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How to come up with research ideas?

As a very new researcher who is exploring the best way to generate ideas, some guidance on this question would be very helpful. I have found that this is NOT easy. Ideas seem to pop out of my Professor every day and I wonder how he does it. This question is broad;

How do you tend to come up with initial/seed ideas? What is your search method (if you have one)?

What proportion of your ideas for past papers come from; (i) colleagues, (ii) intentionally browsing the literature for ideas, (iii) on the spot inspiration, (iv) conferences, (v) other?

How do you prioritize research ideas?

Is there any special, generalizable method that you've discovered to sift out those ideas that are likely to be unrealistic early on in the process of idea generation?

Based on small amounts of anecdotal evidence I have reason to believe that there is vast heterogeneity among professors regarding the above questions. For example, economist Steven Levitt says he works on 22 papers at once. A professor I know will have maybe 25% of this at any one time.

Related but not duplicate: Is there any software or tools for managing developing research ideas?

• research-topic

• 11 Anecdotally - for me it just happens (and I have much more ideas than time to develop them), while either reading or (much more) solving other problems. Just sparks of "what if?" or "can I generalized it?". For me it rarely happens on purpose - it it not hard, but impossible to force myself to be creative (on research or anything else). Related - Paul Graham, "How to get startup ideas" . –  Piotr Migdal Commented Dec 18, 2012 at 18:53

9 Answers 9

Okay, as you say, this is very broad, and possibly argumentative. So, I'll try to section off my answer for your various sub-questions, and talk not so much about how I do come up (and organize) research ideas, but how I see it done by everyone (including me).

Coming up with ideas

The most exciting phrase to hear in science, the one that heralds new discoveries, is not “Eureka!” but rather, “hmm... that's funny...” — Isaac Asimov

It's probably very akin to asking a large number of artists “how do you come up with inspiration?” , i.e. you can probably get one thousand different answers, and yet not useful answer at the same time. However, there are some elements that I think are common to all. You can't “trigger” new ideas to come into your mind, but you can put your mind into the right disposition to host these new ideas: recognize them and welcome them. Below is a list, certainly partial and limited, trying to detail my perspective in this matter:

Be challenged! Nothing sparks ideas more than being confronted with contradiction, healthy criticism, a spirited debate, maybe a bit of competition. Some people manage to do that by themselves, arguing against their ideas and improving them. I myself (and most of the colleagues and students I have seen) need an echo chamber, someone to discuss things with. If they're not exactly from your field, all the better, as they may have unusual/naïve/silly questions or expectations.

To give an example, some of the most “successful” ideas I have had came while answering questions, for example from a PhD student or colleague, and replying by “no, it doesn't work like that… in fact, it's probably always guaranteed to be false, because… see, it's linked to X… or maybe it's not? hum…”

Be curious! Ideas come from problems. Identifying worthy problems in your field of research, and dissecting larger issues into of specific problems of manageable scope, is at least as hard as coming up with new ideas. In the end my feeling is that, especially for a researcher, all ideas are the result of one’s curiosity.

Manage to get some free time for thinking (and not: teaching, supervising, tutoring, reviewing, writing, sleeping, …). Body and mind. Sure, an idea can pop into your head any time, but it's probably less likely to happen when you teach basic calculus all day that when you get some time to really think .

Know your field, know where a new development need to occur, what is currently missing. Read review papers, search for such ideas through people's articles or blog posts , discuss with senior colleagues who have a comprehensive view of the field, …

One of the ways you can come with ideas is by analyzing how different groups work in your fields, seeing what has been addressed and avoided, what big questions are still open, and how you can link between different works to build a coherent global picture… This is not always successful, but it usually generates some good ideas along the way!

Explore more or less closely related fields, and see if there is something from your background that you could apply to their problems, or ways you could build something together. Such ideas tend to be very strong, because you can oftentimes apply an entire branch of knowledge (ideas, methods, algorithms, etc.) to a very different problem. In that case, the added value comes from your different perspective, as you might try things that others would not think of.

Ways have been devised to come up with new ideas on a given topic, either alone or in group sessions. Brainstorming is probably the best know such method (and might be the most popular, in one form or another), but a really large number of creativity techniques have been developed. They can be applied both to enhance creativity or to boost problem solving efficiency.

Organizing ideas

A quote often attributed to Kant: “someone’s intelligence can be measured by the quantity of uncertainties that he can bear” . If that true, that has serious consequences for research. Accepting that your mind can only efficiently support a finite number of ongoing research ideas, you have to come up with ways to write them down, organize them, prioritize them, come back to them later, etc. Just as you cannot juggle with as many balls as you'd like, such “external” tools will help your brain focus on the ones that you assign high priority (or the ones to which it gives high priority; the brain works in funny ways).

Most people use very low-tech tools for that:

Notebooks , either sorted chronologically or thematically; in the later case, open a series of blanks pages for each new project/idea, and flip through the book whenever you want to check on them. I use a Moleskine ( WP ) for that purpose; having a nice, leather-bound notebook somehow helps me “value” it more and treat it with care (always have it with me, actually use it).

Post-it’s scattered through one’s (real or virtual) desktop. Downsides are obvious.

More people than I thought actually don't use any tools, and just keep all in their mind. Apparently it can be done, but I don't advise it.

But more complicated methodologies have been devised, that are supposed to help you with it:

• Mind mapping , either on paper or software-based.
• Using todo-list flat or two-dimensional todo-list software, or more complex task-tracking software (see, e.g. Trello ).
• The software side of this question is already covered (though possibly not extensively) here on this very Q&A site .

Finally, don't underestimate the possibilities opened by delegating: people in charge of a specific project or sub-project (PhD students or post-docs) can be tasked with maintaining a list of ideas by all contributors of the project, to come to later on.

Answers to your miscellaneous smaller questions:

Most ideas are hardly “traceable” to one source or another. A given idea might have formed in my head during a conference, seeing how people were failing to address a certain issue, then crystallized during a discussion with colleagues, but would never have occurred to me if not for a literature review I had performed a few months before.

I'll come back a bit later and continue working on this answer :)

• 5 +1 for the Asimov quotation. I had this as an epigram in my PhD thesis. –  Nicholas Commented Dec 19, 2012 at 9:37
• Agreed! +1 for Asimov! –  Ben Norris Commented Dec 19, 2012 at 11:44
• What do you think of using something like OneNote as a surrogate to the physical notebooks you were recommending? Latex equation typesetting websites can effortlessly generate .gif pictures of your equations that you can copy into OneNote. Wouldn't this do the same thing with the same level of efficiency (or even more because you won't have any clutter) as a physical notebook? –  Jase Commented Dec 23, 2012 at 3:26
• I've only been using it for a few days, but TiddlyWiki seems promising as a non-linear notebook for ideas. –  Detached Laconian Commented Jul 21, 2018 at 7:07

I'll address two points in your question (the overall question is quite broad):

Ideas seem to pop out of my Professor every day : If you've worked on enough problems, you amass a collection of tools and mental shorthands that you can apply to a new problem. It's a matter of experience. You also might see someone else's paper and realize that they are doing something in a clumsy way and you have learnt a better way to do it, and so on.

I wouldn't worry too much about this: it's a matter of time and experience, and will happen on its own. You're not evaluated on the number of ideas you have in any case. You might want to check how many of these ideas are actually good ones :).

How do you tend to come up with initial/seed ideas? : When you're first staring at a problem, it can be intimidating and difficult. While there's no single strategy for getting a "leg up", some useful techniques (and these might be very math/CS specific) are:

• simplify the problem : can you solve a simpler version ? if not, can you simplify even further ? Often, finding the largest solvable element starts to get your mind rolling
• pattern match : does this problem look like something related that has been solved ? can you borrow a method from there ? if not, why not ? again, the goal is to get your mind off the "ZOMG THIS PROBLEM IS TEH HARD" and onto "Here's a tiny piece that I can chew on".

