] is kind of a lingua franca,” AlQuraishi said. “It’s where everything happens.”

A cell makes proteins by daisy-chaining small molecules called amino acids into long polypeptide strings. The amino acids it chooses depends on the underlying set of instructions provided to it by DNA. Within a fraction of a second of its creation, a polypeptide string bends, buckles and folds precisely into the protein’s final three-dimensional shape. Once off the molecular assembly line, it scurries along to do its biological work.

If proteins didn’t perform this folding process exceedingly well, cascades of disasters would tumble through the body. An incorrectly folded or unraveled protein can lead to toxicity and cell death. Many diseases and disorders, such as sickle cell anemia, are caused by misfolded proteins. And misfolded proteins can aggregate into clumps that are hallmarks of neurodegenerative diseases like Alzheimer’s and Parkinson’s.

Yet no one really knows specifically how protein folding happens. How does the sequence information in these simple molecular chains encode a protein’s complex shape? This is the “most profound question that we can ask,” said George Rose , a biophysics professor emeritus at Johns Hopkins University.

In the 1950s, the biochemist Christian Anfinsen conducted experiments which suggested that a string of amino acids contains an internal code telling it how to fold into a protein — and that there should be a way to predict the protein’s shape from that code. This hypothesis is known as Anfinsen’s dogma.

PBH Images/Alamy

Scientists were probing this question as far back as the 1930s. But efforts really took off in the mid-1950s when the biochemist Christian Anfinsen added proteins to chemical solutions that either unfolded them by breaking their bonds or caused them to fold incorrectly. Then he observed what they did next. Anfinsen saw that the unfolded or misfolded proteins could spontaneously refold into their correct structures. His findings, which later won him a Nobel Prize , demonstrated that proteins form their 3D shapes from an internal code — the one written by their string of amino acids.

Anfinsen hypothesized that there should, therefore, be a way to predict a protein’s shape from the sequence of its amino acids. This became generally known as the protein folding problem.

Once its polypeptide chain is assembled, a protein can fold into its structure within a thousandth of a second — a timescale that perplexed the molecular biologist Cyrus Levinthal. In his 1969 paper “ How to Fold Graciously ,” Levinthal calculated that if a protein were to try out every possible folding option, it would take an impossibly long time to assemble. Clearly, he mused, something must send the protein down the right folding pathway more directly.

Over time, the protein folding problem has forked into new kinds of problems. Now three main questions are posed: Can a protein’s structure be predicted from its amino acid sequence? What is the folding code? And what is the folding mechanism?

These questions started to burrow into scientists’ brains in the early 1960s when the first experimentally determined protein structures became available. Max Perutz and John Kendrew, two biologists at the University of Cambridge, grew proteins into crystals, bombarded them with X-rays and measured how the rays bent — a technique known as X-ray crystallography. By doing so, they determined the 3D structures of hemoglobin and myoglobin. It took them more than two decades. They shared a Nobel Prize for their discoveries.

John Kendrew (left) and Max Perutz (right) meticulously uncovered the structures of hemoglobin and myoglobin using X-ray crystallography. Then they built physical models using balls (atoms) and sticks (chemical bonds).

MRC Laboratory of Molecular Biology

Since then, innumerable researchers have strived to understand not just what different proteins look like, but how they come to look that way. “It’s a very human thing to want to know what things look like, because then you can understand how they function,” said Helen Walden , a structural biologist at the University of Glasgow. Some looked into the chemistry of the problem, others into the physics. Experimentalists reconstructed protein shapes through painstaking lab work. Computational biologists hunted for clues with models and simulations, which they programmed and reprogrammed with different combinations of algorithmic rules.

As more structures came along, the protein science community needed a way to organize and share them. In 1971, the Protein Data Bank was founded as an archive for protein structures. Freely available, the data bank became a dependable tool for anyone who needed to know the structure of a protein to probe a biological question.

When the Protein Data Bank opened, it held the structures of seven proteins. By the time Google DeepMind used it to train AlphaFold2 nearly 50 years later, it held more than 140,000 — each laboriously decoded by the scientists known as structural biologists.

The Experimentalists’ Agony

Starting in the mid-1970s, every few months Janet Thornton reliably received a package in the mail. Inside was a 12-inch magnetic tape containing data about new protein structures deposited in the Protein Data Bank. A biophysicist at the University of Oxford, Thornton would eagerly rip open the package so she could analyze the new structures nearly as soon as they were discovered. The first tape Thornton received had only 20 structures on it.

“I had many students who said, ‘I want to come and solve the protein folding problem,’” said Janet Thornton, a structural biologist who retired from the European Molecular Biology Laboratory last year. “But I didn’t have any new ideas, frankly, about how to do that.”

Jeff Dowling, EMBL-EBI

Every one of those proteins represented years of work. Often a doctoral candidate would spend their four or more years in graduate school crystallizing a single protein, collecting data from it or interpreting that data to figure out the folded structure.

Oxford’s department of biophysics was, at the time, one of the world’s centers for X-ray crystallography. There, in 1965, David Phillips, one of the pioneers of protein crystallography, first determined the structure of an enzyme: lysozyme, which the immune system uses to attack bacteria. Using X-ray crystallography, Oxford biophysicists created maps of proteins’ electron density; the areas in which electrons congregated were likely to contain an atom. Thornton and her colleagues printed these electron density maps onto plastic sheets and stacked them one on top of another to create a “contour map” of the protein’s geography, she said.

X-ray crystallography helps scientists build electron density maps, which visualize where electrons congregate and therefore where atoms likely sit in a molecule. By stacking the maps on top of one another (left), scientists can deduce the structure of a protein or another molecule like penicillin (right).

Science Museum Group

Then they converted the maps into physical models. They placed their plastic maps into a Richards box, named for the Oxford biophysicist Frederic Richards, who invented the device in 1968. Inside a Richards box, an angled mirror reflected the maps into a workspace, allowing the scientists to see exactly where each atom was located relative to others. Then they physically built their model out of balls and sticks.

This method was cumbersome and restrictive. In 1971, Louise Johnson, who would go on to become an eminent crystallographer, was modeling phosphorylase, which at 842 amino acids was at the time the largest protein anyone had worked on. To model it, Johnson had to climb a ladder into a two-story Richards box, which Oxford constructed especially for her project.

Once a model was complete, scientists used a ruler to measure the distances between atoms to come up with coordinates for the protein structure. “It was archaic,” Thornton said. Then they fed the coordinates into a computer. The computerized version looked like a dense forest, she said, with atoms clumped together in a jumble. Only when Thornton looked at the structure through 3D glasses could she start to see the protein’s topology.

“It was a very torturous process,” Thornton said. “It’s amazing that it got done.”

Year by agonizing year, they did it. Once researchers were confident in their protein structure, they submitted it to the Protein Data Bank. By 1984, 152 proteins had been deposited. In 1992, that number climbed to 747.

While the experimentalists toiled on with their physical models, another faction of protein biologists — the computational scientists — took a different approach. But as they pondered Anfinsen’s insight that a protein’s structure should be predictable from its amino acid sequence, they got a bit overconfident.

Writing Their Own Rules

As an undergraduate in the early 1960s, John Moult planned to become a physicist. Then he learned about the protein folding problem. “Somebody came and gave a lecture about biology being too important to leave to the biologists,” he said, “which I arrogantly took seriously.” Captivated, he took his career in a different direction.

After he graduated, Moult went into protein crystallography. He decoded the structures of several proteins, including beta-lactamase, a bacterial enzyme that destroys penicillin, and received his doctorate in molecular biophysics at Oxford in 1970. But as he started his postdoc, he tired of the experimentalist approach and began to drift toward the growing field of protein computation. Computational biologists, as opposed to experimentalists, wrote computer algorithms to try and prove that Anfinsen was right: that they could feed a program a string of amino acids to generate a correct protein structure.

John Moult co-founded the Critical Assessment of Structure Prediction (CASP) experiment to force himself and other computational biologists to test their computer models of proteins against experimentally determined protein structures.

