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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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• Knowledge Base
• Methodology

Research Design | Step-by-Step Guide with Examples

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

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

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

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

Table of contents

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

• Introduction

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

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

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

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

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

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

Practical and ethical considerations when designing research

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

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

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

Prevent plagiarism, run a free check.

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

Types of quantitative research designs

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

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

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

Types of qualitative research designs

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

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

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

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

Defining the population

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

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

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

Sampling methods

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

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

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

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

Case selection in qualitative research

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

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

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

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

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

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

Survey methods

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

Observation methods

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

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

Other methods of data collection

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

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

Secondary data

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

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

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

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

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

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

Operationalisation

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

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

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

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

Reliability and validity

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

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

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

Sampling procedures

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

That means making decisions about things like:

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

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

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

Data management

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

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

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

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

Quantitative data analysis

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

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

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

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

Using inferential statistics , you can:

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

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

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

Qualitative data analysis

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

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

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

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

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

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

Operationalisation means turning abstract conceptual ideas into measurable observations.

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

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

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

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

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Writing about Design

Principles and tips for design-oriented research.

How to define a research question or a design problem

Introduction.

Many texts state that identifying a good research question (or, equivalently, a design problem) is important for research. Wikipedia, for example, starts (as of writing this text, at least) with the following two sentences:

“A research question is ‘a question that a research project sets out to answer’. Choosing a research question is an essential element of both quantitative and qualitative research.” (Wikipedia, 2020)

However, finding a good research question (RQ) can be a painful experience. It may feel impossible to understand what are the criteria for a good RQ, how a good RQ can be found, and to notice when there are problems with some RQ candidate.

In this text, I will address the pains described above. I start by presenting a scenario of a project that has problems with its RQ. The analysis of that scenario allows me then to describe how to turn the situation described in the scenario for a better research or design project.

Scenario of a problematic project

Let us consider a scenario that you are starting a new research or design project. You have already an idea: your work will be related to communication with instant messaging (IM). Because you are a design-minded person, you are planning to design and develop a new IM feature: a possibility to send predefined replies on a mobile IM app. Your idea is that this feature will allow the user to communicate quickly with others in difficult situations where the they can only connect with others through their mobile phone. Your plan is to supply the mobile IM app with messages like “I’m late by 10 minutes but see you soon”, “I can’t answer back now but will do that later today”, and so on.

Therefore, your plan involves designing such an app, maybe first by sketching it and then illustrating its interaction with a prototyping software like Figma or Adobe XD. You may also decide to make your design functional by programming it and letting a selected number of participants to use it. These kinds of activities will let you demonstrate your skills as a designer-researcher.

Although predefined messages for a mobile IM app can be a topic of a great study, there are some problems with this project that require you to think more about it before you start. As the project is currently defined, it is difficult to provide convincing answers to these challenges:

• Challenge 1: Why would this be a relevant topic for research or design? Good studies address topics that may interest also other people than the author only. The current research topic, however, does not do that self-evidently yet: it lacks an explanation why it would make sense to equip mobile IM apps with predefined replies. There is only a guess that this could be useful in some situations, but this may not convince the reader about the ingenuity of this project.
• Challenge 2: How do you demonstrate that your solution is particularly good? For an outsider who will see the project’s outcome, it may not be clear why your final design would be the best one among the other possible designs. If you propose one interaction design for such a feature, what makes that a good one? In other words, the project lacks a yardstick by which its quality should be measured.
• Challenge 3: How does this project lead to learning or new knowledge? Even if you can show that the topic is relevant (point 1) and that the solution works well (2), the solution may feel too “particularized” – not usable in any other design context. This is an important matter in applied research fields like design and human–computer interaction, because these fields require some form of generalizability from their studies. Findings of a study should result in some kind of knowledge, such as skills, sensitivity to important matters, design solutions or patterns, etc. that could be used also at a later time in other projects, preferably by other people too.

All these problems relate to a problem that this study does not have a RQ yet . Identifying a good research question will help clarify all the above matters, as we will see below.

Adding a research question / design problem

RQs are of many kinds, and they are closely tied to the intended finding of the study: what contribution  should the study deliver. A contribution can be, for example, a solution to a problem or creation of novel information or knowledge. Novel information, in turn, can be a new theory, model or hypothesis, analysis that offers deeper understanding, identification of an unattended problem, description about poorly understood phenomenon, a new viewpoint, or many other things.

The researcher or thesis author usually has a lot of freedom in choosing the exact type of contribution that they want to make. This can feel difficult to the author: there may be no-one telling what they should study. In a way, in such a situation, the thesis/article author is the client of their own research: they both define what needs to be done, and then accomplish that work. Some starting points for narrowing down the space of possibilities is offered here.

Most importantly, the RQ needs to be focused on a topic that the author genuinely does not know, and which is important to find out on the path to the intended contribution. In our scenario about a mobile IM app’s predefined replies, there are currently too many alternatives for an intended contribution, and an outsider would not be able to know which one of them to expect:

• Demonstration that mobile IM apps will be better to use when they have this new feature.
• Report on the ways by which people would use the new feature, if their mobile IM apps would have such a feature.
• Requirements analysis for the specific design and detailed features by which the feature should be designed.
• Analysis of the situations where the feature would be most needed, and user groups who would most often be in such situations.

All of these are valid contributions, and the author can choose to focus on any one of them. This depends also on the author’s personal interests. This gives a possibility for formulating a RQ for the project. It is important to notice that each one of the possible contributions listed above calls for a different corresponding RQ:

RQ1: Do predefined replies in mobile IM apps improve their usability?

RQ2: How will users start using the predefined replies in mobile IM apps?

RQ3: How should the interaction in the IM app be designed, and what kind of predefined replies need to be offered to the users?

RQ4: When are predefined replies in IM apps needed?

This list of four RQs, matched with the four possible contributions, shows why the scenario presented in the beginning of this text was problematic. Only after asking these kinds of questions one is able to seek to answer to the earlier-presented three challenges in the end of the previous section. Also, each of the RQs needs a different research or design method, and its own kind of background research.

The choice and fine-tuning of the research question / design problem

Which one of the above RQs should our hypothetical researcher/designer choose? Lists of basic requisites for good RQs have been presented in many websites. They can help identify RQs that will still need refinement. Monash University offers the following kind of helpful list:

• Clear and focused.  In other words, the question should clearly state what the writer needs to do.
• Not too broad and not too narrow.  The question should have an appropriate scope. If the question is too broad it will not be possible to answer it thoroughly within the word limit. If it is too narrow you will not have enough to write about and you will struggle to develop a strong argument.
• Not too easy to answer.  For example, the question should require more than a simple yes or no answer.
• Not too difficult to answer.  You must be able to answer the question thoroughly within the given timeframe and word limit.
• Researchable.  You must have access to a suitable amount of quality research materials, such as academic books and refereed journal articles.
• Analytical rather than descriptive.  In other words, your research question should allow you to produce an analysis of an issue or problem rather than a simple description of it.