I'm sure others will have useful ideas as well. Ultimately, you'll find that getting ideas isn't the problem: it's getting GOOD ideas that is hard.

• 1 +1 for pattern match; found it very effective specially among different graph-like representations. –  seteropere Commented Dec 20, 2012 at 7:15
• +100 because I expect that your simplify the problem and pattern match advice will be very helpful. –  Jase Commented Dec 24, 2012 at 5:04
• Check out e.g. Pólya's "How to solve it" for related suggestions –  vonbrand Commented Jan 11, 2016 at 0:10

Here is some things I found useful:

Attending public seminars at the department could spark nice ideas (even if it seems not related to your research).

Chatting with other graduate students.

• Reading deeply with why? in mind. This means reading a lot and also means stopping more than usual in the assumptions hypothesis and results for different papers.
• Read future work and conclusions of the papers. Some papers have a real list of future research ideas.
• Capture the Big Picture. This usually will result in many whys for what you encountered.
• Ask Questions .. Even what seems as silly and fundamental questions for some can be the key for good ideas.

Do you, when presented with anything related to your research, routinely ask, "Why is that? How can I tell if that is the case?"

If not, try doing so.

If so, you probably won't be short on ideas. Your problem will be sorting the good ones from the bad ones.

Then start asking "Is this important? How can I explain why it is important?"

Be open/curious to what related disciplines are doing. In some you'll see that the way they solve their problems could also applied to your field but hasn't been tried yet.

I am also a green researcher, and similarly to you, I find coming up with ideas a daunting task. I have tried to approach this task in a bit more systematic way, than to just be waiting for Godot. Feel free to draw inspiration!

• Finding a problem to work on

I keep a list of interesting problems. This could be something a hear about at a seminar, read about in an article, or just something I think about. I write it down - usually half a page, only few references - and forget about it. I can then pull out my list, and find something. Some of the problems quickly turn out to be too small to be interesting, others not.

• Starting out

When one of my problems are deemed interesting enough, I turn to lit. study. This goes on until I find someone with an interesting treatise. Then I read it, and try to reproduce the result as they do it. (I should here mention that my field is theoretical)

• Reproducing - talk about it

I can use quite some time reproducing previous authors' work. But it is very fruitful, and you tend to learn something. I try to give a local seminar about the work at this point, the junior people in my department does bi-weekly blackboard seminars, where presentations like this are encouraged for exactly this reason.

At this point it is hopefully possible where I can go in and improve state of matter. So I start. This can sometimes require correspondence with the author of aforementioned work.

• Talk about it - again!

For me, discussing my work with peers is essential. At this point I would try to sneak in two slides about 'ongoing work' in a conference presentation in order to get feedback from peers and seniors from the field.

From this point on it is not so much getting the idea anymore, as following through on it. I will leave that to another day.

The most practical way is to go to the Library and look through journals for articles that interest you.

When you have found a selection, then sort them by a) Is this a current concern in your field? b) Is the prevailing methodology/technique practical - have you the resources? c) Will your supervisor(s) find this project interesting?

When you can answer all three questions as Yes, then do a deeper literature research and assess again whether the project is doable in the time available and publishable (sound and interesting to people in your field).

• 2 I think it would be much more efficient to do it over the internet where every single journal article can be accessed instantly (which is not the case in the library where some references are inaccessible or take 5 minutes of wasted time to find). –  Jase Commented Dec 30, 2012 at 1:21

Maybe you could try approaching the problem from another direction,

"What is it that you would like to achieve? what is the purpose of your research?"

There are millions of problems in life at the moment, and finding things to research is not the problem at all, even though it may seem that way. Inspiration is not purely found in a textbook, but are a function of the mind and soul and body.

Experience is what probably allows your professor to come up with constant questions. He probably practices free thinking, whereby he doesn't feel constrained in any way by other people and current belief systems. Maybe a lot of the problems that actually need to be understood, such as mental health and problems that people and our planet, experience everyday, just aren't being taken into your current world-view.

Science in itself is not an end. Science is a state of being, including understanding; and is a way that you as an intelligent, caring and investigative person (I presume) approach problems. A classic example of a problem is, that we don't understand. However, simply not understanding something is not a problem. A problem is something that has effects in the real world, such as, how can we help infertile couples reproduce and have children? Although it appears that now that we have resolved this in some detail, that it was the problem of not understanding DNA and the details of reproduction that probably is what resulted, with in vitro fertilisation, and even in vivo transplants etc. If one were to take the time to step out of this 'curiosity breeds progress' mindset, it would appear that these problems weren't purely driven by a quest for knowledge, but from real world problems, that have fortunately been solved.

I'd be interested in further discussion, as I have only this evening come up with an idea myself!

There's always a thirst for improvement, and this won't cease until people realize that happiness doesn't come from materials. Happiness is within all of us, all we have to do is tap into it. Being only 24 I have seen some truly eye-opening things and I am very humble to each of our personal strengths, but I do feel its a shame that research has become so fascinated with one-upmanship, and away from the real potential and benefit of being so intelligent.

I would recommend Pragmatic Thinking and Learning: Refactor Your Wetware by Andy Hunt. This book has some very good tips for coming up with ideas, research or otherwise.

• 5 I don't think this is very useful without giving some indication of what is actually in the book. –  David Z Commented Dec 21, 2012 at 2:09

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5 Proven Strategies for Generating Original Research Topic Ideas

Embarking on the journey of academic research often begins with the daunting task of selecting a unique and viable research topic. This article outlines five proven strategies to help you generate original research topic ideas, ensuring a strong foundation for your scholarly work. These strategies are designed to guide students and researchers through the initial and often challenging phase of their research projects.

Key Takeaways

• The 'Academic Project Planner' helps organize and structure your research process, fostering the generation of innovative ideas.
• Utilizing the 'Literature Navigator' can expose gaps in existing research, prompting the development of original topics.
• The 'Writing Wizard's Template' provides a framework for brainstorming and refining research questions and hypotheses.
• Engaging with the 'Thesis Dialogue Blueprint' encourages critical thinking and dialogue, leading to unique research angles.
• The 'Research Proposal Compass' aids in aligning your research interests with academic significance and feasibility.

1. Academic Project Planner

Embarking on a research project can be a daunting task, but with the Academic Project Planner , you can transition smoothly from thesis to project mode. This tool is designed to guide you through structured planning, time management, and stress-free project management, paving the way for academic success. A research plan is a living document that helps you organize your thoughts and make the most of your research project.

To begin, follow these steps:

• Select a topic of project management that sparks your interest.
• Utilize credible sources such as academic journals, books, and websites to gather information.
• Develop a topic that is neither too broad nor too narrow in scope.
• Use the planner to narrow or broaden your topic as necessary.

By following these steps, you ensure that your research topic is well-defined and manageable, setting the stage for a successful academic endeavor.

2. Literature Navigator

Embarking on the quest for a compelling research topic can be daunting, but the Literature Navigator is your ally in this academic adventure. This tool helps you navigate literature confidently with clear instructions, efficient strategies, quality sources, and plagiarism prevention for enhanced research efficiency. It's essential to ensure that your chosen topic has been previously explored, allowing you to find relevant articles for review and build upon existing knowledge.

When developing a research question, consider delving into existing literature reviews on your topic. These reviews can illuminate major themes, showcase how authors structure their arguments, and reveal what aspects of the topic have been thoroughly examined. By doing so, you not only gain insights but also demonstrate a breadth of analysis in your preparation.

Here are some steps to guide you through the Literature Navigator:

• Choose Your Topic : Select a topic that has been researched by others to ensure a wealth of literature to review.
• Identify Key Themes: Look for major themes within the literature to focus your research question.
• Analyze Arguments: Understand how authors have structured their arguments and what gaps may exist.
• Reference Appropriately: Cite all relevant authors to show the comprehensive nature of your review.
• Prevent Plagiarism: Use the Literature Navigator to ensure you are crediting all sources correctly.