Umit Gulsen for Quanta Magazine

The transition from biological experiments to computation was an uneasy one. Moult was used to the slow, careful work of solving a single protein structure. In his new field, computational papers regularly claimed to have solved the protein folding problem and related sub-problems.

Moult was dubious. “The things that were being published in that area were not as rigorous as I was used to,” he said. “This is not because we’re all a load of crooks in this field. It’s because if you’re doing this sort of computational work, you’re doing it in a virtual world.”

In a virtual world, computationalists wrote their own rules when the rules of the natural world didn’t work. They designed their algorithms so that atoms stuck together in a certain way or the protein always folded to the right or the left. Over time, the models drifted farther away from reality. It’s hard to maintain rigor in a world where you have complete control, Moult said.

Still, he could see the value of both sides. Experimentalists worked precisely but slowly; computationalists worked quickly but were so removed from biophysical realities that they were often wrong.

There must be a way, he thought, to bring the best of both approaches together.

The Stamping Begins

In the early 1990s, Moult and his colleague Krzysztof Fidelis had an idea for bringing discipline to the field’s chaos. They set up a community science experiment that they called the Critical Assessment of Structure Prediction, or CASP.

The idea was simple. As CASP’s organizers, Moult and Fidelis would publish a list of amino acid sequences for proteins whose structures had been recently solved and supplied to them by experimentalists, but for which the results hadn’t yet been published. Then computational groups around the world would try their best to predict the protein’s structure using whatever method they wanted. An independent group of scientists would assess the models by comparing their answers to the experimentally confirmed structures.

The idea took off. CASP soon became a proving ground for computational approaches to the protein folding problem. These were the days before artificial intelligence, when computational approaches involved simulating molecular physics. It was a chance for scientists to put their thinking to the test in a public trial against their peers. “It wasn’t supposed to be a competition,” Thornton said. “But it actually has turned out to be a competition.”

Every two years, scientists gathered at the Asilomar conference center, an old chapel near Monterey, California, that used to be a Methodist retreat. During these conferences, the organizers announced the competition’s results and the computationalists gave talks about their methods and approaches. Moult encouraged attendees to stamp their feet on the wooden floors if they didn’t like what they were hearing.

“There was, at the beginning, quite a lot of stamping,” he said.

It was “almost like a drum,” recalled David Jones , a professor of bioinformatics at University College London who studied under Thornton. The biologists stamped if the talks got bogged down in details. They stamped if claims were overblown. They stamped if speakers were repetitive or too much in the weeds. But it was friendly stamping, Jones said: “It wasn’t nasty.”

The early CASP conferences were held at the Asilomar conference center in Monterey, California. When attendees stamped on the wooden floors, it sounded like a drum.

Aramark Destinations

Whatever the reason, when the echoes of stamping started ringing in a speaker’s ears, it was embarrassing. “Thank God I never got stamped on,” Jones said. One year, he and his colleagues presented a computational method called threading, in which amino acid sequences were woven through known protein structures to search for a fit. They didn’t do too badly. “We were quite pleased. … It was all downhill after that,” Jones recalled, laughing. “No, it was fun.”

There was a lot of excitement back then, said Silvio Tosatto , a professor of bioinformatics at the University of Padua. “People thought that they could become millionaires because they had the right algorithm, and some other people thought that they would immediately win the Nobel Prize.”

Neither of those things happened during the early years. When asked what the CASP submissions were like during that time, Moult paused. “‘Random’ is a good word,” he said.

Some methods performed better than expected, such as “homology modeling,” which compared the structures of known proteins to deduce the structures of unknown ones. Others were a dead loss. Most structure predictions were “tortured-looking objects,” Moult said.

“I was loving seeing them fail,” joked Anastassis Perrakis , a structural biologist at the Netherlands Cancer Institute and Utrecht University who gave experimentally determined structures to CASP organizers for use in the competition. “It’s not rivalry, but we like to tease each other in science.”

Through this process, clear leaders emerged. In 1996, after the second CASP wrapped up, a young man named David Baker asked Jones to share a taxi to the airport. Baker had seen Jones’ talk and was working on his own computational model. He didn’t have it ready for this CASP, but he wanted to chat about it. Jones listened to his ideas in the cab and never expected to see him again.

At the next competition in 1998, Baker blew the doors wide open with his algorithm Rosetta. He became “the man to beat,” Jones said.

David Baker, who is now one of the world’s leading protein design experts, was the man to beat at CASP with his high-performing algorithm named Rosetta.

BBVA Foundation

Algorithms like Rosetta modeled interactions between the atoms of amino acid molecules to predict how they would fold. They “showed that you actually could predict protein structure,” Baker said. “But it wasn’t good enough or accurate enough to be useful.”

In 2008, humans were still beating the computers. Baker , who by then was running his own lab at the University of Washington, created a free online computer game called Foldit , in which players folded strings of amino acids into protein structures. In a paper published in Nature , his team reported that human Foldit players outperformed Rosetta in modeling proteins.

But the human lead wouldn’t last long. In the early 2010s, important breakthroughs in a concept known as co-evolution propelled the field forward and would later turn out to be critical for artificial intelligence. The idea, which had been around for decades, was straightforward: By comparing closely related sequences of amino acids in hundreds to thousands of proteins, scientists could identify the amino acids that had mutated — and, importantly, determine whether they’d mutated in step with others. If two amino acids changed together, they were likely linked in some way. “You start to be able to say, ‘Well, these two things are probably close together in space,’” said Adams, the structural biologist at Berkeley Lab.

But until the early 2010s, such predictions of which amino acids were in contact were dismal. Their accuracy hovered between 20% and 24%. Then scientists noticed that their statistical methods were introducing errors, suggesting that some amino acids were in contact when they weren’t. Later, Moult learned that statisticians had been keenly aware of this kind of error for decades. When you looked back, he said, you’d think, “How could I be so stupid?”

Computational biologists cleaned up the statistical tools . By 2016, the accuracy of contact prediction had shot up to 47%. Two years later, it reached 70%. Baker’s algorithm built on this success: In 2014, Rosetta produced two protein structures so accurate that a CASP assessor thought Baker might have solved the protein folding problem.

The co-evolution insights were “fantastic,” Adams said. Without using machine learning, co-evolution was “one of the big things that came that really pushed that field forward.”

However, it got the field only so far. Co-evolution required an abundance of similar proteins to compare, and experimentalists weren’t solving protein structures fast enough to supply computationalists’ needs.

The years flowed by in a punctuated equilibrium, Moult said, using a term from evolutionary biology. Sometimes it felt as though no good ideas had evolved for a billion years — and then something exciting would happen.

Off the Deep End

In 2016, David Jones caught a glimpse of the future in a new paper in Nature . Researchers from Google DeepMind, an artificial intelligence team based in London, detailed how their algorithm, which used a method known as deep learning, had beaten a human champion at an ancient board game called Go.

Jones was amazed. “Things are happening,” he recalled thinking at the time. “I’m really going to have to get into this deep learning.”

Deep learning is a flavor of artificial intelligence loosely inspired by the human brain. In your brain, molecular information is sent across an interconnected web of brain cells called neurons. Neurons have little arms called dendrites that grab molecules dispatched by neighboring neurons which tell the receiving neuron either to fire and propagate a signal or not to fire.

“If enough activity comes at that neuron, then that neuron is going to fire,” said Michael Littman , a computer science professor at Brown University. That results in another wave of molecules being released to the next neuron.

In the 1950s, computer scientists realized that they could wire electronic bits together to create “neural networks.” Every unit in the neural network is a node, which researchers likened to a neuron: A neuron receives information from other neurons, then calculates whether to fire toward the next ones. In neural networks, information propagates across multiple layers of neurons to produce a particular outcome, like recognizing a dog in an image.

The more layers of neurons you have, the more intricate calculations you can perform. But early neural networks were made up of only two layers. In the 1990s, that number increased to three, and it stayed there for two decades. “We could not figure out how to reliably create networks that were deeper than that,” Littman said.

