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How to Write a Research Question: Types and Examples 

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

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

What is a research question?

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

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

A good research question has the following features: 

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

A poorly formulated research question poses several risks. 1   

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

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

Types of research questions

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

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

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

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

• Qualitative research questions  

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

• Mixed-methods studies  

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

What makes a good research question

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

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

Steps for developing a good research question

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

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

How to write a research question

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

Sample Research Questions

The following are some bad and good research question examples 

• Example 1 
• Example 2 

References:  

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

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

What is a Research Question?

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

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

Research Question Writing Tips

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

A good research question should:

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

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

The research question should be specific and focused 

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

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

The research question should be based on the literature 

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

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

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

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

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

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

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

The research question should be in-depth

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

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

Research Question Types

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

Quantitative Research Questions

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

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

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

Categories of quantitative research questions

Qualitative research questions.

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

Categories of qualitative research questions

Quantitative and qualitative research question examples.

Good and Bad Research Question Examples

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

Research Question Example 1

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

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

Research Question Example 2

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

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

Steps for Writing a Research Question

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

1. Start with an interesting and relevant topic

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

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

2. Do preliminary research  

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

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

3. Narrow your research to determine specific research questions

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

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

4. Evaluate your research question

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

Is my research question clear?

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

Is my research question focused and specific?

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

Is my research question sufficiently complex?

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

Editing Your Research Question

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

• Jul 7, 2020

How to Write a Science Research Question

Humans are a very curious species. We are always asking questions. But the way we formulate a question is very important when we think about science and research. Here we’ll lay out how to form a science research question and the concepts needed to formulate a good research question. Luckily, we’ve got some handy visuals to help you along.

In order to inquire about the world, produce new information, and solve a mystery of about the natural world, we always use the scientific process to inform research questions. So, we need to keep in mind the steps of the scientific process :

Observation

Data to be obtained

Ways to analyze data

Conclusions to obtain from the question

First, clearly define your population and your variables.

Now, what is a population ? Defined in ecologic terms, a population are all the individuals of one species in a given area (e.g. population of deer, leatherback turtles, spruce trees, mushrooms, etc.).

Now, what is a variable ? A variable is any factor, trait, or condition that can exist in differing amounts or types (e.g. length, quantity, temperature, speed, mass, distance, depth, etc.).

So, using different combinations of these two components, we can create three different types of research questions: descriptive, comparative, and correlative. These three types also match three of the modern research methodologies. 

Descriptive field investigations involve describing and/or quantifying parts of a natural system. Includes generally 1 population and one distinctive variable (figure 1). Examples of descriptive research questions:

How many pine trees are in the Mammoth Hot Springs area?

What is the wolf pack’s distribution range?

How frequently do humpback whales breed?    

Comparative field investigations involve collecting data on different populations/organisms, or under different conditions (e.g., times of year, locations), to make a comparison. Includes two or more populations and one distinctive variable (figure 2). Examples of comparative research questions:

Is there a difference in body length between male and female tortoises?

Is there a difference in diversity of fungi that live in the forest compared with non-forested areas?  

Correlative field investigations involve measuring or observing two variables and searching for a relationship between them for a distinctive population (figure 3). Examples of correlative research questions:

What is the relationship between length of the tail and age in humpback whales?

How does a spider’s reproduction rate change with a change in season?

To practice how to write a research question, we suggest the following steps:

Find a nice place where you can be alone and connected with nature. Bring nothing else but a journal and a pencil. Take a few moments to breath and observe everything that surrounds you. Use all of your senses to obtain information from your surroundings: smell the flowers around you, feel the leaves, hear the birds, and recognize all the life.

Choose a population that is around you and that interests you (flowers, trees, insects, rocks), and think about what would you like to know about that population. Write down what you want to study from that population (your variable). It is easier to choose the population first and the variables second. Think about a feasible and simple measurement. One easy measurement is counting, since it doesn’t require an instrument.

Write down your question using your population and variable. Remember to write a question that is going to be simple, measurable, attainable, relevant, and limited to a particular time and place. Avoid why questions.

Next, write a prediction that answers your question. This is your hypothesis .

Now that you have a defined population, measure your variable, and obtain data. Don’t forget to write it down in your journal.

Finally, compare your hypothesis with your actual data and write a conclusion about your findings.

These simple and fun steps will help you create great questions that will lead you to find interesting answers and discoveries. But remember, this process not only works for scientific questions but also for daily issues, such as why the car stopped working. You can use it to investigate local environmental problems and provide possible solutions for the benefit of your community and future generations.

You can find more information about this topic in: Ryken, A. E., Otto, P., Pritchard, K., & Owens, K. (2007). Field investigations: Using outdoor environments to foster student learning of scientific processes . Pacific Education Institute. 

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Research Aims, Objectives & Questions

The “Golden Thread” Explained Simply (+ Examples)

By: David Phair (PhD) and Alexandra Shaeffer (PhD) | June 2022

The research aims , objectives and research questions (collectively called the “golden thread”) are arguably the most important thing you need to get right when you’re crafting a research proposal , dissertation or thesis . We receive questions almost every day about this “holy trinity” of research and there’s certainly a lot of confusion out there, so we’ve crafted this post to help you navigate your way through the fog.

Overview: The Golden Thread

• What is the golden thread
• What are research aims ( examples )
• What are research objectives ( examples )
• What are research questions ( examples )
• The importance of alignment in the golden thread

What is the “golden thread”?  

The golden thread simply refers to the collective research aims , research objectives , and research questions for any given project (i.e., a dissertation, thesis, or research paper ). These three elements are bundled together because it’s extremely important that they align with each other, and that the entire research project aligns with them.

Importantly, the golden thread needs to weave its way through the entirety of any research project , from start to end. In other words, it needs to be very clearly defined right at the beginning of the project (the topic ideation and proposal stage) and it needs to inform almost every decision throughout the rest of the project. For example, your research design and methodology will be heavily influenced by the golden thread (we’ll explain this in more detail later), as well as your literature review.

The research aims, objectives and research questions (the golden thread) define the focus and scope ( the delimitations ) of your research project. In other words, they help ringfence your dissertation or thesis to a relatively narrow domain, so that you can “go deep” and really dig into a specific problem or opportunity. They also help keep you on track , as they act as a litmus test for relevance. In other words, if you’re ever unsure whether to include something in your document, simply ask yourself the question, “does this contribute toward my research aims, objectives or questions?”. If it doesn’t, chances are you can drop it.

Alright, enough of the fluffy, conceptual stuff. Let’s get down to business and look at what exactly the research aims, objectives and questions are and outline a few examples to bring these concepts to life.

Research Aims: What are they?

Simply put, the research aim(s) is a statement that reflects the broad overarching goal (s) of the research project. Research aims are fairly high-level (low resolution) as they outline the general direction of the research and what it’s trying to achieve .

Research Aims: Examples  

True to the name, research aims usually start with the wording “this research aims to…”, “this research seeks to…”, and so on. For example:

“This research aims to explore employee experiences of digital transformation in retail HR.”   “This study sets out to assess the interaction between student support and self-care on well-being in engineering graduate students”  

As you can see, these research aims provide a high-level description of what the study is about and what it seeks to achieve. They’re not hyper-specific or action-oriented, but they’re clear about what the study’s focus is and what is being investigated.

Need a helping hand?

Research Objectives: What are they?

The research objectives take the research aims and make them more practical and actionable . In other words, the research objectives showcase the steps that the researcher will take to achieve the research aims.

The research objectives need to be far more specific (higher resolution) and actionable than the research aims. In fact, it’s always a good idea to craft your research objectives using the “SMART” criteria. In other words, they should be specific, measurable, achievable, relevant and time-bound”.

Research Objectives: Examples  

Let’s look at two examples of research objectives. We’ll stick with the topic and research aims we mentioned previously.  

For the digital transformation topic:

To observe the retail HR employees throughout the digital transformation. To assess employee perceptions of digital transformation in retail HR. To identify the barriers and facilitators of digital transformation in retail HR.

And for the student wellness topic:

To determine whether student self-care predicts the well-being score of engineering graduate students. To determine whether student support predicts the well-being score of engineering students. To assess the interaction between student self-care and student support when predicting well-being in engineering graduate students.

  As you can see, these research objectives clearly align with the previously mentioned research aims and effectively translate the low-resolution aims into (comparatively) higher-resolution objectives and action points . They give the research project a clear focus and present something that resembles a research-based “to-do” list.

Research Questions: What are they?

Finally, we arrive at the all-important research questions. The research questions are, as the name suggests, the key questions that your study will seek to answer . Simply put, they are the core purpose of your dissertation, thesis, or research project. You’ll present them at the beginning of your document (either in the introduction chapter or literature review chapter) and you’ll answer them at the end of your document (typically in the discussion and conclusion chapters).  

The research questions will be the driving force throughout the research process. For example, in the literature review chapter, you’ll assess the relevance of any given resource based on whether it helps you move towards answering your research questions. Similarly, your methodology and research design will be heavily influenced by the nature of your research questions. For instance, research questions that are exploratory in nature will usually make use of a qualitative approach, whereas questions that relate to measurement or relationship testing will make use of a quantitative approach.  

Let’s look at some examples of research questions to make this more tangible.

Research Questions: Examples  

Again, we’ll stick with the research aims and research objectives we mentioned previously.  

For the digital transformation topic (which would be qualitative in nature):

How do employees perceive digital transformation in retail HR? What are the barriers and facilitators of digital transformation in retail HR?  

And for the student wellness topic (which would be quantitative in nature):

Does student self-care predict the well-being scores of engineering graduate students? Does student support predict the well-being scores of engineering students? Do student self-care and student support interact when predicting well-being in engineering graduate students?  

You’ll probably notice that there’s quite a formulaic approach to this. In other words, the research questions are basically the research objectives “converted” into question format. While that is true most of the time, it’s not always the case. For example, the first research objective for the digital transformation topic was more or less a step on the path toward the other objectives, and as such, it didn’t warrant its own research question.  

So, don’t rush your research questions and sloppily reword your objectives as questions. Carefully think about what exactly you’re trying to achieve (i.e. your research aim) and the objectives you’ve set out, then craft a set of well-aligned research questions . Also, keep in mind that this can be a somewhat iterative process , where you go back and tweak research objectives and aims to ensure tight alignment throughout the golden thread.

The importance of strong alignment 

Alignment is the keyword here and we have to stress its importance . Simply put, you need to make sure that there is a very tight alignment between all three pieces of the golden thread. If your research aims and research questions don’t align, for example, your project will be pulling in different directions and will lack focus . This is a common problem students face and can cause many headaches (and tears), so be warned.

Take the time to carefully craft your research aims, objectives and research questions before you run off down the research path. Ideally, get your research supervisor/advisor to review and comment on your golden thread before you invest significant time into your project, and certainly before you start collecting data .  

Recap: The golden thread

In this post, we unpacked the golden thread of research, consisting of the research aims , research objectives and research questions . You can jump back to any section using the links below.

As always, feel free to leave a comment below – we always love to hear from you. Also, if you’re interested in 1-on-1 support, take a look at our private coaching service here.

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39 Comments

Thank you very much for your great effort put. As an Undergraduate taking Demographic Research & Methodology, I’ve been trying so hard to understand clearly what is a Research Question, Research Aim and the Objectives in a research and the relationship between them etc. But as for now I’m thankful that you’ve solved my problem.

Well appreciated. This has helped me greatly in doing my dissertation.

An so delighted with this wonderful information thank you a lot.

so impressive i have benefited a lot looking forward to learn more on research.

I am very happy to have carefully gone through this well researched article.

Infact,I used to be phobia about anything research, because of my poor understanding of the concepts.

Now,I get to know that my research question is the same as my research objective(s) rephrased in question format.

I please I would need a follow up on the subject,as I intends to join the team of researchers. Thanks once again.

Thanks so much. This was really helpful.

I know you pepole have tried to break things into more understandable and easy format. And God bless you. Keep it up

i found this document so useful towards my study in research methods. thanks so much.

This is my 2nd read topic in your course and I should commend the simplified explanations of each part. I’m beginning to understand and absorb the use of each part of a dissertation/thesis. I’ll keep on reading your free course and might be able to avail the training course! Kudos!

Thank you! Better put that my lecture and helped to easily understand the basics which I feel often get brushed over when beginning dissertation work.

This is quite helpful. I like how the Golden thread has been explained and the needed alignment.

This is quite helpful. I really appreciate!

The article made it simple for researcher students to differentiate between three concepts.

Very innovative and educational in approach to conducting research.

I am very impressed with all these terminology, as I am a fresh student for post graduate, I am highly guided and I promised to continue making consultation when the need arise. Thanks a lot.

A very helpful piece. thanks, I really appreciate it .

Very well explained, and it might be helpful to many people like me.

Wish i had found this (and other) resource(s) at the beginning of my PhD journey… not in my writing up year… 😩 Anyways… just a quick question as i’m having some issues ordering my “golden thread”…. does it matter in what order you mention them? i.e., is it always first aims, then objectives, and finally the questions? or can you first mention the research questions and then the aims and objectives?

Thank you for a very simple explanation that builds upon the concepts in a very logical manner. Just prior to this, I read the research hypothesis article, which was equally very good. This met my primary objective.

My secondary objective was to understand the difference between research questions and research hypothesis, and in which context to use which one. However, I am still not clear on this. Can you kindly please guide?

In research, a research question is a clear and specific inquiry that the researcher wants to answer, while a research hypothesis is a tentative statement or prediction about the relationship between variables or the expected outcome of the study. Research questions are broader and guide the overall study, while hypotheses are specific and testable statements used in quantitative research. Research questions identify the problem, while hypotheses provide a focus for testing in the study.

Exactly what I need in this research journey, I look forward to more of your coaching videos.

This helped a lot. Thanks so much for the effort put into explaining it.

What data source in writing dissertation/Thesis requires?

What is data source covers when writing dessertation/thesis

This is quite useful thanks

I’m excited and thankful. I got so much value which will help me progress in my thesis.

where are the locations of the reserch statement, research objective and research question in a reserach paper? Can you write an ouline that defines their places in the researh paper?

Very helpful and important tips on Aims, Objectives and Questions.

Thank you so much for making research aim, research objectives and research question so clear. This will be helpful to me as i continue with my thesis.

Thanks much for this content. I learned a lot. And I am inspired to learn more. I am still struggling with my preparation for dissertation outline/proposal. But I consistently follow contents and tutorials and the new FB of GRAD Coach. Hope to really become confident in writing my dissertation and successfully defend it.

As a researcher and lecturer, I find splitting research goals into research aims, objectives, and questions is unnecessarily bureaucratic and confusing for students. For most biomedical research projects, including ‘real research’, 1-3 research questions will suffice (numbers may differ by discipline).

Awesome! Very important resources and presented in an informative way to easily understand the golden thread. Indeed, thank you so much.

Well explained

The blog article on research aims, objectives, and questions by Grad Coach is a clear and insightful guide that aligns with my experiences in academic research. The article effectively breaks down the often complex concepts of research aims and objectives, providing a straightforward and accessible explanation. Drawing from my own research endeavors, I appreciate the practical tips offered, such as the need for specificity and clarity when formulating research questions. The article serves as a valuable resource for students and researchers, offering a concise roadmap for crafting well-defined research goals and objectives. Whether you’re a novice or an experienced researcher, this article provides practical insights that contribute to the foundational aspects of a successful research endeavor.

A great thanks for you. it is really amazing explanation. I grasp a lot and one step up to research knowledge.

I really found these tips helpful. Thank you very much Grad Coach.

I found this article helpful. Thanks for sharing this.

thank you so much, the explanation and examples are really helpful

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Developing a Research Question

From Laurier Library. 

Selecting and Narrowing a Topic

When starting out on your research, it is important to choose a research topic that is not only of interest to you, but can also be covered effectively in the space that you have available. You may not know right away what your research question is - that's okay! Start out with a broad topic, then conduct some background research to explore possibilities and narrow your topic to something more manageable.    

Choose an interesting general topic.  If you’re interested in your topic, others probably will be too! And your research will be a lot more fun. Once you have a general topic of interest, you can begin to explore more focused areas within that broad topic. 

Gather background information.  Do a few quick searches in OneSearch@IU  or in other relevant sources.  See what other researchers have already written to help narrow your focus.  

• What subtopics relate to the broader topic? 
• What questions do these sources raise?
• What piques your interest? What might you like to say about the topic? 

Consider your audience.  Who would be interested in this issue? For whom are you writing? 

Adapted from: George Mason University Writing Center. (2008). How to write a research question. Retrieved from  http://writingcenter.gmu.edu/writing-resources/wc-quick-guides  

From Topic to Research Question

Once you have done some background research and narrowed down your topic, you can begin to turn that topic into a research question that you will attempt to answer in the course of your research.  Keep in mind that your question may change as you gather more information and as you write. However, having some sense of your direction can help you evaluate sources and identify relevant information throughout your research process. 

Explore questions.

• Ask open-ended “how” and “why” questions about your general topic.  
• Consider the “so what?” of your topic. Why does this topic matter to you? Why should it matter to others?

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

• Is your question clear? Do you have a specific aspect of your general topic that you are going to explore further?   
• Is your question focused? Will you be able to cover the topic adequately in the space available?   
• Is your question sufficiently complex? (cannot be answered with a simple yes/no response, requires research and analysis)

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

• If you are making an argument, what will you say?  
• Why does your argument matter?  
• What kinds of sources will you need in order to support your argument?  
• How might others challenge your argument?

Adapted from: George Mason University Writing Center. (2008). How to write a research question. Retrieved from http://writingcenter.gmu.edu/writing-resources/wc-quick-guides

Sample Research Questions

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

Unclear : Why are social networking sites harmful?

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

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

Focused:  How is glacial melting affecting penguins in Antarctica?

Simple vs Complex

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

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

Adapted from: George Mason University Writing Center. (2008). How to write a research question. Retrieved from  http://writingcenter.gmu.edu/writing-resources/wc-quick-guides

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A note on citation: Reference sources are generally not cited since they usually consist of common knowledge (e.g. who was the first United States President).  But if you're unsure whether to cite something it's best to do so. Specific pieces of information and direct quotes should always be cited. 

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Examples of Good and Bad Research Questions

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Written by  Scribendi

So, you've got a research grant in your sights or you've been admitted to your school of choice, and you now have to write up a proposal for the work you want to perform. You know your topic, have done some reading, and you've got a nice quiet place where nobody will bother you while you try to decide where you'll go from here. The question looms:     

What Is a Research Question?

Your research question will be your focus, the sentence you refer to when you need to remember why you're researching. It will encapsulate what drives you and be something your field needs an answer for but doesn't have yet. 

Whether it seeks to describe a phenomenon, compare things, or show how one variable influences another, a research question always does the same thing: it guides research that will be judged based on how well it addresses the question.

So, what makes a research question good or bad? This article will provide examples of good and bad research questions and use them to illustrate both of their common characteristics so that you can evaluate your research question and improve it to suit your needs.

How to Choose a Research Question

At the start of your research paper, you might be wondering, "What is a good research question?"

A good research question focuses on one researchable problem relevant to your subject area.

To write a research paper , first make sure you have a strong, relevant topic. Then, conduct some preliminary research around that topic. It's important to complete these two initial steps because your research question will be formulated based on this research.

With this in mind, let's review the steps that help us write good research questions.

1. Select a Relevant Topic

When selecting a topic to form a good research question, it helps to start broad. What topics interest you most? It helps when you care about the topic you're researching!

Have you seen a movie recently that you enjoyed? How about a news story? If you can't think of anything, research different topics on Google to see which ones intrigue you the most and can apply to your assignment.

Also, before settling on a research topic, make sure it's relevant to your subject area or to society as a whole. This is an important aspect of developing your research question, because, in general, your research should add value to existing knowledge .

2. Thoroughly Research the Topic

Now that you've chosen a broad but relevant topic for your paper, research it thoroughly to see which avenues you might want to explore further.

For example, let's say you decide on the broad topic of search engines. During this research phase, try skimming through sources that are unbiased, current, and relevant, such as academic journals or sources in your university library.

Check out: 21 Legit Research Databases for Free Articles in 2022

Pay close attention to the subtopics that come up during research, such as the following: Which search engines are the most commonly used? Why do some search engines dominate specific regions? How do they really work or affect the research of scientists and scholars?

Be on the lookout for any gaps or limitations in the research. Identifying the groups or demographics that are most affected by your topic is also helpful, in case that's relevant to your work.

3. Narrow Your Topic to a Single Point

Now that you've spent some time researching your broad topic, it's time to narrow it down to one specific subject. A topic like search engines is much too broad to develop a research paper around. What specifically about search engines could you explore?

When refining your topic, be careful not to be either too narrow or too broad. You can ask yourself the following questions during this phase:

Can I cover this topic within the scope of my paper, or would it require longer, heavier research? (In this case, you'd need to be more specific.)

Conversely, is there not enough research about my topic to write a paper? (In this case, you'd need to be broader.)

Keep these things in mind as you narrow down your topic. You can always expand your topic later if you have the time and research materials.

4. Identify a Problem Related to Your Topic

When narrowing down your topic, it helps to identify a single issue or problem on which to base your research. Ask open-ended questions, such as why is this topic important to you or others? Essentially, have you identified the answer to "so what"?

For example, after asking these questions about our search engine topic, we might focus only on the issue of how search engines affect research in a specific field. Or, more specifically, how search engine algorithms manipulate search results and prevent us from finding the critical research we need.

Asking these "so what" questions will help us brainstorm examples of research questions we can ask in our field of study.

5. Turn Your Problem into a Question

Now that you have your main issue or problem, it's time to write your research question. Do this by reviewing your topic's big problem and formulating a question that your research will answer.

For example, ask, "so what?" about your search engine topic. You might realize that the bigger issue is that you, as a researcher, aren't getting the relevant information you need from search engines.

How can we use this information to develop a research question? We might phrase the research question as follows:

"What effect does the Google search engine algorithm have on online research conducted in the field of neuroscience?"

Note how specific we were with the type of search engine, the field of study, and the research method. It's also important to remember that your research question should not have an easy yes or no answer. It should be a question with a complex answer that can be discovered through research and analysis.

Perfect Your Paper

Hire an expert academic editor , or get a free sample, how to find good research topics for your research.

It can be fun to browse a myriad of research topics for your paper, but there are a few important things to keep in mind.

First, make sure you've understood your assignment. You don't want to pick a topic that's not relevant to the assignment goal. Your instructor can offer good topic suggestions as well, so if you get stuck, ask them!

Next, try to search for a broad topic that interests you. Starting broad gives you more options to work with. Some research topic examples include infectious diseases, European history, and smartphones .

Then, after some research, narrow your topic to something specific by extracting a single element from that subject. This could be a current issue on that topic, a major question circulating around that topic, or a specific region or group of people affected by that topic.

It's important that your research topic is focused. Focus lets you clearly demonstrate your understanding of the topic with enough details and examples to fit the scope of your project.

For example, if Jane Austen is your research topic, that might be too broad for a five-page paper! However, you could narrow it down to a single book by Austen or a specific perspective.

To keep your research topic focused, try creating a mind map. This is where you put your broad topic in a circle and create a few circles around it with similar ideas that you uncovered during your research. 

Mind maps can help you visualize the connections between topics and subtopics. This could help you simplify the process of eliminating broad or uninteresting topics or help you identify new relationships between topics that you didn't previously notice. 

Keeping your research topic focused will help you when it comes to writing your research question!

2. Researchable

A researchable question should have enough available sources to fill the scope of your project without being overwhelming. If you find that the research is never-ending, you're going to be very disappointed at the end of your paper—because you won't be able to fit everything in! If you are in this fix, your research question is still too broad.

Search for your research topic's keywords in trusted sources such as journals, research databases , or dissertations in your university library. Then, assess whether the research you're finding is feasible and realistic to use.

If there's too much material out there, narrow down your topic by industry, region, or demographic. Conversely, if you don't find enough research on your topic, you'll need to go broader. Try choosing two works by two different authors instead of one, or try choosing three poems by a single author instead of one.

3. Reasonable

Make sure that the topic for your research question is a reasonable one to pursue. This means it's something that can be completed within your timeframe and offers a new perspective on the research.

Research topics often end up being summaries of a topic, but that's not the goal. You're looking for a way to add something relevant and new to the topic you're exploring. To do so, here are two ways to uncover strong, reasonable research topics as you conduct your preliminary research:

Check the ends of journal articles for sections with questions for further discussion. These make great research topics because they haven't been explored!

Check the sources of articles in your research. What points are they bringing up? Is there anything new worth exploring? Sometimes, you can use sources to expand your research and more effectively narrow your topic.

4. Specific

For your research topic to stand on its own, it should be specific. This means that it shouldn't be easily mistaken for another topic that's already been written about.

If you are writing about a topic that has been written about, such as consumer trust, it should be distinct from everything that's been written about consumer trust so far.

There is already a lot of research done on consumer trust in specific products or services in the US. Your research topic could focus on consumer trust in products and services in a different region, such as a developing country.

If your research feels similar to existing articles, make sure to drive home the differences.

Whether it's developed for a thesis or another assignment, a good research topic question should be complex enough to let you expand on it within the scope of your paper.

For example, let's say you took our advice on researching a topic you were interested in, and that topic was a new Bridezilla reality show. But when you began to research it, you couldn't find enough information on it, or worse, you couldn't find anything scholarly.

In short, Bridezilla reality shows aren't complex enough to build your paper on. Instead of broadening the topic to all reality TV shows, which might be too overwhelming, you might consider choosing a topic about wedding reality TV shows specifically.

This would open you up to more research that could be complex enough to write a paper on without being too overwhelming or narrow.

6. Relevant

Because research papers aim to contribute to existing research that's already been explored, the relevance of your topic within your subject area can't be understated.

Your research topic should be relevant enough to advance understanding in a specific area of study and build on what's already been researched. It shouldn't duplicate research or try to add to it in an irrelevant way.

For example, you wouldn't choose a research topic like malaria transmission in Northern Siberia if the mosquito that transmits malaria lives in Africa. This research topic simply isn't relevant to the typical location where malaria is transmitted, and the research could be considered a waste of resources.

Do Research Questions Differ between the Humanities, Social Sciences, and Hard Sciences?

The art and science of asking questions is the source of all knowledge. 

–Thomas Berger

First, a bit of clarification: While there are constants among research questions, no matter what you're writing about, you will use different standards for the humanities and social sciences than for hard sciences, such as chemistry. The former depends on subjectivity and the perspective of the researcher, while the latter requires answers that must be empirically tested and replicable.

For instance, if you research Charles Dickens' writing influences, you will have to explain your stance and observations to the reader before supporting them with evidence. If you research improvements in superconductivity in room-temperature material, the reader will not only need to understand and believe you but also duplicate your work to confirm that you are correct.

Do Research Questions Differ between the Different Types of Research?

Research questions help you clarify the path your research will take. They are answered in your research paper and usually stated in the introduction.

There are two main types of research—qualitative and quantitative. 

If you're conducting quantitative research, it means you're collecting numerical, quantifiable data that can be measured, such as statistical information.

Qualitative research aims to understand experiences or phenomena, so you're collecting and analyzing non-numerical data, such as case studies or surveys.

The structure and content of your research question will change depending on the type of research you're doing. However, the definition and goal of a research question remains the same: a specific, relevant, and focused inquiry that your research answers.

Below, we'll explore research question examples for different types of research.

Comparative Research

Comparative research questions are designed to determine whether two or more groups differ based on a dependent variable. These questions allow researchers to uncover similarities and differences between the groups tested.

Because they compare two groups with a dependent variable, comparative research questions usually start with "What is the difference in…"

A strong comparative research question example might be the following:

"What is the difference in the daily caloric intake of American men and women?" ( Source .)

In the above example, the dependent variable is daily caloric intake and the two groups are American men and women.

A poor comparative research example might not aim to explore the differences between two groups or it could be too easily answered, as in the following example:

"Does daily caloric intake affect American men and women?"

Always ensure that your comparative research question is focused on a comparison between two groups based on a dependent variable.

Descriptive Research

Descriptive research questions help you gather data about measurable variables. Typically, researchers asking descriptive research questions aim to explain how, why, or what.

These research questions tend to start with the following:

What percentage?

How likely?

What proportion?

For example, a good descriptive research question might be as follows:

"What percentage of college students have felt depressed in the last year?" ( Source .)

A poor descriptive research question wouldn't be as precise. This might be something similar to the following:

"What percentage of teenagers felt sad in the last year?"

The above question is too vague, and the data would be overwhelming, given the number of teenagers in the world. Keep in mind that specificity is key when it comes to research questions!

Correlational Research

Correlational research measures the statistical relationship between two variables, with no influence from any other variable. The idea is to observe the way these variables interact with one another. If one changes, how is the other affected?

When it comes to writing a correlational research question, remember that it's all about relationships. Your research would encompass the relational effects of one variable on the other.

For example, having an education (variable one) might positively or negatively correlate with the rate of crime (variable two) in a specific city. An example research question for this might be written as follows:

"Is there a significant negative correlation between education level and crime rate in Los Angeles?"