3. Writing Wizard's Template

Embarking on the journey of thesis writing can be daunting, but with the Writing Wizard's Template , you can navigate the process with greater ease. This template, offered by platforms like Research Rebels, is designed to reduce your anxiety and improve your writing skills. Prof. Jan founded Research Rebels to specifically address the challenges of thesis writing, ensuring that you have a structured approach to crafting your academic masterpiece.

The Writing Wizard's Template simplifies the writing process into manageable steps. Here's a brief overview of what you might expect:

• Understanding the Assignment : Grasping the requirements and expectations.
• Topic Selection : Generating and refining research ideas.
• Outline Creation : Structuring your thesis in a logical flow.
• Drafting : Writing with clarity and academic rigor.
• Revisions : Polishing your work for submission.

By following these steps, you can transform the overwhelming task of thesis writing into a series of achievable goals. Remember, the key to a successful thesis is not just the content but also the presentation. The Writing Wizard's Template ensures that your ideas are well-organized and articulated, paving the way for academic excellence.

4. Thesis Dialogue Blueprint

Embarking on your thesis can be a daunting task, but with the Thesis Dialogue Blueprint , you can navigate the complexities of academic writing with ease. This blueprint is designed to help you engage in the scholarly conversations that are essential to formulating a robust research question. It ensures that your work resonates with ongoing academic debates and demonstrates that you have 'listened' to relevant discussions among scholars before joining in.

To begin, identify the key conversations in your field by reviewing peer-reviewed scholarship, such as books, research reports, and journal articles. Then, delineate alternative approaches to explaining your research problem. If you find a gap in how scholars have understood a problem, your blueprint can guide you in addressing it. Here are the items you'll need before you try to create a thesis proposal :

• Basic questions

By following these steps, you can craft a proposal that is both innovative and grounded in scholarly tradition. Websites that offer tools for thesis writing , including worksheets and templates, can be particularly helpful. They provide articles on research techniques and niche identification, which can further support your academic journey. Remember, a well-structured thesis proposal is your first step towards a successful research project.

5. Research Proposal Compass

Embarking on the journey of crafting a research proposal can be daunting, but with the Research Proposal Compass , you're never alone. This comprehensive guide is designed to steer you through the intricate process of developing a successful research proposal. It provides step-by-step guidance , ensuring that you can navigate each stage with confidence, from formulating your research question to presenting a persuasive argument to academic committees.

The Research Proposal Compass is tailored for students at all academic levels. Whether you're an undergraduate starting your first major project or a doctoral candidate seeking approval for your dissertation, this tool is your ally. It offers resources for thorough investigation, critical analysis, and scholarly discourse, which are essential for encouraging further research and exploration.

To maximize the effectiveness of your proposal, consider the following points:

• Define clear, achievable objectives.
• Establish a solid theoretical framework.
• Justify the significance of your research.
• Outline your methodology with precision.
• Anticipate potential challenges and address them proactively.

Remember, a well-crafted research proposal not only opens the door to academic inquiry but also lays the groundwork for a compelling thesis. Utilize the Research Proposal Compass to chart your course and set sail towards academic excellence.

Embark on your thesis journey with confidence using our proven Thesis Action Plan at Research Rebels . Our step-by-step guide, crafted by academic experts, is designed to alleviate your anxiety and provide clarity on every aspect of thesis writing. Don't let sleepless nights and overwhelming stress dominate your academic experience. Visit our website now to claim your special offer and take the first step towards a successful and stress-free thesis completion. Your path to academic excellence is just a click away!

In conclusion, generating original research topic ideas is a critical step in the scholarly journey, requiring creativity, strategic thinking, and a deep understanding of the academic landscape. The strategies outlined in this article provide a structured approach to uncovering fresh perspectives and unexplored territories within your field of study. By employing these methods, researchers can navigate the complexities of topic selection with greater confidence and lay the groundwork for impactful and meaningful contributions to their discipline. Remember, the pursuit of knowledge is an iterative and evolving process; stay curious, be open to interdisciplinary influences, and embrace the iterative nature of refining your research questions. The path to discovery is paved with the stones of perseverance and intellectual rigor, leading to the ultimate reward of advancing human understanding.

Frequently Asked Questions

How can the academic project planner help me generate original research topics.

The Academic Project Planner is designed to help you structure your academic projects systematically. By breaking down your interests and the current research landscape, it can guide you in identifying gaps in the literature and formulating original research questions.

Is the Literature Navigator suitable for all academic disciplines?

Yes, the Literature Navigator is a versatile tool that can be adapted to any academic discipline. It helps you navigate through existing literature, identify trends, and spot areas that lack sufficient research, thereby inspiring original topic ideas.

What advantages does the Writing Wizard's Template offer for topic generation?

The Writing Wizard's Template provides a structured approach to writing, which can stimulate your critical thinking and creativity. This can lead to the discovery of unique angles and original topics for your research.

Can the Thesis Dialogue Blueprint help if I've found my idea has already been studied?

Absolutely, the Thesis Dialogue Blueprint encourages you to engage in a 'dialogue' with existing research. This can help you refine your idea, find a new perspective, or identify a more specific niche that has not yet been explored.

How does the Research Proposal Compass assist in formulating research questions?

The Research Proposal Compass guides you through the process of crafting a research proposal, which includes defining clear and compelling research questions. It encourages you to consider the significance, feasibility, and originality of your potential topic.

What should I do if my research topic idea is not entirely original?

Don't worry if your idea isn't entirely new; focus on adding value by approaching the topic from a different angle, using a new methodology, or exploring an understudied population or variable. Originality can also come from building upon and expanding existing research.

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• What Is a Research Design | Types, Guide & Examples

What Is a Research Design | Types, Guide & Examples

Published on June 7, 2021 by Shona McCombes . Revised on September 5, 2024 by Pritha Bhandari.

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

• Your overall research objectives and approach
• Whether you’ll rely on primary research or secondary research
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

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

You might have to write up a research design as a standalone assignment, or it might be part of a larger   research proposal or other project. In either case, you should carefully consider which methods are most appropriate and feasible for answering your question.

Table of contents

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

• Introduction

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

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

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

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

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

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

Practical and ethical considerations when designing research

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

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

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

Prevent plagiarism. Run a free check.

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

Types of quantitative research designs

Quantitative designs can be split into four main types.

• Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships
• Descriptive and correlational designs allow you to measure variables and describe relationships between them.

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

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

Types of qualitative research designs

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

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

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

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

Defining the population

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

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

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

• Sampling methods

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

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

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

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

Case selection in qualitative research

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

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

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

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

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

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

Survey methods

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

Observation methods

Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors or social interactions without relying on self-reporting.

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

Other methods of data collection

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

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

Secondary data

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

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

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

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

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

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

Operationalization

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

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

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

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

Reliability and validity

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

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

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

Sampling procedures

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

That means making decisions about things like:

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

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

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

Data management

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

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

Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability ).

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

Quantitative data analysis

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

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

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

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

Using inferential statistics , you can:

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

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

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

Qualitative data analysis

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

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

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

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

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

 Statistics

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

Research bias

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

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

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

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

Operationalization means turning abstract conceptual ideas into measurable observations.

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

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

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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From ideas to studies: how to get ideas and sharpen them into research questions

Jan p vandenbroucke.