Structural biologists, including Jones and Moult, had tried using neural networks in protein science since the 1990s, but the limitations of shallow networks and sparse data held them back. Then, in the early 2010s, computer scientists learned how to better structure neural networks to allow reliable training of more layers. Networks deepened to 20, 50, 100 and then thousands of layers. “To distinguish that from the way we were doing it in the ’90s, people started to call it ‘deep learning,’” Littman said. “Because if machine learning people are good at one thing, it’s making up sexy names.”

Deep learning transformed artificial intelligence, leading to algorithms that excelled at recognizing features in photos or voices — and, it turns out, at beating humans in games.

In March 2016, when DeepMind co-founder Demis Hassabis was in Seoul watching his AI system AlphaGo beat a human world champion in the ancient game of Go, he flashed back to playing Foldit as an undergraduate. He wondered: If DeepMind researchers could write an algorithm to mimic the intuition of Go masters , couldn’t they write one to mimic the intuition of Foldit gamers, who knew nothing about biology but could fold proteins?

In 2016, AlphaGo, an AI system by Google DeepMind, defeated Lee Sedol (right), the world champion in the ancient game of Go. Its ability to mimic human intuition drew biologists’ attention to the potential power of deep learning in protein science.

Google DeepMind

Jinbo Xu , a professor at the Toyota Technological Institute at Chicago, also recognized the potential of using deep learning to attack the protein folding problem. He was inspired by what these networks were doing in image recognition. By then, computer scientists had had great success with convolutional networks, which program deep learning algorithms to break images into pieces and identify patterns between them. Xu brought this technique to protein folding. He used a mathematical object called a matrix to represent which amino acids were close together in space, then fed the data into a convolutional network as an image. The algorithm looked for patterns among these images to predict the 3D coordinates of the atoms that make up a protein.

In 2016, Xu posted a preprint of this work on arxiv.org (it was later published in PLOS Computational Biology ) that “was quite influential” for the field, Moult said. It showed people “the sort of things you might do with deep learning.”

Before long, protein structure groups started dabbling in deep learning. AlQuraishi and his team were the first to develop an approach that could directly predict protein structure exclusively with neural networks, in what’s called an “end-to-end” method — it just didn’t work very well. Others wondered how they could dip their toes into a new approach that felt so momentous.

“I didn’t know exactly what I wanted to do with deep learning, but I realized I needed to be doing deep learning,” Jones said.

He had started to write grant applications to find his way in when he received an email from Google DeepMind. They asked Jones about the CASP competition and offered help. “I just assumed they meant: We’ve got lots of computer power,” Jones said.

After he met them, it became obvious that Google had larger ambitions. But to pull them off, the tech giant would need more scientific brainpower.

A New Player on the Field

In 2016, when Jones started working as a consultant for Google DeepMind on a project that would later be known as AlphaFold, John Jumper was completing his doctorate in theoretical chemistry at the University of Chicago.

As a teenager, Jumper had taught himself how to program computers. He also had a knack for physics. So when it came time to go to college, he decided to study math and physics even though his parents, both engineers, were worried he’d never be able to find a job.

“I thought I was going to be a ‘laws of the universe’ physicist all the way through,” Jumper said. “I’ve always loved this notion of discovering something true of the universe.”

As an undergraduate at Vanderbilt University, he joined a collaboration with researchers at the Fermi National Accelerator Laboratory to study the strange properties of subatomic particles called quarks . One day, when he was sitting at a lunch table with the researchers, he got some sour news. “So, this experiment that we’re working on — when’s it going to turn on?” Jumper recalled asking them. One of the professors said that he’d probably retire first. The other, a bit older, said he might not live to see it.

“I wanted to do science in a little shorter timeframe than that,” Jumper said. After finishing undergrad, he started a doctoral program in theoretical condensed matter physics — and quickly dropped out. He had gotten a job at D.E. Shaw Research, a New York company that, at the time, was creating basic simulations of proteins. By understanding how proteins move and change, they hoped to better understand the mechanisms of various ailments like lung cancer.

It was the first time Jumper grasped the potential significance of his work. “It’s about health and extending people’s lives,” he said. For the next three years, Jumper modeled protein movements on the company’s supercomputers, which they had built specifically to simulate molecules faster. “I was doing more simulation on a Tuesday of some weeks than I was going to do in my entire Ph.D.,” he said.

In 2011, he gave graduate school another shot, this time studying theoretical chemistry at the University of Chicago. He was still interested in protein structure and movement. But he was frustrated by the slow pace of academia. “I no longer had this access to this custom computer hardware” he had used at D.E. Shaw, Jumper said. He wondered if he could use artificial intelligence — “at the time we called it statistical physics” — to reach a level of quick simulation that otherwise required advanced machines. He began dabbling in machine learning and neural networks.

During this time, he also started thinking about the protein folding problem. He suspected that the problem should be solvable with the training data available in the Protein Data Bank — by 2012, it contained more than 76,000 protein structures.

“I believed that the data were sufficient,” Jumper said. But “the ideas weren’t.”

In 2017, Jumper heard a rumor that Google DeepMind was getting into protein structure prediction. He had just completed his doctorate, using machine learning to simulate protein folding and dynamics. He applied for a job as a research scientist.

“The project was still secret,” he said. If he raised the subject of protein folding in an interview, the DeepMind team changed the subject. “You can only do that so many times before I’m pretty sure what you’re doing,” Jumper said.

In October 2017, he arrived at DeepMind’s London office. With Jones’ help as a consultant, the team was already deep into the development of AlphaFold. “It was a great fun time where we were just throwing ideas at the wall,” Jones said. “Eventually, a good core idea emerged, and they ran with it.”

To train their algorithm, the DeepMind team used more than 140,000 structures from the Protein Data Bank. They fed this information into a convolutional network, but didn’t change much about the AI architecture itself. It was “standard machine learning,” Jumper said.

By the spring of 2018, AlphaFold was ready to join CASP and compete against bona fide protein scientists. “It’s a bit like Formula One racing,” Jones reflected. “You think you’ve built the best car, but you just don’t know what the other teams have built.” The stakes felt high. The DeepMind team debated whether they should compete anonymously; they didn’t want to risk humiliation.

“Nobody wants to fail,” Jones said. In academia, it’s part of the job; you fail, and you move on because you don’t have a choice. “But obviously if you’re a multibillion-dollar tech company, it’s not a good look if you tried to do something and failed.”

They ultimately decided to submit their results under the Google DeepMind name. A few months before the December meeting, Jones heard from CASP’s organizers. They suggested that the DeepMind team come along to the meeting because AlphaFold had performed really well.

Their victory wasn’t massive — they were about 2.5 times better at predicting protein structures compared to the next-best team — but their win made an impression. “It was clear something interesting had happened,” Moult said.

Rebooting the Algorithm

The win should have energized the DeepMind team. But they knew they weren’t close to solving the protein folding problem. Hassabis had gathered them a few months earlier. “Are we going to go after solving this or not?” Jumper recalled him saying. “If not, let’s find problems that we can have this really, really big impact on.”

“We had this moment where we really decided: We are going to go after solving it,” Jumper said. They went back to the drawing board.

John Jumper suspected that biologists had studied enough protein structures to solve the protein folding problem. “I believed that the data were sufficient,” said Jumper, who started working at Google DeepMind in 2017. But “the ideas weren’t.”

With his diverse background in physics, chemistry, biology and computation, Jumper brought original insights to brainstorming sessions. Soon, he was leading the team, which had grown from six to 15 people. “There was something very unique going on,” said Raphael Townshend, who interned at Google DeepMind in 2019 and later founded Atomic AI, an AI-driven biotech company.

In academia, experts are often siloed from one another, each pursuing independent projects with little collaboration. At DeepMind, experts in statistics, structural biology, computational chemistry, software engineering and more worked together on the protein folding problem. They also had the massive financial and computational resources of Google behind them. “Things that would have taken me months to do as a Ph.D. student, I was doing in a single day,” Townshend said.