A bad correlational research question might not use relationships at all. In fact, correlational research questions are often confused with causal research questions, which imply cause and effect. For example:

"How does the education level in Los Angeles influence the crime rate?"

The above question wouldn't be a good correlational research question because the relationship between Los Angeles and the crime rate is already inherent in the question—we are already assuming the education level in Los Angeles affects the crime rate in some way.

Be sure to use the right format if you're writing a correlational research question.

How to Avoid a Bad Question

Ask the right questions, and the answers will always reveal themselves. 

–Oprah Winfrey

If finding the right research question was easy, doing research would be much simpler. However, research does not provide useful information if the questions have easy answers (because the questions are too simple, narrow, or general) or answers that cannot be reached at all (because the questions have no possible answer, are too costly to answer, or are too broad in scope).

For a research question to meet scientific standards, its answer cannot consist solely of opinion (even if the opinion is popular or logically reasoned) and cannot simply be a description of known information.

However, an analysis of what currently exists can be valuable, provided that there is enough information to produce a useful analysis. If a scientific research question offers results that cannot be tested, measured, or duplicated, it is ineffective.

Bad Research Question Examples

Here are examples of bad research questions with brief explanations of what makes them ineffective for the purpose of research.

"What's red and bad for your teeth?"

This question has an easy, definitive answer (a brick), is too vague (What shade of red? How bad?), and isn't productive.

"Do violent video games cause players to act violently?"

This question also requires a definitive answer (yes or no), does not invite critical analysis, and allows opinion to influence or provide the answer.

"How many people were playing balalaikas while living in Moscow on July 8, 2019?"

This question cannot be answered without expending excessive amounts of time, money, and resources. It is also far too specific. Finally, it doesn't seek new insight or information, only a number that has no conceivable purpose.

How to Write a Research Question

The quality of a question is not judged by its complexity but by the complexity of thinking it provokes. 

–Joseph O'Connor

What makes a good research question? A good research question topic is clear and focused. If the reader has to waste time wondering what you mean, you haven't phrased it effectively.

It also needs to be interesting and relevant, encouraging the reader to come along with you as you explain how you reached an answer. 

Finally, once you explain your answer, there should be room for astute or interested readers to use your question as a basis to conduct their own research. If there is nothing for you to say in your conclusion beyond "that's the truth," then you're setting up your research to be challenged.

Good Research Question Examples

Here are some examples of good research questions. Take a look at the reasoning behind their effectiveness.

"What are the long-term effects of using activated charcoal in place of generic toothpaste for routine dental care?"

This question is specific enough to prevent digressions, invites measurable results, and concerns information that is both useful and interesting. Testing could be conducted in a reasonable time frame, without excessive cost, and would allow other researchers to follow up, regardless of the outcome.

"Why do North American parents feel that violent video game content has a negative influence on their children?"

While this does carry an assumption, backing up that assumption with observable proof will allow for analysis of the question, provide insight on a significant subject, and give readers something to build on in future research. 

It also discusses a topic that is recognizably relevant. (In 2022, at least. If you are reading this article in the future, there might already be an answer to this question that requires further analysis or testing!)

"To what extent has Alexey Arkhipovsky's 2013 album, Insomnia , influenced gender identification in Russian culture?"

While it's tightly focused, this question also presents an assumption (that the music influenced gender identification) and seeks to prove or disprove it. This allows for the possibilities that the music had no influence at all or had a demonstrable impact.

Answering the question will involve explaining the context and using many sources so that the reader can follow the logic and be convinced of the author's findings. The results (be they positive or negative) will also open the door to countless other studies.

How to Turn a Bad Research Question into a Good One

If something is wrong, fix it if you can. But train yourself not to worry. Worry never fixes anything.

–Ernest Hemingway

How do you turn something that won't help your research into something that will? Start by taking a step back and asking what you are expected to produce. While there are any number of fascinating subjects out there, a grant paying you to examine income disparity in Japan is not going to warrant an in-depth discussion of South American farming pollution. 

Use these expectations to frame your initial topic and the subject that your research should be about, and then conduct preliminary research into that subject. If you spot a knowledge gap while researching, make a note of it, and add it to your list of possible questions.

If you already have a question that is relevant to your topic but has flaws, identify the issues and see if they can be addressed. In addition, if your question is too broad, try to narrow it down enough to make your research feasible.

Especially in the sciences, if your research question will not produce results that can be replicated, determine how you can change it so a reader can look at what you've done and go about repeating your actions so they can see that you are right.

Moreover, if you would need 20 years to produce results, consider whether there is a way to tighten things up to produce more immediate results. This could justify future research that will eventually reach that lofty goal.

If all else fails, you can use the flawed question as a subtopic and try to find a better question that fits your goals and expectations.

Parting Advice

When you have your early work edited, don't be surprised if you are told that your research question requires revision. Quite often, results or the lack thereof can force a researcher to shift their focus and examine a less significant topic—or a different facet of a known issue—because testing did not produce the expected result. 

If that happens, take heart. You now have the tools to assess your question, find its flaws, and repair them so that you can complete your research with confidence and publish something you know your audience will read with fascination.

Of course, if you receive affirmation that your research question is strong or are polishing your work before submitting it to a publisher, you might just need a final proofread to ensure that your confidence is well placed. Then, you can start pursuing something new that the world does not yet know (but will know) once you have your research question down.

Master Your Research with Professional Editing

About the author.

Scribendi's in-house editors work with writers from all over the globe to perfect their writing. They know that no piece of writing is complete without a professional edit, and they love to see a good piece of writing transformed into a great one. Scribendi's in-house editors are unrivaled in both experience and education, having collectively edited millions of words and obtained nearly 20 degrees. They love consuming caffeinated beverages, reading books of various genres, and relaxing in quiet, dimly lit spaces.

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Internet Resources

I. developing scientific literacy.

• 1 - Building a Scientific Vocabulary
• 2 - Developing Science Reading Skills
• 3 - Developing Science Writing Skills
• 4 - Science, Technology & Society

II. Developing Scientific Reasoning

• 5 - Employing Scientific Methods
• 6 - Developing Scientific Reasoning
• 7 - Thinking Critically & Misconceptions

III. Developing Scientific Understanding

• 8 - Organizing Science Information
• 9 - Graphic Oganizers for Science
• 10 - Learning Science with Analogies
• 11 - Improving Memory in Science
• 12 - Structure and Function in Science
• 13 - Games for Learning Science

IV. Developing Scientific Problem Solving

• 14 - Science Word Problems
• 15 - Geometric Principles in Science
• 16 - Visualizing Problems in Science
• 17 - Dimensional Analysis
• 18 - Stoichiometry

V. Developing Scientific Research Skills

• 19 - Scientific Databases
• 20 - Graphing & Data Analysis
• 21 - Mapping & Visualizing Data
• 22 - Science Inquiry & Research
• 23 - Science Projects & Fairs

VI. Resources for Teaching Science

• 24 - Science Curriculum & Instruction
• 25 - Planning Science Instruction
• 26 - The Science Laboratory
• 27 - Science Reference Information

Types of Research Questions

Check out the science fair sites for sample research questions.

Descriptive Designed primarily to describe what is going on or what exists

• What are the characteristics of a burning candle ?
• Which stage of mitosis is longest?
• Which is more common, right-eye or left-eye dominance ?
• If two sounds have the same pitch, do they have the same frequency ?
• What complimentary colors do color blind individuals see?
• Is there any pattern to occurrence of earthquakes ?
• How can one determine the center of gravity ?
• Are all printed materials composed of the same colors ?
• What is affect of exercise on heart rate?
• What is the effect hand fatigue on reaction time ?
• What are the most potent vectors for disease transmission?
• How does exercise affect the rate of carbon dioxide production ?
• How is the diffusion of air freshener influenced by temperature?
• How does concentration of silver nitrate affect the formation of silver crystals?
• Norman Herr, Ph.D.

• Translators
• Graphic Designers

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415 Research Question Examples Across 15 Disciplines

A research question is a clearly formulated query that delineates the scope and direction of an investigation. It serves as the guiding light for scholars, helping them to dissect, analyze, and comprehend complex phenomena. Beyond merely seeking answers, a well-crafted research question ensures that the exploration remains focused and goal-oriented.

The significance of framing a clear, concise, and researchable question cannot be overstated. A well-defined question not only clarifies the objective of the research but also determines the methodologies and tools a researcher will employ. A concise question ensures precision, eliminating the potential for ambiguity or misinterpretation. Furthermore, the question must be researchable—posing a question that is too broad, too subjective, or unanswerable can lead to inconclusive results or an endless loop of investigation. In essence, the foundation of any meaningful academic endeavor rests on the articulation of a compelling and achievable research question.

Research questions can be categorized based on their intent and the nature of the information they seek. Recognizing the different types is essential for crafting an effective inquiry and guiding the research process. Let's delve into the various categories:

• Descriptive Research Questions: These types of questions aim to outline and characterize specific phenomena or attributes. They seek to provide a clear picture of a situation or context without necessarily diving into causal relationships. For instance, a question like "What are the main symptoms of the flu?" is descriptive as it seeks to list the symptoms.
• Explanatory (or Causal) Research Questions: Explanatory questions delve deeper, trying to uncover the reasons or causes behind certain phenomena. They are particularly common in experimental research where researchers are attempting to establish cause-and-effect relationships. An example might be, "Does smoking increase the risk of lung cancer?"
• Exploratory Research Questions: As the name suggests, these questions are used when researchers are entering uncharted territories. They are designed to gather preliminary information on topics that haven't been studied extensively. A question like "How do emerging technologies impact remote tribal communities?" can be seen as exploratory if there's limited existing research on the topic.
• Comparative Research Questions: These questions are formulated when the objective is to compare two or more groups, conditions, or variables. Comparative questions might look like "How do test scores differ between students who study regularly and those who cram?"
• Predictive Research Questions: The goal here is to forecast or predict potential outcomes based on certain variables or conditions. Predictive research might pose questions such as "Based on current climate trends, how will average global temperatures change by 2050?"

Here are examples of research questions across various disciplines, shedding light on queries that stimulate intellectual curiosity and advancement. In this post, we will delve into disciplines ranging from the Natural Sciences, such as Physics and Biology, to the Social Sciences, including Sociology and Anthropology, as well as the Humanities, like Literature and Philosophy. We'll also explore questions from fields as varied as Health Sciences, Engineering, Business, Environmental Sciences, Mathematics, Education, Law, Agriculture, Arts, Computer Science, Architecture, and Languages. This comprehensive overview aims to illustrate the breadth and depth of inquiries that shape our world of knowledge.

Agriculture and forestry examples

Architecture and planning examples, arts and design examples, business and finance examples, computer science and informatics examples, education examples, engineering and technology examples, environmental sciences examples, health sciences examples, humanities examples, languages and linguistics examples, law examples, mathematics and statistics examples, natural sciences examples, social sciences examples.

• Descriptive: What are the primary factors that influence crop yield in temperate climates?
• Explanatory: Why do certain soil types yield higher grain production than others?
• Exploratory: How might new organic farming techniques influence soil health over a decade?
• Comparative: How do the growth rates differ between genetically modified and traditional corn crops?
• Predictive: Based on current climate models, how will changing rain patterns impact wheat production in the next 20 years?

Animal science

• Descriptive: What are the common behavioral traits of domesticated cattle in grass-fed conditions?
• Explanatory: Why do certain breeds of chickens have a higher egg production rate?
• Exploratory: What potential benefits could arise from integrating tech wearables in livestock management?
• Comparative: How does the milk yield differ between Holstein and Jersey cows when given the same diet?
• Predictive: How might increasing global temperatures influence the reproductive cycles of swine?

Aquaculture

• Descriptive: What are the most commonly farmed fish species in Southeast Asia?
• Explanatory: Why do shrimp farms have a higher disease outbreak rate compared to fish farms?
• Exploratory: How might innovative recirculating aquaculture systems revolutionize the industry's environmental impact?
• Comparative: How do growth rates of salmon differ between open-net pens and land-based tanks?
• Predictive: What will be the impact of ocean acidification on mollusk farming over the next three decades?
• Descriptive: What tree species dominate the temperate rainforests of North America?
• Explanatory: Why are certain tree species more resistant to pest infestations?
• Exploratory: What are the potential benefits of integrating drone technology in forest health monitoring?
• Comparative: How do deforestation rates compare between legally protected and unprotected areas in the Amazon?
• Predictive: Given increasing global demand for timber, how might tree populations in Siberia change in the next half-century?

Horticulture

• Descriptive: What are the common characteristics of plants suitable for urban vertical farming?
• Explanatory: Why do roses require specific pH levels in the soil for optimal growth?
• Exploratory: What potential methods might promote year-round vegetable farming in colder regions?
• Comparative: How does fruit yield differ between traditionally planted orchards and high-density planting systems?
• Predictive: How might changing global temperatures affect wine grape production in traditional regions?

Soil science

• Descriptive: What are the main components of loamy soil?
• Explanatory: Why does clay-rich soil retain more water compared to sandy soil?
• Exploratory: How might biochar applications transform nutrient availability in degraded soils?
• Comparative: How do nutrient levels vary between soils managed with organic versus inorganic fertilizers?
• Predictive: Based on current farming practices, how will soil quality in the Midwest U.S. evolve over the next 30 years?

Architectural design

• Descriptive: What are the dominant architectural styles of public buildings constructed in the 21st century?
• Explanatory: Why do certain architectural elements from classical periods continue to influence modern designs?
• Exploratory: How might sustainable materials revolutionize the future of architectural design?
• Comparative: How do energy consumption levels differ between buildings with passive design elements and those without?
• Predictive: Based on urbanization trends, how will the design of residential buildings evolve in the next two decades?

Landscape architecture

• Descriptive: What are the primary components of a successful urban park design?
• Explanatory: Why do certain types of vegetation promote greater biodiversity in urban settings?
• Exploratory: What innovative techniques can be employed to restore and integrate wetlands into urban landscapes?
• Comparative: How does visitor satisfaction vary between nature-inspired landscapes and more structured, geometric designs?
• Predictive: With the effects of climate change, how might coastal landscape architecture adapt to rising sea levels over the coming century?

Urban planning

• Descriptive: What are the main components of a pedestrian-friendly city center?
• Explanatory: Why do certain urban layouts promote more efficient traffic flow than others?
• Exploratory: How might the integration of vertical farming impact urban food security and cityscape aesthetics?
• Comparative: How do the air quality levels differ between cities with green belts and those without?
• Predictive: Based on increasing telecommuting trends, how will urban planning strategies adjust to potentially reduced daily commutes in the future?

Graphic design

• Descriptive: What are the prevailing typography trends in modern branding?
• Explanatory: Why do certain color schemes evoke specific emotions or perceptions in consumers?
• Exploratory: How is augmented reality reshaping the landscape of interactive graphic design?
• Comparative: How do print and digital designs differ in terms of elements and principles when targeting a young adult audience?
• Predictive: Based on evolving digital platforms, what are potential future trends in web design aesthetics?

Industrial design

• Descriptive: What characterizes the ergonomic features of leading office chairs in the market?
• Explanatory: Why have minimalist designs become more prevalent in consumer electronics over the past decade?
• Exploratory: How might bio-inspired design influence the future of transportation vehicles?
• Comparative: How does user satisfaction differ between traditional versus modular product designs?
• Predictive: Given the push towards sustainability, how will material selection evolve in the next decade of product design?

Multimedia arts

• Descriptive: What techniques define the most popular virtual reality (VR) experiences currently available?
• Explanatory: Why do certain sound designs enhance immersion in video games more effectively than others?
• Exploratory: How might holographic technologies revolutionize stage performances or public installations in the future?
• Comparative: How do user engagement levels differ between 2D animations and 3D animations in educational platforms?
• Predictive: With the rise of augmented reality (AR) wearables, what might be the next frontier in multimedia art installations?

Performing arts

• Descriptive: What styles of dance are currently predominant in global theater productions?
• Explanatory: Why do certain rhythms or beats universally resonate with audiences across cultures?
• Exploratory: How might digital avatars or AI entities play roles in future theatrical performances?
• Comparative: How does audience reception differ between traditional plays and experimental, interactive performances?
• Predictive: Considering global digitalization, how might virtual theaters redefine the experience of live performances in the future?

Visual arts

• Descriptive: What themes are prevalent in contemporary art exhibitions worldwide?
• Explanatory: Why have mixed media installations gained prominence in the 21st-century art scene?
• Exploratory: How is the intersection of technology and art opening new mediums or platforms for artists?
• Comparative: How do traditional painting techniques, such as oil and watercolor, contrast in terms of texture and luminosity?
• Predictive: With the evolution of digital art platforms, how might the definition and appreciation of "original" artworks change in the coming years?

Entrepreneurship

• Descriptive: What are the main challenges faced by startups in the tech industry?
• Explanatory: Why do some entrepreneurial ventures succeed while others fail within their first five years?
• Exploratory: How are emerging digital platforms reshaping the entrepreneurial landscape?
• Comparative: How do funding opportunities for entrepreneurs differ between North America and Europe?
• Predictive: What sectors are predicted to see the most startup growth in the next decade?
• Descriptive: What are the primary sources of external funding for large corporations?
• Explanatory: Why did the stock market experience a significant drop in Q4 2022?
• Exploratory: How might blockchain technology revolutionize the future of banking?
• Comparative: How do the financial markets in developing countries compare to those in developed countries?
• Predictive: Based on current economic indicators, what is the forecasted health of the global economy for the next five years?

Human resources

• Descriptive: What are the most sought-after employee benefits in the tech industry?
• Explanatory: Why is there a high turnover rate in the retail sector?
• Exploratory: How might the rise of remote work affect HR practices in the next decade?
• Comparative: How do HR practices in multinational corporations differ from those in local companies?
• Predictive: What skills will be in highest demand in the workforce by 2030?
• Descriptive: What are the core responsibilities of middle management in large manufacturing firms?
• Explanatory: Why do some management strategies fail in diverse cultural environments?
• Exploratory: How are companies adapting their management structures in response to the gig economy?
• Comparative: How does management style in Eastern companies compare with Western businesses?
• Predictive: How might artificial intelligence reshape management practices in the next decade?
• Descriptive: What are the most effective digital marketing channels for e-commerce businesses?
• Explanatory: Why did a particular viral marketing campaign succeed in reaching a global audience?
• Exploratory: How might virtual reality change the landscape of product advertising?
• Comparative: How do marketing strategies differ between B2B and B2C sectors?
• Predictive: What consumer behaviors are forecasted to dominate online shopping trends in the next five years?

Operations research

• Descriptive: What are the primary optimization techniques used in supply chain management?
• Explanatory: Why do certain optimization algorithms perform better in specific industries?
• Exploratory: How can quantum computing impact the future of operations research?
• Comparative: How does operations strategy differ between service and manufacturing industries?
• Predictive: Based on current technological advancements, how might automation reshape supply chain strategies by 2035?

Artificial intelligence

• Descriptive: What are the primary algorithms used in deep learning?
• Explanatory: Why do certain neural network architectures outperform others in image recognition tasks?
• Exploratory: How might quantum computing influence the development of AI models?
• Comparative: How do reinforcement learning methods compare to supervised learning in game playing scenarios?
• Predictive: Based on current trends, how will AI impact the job market over the next decade?

Cybersecurity

• Descriptive: What are the most common types of cyberattacks reported in 2022?
• Explanatory: Why are certain industries more vulnerable to ransomware attacks?
• Exploratory: How might advances in quantum computing challenge existing encryption methods?
• Comparative: How do open-source software vulnerabilities compare to those in proprietary systems?
• Predictive: Given emerging technologies, what types of cyber threats will likely dominate in the next five years?

Data science

• Descriptive: What are the main tools used by data scientists in large-scale data analysis?
• Explanatory: Why does algorithm X yield more accurate predictions than algorithm Y for certain datasets?
• Exploratory: How can machine learning models improve real-time data processing in IoT devices?
• Comparative: How does the performance of traditional statistical models compare to machine learning models in predicting stock prices?
• Predictive: Based on current data trends, what industries will likely benefit the most from data analytics advancements in the coming decade?

Information systems

• Descriptive: What are the core components of a modern enterprise resource planning (ERP) system?
• Explanatory: Why have cloud-based information systems seen a rapid adoption rate in recent years?
• Exploratory: How might the integration of blockchain technology revolutionize supply chain information systems?
• Comparative: How do information system strategies differ between e-commerce and brick-and-mortar retailers?
• Predictive: Given the rise of remote work, how will information systems evolve to support decentralized teams in the future?

Software engineering

• Descriptive: What are the standard practices in agile software development?
• Explanatory: Why do some software projects face significant delays despite rigorous planning?
• Exploratory: How are emerging programming languages shaping the future of software development?
• Comparative: How does the software development lifecycle in startup environments compare to that in large corporations?
• Predictive: Based on current development trends, which software platforms are forecasted to dominate market share by 2030?

Adult education

• Descriptive: What are the primary motivations behind adults seeking further education later in life?
• Explanatory: Why do some adult education programs have a higher success rate compared to others?
• Exploratory: How might online learning platforms revolutionize adult education in the next decade?
• Comparative: How do adult education methodologies differ from traditional collegiate teaching techniques?
• Predictive: Given current trends, how will the demand for adult education courses change in the upcoming years?

Curriculum studies

• Descriptive: What are the core components of a modern high school curriculum in the United States?
• Explanatory: Why have certain subjects, like financial literacy, become more emphasized in recent curriculum updates?
• Exploratory: How can interdisciplinary studies be better incorporated into traditional curricula?
• Comparative: How does the math curriculum in the US compare to that in other developed countries?
• Predictive: Based on pedagogical research, what subjects are forecasted to gain prominence in curricula over the next decade?

Educational administration

• Descriptive: What are the main responsibilities of a school principal in large urban schools?
• Explanatory: Why do some schools consistently perform better in standardized testing than others, despite similar resources?
• Exploratory: How might emerging technologies shape the administrative tasks of educational institutions in the future?
• Comparative: How does school administration differ between private and public educational institutions?
• Predictive: Given the rise of online education, how will the role of educational administrators evolve in the coming years?

Educational psychology

• Descriptive: What cognitive strategies are commonly used by students to enhance memory retention during studies?
• Explanatory: Why do certain teaching methodologies resonate better with students having specific learning styles?
• Exploratory: How can insights from behavioral psychology improve student engagement in virtual classrooms?
• Comparative: How does the motivation level of students differ between self-paced versus instructor-led courses?
• Predictive: With the increasing integration of technology in education, how will student learning behaviors change in the next decade?

Special education

• Descriptive: What interventions are commonly used to support students with autism spectrum disorders in inclusive classrooms?
• Explanatory: Why do some special education programs yield better academic outcomes for students with specific learning disabilities?
• Exploratory: How can augmented reality technologies be utilized to enhance learning for students with visual impairments?
• Comparative: How does special education support differ between urban and rural school districts?
• Predictive: Based on advancements in assistive technologies, how will the landscape of special education transform in the near future?

Aerospace engineering

• Descriptive: What are the key materials and technologies utilized in modern spacecraft design?
• Explanatory: Why are certain alloys preferred in high-temperature aerospace applications?
• Exploratory: How might advances in propulsion technologies revolutionize space travel in the next decade?
• Comparative: How do commercial aircraft designs differ from military aircraft designs in terms of aerodynamics?
• Predictive: Given current research trends, how will the efficiency of jet engines change in the upcoming years?

Biomedical engineering

• Descriptive: What are the foundational principles behind the design of modern prosthetic limbs?
• Explanatory: Why have bio-compatible materials like titanium become crucial in implantable medical devices?
• Exploratory: How can nanotechnology be leveraged to improve drug delivery systems in the future?
• Comparative: How do MRI machines differ from CT scanners in terms of their underlying technology and application?
• Predictive: Based on emerging trends, how will wearable health monitors evolve in the next decade?

Chemical engineering

• Descriptive: What processes are involved in the large-scale production of ethylene?
• Explanatory: Why is distillation the most common separation method in the petroleum industry?
• Exploratory: How might green chemistry principles transform traditional chemical manufacturing processes?
• Comparative: How does the production of biofuels compare to traditional fossil fuels in terms of yield and environmental impact?
• Predictive: Given global sustainability goals, how will the chemical industry's reliance on fossil resources shift in the future?

Civil engineering

• Descriptive: What are the primary considerations in the structural design of skyscrapers in earthquake-prone regions?
• Explanatory: Why are steel-reinforced concrete beams commonly used in bridge construction?
• Exploratory: How can smart city concepts influence the infrastructure planning of urban centers in the future?
• Comparative: How do tunneling methods differ between soft soil and hard rock terrains?
• Predictive: With the increasing threat of climate change, how will coastal infrastructure design criteria change to account for rising sea levels?

Computer engineering

• Descriptive: What are the main components of a modern central processing unit (CPU) and their functions?
• Explanatory: Why is silicon predominantly used in semiconductor manufacturing?
• Exploratory: How might quantum computing redefine the landscape of traditional computing architectures?
• Comparative: How do solid-state drives (SSDs) compare to traditional hard disk drives (HDDs) in terms of performance and longevity?
• Predictive: Given advancements in chip miniaturization, how will the form factor of consumer electronics evolve in the coming years?

Electrical engineering

• Descriptive: What are the standard stages involved in the transmission and distribution of electrical power?
• Explanatory: Why are transformers essential in the power distribution network?
• Exploratory: How can emerging smart grid technologies improve the efficiency and reliability of electrical distribution systems?
• Comparative: How do AC and DC transmission methods differ in terms of efficiency and infrastructure requirements?
• Predictive: With the rise of renewable energy sources, how will power grid management complexities change in the next decade?

Mechanical engineering

• Descriptive: What are the fundamental principles behind the operation of a four-stroke internal combustion engine?
• Explanatory: Why are certain polymers used as vibration dampeners in machinery?
• Exploratory: How might advancements in materials science impact the design of future automotive systems?
• Comparative: How do hydraulic systems compare to pneumatic systems in terms of energy efficiency and application?
• Predictive: With the push towards sustainability, how will traditional manufacturing methods evolve to reduce their carbon footprint?

Climatology

• Descriptive: What are the primary factors that influence the El Niño and La Niña phenomena?
• Explanatory: Why have certain regions experienced more intense and frequent heatwaves in the past decade?
• Exploratory: How might changing atmospheric CO2 concentrations impact global wind patterns in the future?
• Comparative: How do urban areas differ from rural areas in terms of microclimate conditions?
• Predictive: Given current greenhouse gas emission trends, what will be the average global temperature increase by the end of the century?

Conservation science

• Descriptive: What are the primary threats faced by tropical rainforests around the world?
• Explanatory: Why are certain species more vulnerable to habitat fragmentation than others?
• Exploratory: How can community involvement enhance conservation efforts in protected areas?
• Comparative: How does the effectiveness of in-situ conservation compare to ex-situ conservation for endangered species?
• Predictive: If current deforestation rates continue, how many species are predicted to go extinct in the next 50 years?
• Descriptive: What are the dominant flora and fauna in a temperate deciduous forest biome?
• Explanatory: Why do certain ecosystems, like wetlands, have higher biodiversity than others?
• Exploratory: How might the spread of invasive species alter nutrient cycling in freshwater lakes?
• Comparative: How do the trophic dynamics of grassland ecosystems differ from those of desert ecosystems?
• Predictive: How will global ecosystems change if bee populations continue to decline at current rates?

Environmental health

• Descriptive: What are the major pollutants found in urban air?
• Explanatory: Why do certain pollutants cause respiratory diseases in humans?
• Exploratory: How might green building designs reduce the health risks associated with indoor air pollutants?
• Comparative: How do the health impacts of living near coal-fired power plants compare to living near nuclear power plants?
• Predictive: Given increasing urbanization trends, how will air quality in major cities change over the next two decades?

Marine biology

• Descriptive: What are the primary species that comprise a coral reef ecosystem?
• Explanatory: Why are coral reefs particularly sensitive to changes in sea temperature?
• Exploratory: How might deep-sea exploration reveal unknown marine species and their adaptations?
• Comparative: How do the feeding strategies of pelagic fish differ from benthic fish in oceanic ecosystems?
• Predictive: If ocean acidification trends continue, what will be the impact on shell-forming marine organisms in the next 30 years?
• Descriptive: What are the most common oral health issues faced by elderly individuals?
• Explanatory: Why do sugary foods lead to a higher prevalence of cavities?
• Exploratory: How might emerging technologies revolutionize dental procedures in the coming decade?
• Comparative: How do the effects of electric toothbrushes compare to manual ones in reducing plaque?
• Predictive: Given current trends, how might the prevalence of gum diseases change in populations with increased sugar consumption over the next decade?