1 Leiden University Medical Center, Leiden, the Netherlands

2 Department of Clinical Epidemiology, Aarhus University, Aarhus, Denmark

3 Department of Medical Statistics and Centre for Global NCDs, London School of Hygiene and Tropical Medicine, London, UK

Neil Pearce

Where do new research questions come from? This is at best only partially taught in courses or textbooks about clinical or epidemiological research. Methods are taught under the assumption that a researcher already knows the research question and knows which methods will fit that question. Similarly, the real complexity of the thought processes that lead to a scientific undertaking is almost never described in published papers. In this paper, we first discuss how to get an idea that is worth researching. We describe sources of new ideas and how to foster a creative attitude by “cultivating your thoughts”. Only a few of these ideas will make it into a study. Next, we describe how to sharpen and focus a research question so that a study becomes feasible and a valid test of the underlying idea. To do this, the idea needs to be “pruned”. Pruning a research question means cutting away anything that is unnecessary, so that only the essence remains. This includes determining both the latent and the stated objectives, specific pruning questions, and the use of specific schemes to structure reasoning. After this, the following steps include preparation of a brief protocol, conduct of a pilot study, and writing a draft of the paper including draft tables. Then you are ready to carry out your research.

Introduction

How do you get an idea for a study? How do you turn your idea into a testable hypothesis, and turn this into an appropriate and feasible study design? This is usually at best only partially taught in epidemiology courses. Most courses and textbooks assume that you know your research question and the general methods that you will need to answer it. Somehow it is assumed that you can readily translate your idea into a specific framework, such as the PICO framework (Patient, Intervention, Control or Comparison, Outcome) 1 or the FINER framework (Feasible, Interesting, Novel, Ethical, and Relevant) 2 or that you can fit it into counterfactual reasoning. 3 However, before describing your project in one of these frameworks, you first need to have an idea for your study and think about it in general terms: why you might do a study and how you might do a study.

This paper considers the complex process of having ideas, keeping track of them, turning them into studies, trying them out in pilot studies, and writing a draft paper before you finally embark on your study.

The paper is intended for novice researchers in clinical or public health epidemiology. It is not intended to be a comprehensive literature review about creativity, nor a sociology or philosophical treatise about why scientists get particular ideas (and not other ideas). It is based on our personal experience of (a combined) 70+ epidemiologic research-years. We have worked on very different topics, mostly on opposite sides of the globe, yet found that our experiences are quite similar. The fact that these issues are rarely covered in epidemiology courses has provided motivation to reflect on our experience.

Getting new ideas

So how do you get an idea? How some juxtaposition of neural patterns in our brain suddenly creates a new idea is a process that we are far from understanding. According to Karl Popper, the origin of new ideas does not matter; the only thing of interest is to devise how to test them. 4 Over the past decades, the literature has been enriched with new ideas about “being creative” in science – as witnessed in the book Innovation Generation by Ness. 5

In the present paper, we will not cover the literature about creativity and discovery in depth, but we will discuss the issues that we consider relevant to epidemiologic research. We will first consider the more general principles.

The real complexity of the thought processes that lead to a scientific undertaking is almost never described in published papers. Immunologist Medawar claimed that in this respect almost all scientific papers may be a fraud – not in the sense that scientists deliberately produce misleading data, but in the sense that the real thought processes that lead to the data and conclusions are not mentioned. 6 Scientists tell us about their real thought processes in memoirs, inaugural, or valedictory lectures – which is why these are so much more interesting than “standard” papers or presentations.

What strikes our minds: regularities or anomalies?

All sciences study a particular “object of knowledge” (eg, “matter”, “life”). Ideas come from experience and previous knowledge or facts about this object of knowledge, although this knowledge is always filtered through the perspective of one or more theories. 7 Epidemiology studies the distribution and determinants of disease in human populations, 8 and epidemiological ideas arise from observing and thinking about populations. 9 These could be clinical populations (ie, clinical experience, sometimes involving just a few patients), exposure-based populations (eg, workers exposed to a particular chemical), or general populations (geographically defined or sociologically defined). Whatever the population we are interested in, ideas come from observing either regularities or anomalies.

The observation of regularities (“induction”) is a common origin of new ideas. 4 , 10 – 13 Philosopher David Hume described “Induction” as: regularly seeing two things happening in succession (like pushing a switch and a light going on) leads to suspicions of causality. As he pointed out, causality can never be proven by the mere observation of “constant conjunctions”, but observing regularities can start our train of thought. 12

An anomaly (or irregularity) strikes our mind, because it defies our expectations. The regularity that we expected was our “hypothesis” (even if it was not really explicitly formulated); the anomaly is a “refutation”. 4 , 13 It forces us to think about other explanations, and these lead to new hypotheses that we then try to test. Thus, scientists do not usually start from hypotheses that are nicely formulated “out of the blue”, but instead start from previous knowledge and experience; when they are challenged by anomalies, scientists seek new explanations. 14

An interesting way to discover anomalies is to enter a new field of research; since you have other background experience than the people already in the field, you see things that they take for granted but that strike you as odd – at the same time, you may also see new explanations for these anomalies. One of the pioneers of clinical epidemiology, Sackett, once wrote that scientists should “retire” from a field as soon as they become “experts”. 15 When you are too long in a field, you will no longer see the anomalies, and you may even obstruct newcomers with new explanations. Of course, there are differences between scientists: some roam across various fields and others stick to a problem area that they explore with increasing depth – then the increasing depth and the new techniques that one needs for advancing one’s thoughts will be like a “new field”.

Taxonomies of discovery

Few researchers have listed the different ways in which one can arrive at new ideas, that is, lists of ways of discovery. We will present two of them – which have very different origins but remarkable similarities. Several examples of studies corresponding to items on these two lists are given in Appendix Examples A1–A10 .

Sources for new ideas about health care evaluation were described by Crombie and Davies in the chapter “Developing the research question” of their book on Research in Health Care that reflects a UK public health experience. 16

• “Review existing practice […] the current organisation and delivery of health care is not as good as it could be […]”
• “Challenge accepted ideas […] much of health care is based on accepted practice rather than research evidence […]” ( Appendix Example A3 )
• “Look for conflicting views […] which indicate either that there is not enough evidence, or that some practitioners are misinformed”
• “Investigate geographical variation […] reflecting on the reasons [for geographical variation] can be a fruitful source of research questions […]” ( Appendix Example A6 )
• “Identify Cinderella topics […] important areas of health care are often overlooked […]”
• “Let loose the imagination […] look for wild or impossible ideas […] free the mind from the constraints of conventional wisdom […].”

A taxonomy for sources of clinical research questions about medical care and clinical problems was proposed by Hulley and Cummings, in the context of clinical research in the US: 2

• “Build on experience;” your own experience, that of close colleagues with whom you can freely discuss your research ideas, and that of a good mentor, because young researchers might not yet have much experience, “An essential strategy for a young investigator is to apprentice himself to an experienced senior scientist who has the time and interest to work with him regularly.”
• ○ By harvesting “the medical literature and attending journal clubs, national and international meetings, seeking informal conversations with other scientists and colleagues”
• ○ “A sceptical attitude about prevailing beliefs can stimulate good research questions”
• ○ Be alert to “careful observation of patients, which has historically been one of the major sources of descriptive studies” ( Appendix Examples A1 and A2 )
• ○ Your experiences in teaching; having to explain something may make you aware of gaps in your knowledge; questions by patients and colleagues may similarly identify things that we do not fully understand or ignore
• “Keep the imagination roaming […]” by a mixture of creativity and tenacity; “put an unresolved question clearly in view and turn on the mental switch that lets the mind run freely toward it”.

A special mention needs to be made about the last categories of both the lists: “Let loose the imagination” and “Keep the imagination roaming”. These are especially important to find innovative solutions. In many situations wherein you cannot do a perfect study and you run a grave danger of potential confounding or bias, it helps to “get deeply immersed”: to understand the problem biologically, clinically, socially, organizationally, and environmentally will help you to think about what is happening, why it is happening, and whether you can find situations in which the potential confounders or biases do not exist or exists in reverse. You should forget formal designs and think out of the box: you will find instances of studies that mutually reinforce each other and may even arrive at formulating new designs or analytic solutions (see Appendix Examples A7–A10 ).