If a study meets the above criteria, it has a good chance of avoiding a problem of presenting a “non-contribution” : A laboriously produced finding that nonetheless does not provide new, interesting information. The points 3 and 6 above particularly guard against such studies: they warn the readers from focusing their efforts on something that is already known (3) and only describing what was done or what observations were made, instead of analysing them in more detail (6).

In fine-tuning a possible RQ, it is important to situate it to the right scope. The first possible RQ that comes to one’s mind is often too broad and needs to be narrowed. RQ4 above (“ When are predefined replies in IM apps most needed? ”), for example, is a very relevant question, but it is probably too broad.

Why is RQ4 too broad? The reason is that RQs are usually considered very literally. If you leave an aspect in your RQ unspecified, then it means that you intend that your RQ and your findings will be generalisable (i.e., applicable) to all the possible contexts and cases that your RQ can be applied to. Consider the following diagram:

With a question “ When are predefined replies in IM apps most needed?”, you are asking a question that covers both leisure-oriented and work-oriented IM apps which can be of very different kinds. Some of the IM apps are mobile-oriented (such as WhatsApp) and others are desktop-oriented (such as Slack or Teams). Unless you specify your RQ more narrowly, your findings should be applicable to all these kinds of apps. Also, RQ4 is unspecific also about the people that you are thinking as communication partners. It may be impossible for you to make a study so broad that it applies to all of these cases.

Therefore, a more manageable-sized scoping could be something like this:

RQ4 (version 2): In which away-from-desktop leisure life situations are predefined replies in IM apps most needed?

Furthermore, you can also narrow down your focus theoretically. In our example scenario, the researcher/designer can decide, for example, that they will consider predefined IM replies from the viewpoint of “face-work” in social interaction. By adopting this viewpoint, the researcher/designer can decide that they will design the IM’s replies with a goal that they help the user to maintain an active, positive image in the eyes of others. When they start designing the reply feature, they can now ask much more specific questions. For example: how could my design help a user in doing face-work in cases where they are in a hurry and can only send a short and blunt message to another person? How could the predefined replies help in situations where the users would not have time to answer but they know they should? Ultimately, would the predefined replies make it easier for users to do face-work in computer-mediated communications (CMC)?

You can therefore further specify RQ4 into this:

RQ4 (version 3): In which away-from-desktop leisure life situations are predefined replies in IM apps most needed when it is important to react quickly to arriving messages?

As you may notice, it is possible to scope the RQ too narrowly so that it starts to be close to absurd. But if that does not become a problem, the choice of methods (i.e., the research design ) becomes much easier to do.

The benefit of theoretically narrowed-down RQs (in this case, building on the concept of face-work in RQ4 version 3) have the benefit that they point you to useful background literature. Non-theoretical RQs (e.g., RQ4 version 2), in contrast, require that you identify the relevant literature more independently, relying on your own judgment. In the present case, you can base your thinking about IM apps’ on sociological research on interpersonal interaction and self-presentation (e.g., Goffman 1967) and its earlier applications to CMC (Nardi et al., 2000; Salovaara et al., 2011). Such a literature provides the starting points for deeper design considerations. Deeper considerations, in turn, increase the contribution of the research, and make it interesting for the readers.

As said, the first RQ that one comes to think of is not necessarily the best and final one. The RQ may need to be adapted (and also can be adapted) over the course of the research. In qualitative research this is very typical, and the same applies to exploratory design projects that proceed through small design experiments (i.e., through their own smaller RQs).

This text promised to address the pains that definition of a RQ or a design problem may pose for a student or a researcher. The main points of the answer may be summarized as follows:

• The search for a good RQ is a negotiation process between three objectives : what is personally motivating, what is realistically possible to do (e.g., that the work can be built on some earlier literature and there is a method that can answer to the RQ), and what motivates its relevance (i.e., can it lead to interesting findings).
• The search for a RQ or a design problem is a process and not a task that must be fixed immediately . It is, however, good to get started somewhere, since a RQ gives a lot of focus for future activities: what to read and what methods to choose, for example.

With the presentation of the scenario and its analysis, I sought to demonstrate why and how choosing an additional analytical viewpoint can be a useful strategy. With it, a project whose meaningfulness may be otherwise questionable for an outsider can become interesting when its underpinnings and assumptions are explicated. That helps ensure that the reader will appreciate the work that the author has done with their research.

In the problematization of the scenario, I presented the three challenges related to it. I can now offer possible answers to them, by highlighting why a RQ can serve as a tool for finding them:  

• Why would this be a relevant topic for research or design? Choice of a RQ often requires some amount of background research that helps the researcher/designer to understand how much about the problem has already been solved by others. This awareness helps shape the RQ to focus on a topic where information is not yet known and more information is needed for a high-quality outcome.
• How do you demonstrate that your solution is particularly good? By having a question, it is possible to analyse what are the right methods for answering it. The quality of executing these becomes then evaluatable. The focus on a particular question also will permit that the author compromises optimality in other, less central outcomes. For example, if smoothness of interaction is in the focus, then it is easy to explain why long-term robustness and durability of a prototype may not be critical.
• How does this project lead to learning or new knowledge? Presentation of the results or findings allows the researcher/design to devote their Discussion section (see the IMRaD article format ) to topics that would have been impossible to predict before the study. That will demonstrate that the project has generated novel understanding: it has generated knowledge that can be considered insightful.

If and when the researcher/designer pursues further in design and research, the experience of thinking about RQs and design problems accumulates. As one reads literature , the ability to consider different research questions becomes better too. Similarly, as one carries out projects with different RQs and problems, and notices how adjusting them along the way helps shape one’s work, the experience similarly grows. Eventually, one may even learn to enjoy the analytical process of identifying a good research question.

As a suggestion for further reading, Carsten Sørensen’s text  (2002) about writing and planning an article in information systems research field is a highly recommended one. It combines the question of choosing the RQ with the question on how to write a paper about it.