The London DeepMind office was high-energy, he said, and much of that energy was generated by Jumper. “He’s a true genius, I would say, and also a very humble person,” said the computer scientist Ellen Zhong , who interned at DeepMind in 2021 and is now an assistant professor at Princeton University. “He was beloved by the team.”

Under Jumper’s leadership, AlphaFold was reconstructed. DeepMind designed a new type of transformer architecture — a type of deep learning that has “powered basically every single machine learning breakthrough that’s happened in the last five years,” Townshend said. The neural network tweaked the strength of its connections to create more accurate representations of the data, in this case protein evolutionary and structure data. It ran that data through a second transformer to predict the 3D structure of a protein. The algorithm then honed the structure further by running it, together with some of the revised data, back through its transformers a few more times.

When they first started working on AlphaFold2, their algorithm was “terrible, but not as terrible as we expected,” Jumper said. “[It] made helices that kind of vaguely looked like a protein.” But as they honed it further, they noticed enormous increases in the efficiency and accuracy of their predictions.

“It was actually terrifying,” Jumper said. If it’s working too well, that usually means “you’re doing the wrong thing.” They checked, but there wasn’t a problem. It was simply working.

The team decided to run an internal experiment to see whether their system would be helpful to biologists. They identified roughly 50 papers published in high-end journals like Science , Nature and Cell that not only described a new protein structure but also generated insights about the protein’s function from the structure. They wanted to see if AlphaFold2 would stand up to the experimentalists’ laborious approach.

They entered the amino acid sequences. AlphaFold2 ran its prediction engine. For each sequence, it spat out a prediction close to the experimental structure presented in the papers. However, in the team’s view, it wasn’t accurate enough. The structures were missing key details that the experimentalists learned about their proteins. “You feel like you’ve finished the race and it’s like finding out that you’ve got the second half,” Jumper said.

The team further honed the system over the next six months, minor improvement by minor improvement. A few weeks before the protein candidates were released for the 2020 CASP competition, they performed another usefulness test. Jumper was satisfied. Google DeepMind submitted their predictions to CASP in the spring of 2020. And then they waited.

The Earthquake

In early summer, Moult received an email from a CASP assessor: “Look at this, it’s pretty impressive.” Attached to the email was a protein structure solved by Google DeepMind. Moult was indeed impressed, but he thought it was a one-off.

Then he got another email, and another. “That’s strange,” he recalled thinking. There were three, four, a whole slew of near-perfect protein predictions — and all from DeepMind. By the end of the summer, “we rapidly realized … something very, very extraordinary had happened,” Moult said.

CASP assessors score each submission by comparing the predicted protein structure to its proven experimental structure. A perfect score, in which the model and reality match atom by atom, is 100. Moult had always believed that anything above 90 would indicate that an algorithm had effectively solved a protein’s structure. Most of AlphaFold’s structures hit or surpassed the 90 mark.

A few months before the meeting, Moult called Jumper with the news. “I cursed out loud,” Jumper recalled. “My wife asked if I was OK.”

In December 2020, less than a year into the Covid-19 pandemic, Jumper presented AlphaFold2 over Zoom at the virtual CASP meeting.

Like the rest of the attendees, Jones watched from home. “I was just stuck … watching this unfold,” he said. “There’s no outlet because your colleagues aren’t nearby. … We’re all under lockdown so we can’t go anywhere.”

For anyone who wasn’t a neural network expert, the ideas were complex. Even so, the conclusions were clear. DeepMind had solved the structure prediction part of the protein folding problem. AlphaFold2 could accurately predict the structure of a protein from its amino acid sequence.

“Ugh, my favorite subject is dead,” Jones recalled thinking. “DeepMind shot it, and it’s the end.”

For years, Anastassis Perrakis had contributed unpublished experimental results to CASP for the competition. When he saw AlphaFold2’s results for a protein his team had sweated over, he thought, “Uh-oh.” AlphaFold2 had gotten it perfectly right.

Alone at home in lockdown, the scientists were united in thinking that the world of protein science had changed forever. As its inhabitants looked out across the new landscape, they had one question in mind: What now?

Shock and Awe

Structural biology suddenly became unstructured.

At first there was “a lot of soul searching,” said Silvio Tosatto, who had competed in CASP since its earliest days. Some structural biologists feared their jobs would become obsolete. Others grew defensive and claimed that AlphaFold2 wasn’t accurate.

The computational biologists who had been trying to solve this problem, some for decades, found the moment bittersweet. In a blog post he wrote after CASP, AlQuraishi cited an attendee who described feeling like someone whose child had left home for the first time.

Mohammed AlQuraishi, a systems biologist at Columbia University’s Program for Mathematical Genomics, hopes that deep learning will be able to simulate an entire cell and all the structures and dynamics within it by 2040.

Nicole Pereira

But even amid their trepidation around this shiny new tool, many scientists were ecstatic. Those who didn’t do structural work used to have to collaborate with structural biologists to determine protein structures for their broader experiments. Now, they could just press a few buttons and get the structure on their own.

In the media, AlphaFold2 became the shiny new artificial intelligence breakthrough that would “ change everything .” But it took months and years for scientists to tease apart what AlphaFold2 could and couldn’t do. Around six months after Jumper’s talk, Google DeepMind published their results and shared AlphaFold2’s underlying code. “When AlphaFold2 was out, the next day we were trying to install it in our GPU servers,” Perrakis said. Biologists began to play.

“I expected [AlphaFold2] to fall down,” Thornton said. “But actually my impression is that it’s been remarkably successful.”

It started to become clear that rather than being a threat, AlphaFold2 might be a catalyst for accelerating research. Rather than put structural biologists out of a job, it gave them a new tool to do their jobs better. “If you view a structural biologist simply as a technical specialist who works out the structure of proteins, then yes, of course structural biologists are out of the job,” Walden said. But that would be like saying that the Human Genome Project made genomicists obsolete because they could no longer publish a paper describing the sequence of a single gene.

In many cases, a structural biologist’s goal is to discover the function of a protein. With AlphaFold2, they could create a hypothesis within minutes rather than wait for months or years to work out a structure through experiments.

“This changes structural biology in many good ways, and not bad ways,” Adams said. “This only makes this a more exciting field to work in.”

However, it didn’t immediately result in all kinds of new drugs as some people had predicted — and researchers soon learned that the tool has its limitations. AlphaFold2 predictions aren’t perfect. They require experimental validation, Perrakis said. But “you can move much quicker to the actual study of the structures.” Now when his students start a new project, they first use AlphaFold2 to predict the structure of a particular protein. Then they conduct experiments to validate it.

Perrakis suspects that he and other researchers will continue to use X-ray crystallography to a degree. But to develop initial protein structures, many are starting to combine deep learning predictions with advanced electron microscope techniques such as cryo-EM, which involves flash-freezing biological samples and bombarding them with electrons. Then they can get to the interesting questions about what their proteins do. AlphaFold2 has “turbo-boosted” cryo-EM, AlQuraishi said.

That shift has already begun. In June 2022, a special issue of Science revealed the near-atomic structure of a human nuclear pore complex. This massive, complicated structure — built of 30 different proteins — had been a biological quandary for decades. The scientists used AlphaFold2 predictions to fill in gaps in the proteins’ structures left unsolved by cryo-EM.

Seeing that paper, in which other scientists used AlphaFold2 to make a biological breakthrough, was the “moment that I knew that [AlphaFold] really, really mattered,” Jumper said.

Discoveries like the nuclear pore complex dot the timeline of the last three years of protein science. Already, AlphaFold2 has predicted protein structures that have been used to study diseases and create new tools for drug delivery. “It’s been hugely helpful for us,” said Feng Zhang , a molecular biologist at the Broad Institute who used AlphaFold2 to engineer a molecular syringe to deliver drugs into human cells. Knowing a protein’s structure can also help develop drugs if researchers can identify molecules to latch onto a protein’s shape, for example, and change its behavior. While some studies have suggested that AlphaFold2 predictions aren’t as useful as experimental structures in this realm, others have shown that they work just as well . The full impact of AI tools on drug discovery is still unfolding.