Kinesiology

• Descriptive: What are the primary physiological changes that occur during aerobic exercise?
• Explanatory: Why do some athletes experience muscle cramps during extensive physical activity?
• Exploratory: How might different stretching routines impact athletic performance?
• Comparative: How do the biomechanics of running on a treadmill differ from running outdoors?
• Predictive: If sedentary lifestyles continue to rise, what could be the potential impact on musculoskeletal health in the next 20 years?
• Descriptive: What are the main symptoms associated with the early stages of Parkinson's disease?
• Explanatory: Why are some viruses, like the flu, more prevalent in colder months?
• Exploratory: How might genetic editing technologies, like CRISPR, be utilized to treat hereditary diseases in the future?
• Comparative: How does the efficacy of traditional chemotherapy compare to targeted therapy in treating certain cancers?
• Predictive: Given advances in telemedicine, how might patient-doctor interactions evolve over the next decade?
• Descriptive: What are the primary responsibilities of nurses in intensive care units?
• Explanatory: Why is there a higher burnout rate among nurses compared to other healthcare professionals?
• Exploratory: How can training programs be improved to better equip nurses for challenges in emergency situations?
• Comparative: How does the patient recovery rate differ when cared for by specialized nurses versus general ward nurses?
• Predictive: How will the role of nurses change with the integration of more AI-based diagnostic tools in hospitals?
• Descriptive: What are the main nutritional components of a Mediterranean diet?
• Explanatory: Why does a diet high in processed sugars lead to increased risks of type 2 diabetes?
• Exploratory: How might gut microbiota be influenced by various diets and what are the potential health implications?
• Comparative: How does the nutritional profile of plant-based proteins compare to animal-based proteins?
• Predictive: If global meat consumption trends continue, what could be the implications for population-wide nutritional health in 30 years?
• Descriptive: What are the primary active ingredients in over-the-counter pain relievers?
• Explanatory: Why do certain medications cause drowsiness as a side effect?
• Exploratory: How might nanoparticle-based drug delivery systems enhance the efficacy of certain treatments?
• Comparative: How do the effects of generic drugs compare to their brand-name counterparts?
• Predictive: Given the rise of antibiotic-resistant bacteria, how might pharmaceutical approaches to bacterial infections change in the future?

Public health

• Descriptive: What are the main factors contributing to public health disparities in urban vs rural areas?
• Explanatory: Why did certain regions have higher transmission rates during the COVID-19 pandemic?
• Exploratory: How can community engagement strategies be optimized for more effective health campaigns?
• Comparative: How do vaccination rates and outcomes differ between countries with public vs private healthcare systems?
• Predictive: Based on current trends, how will global public health challenges evolve over the next 50 years?

Art history

• Descriptive: What are the primary artistic styles observed in the Renaissance era?
• Explanatory: Why did the Baroque art movement emerge after the Renaissance?
• Exploratory: How might newly discovered ancient art pieces reshape our understanding of prehistoric artistic practices?
• Comparative: How does European Romantic art differ from Asian Romantic art of the same period?
• Predictive: Given current trends, how might digital art impact traditional art gallery setups in the next decade?
• Descriptive: What are the primary themes in Homer's "Odyssey"?
• Explanatory: Why did Greek tragedies place a strong emphasis on the concept of fate?
• Exploratory: Are there undiscovered works that might provide more insight into daily life in ancient Rome?
• Comparative: How do Roman epics compare to their Greek counterparts in terms of character development?
• Predictive: How will emerging technologies like virtual reality affect the study of ancient ruins?

Cultural studies

• Descriptive: How is the concept of family portrayed in contemporary American media?
• Explanatory: Why has the influence of Western culture grown in certain Eastern countries over the last century?
• Exploratory: What are the emerging subcultures in the digital age and how do they communicate?
• Comparative: How does the representation of masculinity vary between Eastern and Western films?
• Predictive: In what ways might globalization affect cultural identities in the next two decades?
• Descriptive: What events led to the fall of the Berlin Wall?
• Explanatory: Why did the Industrial Revolution begin in Britain?
• Exploratory: Are there undocumented civilizational interactions in ancient times that new archaeological findings might reveal?
• Comparative: How did the responses to the Black Plague differ between European and Asian nations?
• Predictive: Given historical patterns, how might major global powers react to dwindling natural resources in the future?
• Descriptive: What are the main narrative techniques used in James Joyce's "Ulysses"?
• Explanatory: Why did the Gothic novel become popular in 19th-century England?
• Exploratory: How might translations of ancient texts reveal different interpretations based on the translator's cultural background?
• Comparative: How does the portrayal of war differ between post-WWII American and French literature?
• Predictive: How might the rise of AI-authored literature change the publishing industry?
• Descriptive: What are the core principles of existentialism as described by Jean-Paul Sartre?
• Explanatory: Why did the philosophy of existentialism gain prominence post-WWII?
• Exploratory: How might ancient Eastern philosophies provide insights into modern ethical dilemmas surrounding technology?
• Comparative: How does Nietzsche's concept of the "Ubermensch" compare to Aristotle's "virtuous person"?
• Predictive: As AI becomes more prevalent, how might philosophical discussions around consciousness evolve?

Religious studies

• Descriptive: What are the Five Pillars of Islam?
• Explanatory: Why did Protestantism emerge within Christianity during the 16th century?
• Exploratory: Are there common motifs in creation myths across various religions?
• Comparative: How do concepts of the afterlife compare between Christianity, Buddhism, and Ancient Egyptian beliefs?
• Predictive: How might interfaith dialogue shape religious practices in multi-faith societies over the next decade?

Classic languages

• Descriptive: What are the primary grammatical structures in Ancient Greek?
• Explanatory: Why did Latin play a foundational role in the development of many modern European languages?
• Exploratory: Are there yet-to-be-deciphered scripts from ancient civilizations that might provide insight into lost languages?
• Comparative: How do the verb conjugation patterns in Latin compare to those in Sanskrit?
• Predictive: Given the ongoing research in classical studies, how might our understanding of certain ancient texts change in the next decade?

Comparative literature

• Descriptive: What are the main themes in Japanese Haiku and English Sonnets?
• Explanatory: Why do certain folklore tales appear with variations across different cultures?
• Exploratory: How might newly translated works from lesser-known languages reshape the world literature canon?
• Comparative: How does the role of the tragic hero in French literature differ from its portrayal in Russian literature?
• Predictive: As global communication becomes more interconnected, how might the study of world literature evolve in universities?

Modern languages

• Descriptive: What are the primary tonal patterns observed in Mandarin Chinese?
• Explanatory: Why has English become a dominant lingua franca in international business and diplomacy?
• Exploratory: Which lesser-studied languages might become more prominent due to socio-political changes in their regions?
• Comparative: How do the grammatical complexities of Russian compare to those of German?
• Predictive: Given current global trends, which languages are predicted to become more widely spoken in the next two decades?
• Descriptive: What are the primary articulatory features of plosive sounds?
• Explanatory: Why do certain accents develop specific pitch fluctuations and intonations?
• Exploratory: How do various environmental factors affect vocal cord vibrations and sound production?
• Comparative: How does the pronunciation of fricatives differ between Spanish and Portuguese speakers?
• Predictive: How might advancements in voice recognition technology influence phonetics research in the next decade?
• Descriptive: What are the primary signs and symbols used in American road signage?
• Explanatory: Why do red roses universally symbolize love or passion in many cultures?
• Exploratory: Are there emerging symbols in digital communication that could become universally recognized signs in the future?
• Comparative: How do the semiotic structures in print advertisements differ between Western and Eastern cultures?
• Predictive: As emoji usage becomes more widespread, how might they impact written language semantics in the coming years?
• Descriptive: What are the key statutes governing tenant rights in residential leases?
• Explanatory: Why do personal injury claims vary significantly in settlement amounts even under similar circumstances?
• Exploratory: How might alternative dispute resolution mechanisms evolve in civil law contexts over the next decade?
• Comparative: How do defamation laws differ between jurisdictions that adopt the British common law system versus the Napoleonic code?
• Predictive: How might the rise of online transactions affect the volume and nature of civil law cases related to contract disputes?

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• Descriptive: What are the main principles enshrined in the First Amendment of the U.S. Constitution?
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• Comparative: How does the protection of freedom of speech differ between the U.S. Constitution and the German Basic Law?
• Predictive: Given global socio-political trends, how might constitutional democracies adjust their foundational texts in the next two decades?

Corporate law

• Descriptive: What are the primary duties and liabilities of a board of directors in a publicly traded company?
• Explanatory: Why do mergers and acquisitions often involve extensive due diligence processes?
• Exploratory: How might the rise of digital currencies impact the regulatory landscape for corporations in the finance sector?
• Comparative: How does the legal framework for shareholder rights in the U.S. compare to that of Japan?
• Predictive: How might changing global trade dynamics influence corporate structuring and international partnerships?

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• Descriptive: What constitutes first-degree murder in the majority of jurisdictions?
• Explanatory: Why are certain offenses classified as misdemeanors while others are felonies?
• Exploratory: Are there emerging patterns in cybercrime that suggest new areas of legal vulnerability?
• Comparative: How does the treatment of juvenile offenders differ between Scandinavian countries and the U.S.?
• Predictive: Given advancements in technology, how might criminal law evolve to address potential misuses of artificial intelligence?

International law

• Descriptive: What are the foundational principles of the Geneva Conventions?
• Explanatory: Why have some nations refused to recognize or be bound by certain international treaties?
• Exploratory: How might global climate change reshape international agreements and treaties in the coming years?
• Comparative: How do regional trade agreements in Africa compare to those in Southeast Asia in terms of provisions and enforcement mechanisms?
• Predictive: How might geopolitical shifts influence the role and effectiveness of international courts in resolving state disputes?

Applied mathematics

• Descriptive: What are the primary mathematical models used to predict the spread of infectious diseases?
• Explanatory: Why does the Navier–Stokes equation play a pivotal role in fluid dynamics?
• Exploratory: How might new computational methods enhance the efficiency of existing algorithms in applied mathematics?
• Comparative: How do optimization techniques in operations research differ from those in machine learning applications?
• Predictive: Given the rapid growth of quantum computing, how might it reshape the landscape of applied mathematical problems in the next decade?

Applied statistics

• Descriptive: What are the standard procedures for handling missing data in a large-scale survey?
• Explanatory: Why do statisticians use bootstrapping techniques in hypothesis testing?
• Exploratory: How might emerging data sources, like wearables and IoT devices, introduce new challenges and opportunities in applied statistics?
• Comparative: How does the performance of Bayesian methods compare to frequentist methods in complex hierarchical models?
• Predictive: With the increasing availability of big data, how might the role of applied statisticians evolve in the next five years?

Pure mathematics

• Descriptive: What are the axioms underpinning Euclidean geometry?
• Explanatory: Why is Gödel's incompleteness theorem considered a foundational result in the philosophy of mathematics?
• Exploratory: Are there newly emerging areas of study within number theory due to advancements in computational mathematics?
• Comparative: How do algebraic structures differ between rings and fields?
• Predictive: Considering current research trends, what areas of pure mathematics are poised for significant breakthroughs in the next decade?

Theoretical statistics

• Descriptive: What foundational principles underlie the Central Limit Theorem?
• Explanatory: Why is the concept of sufficiency crucial in the design of statistical tests?
• Exploratory: How might advances in artificial intelligence influence theoretical developments in statistical inference?
• Comparative: How do likelihood-based inference methods compare to Bayesian methods in terms of theoretical underpinnings?
• Predictive: As data generation mechanisms evolve, how might the theoretical foundations of statistics need to adapt in the future?
• Descriptive: What are the key features and behaviors of black holes?
• Explanatory: Why does the expansion of the universe appear to be accelerating?
• Exploratory: What potential insights might the study of exoplanets provide about the conditions necessary for life?
• Comparative: How do the properties of spiral galaxies differ from those of elliptical galaxies?
• Predictive: Based on current data, what are the projected future behaviors of our sun as it ages?
• Descriptive: What are the primary functions and structures of ribosomes in a cell?
• Explanatory: Why does DNA replication occur semi-conservatively?
• Exploratory: How might emerging technologies like CRISPR redefine our understanding of genetic engineering?
• Comparative: How do the metabolic processes of prokaryotic cells differ from those of eukaryotic cells?
• Predictive: Given the current trajectory of climate change, how might the biodiversity in tropical rainforests be affected over the next century?
• Descriptive: What are the key properties and uses of the noble gases?
• Explanatory: Why do exothermic reactions release heat?
• Exploratory: How might advances in nanochemistry influence drug delivery systems?
• Comparative: How do ionic bonds differ in strength and characteristics from covalent bonds?
• Predictive: Considering the rise in antibiotic-resistant bacteria, how might the field of medicinal chemistry adapt to produce effective treatments in the future?

Earth science

• Descriptive: What are the primary layers of Earth's atmosphere and their respective characteristics?
• Explanatory: Why do certain regions experience more seismic activity than others?
• Exploratory: How might the study of ancient ice cores provide insights into past climate conditions?
• Comparative: How do the processes of weathering differ between arid and humid climates?
• Predictive: Given current data on deforestation, what could be its impact on global soil quality and erosion patterns over the next 50 years?
• Descriptive: What are the fundamental principles underlying quantum mechanics?
• Explanatory: Why does the speed of light in a vacuum remain constant regardless of the observer's frame of reference?
• Exploratory: How might studies in string theory reshape our understanding of the universe at the smallest scales?
• Comparative: How do the effects of general relativity contrast with predictions from Newtonian physics under extreme gravitational conditions?
• Predictive: With advancements in particle physics, what potential new particles or phenomena might be discovered in the next decade?

Anthropology

• Descriptive: What are the primary rituals and customs of the indigenous tribes of the Amazon?
• Explanatory: Why did the ancient Mayan civilization collapse?
• Exploratory: How might modern urbanization impact the preservation of ancient burial sites?
• Comparative: How do hunter-gatherer societies differ from agricultural societies in terms of social structures?
• Predictive: Given global trends, how might indigenous cultures evolve over the next century?

Communication

• Descriptive: What are the main modes of communication used by millennials compared to baby boomers?
• Explanatory: Why has the usage of social media platforms surged in the last two decades?
• Exploratory: How might advancements in virtual reality reshape interpersonal communication in the future?
• Comparative: How do written communication skills differ between those educated in traditional schools versus online schools?
• Predictive: How might the nature of journalism change with the rise of automated content generation?
• Descriptive: What are the primary components of a nation's gross domestic product (GDP)?
• Explanatory: Why did the economic recession of 2008 occur?
• Exploratory: How might the concept of universal basic income impact labor market dynamics?
• Comparative: How do free market economies differ from command economies in terms of resource allocation?
• Predictive: Based on current global economic trends, which industries are predicted to boom in the next decade?
• Descriptive: What are the geographical features of the Himalayan mountain range?
• Explanatory: Why do desert regions exist on the western coasts of continents, such as the Atacama in South America?
• Exploratory: How might rising sea levels reshape the world's coastlines over the next century?
• Comparative: How does urban planning in European cities differ from that in American cities?
• Predictive: Given current urbanization rates, which cities are poised to become megacities by 2050?

Political science

• Descriptive: What are the foundational principles of a parliamentary democracy?
• Explanatory: Why do certain nations adopt federal systems while others prefer unitary systems?
• Exploratory: How might the rise of populism influence global diplomatic relations in the 21st century?
• Comparative: How do the rights of citizens in liberal democracies differ from those in authoritarian regimes?
• Predictive: Based on current political trends, which nations might see significant shifts in governance models over the next two decades?
• Descriptive: What are the primary stages of cognitive development in children according to Piaget?
• Explanatory: Why do certain individuals develop phobias?
• Exploratory: How might emerging neuroscientific tools, like fMRI, alter our understanding of human emotions?
• Comparative: How do coping mechanisms differ between individuals with high resilience versus those with low resilience?
• Predictive: Given the rise in digital communication, how might human attention spans evolve in future generations?

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• Descriptive: What are the core principles and practices in child protective services?
• Explanatory: Why do certain communities have higher rates of child neglect and abuse?
• Exploratory: How might the integration of artificial intelligence in social work affect decision-making in child welfare cases?
• Comparative: How do intervention strategies for substance abuse differ between urban and rural settings?
• Predictive: Based on current societal trends, what challenges might social workers face in the next decade?
• Descriptive: What are the defining characteristics of Generation Z as a social cohort?
• Explanatory: Why have nuclear families become less prevalent in Western societies?
• Exploratory: How might the widespread adoption of virtual realities impact social interactions and community structures in the future?
• Comparative: How do the roles and perceptions of elderly individuals differ between Eastern and Western societies?
• Predictive: Given the rise in remote work, how might urban and suburban living patterns change over the next three decades?

In synthesizing the vast range of research questions posed across diverse disciplines, it becomes clear that every academic field, from the humanities to the social sciences, offers unique perspectives and methodologies to uncover and understand various facets of our world. These questions, whether descriptive, explanatory, exploratory, comparative, or predictive, serve as guiding lights, driving scholarship and innovation. As academia continues to evolve and adapt, these inquiries not only define the boundaries of current knowledge but also pave the way for future discoveries and insights, emphasizing the invaluable role of continuous inquiry in the ever-evolving tapestry of human understanding.

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• v.53(4); 2010 Aug

Research questions, hypotheses and objectives

Patricia farrugia.

* Michael G. DeGroote School of Medicine, the

Bradley A. Petrisor

† Division of Orthopaedic Surgery and the

Forough Farrokhyar

‡ Departments of Surgery and

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

Mohit Bhandari

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

Objectives of this article

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

Research question

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

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

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

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

FINER criteria for a good research question

Adapted with permission from Wolters Kluwer Health. 2

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

PICOT criteria 1

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

Research hypothesis

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

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

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

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

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

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

Research objective

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

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

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

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

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

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

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

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

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

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

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

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

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Innovations In  10 May 2018

The biggest questions in science

In recent centuries we have learned so much about the worlds around and within us that it may sometimes seem that no nook is left unexplored. The truth is, though, that every new discovery leads us to ever deeper questions. Innovations In: The Biggest Questions in Science is a special report on the state of inquiry into these questions—the latest research on the nature of spacetime, the identity of dark matter, the origins of life, the source of consciousness, and more.

This special report from Nature and Scientific American is editorially independent. It is produced with third-party financial support. About this content .

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Quick links

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Write a Research Question

Before you begin writing your research question, it is first important to craft a purpose statement. What can be a purpose of your study?

Examples of a purpose for a quantitative study include:

• Examining a relationship between students who take computing classes in high school and those who pursue computer science as a major in college,
• Evaluating the effectiveness of an outreach activity among underrepresented students, or
• Measuring engagement or interest in computing among middle school students.

Examples for a qualitative study include:

• Exploring parent stories about helping their students with computing homework or
• Developing a theory of effective management techniques in a computer lab.

Once you define the purpose of your study, you can then create a clear purpose statement. Purpose statements help you define your research in a straightforward manner. Here is an example of a well-defined purpose statement.

The purpose of this study is to examine the relationship between the completion of an 9-week computational thinking unit among 7th and 8th grade students in a rural middle school and student achievement on mathematics exams.

This purpose statement explicitly answers these questions:

• What is the intent of the study?
• What population group is targeted in the study (i.e., age, location, etc.)?
• What was the intervention (activity or curriculum), including its duration?

After you have decided on the purpose of your study and have written your purpose statement, you can then craft your research question.

Writing a Well-crafted Research Question

Research questions provide an overarching direction for your study to follow. It guides the type of study you will choose, the type of data you will collect, and the type of analysis on the data that you will perform.

Writing good research questions, then, is an important step in framing your study. What makes a good research question? Research questions should be clear, concise, specific, neutral, and focused. They should also be complex enough that the question requires more than just a “yes” or “no” answer. An example of a thorough research question for a quantitative study follows:

Does guardian understanding of computational thinking affect student performance on computational thinking tasks among primary school students in an urban school district?

For a qualitative study, a thorough research question may look like this:

What are the major challenges teachers face when teaching computational thinking to Kindergarten, 1st grade and 2nd grade students in the United States?

Typically, research questions are not the exact question that you will actually ask the participants in your study. They, however, guide those questions.

Research questions should:

• Define what is being measured
• Define the population group
• Be neutral (not assume the intervention being studied is effective or not)
• Be able to be answered in the timeframe you have planned for the study

Depending on the study length, more than one research question can be appropriate. Your research questions will most likely be related in some way, since they will be designed to support your purpose statement.

Defining What is being Measured

Defining what is being measured is important for narrowing your research. Consider the following questions:

1A: Does participation in a one-week teacher professional development around the Exploring Computer Science curriculum result in improved teaching practices? 1B: Does participation in a one-week teacher professional development around the Exploring Computer Science curriculum result in more frequent use of inquiry-based learning pedagogical methods?

In the example above, Question 1A refers to “improved teaching practices”. “Improved teaching practices” is unclear, since there is no context for “improved” against the status quo. In Question 1B, one teaching practice, inquiry-based learning, is chosen for the study.

Defining the Population Group

Defining the population group is often missing in research questions, but it is very easy to add. Consider the following questions:

2A: Do participants in a week-long Lego Robotics summer camp have an increased likelihood of taking computer science courses at the college level?

2B: Do 11th and 12th grade students in central Illinois who participate in a week-long Lego Robotics summer camp have an increased likelihood of taking computer science courses during their first year of college?

In the Question 2A, we do not know who the participants are or where they are located. As a research question, clearly stating the population group is important for identifying the group that will be targeted in your study. Sometimes this information may be provided in context within preceding paragraphs. However, restating the population group within the research question makes the target of your study clear to your reader.

In our review of hundreds of articles for this site, we have encountered many articles that do not state whether the group is undergraduate students, primary school students, or secondary school students or in which country or setting the study takes place. It is difficult for other researchers to use or build upon research that hasn’t clearly stated the population group. Embedding this into your research question will enable others to know who the participants in your study were.

Writing Neutral Statements

A neutral statement will exclude any pre-conceived bias. Consider these questions:

3A: What elements of AP Computer Science Principles make it a more appealing course to high school-aged girls than AP Computer Science A? 3B: Is the AP Computer Science Principles course more appealing to high school-aged girls than AP Computer Science A? If so, what is seen as different and/or more appealing?

Question A assumes that the AP Computer Science Principles course is more appealing than the AP Computer Science A course for the target population (high school-aged girls). If this has been previously established in prior research and the researchers are making this a follow-up study, then Question 3A may be seen as neutral. However, if this has not been previously established, then Question 3B may be a more appropriately worded research question.

Defining a Scope/Timeframe

Research studies are projects, and just like any project, it is important to manage scope. Scope is based on your timeframe and your resources. Consider the following questions:

4A: Are middle-school girls who participate in a summer camp more likely to pursue careers in computing fields than those who do not participate in the camp? 4B: Are middle-school girls who participate in a summer camp more likely to express interest in computing-related careers than those who do not participate in the camp?

Question 4A implies that girls will be tracked from middle school through college and into their careers. This longitudinal study would take a minimum of seven years, likely more, if you count the years it would take for a 6th grader to start her career. Question 4B is finite and could be evaluated at the end of camp, three months after the camp has ended, or even the following year.

In this example, both questions could be suitable and is entirely dependent on your timeframe for your study as well as your resources.

Additional Examples

Take a look at these examples that illustrate different types of requirements for well-crafted research questions.

Example 5A: How is Scratch used to teach computational thinking? Example 5B: How are Native American high school teachers in North Dakota using Scratch to teach computational thinking?

The 5A research question is very vague. We know nothing about the population group being studied or the intervention other than one computing education tool being used (Scratch) and the concept being taught. The 5B question specifies how Scratch is being used and the population group being targeted.

Example 6A: Does a game design camp make girls interested in computing? Example 6B: What is the impact of a one-week game design camp on the interest levels in computing among 7th and 8th grade girls located in Chicago’s West Side?

Example 6A is very broad. It may be fine if you are planning on writing a book or a 200-page dissertation. For most of us, though, our studies need to be more focused so that we can complete it in 6 months or 1 or 2 years. Example 6B looks at a specific cause (impact of a one-week game design camp), a specific locale (Chicago’s West Side), and a specific group (7th and 8th grade girls). By making your research question(s) well-defined, you are more likely to be able to answer the question in the timeframe for your study.

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Research Questions in Data Science

• First Online: 16 February 2018

Cite this chapter

• Sherri Rose 8 &
• Mark J. van der Laan 9  

Part of the book series: Springer Series in Statistics ((SSS))

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The types of research questions we face in medicine, technology, and business continue to increase in their complexity with our growing ability to obtain novel forms of data. Much of the data in both observational and experimental studies is gathered over lengthy periods of time with multiple measures collected at intermediate time points. Some of these data are streaming (such as posts on Twitter), images, DNA sequences, or electronic health records. Statistical learning methods must be developed and adapted for these new challenges.

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Data Analysis

O. Aalen, Nonparametric estimation of partial transition probabilities in multiple decrement models. Ann. Stat. 6 , 534–545 (1978)

Article   MathSciNet   MATH   Google Scholar  

A. Abadie, G. Imbens, Simple and bias-corrected matching estimators for average treatment effects. Technical Report 283. NBER Working Paper (2002)

Google Scholar  

Action to Control Cardiovascular Risk in Diabetes Study Group. Effects of intensive glucose lowering in type 2 diabetes. N. Engl. J. Med. 358 , 2545–2549 (2008)

ADVANCE Collaborative Group, Intensive blood glucose control and vascular outcomes in patients with type 2 diabetes. N. Engl. J. Med. 358 , 2560–2562 (2008)

A. Afifi, S. Azen, Statistical Analysis: A Computer Oriented Approach , 2nd edn. (Academic, New York, 1979)

MATH   Google Scholar  

C. Anderson, The end of theory: the data deluge makes the scientific method obsolete. Wired (2008)

T.J. Aragon, epitools: Epidemiology tools (2012). http://cran.r-project.org/package=epitools

S. Aral, D. Walker, Identifying social influence in networks using randomized experiments. IEEE Intell. Syst. 26 (5), 91–96 (2011)

Article   Google Scholar  

S. Aral, D. Walker, Tie strength, embeddedness, and social influence: a large-scale networked experiment. Manag. Sci. 60 (6), 1352–1370 (2014)

P.M. Aronow, C. Samii, Estimating average causal effects under interference between units. ArXiv e-prints, May (2013)

J.Y. Audibert, A.B. Tsybakov, Fast learning rates for plug-in classifiers. Ann. Stat. 35 (2), 608–633 (2007)

L. Auret, C. Aldrich, Empirical comparison of tree ensemble variable importance measures. Chemom. Intel. Lab. Syst. 105 (2), 157–170 (2011)

P.C. Austin, A. Manca, M. Zwarensteina, D.N. Juurlinka, M.B. Stanbrook, A substantial and confusing variation exists in handling of baseline covariates in randomized controlled trials: a review of trials published in leading medical journals. J. Clin. Epidemiol. 63 , 142–153 (2010)

C. Avin, I. Shpitser, J. Pearl, Identifiability of path-specific effects. Proceedings of International Joint Conference on Artificial Intelligence, 357–363 (2005)

S. Balakrishnan, D. Madigan, Algorithms for sparse linear classifiers in the massive data setting. J. Mach. Learn. Res. 9 , 313–337 (2008)

L. Balzer, M. Petersen, M.J. van der Laan, Adaptive pair-matching in randomized trials with unbiased and efficient effect estimation. Stat. Med. 34 (6), 999–1011 (2015)

Article   MathSciNet   Google Scholar  

L. Balzer, J. Ahern, S. Galea, M.J. van der Laan, Estimating effects with rare outcomes and high dimensional covariates: Knowledge is power. Epidemiol. Methods. 5 (1), 1–18 (2016a)

L. Balzer, M. van der Laan, M. Petersen, the SEARCH Collaboration, Adaptive pre-specification in randomized trials with and without pair-matching. Stat. Med. 35 (25), 4528–4545 (2016b)

L.B. Balzer, M.L. Petersen, M.J. van der Laan, the SEARCH Collaboration, Targeted estimation and inference of the sample average treatment effect in trials with and without pair-matching. Stat. Med. 35 (21), 3717–3732 (2016c)

H. Bang, J.M. Robins, Doubly robust estimation in missing data and causal inference models. Biometrics 61 , 962–972 (2005)

A.-L. Barabási, R. Albert, Emergence of scaling in random networks. Science 286 (5439), 509–512 (1999)

E. Bareinboim, J. Pearl, A general algorithm for deciding transportability of experimental results. J. Causal Inf. 1 (1), 107–134 (2013)

G.W. Basse, E.M. Airoldi, Optimal design of experiments in the presence of network-correlated outcomes. ArXiv e-prints, July (2015)

C. Beck, B. Lu, R. Greevy, nbpMatching: functions for optimal non-bipartite optimal matching (2016). https://CRAN.R-project.org/package=nbpMatching

O. Bembom, M.J. van der Laan, A practical illustration of the importance of realistic individualized treatment rules in causal inference. Electron. J. Stat. 1 , 574–596 (2007)

O. Bembom, M.J. van der Laan, Analyzing sequentially randomized trials based on causal effect models for realistic individualized treatment rules. Stat. Med. 27 , 3689–3716 (2008)