Goffman, E. (1967). On face-work: An analysis of ritual elements in social interaction. Psychiatry , 18 (3), 213–231.  https://doi.org/10.1080/00332747.1955.11023008

Nardi, B. A., Whittaker, S., & Bradner, E. (2000). Interaction and outeraction: Instant messaging in action. In Proceedings of the 2000 ACM Conference on Computer Supported Cooperative Work (CSCW 2000) (pp. 79–88). New York, NY: ACM Press. https://doi.org/10.1145/358916.358975

Salovaara, A., Lindqvist, A., Hasu, T., & Häkkilä, J. (2011). The phone rings but the user doesn’t answer: unavailability in mobile communication. In Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services (MobileHCI 2011) (pp. 503–512). New York, NY: ACM Press. https://doi.org/10.1145/2037373.2037448

Sørensen, C. (2002): This is Not an Article — Just Some Food for Thoughts on How to Write One. Working Paper. Department of Information Systems, The London School of Economics and Political Science. No. 121.

Wikipedia (2020). Research question. Retrieved from https://en.wikipedia.org/wiki/Research_question (30 November 2020).

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Research Questions – Types, Examples and Writing Guide

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Research Questions

Definition:

Research questions are the specific questions that guide a research study or inquiry. These questions help to define the scope of the research and provide a clear focus for the study. Research questions are usually developed at the beginning of a research project and are designed to address a particular research problem or objective.

Types of Research Questions

Types of Research Questions are as follows:

Descriptive Research Questions

These aim to describe a particular phenomenon, group, or situation. For example:

• What are the characteristics of the target population?
• What is the prevalence of a particular disease in a specific region?

Exploratory Research Questions

These aim to explore a new area of research or generate new ideas or hypotheses. For example:

• What are the potential causes of a particular phenomenon?
• What are the possible outcomes of a specific intervention?

Explanatory Research Questions

These aim to understand the relationship between two or more variables or to explain why a particular phenomenon occurs. For example:

• What is the effect of a specific drug on the symptoms of a particular disease?
• What are the factors that contribute to employee turnover in a particular industry?

Predictive Research Questions

These aim to predict a future outcome or trend based on existing data or trends. For example :

• What will be the future demand for a particular product or service?
• What will be the future prevalence of a particular disease?

Evaluative Research Questions

These aim to evaluate the effectiveness of a particular intervention or program. For example:

• What is the impact of a specific educational program on student learning outcomes?
• What is the effectiveness of a particular policy or program in achieving its intended goals?

How to Choose Research Questions

Choosing research questions is an essential part of the research process and involves careful consideration of the research problem, objectives, and design. Here are some steps to consider when choosing research questions:

• Identify the research problem: Start by identifying the problem or issue that you want to study. This could be a gap in the literature, a social or economic issue, or a practical problem that needs to be addressed.
• Conduct a literature review: Conducting a literature review can help you identify existing research in your area of interest and can help you formulate research questions that address gaps or limitations in the existing literature.
• Define the research objectives : Clearly define the objectives of your research. What do you want to achieve with your study? What specific questions do you want to answer?
• Consider the research design : Consider the research design that you plan to use. This will help you determine the appropriate types of research questions to ask. For example, if you plan to use a qualitative approach, you may want to focus on exploratory or descriptive research questions.
• Ensure that the research questions are clear and answerable: Your research questions should be clear and specific, and should be answerable with the data that you plan to collect. Avoid asking questions that are too broad or vague.
• Get feedback : Get feedback from your supervisor, colleagues, or peers to ensure that your research questions are relevant, feasible, and meaningful.

How to Write Research Questions

Guide for Writing Research Questions:

• Start with a clear statement of the research problem: Begin by stating the problem or issue that your research aims to address. This will help you to formulate focused research questions.
• Use clear language : Write your research questions in clear and concise language that is easy to understand. Avoid using jargon or technical terms that may be unfamiliar to your readers.
• Be specific: Your research questions should be specific and focused. Avoid broad questions that are difficult to answer. For example, instead of asking “What is the impact of climate change on the environment?” ask “What are the effects of rising sea levels on coastal ecosystems?”
• Use appropriate question types: Choose the appropriate question types based on the research design and objectives. For example, if you are conducting a qualitative study, you may want to use open-ended questions that allow participants to provide detailed responses.
• Consider the feasibility of your questions : Ensure that your research questions are feasible and can be answered with the resources available. Consider the data sources and methods of data collection when writing your questions.
• Seek feedback: Get feedback from your supervisor, colleagues, or peers to ensure that your research questions are relevant, appropriate, and meaningful.

Examples of Research Questions

Some Examples of Research Questions with Research Titles:

Research Title: The Impact of Social Media on Mental Health

• Research Question : What is the relationship between social media use and mental health, and how does this impact individuals’ well-being?

Research Title: Factors Influencing Academic Success in High School

• Research Question: What are the primary factors that influence academic success in high school, and how do they contribute to student achievement?

Research Title: The Effects of Exercise on Physical and Mental Health

• Research Question: What is the relationship between exercise and physical and mental health, and how can exercise be used as a tool to improve overall well-being?

Research Title: Understanding the Factors that Influence Consumer Purchasing Decisions

• Research Question : What are the key factors that influence consumer purchasing decisions, and how do these factors vary across different demographics and products?

Research Title: The Impact of Technology on Communication

• Research Question : How has technology impacted communication patterns, and what are the effects of these changes on interpersonal relationships and society as a whole?

Research Title: Investigating the Relationship between Parenting Styles and Child Development

• Research Question: What is the relationship between different parenting styles and child development outcomes, and how do these outcomes vary across different ages and developmental stages?

Research Title: The Effectiveness of Cognitive-Behavioral Therapy in Treating Anxiety Disorders

• Research Question: How effective is cognitive-behavioral therapy in treating anxiety disorders, and what factors contribute to its success or failure in different patients?

Research Title: The Impact of Climate Change on Biodiversity

• Research Question : How is climate change affecting global biodiversity, and what can be done to mitigate the negative effects on natural ecosystems?

Research Title: Exploring the Relationship between Cultural Diversity and Workplace Productivity

• Research Question : How does cultural diversity impact workplace productivity, and what strategies can be employed to maximize the benefits of a diverse workforce?

Research Title: The Role of Artificial Intelligence in Healthcare

• Research Question: How can artificial intelligence be leveraged to improve healthcare outcomes, and what are the potential risks and ethical concerns associated with its use?

Applications of Research Questions

Here are some of the key applications of research questions:

• Defining the scope of the study : Research questions help researchers to narrow down the scope of their study and identify the specific issues they want to investigate.
• Developing hypotheses: Research questions often lead to the development of hypotheses, which are testable predictions about the relationship between variables. Hypotheses provide a clear and focused direction for the study.
• Designing the study : Research questions guide the design of the study, including the selection of participants, the collection of data, and the analysis of results.
• Collecting data : Research questions inform the selection of appropriate methods for collecting data, such as surveys, interviews, or experiments.
• Analyzing data : Research questions guide the analysis of data, including the selection of appropriate statistical tests and the interpretation of results.
• Communicating results : Research questions help researchers to communicate the results of their study in a clear and concise manner. The research questions provide a framework for discussing the findings and drawing conclusions.