Some biologists, however, are already looking beyond AlphaFold2’s use in discerning the structures and functions of known proteins and toward designing ones that don’t exist in nature — a technique pivotal for designing novel medicines.

The Next Frontier

Almost immediately after seeing Jumper’s talk at the 2020 CASP conference, Baker got back to work on his Rosetta algorithm. Google hadn’t yet shared AlphaFold2’s underlying source code. Still, “we started playing with some of the ideas that they introduced,” Baker said. On the same day that Google DeepMind published AlphaFold2 in Nature , he and his team announced RoseTTAFold , a highly accurate rival to AlphaFold. RoseTTAFold also uses deep learning to predict protein structures but has a very different underlying architecture than AlphaFold2.

“Once the scientific idea is out there, it’s possible for people, at least the ones who have enough resources, to reverse engineer it and try to build on top of that,” Tosatto said.

RoseTTAFold wasn’t alone. Other AlphaFold competitors, including Meta, crafted their own algorithms to address protein structure prediction or related problems. Some, including Townshend’s biotech startup Atomic AI, have expanded beyond proteins to use deep learning to understand RNA structures. However, in the realm of single-structure predictions, no one has been able to match AlphaFold’s accuracy so far, Thornton said. “I’m sure they will, but I think getting another … AlphaFold moment like that will be very difficult.”

Last year, David Baker (pictured here), with John Jumper and Demis Hassabis, received the Frontiers of Knowledge Award in Biology and Biomedicine for their work revolutionizing the study and design of proteins with artificial intelligence.

At least in public, Baker and Jumper have continued the tradition of productive competition established by CASP. “They might feel that I compete with them, but I feel like they’ve just been inspirational for us,” Baker said.

Jumper welcomes it. “It’s really important that people build on this science,” he said. “It would be sad to me if there was no intellectual lineage of AlphaFold.”

Baker is already evolving his program’s lineage to focus on a new frontier in protein science: protein design. Right now, biologists are confined to studying the proteins already invented by nature. Baker envisions a science in which they could design novel proteins — ones crafted specifically to harness sunlight, break down plastic, or form the basis of drugs or vaccines.

“The number of different types of protein structures or shapes in nature at the moment is quite limited,” said Danny Sahtoe , a structural biologist at the Hubrecht Institute in the Netherlands who did his postdoc under Baker. “In theory, more should be possible, and if you can have more shapes, that also means that you can have more functions.”

Protein design is essentially the “inverse protein folding problem,” said Baker, who directs the Institute for Protein Design at the University of Washington. Rather than feed an amino acid sequence to a deep learning algorithm and ask it to spit out a protein structure, a protein designer feeds a structure into an algorithm and asks it to spit out a sequence. Then, using that amino acid sequence, the designer builds the protein in the lab.

AlphaFold and RoseTTAFold by themselves can’t spit out these sequences; they are programmed to do the opposite. But Baker created a design-specific iteration of RoseTTAFold, known as RoseTTAFold diffusion or RF diffusion , based on its neural architecture.

The field of protein design has existed for a long time, but deep learning has accelerated it, Sahtoe said. It makes the process of designing realistic computer models of proteins “incredibly fast.” It used to take weeks or months for trained protein designers to create the backbone of a new protein. Now they can make one in days, sometimes even overnight.

Foldit , an online game developed by Baker’s lab, has players predict protein structures.

Baker also updated Foldit to incorporate his obsession: Instead of building protein structures, players design proteins. It’s been productive. Baker’s lab has written papers on several of the player-designed proteins. One of the world’s top Foldit players is now a graduate student working with one of Baker’s colleagues at the University of Washington.

“Do we understand protein folding? Well, if we can design new sequences that fold up to new structures, then that shows we understand quite a lot about protein folding,” Baker said. “That, in a sense, you could view as a solution to the protein folding problem too.”

Trust Exercise

AlphaFold2’s success has undeniably shifted biologists’ attitudes toward artificial intelligence. For a long time, many experimental biologists distrusted computation. They understood that some machine learning approaches can make data appear better than it is. Then Google DeepMind demonstrated “unequivocally that you could do serious work with this,” AlQuraishi said. Any skepticism is now matched with: “Well, what about AlphaFold?”

“Biologists now believe our prediction results,” said Xu, the computational biologist who advanced convolutional networks. “Before, biologists always suspected if our prediction is reliable.”

Playing into this trust is a feature of the AlphaFold2 platform: It not only generates a 3D model of a protein, but also self-assesses the accuracy of its prediction by grading different parts of the structure on a confidence scale from zero to 100.

In July 2022, after Google DeepMind released the structure predictions of 218 million proteins — nearly all those known in the world — Adams decided to analyze AlphaFold2’s self-reports. He compared the predictions to the proteins’ solved structures and independently assessed their accuracy.

The “good news is that when AlphaFold thinks that it’s right, it often is very right,” Adams said. “When it thinks it’s not right, it generally isn’t.” However, in about 10% of the instances in which AlphaFold2 was “very confident” about its prediction (a score of at least 90 out of 100 on the confidence scale), it shouldn’t have been, he reported: The predictions didn’t match what was seen experimentally.

That the AI system seems to have some self-skepticism may inspire an overreliance on its conclusions. Most biologists see AlphaFold2 for what it is: a prediction tool. But others are taking it too far. Some cell biologists and biochemists who used to work with structural biologists have replaced them with AlphaFold2 — and take its predictions as truth. Sometimes scientists publish papers featuring protein structures that, to any structural biologist, are obviously incorrect, Perrakis said. “And they say: ‘Well, that’s the AlphaFold structure.’”

“Some people are overconfident — like, way overconfident — in what these deep learning models can do,” said Lauren Porter , an investigator at the National Institutes of Health. “We should use these deep learning models for as much as we can, but we also need to approach them with caution and humility.”

Jones has heard of scientists struggling to get funding to determine structures computationally. “The general perception is that DeepMind did it, you know, and why are you still doing it?” Jones said. But that work is still necessary, he argues, because AlphaFold2 is fallible.

“There are very large gaps,” Jones said. “There are things that it can’t do quite clearly.”

While AlphaFold2 is excellent at predicting the structures of small, simple proteins, it’s less accurate at predicting those containing multiple parts. It also can’t account for the protein’s environment or bonds with other molecules, which alter a protein’s shape in the wild. Sometimes a protein needs to be surrounded by certain ions, salts or metals to fold properly.

“At the moment, AlphaFold is a little bit of a ways away from being able to determine context,” Walden said. Her group has determined several structures experimentally that AlphaFold2 couldn’t predict.

There are also several types of dynamic proteins that AlphaFold2 predicts poorly but that are no less important in function. Shape-shifting proteins, also known as fold-switching proteins, are not static: Their shapes change as they interact with other molecules. Some fold into dramatically different shapes, despite having the same amino acid sequence. Fold-switching proteins “challenge the paradigm that sequences encode one structure,” Porter said, “because clearly they don’t.”

Fold-switching proteins like RfaH, shown here, can change conformations to perform different tasks. When in its alpha helix form, the RfAH protein can’t bind to its target — but when it switches its form to a beta sheet, it can.

Courtesy of Lauren Porter

Compared to the hundreds of thousands of static, single-structure proteins that the DeepMind algorithm trained on, there are only about 100 examples of fold-switching proteins — although more surely exist. It’s perhaps no surprise, Porter said, that “generally speaking, these algorithms were made to predict a single fold.”

And then there are the proteins that flail about like an air dancer outside a car dealership. Intrinsically disordered proteins or protein regions lack a stable structure . They wiggle and re-form constantly. “They’ve been in many ways ignored simply because they were a little bit annoying,” said Kresten Lindorff-Larsen , a professor of computational protein biophysics at the University of Copenhagen. Around 44% of human proteins have a disordered region made up of at least 30 amino acids. “It’s a relatively large fraction of them,” Lindorff-Larsen said.

AlphaFold2 can predict when a region is likely to be intrinsically disordered — but it can’t tell you what that disorder looks like.