O. Bembom, M.L. Petersen, S.-Y. Rhee, W.J. Fessel, S.E. Sinisi, R.W. Shafer, M.J. van der Laan, Biomarker discovery using targeted maximum likelihood estimation: application to the treatment of antiretroviral resistant HIV infection. Stat. Med. 28 , 152–72 (2009)

J. Benichou, M.H. Gail, Estimates of absolute cause-specific risk in cohort studies. Biometrics 46 , 813–826 (1990)

Y. Benjamini, Y. Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B 57 , 289–300 (1995)

MathSciNet   MATH   Google Scholar  

D. Benkeser, M.J. van der Laan, The highly adaptive lasso estimator, in IEEE International Conference on Data Science and Advanced Analytics , pp. 689–696 (2016)

D. Benkeser, M. Carone, M.J. van der Laan, P. Gilbert, Doubly-robust nonparametric inference on the average treatment effect. Biometrika. 104 (4), 863–880 (2017a)

D. Benkeser, S.D. Lendle, J. Cheng, M.J. van der Laan, Online cross-validation-based ensemble learning. Stat. Med. 37 (2), 249–260 (2017b)

P. Bertail, A. Chambaz, E. Joly, Practical targeted learning from large data sets by survey sampling. ArXiv e-prints, June (2016)

P. Bertail, E. Chautru, S. Clémençon, Empirical processes in survey sampling with (conditional) Poisson designs. Scand. J. Stat. 44 (1), 97–111 (2017)

P.J. Bickel, On adaptive estimation. Ann. Stat. 10 , 647–671 (1982)

Article   MATH   MathSciNet   Google Scholar  

P.J. Bickel, F. Götze, W.R. van Zwet, Resampling fewer than n observations: gains, losses, and remedies for losses. Stat. Sin. 7 (1), 1–31 (1997a)

P.J. Bickel, C.A.J. Klaassen, Y. Ritov, J. Wellner, Efficient and Adaptive Estimation for Semiparametric Models (Springer, Berlin, Heidelberg, New York, 1997b)

L. Bondesson, I. Traat, A. Lundqvist, Pareto sampling versus Sampford and conditional Poisson sampling. Scand. J. Stat. Theory Appl. 33 (4), 699–720 (2006)

L. Bottou, Large-scale machine learning with stochastic gradient descent, in Proceedings of COMPSTAT’2010 (Springer, Berlin, 2010), pp. 177–186

L. Bottou, Stochastic gradient descent tricks, in Neural Networks: Tricks of the Trade (Springer, Berlin, 2012), pp. 421–436

Book   Google Scholar  

J. Bowers, M.M. Fredrickson, C. Panagopoulos, Reasoning about interference between units: a general framework. Polit. Anal. 21 (1), 97–124 (2013)

L. Breiman, Random forests. Mach. Learn. 45 , 5–32 (2001)

Article   MATH   Google Scholar  

L. Breiman, J.H. Friedman, R. Olshen, C.J. Stone, Classification and Regression Trees (Chapman & Hall, Boca Raton, 1984)

L. Breiman et al., Statistical modeling: the two cultures (with comments and a rejoinder by the author). Stat. Sci. 16 (3), 199–231 (2001)

D.I. Broadhurst, D.B. Kell, Statistical strategies for avoiding false discoveries in metabolomics and related experiments. Metabolomics 2 (4), 171–196 (2006)

D.W. Brock, D. Wikler, Ethical challenges in long-term funding for HIV/AIDS. Health Aff. 28 (6), 1666–1676 (2009)

J.C. Brooks, Super learner and targeted maximum likelihood estimation for longitudinal data structures with applications to atrial fibrillation. PhD thesis, University of California, Berkeley (2012)

L.E. Cain, J.M. Robins, E. Lanoy, R. Logan, D. Costagliola, M.A. Hernan, When to start treatment? A systematic approach to the comparison of dynamic regimes using observational data. Int. J. Biostat. 6 , Article 18 (2010)

R.M. Califf, D.A. Zarin, J.M. Kramer, R.E. Sherman, L.H. Aberle, and A. Tasneem, Characteristics of clinical trials registered in ClinicalTrials.gov, 2007–2010. J. Am. Med. Assoc. 307 (17), 1838–1847 (2012)

A.C. Cameron, J.B. Gelbach, D.L. Miller, Boostrap-based improvements for inference with clustered errors. Rev. Econ. Stat. 90 (3), 414–427 (2008)

M.J. Campbell, Cluster randomized trials, in Handbook of Epidemiology , 2nd edn., ed. by W. Ahrens, I. Pigeot (Springer, Berlin, 2014)

M.J. Campbell, A. Donner, N. Klar, Developments in cluster randomized trials and statistics in medicine. Stat. Med. 26 , 2–19 (2007)

M. Carone, I. Díaz, M.J. van der Laan, Higher-order targeted minimum loss-based estimation. Technical Report, Division of Biostatistics, University of California, Berkeley

B. Chakraborty, E.E. Moodie, Statistical Methods for Dynamic Treatment Regimes (Springer, Berlin, Heidelberg, New York, 2013)

Book   MATH   Google Scholar  

B. Chakraborty, E.B. Laber, Y.-Q. Zhao, Inference about the expected performance of a data-driven dynamic treatment regime. Clin. Trials 11 (4), 408–417 (2014)

A. Chambaz, tsml.cara.rct: targeted sequential minimum loss CARA RCT design and inference (2016). https://github.com/achambaz/tsml.cara.rct

A. Chambaz, P. Neuvial, Targeted, integrative search of associations between DNA copy number and gene expression, accounting for DNA methylation. Bioinformatics 31 (18), 3054–3056 (2015)

A. Chambaz, P. Neuvial, Targeted learning of a non-parametric variable importance measure of a continuous exposure (2016). http://CRAN.R-project.org/package=tmle.npvi

A. Chambaz, M.J. van der Laan, Inference in targeted group-sequential covariate-adjusted randomized clinical trials. Scand. J. Stat. 41 (1), 104–140 (2014)

A. Chambaz, M.J. van der Laan, Targeting the optimal design in randomized clinical trials with binary outcomes and no covariate: theoretical study. Int. J. Biostat. 7 (1), Article 10 (2011a)

A. Chambaz, M.J. van der Laan, Targeting the optimal design in randomized clinical trials with binary outcomes and no covariate: simulation study. Int. J. Biostat. 7 (1), Article 11 (2011b)

A. Chambaz, M.J. van der Laan, TMLE in adaptive group sequential covariate-adjusted RCTs, in Targeted Learning: Causal Inference for Observational and Experimental Data , ed. by M.J. van der Laan, S. Rose (Springer, Berlin Heidelberg, New York, 2011c)

A. Chambaz, P. Neuvial, M.J. van der Laan, Estimation of a non-parametric variable importance measure of a continuous exposure. Electron. J. Stat. 6 , 1059–1099 (2012)

A. Chambaz, D. Choudat, C. Huber, J.C. Pairon, M.J. van der Laan, Analysis of the effect of occupational exposure to asbestos based on threshold regression modeling of case–control data. Biostatistics 15 (2), 327–340 (2014)

A. Chambaz, M.J. van der Laan, W. Zheng, Targeted covariate-adjusted response-adaptive lasso-based randomized controlled trials, in Modern Adaptive Randomized Clinical Trials: Statistical, Operational, and Regulatory Aspects , ed. by A. Sverdlov (CRC Press, Boca Raton, 2015), pp. 345–368

A. Chambaz, W. Zheng, M.J. van der Laan, Targeted sequential design for targeted learning of the optimal treatment rule and its mean reward. Ann Stat. 45 (6), 1–28 (2017)

T. Chen, C. Guestrin, Xgboost: a scalable tree boosting system, in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM, New York, 2016), pp. 785–794

O.Y. Chén, C. Crainiceanu, E.L. Ogburn, B.S. Caffo, T.D. Wager, M.A. Lindquist, High-dimensional multivariate mediation with application to neuroimaging data. Biostatistics (2017, in press)

D.S. Choi, Estimation of monotone treatment effects in network experiments. ArXiv e-prints, August (2014)

N.A. Christakis, J.H. Fowler, The spread of obesity in a large social network over 32 years. N. Engl. J. Med. 357 (4), 370–379 (2007)

N.A. Christakis, J.H. Fowler, Social contagion theory: examining dynamic social networks and human behavior. Stat. Med. 32 (4), 556–577 (2013)

W.G. Cochran, Analysis of covariance: its nature and uses. Biometrics 13 , 261–281 (1957)

E. Colantuoni, M. Rosenblum, Leveraging prognostic baseline variables to gain precision in randomized trials. Technical Report 263, Johns Hopkins University, Department of Biostatistics Working Papers (2015)

S.R. Cole, E.A. Stuart, Generalizing evidence from randomized clinical trials to target populations: the ACTG 320 trial. Am. J. Epidemiol. 172 (1), 107–115 (2010)

S.R. Cole, M.A. Hernan, J.M. Robins, K. Anastos, J. Chmiel, R. Detels, C. Ervin, J. Feldman, R. Greenblatt, L. Kingsley, S. Lai, M. Young, M. Cohen, A. Munoz, Effect of highly active antiretroviral therapy on time to acquired immunodeficiency syndrome or death using marginal structural models. Am. J. Epidemiol. 158 (7), 687–694 (2003)

D.R. Cox, P. McCullagh, Some aspects of analysis of covariance. Biometrics 38 (3), 541–561 (1982)

K. Crammer, O. Dekel, J. Keshet, S. Shalev-Shwartz, Y. Singer, Online passive-aggressive algorithms. J. Mach. Learn. Res. 7 , 551–585 (2006)

G.B. Dantzig, Discrete-variable extremum problems. Oper. Res. 5 (2), 266–288 (1957)

A.C. Davison, D.V. Hinkley, Bootstrap methods and Their Application . Cambridge Series in Statistical and Probabilistic Mathematics, vol. 1 (Cambridge University Press, Cambridge, Cambridge, 1997)

A.P. Dawid, V. Didelez, Identifying the consequences of dynamic treatment strategies: a decision-theoretic overview. Stat. Surv. 4 , 184–231 (2010)

V.H. de la Peña, E. Giné, Decoupling, in Probability and its Applications (Springer, New York, 1999)

L. Denby, C. Mallows, Variations on the histogram. J. Comput. Graph. Stat. 18 (1), 21–31 (2009)

I. Díaz, M. van der Laan, Super learner-based conditional density estimation with application to marginal structural models. Int. J. Biostat. 7 (1), 38 (2011)

I. Díaz, M. van der Laan, Population intervention causal effects based on stochastic interventions. Biometrics 68 (2), 541–549 (2012)

I. Díaz, M.J. van der Laan, Assessing the causal effect of policies: an example using stochastic interventions. Int. J. Biostat. 9 (2), 161–174 (2013a)

I. Díaz, M.J. van der Laan, Sensitivity analysis for causal inference under unmeasured confounding and measurement error problems. Int. J. Biostat. 9 (2), 149–160 (2013b)

I. Díaz, M. Carone, M.J. van der Laan, Second-order inference for the mean of a variable missing at random. Int. J. Biostat. 12 (1), 333–349 (2016)

I. Díaz, A. Hubbard, A. Decker, M. Cohen, Variable importance and prediction methods for longitudinal problems with missing variables. PLoS One 10 (3), e0120031 (2015)

T.J. DiCiccio, J.P. Romano, A review of bootstrap confidence intervals. J. R. Stat. Soc. Ser. B (1988)

T.J. DiCiccio, J.P. Romano, Nonparametric confidence limits by resampling methods and least favorable families. Int. Stat. Rev./Revue Internationale de Statistique 58 (1), 59 (1990)

V. Didelez, A.P. Dawid, S. Geneletti, Direct and indirect effects of sequential treatments, in Proceedings of the 22nd Annual Conference on Uncertainty in Artificial Intelligence (2006), pp. 138–146

P. Ding, T. VanderWeele, Sensitivity analysis without assumptions. Epidemiol. 27 (3), 368–377 (2016)

A. Donner, N. Klar, Design and Analysis of Cluster Randomization Trials in Health Research (Arnold, London, 2000)

J. Duchi, E. Hazan, Y. Singer, Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12 , 2121–2159 (2011)

W. Duckworth, C. Abraira, T. Moritz, D. Reda, N. Emanuele, P.D. Reaven, F.J. Zieve, J. Marks, S.N. Davis, R. Hayward, S.R. Warren, S. Goldman, M. McCarren, M.E. Vitek, W.G. Henderson, G.D. Huang for the VADT Investigators, Glucose control and vascular complications in veterans with type 2 diabetes. N. Engl. J. Med. 360 , 129–39 (2009a)

W. Duckworth et al., Glucose control and vascular complications in veterans with type 2 diabetes. N. Engl. J. Med. 360 (2), 129–139 (2009b)

S. Dudoit, M.J. van der Laan, Asymptotics of cross-validated risk estimation in estimator selection and performance assessment. Stat. Methodol. 2 (2), 131–154 (2005)

F. Eberhardt, R. Scheines, Interventions and causal inference. Department of Philosophy. Paper 415 (2006)

B. Efron, Better bootstrap confidence intervals. J. Am. Stat. Assoc. 82 (397), 171–185 (1987)

B. Efron, R.J. Tibshirani, An Introduction to the Bootstrap (Chapman & Hall, Boca Raton, 1993)

U. Einmahl, D.M. Mason, An empirical process approach to the uniform consistency of kernel-type function estimators. J. Theor. Probab. 13 (1) 1–37 (2000)

U. Einmahl, D.M. Mason, Uniform in bandwidth consistency of kernel-type function estimators. Ann. Stat. 33 (3), 1380–1403 (2005)

European Medicines Agency, Guideline on adjustment for baseline covariates in clinical trials. London, February (2015)

J.P. Fine, R.J. Gray, A proportional hazards model for the subdistribution of a competing risk. J. Am. Stat. Assoc. 94 (446), 496–509 (1999)

M. Finster, M. Wood, The Apgar score has survived the test of time. Anesthesiology 102 (4), 855–857 (2005)

R.A. Fisher, Statistical Methods for Research Workers , 4th edn. (Oliver and Boyd Ltd., Edinburgh, 1932)

R.A. Fisher, The Design of Experiments , (Oliver and Boyd Ltd, London, 1935)

C.E. Frangakis, T. Qian, Z. Wu, I. Diaz, Deductive derivation and Turing-computerization of semiparametric efficient estimation. Biometrics 71 (4), 867–874 (2015)

L.S. Freedman, M.H. Gail, S.B. Green, D.K. Corle, The COMMIT Research Group, The Efficiency of the matched-pairs design of the community intervention trial for smoking cessation (COMMIT). Control. Clin. Trials 18 (2), 131–139 (1997)

J.H. Friedman, Multivariate adaptive regression splines. Ann. Stat. 19 (1), 1–141 (1991)

J.H. Friedman, Greedy function approximation: a gradient boosting machine. Ann. Stat. 29 , 1189–1232 (2001)

J.H. Friedman, T.J. Hastie, R.J. Tibshirani, Glmnet: lasso and elastic-net regularized generalized linear models (2010). http://CRAN.R-project.org/package=glmnet

K.J. Friston, L. Harrison, W. Penny, Dynamic causal modelling. Neuroimage 19 (4), 1273–1302 (2003)

K. Friston, R. Moran, A.K. Seth, Analysing connectivity with granger causality and dynamic causal modelling. Curr. Opin. Neurobiol. 23 (2), 172–178 (2013)

W.J. Fu, Penalized regressions: the bridge versus the lasso. J. Comput. Graph. Stat. 7 (3), 397–416 (1998)

MathSciNet   Google Scholar  

P. Galison, How Experiments End (University of Chicago Press, Chicago, 1987)

J.J. Gaynor, E.J. Feuer, C.C. Tan, D.H. Wu, C.R. Little, D.J. Straus, B.D. Clarkson, M.F. Brennan, On the use of cause-specific failure and conditional failure probabilities: examples from clinical oncology data. J. Am. Stat. Assoc. 88 (422), 400–409 (1993)

A. Gelman, C. Shalizi, Philosophy and the practice of bayesian statistics. Br. J. Math. Stat. Psychol. 66(1), 8–38 (2013)

A. Gelman, Y.-S. Su, M. Yajima, J. Hill, M.G. Pittau, J. Kerman, T. Zheng, Arm: data analysis using regression and multilevel/hierarchical models (2010). http://CRAN.R-project.org/package=arm

H.C. Gerstein et al., Effects of intensive glucose lowering in type 2 diabetes. N. Engl. J. Med. 358 (24), 2545–2559 (2008)

G. Gigerenzer, The Empire of Chance: How Probability Changed Science and Everyday Life (Cambridge University Press, Cambridge, 1989)

P.B. Gilbert, Comparison of competing risks failure time methods and time-independent methods for assessing strain variations in vaccine protection. Stat. Med. 19 (22), 3065–3086 (2000)

P.B. Gilbert, S.G. Self, M.A. Ashby, Statistical methods for assessing differential vaccine protection against human immunodeficiency virus types. Biometrics 54 (3), 799–814 (1998)

P.B. Gilbert, S.G. Self, M. Rao, A. Naficy, J. Clemens, Sieve analysis: methods for assessing from vaccine trial data how vaccine efficacy varies with genotypic and phenotypic pathogen variation. J. Clin. Epidemiol. 54 (1), 68–85 (2001)

R.D. Gill, Non- and semiparametric maximum likelihood estimators and the von Mises method (Part 1). Scand. J. Stat. 16 , 91–128 (1989)

R.D. Gill, J.M. Robins, Causal inference in complex longitudinal studies: continuous case. Ann. Stat. 29 (6), 1785–1811 (2001)

R.D. Gill, M.J. van der Laan, J.A. Wellner, Inefficient estimators of the bivariate survival function for three models. Ann. l’Institut Henri Poincaré 31 (3), 545–597 (1995)

Y. Goldberg, R. Song, D. Zeng, M.R. Kosorok, Comment on “Dynamic treatment regimes: technical challenges and applications”. Electron. J. Stat. 8 , 1290–1300 (2014)

N. Grambauer, M. Schumacher, J. Beyersmann, Proportional subdistribution hazards modeling offers a summary analysis, even if misspecified. Stat. Med. 29 (7–8), 875–884 (2010)

R. Greevy, B. Lu, J.H. Silber, P. Rosenbaum, Optimal multivariate matching before randomization. Biostatistics 5 (2), 263–275 (2004)

U. Grömping, Variable importance assessment in regression: linear regression versus random forest. Am. Stat. 63 (4) (2009)

H. Grosskurth, F. Mosha, J. Todd, E. Mwijarubi, A. Klokke, K. Senkoro, P. Mayaud, J. Changalucha, A. Nicoll, G. ka-Gina, J. Newell, K. Mugeye, D. Mabey, R. Hayes, Impact of improved treatment of sexually transmitted diseases on HIV infection in rural Tanzania: randomised controlled trial. Lancet 346 (8974), 530–536 (1995)

S. Gruber, M.J. van der Laan, An application of collaborative targeted maximum likelihood estimation in causal inference and genomics. Int. J. Biostat. 6 (1) (2010a)

S. Gruber, M.J. van der Laan, A targeted maximum likelihood estimator of a causal effect on a bounded continuous outcome. Int. J. Biostat. 6 (1), Article 26 (2010b)

S. Gruber, M.J. van der Laan, tmle: an R package for targeted maximum likelihood estimation. J. Stat. Softw. 51(13) (2012a)

S. Gruber, M.J. van der Laan, Targeted minimum loss based estimator that outperforms a given estimator. Int. J. Biostat. 8 (1), (2012b)

I. Hacking, The Emergence of Probability (Cambridge University Press, Cambridge, 1975)

I. Hacking, The Taming of Chance (1990) (Cambridge University Press, Cambridge, 1990)

D.M. Hafeman, T.J. VanderWeele, Alternative assumptions for the identification of direct and indirect effects. Epidemiology 22 , 753–764 (2010)

J. Hahn, On the role of the propensity score in efficient semiparametric estimation of average treatment effects. Econometrica 2 , 315–331 (1998)

J. Hajek, Asymptotic theory of rejective sampling with varying probabilities from a finite population. Ann. Math. Stat. 35 (4), 1491–1523, 12 (1964)

P Hall, Theoretical comparison of bootstrap confidence intervals. Ann. Stat. 16 , 927–953 (1988)

P. Hall, The Bootstrap and Edgeworth Expansion . Springer Series in Statistics (Springer, New York, NY, 1992)

M.E. Halloran, C.J. Struchiner, Causal inference in infectious diseases. Epidemiology 6 (2), 142–151 (1995)

S.M. Hammer, M.E. Sobieszczyk, H. Janes, S.T. Karuna, M.J. Mulligan, D. Grove, B.A. Koblin, S.P. Buchbinder, M.C. Keefer, G.D. Tomaras, Efficacy trial of a DNA/rAd5 HIV-1 preventive vaccine. N. Engl. J. Med. 369 (22), 2083–2092 (2013)

S. Haneuse, A. Rotnitzky, Estimation of the effect of interventions that modify the received treatment. Stat. Med. (2013)

M. Hanif, K.R.W. Brewer, Sampling with unequal probabilities without replacement: a review. International Statistical Review/Revue Internationale de Statistique, pp. 317–335 (1980)

E. Hartman, R. Grieve, R. Ramsahai, J.S. Sekhon, From sample average treatment effect to population average treatment effect on the treated: combining experimental with observational studies to estimate population treatment effects. J. R. Stat. Soc. Ser. A 178 (3), 757–778 (2015)

T. Hastie, gam: generalized additive models (2011) http://CRAN.R-project.org/package=gam

T.J. Hastie, R.J. Tibshirani, J.H. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer, Berlin Heidelberg New York, 2001)

R.J. Hayes, L.H. Moulton, Cluster Randomised Trials . (Chapman & Hall/CRC, Boca Raton, 2009)

M.A. Hearst, S.T Dumais, E. Osman, J. Platt, B. Scholkopf. Support vector machines. IEEE Intell. Syst. Appl. 13 (4), 18–28 (1998)

M.A. Hernan, B.A. Brumback, J.M. Robins, Estimating the causal effect of zidovudine on CD4 count with a marginal structural model for repeated measures. Stat. Med. 21 , 1689–1709 (2002)

M.A. Hernan, B. Brumback, J.M. Robins, Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men. Epidemiology 11 (5), 561–570 (2000)

M.A. Hernan, E. Lanoy, D. Costagliola, J.M. Robins, Comparison of dynamic treatment regimes via inverse probability weighting. Basic Clin. Pharmacol. 98 , 237–242 (2006)

R. Holiday, What the failed $1m Netflix prize says about business advice. Forbes (2012)

R.R. Holman, S.K. Paul, M.A. Bethel, D.R. Matthews, H.A. Neil, 10-year follow-up of intensive glucose control in type 2 diabetes. N. Engl. J. Med. 359 , 1577–89 (2008)

J.L. Horowitz, C.F. Manski, Nonparametric analysis of randomized experiments with missing covariate and outcome data. J. Am. Stat. Assoc. 95 (449), 77–84 (2000)

D.G. Horvitz, D.J. Thompson, A generalization of sampling without replacement from a finite universe. J. Am. Stat. Assoc. 47 , 663–685 (1952)

J.B. Hough, M. Krishnapur, Y. Peres, B. Virág, Determinantal processes and independence. Probab. Surv. 3 , 206–229 (2006)

F. Hu, W.F. Rosenberger, The Theory of Response Adaptive Randomization in Clinical Trials (Wiley, New York, 2006)

A.E. Hubbard, M.J. van der Laan, Mining with inference: data adaptive target parameters, in Handbook of Big Data . Chapman-Handbooks-Statistical-Methods, ed. by P. Buhlmann, P. Drineas, M. Kane, M.J. van der Laan (Chapman & Hall/CRC, Boca Raton, 2016)

A.E. Hubbard, I Diaz Munoz, A. Decker, J.B. Holcomb, M.A. Schreiber, E.M. Bulger, K.J. Brasel, E.E. Fox, D.J. del Junco, C.E. Wade et al., Time-dependent prediction and evaluation of variable importance using superlearning in high-dimensional clinical data. J. Trauma-Injury Infect. Crit. Care 75 (1), S53–S60 (2013)

A.E. Hubbard, S. Kherad-Pajouh, M.J. van der Laan, Statistical inference for data adaptive target parameters. Int. J. Biostat. 12 (1), 3–19 (2016)

M.G. Hudgens, M.E. Halloran, Toward causal inference with interference. J. Am. Stat. Assoc. 103 (482), 832–842 (2008)

I.A. Ibragimov, R.Z. Khasminskii, Statistical Estimation (Springer, Berlin, 1981)

ICH Harmonised Tripartite Guideline, Statistical principles for clinical trials E9, February (1998)

K. Imai, Variance identification and efficiency analysis in randomized experiments under the matched-pair design. Stat. Med. 27 (24), 4857–4873 (2008)

K. Imai, G. King, C. Nall, The essential role of pair matching in cluster-randomized experiments, with application to the Mexican universal health insurance evaluation. Stat. Sci. 24 (1), 29–53 (2009)

K. Imai, L. Keele, D. Tingley, A general approach to causal mediation analysis. Psychol methods 15 (4), 309–334 (2010a)

K. Imai, L. Keele, T. Yamamoto, Identification, inference and sensitivity analysis for causal mediation effects. Stat. Sci. 25 (1), 51–71 (2010b)

G.W. Imbens, Nonparametric estimation of average treatment effects under exogeneity: a review. Rev. Econ. Stat. 86 (1), 4–29 (2004)

G.W. Imbens, Experimental design for unit and cluster randomized trials. Technical Report. NBER Working Paper (2011)

G. Imbens, D.B. Rubin, Causal Inference for Statistics, Social, and Biomedical Sciences (Cambridge University Press, New York, 2015)

J.P. Ioannidis, Why most discovered true associations are inflated. Epidemiology 19 (5), 640–648 (2008)

F. Ismail-Beigi, T. Craven, M.A. Banerji, J. Basile, J. Calles, R.M. Cohen, R. Cuddihy, W.C Cushman, S. Genuth, R.H. Grimm, B.P. Hamilton, B. Hoogwerf, D. Karl, L. Katz, A. Krikorian, P. O’Connor, R. Pop-Busui, U. Schubart, D. Simmons, H. Taylor, A. Thomas, D. Weiss, I. Hramiak for the ACCORD trial group, Effect of intensive treatment of hyperglycaemia on microvascular outcomes in type 2 diabetes: an analysis of the ACCORD randomised trial. Lancet 376 , 419–430 (2010)

Joint National Committee, The fifth report of the joint national committee on detection, evaluation, and treatment of high blood pressure (JNC V). Arch. Intern. Med. 153 (2), 154–183 (1993)

B.C. Kahn, V. Jairath, C.J. Doré, T.P. Morris, The risks and rewards of covariate adjustment in randomized trials: an assessment of 12 outcomes from 8 studies. Trials 15 (139), 1–7 (2014)

R.M. Karp, Reducibility Among Combinatorial Problems (Springer, New York, Berlin, Heidelberg, 1972)

S. Keleş, M.J. van der Laan, S. Dudoit, Asymptotically optimal model selection method for regression on censored outcomes. Technical Report, Division of Biostatistics, University of California, Berkeley (2002)

E.H. Kennedy, Z. Ma, M.D. McHugh, D.S. Small, Nonparametric methods for doubly robust estimation of continuous treatment effects. ArXiv e-prints (2015)

R. Kessler, S. Rose, K. Koenen et al., How well can post-traumatic stress disorder be predicted from pre-trauma risk factors? an exploratory study in the who world mental health surveys. World Psychiatry 13 (3), 265–274 (2014)

D. Kibler, D.W. Aha, M.K. Albert, Instance-based prediction of real-valued attributes. Comput. Intell. 5 , 51 (1989)

J. Kivinen, A.J. Smola, R.C. Williamson, Online learning with kernels. IEEE Trans. Signal Process. 52 (8), 2165–2176 (2004)

N. Klar, A. Donner, The merits of matching in community intervention trials: a cautionary tale. Stat. Med. 16 (15), 1753–1764 (1997)

D.C. Knill, A. Pouget, The Bayesian brain: the role of uncertainty in neural coding and computation. Trends Neurosci. 27 (12), 712–719 (2004)

K. Korb, L. Hope, A. Nicholson, K. Axnick, Varieties of causal intervention. in PRICAI 2004: Trends in Artificial Intelligence , ed. by C. Zhang, H.W. Guesgen, W.-K. Yeap. Lecture Notes in Computer Science, vol. 3157 (Springer, Berlin, Heidelberg, 2004), pp. 322–331

B. Korte, J. Vygen, Combinatorial Optimization , 5th edn. (Springer, Berlin, Heidelberg, New York, 2012)

M.S. Kramer, B. Chalmers, E.D. Hodnett, Z. Sevkovskaya, I. Dzikovich, S. Shapiro, J.P. Collet, I. Vanilovich, I. Mezen, T. Ducruet, G. Shishko, V. Zubovich, D. Mknuik, E. Gluchanina, V. Dombrovskiy, A. Ustinovitch, T. Kot, N. Bogdanovich, L. Ovchinikova, E. Helsing, PROmotion of breastfeeding intervention trial (PROBIT). J. Am. Med. Assoc. 285 (4), 413–420 (2001)