Keeping track of your ideas

It is not only important to have good ideas but also important to develop them. Researchers who work in laboratories have the habit of keeping “lab logs”. They write down briefly the results of an experiment, note why they think it went wrong, and how they will perform the next experiment. This permits them to trace how they changed the experiments or even the content and the direction of their research. We should do the same in epidemiologic and clinical research, particularly in the stage of creating new ideas. Such notes about ideas can include not only hypotheses and views or results by others but also drawing directed acyclic graphs (DAGs) (see “Intermezzo: specific schemes to structure reasoning” section) to make the causal structures of ideas clear.

The greatest minds kept track of their thoughts. Charles Darwin’s notebooks document his ideas, his observations, his readings, and new theories and facts that struck him. 17 For example, Darwin noted a story that he heard from his father, a medical practitioner. His father recounted that he had been struck by one of his patients’ ways of expressing himself, because he had attended a parent of the patient who had had the same mannerisms – even though the parent had died when the patient was still an infant. Remarks like these still have relevance today when we think about the heredity and evolution of behavior.

The sociologist C Wright Mills carried the description of the process one step further in the appendix of his book on The Sociological Imagination . 18 He encourages young sociologists to set up a file of stacked cards to keep track of “[…] personal experience and professional activities, studies underway and studies planned […]” which “[…] encourages you to capture ‘fringe thoughts’: various ideas which may be by-products of everyday life, stretches of conversations […]”. These notes are continuously reshuffled, regrouped under new headings, and pondered. Mills denounced the habit of most (social) scientists who feel the need to write about their plans only when they are going to apply for a grant. He thought that scientists should continually work with their file of ideas and regularly take stock of how these have evolved.

Such strategies are still relevant today, even if our “logs” are kept in electronic form, particularly because grant writing has become more demanding, hectic, and time-consuming. From such files, new research projects are born: while your ideas gradually develop, you keep wondering what data you might need to prove a certain proposition, and how you might get those data in the easiest way possible. Often, ideas are reshuffled and regrouped under new headings. A new observation, a new piece of literature may make old ones fall into place, or there may suddenly be a new opportunity to work out an old idea.

A complementary advice recently came in a blog from a contemporary sociologist, Aldrich: his advice is to “Write as if you don’t have the data”, that is, to write “[…] the literature review and planning phase of a project, preferably before it has been locked into a specific research design”. 19

The role of emotions

Underlying the discovery process, there are often two emotions: “surprise” and “indignation”. Surprise is the intellectual emotion when we see something happening against expectation: a patient with an unusual exposure, unusual disease manifestation, sudden cure, or sudden ill-understood deterioration; a laboratory result that is an anomaly; and a sudden epidemic of disease in a population. Indignation is the moral emotion: a group of patients is not being treated well because we lack sufficient knowledge, or because we are blundering in organizing health care or in transmitting and applying public health knowledge. Some passion is useful to bring any undertaking to a good end, be it that the passion should be restrained and channeled into polite undertakings, like in a research protocol. While doing the research project, maintaining some of the original passion will help you to find ways to overcome the daily hassles of research, the misadventures, the difficulties of getting others to collaborate, and the difficulties of getting published ( Appendix Example A11 ).

Sharpening the research question: the pruning

Pruning a research question means cutting away anything that is unnecessary, so that only the essence remains.

The initial spark of an idea will usually lead to some rather general research question. Invariably, this is too ambitious, or so all-encompassing that it cannot be researched (at least not within the time frame of a single grant or PhD project). You have to refine your research question into something that is interesting, yet feasible. To do so, you have to know clearly where you are heading. The emphasis on a clear preconceived idea about what you want to attain by your research often comes as a surprise; some people object: “[…] isn’t research about discovery? How can you know in advance what you want to find?”

The social scientist Verschuren proposed the “wristwatch metaphor”. 20 A researcher is not like a beachcomber, who strolls along the beach to see whether anything valuable washed ashore. Rather, a researcher is like someone who has lost her wristwatch on the beach and returns to search for it. She knows what part of the beach to look, she can describe her wristwatch in detail, and once she has found it, she knows that this is the watch she was looking for. Some further background to these ideas can be found in Appendix B .

Charles Medawar wrote in his Advice to a Young Scientist (page 18) 21 that as much as politics is the ‘art of the possible’, research is the ‘art of the soluble’. A research question should be limited to a question that can be solved with the resources at hand. This does not mean that you should preferentially study “trivial” questions with easy solutions. It does mean that you should seek out your particular niche: something specific, something that was overlooked by others, or some new twist to a general question, so that you can make your own contribution.

The concept of “serendipity” is often invoked when thinking of “seeking novelty”: it means finding something that you were not looking for. For a full discussion of the more complex reality that shows how, in reality, “chance favors a prepared mind”, see Appendix C .

Proceed in the inverse order of the paper that you will write

From the aforementioned, we know that we need a precise aim and a soluble research question.

How can we achieve this? The best approach is to “begin at the end”, that is, the conclusion that you hope to support when you eventually publish your research findings, perhaps many years from now. 22 Most medical research papers have a fixed format: introduction, methods, results, discussion. Usually, the discussion has three parts: summary of the results, discussion of the strengths and limitations, and the importance and interpretation of the findings. There you start: you try to imagine what such last lines of the eventual paper might be – in particular what their intent, their message to the reader might be. Another useful strategy would be to imagine what might be written in the separate box “What this paper adds” that many journals nowadays ask to convey the message from the authors clearly and succinctly to the readers.

The “latent” versus the “stated” objective

The pioneer clinical epidemiologist Feinstein wrote that a good research consultant should be like a good clinician, who first wants to learn from the patient: “What is the chief complaint?”, that is, which is the problem that you want to study. Next, “What will you do with the answer?” 22 The latter question is not just about the potential conclusions of the research paper, but more importantly, their meaning. What is the intended effect (or impact) of the findings? He called this the “latent objective”: what do you want to achieve or change by your project; the “stated objective” is different, it is the type of result that the study will deliver. For example, the stated objective can be that you want to do a randomized trial to compare one intervention versus another and that you will look at recurrence of disease. The latent objective might be that you are concerned that one intervention may be harmful to patients, driven by special interests, and that if this is the case it should be abolished.

Rather analogously, the long-time editor of the Annals of Internal Medicine , Edward Huth, proposed in his book about medical publishing the “So-What” and the “Who-Cares” tests: “What may happen if the paper’s message is correct?”; may it change concepts and treatment or stimulate further exciting research? 23 In fact, many funders now require such an “impact statement” as part of the grant application process.

Experienced research consultants know that when trying to discover the latent objective, it is useful to brush aside the detailed protocol and to ask directly what the meaning of the research is. The meaning of the research is often not clearly stated in a formal study protocol that limits itself more or less to “stated aims”. 24 Like a patient who cannot articulate her/his complaints very well, would-be researchers lose themselves in trivial “side issues” or operational details of the protocol. Appendix Examples A2 and A11 explain the importance of elucidating the underlying frustration of the clinician-researcher to clearly guide a research effort.

After initial questions have set the scene and clarified the “latent objective” of a project, the next questions are more operational, translating the latent objective back into a “stated objective”. 22 The stated objective should be a feasible research project. According to Feinstein, one should ask: what maneuver is to be executed (what intervention, deliberate or not, and how is it administered), what groups are to be compared (and why those groups), and what is the outcome that we will study?

In these phases of discussion, one needs to immerse oneself into the problem: one has to understand it biologically and clinically, and how it is dealt with in the daily practice of health care in the setting in which you will do research. Getting deeply immersed in the problem is the only way of arriving at shrewd or new solutions for studies on vexing medical or public health problems ( Appendix Example A9 ). Mere discussion of technical or procedural aspects of a proposed design, data collection, or analysis will usually not lead to new insights.

Specific pruning questions, to ask yourself or others

In initial discussions, one goes back and forth between the general aim (the latent objective), the scientific questions that follow from it, and the possible research designs (with stated objectives). After feeling secure about the “latent” aim, proceed with more specific questions.