Characteristics of Research Questions

Characteristics of Research Questions are as follows:

• Clear and Specific : A good research question should be clear and specific. It should clearly state what the research is trying to investigate and what kind of data is required.
• Relevant : The research question should be relevant to the study and should address a current issue or problem in the field of research.
• Testable : The research question should be testable through empirical evidence. It should be possible to collect data to answer the research question.
• Concise : The research question should be concise and focused. It should not be too broad or too narrow.
• Feasible : The research question should be feasible to answer within the constraints of the research design, time frame, and available resources.
• Original : The research question should be original and should contribute to the existing knowledge in the field of research.
• Significant : The research question should have significance and importance to the field of research. It should have the potential to provide new insights and knowledge to the field.
• Ethical : The research question should be ethical and should not cause harm to any individuals or groups involved in the study.

Purpose of Research Questions

Research questions are the foundation of any research study as they guide the research process and provide a clear direction to the researcher. The purpose of research questions is to identify the scope and boundaries of the study, and to establish the goals and objectives of the research.

The main purpose of research questions is to help the researcher to focus on the specific area or problem that needs to be investigated. They enable the researcher to develop a research design, select the appropriate methods and tools for data collection and analysis, and to organize the results in a meaningful way.

Research questions also help to establish the relevance and significance of the study. They define the research problem, and determine the research methodology that will be used to address the problem. Research questions also help to determine the type of data that will be collected, and how it will be analyzed and interpreted.

Finally, research questions provide a framework for evaluating the results of the research. They help to establish the validity and reliability of the data, and provide a basis for drawing conclusions and making recommendations based on the findings of the study.

Advantages of Research Questions

There are several advantages of research questions in the research process, including:

• Focus : Research questions help to focus the research by providing a clear direction for the study. They define the specific area of investigation and provide a framework for the research design.
• Clarity : Research questions help to clarify the purpose and objectives of the study, which can make it easier for the researcher to communicate the research aims to others.
• Relevance : Research questions help to ensure that the study is relevant and meaningful. By asking relevant and important questions, the researcher can ensure that the study will contribute to the existing body of knowledge and address important issues.
• Consistency : Research questions help to ensure consistency in the research process by providing a framework for the development of the research design, data collection, and analysis.
• Measurability : Research questions help to ensure that the study is measurable by defining the specific variables and outcomes that will be measured.
• Replication : Research questions help to ensure that the study can be replicated by providing a clear and detailed description of the research aims, methods, and outcomes. This makes it easier for other researchers to replicate the study and verify the results.

Limitations of Research Questions

Limitations of Research Questions are as follows:

• Subjectivity : Research questions are often subjective and can be influenced by personal biases and perspectives of the researcher. This can lead to a limited understanding of the research problem and may affect the validity and reliability of the study.
• Inadequate scope : Research questions that are too narrow in scope may limit the breadth of the study, while questions that are too broad may make it difficult to focus on specific research objectives.
• Unanswerable questions : Some research questions may not be answerable due to the lack of available data or limitations in research methods. In such cases, the research question may need to be rephrased or modified to make it more answerable.
• Lack of clarity : Research questions that are poorly worded or ambiguous can lead to confusion and misinterpretation. This can result in incomplete or inaccurate data, which may compromise the validity of the study.
• Difficulty in measuring variables : Some research questions may involve variables that are difficult to measure or quantify, making it challenging to draw meaningful conclusions from the data.
• Lack of generalizability: Research questions that are too specific or limited in scope may not be generalizable to other contexts or populations. This can limit the applicability of the study’s findings and restrict its broader implications.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Formulation of Research Question – Stepwise Approach

Simmi k. ratan.

Department of Pediatric Surgery, Maulana Azad Medical College, New Delhi, India

1 Department of Community Medicine, North Delhi Municipal Corporation Medical College, New Delhi, India

2 Department of Pediatric Surgery, Batra Hospital and Research Centre, New Delhi, India

Formulation of research question (RQ) is an essentiality before starting any research. It aims to explore an existing uncertainty in an area of concern and points to a need for deliberate investigation. It is, therefore, pertinent to formulate a good RQ. The present paper aims to discuss the process of formulation of RQ with stepwise approach. The characteristics of good RQ are expressed by acronym “FINERMAPS” expanded as feasible, interesting, novel, ethical, relevant, manageable, appropriate, potential value, publishability, and systematic. A RQ can address different formats depending on the aspect to be evaluated. Based on this, there can be different types of RQ such as based on the existence of the phenomenon, description and classification, composition, relationship, comparative, and causality. To develop a RQ, one needs to begin by identifying the subject of interest and then do preliminary research on that subject. The researcher then defines what still needs to be known in that particular subject and assesses the implied questions. After narrowing the focus and scope of the research subject, researcher frames a RQ and then evaluates it. Thus, conception to formulation of RQ is very systematic process and has to be performed meticulously as research guided by such question can have wider impact in the field of social and health research by leading to formulation of policies for the benefit of larger population.

I NTRODUCTION

A good research question (RQ) forms backbone of a good research, which in turn is vital in unraveling mysteries of nature and giving insight into a problem.[ 1 , 2 , 3 , 4 ] RQ identifies the problem to be studied and guides to the methodology. It leads to building up of an appropriate hypothesis (Hs). Hence, RQ aims to explore an existing uncertainty in an area of concern and points to a need for deliberate investigation. A good RQ helps support a focused arguable thesis and construction of a logical argument. Hence, formulation of a good RQ is undoubtedly one of the first critical steps in the research process, especially in the field of social and health research, where the systematic generation of knowledge that can be used to promote, restore, maintain, and/or protect health of individuals and populations.[ 1 , 3 , 4 ] Basically, the research can be classified as action, applied, basic, clinical, empirical, administrative, theoretical, or qualitative or quantitative research, depending on its purpose.[ 2 ]

Research plays an important role in developing clinical practices and instituting new health policies. Hence, there is a need for a logical scientific approach as research has an important goal of generating new claims.[ 1 ]

C HARACTERISTICS OF G OOD R ESEARCH Q UESTION

“The most successful research topics are narrowly focused and carefully defined but are important parts of a broad-ranging, complex problem.”