M.S. Kramer, T. Guo, R.W. Platt, S. Shapiro, J.P. Collet, B. Chalmers, E. Hodnett, Z. Sevkovskaya, I. Dzikovich, I. Vanilovich, Breastfeeding and infant growth: biology or bias? Pediatrics 110 (2), 343–347 (2002)

L. Krüger, L. Daston, M. Heidelberger, G. Gigerenzer, M.S. Morgan, The Probabilistic Revolution . (MIT Press, Cambridge, 1987)

L. Kunz, S. Rose, D. Spiegelman, S.-L. Normand, Causal inference methods in comparative effectiveness research, in Methods in Comparative Effectiveness Research , ed. by C. Gatsonis, S.C. Morton (Chapman & Hall, Boca Raton, 2017)

E.B. Laber, D.J. Lizotte, M. Qian, W.E. Pelham, S.A. Murphy, Dynamic treatment regimes: Technical challenges and applications. Electron. J. Stat. 8 (1), 1225–1272 (2014a)

E.B. Laber, D.J. Lizotte, M. Qian, W.E. Pelham, S.A. Murphy, Rejoinder of “Dynamic treatment regimes: technical challenges and applications”. Electron. J. Stat. 8 (1), 1312–1321 (2014b)

J. Langford, L. Li, T. Zhang, Sparse online learning via truncated gradient. J. Mach. Learn. Res. 10 , 777–801 (2009)

P. Lavori, R. Dawson, Adaptive treatment strategies in chronic disease. Annu. Rev. Med. 59 , 443–453 (2008)

P.W. Lavori, R. Dawson, A design for testing clinical strategies: Biased adaptive within-subject randomization. J. R. Stat. Soc. Ser. A 163 29–38 (2000)

D. Lazer, R. Kennedy, What we can learn from the epic failure of Google flu trends. Wired (2015)

S.D. Lendle, M.J. van der Laan, Identification and efficient estimation of the natural direct effect among the untreated. Technical Report, Division of Biostatistics, University of California, Berkeley (2011)

S.D. Lendle, B. Fireman, M.J. van der Laan, Balancing score adjusted targeted minimum loss-based estimation. Technical Report, Division of Biostatistics, University of California, Berkeley (2013)

S. Lendle, J. Schwab, M.L. Petersen, M.J. van der Laan, ltmle: an R package for implementing targeted minimum loss-based estimation for longitudinal data. J. Stat. Softw. 81 (1) (2017)

B.Y. Levit, On the efficiency of a class of non-parametric estimates. Theory Probab. Appl. 20 (4), 723–740 (1975)

L. Li, E. Tchetgen Tchetgen, A.W. van der Vaart, J.M. Robins, Higher order inference on a treatment effect under low regularity conditions. Stat. Probab. Lett. 81 (7), 821–828 (2011)

A. Liaw, M. Wiener, Classification and regression by randomforest. R News  2 (3), 18– 22 (2002)

L. Liu, M.G. Hudgens, Large sample randomization inference of causal effects in the presence of interference. J. Am. Stat. Assoc. 109 (505), 288–301 (2014). ISSN 0162-1459

Z. Liu, T. Stengos, Nonlinearities in cross country growth regressions: a semiparametric approach. J. Appl. Econom. 14 , 527–538 (1999)

V. Loonis, X. Mary, Determinantal sampling designs. ArXiv e-prints, October (2015)

B. Lu, R. Greevy, X. Xu, C. Beck, Optimal nonbipartite matching and its statistical applications. Am. Stat. 65 (1), 21–30 (2011)

A.R. Luedtke, M.J. van der Laan, Statistical inference for the mean outcome under a possibly non-unique optimal treatment strategy. Ann. Stat. 44 (2), 713–742 (2016a)

A.R. Luedtke, M.J. van der Laan, Super-learning of an optimal dynamic treatment rule. Int. J. Biostat. 12 (1), 305–332 (2016b)

A.R. Luedtke, M.J. van der Laan, Optimal individualized treatments in resource-limited settings. Int. J. Biostat. 12 (1), 283–303 (2016c)

A..R Luedtke, M. Carone, M.J. van der Laan, Discussion of deductive derivation and turing-computerization of semiparametric efficient estimation by Frangakis et al. Biometrics 71 (4), 875–879 (2015a)

A.R. Luedtke, I. Díaz, M.J. van der Laan, The statistics of sensitivity analyses. Technical Report, Division of Biostatistics, University of California, Berkeley (2015b)

K. Lum, Limitations of mitigating judicial bias with machine learning. Nat Hum. Behav. 1 , 0141 (2017)

M. Lunn, D. McNeil, Applying Cox regression to competing risks. Biometrics 51 , 524–532 (1995). ISSN 0006-341X

R. Lyons, Determinantal probability measures. Publications Mathématiques de l’Institut des Hautes Études Scientifiques 98 , 167–212 (2003)

R. Lyons, The spread of evidence-poor medicine via flawed social-network analysis. Stat. Politics Policy 2 (1) 1–26 (2010)

O. Macchi, The coincidence approach to stochastic point processes. Adv. Appl. Probab. 7 , 83–122 (1975)

R. Macklin, E. Cowan, Given financial constraints, it would be unethical to divert antiretroviral drugs from treatment to prevention. Health Aff. 31 (7), 1537–1544 (2012)

R.F. MacLehose, S. Kaufman, J.S. Kaufman, C. Poole, Bounding causal effects under uncontrolled confounding using counterfactuals. Epidemiology 16 (4), 548–555 (2005)

E. Mammen, A.B. Tsybakov, Smooth discrimination analysis. Ann. Stat. 27 (6), 1808–1829 (1999)

J.K. Mann, J.R. Balmes, T.A. Bruckner, K.M. Mortimer, H.G. Margolis, B. Pratt, S.K. Hammond, F.W. Lurmann, I.B. Tager, Short-term effects of air pollution on wheeze in asthmatic children in Fresno, California. Environ Health Perspect. 118 (10), 06 (2010)

C.F. Manski, Partial Identification of Probability Distributions (Springer, Berlin, Heidelberg, New York, 2003)

C.F. Manski, Nonparametric bounds on treatment effects. Am. Econ. Rev. 80 , 319–323 (1990)

D. Mayo, Error and the Growth of Experimental Knowledge (University of Chicago Press, Chicago, 1996)

D. Mayo, Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science (Cambridge, Chicago, 2010)

S. Milborrow, T Hastie, R Tibshirani, Earth: multivariate adaptive regression spline models. R package version 3.2-7 (2014)

T. Mildenberger, Y. Rozenholc, D. Zasada, histogram: Construction of regular and irregular histograms with different options for automatic choice of bins (2009). http://CRAN.R-project.org/package=histogram

E.E.M. Moodie, T.S. Richardson, D.A. Stephens, Demystifying optimal dynamic treatment regimes. Biometrics 63 (2), 447–455 (2007)

K.L. Moore, M.J. van der Laan, Application of time-to-event methods in the assessment of safety in clinical trials, in Design, Summarization, Analysis & Interpretation of Clinical Trials with Time-to-Event Endpoints , ed. by K.E. Peace (Chapman & Hall, Boca Raton, 2009a)

K.L. Moore, M.J. van der Laan, Covariate adjustment in randomized trials with binary outcomes: targeted maximum likelihood estimation. Stat. Med. 28 (1), 39–64 (2009b)

K.L. Moore, M.J. van der Laan, Increasing power in randomized trials with right censored outcomes through covariate adjustment. J. Biopharm. Stat. 19 (6), 1099–1131 (2009c)

K.L. Moore, R. Neugebauer, T. Valappil, M.J. van der Laan, Robust extraction of covariate information to improve estimation efficiency in randomized trials. Stat. Med. 30 (19), 2389–2408 (2011)

N. Murata, A statistical study of on-line learning, in Online Learning and Neural Networks (Cambridge University Press, Cambridge, 1998)

S.A. Murphy, Optimal dynamic treatment regimes. J. R. Stat. Soc. Ser. B 65 (2), 331–66 (2003)

S.A. Murphy, An experimental design for the development of adaptive treatment strategies. Stat. Med. 24 , 1455–1481 (2005)

S.A. Murphy, M.J. van der Laan, J.M. Robins, Marginal mean models for dynamic treatment regimens. J. Am. Stat. Assoc. 96 , 1410–1424 (2001)

E.A. Nadaraya, On estimating regression. Theory Probab. Appl. 9 (1), 141–142 (1964)

A.I Naimi, E.E.M. Moodie, N. Auger, J.S. Kaufman, Stochastic mediation contrasts in epidemiologic research: interpregnancy interval and the educational disparity in preterm delivery. Am. J. Epidemiol. 180 (4), 436–445 (2014)

D.M. Nathan, J.B. Buse, M.B. Davidson, E. Ferrannini, R.R. Holman, R. Sherwin, B. Zinman, Medical management of hyperglycemia in type 2 diabetes: a consensus algorithm for the initiation and adjustment of therapy: a consensus statement of the American Diabetes Association and the European Association for the Study of Diabetes. Diab. Care 32 (1), 193–203 (2009)

D.M. Nathan, P. A. Cleary, J.Y. Backlund, S.M. Genuth, J.M. Lachin, T.J. Orchard, P. Raskin, B. Zinman, Diabetes control and complications trial/epidemiology of diabetes interventions and complications (DCCT/EDIC) study research group. Intensive diabetes treatment and cardiovascular disease in patients with type 1 diabetes. N. Engl. J. Med. 22 (353), 2643–2653 (2005)

D.M. Nathan, J.B. Buse, M.B. Davidson, R.J. Heine, R.R. Holman, R. Sherwin, B. Zinman, Management of hyperglycemia in type 2 diabetes: a consensus algorithm for the initiation and adjustment of therapy: a consensus statement from the American Diabetes Association and the European Association for the Study of Diabetes. Diab. Care 29 , 1963–1972 (2006)

NCEP (2002), Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection (2002)

R. Neugebauer, J. Bullard, DSA: data-adaptive estimation with cross-validation and the D/S/A algorithm (2010). http://www.stat.berkeley.edu/~laan/Software/

R. Neugebauer, M.J. van der Laan, Nonparametric causal effects based on marginal structural models. J. Stat. Plann. Infererence 137 (2), 419–434 (2007)

R. Neugebauer, M.J. Silverberg, M.J. van der Laan, Observational study and individualized antiretroviral therapy initiation rules for reducing cancer incidence in HIV-infected patients, chap. 26 (Springer, New York, 2011), pp. 436–456

R. Neugebauer, B. Fireman, J.A. Roy, P.J. O’Connor, J.V. Selby, Dynamic marginal structural modeling to evaluate the comparative effectiveness of more or less aggressive treatment intensification strategies in adults with type 2 diabetes. Pharmacoepidemiol. Drug Saf. 21 (Suppl. 2), 99–113 (2012)

R. Neugebauer, B. Fireman, J.A. Roy, P.J. O’Connor, Impact of specific glucose-control strategies on microvascular and macrovascular outcomes in 58,000 adults with type 2 diabetes. Diab. Care 36 (11), 3510–3516 (2013)

R. Neugebauer, J. Schmittdiel, M.J. Laan, Targeted learning in real-world comparative effectiveness research with time-varying interventions. Stat. Med. 33 (14), 2480–2520 (2014a)

R. Neugebauer, J.A. Schmittdiel, Z. Zhu, J.A. Rassen, J.D. Seeger, S. Schneeweiss, High-dimensional propensity score algorithm in comparative effectiveness research with time-varying interventions. Stat. Med. 34 (5), 753–781 (2014b)

R. Neugebauer, J.A. Schmittdiel, M.J. van der Laan, A case study of the impact of data-adaptive versus model-based estimation of the propensity scores on causal inferences from three inverse probability weighting estimators. Int. J. Biostat. 12 (1), 131–155 (2016)

J. Neyman, Sur les applications de la theorie des probabilites aux experiences agricoles: Essai des principes (In Polish). English translation by D.M. Dabrowska and T.P. Speed (1990). Stat. Sci. 5 , 465–480 (1923)

P.J. O’Connor, F. Ismail-Beigi, Near-normalization of glucose and microvascular diabetes complications: data from ACCORD and ADVANCE. Ther. Adv. Endocrinol. Metab. 2 (1), 17–26 (2011)

E.L. Ogburn, T.J. VanderWeele, Vaccines, contagion, and social networks. ArXiv e-prints, March (2014)

E.L. Ogburn, O. Sofrygin, M.J. van der Laan, I. Diaz, Causal inference for social network data with contagion. ArXiv e-prints, October (2017)

B.A. Olken, Pre-analysis plans in economics. Technical report, Massachusetts Institute of Technology Department of Economics (2015)

C. O’Neil, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy (Crown Publishing Group, New York, 2016)

L. Orellana, A. Rotnitzky, J.M. Robins, Dynamic regime marginal structural mean models for estimation of optimal treatment regimes, part I: main content. Int. J. Biostat. 6 (2), Article 8 (2010)

E. Parzen, On estimation of a probability density function and mode. Ann. Math. Stat. 33 (3), 1065–1076 (1962)

A. Patel, S. MacMahon, J. Chalmers, B. Neal, L. Billot, M. Woodward, M. Marre, M. Cooper, P. Glasziou, D. Grobbee, P. Hamet, S. Harrap, S. Heller, Intensive blood glucose control and vascular outcomes in patients with type 2 diabetes. N. Engl. J. Med. 358 (24), 2560–2572 (2008)

J. Pearl, Causal diagrams for empirical research. Biometrika 82 , 669–710 (1995)

J. Pearl, Direct and indirect effects, in Proceedings of the 17th Conference Uncertainty in Artificial Intelligence (Morgan Kaufmann, San Francisco, 2001)

J. Pearl, Causality: Models, Reasoning, and Inference , 2nd edn. (Cambridge, New York, 2009a)

J. Pearl, Myth, confusion, and science in causal analysis. Technical Report R-348, Cognitive Systems Laboratory, Computer Science Department University of California, Los Angeles, Los Angeles, CA, May 2009b

J. Pearl, On the consistency rule in causal inference: axiom, definition, assumption, or theorem? Epidemiology 21 (6), 872–875 (2010)

J. Pearl, The mediation formula: a guide to the assessment of causal pathways in nonlinear models, in Causality: Statistical Perspectives and Applications , ed. by C. Berzuini, P. Dawid, L. Bernardinelli (Springer, Berlin, 2011)

R. Pemantle, Y. Peres, Concentration of Lipschitz functionals of determinantal and other strong Rayleigh measures. Comb. Probab. Comput. 23 (1), 140–160 (2014)

W.D. Penny, K.E. Stephan, A. Mechelli, K.J. Friston, Modelling functional integration: a comparison of structural equation and dynamic causal models. Neuroimage 23 , S264–S274 (2004)

G. Peoples, New study from Pandora touts the Pandora effect on music sales. Billboard (2014)

A. Peters, T. Hothorn, ipred: improved predictors (2009) http://CRAN.R-project.org/package=ipred

M. Petersen, J. Schwab, S. Gruber, N. Blaser, M. Schomaker, M.J. van der Laan, Targeted maximum likelihood estimation for dynamic and static longitudinal marginal structural working models. J. Causal Inference 2 (2), 147–185 (2014)

M.L. Petersen, E. LeDell, J. Schwab, V. Sarovar, R. Gross, N. Reynolds, J.E. Haberer, K. Goggin, C. Golin, J. Arnsten et al., Super learner analysis of electronic adherence data improves viral prediction and may provide strategies for selective HIV RNA monitoring. J. Acquir. Immune Defic. Syndr. 69 (1), 109 (2015)

J. Pfanzagl, Contributions to a General Asymptotic Statistical Theory (Springer, Berlin, 1982)

J. Pfanzagl, Asymptotic Expansions for General Statistical Models , vol. 31 (Springer, Berlin, 1985)

J. Pfanzagl, Estimation in Semiparametric Models (Springer, Berlin, Heidelberg, New York, 1990)

I. Phyllis, F. Russo. Causality; Philosophical Theory meets Scientific Practice (Oxford University Press, Oxford, 2016)

M. Pintilie, Analysing and interpreting competing risk data. Stat. Med. 26 (6), 1360–1367 (2007)

R. Pirracchio, M.L. Petersen, M.J. van der Laan, Improving propensity score estimators’ robustness to model misspecification using super learner. Am. J. Epidemiol. 181 (2), 108–119 (2014)

R. Pirracchio, M.L. Petersen, M. Carone, M.R. Rigon, S. Chevret, M.J. van der Laan, Mortality prediction in intensive care units with the super ICU learner algorithm (SICULA): a population-based study. Lancet Respir. Med. 3 (1), 42–52 (2015)

R.W. Platt, E.F. Schisterman, S.R. Cole, Time-modified confounding. Am. J. Epidemiol. 170 (6), 687–694 (2009)

S.J. Pocock, S.E. Assmann, L.E. Enos, L.E. Kasten, Subgroup analysis, covariate adjustment, and baseline comparisons in clinical trial reporting: current practice and problems. Stat. Med. 21 , 2917–2930 (2002)

E.C. Polley, M.J. van der Laan, SuperLearner: super learner prediction (2013). http://CRAN.R-project.org/package=SuperLearner

E.C. Polley, M.J. van der Laan, Predicting optimal treatment assignment based on prognostic factors in cancer patients. in Design, Summarization, Analysis & Interpretation of Clinical Trials with Time-to-Event Endpoints , ed. by K.E. Peace (Boca Raton, Chapman & Hall, 2009)

E.C. Polley, M.J. van der Laan, Super learner in prediction. Technical Report 266, Division of Biostatistics, University of California, Berkeley (2010)

E.C Polley, S. Rose, M.J. van der Laan, Super-learning, in Targeted Learning: Causal Inference for Observational and Experimental Data , ed. by M.J. van der Laan, S. Rose (Springer, Berlin, Heidelberg, New York, 2011)

E.C. Polley, E. LeDell, C. Kennedy, M.J. van der Laan, SuperLearner: super learner prediction (2017). https://github.com/ecpolley/SuperLearner

B.T. Polyak, A.B. Juditsky, Acceleration of stochastic approximation by averaging. SIAM J. Control. Optim. 30 (4), 838–855 (1992)

T.M. Porter, The Rise of Statistical Thinking (Princeton University Press, Princeton, 1986)

T.M. Porter, Trust in Numbers: The Pursuit of Objectivity in Science and Public Life (Princeton University Press, Princeton, 1995)

K.E Porter, S. Gruber, M.J. van der Laan, J.S. Sekhon, The relative performance of targeted maximum likelihood estimators. Int. J. Biostat. 7(1) (2011)

R.L. Prentice, J.D. Kalbfleisch, A.V. Peterson Jr, N. Flournoy, V.T. Farewell, N.E. Breslow, The analysis of failure times in the presence of competing risks. Biometrics 34 (4), 541–554 (1978)

M. Qian, S.A. Murphy, Performance guarantees for individualized treatment rules. Ann. Stat. 39 (2), 1180–1210 (2011)

R Development Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna (2016). http://www.R-project.org .

K.K. Ray, S.R. Seshasai, S. Wijesuriya, R. Sivakumaran, S. Nethercott, D. Preiss, S. Erqou, N. Sattar, Effect of intensive control of glucose on cardiovascular outcomes and death in patients with diabetes mellitus: a meta-analysis of randomised controlled trials. Lancet 373 , 1765–72 (2009)

J.M. Robins, A new approach to causal inference in mortality studies with sustained exposure periods–application to control of the healthy worker survivor effect. Math. Modell. 7 , 1393–1512 (1986)

J.M. Robins, Addendum to: “A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect”. Comput. Math. Appl. 14 (9–12), 923–945 (1987)

J.M. Robins, Marginal structural models, in 1997 Proceedings of the American Statistical Association. Section on Bayesian Statistical Science , pp. 1–10 (1998)

J.M. Robins, Association, causation and marginal structural models. Synthese 121 , 151–179 (1999)

J.M. Robins, Robust estimation in sequentially ignorable missing data and causal inference models, in Proceedings of the American Statistical Association (2000)

J.M. Robins, Optimal structural nested models for optimal sequential decisions, in Proceedings of the Second Seattle Symposium in Biostatistics: Analysis of Correlated Data (2004)

J.M. Robins, S. Greenland, Identifiability and exchangeability for direct and indirect effects. Epidemiol 3 , 143–155 (1992)

J.M. Robins, Y. Ritov, Toward a curse of dimensionality appropriate (coda) asymptotic theory for semi-parametric models. Stat. Med. 16 , 285–319 (1997)

J.M. Robins, A. Rotnitzky, Recovery of information and adjustment for dependent censoring using surrogate markers, in AIDS Epidemiology (Birkhäuser, Basel, 1992)

J.M. Robins, A. Rotnitzky, L.P. Zhao, Estimation of regression coefficients when some regressors are not always observed. J. Am. Stat. Assoc. 89 (427), 846–866 (1994)

J.M. Robins, A. Rotnitzky, D.O. Scharfstein, Sensitivity analysis for selection bias and unmeasured confounding in missing data and causal inference models, in Statistical Models in Epidemiology, the Environment and Clinical Trials . IMA Volumes in Mathematics and Its Applications (Springer, Berlin, 1999)

J.M. Robins, M.A. Hernan, B. Brumback, Marginal structural models and causal inference in epidemiology. Epidemiology 11 (5), 550–560 (2000)

J.M. Robins, M.A. Hernán, U. Siebert, Effects of multiple interventions, in Comparative Quantification of Health Risks: Global and Regional Burden of Disease Attributable to Selected Major Risk Factors , vol. 1 (World Health Organization, Geneva, 2004), pp. 2191–2230

J.M. Robins, L. Li, E. Tchetgen Tchetgen, A.W. van der Vaart, Higher order influence functions and minimax estimation of nonlinear functionals, in Probability and Statistics: Essays in Honor of David A. Freedman , (Institute of Mathematical Statistics, 2008a), pp. 335–421

J.M. Robins, L. Orellana, A. Rotnitzky, Estimation and extrapolation of optimal treatment and testing strategies. Stat. Med. 27 , 4678–4721 (2008b)

J.M. Robins, L. Li, E. Tchetgen Tchetgen, A.W. van der Vaart, Quadratic Semiparametric Von Mises calculus. Metrika 69 (2–3), 227–247 (2009)

M. Rolland, P.T. Edlefsen, B.B. Larsen, S. Tovanabutra, E. Sanders-Buell, T. Hertz, C. Carrico, S. Menis, C.A. Magaret, H. Ahmed, Increased HIV-1 vaccine efficacy against viruses with genetic signatures in Env V2. Nature 490 (7420), 417–420 (2012). ISSN 0028-0836

S. Rose, Mortality risk score prediction in an elderly population using machine learning. Am. J. Epidemiol. 177 (5), 443–452 (2013)

S. Rose, Targeted learning for pre-analysis plans in public health and health policy research. Observational Stud. 1 , 294–306 (2015)

S. Rose, A machine learning framework for plan payment risk adjustment. Health Serv. Res. 51 (6), 2358–2374 (2016)

S. Rose, Robust machine learning variable importance analyses of medical conditions for health care spending. Health Serv. Res. (2018, in press)

S. Rose, S. Bergquist, T. Layton, Computational health economics for identification of unprofitable health care enrollees. Biostatistics 18 (4), 682–694 (2017)

S. Rose, M.J. van der Laan, Simple optimal weighting of cases and controls in case-control studies. Int. J. Biostat. 4 (1), Article 19 (2008)

S. Rose, M.J. van der Laan, Why match? Investigating matched case-control study designs with causal effect estimation. Int. J. Biostat. 5 (1), Article 1 (2009)

S. Rose, M.J. van der Laan, A targeted maximum likelihood estimator for two-stage designs. Int. J. Biostat. 7 (1), Article 17 (2011)

S. Rose, M.J. van der Laan, A double robust approach to causal effects in case-control studies. Am. J. Epidemiol. 179 (6), 663–669 (2014a)

S. Rose, M.J. van der Laan, Rose and van der Laan respond to “Some advantages of RERI”. Am. J. Epidemiol. 179 (6), 672–673 (2014b)

P.R. Rosenbaum, D.B. Rubin, Assessing sensitivity to an unobserved binary covariate in an observational study with binary outcome. J. R. Stat. Soc. Ser. B 45 , 212–218 (1983a)

P.R. Rosenbaum, Interference Between Units in Randomized Experiments. J. Am. Stat. Assoc. 102 (477), 191–200 (2007)

P.R. Rosenbaum, D.B. Rubin, The central role of the propensity score in observational studies for causal effects. Biometrika 70 , 41–55 (1983b)

M. Rosenblatt, Remarks on some nonparametric estimates of a density function. Ann. Math. Stat. 27 (3), 832–837 (1956)

M. Rosenblum, M.J. van der Laan, Using regression models to analyze randomized trials: asymptotically valid hypothesis tests despite incorrectly specified models. Biometrics 65 (3), 937–945 (2009)

M. Rosenblum, M.J. van der Laan, Targeted maximum likelihood estimation of the parameter of a marginal structural model. Int. J. Biostat. 6 (2), 19 (2010a)

M. Rosenblum, M.J. van der Laan, Simple, efficient estimators of treatment effects in randomized trials using generalized linear models to leverage baseline variables. Int. J. Biostat. 6 (1), Article 13 (2010b)

M. Rosenblum, S.G. Deeks, M.J. van der Laan, D.R. Bangsberg, The risk of virologic failure decreases with duration of HIV suppression, at greater than 50% adherence to antiretroviral therapy. PLoS ONE 4 (9), e7196 (2009)

R.H. Rosenman, M. Friedman, R. Straus, M. Wurm, R. Kositchek, W. Hahn, N.T. Werthessen, A predictive study of coronary heart disease: the western collaborative group study. J. Am. Med. Assoc. 189 (1), 15–22 (1964)

R.H. Rosenman, R.J. Brand, C.D. Jenkins, M. Friedman, R. Straus, M. Wurm, Coronary heart disease in the western collaborative group study: final follow-up experience of 8 1/2 years. J. Am. Med. Assoc. 233 (8), 872–877 (1975)

B. Rosner, Fundamentals of Biostatistics , 5th edn. (Duxbury, Pacific Grove, 1999)

S. Rosthø j, C. Fullwood, R. Henderson, S. Stewart, Estimation of optimal dynamic anticoagulation regimes from observational data: a regret-based approach. Stat. Med. 88 , 4197–4215 (2006)

A. Rotnitzky, D. Scharfstein, S. Ting-Li Su, J. Robins, Methods for conducting sensitivity analysis of trials with potentially nonignorable competing causes of censoring. Biometrics 57 (1), 103–113 (2001)

A. Rotnitzky, J.M. Robins, D.O. Scharfstein, Semiparametric regression for repeated outcomes with nonignorable nonresponse. J. Am. Med. Assoc. 93 (444), 1321–1339 (1998)

Y. Rozenholc, T. Mildenberger, U. Gather, Combining regular and irregular histograms by penalized likelihood. Comput. Stat. Data Anal. 54 (12), 3313–3323 (2010)

D.B. Rubin, Randomization analysis of experimental data: The fisher randomization test comment. J. Am. Stat. Assoc. 75 (371), 591–593 (1980)

D.B. Rubin, Estimating causal effects of treatments in randomized and nonrandomized studies. J. Educ. Psychol. 66 , 688–701 (1974)

D.B. Rubin, Multivariate matching methods that are equal percent bias reducing, II: maximums on bias reduction for fixed sample sizes. Biometrics 32 (1), 121–132 (1976)

D.B. Rubin, Comment: Neyman (1923) and causal inference in experiments and observational studies. Stat. Sci. 5 (4), 472–480 (1990)

D.B. Rubin, Matched Sampling for Causal Effects (Cambridge, Cambridge, MA, 2006)

D.B. Rubin, M.J. van der Laan, Empirical efficiency maximization: improved locally efficient covariate adjustment in randomized experiments and survival analysis. Int. J. Biostat. 4 (1), Article 5 (2008)

D.B. Rubin, M.J. van der Laan, Targeted ANCOVA estimator in RCTs, in Targeted Learning (Springer, Berlin, 2011), pp. 201–215

D.B. Rubin, M.J. van der Laan, Statistical issues and limitations in personalized medicine research with clinical trials. Int. J. Biostat. 8 (1), Article 1 (2012)

M. Rudelson, R. Vershynin, Hanson-Wright inequality and subGaussian concentration. Electron. Commun. Probab. 18 (82), 1–9 (2013)

M.R. Sampford, On sampling without replacement with unequal probabilities of selection. Biometrika 54 (3–4), 499–513 (1967)

S. Sapp, M.J. van der Laan, K. Page, Targeted estimation of binary variable importance measures with interval-censored outcomes. Int. J. Biostat. 10 (1), 77–97 (2014)