• Try to describe exactly the knowledge gap that you want to fill (ie, the watch that you lost at the beach). Is it about etiology, about pathogenesis, about prognosis? What should change for the benefit of a particular group of patients? Try to be as specific as possible. Do your colleagues see these problems and their solutions as you do? – and if not, why don’t they?
• Once you know the point you want to make, describe what table or figure you need to fill the gap in knowledge, that is, what would your results look like? This means drawing a simple table or graph. Are these the data you want? Will these tables convince your colleagues? What objections might they have? Keep in mind that if the research results go against ingrained beliefs, they will be scrutinized mercilessly, so the important aspects of your research should be able to withstand likely objections.
• Thereafter, the questions become more practical: what study design is needed to produce this table, this figure? Can we do this? Do we have the resources or can we find them?

Be self-critical

You should always remain self-critical about the aspects that threaten the validity of your study ( Appendix Example A12 ). 25 If the practical problems are too large, or the research question too unfeasibly grandiose, it might be wise to settle for a less ambitious aim ( Appendix Example A13 ).

Paraphrasing Miettinen, 26 the first decision is whether you should do the study at all. There might be several reasons to decide not to pursue a study. One might be that arriving at a satisfactory design will be impossible, because of biases that you are unable to solve. It serves no purpose to add another study that suffers from the same unsolved problems as previous studies. For example, it does not serve any purpose to do yet another study that shows lower mortality in vegetarians, if you cannot solve the problems of confounding that vegetarians are persons who have different lifestyles in comparison with others. 27 (If, however, you have found a solution – pursue it at all means!) Nevertheless, thinking about the potential problems and ultimate aims of a seemingly impossible question can foster the development of a new study design or a new method of analysis, ( Appendix Examples A2, A9, and A10 ). In the same vein, deciding that you cannot do a study yourself might make you look for collaboration with persons who have the type of data that you do not, for example, in a different population where it is believed that confounding is not so severe or may even be in the opposite direction.

All studies have imperfections, but you need to be aware which ones you can tolerate. 28 In the early stages of an enquiry, an “imperfect” study might still be worthwhile to see whether “there might be something in it”. For example, time trends or ecological comparisons are often seen as poor study designs to assess causality by themselves, but they can be very valuable in helping to develop ideas, as well as providing a “reality check” about the potential credibility of some hypothesis. 29

Conversely, it is pointless to add yet another study, however perfect, showing what is already known very well – unless you have to do it for “political” purposes, say, for convincing decision makers in your own country.

Finally, it is not a good use of your time to chase something completely improbable or futile. For example, at the present state of the debate, it serves no purpose to add another study about the presence or absence of clinical benefits or harms of homeopathy: no one will change his or her mind about the issue. 30 , 31 An exception might be something that is highly improbable, but that if true might lead to completely revolutionary insights – such an idea might be worth pursuing, even if the initial reaction of outsiders might remain incredulousness. Still, you should pursue unlikely hypotheses knowingly, that is, with the right amount of self-criticism – in particular, to make yourself aware when you are in a blind alley.

To keep yourself on the “straight and narrow”, it helps to form a group of people who cover different aspects of the problem you want to study: clinical, biochemical and physiological, and methodological – to discuss the project as equals. Such discussions can not only be tremendous fun but also will invariably lead to more profound and diverse research questions and will help to find solutions for practical as well as theoretical problems. In the right circumstances of a “machtsfreie Dialog” 32 (a communication in which all are equal and that is only based on rational arguments and not on power – which all scientific debates should be), such a circle of colleagues and friends will help you to be self-critical.

Finally, when pursuing one’s research interests, one should be prepared to learn new skills from other fields or collaborate with others from these fields. If one stays only with the techniques and skills that one knows, it might not lead to the desired answers. 33

What if the data already exist? And you are employed to do a particular analysis with an existing protocol?

Even in the circumstance that the data already exist, it greatly helps to not jump into an analysis, but to think for yourself what you would ideally like to do – if there were no constraints. As Aldrich mentioned, 19 also in that circumstance researchers should still

[…] begin their literature review and conceptual modeling as if they had the luxury of a blank slate […]. Writing without data constraints will, I believe, free their imaginations to range widely over the realm of possibilities, before they are brought to earth by practical necessities.

Moreover, this will make clear what compromises one will make by accepting the available data and the existing analysis protocol. Otherwise, one starts an analysis without being sufficiently aware of the limitations of a particular analysis on particular data.

The difference between explanatory and pragmatic research

A useful distinction is between explanatory and pragmatic research: the former is research that aims at discovery and explanation, whereas the latter is intended to evaluate interventions or diagnostic procedures. The first type of research consists of chasing explanations by pursuing different and evolving hypotheses; the second type of research aims at making decisions about actions in future patients. 27 The two opposites differ strongly in their thinking about the types of studies to pursue (eg, observational vs randomized), about the role of prior specification of a research hypothesis, about the need for “sticking to a prespecified protocol”, and about subgroup analyses and multiplicity of analyses. Some of these will be explained in the following subheadings.

The difference between explanatory and pragmatic trials is sometimes thought to mirror the difference between doing randomized trials versus observational research. However, even for randomized trials, a difference exists between “ pragmatic” and “explanatory” trials (coined first by Schwartz and Lellouch). 34 Because it is not always easy to delineate what aspects of a randomized trial are “pragmatic” or “explanatory”, instruments have been crafted to help researchers and evaluators. 35 , 36 Conversely, not all observational studies are explanatory: some are needed for pragmatic decisions (think about adverse effects of drugs and also about diagnostic evaluations where studies should influence practice guidelines) – while other studies aim at explaining how nature works.

Which iterations should you allow yourself? Anticipating the next project

Thinking about a research problem is a strongly iterative process. 2 , 33 , 37 One starts with a broad aim and then tries out several possible ideas about studies that might lead to better understanding or to better solutions.

Likewise, project proposals characteristically go through many iterations. In the early phases of the research, it is commonplace that the study design or even the research question is changed. Specific suggestions about common research problems and their potential solutions were given by Hulley and Cummings, 2 which we reproduce in Appendix D .

The revision of the aims of a project may be profound, in particular in explanatory research (see “The difference between explanatory and pragmatic research” section), in contrast to pragmatic research (see “Shouldn’t you stick to a predefined protocol?” section). The chemist Whitesides wrote: “Often the objectives of a paper when it is finished are different from those used to justify starting the work. Much of good science is opportunistic and revisionist”. 38 Along a similar line, Medawar proposed that to do justice to the real thought processes of a research undertaking, the discussion section of a paper should come at the beginning, since the thought processes of a scientist start with an expectation about particular results. The expectation determines which findings are of interest and why they will be interpreted in a particular way. 6 He added that in real scientific life, scientists get new ideas (ie, new expectations) while doing their research, but “[…] many of them apparently are ashamed to admit, that hypotheses appear in their mind along uncharted byways of thought”. 6

“Seeing something in the data” can be an important part of scientific discovery. This is often decried as “data dredging”, which it is not: one sees something because of one’s background knowledge and thereby there always is some “prior” that exists – even if that was not specified beforehand in the study protocol. 27 , 39 The word “exploratory” is often misused when it is used to characterize a study. True “exploratory” data analysis would only exists if it is mindlessly done, such as a Genome Wide Association Study (GWAS) analysis – but even GWAS analyses have specific aims, which becomes clear when results are interpreted and some findings are designated as “important” and others not. As stated by Rothman:

Hypotheses are not generated by data; they are proposed by scientists. The process by which scientists use their imagination to create hypotheses has no formal methodology […]. Any study, whether considered exploratory or not, can serve to refute a hypothesis. 40

Appendix Examples A5 and A7 show how projects changed mid-course because of a new discovery in the data or in the background knowledge about a research topic.

Generally, it is a good habit to think through what the next project might be, once you will have the result of the project you are currently thinking about, so as to know what direction your research might take. 33

Shouldn’t you stick to a predefined protocol?