A good RQ is an asset as it:

• Details the problem statement
• Further describes and refines the issue under study
• Adds focus to the problem statement
• Guides data collection and analysis
• Sets context of research.

Hence, while writing RQ, it is important to see if it is relevant to the existing time frame and conditions. For example, the impact of “odd-even” vehicle formula in decreasing the level of air particulate pollution in various districts of Delhi.

A good research is represented by acronym FINERMAPS[ 5 ]

Interesting.

• Appropriate
• Potential value and publishability
• Systematic.

Feasibility means that it is within the ability of the investigator to carry out. It should be backed by an appropriate number of subjects and methodology as well as time and funds to reach the conclusions. One needs to be realistic about the scope and scale of the project. One has to have access to the people, gadgets, documents, statistics, etc. One should be able to relate the concepts of the RQ to the observations, phenomena, indicators, or variables that one can access. One should be clear that the collection of data and the proceedings of project can be completed within the limited time and resources available to the investigator. Sometimes, a RQ appears feasible, but when fieldwork or study gets started, it proves otherwise. In this situation, it is important to write up the problems honestly and to reflect on what has been learned. One should try to discuss with more experienced colleagues or the supervisor so as to develop a contingency plan to anticipate possible problems while working on a RQ and find possible solutions in such situations.

This is essential that one has a real grounded interest in one's RQ and one can explore this and back it up with academic and intellectual debate. This interest will motivate one to keep going with RQ.

The question should not simply copy questions investigated by other workers but should have scope to be investigated. It may aim at confirming or refuting the already established findings, establish new facts, or find new aspects of the established facts. It should show imagination of the researcher. Above all, the question has to be simple and clear. The complexity of a question can frequently hide unclear thoughts and lead to a confused research process. A very elaborate RQ, or a question which is not differentiated into different parts, may hide concepts that are contradictory or not relevant. This needs to be clear and thought-through. Having one key question with several subcomponents will guide your research.

This is the foremost requirement of any RQ and is mandatory to get clearance from appropriate authorities before stating research on the question. Further, the RQ should be such that it minimizes the risk of harm to the participants in the research, protect the privacy and maintain their confidentiality, and provide the participants right to withdraw from research. It should also guide in avoiding deceptive practices in research.

The question should of academic and intellectual interest to people in the field you have chosen to study. The question preferably should arise from issues raised in the current situation, literature, or in practice. It should establish a clear purpose for the research in relation to the chosen field. For example, filling a gap in knowledge, analyzing academic assumptions or professional practice, monitoring a development in practice, comparing different approaches, or testing theories within a specific population are some of the relevant RQs.

Manageable (M): It has the similar essence as of feasibility but mainly means that the following research can be managed by the researcher.

Appropriate (A): RQ should be appropriate logically and scientifically for the community and institution.

Potential value and publishability (P): The study can make significant health impact in clinical and community practices. Therefore, research should aim for significant economic impact to reduce unnecessary or excessive costs. Furthermore, the proposed study should exist within a clinical, consumer, or policy-making context that is amenable to evidence-based change. Above all, a good RQ must address a topic that has clear implications for resolving important dilemmas in health and health-care decisions made by one or more stakeholder groups.

Systematic (S): Research is structured with specified steps to be taken in a specified sequence in accordance with the well-defined set of rules though it does not rule out creative thinking.

Example of RQ: Would the topical skin application of oil as a skin barrier reduces hypothermia in preterm infants? This question fulfills the criteria of a good RQ, that is, feasible, interesting, novel, ethical, and relevant.

Types of research question

A RQ can address different formats depending on the aspect to be evaluated.[ 6 ] For example:

• Existence: This is designed to uphold the existence of a particular phenomenon or to rule out rival explanation, for example, can neonates perceive pain?
• Description and classification: This type of question encompasses statement of uniqueness, for example, what are characteristics and types of neuropathic bladders?
• Composition: It calls for breakdown of whole into components, for example, what are stages of reflux nephropathy?
• Relationship: Evaluate relation between variables, for example, association between tumor rupture and recurrence rates in Wilm's tumor
• Descriptive—comparative: Expected that researcher will ensure that all is same between groups except issue in question, for example, Are germ cell tumors occurring in gonads more aggressive than those occurring in extragonadal sites?
• Causality: Does deletion of p53 leads to worse outcome in patients with neuroblastoma?
• Causality—comparative: Such questions frequently aim to see effect of two rival treatments, for example, does adding surgical resection improves survival rate outcome in children with neuroblastoma than with chemotherapy alone?
• Causality–Comparative interactions: Does immunotherapy leads to better survival outcome in neuroblastoma Stage IV S than with chemotherapy in the setting of adverse genetic profile than without it? (Does X cause more changes in Y than those caused by Z under certain condition and not under other conditions).

How to develop a research question

• Begin by identifying a broader subject of interest that lends itself to investigate, for example, hormone levels among hypospadias
• Do preliminary research on the general topic to find out what research has already been done and what literature already exists.[ 7 ] Therefore, one should begin with “information gaps” (What do you already know about the problem? For example, studies with results on testosterone levels among hypospadias
• What do you still need to know? (e.g., levels of other reproductive hormones among hypospadias)
• What are the implied questions: The need to know about a problem will lead to few implied questions. Each general question should lead to more specific questions (e.g., how hormone levels differ among isolated hypospadias with respect to that in normal population)
• Narrow the scope and focus of research (e.g., assessment of reproductive hormone levels among isolated hypospadias and hypospadias those with associated anomalies)
• Is RQ clear? With so much research available on any given topic, RQs must be as clear as possible in order to be effective in helping the writer direct his or her research
• Is the RQ focused? RQs must be specific enough to be well covered in the space available
• Is the RQ complex? RQs should not be answerable with a simple “yes” or “no” or by easily found facts. They should, instead, require both research and analysis on the part of the writer
• Is the RQ one that is of interest to the researcher and potentially useful to others? Is it a new issue or problem that needs to be solved or is it attempting to shed light on previously researched topic
• Is the RQ researchable? Consider the available time frame and the required resources. Is the methodology to conduct the research feasible?
• Is the RQ measurable and will the process produce data that can be supported or contradicted?
• Is the RQ too broad or too narrow?
• Create Hs: After formulating RQ, think where research is likely to be progressing? What kind of argument is likely to be made/supported? What would it mean if the research disputed the planned argument? At this step, one can well be on the way to have a focus for the research and construction of a thesis. Hs consists of more specific predictions about the nature and direction of the relationship between two variables. It is a predictive statement about the outcome of the research, dictate the method, and design of the research[ 1 ]
• Understand implications of your research: This is important for application: whether one achieves to fill gap in knowledge and how the results of the research have practical implications, for example, to develop health policies or improve educational policies.[ 1 , 8 ]

Brainstorm/Concept map for formulating research question

• First, identify what types of studies have been done in the past?
• Is there a unique area that is yet to be investigated or is there a particular question that may be worth replicating?
• Begin to narrow the topic by asking open-ended “how” and “why” questions
• Evaluate the question
• Develop a Hypothesis (Hs)
• Write down the RQ.