D.O. Scharfstein, J.M. Robins, Estimation of the failure time distribution in the presence of informative censoring. Biometrika 89 (3), 617–634 (2002)

D.O. Scharfstein, A. Rotnitzky, J.M. Robins, Adjusting for nonignorable drop-out using semiparametric nonresponse models, (with discussion and rejoinder). J. Am. Stat. Assoc. 94 , 1096–1120, 1121–1146 (1999)

M.E. Schnitzer, J. Lok, S. Gruber, Variable selection for confounder control, flexible modeling and collaborative targeted minimum loss-based estimation in causal inference. Int. J. Biostat. 12 (1), 97–115 (2016)

M.E. Schnitzer, M.J. van der Laan, E.E.M. Moodie, R.W. Platt, Effect of breastfeeding on gastrointestinal infection in infants: a targeted maximum likelihood approach for clustered longitudinal data. Ann. Appl. Stat. 8 (2), 703–725 (2014)

P. Schochet, Estimators for clustered education RCTs using the Neyman model for causal inference. J. Educ. Behav. Stat. 38 (3), 219–238 (2013)

M.S. Schuler, S. Rose, Targeted maximum likelihood estimation for causal inference in observational studies. Am. J. Epidemiol. 185 (1), 65–73 (2017)

S. Selvaraj, V. Prasad. Characteristics of cluster randomized trials: Are they living up to the randomized trial? JAMA Intern. Med. 173 (23), 313 (2013)

S. Shalev-Shwartz, Online learning and online convex optimization. Found. Trends Mach. Learn. 4 (2), 107–194 (2011)

S. Shalev-Shwartz, Y. Singer, N. Srebro, A. Cotter, Pegasos: primal estimated sub-gradient solver for SVM. Math. Programm. 127 (1), 3–30 (2011)

C.R. Shalizi, A.C. Thomas, Homophily and contagion are generically confounded in observational social network studies. Sociol. Methods Res. 40 (2), 211–239 (2011)

C. Shen, X. Li, L. Li, Inverse probability weighting for covariate adjustment in randomized studies. Stat. Med. 33 , 555–568 (2014)

A. Shrestha, S. Bergquist, E. Montz, S. Rose, Mental health risk adjustment with clinical categories and machine learning. Health Serv. Res. (2018, in press)

J.A. Singh, Antiretroviral resource allocation for HIV prevention. AIDS 27 (6), 863–865 (2013)

S.E. Sinisi, M.J. van der Laan, Deletion/Substitution/Addition algorithm in learning with applications in genomics. Stat. Appl. Genet. Mol. 3 (1), Article 18 (2004)

J.S. Skyler, R. Bergenstal, R.O. Bonow, J. Buse, P. Deedwania, E.A.M. Gale, B.V. Howard, M.S. Kirkman, M. Kosiborod, P. Reaven, R.S. Sherwin, Intensive Glycemic Control and the prevention of cardiovascular events: implications of the accord, advance, and VA diabetes trials: a position statement of the American Diabetes Association and a scientific statement of the American College of Cardiology Foundation and the American Heart Association. Diab. Care 32 , 187–92 (2009)

J.W. Smith, J.E. Everhart, W.C. Dickson, W.C. Knowler, R.S. Johannes, Using the adap learning algorithm to forecast the onset of diabetes mellitus, in Proceedings of the Annual Symposium on Computer Application in Medical Care (American Medical Informatics Association, Bethesda, 1988), p. 261

J.M. Snowden, S. Rose, K.M. Mortimer, Implementation of g-computation on a simulated data set: demonstration of a causal inference technique. Am. J. Epidemiol. 173 (7), 731–738 (2011)

M. Sobel, What do randomized studies of housing mobility demonstrate? J. Am. Stat. Assoc. 101 (476), 1398–1407 (2006)

O. Sofrygin, M.J. van der Laan, R. Neugebauer, Simcausal R package: conducting transparent and reproducible simulation studies of causal effect estimation with complex longitudinal data. J. Stat. Softw. 81, 2 (2017)

O. Sofrygin, M.J. van der Laan, tmlenet: targeted maximum likelihood estimation for network data (2015)

O. Sofrygin, M.J. van der Laan, Semi-parametric estimation and inference for the mean outcome of the single time-point intervention in a causally connected population. J. Causal Inference 5 (1), 20160003 (2017)

A. Soshnikov, Gaussian limit for determinantal random point fields. Ann. Probab. 30 (1), 171–187 (2000)

K. Stanley, Design of randomized controlled trials. Circulation 115 , 1164–1169 (2007)

R.J.C.M. Starmans, Models, inference, and truth: probabilistic reasoning in the information era, in Targeted Learning: Causal Inference for Observational and Experimental Data , ed. by M. van der Laan, S. Rose (Springer, Berlin, 2011)

R.J.C.M. Starmans, The reality behind the model and the cracks in the mirror of nature (in Dutch), in Filosofie, Tweemaandelijks Vlaams-Nederlands Tijdschrift, jaargang , vol. 21 (Garant Publishers, Antwerpen, Apeldoorn, 2011a)

R.J.C.M. Starmans, Ethics and statistics; the progress of a laborious dialogue (in Dutch), in Filosofie, Tweemaandelijks Vlaams-Nederlands Tijdschrift, jaargang , vol. 22 (Garant Publishers, Antwerpen, Apeldoorn, 2012a)

R.J.C.M. Starmans, Statistics, discomfort and the human dimension (in Dutch), in STAtOR , vol. 13 (2012b)

R.J.C.M. Starmans, The world of values; statistics, evolution and ethics (in Dutch), in STAtOR , vol. 13 (2012c)

R.J.C.M. Starmans, Idols and ideals; francis bacon, induction and the hypothetico-deductive model (in Dutch). in Filosofie, Tweemaandelijks Vlaams-Nederlands Tijdschrift, jaargang , vol. 23 (Garant Publishers, Antwerpen, Apeldoorn, 2013)

R.J.C.M. Starmans, Between hobbes and turing; george boole and the laws of thinking (in Dutch), in Filosofie, Tweemaandelijks Vlaams-Nederlands Tijdschrift, jaargang , vol. 25 (Garant Publishers, Antwerpen, Apeldoorn, 2015a)

R.J.C.M. Starmans, With google toward the automatic statistician (in Dutch), in STAtOR , vol. 16 ( 2015b)

R.J.C.M. Starmans, Shannon; information, entropy and the probabilistic worldview (in Dutch), in Filosofie Tweemaandelijks Vlaams-Nederlands Tijdschrift, jaargang , vol. 26 (Garant Publishers, Antwerpen, Apeldoorn, 2016a)

R.J.C.M. Starmans, The advent of data science - some considerations on the unreasonable effectiveness of data, in Handbook of Big Data - Handbooks of Modern Statistical Methods , ed. by P. Buhlmann, P. Drineas, M. Kane, M.J. van der Laan (Chapman & Hall/CRC, New York, 2016b)

R.J.C.M. Starmans, From heraclitus to shannon: the velvet revolution of data in context and flux (in Dutch), in STAtOR , vol. 18 (2017a)

R.J.C.M. Starmans, The end of theory or the unreasonableness of data (in Dutch), in Filosofie, Tweemaandelijks Vlaams-Nederlands Tijdschrift, jaargang , vol. 27 (Garant Publishers, Antwerpen, Apeldoorn, 2017b), p. 2

R.J.C.M. Starmans, The new house of salomon: Peter galison and the empirical tradition (in Dutch), in Filosofie, Tweemaandelijks Vlaams-Nederlands Tijdschrift, jaargang , vol. 27 (Garant Publishers, Antwerpen, Apeldoorn, 2017c), p. 4

R.J.C.M. Starmans, The tryptych of the Bayesian paradigm: confirmation, inference and algoritmics, in Filosofie, Tweemaandelijks Vlaams-Nederlands Tijdschrift, jaargang , vol. 27 (Garant Publishers, Antwerpen, Apeldoorn, 2017d)

C. Steglich, T.A.B. Snijders, M. Pearson, Dynamic networks and behavior: separating selection from influence. Sociol. Methodol. 40 (1), 329–393 (2010)

S. Stigler, The History of Statistics: The Measurement of Uncertainty Before 1900 (Harvard University Press, Cambridge, MA, 1986)

S. Stigler, The History of Statistical Concepts and Methods (Harvard University Press, Cambridge, MA, 1999)

S. Stigler, The Seven Pillars of Statistical Wisdom (Harvard University Press, Cambridge, MA, 2016)

O.M. Stitelman, V. De Gruttola, M.J. van der Laan, A general implementation of TMLE for longitudinal data applied to causal inference in survival analysis. Int. J. Biostat. 8 (1), 1–37 (2012)

O.M. Stitelman, M.J. van der Laan, Collaborative targeted maximum likelihood for time-to-event data. Int. J. Biostat. 6 (1), Article 21 (2010)

O.M. Stitelman, M.J. van der Laan. Targeted maximum likelihood estimation of effect modification parameters in survival analysis. Int. J. Biostat. 7 (1), 1–34 (2011)

O.M. Stitelman, V. De Gruttola, C.W. Wester, M.J. van der Laan, Rcts with time-to-event outcomes and effect modification parameters, in Targeted Learning: Causal Inference for Observational and Experimental Data , ed. by M. J. van der Laan, S. Rose (Springer, Berlin, 2011)

C.A. Struthers, J.D. Kalbfleisch, Misspecified proportional hazard models. Biometrika 73 (2), 363–369 (1986)

E.A. Stuart, S.R. Cole, C.P. Bradshaw, P.J. Leaf, The use of propensity scores to assess the generalizability of results from randomized trials. J. R. Stat. Soc. Ser. A 174 (Part 2), 369–386 (2011)

J. Tacq, Causality in qualitative and quantitative research. Qual. Quant. 45 (2), 263–291 (2011)

I. Tager, M. Hollenberg, W. Satariano, Self-reported leisure-time physical activity and measures of cardiorespiratory fitness in an elderly population. Am. J. Epidemiol. 147 , 921–931 (1998)

E.J. Tchetgen Tchetgen, I. Shpitser, Semiparametric theory for causal mediation analysis: efficiency bounds, multiple robustness, and sensitivity analysis. Technical report 130, Biostatistics, Harvard University, June (2011a)

E.J. Tchetgen Tchetgen, I. Shpitser, Semiparametric estimation of models for natural direct and indirect effects. Technical Report 129, Biostatistics, Harvard University, June (2011b)

E.J. Tchetgen Tchetgen, T.J. VanderWeele. On causal inference in the presence of interference. Stat. Methods Med. Res. 21 (1), 55–75 (2012)

P. Thall, H. Sung, E. Estey, Selecting therapeutic strategies based on efficacy and death in multicourse clinical trials. J. Am. Stat. Assoc. 39 , 29–39 (2002)

The Diabetes Control and Complications Trial Research Group, The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N. Engl. J. Med. 329 , 977–86 (1993)

M. Toftager, L.B. Christiansen, P.L. Kristensen, J. Troelsen, Space for physical activity-a multicomponent intervention study: study design and baseline findings from a cluster randomized controlled trial. BMC Public Health 11 , 777 (2011)

P. Toulis, E. Kao, Estimation of causal peer influence effects, in Proceedings of The 30th International Conference on Machine Learning (2013), pp. 1489–1497

A.A. Tsiatis, Semiparametric Theory and Missing Data . (Springer, Berlin, Heidelberg, New York, 2006)

A.A. Tsiatis, M. Davidian, M. Zhang, X. Lu, Covariate adjustment for two-sample treatment comparisons in randomized clinical trials: a principled yet flexible approach. Stat. Med. 27 , 4658–4677 (2008)

C. Tuglus, M.J. van der Laan, Targeted methods for biomarker discovery, in Targeted Learning: Causal Inference for Observational and Experimental Data . ed. by M.J. van der Laan, S. Rose (Springer, Berlin, 2011)

UK Prospective Diabetes Study (UKPDS) Group, Effect of intensive blood-glucose control with metformin on complications in overweight patients with type 2 diabetes (UKPDS 34). Lancet 352 , 854–865 (1998)

M.J. van der Laan, Causal effect models for intention to treat and realistic individualized treatment rules. Technical Report, Division of Biostatistics, University of California, Berkeley (2006a)

M.J. van der Laan, Statistical inference for variable importance. Int. J. Biostat. 2 (1), Article 2 (2006b)

M.J. van der Laan, Estimation based on case-control designs with known prevalence probability. Int. J. Biostat. 4 (1), Article 17 (2008a)

M.J. van der Laan, The construction and analysis of adaptive group sequential designs. Technical Report 232, Division of Biostatistics, University of California, Berkeley (2008b)

M.J. van der Laan, Targeted maximum likelihood based causal inference: Part I. Int. J. Biostat. 6 (2), Article 2 (2010a)

M.J. van der Laan, Targeted maximum likelihood based causal inference: Part II. Int. J. Biostat. 6 (2), Article 3 (2010b)

M.J. van der Laan, Estimation of causal effects of community-based interventions. Technical Report 268, Division of Biostatistics, University of California, Berkeley (2010c)

M.J. van der Laan, Causal inference for networks. Technical Report, Division of Biostatistics, University of California, Berkeley (2012)

M.J. van der Laan, Causal inference for a population of causally connected units. J. Causal Inference 2 (1), 13–74 (2014a)

M.J. van der Laan, Targeted estimation of nuisance parameters to obtain valid statistical inference. Int. J. Biostat. 10 (1), 29–57 (2014b)

M.J. van der Laan, A generally efficient targeted minimum loss based estimator. Int. J. Biostat. 13 (2), 1106–1118 (2017)

M.J. van der Laan, S. Dudoit, Unified cross-validation methodology for selection among estimators and a general cross-validated adaptive epsilon-net estimator: finite sample oracle inequalities and examples. Technical Report, Division of Biostatistics, University of California, Berkeley (2003)

M.J. van der Laan, S. Gruber, Collaborative double robust penalized targeted maximum likelihood estimation. Int. J. Biostat. 6 (1), Article 17 (2010)

M.J. van der Laan, S. Gruber, Targeted minimum loss based estimation of causal effects of multiple time point interventions. Int. J. Biostat. 8 (1), Article 9 (2012)

M.J. van der Laan, S. Gruber, One-step targeted minimum loss-based estimation based on universal least favorable one-dimensional submodels. Int. J. Biostat. 12 (1), 351–378 (2016)

M.J. van der Laan, S. Lendle, Online targeted learning. Technical Report, Division of Biostatistics, University of California, Berkeley (2014)

M.J. van der Laan, A.R. Luedtke, Targeted learning of an optimal dynamic treatment, and statistical inference for its mean outcome. Technical Report, Division of Biostatistics, University of California, Berkeley

M.J. van der Laan, A.R. Luedtke, Targeted learning of the mean outcome under an optimal dynamic treatment rule. J. Causal Inference 3 (1), 61–95 (2015)

M.J. van der Laan, M.L. Petersen, Causal effect models for realistic individualized treatment and intention to treat rules. Int. J. Biostat. 3 (1), Article 3 (2007)

M.J. van der Laan, M.L. Petersen, Direct effect models. Int. J. Biostat. 4 (1), Article 23 (2008)

M.J. van der Laan, K.S. Pollard, Hybrid clustering of gene expression data with visualization and the bootstrap. J. Stat. Plann. Inference 117 , 275–303 (2003)

M.J. van der Laan, E.C. Polley, A.E. Hubbard, Super learner. Stat. Appl. Genet. Mol. 6 (1), Article 25 (2007)

M.J. van der Laan, J.M. Robins, Unified Methods for Censored Longitudinal Data and Causality (Springer, Berlin Heidelberg New York, 2003)

M.J. van der Laan, S. Rose, Targeted Learning: Causal Inference for Observational and Experimental Data (Springer, Berlin, Heidelberg, New York, 2011)

M.J. van der Laan, D.B. Rubin, Targeted maximum likelihood learning. Int. J. Biostat. 2 (1), Article 11 (2006)

M.J. van der Laan, R.J.C.M. Starmans, Entering the era of data science: targeted learning and the integration of statistics and computational data analysis. Adv. Stat. 2014, 502678 (2014)

M.J. van der Laan, S. Dudoit, S. Keleş, Asymptotic optimality of likelihood-based cross-validation. Stat. Appl. Genet. Mol. 3 (1), Article 4 (2004)

M.J. van der Laan, S. Dudoit, A.W. van der Vaart. The cross-validated adaptive epsilon-net estimator. Stat. Decis. 24 (3), 373–395 (2006)

M.J. van der Laan, L.B. Balzer, M.L. Petersen, Adaptive matching in randomized trials and observational studies. J. Stat. Res. 46 (2), 113–156 (2013a)

M.J. van der Laan, M. Petersen, W. Zheng, Estimating the effect of a community-based intervention with two communities. J. Causal Inference 1 (1), 83–106 (2013b)

M.J. van der Laan, A.R. Luedtke, I. Díaz, Discussion of identification, estimation and approximation of risk under interventions that depend on the natural value of treatment using observational data, by Jessica Young, Miguel Hernán, and James Robins. Epidemiol Methods 3 (1), 21–31 (2014)

M.J. van der Laan, M. Carone, A.R. Luedtke, Computerizing efficient estimation of a pathwise differentiable target parameter. Technical Report, Division of Biostatistics, University of California, Berkeley (2015)

A.W. van der Vaart, Asymptotic Statistics (Cambridge, New York, 1998)

A.W. van der Vaart, Higher order tangent spaces and influence functions. Stat. Sci. 29 (4), 679–686 (2014)

A.W. van der Vaart, J.A. Wellner, Weak Convergence and Empirical Processes (Springer, Berlin, Heidelberg, New York, 1996)

A.W. van der Vaart, J.A. Wellner, A local maximal inequality under uniform entropy. Electron. J. Stat. 5 , 192–203 (2011)

A.W. van der Vaart, S. Dudoit, M.J. van der Laan, Oracle inequalities for multi-fold cross-validation. Stat. Decis. 24 (3), 351–371 (2006)

R. van Handel, On the minimal penalty for Markov order estimation. Probab. Theory Relat. Fields 150 , 709–738 (2009)

T.J. VanderWeele, Marginal structural models for the estimation of direct and indirect effects. Epidemiology 20 , 18–26 (2009)

T.J. VanderWeele, Bias formulas for sensitivity analysis for direct and indirect effects. Epidemiology 21 (4), 540 (2010)

T.J VanderWeele, Sensitivity analysis for contagion effects in social networks. Sociol. Methods Res. 40 (2), 240–255 (2011)

T.J. VanderWeele, Inference for influence over multiple degrees of separation on a social network. Stat. Med. 32 (4), 591–596 (2013)

T.J. VanderWeele, W. An, Social networks and causal inference, in Handbook of Causal Analysis for Social Research (Springer, Berlin, 2013), pp. 353–374

T.J. VanderWeele, O.A. Arah, Unmeasured confounding for general outcomes, treatments, and confounders: bias formulas for sensitivity analysis. Epidemiology 22 (1), 42 (2011)

T.J. VanderWeele, M.A. Hernán, Causal inference under multiple versions of treatment. J. Causal Inference 1 (1), 1–20 (2013)

T.J. VanderWeele, E.J. Tchetgen Tchetgen, Mediation analysis with time-varying exposures and mediators. J. R. Stat. Soc. Ser. B 79 (3), 917–938 (2017)

T.J. VanderWeele, B. Mukherjee, J. Chen, Sensitivity analysis for interactions under unmeasured confounding. Stat. Med. 31 (22), 2552–2564 (2012a)

T.J. VanderWeele, J.P. Vandenbrouke, E.J. Tchetgen Tchetgen, J.M. Robins, A mapping between interactions and interference: implications for vaccine trials. Epidemiology 23 (3), 285–292 (2012b)

T.J. VanderWeele, E.L. Ogburn, E.J. Tchetgen Tchetgen, Why and when “flawed” social network analyses still yield valid tests of no contagion. Stat. Polit. Policy 3 (1), 2151–2160 (2012c)

T.J. VanderWeele, S. Vansteelandt, J.M. Robins, Effect decomposition in the presence of an exposure-induced mediator-outcome confounder. Epidemiology 25 (2), 300–306 (2014a)

T.J. VanderWeele, E.J. Tchetgen Tchetgen, M.E. Halloran, Interference and sensitivity analysis. Stat. Sci. 29 (4), 687–706 (2014b)

S. Vansteelandt, E. Goetghebeur, M.G. Kenward, G. Molenberghs, Ignorance and uncertainty regions as inferential tools in a sensitivity analysis. Stat. Sin. 16 (3), 953–979 (2006)

W.N. Venables, B.D. Ripley, Modern Applied Statistics with S , 4th edn. (Springer, Berlin, Heidelberg, New York, 2002)

T.M. Vogt, J. Elston-Lafata, D. Tolsma, S.M. Greene, The role of research in integrated healthcare systems: the HMO Research Network. Am. J. Manag. Care 10 (9), 643–648 (2004)

E. Wagner, B. Austin, C. Davis, M. Hindmarsh, J. Schaefer, A. Bonomi, Improving chronic illness care: translating evidence into action. Health Aff. 20 , 64–78 (2001)

D. Walker, L. Muchnik, Design of randomized experiments in networks. Proc. IEEE 102 (12), 1940–1951 (2014)

H. Wang, M.J. van der Laan, Dimension reduction with gene expression data using targeted variable importance measurement. BMC Bioinf. 12 (1), 312 (2011)

H. Wang, S. Rose, M.J. van der Laan, Finding quantitative trait loci genes with collaborative targeted maximum likelihood learning. Stat. Probab. Lett. 81 (7), 792–796 (2011a)

H. Wang, S. Rose, M.J. van der Laan. Finding quantitative trait loci genes, in Targeted Learning: Causal Inference for Observational and Experimental Data , ed. by M.J. van der Laan, S. Rose (Springer, Berlin Heidelberg, New York, 2011b)

H. Wang, Z. Zhang, S. Rose, M.J. van der Laan, A novel targeted learning methods for quantitative trait Loci mapping. Genetics 198 (4), 1369–1376 (2014)

G.S. Watson, Smooth regression analysis. Sankhyā Indian J. Stat. Ser. A 359–372 (1964)

L. Watson, R. Small, S. Brown, W. Dawson, J. Lumley, Mounting a community-randomized trial: sample size, matching, selection, and randomization issues in PRISM. Control. Clin. Trials 25 (3), 235–250 (2004)

S. Weinberg, Dreams of a Final Theory: The Scientist’s Search for the Ultimate Laws of Nature (Random House Inc., New York, 1993)

D. Wied, R. Weißbach, Consistency of the kernel density estimator: a survey. Stat. Pap. 53 (1), 1–21 (2012)

R.J. Wieringa, Design Science Methodology for Information Systems and Software Engineering (Springer, New York, 2014)

J. Williamson, Probabilistic theories of causality, in The Oxford Handbook of Causation , ed. by H. Beebee, C. Hitchcock, P. Menzies (Oxford University Press, Oxford, 2009), pp. 185–212

P. Wilson, R.B. D’Agostino, D. Levy, A.M. Belanger, H. Silbershatz, W.B. Kannel, Prediction of coronary heart disease using risk factor categories. Circulation 97 (18), 1837–1847 (1998)

T Woutersen, A simple way to calculate confidence intervals for partially identified parameters. Technical Report, Johns Hopkins University (2006)

W. Xu, Towards optimal one pass large scale learning with averaged stochastic gradient descent. ArXiv e-prints, December (2011)

J.G. Young, M.A. Hernán, J.M. Robins, Identification, estimation and approximation of risk under interventions that depend on the natural value of treatment using observational data. Epidemiol. Methods 3 (1), 1–19 (2014)

S. Yuan, H.H. Zhang, M. Davidian, Variable selection for covariate-adjusted semiparametric inference in randomized clinical trials. Stat. Med. 31 , 3789–3804 (2012)

M.D. Zeiler, Adadelta: an adaptive learning rate method. arXiv e-prints, December (2012)

K. Zhang, D.S. Small, Comment: the essential role of pair matching in cluster-randomized experiments, with application to the Mexican universal health insurance evaluation. Stat. Sci. 25 (1), 59–64 (2009)

B. Zhang, A. Tsiatis, M. Davidian, M. Zhang, E. Laber, A robust method for estimating optimal treatment regimes. Biometrics 68 , 1010–1018 (2012a)

B. Zhang, A. Tsiatis, M. Davidian, M. Zhang, E. Laber, Estimating optimal treatment regimes from a classification perspective. Stat 68 (1), 103–114 (2012b)

M. Zhang, A.A. Tsiatis, M. Davidian, Improving efficiency of inferences in randomized clinical trials using auxiliary covariates. Biometrics 64 (3), 707–715 (2008)

T. Zhang, J. Wu, F. Li, B. Caffo, D. Boatman-Reich, A dynamic directional model for effective brain connectivity using electrocorticographic (ECoG) time series. J. Am. Stat. Assoc. 110 (509), 93–106 (2015)

Y. Zhao, D. Zeng, A. Rush, M Kosorok, Estimating individual treatment rules using outcome weighted learning. J. Am. Stat. Assoc. 107 , 1106–1118 (2012)

Y. Zhao, D. Zeng, E.B. Laber, M.R. Kosorok, New statistical learning methods for estimating optimal dynamic treatment regimes. J. Am. Stat. Assoc. 110 (510), 583–598 (2015)

W. Zheng, M.J. van der Laan, Asymptotic theory for cross-validated targeted maximum likelihood estimation. Technical Report, Division of Biostatistics, University of California, Berkeley (2010)

W. Zheng, M.J. van der Laan, Causal mediation in a survival setting with time-dependent mediators. Technical Report, Division of Biostatistics, University of California, Berkeley (2012a)

W. Zheng, M.J. van der Laan, Targeted maximum likelihood estimation of natural direct effects. Int. J. Biostat. 8 (1), 1–40 (2012b)

W. Zheng, M.J. van der Laan, Longitudinal mediation analysis with time-varying mediators and exposures, with application to survival outcomes. J. Causal Inference 5 (2), 20160006 (2017)

W. Zheng, A. Chambaz, M.J. van der Laan, Drawing valid targeted inference when covariate-adjusted response-adaptive RCT meets data-adaptive loss-based estimation, with an application to the lasso. Technical Report, Division of Biostatistics, University of California, Berkeley (2015)

M. Zinkevich, Online convex programming and generalized infinitesimal gradient ascent. Proceedings of ICML (2003)

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Rose, S., van der Laan, M.J. (2018). Research Questions in Data Science. In: Targeted Learning in Data Science. Springer Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-65304-4_1

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Cultural Relativity and Acceptance of Embryonic Stem Cell Research

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There is a debate about the ethical implications of using human embryos in stem cell research, which can be influenced by cultural, moral, and social values. This paper argues for an adaptable framework to accommodate diverse cultural and religious perspectives. By using an adaptive ethics model, research protections can reflect various populations and foster growth in stem cell research possibilities.

INTRODUCTION

Stem cell research combines biology, medicine, and technology, promising to alter health care and the understanding of human development. Yet, ethical contention exists because of individuals’ perceptions of using human embryos based on their various cultural, moral, and social values. While these disagreements concerning policy, use, and general acceptance have prompted the development of an international ethics policy, such a uniform approach can overlook the nuanced ethical landscapes between cultures. With diverse viewpoints in public health, a single global policy, especially one reflecting Western ethics or the ethics prevalent in high-income countries, is impractical. This paper argues for a culturally sensitive, adaptable framework for the use of embryonic stem cells. Stem cell policy should accommodate varying ethical viewpoints and promote an effective global dialogue. With an extension of an ethics model that can adapt to various cultures, we recommend localized guidelines that reflect the moral views of the people those guidelines serve.

Stem cells, characterized by their unique ability to differentiate into various cell types, enable the repair or replacement of damaged tissues. Two primary types of stem cells are somatic stem cells (adult stem cells) and embryonic stem cells. Adult stem cells exist in developed tissues and maintain the body’s repair processes. [1] Embryonic stem cells (ESC) are remarkably pluripotent or versatile, making them valuable in research. [2] However, the use of ESCs has sparked ethics debates. Considering the potential of embryonic stem cells, research guidelines are essential. The International Society for Stem Cell Research (ISSCR) provides international stem cell research guidelines. They call for “public conversations touching on the scientific significance as well as the societal and ethical issues raised by ESC research.” [3] The ISSCR also publishes updates about culturing human embryos 14 days post fertilization, suggesting local policies and regulations should continue to evolve as ESC research develops. [4]  Like the ISSCR, which calls for local law and policy to adapt to developing stem cell research given cultural acceptance, this paper highlights the importance of local social factors such as religion and culture.