For his part, Jumper’s biggest frustration is that AlphaFold2 doesn’t register the difference between two proteins that vary by a single amino acid, known as a point mutation. Point mutations can “have quite dramatic effects, sometimes on structure and often on function of proteins,” he said. “AlphaFold is relatively blind” to them, in that it will produce the same structure for both sequences.

In September 2023 DeepMind released AlphaMissense , a deep learning algorithm that predicts the effects of such mutations. It can’t show the change to the structure, but it informs the user if the mutation might turn the protein pathogenic or introduce dysfunction based on similar mutations in known pathogenic proteins.

However, even if AlphaFold2 could predict all proteins perfectly, it would be far from modeling biological reality. That’s because in a cell, proteins never act alone.

Cellular Complexity

The insides of cells are complicated and chaotic. A cell’s external membrane envelops a biochemical environment densely crowded with molecular parts — proteins, signaling molecules, messenger RNA, organelles and more. Proteins bind to each other and to other molecules, which alters their forms and functions.

Proteins don’t work alone: They interact constantly with other molecules. This rendering of a cellular landscape is made of models of real proteins and other molecules in “an attempt to visualize the great complexity and beauty of the cell’s molecular choreography,” wrote its creator, Gael McGill .

Evan Ingersoll and Gaël McGill, PhD/Digizyme Inc

AlphaFold2’s ability to predict the structure of a single protein doesn’t get biologists close to understanding proteins in this intricate native environment. But that’s the direction the field is now heading. Protein science’s artificial intelligence giants, Google DeepMind and David Baker’s Institute for Protein Design, are now evolving their deep learning algorithms to predict the structures that proteins assume while interacting with other molecules.

In spring 2024, they both published papers describing similar developments in this area. Updates to their algorithms — launched with the new names AlphaFold3 and RoseTTAFold All-Atom — enable them to predict the structures of proteins bound to each other, DNA, RNA and other small molecules.

Biologists are just starting to test out these updates. So far, AlphaFold3 is much more accurate than RoseTTAFold All-Atom, AlQuraishi said — but it’s not as big of a leap as an “AlphaFold2 moment.” For some macromolecules, such as RNA structures, its accuracy remains below that of other physics-based systems and experiments.

AlphaFold3 predicts the structures of molecular complexes, such as this enzyme found in a plant-damaging fungus. In this model structure, the protein (blue) is linked to simple sugars (yellow) and an ion (yellow sphere).

Even so, the new algorithms are a step in the right direction. The interactions between proteins and other molecules are critical to their functioning in cells. To develop drugs that can dock onto proteins and alter their activity as desired, researchers need to understand what those complexes look like. It’s unlikely, though, that either algorithm will lead to new medicines anytime soon, Adams said. “Both methods are still limited in their accuracy, [but] both are dramatic improvements on what was possible.”

There is one other major change in DeepMind’s new product. AlphaFold2’s underlying code was open-source so that other researchers could study the algorithm and remake it for their own projects. However, rather than share AlphaFold3’s source code, Google has so far opted to protect it as a trade secret. “For the time being, at least, no one can run and use it like they did with [AlphaFold2],” AlQuraishi said.

Even before the release of AlphaFold3, researchers had been testing AlphaFold2 to see if it could provide useful information on proteins in different conformations. Brenda Rubenstein , an associate professor of chemistry and physics at Brown University, was interested in kinases, a type of protein that activates other proteins. Specifically, she wanted to understand the mechanism of a kinase that causes cancer so that she could develop more precise drugs against it. Her lab modeled the kinase’s structure using a physics-based approach, which maps the 3D coordinates of atoms using Newton’s laws. It took two and a half years.

“About a year ago, we said: Can we do this faster?” Rubenstein said. They tried using AlphaFold2 in a novel way. By feeding data about related proteins to the algorithm, she found that it could predict her kinase in different conformations with more than 80% accuracy.

Rubenstein’s is one of several labs finding that “if you poke AlphaFold in the right way, you get it to kind of spit out alternate conformations,” AlQuraishi said. “That’s been encouraging.”

AlQuraishi hopes that by 2040, deep learning will be able to simulate an entire cell and all the structures and dynamics within it. Getting there, however, will require leaps on both the experimental and computational sides.

An Outsider’s Take

For many biologists, AlphaFold2 was the breakthrough they had been waiting for. The goal of CASP had been to create computing tools that predict protein structure from sequence. Still, many can’t help but ask: Why was a relative newcomer able to crack the protein code when so many experts had struggled for decades?

The insights that Google DeepMind’s team of computer and protein scientists brought to the problem are undeniable. At the same time, the ground of protein science was fertile and ready to yield a deep learning revolution, AlQuraishi said. “These things don’t appear out of nowhere.”

By the time CASP 2020 came around, many researchers expected a breakthrough in structure prediction to come through artificial intelligence. “It was all heading in that direction,” Townshend said. But they didn’t expect it to come from a multibillion-dollar technology company, and they didn’t expect it so soon. Some said AlphaFold2 wasn’t a feat of new science but rather clever engineering. Some were surprised that David Baker’s algorithms didn’t take the trophy. Others were less surprised because of Google DeepMind’s unmatched resources.

Around 100 labs participate in CASP every year, and though they had begun to adopt AI technologies, they “probably didn’t have the expertise that DeepMind had, nor the computing power,” Thornton said. DeepMind “had access to basically unlimited computing power.”

She also speculated that Google’s lack of expertise in protein science may have freed them creatively. “They were single-minded,” Thornton said, and focused on building a great neural network. Protein biologists had baggage. As they worked on their AI tools, they wanted to capture the atomic-level molecular physics and chemistry involved in protein folding. DeepMind had a different approach: We will transform sequence data into a 3D structure, and it doesn’t matter how we get there.

“Rather than trying to solve the protein folding problem, which I think a lot of previous predictions tried to do, they actually just went with the brute force” of mapping out the atoms’ final positions in space, Walden said. “Rather interestingly, they have therefore probably solved the problem.”

The Princeton University computer scientist Ellen Zhong, who was an intern on Google DeepMind’s AlphaFold2 team in 2021, pioneered the use of deep learning in combination with cryo-EM to study protein dynamics.

Tori Repp/Fotobuddy for Princeton University

To some biologists, that approach leaves the protein folding problem incomplete. From the earliest days of structural biology, researchers hoped to learn the rules of how an amino acid string folds into a protein. With AlphaFold2, most biologists agree that the structure prediction problem is solved. However, the protein folding problem is not. “Right now, you just have this black box that can somehow tell you the folded states, but not actually how you get there,” Zhong said.

“It’s not solved the way a scientist would solve it,” said Littman, the Brown University computer scientist.

This might sound like “semantic quibbling,” said George Rose, the biophysics professor emeritus at Johns Hopkins. “But of course it isn’t.” AlphaFold2 can recognize patterns in how a given amino acid sequence might fold up based on its analysis of hundreds of thousands of protein structures. But it can’t tell scientists anything about the protein folding process.

“For many people, you don’t need to know. They don’t care,” Rose said. “But science, at least for the past 500 years or so … has been involved with trying to understand the process by which things occur.” To understand the dynamics, mechanisms, functions and nature of protein-based life, Rose argued, you need the full story — one that deep learning algorithms can’t tell us.

To Moult, it doesn’t matter that the machine does something he doesn’t understand. “We’re all used to machines doing things we can’t. You know, I can’t run as fast as my car,” he said. To molecular biologists who are trying to study a protein and just need to know roughly what it looks like, how they get there doesn’t really matter.

But “until we know really how it works, we’re never going to have a 100% reliable predictor,” Porter said. “We have to understand the fundamental physics to be able to make the most informed predictions we can.”

“We keep moving the goalpost,” AlQuraishi said. “I do think that core problem has been solved, so now it’s very much about what comes next.”

Even as biologists continue to debate these topics, others are looking forward to a field that’s undeniably changed — and backward toward its recent past.