Writing down the research question

• State the question in your own words
• Write down the RQ as completely as possible.

For example, Evaluation of reproductive hormonal profile in children presenting with isolated hypospadias)

• Divide your question into concepts. Narrow to two or three concepts (reproductive hormonal profile, isolated hypospadias, compare with normal/not isolated hypospadias–implied)
• Specify the population to be studied (children with isolated hypospadias)
• Refer to the exposure or intervention to be investigated, if any
• Reflect the outcome of interest (hormonal profile).

Another example of a research question

Would the topical skin application of oil as a skin barrier reduces hypothermia in preterm infants? Apart from fulfilling the criteria of a good RQ, that is, feasible, interesting, novel, ethical, and relevant, it also details about the intervention done (topical skin application of oil), rationale of intervention (as a skin barrier), population to be studied (preterm infants), and outcome (reduces hypothermia).

Other important points to be heeded to while framing research question

• Make reference to a population when a relationship is expected among a certain type of subjects
• RQs and Hs should be made as specific as possible
• Avoid words or terms that do not add to the meaning of RQs and Hs
• Stick to what will be studied, not implications
• Name the variables in the order in which they occur/will be measured
• Avoid the words significant/”prove”
• Avoid using two different terms to refer to the same variable.

Some of the other problems and their possible solutions have been discussed in Table 1 .

Potential problems and solutions while making research question

G OING B EYOND F ORMULATION OF R ESEARCH Q UESTION–THE P ATH A HEAD

Once RQ is formulated, a Hs can be developed. Hs means transformation of a RQ into an operational analog.[ 1 ] It means a statement as to what prediction one makes about the phenomenon to be examined.[ 4 ] More often, for case–control trial, null Hs is generated which is later accepted or refuted.

A strong Hs should have following characteristics:

• Give insight into a RQ
• Are testable and measurable by the proposed experiments
• Have logical basis
• Follows the most likely outcome, not the exceptional outcome.

E XAMPLES OF R ESEARCH Q UESTION AND H YPOTHESIS

Research question-1.

• Does reduced gap between the two segments of the esophagus in patients of esophageal atresia reduces the mortality and morbidity of such patients?

Hypothesis-1

• Reduced gap between the two segments of the esophagus in patients of esophageal atresia reduces the mortality and morbidity of such patients
• In pediatric patients with esophageal atresia, gap of <2 cm between two segments of the esophagus and proper mobilization of proximal pouch reduces the morbidity and mortality among such patients.

Research question-2

• Does application of mitomycin C improves the outcome in patient of corrosive esophageal strictures?

Hypothesis-2

In patients aged 2–9 years with corrosive esophageal strictures, 34 applications of mitomycin C in dosage of 0.4 mg/ml for 5 min over a period of 6 months improve the outcome in terms of symptomatic and radiological relief. Some other examples of good and bad RQs have been shown in Table 2 .

Examples of few bad (left-hand side column) and few good (right-hand side) research questions

R ESEARCH Q UESTION AND S TUDY D ESIGN

RQ determines study design, for example, the question aimed to find the incidence of a disease in population will lead to conducting a survey; to find risk factors for a disease will need case–control study or a cohort study. RQ may also culminate into clinical trial.[ 9 , 10 ] For example, effect of administration of folic acid tablet in the perinatal period in decreasing incidence of neural tube defect. Accordingly, Hs is framed.

Appropriate statistical calculations are instituted to generate sample size. The subject inclusion, exclusion criteria and time frame of research are carefully defined. The detailed subject information sheet and pro forma are carefully defined. Moreover, research is set off few examples of research methodology guided by RQ:

• Incidence of anorectal malformations among adolescent females (hospital-based survey)
• Risk factors for the development of spontaneous pneumoperitoneum in pediatric patients (case–control design and cohort study)
• Effect of technique of extramucosal ureteric reimplantation without the creation of submucosal tunnel for the preservation of upper tract in bladder exstrophy (clinical trial).

The results of the research are then be available for wider applications for health and social life

C ONCLUSION

A good RQ needs thorough literature search and deep insight into the specific area/problem to be investigated. A RQ has to be focused yet simple. Research guided by such question can have wider impact in the field of social and health research by leading to formulation of policies for the benefit of larger population.

Financial support and sponsorship

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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.

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.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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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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Research questions

Because research questions define a research project, this is the place to start your investigation. Different sorts of questions demand different types of research designs, methodologies, data collection methods, and analysis techniques.

Broadly speaking, your research questions should pose a unitary query using unambiguous terminology and they should be reasonably answerable given available resources.

What to avoid

• Compound research questions. For example: "Do new learning spaces inspire faculty members to employ new active-learning techniques that enhance student motivation and learning?" This question contains several distinct strands that should be disentangled and listed as separate questions.
• Ambiguous terminology. For instance: “Are students more engaged after a service learning experience than they were before?” Student engagement is a popular concept in current educational discourse, but its meaning is not entirely clear. Do we mean affective engagement (improved feelings or emotions)? Cognitive engagement (increased intellectual interest)? Social engagement (greater interaction with peers or instructors around class issues)? Specifying a way of measuring engagement will often lead to greater precision in terminology.
• Overly ambitious questions. For example: “Does using a problem-based approach to teaching evolutionary theory in introductory biology courses cause greater student acceptance of evolution?” Causal questions are among the most difficult questions to answer because an affirmative answer must not only document change over time, but also justify attributing that change to the putative causal factor. This can be challenging, particularly if circumstances do not permit you to use a comparative research design that controls for extraneous factors.

Research design

The design of a study (or its methodology ) refers to its overall structure and to the important components of the research contained within that structure.

A research design defines:

• who or what is being studied
• the framework within which the study's research questions will be addressed
• the information to be gathered
• whether there will be any manipulation of study conditions
• what are the hypothesized relationships among the matters of concern in the study

One important distinction in research design has to do with whether a study involves a single group, or multiple groups. In educational research, these are likely to be a group or groups of instructors, or students, or classes. Studies that examine just one group are appropriate when what is being studied is a new and relatively unknown phenomenon. When that is the case, the study is likely to be exploratory and descriptive in nature, built around questions such as:

How do faculty members adapt their classes to employ new types of learning activities? What difficulties do they encounter? What benefits do they perceive?