I.     Global Cultural Perspective of Embryonic Stem Cells

Views on ESCs vary throughout the world. Some countries readily embrace stem cell research and therapies, while others have stricter regulations due to ethical concerns surrounding embryonic stem cells and when an embryo becomes entitled to moral consideration. The philosophical issue of when the “someone” begins to be a human after fertilization, in the morally relevant sense, [5] impacts when an embryo becomes not just worthy of protection but morally entitled to it. The process of creating embryonic stem cell lines involves the destruction of the embryos for research. [6] Consequently, global engagement in ESC research depends on social-cultural acceptability.

a.     US and Rights-Based Cultures

In the United States, attitudes toward stem cell therapies are diverse. The ethics and social approaches, which value individualism, [7] trigger debates regarding the destruction of human embryos, creating a complex regulatory environment. For example, the 1996 Dickey-Wicker Amendment prohibited federal funding for the creation of embryos for research and the destruction of embryos for “more than allowed for research on fetuses in utero.” [8] Following suit, in 2001, the Bush Administration heavily restricted stem cell lines for research. However, the Stem Cell Research Enhancement Act of 2005 was proposed to help develop ESC research but was ultimately vetoed. [9] Under the Obama administration, in 2009, an executive order lifted restrictions allowing for more development in this field. [10] The flux of research capacity and funding parallels the different cultural perceptions of human dignity of the embryo and how it is socially presented within the country’s research culture. [11]

b.     Ubuntu and Collective Cultures

African bioethics differs from Western individualism because of the different traditions and values. African traditions, as described by individuals from South Africa and supported by some studies in other African countries, including Ghana and Kenya, follow the African moral philosophies of Ubuntu or Botho and Ukama , which “advocates for a form of wholeness that comes through one’s relationship and connectedness with other people in the society,” [12] making autonomy a socially collective concept. In this context, for the community to act autonomously, individuals would come together to decide what is best for the collective. Thus, stem cell research would require examining the value of the research to society as a whole and the use of the embryos as a collective societal resource. If society views the source as part of the collective whole, and opposes using stem cells, compromising the cultural values to pursue research may cause social detachment and stunt research growth. [13] Based on local culture and moral philosophy, the permissibility of stem cell research depends on how embryo, stem cell, and cell line therapies relate to the community as a whole . Ubuntu is the expression of humanness, with the person’s identity drawn from the “’I am because we are’” value. [14] The decision in a collectivistic culture becomes one born of cultural context, and individual decisions give deference to others in the society.

Consent differs in cultures where thought and moral philosophy are based on a collective paradigm. So, applying Western bioethical concepts is unrealistic. For one, Africa is a diverse continent with many countries with different belief systems, access to health care, and reliance on traditional or Western medicines. Where traditional medicine is the primary treatment, the “’restrictive focus on biomedically-related bioethics’” [is] problematic in African contexts because it neglects bioethical issues raised by traditional systems.” [15] No single approach applies in all areas or contexts. Rather than evaluating the permissibility of ESC research according to Western concepts such as the four principles approach, different ethics approaches should prevail.

Another consideration is the socio-economic standing of countries. In parts of South Africa, researchers have not focused heavily on contributing to the stem cell discourse, either because it is not considered health care or a health science priority or because resources are unavailable. [16] Each country’s priorities differ given different social, political, and economic factors. In South Africa, for instance, areas such as maternal mortality, non-communicable diseases, telemedicine, and the strength of health systems need improvement and require more focus. [17] Stem cell research could benefit the population, but it also could divert resources from basic medical care. Researchers in South Africa adhere to the National Health Act and Medicines Control Act in South Africa and international guidelines; however, the Act is not strictly enforced, and there is no clear legislation for research conduct or ethical guidelines. [18]

Some parts of Africa condemn stem cell research. For example, 98.2 percent of the Tunisian population is Muslim. [19] Tunisia does not permit stem cell research because of moral conflict with a Fatwa. Religion heavily saturates the regulation and direction of research. [20] Stem cell use became permissible for reproductive purposes only recently, with tight restrictions preventing cells from being used in any research other than procedures concerning ART/IVF.  Their use is conditioned on consent, and available only to married couples. [21] The community's receptiveness to stem cell research depends on including communitarian African ethics.

c.     Asia

Some Asian countries also have a collective model of ethics and decision making. [22] In China, the ethics model promotes a sincere respect for life or human dignity, [23] based on protective medicine. This model, influenced by Traditional Chinese Medicine (TCM), [24] recognizes Qi as the vital energy delivered via the meridians of the body; it connects illness to body systems, the body’s entire constitution, and the universe for a holistic bond of nature, health, and quality of life. [25] Following a protective ethics model, and traditional customs of wholeness, investment in stem cell research is heavily desired for its applications in regenerative therapies, disease modeling, and protective medicines. In a survey of medical students and healthcare practitioners, 30.8 percent considered stem cell research morally unacceptable while 63.5 percent accepted medical research using human embryonic stem cells. Of these individuals, 89.9 percent supported increased funding for stem cell research. [26] The scientific community might not reflect the overall population. From 1997 to 2019, China spent a total of $576 million (USD) on stem cell research at 8,050 stem cell programs, increased published presence from 0.6 percent to 14.01 percent of total global stem cell publications as of 2014, and made significant strides in cell-based therapies for various medical conditions. [27] However, while China has made substantial investments in stem cell research and achieved notable progress in clinical applications, concerns linger regarding ethical oversight and transparency. [28] For example, the China Biosecurity Law, promoted by the National Health Commission and China Hospital Association, attempted to mitigate risks by introducing an institutional review board (IRB) in the regulatory bodies. 5800 IRBs registered with the Chinese Clinical Trial Registry since 2021. [29] However, issues still need to be addressed in implementing effective IRB review and approval procedures.

The substantial government funding and focus on scientific advancement have sometimes overshadowed considerations of regional cultures, ethnic minorities, and individual perspectives, particularly evident during the one-child policy era. As government policy adapts to promote public stability, such as the change from the one-child to the two-child policy, [30] research ethics should also adapt to ensure respect for the values of its represented peoples.

Japan is also relatively supportive of stem cell research and therapies. Japan has a more transparent regulatory framework, allowing for faster approval of regenerative medicine products, which has led to several advanced clinical trials and therapies. [31] South Korea is also actively engaged in stem cell research and has a history of breakthroughs in cloning and embryonic stem cells. [32] However, the field is controversial, and there are issues of scientific integrity. For example, the Korean FDA fast-tracked products for approval, [33] and in another instance, the oocyte source was unclear and possibly violated ethical standards. [34] Trust is important in research, as it builds collaborative foundations between colleagues, trial participant comfort, open-mindedness for complicated and sensitive discussions, and supports regulatory procedures for stakeholders. There is a need to respect the culture’s interest, engagement, and for research and clinical trials to be transparent and have ethical oversight to promote global research discourse and trust.

d.     Middle East

Countries in the Middle East have varying degrees of acceptance of or restrictions to policies related to using embryonic stem cells due to cultural and religious influences. Saudi Arabia has made significant contributions to stem cell research, and conducts research based on international guidelines for ethical conduct and under strict adherence to guidelines in accordance with Islamic principles. Specifically, the Saudi government and people require ESC research to adhere to Sharia law. In addition to umbilical and placental stem cells, [35] Saudi Arabia permits the use of embryonic stem cells as long as they come from miscarriages, therapeutic abortions permissible by Sharia law, or are left over from in vitro fertilization and donated to research. [36] Laws and ethical guidelines for stem cell research allow the development of research institutions such as the King Abdullah International Medical Research Center, which has a cord blood bank and a stem cell registry with nearly 10,000 donors. [37] Such volume and acceptance are due to the ethical ‘permissibility’ of the donor sources, which do not conflict with religious pillars. However, some researchers err on the side of caution, choosing not to use embryos or fetal tissue as they feel it is unethical to do so. [38]

Jordan has a positive research ethics culture. [39] However, there is a significant issue of lack of trust in researchers, with 45.23 percent (38.66 percent agreeing and 6.57 percent strongly agreeing) of Jordanians holding a low level of trust in researchers, compared to 81.34 percent of Jordanians agreeing that they feel safe to participate in a research trial. [40] Safety testifies to the feeling of confidence that adequate measures are in place to protect participants from harm, whereas trust in researchers could represent the confidence in researchers to act in the participants’ best interests, adhere to ethical guidelines, provide accurate information, and respect participants’ rights and dignity. One method to improve trust would be to address communication issues relevant to ESC. Legislation surrounding stem cell research has adopted specific language, especially concerning clarification “between ‘stem cells’ and ‘embryonic stem cells’” in translation. [41] Furthermore, legislation “mandates the creation of a national committee… laying out specific regulations for stem-cell banking in accordance with international standards.” [42] This broad regulation opens the door for future global engagement and maintains transparency. However, these regulations may also constrain the influence of research direction, pace, and accessibility of research outcomes.

e.     Europe

In the European Union (EU), ethics is also principle-based, but the principles of autonomy, dignity, integrity, and vulnerability are interconnected. [43] As such, the opportunity for cohesion and concessions between individuals’ thoughts and ideals allows for a more adaptable ethics model due to the flexible principles that relate to the human experience The EU has put forth a framework in its Convention for the Protection of Human Rights and Dignity of the Human Being allowing member states to take different approaches. Each European state applies these principles to its specific conventions, leading to or reflecting different acceptance levels of stem cell research. [44]

For example, in Germany, Lebenzusammenhang , or the coherence of life, references integrity in the unity of human culture. Namely, the personal sphere “should not be subject to external intervention.” [45]  Stem cell interventions could affect this concept of bodily completeness, leading to heavy restrictions. Under the Grundgesetz, human dignity and the right to life with physical integrity are paramount. [46] The Embryo Protection Act of 1991 made producing cell lines illegal. Cell lines can be imported if approved by the Central Ethics Commission for Stem Cell Research only if they were derived before May 2007. [47] Stem cell research respects the integrity of life for the embryo with heavy specifications and intense oversight. This is vastly different in Finland, where the regulatory bodies find research more permissible in IVF excess, but only up to 14 days after fertilization. [48] Spain’s approach differs still, with a comprehensive regulatory framework. [49] Thus, research regulation can be culture-specific due to variations in applied principles. Diverse cultures call for various approaches to ethical permissibility. [50] Only an adaptive-deliberative model can address the cultural constructions of self and achieve positive, culturally sensitive stem cell research practices. [51]

II.     Religious Perspectives on ESC

Embryonic stem cell sources are the main consideration within religious contexts. While individuals may not regard their own religious texts as authoritative or factual, religion can shape their foundations or perspectives.

The Qur'an states:

“And indeed We created man from a quintessence of clay. Then We placed within him a small quantity of nutfa (sperm to fertilize) in a safe place. Then We have fashioned the nutfa into an ‘alaqa (clinging clot or cell cluster), then We developed the ‘alaqa into mudgha (a lump of flesh), and We made mudgha into bones, and clothed the bones with flesh, then We brought it into being as a new creation. So Blessed is Allah, the Best of Creators.” [52]

Many scholars of Islam estimate the time of soul installment, marked by the angel breathing in the soul to bring the individual into creation, as 120 days from conception. [53] Personhood begins at this point, and the value of life would prohibit research or experimentation that could harm the individual. If the fetus is more than 120 days old, the time ensoulment is interpreted to occur according to Islamic law, abortion is no longer permissible. [54] There are a few opposing opinions about early embryos in Islamic traditions. According to some Islamic theologians, there is no ensoulment of the early embryo, which is the source of stem cells for ESC research. [55]

In Buddhism, the stance on stem cell research is not settled. The main tenets, the prohibition against harming or destroying others (ahimsa) and the pursuit of knowledge (prajña) and compassion (karuna), leave Buddhist scholars and communities divided. [56] Some scholars argue stem cell research is in accordance with the Buddhist tenet of seeking knowledge and ending human suffering. Others feel it violates the principle of not harming others. Finding the balance between these two points relies on the karmic burden of Buddhist morality. In trying to prevent ahimsa towards the embryo, Buddhist scholars suggest that to comply with Buddhist tenets, research cannot be done as the embryo has personhood at the moment of conception and would reincarnate immediately, harming the individual's ability to build their karmic burden. [57] On the other hand, the Bodhisattvas, those considered to be on the path to enlightenment or Nirvana, have given organs and flesh to others to help alleviate grieving and to benefit all. [58] Acceptance varies on applied beliefs and interpretations.

Catholicism does not support embryonic stem cell research, as it entails creation or destruction of human embryos. This destruction conflicts with the belief in the sanctity of life. For example, in the Old Testament, Genesis describes humanity as being created in God’s image and multiplying on the Earth, referencing the sacred rights to human conception and the purpose of development and life. In the Ten Commandments, the tenet that one should not kill has numerous interpretations where killing could mean murder or shedding of the sanctity of life, demonstrating the high value of human personhood. In other books, the theological conception of when life begins is interpreted as in utero, [59] highlighting the inviolability of life and its formation in vivo to make a religious point for accepting such research as relatively limited, if at all. [60] The Vatican has released ethical directives to help apply a theological basis to modern-day conflicts. The Magisterium of the Church states that “unless there is a moral certainty of not causing harm,” experimentation on fetuses, fertilized cells, stem cells, or embryos constitutes a crime. [61] Such procedures would not respect the human person who exists at these stages, according to Catholicism. Damages to the embryo are considered gravely immoral and illicit. [62] Although the Catholic Church officially opposes abortion, surveys demonstrate that many Catholic people hold pro-choice views, whether due to the context of conception, stage of pregnancy, threat to the mother’s life, or for other reasons, demonstrating that practicing members can also accept some but not all tenets. [63]

Some major Jewish denominations, such as the Reform, Conservative, and Reconstructionist movements, are open to supporting ESC use or research as long as it is for saving a life. [64] Within Judaism, the Talmud, or study, gives personhood to the child at birth and emphasizes that life does not begin at conception: [65]

“If she is found pregnant, until the fortieth day it is mere fluid,” [66]

Whereas most religions prioritize the status of human embryos, the Halakah (Jewish religious law) states that to save one life, most other religious laws can be ignored because it is in pursuit of preservation. [67] Stem cell research is accepted due to application of these religious laws.

We recognize that all religions contain subsets and sects. The variety of environmental and cultural differences within religious groups requires further analysis to respect the flexibility of religious thoughts and practices. We make no presumptions that all cultures require notions of autonomy or morality as under the common morality theory , which asserts a set of universal moral norms that all individuals share provides moral reasoning and guides ethical decisions. [68] We only wish to show that the interaction with morality varies between cultures and countries.

III.     A Flexible Ethical Approach

The plurality of different moral approaches described above demonstrates that there can be no universally acceptable uniform law for ESC on a global scale. Instead of developing one standard, flexible ethical applications must be continued. We recommend local guidelines that incorporate important cultural and ethical priorities.

While the Declaration of Helsinki is more relevant to people in clinical trials receiving ESC products, in keeping with the tradition of protections for research subjects, consent of the donor is an ethical requirement for ESC donation in many jurisdictions including the US, Canada, and Europe. [69] The Declaration of Helsinki provides a reference point for regulatory standards and could potentially be used as a universal baseline for obtaining consent prior to gamete or embryo donation.

For instance, in Columbia University’s egg donor program for stem cell research, donors followed standard screening protocols and “underwent counseling sessions that included information as to the purpose of oocyte donation for research, what the oocytes would be used for, the risks and benefits of donation, and process of oocyte stimulation” to ensure transparency for consent. [70] The program helped advance stem cell research and provided clear and safe research methods with paid participants. Though paid participation or covering costs of incidental expenses may not be socially acceptable in every culture or context, [71] and creating embryos for ESC research is illegal in many jurisdictions, Columbia’s program was effective because of the clear and honest communications with donors, IRBs, and related stakeholders.  This example demonstrates that cultural acceptance of scientific research and of the idea that an egg or embryo does not have personhood is likely behind societal acceptance of donating eggs for ESC research. As noted, many countries do not permit the creation of embryos for research.

Proper communication and education regarding the process and purpose of stem cell research may bolster comprehension and garner more acceptance. “Given the sensitive subject material, a complete consent process can support voluntary participation through trust, understanding, and ethical norms from the cultures and morals participants value. This can be hard for researchers entering countries of different socioeconomic stability, with different languages and different societal values. [72]

An adequate moral foundation in medical ethics is derived from the cultural and religious basis that informs knowledge and actions. [73] Understanding local cultural and religious values and their impact on research could help researchers develop humility and promote inclusion.

IV.     Concerns

Some may argue that if researchers all adhere to one ethics standard, protection will be satisfied across all borders, and the global public will trust researchers. However, defining what needs to be protected and how to define such research standards is very specific to the people to which standards are applied. We suggest that applying one uniform guide cannot accurately protect each individual because we all possess our own perceptions and interpretations of social values. [74] Therefore, the issue of not adjusting to the moral pluralism between peoples in applying one standard of ethics can be resolved by building out ethics models that can be adapted to different cultures and religions.

Other concerns include medical tourism, which may promote health inequities. [75] Some countries may develop and approve products derived from ESC research before others, compromising research ethics or drug approval processes. There are also concerns about the sale of unauthorized stem cell treatments, for example, those without FDA approval in the United States. Countries with robust research infrastructures may be tempted to attract medical tourists, and some customers will have false hopes based on aggressive publicity of unproven treatments. [76]

For example, in China, stem cell clinics can market to foreign clients who are not protected under the regulatory regimes. Companies employ a marketing strategy of “ethically friendly” therapies. Specifically, in the case of Beike, China’s leading stem cell tourism company and sprouting network, ethical oversight of administrators or health bureaus at one site has “the unintended consequence of shifting questionable activities to another node in Beike's diffuse network.” [77] In contrast, Jordan is aware of stem cell research’s potential abuse and its own status as a “health-care hub.” Jordan’s expanded regulations include preserving the interests of individuals in clinical trials and banning private companies from ESC research to preserve transparency and the integrity of research practices. [78]

The social priorities of the community are also a concern. The ISSCR explicitly states that guidelines “should be periodically revised to accommodate scientific advances, new challenges, and evolving social priorities.” [79] The adaptable ethics model extends this consideration further by addressing whether research is warranted given the varying degrees of socioeconomic conditions, political stability, and healthcare accessibilities and limitations. An ethical approach would require discussion about resource allocation and appropriate distribution of funds. [80]

While some religions emphasize the sanctity of life from conception, which may lead to public opposition to ESC research, others encourage ESC research due to its potential for healing and alleviating human pain. Many countries have special regulations that balance local views on embryonic personhood, the benefits of research as individual or societal goods, and the protection of human research subjects. To foster understanding and constructive dialogue, global policy frameworks should prioritize the protection of universal human rights, transparency, and informed consent. In addition to these foundational global policies, we recommend tailoring local guidelines to reflect the diverse cultural and religious perspectives of the populations they govern. Ethics models should be adapted to local populations to effectively establish research protections, growth, and possibilities of stem cell research.

For example, in countries with strong beliefs in the moral sanctity of embryos or heavy religious restrictions, an adaptive model can allow for discussion instead of immediate rejection. In countries with limited individual rights and voice in science policy, an adaptive model ensures cultural, moral, and religious views are taken into consideration, thereby building social inclusion. While this ethical consideration by the government may not give a complete voice to every individual, it will help balance policies and maintain the diverse perspectives of those it affects. Embracing an adaptive ethics model of ESC research promotes open-minded dialogue and respect for the importance of human belief and tradition. By actively engaging with cultural and religious values, researchers can better handle disagreements and promote ethical research practices that benefit each society.

This brief exploration of the religious and cultural differences that impact ESC research reveals the nuances of relative ethics and highlights a need for local policymakers to apply a more intense adaptive model.

[1] Poliwoda, S., Noor, N., Downs, E., Schaaf, A., Cantwell, A., Ganti, L., Kaye, A. D., Mosel, L. I., Carroll, C. B., Viswanath, O., & Urits, I. (2022). Stem cells: a comprehensive review of origins and emerging clinical roles in medical practice.  Orthopedic reviews ,  14 (3), 37498. https://doi.org/10.52965/001c.37498

[2] Poliwoda, S., Noor, N., Downs, E., Schaaf, A., Cantwell, A., Ganti, L., Kaye, A. D., Mosel, L. I., Carroll, C. B., Viswanath, O., & Urits, I. (2022). Stem cells: a comprehensive review of origins and emerging clinical roles in medical practice.  Orthopedic reviews ,  14 (3), 37498. https://doi.org/10.52965/001c.37498

[3] International Society for Stem Cell Research. (2023). Laboratory-based human embryonic stem cell research, embryo research, and related research activities . International Society for Stem Cell Research. https://www.isscr.org/guidelines/blog-post-title-one-ed2td-6fcdk ; Kimmelman, J., Hyun, I., Benvenisty, N.  et al.  Policy: Global standards for stem-cell research.  Nature   533 , 311–313 (2016). https://doi.org/10.1038/533311a

[4] International Society for Stem Cell Research. (2023). Laboratory-based human embryonic stem cell research, embryo research, and related research activities . International Society for Stem Cell Research. https://www.isscr.org/guidelines/blog-post-title-one-ed2td-6fcdk

[5] Concerning the moral philosophies of stem cell research, our paper does not posit a personal moral stance nor delve into the “when” of human life begins. To read further about the philosophical debate, consider the following sources:

Sandel M. J. (2004). Embryo ethics--the moral logic of stem-cell research.  The New England journal of medicine ,  351 (3), 207–209. https://doi.org/10.1056/NEJMp048145 ; George, R. P., & Lee, P. (2020, September 26). Acorns and Embryos . The New Atlantis. https://www.thenewatlantis.com/publications/acorns-and-embryos ; Sagan, A., & Singer, P. (2007). The moral status of stem cells. Metaphilosophy , 38 (2/3), 264–284. http://www.jstor.org/stable/24439776 ; McHugh P. R. (2004). Zygote and "clonote"--the ethical use of embryonic stem cells.  The New England journal of medicine ,  351 (3), 209–211. https://doi.org/10.1056/NEJMp048147 ; Kurjak, A., & Tripalo, A. (2004). The facts and doubts about beginning of the human life and personality.  Bosnian journal of basic medical sciences ,  4 (1), 5–14. https://doi.org/10.17305/bjbms.2004.3453

[6] Vazin, T., & Freed, W. J. (2010). Human embryonic stem cells: derivation, culture, and differentiation: a review.  Restorative neurology and neuroscience ,  28 (4), 589–603. https://doi.org/10.3233/RNN-2010-0543

[7] Socially, at its core, the Western approach to ethics is widely principle-based, autonomy being one of the key factors to ensure a fundamental respect for persons within research. For information regarding autonomy in research, see: Department of Health, Education, and Welfare, & National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research (1978). The Belmont Report. Ethical principles and guidelines for the protection of human subjects of research.; For a more in-depth review of autonomy within the US, see: Beauchamp, T. L., & Childress, J. F. (1994). Principles of Biomedical Ethics . Oxford University Press.

[8] Sherley v. Sebelius , 644 F.3d 388 (D.C. Cir. 2011), citing 45 C.F.R. 46.204(b) and [42 U.S.C. § 289g(b)]. https://www.cadc.uscourts.gov/internet/opinions.nsf/6c690438a9b43dd685257a64004ebf99/$file/11-5241-1391178.pdf

[9] Stem Cell Research Enhancement Act of 2005, H. R. 810, 109 th Cong. (2001). https://www.govtrack.us/congress/bills/109/hr810/text ; Bush, G. W. (2006, July 19). Message to the House of Representatives . National Archives and Records Administration. https://georgewbush-whitehouse.archives.gov/news/releases/2006/07/20060719-5.html

[10] National Archives and Records Administration. (2009, March 9). Executive order 13505 -- removing barriers to responsible scientific research involving human stem cells . National Archives and Records Administration. https://obamawhitehouse.archives.gov/the-press-office/removing-barriers-responsible-scientific-research-involving-human-stem-cells

[11] Hurlbut, W. B. (2006). Science, Religion, and the Politics of Stem Cells.  Social Research ,  73 (3), 819–834. http://www.jstor.org/stable/40971854

[12] Akpa-Inyang, Francis & Chima, Sylvester. (2021). South African traditional values and beliefs regarding informed consent and limitations of the principle of respect for autonomy in African communities: a cross-cultural qualitative study. BMC Medical Ethics . 22. 10.1186/s12910-021-00678-4.

[13] Source for further reading: Tangwa G. B. (2007). Moral status of embryonic stem cells: perspective of an African villager. Bioethics , 21(8), 449–457. https://doi.org/10.1111/j.1467-8519.2007.00582.x , see also Mnisi, F. M. (2020). An African analysis based on ethics of Ubuntu - are human embryonic stem cell patents morally justifiable? African Insight , 49 (4).

[14] Jecker, N. S., & Atuire, C. (2021). Bioethics in Africa: A contextually enlightened analysis of three cases. Developing World Bioethics , 22 (2), 112–122. https://doi.org/10.1111/dewb.12324

[15] Jecker, N. S., & Atuire, C. (2021). Bioethics in Africa: A contextually enlightened analysis of three cases. Developing World Bioethics, 22(2), 112–122. https://doi.org/10.1111/dewb.12324

[16] Jackson, C.S., Pepper, M.S. Opportunities and barriers to establishing a cell therapy programme in South Africa.  Stem Cell Res Ther   4 , 54 (2013). https://doi.org/10.1186/scrt204 ; Pew Research Center. (2014, May 1). Public health a major priority in African nations . Pew Research Center’s Global Attitudes Project. https://www.pewresearch.org/global/2014/05/01/public-health-a-major-priority-in-african-nations/

[17] Department of Health Republic of South Africa. (2021). Health Research Priorities (revised) for South Africa 2021-2024 . National Health Research Strategy. https://www.health.gov.za/wp-content/uploads/2022/05/National-Health-Research-Priorities-2021-2024.pdf

[18] Oosthuizen, H. (2013). Legal and Ethical Issues in Stem Cell Research in South Africa. In: Beran, R. (eds) Legal and Forensic Medicine. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32338-6_80 , see also: Gaobotse G (2018) Stem Cell Research in Africa: Legislation and Challenges. J Regen Med 7:1. doi: 10.4172/2325-9620.1000142

[19] United States Bureau of Citizenship and Immigration Services. (1998). Tunisia: Information on the status of Christian conversions in Tunisia . UNHCR Web Archive. https://webarchive.archive.unhcr.org/20230522142618/https://www.refworld.org/docid/3df0be9a2.html

[20] Gaobotse, G. (2018) Stem Cell Research in Africa: Legislation and Challenges. J Regen Med 7:1. doi: 10.4172/2325-9620.1000142

[21] Kooli, C. Review of assisted reproduction techniques, laws, and regulations in Muslim countries.  Middle East Fertil Soc J   24 , 8 (2020). https://doi.org/10.1186/s43043-019-0011-0 ; Gaobotse, G. (2018) Stem Cell Research in Africa: Legislation and Challenges. J Regen Med 7:1. doi: 10.4172/2325-9620.1000142

[22] Pang M. C. (1999). Protective truthfulness: the Chinese way of safeguarding patients in informed treatment decisions. Journal of medical ethics , 25(3), 247–253. https://doi.org/10.1136/jme.25.3.247

[23] Wang, L., Wang, F., & Zhang, W. (2021). Bioethics in China’s biosecurity law: Forms, effects, and unsettled issues. Journal of law and the biosciences , 8(1).  https://doi.org/10.1093/jlb/lsab019 https://academic.oup.com/jlb/article/8/1/lsab019/6299199

[24] Wang, Y., Xue, Y., & Guo, H. D. (2022). Intervention effects of traditional Chinese medicine on stem cell therapy of myocardial infarction.  Frontiers in pharmacology ,  13 , 1013740. https://doi.org/10.3389/fphar.2022.1013740

[25] Li, X.-T., & Zhao, J. (2012). Chapter 4: An Approach to the Nature of Qi in TCM- Qi and Bioenergy. In Recent Advances in Theories and Practice of Chinese Medicine (p. 79). InTech.

[26] Luo, D., Xu, Z., Wang, Z., & Ran, W. (2021). China's Stem Cell Research and Knowledge Levels of Medical Practitioners and Students.  Stem cells international ,  2021 , 6667743. https://doi.org/10.1155/2021/6667743

[27] Luo, D., Xu, Z., Wang, Z., & Ran, W. (2021). China's Stem Cell Research and Knowledge Levels of Medical Practitioners and Students.  Stem cells international ,  2021 , 6667743. https://doi.org/10.1155/2021/6667743

[28] Zhang, J. Y. (2017). Lost in translation? accountability and governance of Clinical Stem Cell Research in China. Regenerative Medicine , 12 (6), 647–656. https://doi.org/10.2217/rme-2017-0035

[29] Wang, L., Wang, F., & Zhang, W. (2021). Bioethics in China’s biosecurity law: Forms, effects, and unsettled issues. Journal of law and the biosciences , 8(1).  https://doi.org/10.1093/jlb/lsab019 https://academic.oup.com/jlb/article/8/1/lsab019/6299199

[30] Chen, H., Wei, T., Wang, H.  et al.  Association of China’s two-child policy with changes in number of births and birth defects rate, 2008–2017.  BMC Public Health   22 , 434 (2022). https://doi.org/10.1186/s12889-022-12839-0

[31] Azuma, K. Regulatory Landscape of Regenerative Medicine in Japan.  Curr Stem Cell Rep   1 , 118–128 (2015). https://doi.org/10.1007/s40778-015-0012-6

[32] Harris, R. (2005, May 19). Researchers Report Advance in Stem Cell Production . NPR. https://www.npr.org/2005/05/19/4658967/researchers-report-advance-in-stem-cell-production

[33] Park, S. (2012). South Korea steps up stem-cell work.  Nature . https://doi.org/10.1038/nature.2012.10565

[34] Resnik, D. B., Shamoo, A. E., & Krimsky, S. (2006). Fraudulent human embryonic stem cell research in South Korea: lessons learned.  Accountability in research ,  13 (1), 101–109. https://doi.org/10.1080/08989620600634193 .