Sometimes Perrakis is hit by a wave of nostalgia for the old ways of doing things. In 2022, his team described an enzyme involved in modifying microtubules (giant, rod-shaped molecules that provide structures to cells) that they had determined using X-ray crystallography. “I realized that I’m never going to do that [again],” he said. “Having the first structure appearing after months of work was a very particular satisfaction.”

AlphaFold2 hasn’t made those experiments obsolete. On the contrary, it’s illuminated just how necessary they are. It has stitched together two historically disparate disciplines, launching a new and stimulating conversation.

The New World

Seventy years ago, proteins were thought to be a gelatinous substance, Porter said. “Now look at what we can see”: structure after structure of a vast world of proteins, whether they exist in nature or were designed.

The field of protein biology is “more exciting right now than it was before AlphaFold,” Perrakis said. The excitement comes from the promise of reviving structure-based drug discovery, the acceleration in creating hypotheses and the hope of understanding complex interactions happening within cells.

“It [feels] like the genomics revolution,” AlQuraishi said. There is so much data, and biologists, whether in their wet labs or in front of their computers, are just starting to figure out what to do with it all.

But like other artificial intelligence breakthroughs sparking across the world, this one might have a ceiling.

AlphaFold2’s success was founded on the availability of training data — hundreds of thousands of protein structures meticulously determined by the hands of patient experimentalists. While AlphaFold3 and related algorithms have shown some success in determining the structures of molecular compounds, their accuracy lags behind that of their single-protein predecessors. That’s in part because there is significantly less training data available.

The protein folding problem was “almost a perfect example for an AI solution,” Thornton said, because the algorithm could train on hundreds of thousands of protein structures collected in a uniform way. However, the Protein Data Bank may be an unusual example of organized data sharing in biology. Without high-quality data to train algorithms, they won’t make accurate predictions.

“We got lucky,” Jumper said. “We met the problem at the time it was ready to be solved.”

No one knows if deep learning’s success at addressing the protein folding problem will carry over to other fields of science, or even other areas of biology. But some, like AlQuraishi, are optimistic. “Protein folding is really just the tip of the iceberg,” he said. Chemists, for example, need to perform computationally expensive calculations. With deep learning, these calculations are already being computed up to a million times faster than before, AlQuraishi said.

Artificial intelligence can clearly advance specific kinds of scientific questions. But it may get scientists only so far in advancing knowledge. “Historically, science has been about understanding nature,” AlQuraishi said — the processes that underlie life and the universe. If science moves forward with deep learning tools that reveal solutions and no process, is it really science?

“If you can cure cancer, do you care about how it really works?” AlQuraishi said. “It is a question that we’re going to wrestle with for years to come.”

If many researchers decide to give up on understanding nature’s processes, then artificial intelligence will not just have changed science — it will have changed the scientists too.

Meanwhile, the CASP organizers are wrestling with a different question: how to continue their competition and conference. AlphaFold2 is a product of CASP, and it solved the main problem the conference was organized to address. “It was a big shock for us in terms of: Just what is CASP anymore?” Moult said.

In 2022, the CASP meeting was held in Antalya, Turkey. Google DeepMind didn’t enter, but the team’s presence was felt. “It was more or less just people using AlphaFold,” Jones said. In that sense, he said, Google won anyway.

Some researchers are now less keen on attending. “Once I saw that result, I switched my research,” Xu said. Others continue to hone their algorithms. Jones still dabbles in structure prediction, but it’s more of a hobby for him now. Others, like AlQuraishi and Baker, continue on by developing new algorithms for structure prediction and design, undaunted by the prospect of competing against a multibillion-dollar company.

Moult and the conference organizers are trying to evolve. The next round of CASP opened for entries in May. He is hoping that deep learning will conquer more areas of structural biology, like RNA or biomolecular complexes. “This method worked on this one problem,” Moult said. “There are lots of other related problems in structural biology.”

The next meeting will be held in December 2024 by the aqua waters of the Caribbean Sea. The winds are cordial, as the conversation will probably be. The stamping has long since died down — at least out loud. What this year’s competition will look like is anyone’s guess. But if the past few CASPs are any indication, Moult knows to expect only one thing: “surprises.”

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• An introduction to WHO in the European Region

74th session of the WHO Regional Committee for Europe

First qualitative research study conducted in Turkmenistan focuses on HPV vaccination

Within the framework of a WHO–European Union joint project on immunization in central Asia, the WHO Country Office in Turkmenistan and the Ministry of Health and Medical Industry of Turkmenistan jointly conducted the country’s first qualitative research study.

The project aimed at identifying factors influencing parents' decisions related to human papillomavirus (HPV) vaccination for their children. Consisting of focus-group discussions and in-depth interviews, the research provided an understanding of parents’ attitudes and beliefs about HPV as well as barriers to HPV vaccination.

The results of the research conducted over 3 weeks in late 2023 will serve as the basis for activities to increase public awareness about HPV and sustain confidence in HPV vaccination in the future.

HPV vaccination in Turkmenistan

Turkmenistan included the HPV vaccine in its routine immunization schedule starting in 2016, for boys and girls at 9 years of age. Although national vaccination coverage remains high, a slight downward trend has been observed in both urban and rural areas: from 99.2% in 2021 to 98.5% in 2023.

With a relatively young population increasingly turning to the internet for information, it is important that evidence-based answers to potential questions about vaccination are readily available. However, official online information about vaccines remains limited, creating an opportunity for misinformation to spread with the potential to decrease vaccination uptake in the coming years.

The Ministry asked WHO to conduct a qualitative research study to identify what parents know about HPV, the diseases it can cause, the effectiveness of vaccination in preventing these diseases, and especially what questions or concerns they have on HPV vaccination that need to be addressed in a transparent and accessible manner.

The study, conducted jointly by experts from the Ministry and WHO, aimed to develop targeted interventions to better inform the public and health-care workers about HPV vaccines. Focus groups and in-depth interviews with health-care providers, parents and staff of public organizations were conducted to identify participants’ knowledge, attitudes and behavioural determinants for uptake of HPV vaccine and childhood vaccines in general.

The study was conducted in cities, including the capital, as well as in rural sites in 2 regions. Data collection and analysis were conducted using the COM-B framework, which looks at 3 key components: capability, opportunity and motivation for behaviour change.

Study outcomes

Study findings revealed that attitudes toward HPV were generally positive, partially due to positive attitudes toward vaccination in general but also due to preparatory steps taken by health authorities prior to introducing the HPV vaccine in 2016.

These steps included informing and training health workers to administer and answer questions about the vaccine and to inform parents and children about the benefits of HPV vaccination in preventing HPV infection, emphasizing its role in preventing the spread of the virus rather than only in preventing cervical cancer.

Despite high levels of knowledge and trust in vaccination, study participants did reveal certain gaps in knowledge and potential vulnerability to misinformation. Based on the findings, researchers proposed several measures, including:

• making up-to-date information on childhood vaccination available through a single online portal to ensure accessibility and availability for the public;
• training health workers to increase their capacity to effectively communicate with parents on HPV and other vaccines in the routine immunization schedule; and
• using existing facility-level data and ongoing activities to conduct local, community-based interventions to effectively engage the minority of parents delaying or rejecting vaccination.

Based on these recommendations, the Ministry is developing an action plan that will include regular training for health workers and provision of information to parents via online resources and individual consultations.

With an eye to sustaining high demand for vaccination in the future, the Ministry is also planning to pilot an education module for 10–12-year-olds called Immune Patrol in several schools. WHO developed Immune Patrol to increase health literacy, resistance to misinformation, and knowledge about the immune system and immunization. WHO will provide technical support to the Ministry to implement the action plan and to pilot the Immune Patrol package in 2024 and beyond.

Partnering with the European Union to support and strengthen vaccination

Questions and answers about human papillomavirus, second edition

Field guide to qualitative research for new vaccine introduction

Supporting the prevention, detection and treatment of cervical cancer

The First Descendant FAQ: Release date, cross-play, microtransactions, and other questions answered

Here's everything you need to know about The First Descendant, a free-to-play looter-shooter that looks to rival Warframe and Destiny 2.