Here, what is wanted is not a controlled experiment, but instead a detailed narrative that provides richness of context, which other faculty members can look to for guidance.

Studies that examine more than one group are appropriate when the objective of the research is to answer questions that call for comparative data, such as questions about change over time; about the association between two or more variables; or about cause and effect. The research designs appropriate for answering these questions are described in a detailed but accessible way in William Trochim’s Research Methods Knowledge Base .

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Conducting sustainability research in the anthropocene: toward a relational approach

• Original Article
• Open access
• Published: 24 May 2024

Cite this article

You have full access to this open access article

• Jessica Böhme   ORCID: orcid.org/0000-0003-4591-3754 1   na1 ,
• Eva-Maria Spreitzer 2 &
• Christine Wamsler   ORCID: orcid.org/0000-0003-4511-1532 3   na1  

Scholars and practitioners are urgently highlighting the need to apply a relational approach to effectively address societal crises. At the same time, little is known about the associated challenges, and there is little advice regarding how to operationalize this approach in sustainability science. Against this background, this article explores how we can break out of our current paradigms and approaches, and instead apply relational thinking, being, and acting in the way we conduct research. To achieve this, we systematically list all major research phases, and assess possible pathways for integrating a relational paradigm for each step. We show that moving toward a relational paradigm requires us to methodically question and redefine existing theories of change, concepts, and approaches, for instance by combining abductive reasoning, first-person inquiries, and decentering the human through critical complexity theory. Challenging mainstream thought, and daring to ask different questions in each step is crucial to ultimately shift scientific norms and systems. Hence, we offer a catalog of questions that may help to systematically integrate relational being, thinking, and acting into the process, as a tool for transforming current paradigms in research, and associated education and practice. Finally, we highlight the importance of further research to develop and refine our outcomes.

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Solutions-oriented research for sustainability: Turning knowledge into action

Coevolving ostrom’s social–ecological systems (ses) framework and sustainability science: four key co-benefits, sociology for sustainability science.

Avoid common mistakes on your manuscript.

Introduction

The anthropocene is characterized by significant human impact on the Earth’s geology and ecosystems; examples include biodiversity loss, climate change, social inequalities, and conflicts (IPCC 2021 ). These challenges are part of an underlying metacrisis of accelerating, causally entangled, complex grand challenges (Jørgsen et al. 2023 ; Rosa 2019 ). In fact, there is mounting evidence that today’s societal crises have one common denominator, or root cause: they are a reflection of an inner, human crisis of disconnection or separation from self, others, and nature, which is grounded in modern societies’ social paradigm (Ives et al. 2023 ; Leichenko and O’Brien 2020 ; Rowson 2021 ; Wamsler et al. 2021 ; Wamsler and Bristow 2022 ). Hence, the current focus on external, technological approaches is insufficient to support transformation toward sustainable and just futures (ibid).

Consequently, there is also a need for sustainability science to re-consider and expand its ontological, epistemological, and ethical foundations, and associated approaches for researching and engaging with complex, wicked sustainability challenges (Alford and Head 2017 ; Ives et al. 2023 ; Lang et al. 2012 ; Lönngren and van Peock 2020 ; Mauser et al. 2013 ; Wiek and Lang 2016 ; Xiang 2013 ). Accordingly, an increasing number of scholars and practitioners argue that effectively addressing and researching sustainability challenges requires a shift in paradigms Footnote 1 to address societal crises differently (Ives et al. 2023 ; Walsh et al. 2020 ; Wamsler et al. 2021 ).

The dominant social paradigm in the modern, industrialized world is what we refer to in the following as the ‘mechanistic paradigm’.Scholars and practitioners highlight that current mechanistic approaches, and associated reductionist strategies and perspectives, are inadequate for tackling sustainability issues (Leichenko and O’Brien 2020 ; Porter and Reischer 2018 ). Furthermore, it can be argued that the paradigm’s underlying values (individualism, materialism, capitalism) and the associated norms, Footnote 2 mechanisms, and structures enhance separation from self, others and nature, and a kind of alienation, as an integral element of modern life, forms (Wamsler and Bristow 2022 ; Rosa 2019 ).

The core pattern that emerges from the mechanistic paradigm, which is especially relevant in the context of todays’ sustainability crises and associated research, is that we are increasingly exhausting and exploiting ourselves, others, and nature (Wamsler and Bristow 2022 ). This is based on the perception that humans are separate from each other, that they are separate and superior to the rest of the natural world, and that nature, like any other system, behaves like a machine, and can be controlled and known by reducing it to its parts (Capra and Luisi 2014 ; Redclift and Sage 1994 ; Rees 1999 ; Walsh et al. 2020 ). The result is separation between self, others, and the more-than-human world (Ives et al. 2019 ; Wamsler and Bristow 2022 ).

The mechanistic paradigm has dominated both policy-making and research. It favors “outer” approaches and solutions (IPCC 2022a ; b ; Wendt 2015 ; Todd 2016 ; Wamsler and Bristow 2022 ), while largely ignoring the inner dimension of sustainability, which includes people’s individual and collective mindsets, beliefs, values, worldviews, and associated inner qualities/capacities (Capra and Luisi 2014 ; Redclift and Sage 1994 ; Rees 1999 ; Wamsler 2020 ; Wamsler et al. 2021 , 2022a ). This has, in turn, narrowed the possibilities for deeper change that can tackle the underlying root causes of today’s crises, while fostering mechanistic and unsustainable interactions with the living world around us (Leal Filho and Consorte McCrea 2019 ; Wamsler et al. 2021 ).

To address this gap, an increasing number of scholars advocate for a shift toward a relational paradigm (e.g., Audouin et al. 2013 ; Böhme et al. 2022 ; Hertz et al. 2020 ; Ives et al. 2023 ; Mancilla Garcia et al. 2020 ; Stalhammar and Thorén 2019 ; Walsh et al. 2020 ; Wamsler et al. 2021 , 2022a ; West et al. 2020 ). A relational paradigm Footnote 3 attempts to understand complex phenomena in terms of constitutive processes and relations and recognizes the intricate interconnectedness of humans and the more-than-human world, as well as the associated nonlinear dynamics, uncertainty, and the emergence of change (West et al. 2020 ; Walsh et al. 2020 ). It builds on the ontological premise that inner and outer phenomena are entangled and interconnected across individual, collective, and system levels, and recognizes the multiple potential that is latent within each of us to enable transformative change across these scales (Ives et al. 2023 ). From an epistemological point of view, it requires the inclusion of diverse perspectives, and the expansion of knowledge systems for enhanced “transformation” Footnote 4 toward more sustainable futures (Ives et al. 2023 ; Künkel and Ragnarsdottir 2022 ).