Different research aims, in particular along the “explanatory” versus “pragmatic” continuum, may lead to different attitudes on the amount of change that protocols may endure while doing research. 27 , 39 For randomized trials, and also for pragmatic observational research, the research question is usually fixed: does a new therapy lead to better outcomes for a particular group of patients in a particular setting? Because findings from randomized trials or pragmatic observational research may lead to millions of patients to adopt or avoid a particular therapy (which means that their well-being or even life depends on the research) researchers are generally not at liberty to change their hypotheses at the last moment – for example, by suddenly declaring an interest in a particular subgroup. They should stick to the predefined protocol. If a change is needed for practical reasons, it should be clearly stated in the resulting publications. This makes thinking about research questions and doing pilot studies beforehand all the more important (see “Pilot Study” section).

In contrast, much epidemiologic and clinical research tries to explain how nature works. This gives greater leeway: exploration of data can lead to new insights. Thus, “sticking to the protocol” is a good rule for randomized trials and pragmatic observational research, but may be counterproductive for explanatory research. 39 , 41 Nevertheless, it is good to keep track of the changes in your thoughts and in the protocol, even if only for yourself. In practice, many situations are intermediate; in particular when using large available data sets, it often happens that one envisages in a protocol what one would do with the data, only to discover upon opening the data files that the data fall short or are more complex than imagined; this is another reason for doing pilot studies, even with large available data sets (see “Pilot Study” section).

How much literature should you read?

If you are setting up a new research project in a new area, do not start by reading too much. You will quickly drown in the ideas of others. Rather, read a few general reviews that identify unanswered problems. Only return to the literature after you have defined your research question and provisionally your study design. Now, the literature suddenly becomes extremely interesting, since you know what types of papers you need. You also know what the potential objections and shortcomings are of the different design options, because you thought about them yourself. The number of relevant papers usually greatly shrinks, see Appendix Example A4 .

Shouldn’t you do a systematic review first?

It is argued that before embarking on a new piece of research, one should first do a systematic review and/or meta-analysis, because this may help to define the gaps in knowledge more precisely, and guide new research – or may show that the question has been solved. This argument is somewhat circular. A systematic review is a piece of research in itself, intended for publication, and requires much time and effort. Like any piece of research, it requires a clear research question. As such it does not “identify gaps”: a systematic review is about a research question which is already specified, but for which more information is needed. Thus, the main function of the advice to first do a systematic review is to know whether the research question that one has in mind has not yet been solved by others. Perusing the literature in depth is absolutely needed, for example, before embarking on a randomized trial or on a major observational study. However, this is not the same as doing a formal systematic review. In-depth scoping of the literature will suffice. If it is found that potentially valuable studies already exist on the research question that one has in mind, then the new study that one is thinking about may be discarded, and a systematic review should be done instead.

Intermezzo: specific schemes to structure reasoning

Specific schemes have been proposed to guide our reasoning between the stage of delineation of the “gap in knowledge” and the stage of proposing the research design.

The acronym FINER (feasible, interesting, novel, ethical, and relevant) was coined by Hulley and Cummings 2 and denotes the different aspects that one should consider to judge a budding research proposal. These words are a good checklist for an in-depth self-scrutiny of your research. The central aspects are the feasibility and whether the possible answers are exciting (and/or much needed).

The PICO format (Patient, Intervention, Control or Comparison, Outcome) is advocated by the evidence-based medicine and Cochrane movements and is very useful for clinical therapeutic research, particularly randomized controlled trials (RCTs). 1 , 42 Questions about therapeutic interventions are highly specific, for example, a particular chemotherapeutic scheme (the intervention) is proposed to study survival (the outcome) among young women with a particular form of stage III breast cancer (the patients). This framework is less useful, and becomes a bit pointless, for etiologic research about generalizable questions such as: “Does smoking cause lung cancer?” which applies to all humans and to different types of smoking. Of course, all research will be done in particular population, with particular smoking habits, but this does not necessarily define the research question. Some of the first investigations about smoking and lung cancer were done in male doctors aged ≥35 years in the UK 43 – this was a very convenient group to research, but being a male doctor in the UK is not part of the research question.

The PICO format is thus most applicable for pragmatic research. A much more detailed and elaborate scheme for pragmatic research was proposed by the US Patient-Centered Outcomes Research Institute (PCORI) which has published Methodology Standards, including “Standards for Formulating Research Questions”. While we would not agree with all six standards, junior investigators may find the structure useful as they think through their options – especially for pragmatic research questions. 44

Counterfactual reasoning 3 emphasizes those aspects of the “ideal randomized trial” that should be mimicked by an observational study. A key question is whether your study is addressing a hypothesis that could in theory be studied in a randomized trial. For example, if the research question is “does smoking cause lung cancer?”, then this is a question that could in theory (but not in practice) be addressed by randomizing study participants to be smokers or nonsmokers. In this situation, it may be useful to design your observational study with the intention of obtaining the same answer that would have been obtained if you had been able to do a randomized trial.

However, the aims of explanatory observational research are different from those of randomized trials. 27 Explanatory research about disease etiology may involve “states” like being female, being old, being obese, having hypertension, having a high serum cholesterol, carrying the BrCa1 gene, and so on, as causes of disease. None of these causes are interventions. In contrast, RCTs focus on what to do to change particular causes: which interventions are feasible and work? For example, being female might expose a person to job discrimination; the intervention might be to have women on the appointment committee or to use some kind of positive discrimination. Likewise, the gene for phenylketonuria leads to disease, but the intervention is to change the diet. For carriers of BRCa1 genes, different strategies can be evaluated in RCTs to evaluate their effectiveness in preventing premature death due to breast cancer: frequent screening, prophylactic mastectomy, hormone treatment, and so on – which may have different effects. For obesity or hypertension or hypercholesterolemia, different types of interventions are possible – with potentially different effects and different adverse effects.

The interventionist outlook, that is, trying to mimic an RCT, can be very useful, for some type of observational studies, for example, about the adverse effects of drugs. It helps to make certain that one can mimic an “intervention” (ie, patients starting to use particular drugs) that is specific and consistent in groups of patients that are comparable (more technically, exchangeable – meaning that the results of the investigation would not change if the persons exposed and nonexposed were swapped). These conditions can be met in a credible way, if there are competing drugs for a similar indication, so that there is an active drug comparator: the interventions (use of different drugs in different patients) will be well defined, and the patients on the different drugs will tend to be comparable. This works particularly well if you are focusing on adverse drug effects that were unknown or unpredictable at the time of prescription. 45 , 46 For example, you may obtain more valid findings in a study that compares the adverse effects of two different beta agonists for asthma care (ie, two different drugs within the same class), than to design a study which compares patients who are prescribed beta agonists with patients who are prescribed other asthma medication, or no medication at all – because the latter might be a highly different group of patients. 47

As mentioned, there are some important studies about causes of diseases where a randomized trial is not feasible, even in theory. In particular, there are various “states” which are major causes of disease (obesity, cholesterol, hypertension, diabetes, etc). These states strongly affect the risks of disease and death, but cannot be randomized. For example, it is difficult to conceive of randomizing study participants to be obese or not obese; however, we could randomize them for the reduction of obesity, for example, through exercise, but such a study would assess the effects of a particular intervention, not of obesity itself. Still, it remains important to estimate the overall effects of obesity, that is, to answer the question “would this group of people have had different health status, on the average, if they had not been obese”. In this situation, the concept of “interventions” is not relevant to designing your study (at least in the way that the term “intervention” is commonly used). What is more relevant is simply to focus on the counterfactual contrast which is being assessed (eg, a body mass index [BMI] of 35 versus a BMI of 25), without specifying how this contrast came about.