• What is it?
• Release date
• How much will it cost?
• What platforms?
• Can you play solo or offline?
• Cross-play and cross-progression?
• System requirements?
• What is the frame rate?
• Gameplay and trailers

•  What is it? •  Release date •  Countdown •  Price and microtransactions •  Platforms •  Game Pass •  Single player or offline •  Minimum requirements • What is the frame rate? •  Trailers

Many were saying it was a slow year for gaming, but I'm having a hard time keeping up with all the games released. As someone who loves XDefiant and recently found their love for a much improved Fallout 76 , I'm staring at the release calendar wondering how I'm going to have time for other games like Flintlock: The Siege of Dawn , Once Human, and coming soon, The First Descendant .

When two of the games I'm already playing are live service titles, and two of the games I'm keen for are also live service, I'm beginning to think I won't be able to play everything. Two is already tough, but four? I'll have to start creating a schedule for specific days, so I can hit everything. 

What is The First Descendant

Best answer: The First Descendant is a free-to-play take on the genre that Destiny and Warframe popularized. First Descendant is an open-world looter-shooter MMO-lite. Expect a mashup of cooperative content that will test the limits of player builds as they take on an ever-increasing level of difficulty across a wide range of content.

The First Descendant  is a free third-person cooperative action RPG shooter featuring cooperative gameplay. Similar to both Destiny and Warframe , players can experience fun and strategic boss fights through 4-player co-op and various unique characters, exciting gunplay, and looting. A variety of equipment is necessary for growth, and you can obtain it through clearing scenario missions within the game or in the World Missions where cooperation is critical to defeating gigantic bosses. The player becomes a Descendant and is given the mission to fight against the invaders for the survival of the human race and to protect the ‘Ingris continent’. The player can experience a spectacular story by growing stronger through various missions and stories to finally discover the Descendants' secret. First Descendant is the first looter-shooter available on Unreal Engine 5.

• Back to the top ⤴

When does The First Descendant release?

Best answer: The First Descendant releases on July 2, 2024 for Xbox Series X|S, Xbox One, Steam, PlayStation 5, and PlayStation 4.

The First Descendant was first shown to the world back in July of 2022. It was only a few months later when The First Descendant went into closed testing. Players from around the world were eligible to sign up. 

Since then, more and better gameplay has been exhibited to the masses, and a few more tests were available to gamers. A couple of weeks before Summer Game Fest, NEXON Games teased a release date reveal was on the horizon. It was then showcased at Summer Game Fest.

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The First Descendant countdown

How much is the first descendant and will there be microtransactions.

Best answer: The First Descendant is free-to-play. While The First Descendant won't cost anything for the base game, there will be microtransactions, including battle passes.

When The First Descendant was announced as free-to-play, there were plenty of people worried as to how the game would be monetized. Given the track record of NEXON with games such as Maple Story, one can only imagine the kinds of things NEXON could potentially do. The main concern is whether the game will be pay-to-win.

For those unfamiliar, pay-to-win is a style of free-to-play game where microtransactions can be made that progress the player forward in some way. Think of it like a time skip compared to everyone else. These are more prevalent in mobile gaming, but lately, things have been getting worse on other platforms.

Maybe you're out of event tokens for some game; just buy some more and continue playing the event! Perhaps you ran out of stamina; again, buy some more. Maybe you want that shiny new weapon, but it takes another 40 hours of grinding to get? Pay $40, and it's yours!

Those are the types of transactions people are worried about, and rightfully so. Hopefully, the microtransactions are balanced around cosmetics only. However, I feel that would have already been announced if it were the case. That's an easy thing to announce for free brownie PR points. 

The First Descendant | See on Xbox

The First Descendant is a third-person looter shooter powered by Unreal Engine 5. Become a Descendant. Fight for the survival of humanity. Descendants have unique abilities to tackle both solo and co-op missions. Up to 4 players use varied mechanics to defeat giant bosses.

See at:  Steam

What platforms is The First Descendant coming to?

Best answer: The First Descendant is coming to Xbox Series X|S, Xbox One, Windows PC, PlayStation 5, and PlayStation 4.

NEXON Games announced that The First Descendant would be coming back to PC, current, and previous generation consoles during its initial reveal. Surprisingly, they've stuck to that. In an era of gaming where previous generation consoles are getting their releases either canceled or are no longer being developed for, it's surprising to see a game launching for last gen 11 years after their initial release.

In fact, I believe this is the first and only announced title coming to last-generation consoles that use Unreal Engine 5 . The same engine that powers the graphical powerhouse of Senua's Saga: Hellblade 2 is the same engine that's going to be running The First Descendant on original Xbox One hardware. That's impressive!

Is The First Descendant coming to Game Pass?

Best answer: No, The First Descendant is already a free-to-play game. There is currently no reason for it to come to Game Pass. However, there could be some added benefits down the road.

Since The First Descendant is already free-to-play, there's no reason for the game to be coming to Game Pass. As it stands, the game will not be going to Game Pass. However, that doesn't mean we won't see some other sort of partnership. 

There are plenty of free-to-play games, such as Apex Legends , that offer benefits to Game Pass users. Things like cosmetic skins or booster packs are regularly available to those who redeem them on the platform. We'll update this document if any such partnership occurs.

Will the First Descendant have single player or offline?

Best answer: Yes and no, The First Descendant can be played solo, but players will have to remain online to play.

The First Descendant is playable for solo players. While I can't speak to the extent as to how far people can make it, it's been reported that running solo is possible. However, it may be more challenging to do so .

Due to the interconnectivity of the title and the nature of the in-game mechanics, The First Descendant cannot be played offline. This is to create both a seamless online experience and protection against cheats, hacks, or memory editing. Hopefully, the servers will be robust on release day!

Will The First Descendant have cross-play or cross-progression?

Best answer: Yes, The First Descendant will have both cross-play and cross-progression between all platforms.

In an interview with God is Geek , Producer for The First Descendant Beom-jun Lee stated both would be coming, “Yes, that’s right. We support both cross-play and cross-progression, so you can play freely on any platform with shared progression.”

This is a huge win for gamers that play on multiple platforms. Having cross-progression between PC platforms and consoles is fantastic for players looking to take advantage of both. I'm one of those players!

The lack of cross-play and cross-progression has become increasingly noticeable in games like Fallout 76 . Having played the PC version for about 30 hours, I had to completely restart when I switched over to Xbox.

What are the system requirements for The First Descendant?

Best Answer: for PC, the system requirements are as follows:

As you can see, compared to other releases over the last couple of years, The First Descendant is relatively light in terms of system requirements. I'm now beginning to understand why it can run on previous-generation systems! I wonder if it's run on iOS.

What is the frame rate for The First Descendant?

Best answer: This largely depends on the platform you're playing on. From video I've seen, Xbox Series X|S and PlayStation 5 are targeting a performance mode of 60 frames per second, with an optional 30 FPS fidelity mode. Xbox One and PlayStation 4, are both uncapped, running between 30 and 60 FPS depending on XB1/PS4 vs XBX/PS4 Pro.

There's a user on YouTube who's been testing the gameplay of The First Descendant on Xbox Series X |S, Xbox One X , and Xbox One . As you can see from the gameplay videos, the performance of the Xbox Series X|S versions is terrific. It's the Xbox One X and Xbox One that suffer and find themselves hovering around 30 FPS in most instances.

The Xbox One X is able to achieve somewhere between 45 and 50 FPS in most circumstances. This allows for VRR enabled televisions running at 120Hz to compensate a bit for performance reasons. I was not able to find a comparable set of testing videos for the PS4 lineup.

The First Descendant gameplay and trailers

Here is a collection of many currently released trailers for The First Descendant.

Michael has been gaming since he was five when his mother first bought a Super Nintendo from Blockbuster. Having written for a now-defunct website in the past, he's joined Windows Central as a contributor to spreading his 30+ years of love for gaming with everyone he can. His favorites include Red Dead Redemption, all the way to the controversial Dark Souls 2. 

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• 4 The First Descendant servers have fallen apart on day two, so you may have to take a break
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