[35] Alahmad, G., Aljohani, S., & Najjar, M. F. (2020). Ethical challenges regarding the use of stem cells: interviews with researchers from Saudi Arabia. BMC medical ethics, 21(1), 35. https://doi.org/10.1186/s12910-020-00482-6

[36] Association for the Advancement of Blood and Biotherapies.  https://www.aabb.org/regulatory-and-advocacy/regulatory-affairs/regulatory-for-cellular-therapies/international-competent-authorities/saudi-arabia

[37] Alahmad, G., Aljohani, S., & Najjar, M. F. (2020). Ethical challenges regarding the use of stem cells: Interviews with researchers from Saudi Arabia.  BMC medical ethics ,  21 (1), 35. https://doi.org/10.1186/s12910-020-00482-6

[38] Alahmad, G., Aljohani, S., & Najjar, M. F. (2020). Ethical challenges regarding the use of stem cells: Interviews with researchers from Saudi Arabia. BMC medical ethics , 21(1), 35. https://doi.org/10.1186/s12910-020-00482-6

Culturally, autonomy practices follow a relational autonomy approach based on a paternalistic deontological health care model. The adherence to strict international research policies and religious pillars within the regulatory environment is a great foundation for research ethics. However, there is a need to develop locally targeted ethics approaches for research (as called for in Alahmad, G., Aljohani, S., & Najjar, M. F. (2020). Ethical challenges regarding the use of stem cells: interviews with researchers from Saudi Arabia. BMC medical ethics, 21(1), 35. https://doi.org/10.1186/s12910-020-00482-6), this decision-making approach may help advise a research decision model. For more on the clinical cultural autonomy approaches, see: Alabdullah, Y. Y., Alzaid, E., Alsaad, S., Alamri, T., Alolayan, S. W., Bah, S., & Aljoudi, A. S. (2022). Autonomy and paternalism in Shared decision‐making in a Saudi Arabian tertiary hospital: A cross‐sectional study. Developing World Bioethics , 23 (3), 260–268. https://doi.org/10.1111/dewb.12355 ; Bukhari, A. A. (2017). Universal Principles of Bioethics and Patient Rights in Saudi Arabia (Doctoral dissertation, Duquesne University). https://dsc.duq.edu/etd/124; Ladha, S., Nakshawani, S. A., Alzaidy, A., & Tarab, B. (2023, October 26). Islam and Bioethics: What We All Need to Know . Columbia University School of Professional Studies. https://sps.columbia.edu/events/islam-and-bioethics-what-we-all-need-know

[39] Ababneh, M. A., Al-Azzam, S. I., Alzoubi, K., Rababa’h, A., & Al Demour, S. (2021). Understanding and attitudes of the Jordanian public about clinical research ethics.  Research Ethics ,  17 (2), 228-241.  https://doi.org/10.1177/1747016120966779

[40] Ababneh, M. A., Al-Azzam, S. I., Alzoubi, K., Rababa’h, A., & Al Demour, S. (2021). Understanding and attitudes of the Jordanian public about clinical research ethics.  Research Ethics ,  17 (2), 228-241.  https://doi.org/10.1177/1747016120966779

[41] Dajani, R. (2014). Jordan’s stem-cell law can guide the Middle East.  Nature  510, 189. https://doi.org/10.1038/510189a

[42] Dajani, R. (2014). Jordan’s stem-cell law can guide the Middle East.  Nature  510, 189. https://doi.org/10.1038/510189a

[43] The EU’s definition of autonomy relates to the capacity for creating ideas, moral insight, decisions, and actions without constraint, personal responsibility, and informed consent. However, the EU views autonomy as not completely able to protect individuals and depends on other principles, such as dignity, which “expresses the intrinsic worth and fundamental equality of all human beings.” Rendtorff, J.D., Kemp, P. (2019). Four Ethical Principles in European Bioethics and Biolaw: Autonomy, Dignity, Integrity and Vulnerability. In: Valdés, E., Lecaros, J. (eds) Biolaw and Policy in the Twenty-First Century. International Library of Ethics, Law, and the New Medicine, vol 78. Springer, Cham. https://doi.org/10.1007/978-3-030-05903-3_3

[44] Council of Europe. Convention for the protection of Human Rights and Dignity of the Human Being with regard to the Application of Biology and Medicine: Convention on Human Rights and Biomedicine (ETS No. 164) https://www.coe.int/en/web/conventions/full-list?module=treaty-detail&treatynum=164 (forbidding the creation of embryos for research purposes only, and suggests embryos in vitro have protections.); Also see Drabiak-Syed B. K. (2013). New President, New Human Embryonic Stem Cell Research Policy: Comparative International Perspectives and Embryonic Stem Cell Research Laws in France.  Biotechnology Law Report ,  32 (6), 349–356. https://doi.org/10.1089/blr.2013.9865

[45] Rendtorff, J.D., Kemp, P. (2019). Four Ethical Principles in European Bioethics and Biolaw: Autonomy, Dignity, Integrity and Vulnerability. In: Valdés, E., Lecaros, J. (eds) Biolaw and Policy in the Twenty-First Century. International Library of Ethics, Law, and the New Medicine, vol 78. Springer, Cham. https://doi.org/10.1007/978-3-030-05903-3_3

[46] Tomuschat, C., Currie, D. P., Kommers, D. P., & Kerr, R. (Trans.). (1949, May 23). Basic law for the Federal Republic of Germany. https://www.btg-bestellservice.de/pdf/80201000.pdf

[47] Regulation of Stem Cell Research in Germany . Eurostemcell. (2017, April 26). https://www.eurostemcell.org/regulation-stem-cell-research-germany

[48] Regulation of Stem Cell Research in Finland . Eurostemcell. (2017, April 26). https://www.eurostemcell.org/regulation-stem-cell-research-finland

[49] Regulation of Stem Cell Research in Spain . Eurostemcell. (2017, April 26). https://www.eurostemcell.org/regulation-stem-cell-research-spain

[50] Some sources to consider regarding ethics models or regulatory oversights of other cultures not covered:

Kara MA. Applicability of the principle of respect for autonomy: the perspective of Turkey. J Med Ethics. 2007 Nov;33(11):627-30. doi: 10.1136/jme.2006.017400. PMID: 17971462; PMCID: PMC2598110.

Ugarte, O. N., & Acioly, M. A. (2014). The principle of autonomy in Brazil: one needs to discuss it ...  Revista do Colegio Brasileiro de Cirurgioes ,  41 (5), 374–377. https://doi.org/10.1590/0100-69912014005013

Bharadwaj, A., & Glasner, P. E. (2012). Local cells, global science: The rise of embryonic stem cell research in India . Routledge.

For further research on specific European countries regarding ethical and regulatory framework, we recommend this database: Regulation of Stem Cell Research in Europe . Eurostemcell. (2017, April 26). https://www.eurostemcell.org/regulation-stem-cell-research-europe   

[51] Klitzman, R. (2006). Complications of culture in obtaining informed consent. The American Journal of Bioethics, 6(1), 20–21. https://doi.org/10.1080/15265160500394671 see also: Ekmekci, P. E., & Arda, B. (2017). Interculturalism and Informed Consent: Respecting Cultural Differences without Breaching Human Rights.  Cultura (Iasi, Romania) ,  14 (2), 159–172.; For why trust is important in research, see also: Gray, B., Hilder, J., Macdonald, L., Tester, R., Dowell, A., & Stubbe, M. (2017). Are research ethics guidelines culturally competent?  Research Ethics ,  13 (1), 23-41.  https://doi.org/10.1177/1747016116650235

[52] The Qur'an  (M. Khattab, Trans.). (1965). Al-Mu’minun, 23: 12-14. https://quran.com/23

[53] Lenfest, Y. (2017, December 8). Islam and the beginning of human life . Bill of Health. https://blog.petrieflom.law.harvard.edu/2017/12/08/islam-and-the-beginning-of-human-life/

[54] Aksoy, S. (2005). Making regulations and drawing up legislation in Islamic countries under conditions of uncertainty, with special reference to embryonic stem cell research. Journal of Medical Ethics , 31: 399-403.; see also: Mahmoud, Azza. "Islamic Bioethics: National Regulations and Guidelines of Human Stem Cell Research in the Muslim World." Master's thesis, Chapman University, 2022. https://doi.org/10.36837/ chapman.000386

[55] Rashid, R. (2022). When does Ensoulment occur in the Human Foetus. Journal of the British Islamic Medical Association , 12 (4). ISSN 2634 8071. https://www.jbima.com/wp-content/uploads/2023/01/2-Ethics-3_-Ensoulment_Rafaqat.pdf.

[56] Sivaraman, M. & Noor, S. (2017). Ethics of embryonic stem cell research according to Buddhist, Hindu, Catholic, and Islamic religions: perspective from Malaysia. Asian Biomedicine,8(1) 43-52.  https://doi.org/10.5372/1905-7415.0801.260

[57] Jafari, M., Elahi, F., Ozyurt, S. & Wrigley, T. (2007). 4. Religious Perspectives on Embryonic Stem Cell Research. In K. Monroe, R. Miller & J. Tobis (Ed.),  Fundamentals of the Stem Cell Debate: The Scientific, Religious, Ethical, and Political Issues  (pp. 79-94). Berkeley: University of California Press.  https://escholarship.org/content/qt9rj0k7s3/qt9rj0k7s3_noSplash_f9aca2e02c3777c7fb76ea768ba458f0.pdf https://doi.org/10.1525/9780520940994-005

[58] Lecso, P. A. (1991). The Bodhisattva Ideal and Organ Transplantation.  Journal of Religion and Health ,  30 (1), 35–41. http://www.jstor.org/stable/27510629 ; Bodhisattva, S. (n.d.). The Key of Becoming a Bodhisattva . A Guide to the Bodhisattva Way of Life. http://www.buddhism.org/Sutras/2/BodhisattvaWay.htm

[59] There is no explicit religious reference to when life begins or how to conduct research that interacts with the concept of life. However, these are relevant verses pertaining to how the fetus is viewed. (( King James Bible . (1999). Oxford University Press. (original work published 1769))

Jerimiah 1: 5 “Before I formed thee in the belly I knew thee; and before thou camest forth out of the womb I sanctified thee…”

In prophet Jerimiah’s insight, God set him apart as a person known before childbirth, a theme carried within the Psalm of David.

Psalm 139: 13-14 “…Thou hast covered me in my mother's womb. I will praise thee; for I am fearfully and wonderfully made…”

These verses demonstrate David’s respect for God as an entity that would know of all man’s thoughts and doings even before birth.

[60] It should be noted that abortion is not supported as well.

[61] The Vatican. (1987, February 22). Instruction on Respect for Human Life in Its Origin and on the Dignity of Procreation Replies to Certain Questions of the Day . Congregation For the Doctrine of the Faith. https://www.vatican.va/roman_curia/congregations/cfaith/documents/rc_con_cfaith_doc_19870222_respect-for-human-life_en.html

[62] The Vatican. (2000, August 25). Declaration On the Production and the Scientific and Therapeutic Use of Human Embryonic Stem Cells . Pontifical Academy for Life. https://www.vatican.va/roman_curia/pontifical_academies/acdlife/documents/rc_pa_acdlife_doc_20000824_cellule-staminali_en.html ; Ohara, N. (2003). Ethical Consideration of Experimentation Using Living Human Embryos: The Catholic Church’s Position on Human Embryonic Stem Cell Research and Human Cloning. Department of Obstetrics and Gynecology . Retrieved from https://article.imrpress.com/journal/CEOG/30/2-3/pii/2003018/77-81.pdf.

[63] Smith, G. A. (2022, May 23). Like Americans overall, Catholics vary in their abortion views, with regular mass attenders most opposed . Pew Research Center. https://www.pewresearch.org/short-reads/2022/05/23/like-americans-overall-catholics-vary-in-their-abortion-views-with-regular-mass-attenders-most-opposed/

[64] Rosner, F., & Reichman, E. (2002). Embryonic stem cell research in Jewish law. Journal of halacha and contemporary society , (43), 49–68.; Jafari, M., Elahi, F., Ozyurt, S. & Wrigley, T. (2007). 4. Religious Perspectives on Embryonic Stem Cell Research. In K. Monroe, R. Miller & J. Tobis (Ed.),  Fundamentals of the Stem Cell Debate: The Scientific, Religious, Ethical, and Political Issues  (pp. 79-94). Berkeley: University of California Press.  https://escholarship.org/content/qt9rj0k7s3/qt9rj0k7s3_noSplash_f9aca2e02c3777c7fb76ea768ba458f0.pdf https://doi.org/10.1525/9780520940994-005

[65] Schenker J. G. (2008). The beginning of human life: status of embryo. Perspectives in Halakha (Jewish Religious Law).  Journal of assisted reproduction and genetics ,  25 (6), 271–276. https://doi.org/10.1007/s10815-008-9221-6

[66] Ruttenberg, D. (2020, May 5). The Torah of Abortion Justice (annotated source sheet) . Sefaria. https://www.sefaria.org/sheets/234926.7?lang=bi&with=all&lang2=en

[67] Jafari, M., Elahi, F., Ozyurt, S. & Wrigley, T. (2007). 4. Religious Perspectives on Embryonic Stem Cell Research. In K. Monroe, R. Miller & J. Tobis (Ed.),  Fundamentals of the Stem Cell Debate: The Scientific, Religious, Ethical, and Political Issues  (pp. 79-94). Berkeley: University of California Press.  https://escholarship.org/content/qt9rj0k7s3/qt9rj0k7s3_noSplash_f9aca2e02c3777c7fb76ea768ba458f0.pdf https://doi.org/10.1525/9780520940994-005

[68] Gert, B. (2007). Common morality: Deciding what to do . Oxford Univ. Press.

[69] World Medical Association (2013). World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA , 310(20), 2191–2194. https://doi.org/10.1001/jama.2013.281053 Declaration of Helsinki – WMA – The World Medical Association .; see also: National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. (1979).  The Belmont report: Ethical principles and guidelines for the protection of human subjects of research . U.S. Department of Health and Human Services.  https://www.hhs.gov/ohrp/regulations-and-policy/belmont-report/read-the-belmont-report/index.html

[70] Zakarin Safier, L., Gumer, A., Kline, M., Egli, D., & Sauer, M. V. (2018). Compensating human subjects providing oocytes for stem cell research: 9-year experience and outcomes.  Journal of assisted reproduction and genetics ,  35 (7), 1219–1225. https://doi.org/10.1007/s10815-018-1171-z https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6063839/ see also: Riordan, N. H., & Paz Rodríguez, J. (2021). Addressing concerns regarding associated costs, transparency, and integrity of research in recent stem cell trial. Stem Cells Translational Medicine , 10 (12), 1715–1716. https://doi.org/10.1002/sctm.21-0234

[71] Klitzman, R., & Sauer, M. V. (2009). Payment of egg donors in stem cell research in the USA.  Reproductive biomedicine online ,  18 (5), 603–608. https://doi.org/10.1016/s1472-6483(10)60002-8

[72] Krosin, M. T., Klitzman, R., Levin, B., Cheng, J., & Ranney, M. L. (2006). Problems in comprehension of informed consent in rural and peri-urban Mali, West Africa.  Clinical trials (London, England) ,  3 (3), 306–313. https://doi.org/10.1191/1740774506cn150oa

[73] Veatch, Robert M.  Hippocratic, Religious, and Secular Medical Ethics: The Points of Conflict . Georgetown University Press, 2012.

[74] Msoroka, M. S., & Amundsen, D. (2018). One size fits not quite all: Universal research ethics with diversity.  Research Ethics ,  14 (3), 1-17.  https://doi.org/10.1177/1747016117739939

[75] Pirzada, N. (2022). The Expansion of Turkey’s Medical Tourism Industry.  Voices in Bioethics ,  8 . https://doi.org/10.52214/vib.v8i.9894

[76] Stem Cell Tourism: False Hope for Real Money . Harvard Stem Cell Institute (HSCI). (2023). https://hsci.harvard.edu/stem-cell-tourism , See also: Bissassar, M. (2017). Transnational Stem Cell Tourism: An ethical analysis.  Voices in Bioethics ,  3 . https://doi.org/10.7916/vib.v3i.6027

[77] Song, P. (2011) The proliferation of stem cell therapies in post-Mao China: problematizing ethical regulation,  New Genetics and Society , 30:2, 141-153, DOI:  10.1080/14636778.2011.574375

[78] Dajani, R. (2014). Jordan’s stem-cell law can guide the Middle East.  Nature  510, 189. https://doi.org/10.1038/510189a

[79] International Society for Stem Cell Research. (2024). Standards in stem cell research . International Society for Stem Cell Research. https://www.isscr.org/guidelines/5-standards-in-stem-cell-research

[80] Benjamin, R. (2013). People’s science bodies and rights on the Stem Cell Frontier . Stanford University Press.

Mifrah Hayath

SM Candidate Harvard Medical School, MS Biotechnology Johns Hopkins University

Olivia Bowers

MS Bioethics Columbia University (Disclosure: affiliated with Voices in Bioethics)

Article Details

This work is licensed under a Creative Commons Attribution 4.0 International License .

How to make ubiquitous plastics biodegradable

Understanding the function of a specific bacterial enzyme has paved the way for the biotechnological degradation of styrene.

Polystyrene is made from styrene building blocks and is the most widely used plastic in terms of volume, for example in packaging. Unlike PET, which can now be produced and recycled using biotechnological methods, the production of polystyrene has so far been a purely chemical process. The plastic can't be broken down by biotechnological means, either. Researchers are looking for ways to rectify this: An international team headed by Dr. Xiaodan Li from the Paul Scherrer Institute, Switzerland, in collaboration with Professor Dirk Tischler, head of the Microbial Biotechnology research group at Ruhr University Bochum, Germany, has decoded a bacterial enzyme that plays a key role in styrene degradation. This paves the way for biotechnological application. The researchers published their findings in the journal Nature Chemistry on May, 14, 2024.

Styrene in the environment

"Several million tons of styrene are produced and transported every year," says Dirk Tischler. "In the process, some of it also gets released unintentionally into the environment." This is not the only source of styrene in the environment, however: It occurs naturally in coal tar and lignite tar, can occur in traces in essential oils from some plants and is formed during the decomposition of plant material. "It is therefore not surprising that microorganisms have learned to handle or even to metabolize it," says the researcher.

Fast, but complex: microbial styrene degradation

Bacteria and fungi, as well as the human body, activate styrene with the help of oxygen and form styrene oxide. While styrene itself is toxic, styrene oxide is even more harmful. Rapid metabolization is therefore crucial. "In some microorganisms as well as in the human body, the epoxide formed by this process usually undergoes glutathione conjugation, which makes it both more water-soluble and easier to break down and excrete," explains Dirk Tischler. "This process is very fast, but also very expensive for the cells. A glutathione molecule has to be sacrificed for every molecule of styrene oxide."

The formation of the glutathione conjugate and whether, or rather how, glutathione can be recovered is part of current research at the MiCon Graduate School at Ruhr University Bochum, funded by the German Research Foundation (DFG). Some microorganisms have developed a more efficient variant. They use a small membrane protein, namely styrene oxide isomerase, to break down the epoxide.

Styrene oxide isomerases are more efficient

"Even after the first enrichment of styrene oxide isomerase from the soil bacterium Rhodococcus, we observed its reddish color and showed that this enzyme is bound in the membrane," explains Dirk Tischler. Over the years, he and his team have studied various enzymes of the family and used them primarily in biocatalysis. All of these styrene oxide isomerases have a high catalytic efficiency, are very fast and don't require any additional substances (co-substrates). They therefore allow rapid detoxification of the toxic styrene oxide in the organism and also a potent biotechnological application in the field of fine chemical synthesis.

"In order to optimize the latter, we do need to understand their function," points out Dirk Tischler. "We made considerable progress in this area in our international collaboration between researchers from Switzerland, Singapore, the Netherlands and Germany." The team showed that the enzyme exists in nature as a trimer with three identical units. The structural analyses revealed that there is a heme cofactor between each subunit and that this is loaded with an iron ion. The heme forms an essential part of the so-called active pocket and is relevant for the fixation and conversion of the substrate. The iron ion of the heme cofactor activates the substrate by coordinating the oxygen atom of the styrene oxide. "This means that a new biological function of heme in proteins has been comprehensively described," concludes Dirk Tischler.

• Biochemistry
• Organic Chemistry
• Alternative Fuels
• Geochemistry
• Environmental Policy
• Environmental Science
• Hazardous Waste
• Polyethylene
• Model rocket
• Economic growth
• Water rocket
• Biodegradation
• Dead zone (ecology)

Story Source:

Materials provided by Ruhr-University Bochum . Original written by Meike Drießen. Note: Content may be edited for style and length.

Journal Reference :

• Basavraj Khanppnavar, Joel P. S. Choo, Peter-Leon Hagedoorn, Grigory Smolentsev, Saša Štefanić, Selvapravin Kumaran, Dirk Tischler, Fritz K. Winkler, Volodymyr M. Korkhov, Zhi Li, Richard A. Kammerer, Xiaodan Li. Structural basis of the Meinwald rearrangement catalysed by styrene oxide isomerase . Nature Chemistry , 2024; DOI: 10.1038/s41557-024-01523-y

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Strange & offbeat.

Not all data are created equal; some are structured, but most of them are unstructured. Structured and unstructured data are sourced, collected and scaled in different ways and each one resides in a different type of database.

In this article, we will take a deep dive into both types so that you can get the most out of your data.

Structured data—typically categorized as quantitative data—is highly organized and easily decipherable by  machine learning algorithms .  Developed by IBM® in 1974 , structured query language (SQL) is the programming language used to manage structured data. By using a  relational (SQL) database , business users can quickly input, search and manipulate structured data.

Examples of structured data include dates, names, addresses, credit card numbers, among others. Their benefits are tied to ease of use and access, while liabilities revolve around data inflexibility:

• Easily used by machine learning (ML) algorithms:  The specific and organized architecture of structured data eases the manipulation and querying of ML data.
• Easily used by business users:  Structured data do not require an in-depth understanding of different types of data and how they function. With a basic understanding of the topic relative to the data, users can easily access and interpret the data.
• Accessible by more tools:  Since structured data predates unstructured data, there are more tools available for using and analyzing structured data.
• Limited usage:  Data with a predefined structure can only be used for its intended purpose, which limits its flexibility and usability.
• Limited storage options:  Structured data are usually stored in data storage systems with rigid schemas (for example, “ data warehouses ”). Therefore, changes in data requirements necessitate an update of all structured data, which leads to a massive expenditure of time and resources.
• OLAP :  Performs high-speed, multidimensional data analysis from unified, centralized data stores.
• SQLite : (link resides outside ibm.com)  Implements a self-contained,  serverless , zero-configuration, transactional relational database engine.
• MySQL :  Embeds data into mass-deployed software, particularly mission-critical, heavy-load production system.
• PostgreSQL :  Supports SQL and JSON querying as well as high-tier programming languages (C/C+, Java,  Python , among others.).
• Customer relationship management (CRM):  CRM software runs structured data through analytical tools to create datasets that reveal customer behavior patterns and trends.
• Online booking:  Hotel and ticket reservation data (for example, dates, prices, destinations, among others.) fits the “rows and columns” format indicative of the pre-defined data model.
• Accounting:  Accounting firms or departments use structured data to process and record financial transactions.

Unstructured data, typically categorized as qualitative data, cannot be processed and analyzed through conventional data tools and methods. Since unstructured data does not have a predefined data model, it is best managed in  non-relational (NoSQL) databases . Another way to manage unstructured data is to use  data lakes  to preserve it in raw form.

The importance of unstructured data is rapidly increasing.  Recent projections  (link resides outside ibm.com) indicate that unstructured data is over 80% of all enterprise data, while 95% of businesses prioritize unstructured data management.

Examples of unstructured data include text, mobile activity, social media posts, Internet of Things (IoT) sensor data, among others. Their benefits involve advantages in format, speed and storage, while liabilities revolve around expertise and available resources:

• Native format:  Unstructured data, stored in its native format, remains undefined until needed. Its adaptability increases file formats in the database, which widens the data pool and enables data scientists to prepare and analyze only the data they need.
• Fast accumulation rates:  Since there is no need to predefine the data, it can be collected quickly and easily.
• Data lake storage:  Allows for massive storage and pay-as-you-use pricing, which cuts costs and eases scalability.
• Requires expertise:  Due to its undefined or non-formatted nature, data science expertise is required to prepare and analyze unstructured data. This is beneficial to data analysts but alienates unspecialized business users who might not fully understand specialized data topics or how to utilize their data.
• Specialized tools:  Specialized tools are required to manipulate unstructured data, which limits product choices for data managers.
• MongoDB :  Uses flexible documents to process data for cross-platform applications and services.
• DynamoDB :  (link resides outside ibm.com) Delivers single-digit millisecond performance at any scale through built-in security, in-memory caching and backup and restore.
• Hadoop :  Provides distributed processing of large data sets using simple programming models and no formatting requirements.
• Azure :  Enables agile cloud computing for creating and managing apps through Microsoft’s data centers.
• Data mining :  Enables businesses to use unstructured data to identify consumer behavior, product sentiment and purchasing patterns to better accommodate their customer base.
• Predictive data analytics :  Alert businesses of important activity ahead of time so they can properly plan and accordingly adjust to significant market shifts.
• Chatbots :  Perform text analysis to route customer questions to the appropriate answer sources.

While structured (quantitative) data gives a “birds-eye view” of customers, unstructured (qualitative) data provides a deeper understanding of customer behavior and intent. Let’s explore some of the key areas of difference and their implications:

• Sources:  Structured data is sourced from GPS sensors, online forms, network logs, web server logs,  OLTP systems , among others; whereas unstructured data sources include email messages, word-processing documents, PDF files, and others.
• Forms:  Structured data consists of numbers and values, whereas unstructured data consists of sensors, text files, audio and video files, among others.
• Models:  Structured data has a predefined data model and is formatted to a set data structure before being placed in data storage (for example, schema-on-write), whereas unstructured data is stored in its native format and not processed until it is used (for example, schema-on-read).
• Storage:  Structured data is stored in tabular formats (for example, excel sheets or SQL databases) that require less storage space. It can be stored in data warehouses, which makes it highly scalable. Unstructured data, on the other hand, is stored as media files or NoSQL databases, which require more space. It can be stored in data lakes, which makes it difficult to scale.
• Uses:  Structured data is used in machine learning (ML) and drives its algorithms, whereas unstructured data is used in  natural language processing  (NLP) and text mining.

Semi-structured data (for example, JSON, CSV, XML) is the “bridge” between structured and unstructured data. It does not have a predefined data model and is more complex than structured data, yet easier to store than unstructured data.

Semi-structured data uses “metadata” (for example, tags and semantic markers) to identify specific data characteristics and scale data into records and preset fields. Metadata ultimately enables semi-structured data to be better cataloged, searched and analyzed than unstructured data.

• Example of metadata usage:  An online article displays a headline, a snippet, a featured image, image alt-text, slug, among others, which helps differentiate one piece of web content from similar pieces.
• Example of semi-structured data vs. structured data:  A tab-delimited file containing customer data versus a database containing CRM tables.
• Example of semi-structured data vs. unstructured data:  A tab-delimited file versus a list of comments from a customer’s Instagram.

Recent developments in  artificial intelligence  (AI) and machine learning (ML) are driving the future wave of data, which is enhancing business intelligence and advancing industrial innovation. In particular, the data formats and models that are covered in this article are helping business users to do the following:

• Analyze digital communications for compliance:  Pattern recognition and email threading analysis software that can search email and chat data for potential noncompliance.
• Track high-volume customer conversations in social media:  Text analytics and sentiment analysis that enables monitoring of marketing campaign results and identifying online threats.
• Gain new marketing intelligence:  ML analytics tools that can quickly cover massive amounts of data to help businesses analyze customer behavior.

Furthermore, smart and efficient usage of data formats and models can help you with the following:

• Understand customer needs at a deeper level to better serve them
• Create more focused and targeted marketing campaigns
• Track current metrics and create new ones
• Create better product opportunities and offerings
• Reduce operational costs

Whether you are a seasoned data expert or a novice business owner, being able to handle all forms of data is conducive to your success. By using structured, semi-structured and unstructured data options, you can perform optimal data management that will ultimately benefit your mission.

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