A technique that has gone hand in hand with counterfactual reasoning in epidemiology is drawing DAGs; several introductions to DAG theory can be found in epidemiologic textbooks. 3 , 48 DAGs can be useful in the brainstorming phase of a study, after the general research question has been defined. At this stage, a general structure for the study is envisaged and the complexity of the causal processes needs clarification. A DAG can be extremely useful for illustrating the context in which a causal question is being asked, the assumptions that will be involved in the analyses (eg, whether a particular risk factor is a confounder, a mediator, or a col-lider), and help us question the validity of our reasoning. 49 Using DAGs helps us also decide which variables we need to collect information on and how they should be measured and defined. Given that DAGs root in causal thinking, their construction is, of necessity, subjective.

Preparation: pilot study, protocol, and advance writing

Doing a pilot study and collecting ancillary information about feasibility.

May I now start? is a question heard after lengthy deliberations about the research question and the potential studies that follow from it. Such deliberations almost invariably produce a lot of enthusiasm and exhilaration – because they are fun. The researcher wants to begin collecting data or start the analysis. However, Crombie and Davies, in their chapter about “Developing the research question” state emphatically: “Don’t rush into a study”. 16 Separate from doing a pilot study, which is about the procedures of your study, you may also need to collect ancillary information before actually starting your study.

Pilot study

Even if you think you are totally certain of what you want, you should first do a pilot study, based on a brief protocol. 2 , 22 That initial protocol should be easy to write. You have already discussed the aim and design of your study. Write them down. You expect a particular type of information that is essential and that will tell the essence of your message (a particular 2-by-2 or X-by-Y table, a particular graph), which you can describe.

Pilot studies are not done to know the likely direction of the results; instead, the aim is to see whether you will be able to perform the procedures of your study – and ultimately whether that really is the study you want to do. 50 The aim is to save yourself from embarrassment: data that very surprisingly do not turn out to be what you expected, questionnaires that are misunderstood or do not deliver the answers that you need or that are not returned, laboratories that do not produce, patients who do not show up, heads of other departments who block access to their patients or materials, or yourself who needs more time to manage the complexity of the undertaking.

We have never heard of someone who was sorry for having done a pilot. Conversely, we know many persons who found out at much personal embarrassment and institutional cost that their project was unfeasible. In intermediate cases, the pilot may show the need to change questionnaires or procedures before the study goes ahead.

In principle, a pilot study should be exactly like your final study and test out all your procedures on a small number of persons. Often, it is better to approach the task piecemeal and pilot different aspects of the research one by one.

A tough question is how to do pilot studies and pilot analyses when ethical or institutional review board approval is necessary for some of the actions in a pilot study. One solution might be to avoid piloting some procedures; for example, try parts of the procedure – for example, you may not be able to randomize in a pilot, but you may be able to try out data collection procedures and forms. There is a degree of circularity about piloting, also in obtaining funding, as one may need funding for the pilot. In practice, the best step might be to ask the ethics committee or review board of your institute which aspects of the research can be piloted and under what conditions.

In Appendix E , several questions that you might ask in pilot studies are listed. They may lead to profound reassessments of your research – particularly if you are piloting the collection of new data, but also if the research involves analyses of existing data.

Ancillary information

It may be necessary to collect additional information about event rates or standard deviations of measurements to calculate the statistical precision that might be obtained. Also, sometimes you need other ways of “testing the water” like procedures to streamlining data collection from different centers in order to know whether the study is feasible. Depending on the study size and importance, such activities may become studies in themselves and actually take a lot of time and money.

Advance writing of paper: before full data collection and/or analysis

Whitesides’ advice is:

The key to efficient use of your and my time is that we start exchanging outlines and proposals as early in a project as possible. Do not, under any circumstances, wait until the collection of data is ‘complete’ before starting to write an outline. 38

After the pilot study, you have a firm grasp of all elements that are necessary for a scientific paper: introduction, materials and methods, results, and discussion. In the introduction, you explain why you have done this research. Almost always, an introduction comprises three ideas: what is the general problem? what is the particular research question? what study will you perform to answer that question? This is followed by the materials and methods section. They have been extensively discussed and have been fine-tuned in the study protocol and the pilot study. Thereafter come the results sections. By now, you know what tables or figures you want and how you can obtain them, but not what the final numbers will look like. You will also have an idea about the auxiliary tables that you might need to explain your data to others (such as a table with the baseline characteristics or an additional table with a subgroup analysis). You can now draft the layouts of all these tables. Visualizing the presentation of your results in advance is the “bare minimum” of writing in advance.

Finally, the discussion section. Can you write a discussion before you know the final data? Of course you can; you even must think ahead. In principle, there are only three possible outcomes: the study can give the results that you hoped for; it can show the inverse; or something indeterminate in between. In all instances, you can imagine how you will react. One possibility is that you are disappointed by the results of your study, and you will tend to find excuses for why it did not produce the results you hoped for. What excuses might your produce? The other possibility is that it does show what you wanted; then you may have to imagine how others will react and what their objections might be. If the results are indeterminate, everybody might be disappointed, and you will need to explain the failure of your research to give clear-cut results. When you detect a specific weakness by imagining this situation, you may wish to change aspects of your study.

As we explain in Appendix F , there is no need to write a very extensive paper as a first draft – on the contrary, it might be more useful to write a short paper, which has the advantage that others will more readily read it and comment on it.

Never be afraid to discuss your study at all stages extensively with others, not only your immediate research colleagues but also semi-outsiders and also in this advance-writing stage. If you know, or are told by others, that a particular direction of your results might not be believed and therefore draw criticism because of some potential deficiency in your study, why not remedy it at this stage? Looking at what you have written, or by discussing potential results with others, you will be able to imagine more clearly what your readers and critical colleagues might object to.

Writing a paper beforehand is the ultimate test of whether the research project is what you wanted, whether your reasoning flows logically, or whether you forgot something. The initial draft will be a yardstick for yourself and for others – whatever happens during the course of your research. This will help you to surmount surprise happenings: you have written down where you started and why, and therefore you will also know very securely when and why you have to take a detour – or even a U-turn.

Writing is difficult and time-consuming. Writing a paper can easily take 5–10 revisions, which might span a full year (inclusive of the time it takes your supervisor or your colleagues to produce comments). During the writing, you will often be obliged to go back to the data and do additional or different analyses. Since your paper will need many revisions, and this will take such a long time, why not take a head-start at the beginning of your data collection? It will save frustration and lost time at the end of your project.

Many guidelines and advices exist about writing, both about the substance (how to use words and phrases) and about the process. All beginning researchers should have a look at some books and papers about writing, and seasoned researchers can still profit from rereading them. Several reporting guidelines exist for several types of studies (RCTs, observational, diagnostic research, etc). They are often very detailed, in describing what should be in title, abstract, and so on. Although they should not be mechanically adhered to, 28 they help writing. In Appendix F , we have collected some wisdom that we particularly liked; several books on writing are listed, as well as reporting guidelines that help researchers to craft papers that are readable and contain all the information that is necessary and useful to others.

Now you can start “your research”

After the piloting and after having written your paper, you are ready to start your data collection, your analysis, or whatever is needed to “do your research”.

The work that is needed before you can start to “do your research” will take a great deal of time and effort. What will you have achieved after setting up a piece of research following the lengthy and involved precepts of this paper? You will have specified a limited research question that you will solve. You will add one little shining stone to the large mosaic of science. At the time that you do the study, you may still be too close to see its effect on the overall picture. That will come over the years.

Further reading

Some texts that we mention in the paper might be especially worthwhile for further reading; see Appendix G .

Acknowledgments

We thank Miguel Hernán, Stuart Pocock, and Bianca De Stavola for their informative comments on an earlier draft manuscript, as well as two anonymous reviewers of Clinical Epidemiology . The Centre for Global NCDs is supported by the Wellcome Trust Institutional Strategic Support Fund (097834/Z/11/B). This work was also supported by the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013 / ERC grant agreement number 668954).

The authors report no conflicts of interest in this work.