On these premises, relational research should not be understood as simple introspection, but as a new form of praxis for integrative inner–outer transformation that includes different modes of activating the inner human dimension across individual, collective, and system levels, and the generation of so-called transformative capacities through intentional practices (Ives et al. 2023 ; Spreitzer 2021 ). “Such cognitive, emotional and relational capacities support the cultivation of values, beliefs, and worldviews regarding how people relate (or reconnect) to themselves, others, nature, and future generations in ways that can support transformation” (Wamsler et al. 2022a , b , p. 9).

In contrast, current scientific mainstream approaches and methods risk reproducing and strengthening the dominant social paradigm that underlies today’s sustainability crises, instead of questioning and reframing the underlying assumptions (Fischer et al. 2015 ; Walsh et al. 2020 ). While these challenges are increasingly addressed in emerging frameworks and perspectives that may form the foundations of transformative approaches toward more sustainable and just futures (e.g., Ison 2018 ; Gearty and Marshall 2020 ; Hertz et al. 2020 ; Wamsler et al. 2021 ), there is little knowledge on how to systemically conduct sustainability science from a relational paradigm perspective (Fischer et al. 2015 ; Walsh et al. 2020 ; West et al. 2020 ).

Sustainability science is both an inter- and transdisciplinary field, and it is concerned with addressing complex challenges that threaten humanity and the planet (Wiek and Lang 2016 ). It bridges natural and social sciences and the humanities in the search for creative solutions to these challenges (Jerneck et al. 2010 ; Kajikawa et al. 2014 ; Miller 2012 ; van Kerkhoff 2013 ). Accordingly, sustainability research tends to combine “descriptive-analytical” and “transformational” approaches with different methodologies, based on systems thinking as an epistemological frame (Miller et al. 2013 ; Wiek and Lang 2016 ). While the descriptive-analytical stream draws mostly on systems modeling for describing and analyzing the causes and effects of complex sustainability challenges, the transformational stream often focuses on evidence-based solutions, by accommodating systems thinking for generating actionable insights into how to address sustainability challenges more effectively (Abson et al. 2017 ; Wiek and Lang 2016 ). Hence, both approaches are built on the premise of addressing un/sustainability by identifying and “solving” wicked problems. In general, however, these premises and their corresponding understanding of systems, and systems change, operate within the dominant social paradigm (Latour 2005 ; Poli 2013 ). In other words, they typically do not align with, or support, a relational paradigm, notably its epistemological, ontological, and praxis dimensions (Ives et al. 2023 ). Despite the above-described call for a relational turn in sustainability science, related endeavors are still in their infancy, and there is a need for further efforts to learn how to nurture more relational and, thus, transformative approaches.

Put together, there is an urgent need for a move toward more relational thinking, being and acting, and thus a related shift in: (1) how we see the world; and (2) how we get to know, (3) engage, and (4) ensure quality and equity considerations across these aspects (Ives et al. 2023 ; Walsh et al. 2020 ; Wamsler et al. 2021 , 2024 ). This involves examining how ethical considerations shape our understanding of reality (ontology), influence the ways we acquire, validate, and apply knowledge (epistemology), and translate it into action (praxis).

Against this background, in this article we explore how we can break out of societies’ dominant social paradigm and apply a relational paradigm to the conduct of sustainability research in more transformative ways. More specifically, we identify key implications and possible ways forward for all major steps typically found in any scientific research process.

Methodological considerations

In the next section, we describe the particularities that result from a relational paradigm for each of the following research steps: (1) identifying the research problem and niche; (2) reviewing the literature; (3) creating research hypotheses; (4) designing the overall approach; (5) data collection and analyses; (6) writing up the results; and (7) disseminating them (Booth et al. 2016 ; Cohen et al. 2018 ; Creswell 2018 ). For each of these steps, we compare: (1) how sustainability research is generally conducted based on the mechanistic paradigm; and (2) how the approach might change if a relational paradigm is applied. Related analyses are based on an exploratory analysis Footnote 5 of the literature that calls for a relational shift in sustainability and social sciences. While our comparison relates to mainstream sustainability approaches that are built on a mechanistic paradigm, we recognize the existence of alternatives (cf. Bradbury 2015 , 2022 ; Drawson et al. 2017 ; Goodchild 2021 ; Mbah et al. 2022 ; Romm 2015 ; Rowell et al. 2017 ).

We do not attempt to present a comprehensive overview of research methodologies based on a relational paradigm. Instead, we critically reflect on existing approaches and review how a relational paradigm could be operationalized in sustainability research, particularly as there is no single, coherent relational paradigm to build upon (Alvesson and Sandberg 2020 ; Böhme et al. 2022 ).

To do so, we do not present tools, methods, or steps with specific prescriptions and instructions for how to move toward a more relational paradigm and overcome related challenges—instead, we offer a proposition that could trigger conditions of emergence (Springgay 2015 ). This is important, because the idea that specific actions lead to defined outcomes is not aligned with a relational perspective and thus on how transformation can be supported in complex systems (Smartt Gullion 2018 ). Moreover, relational epistemologies question the idea that tools can be used to represent reality, without acknowledging the entanglement of the researcher who is co-creating the knowledge (Latour 2005 ). Ultimately, “tools are never ‘mere’ tools ready to be applied: they always modify the goals you had in mind” (Latour 2005 , p. 143). Offering a practical tool runs the risk of offering a simplistic conceptualization that narrows understanding and changes our object of study (Mancilla Garcia et al. 2020 ).

Instead, in each section, we conclude with some questions that can be used to make the implicit explicit when conducting research within a relational paradigm. Making the implicit explicit is an important strategy for dealing with complexity (Audouin et al. 2013 ; Cilliers 2005 ). We thus follow Puig de la Bellacasa ( 2017 ), who suggests that the aim should be a commitment to asking how things could be different, as developing processes and practices of asking can challenge the status quo and, thus, help to increasingly integrate the relational paradigm into current approaches.

Pathways toward a relational paradigm in research

Step 1: identifying the research problem and niche.

The first step in the process is the identification of the research problem and niche.

From a mechanistic paradigm, the problem and niche can be found by identifying and isolating certain parts of a system that relate to a particular sustainability challenge. For example, a focus on carbon emissions in a particular sector (e.g., transportation).

A relational paradigm would require adding a perspective that is based on an understanding of sustainability challenges as evolving, complex adaptive systems marked by interdependencies, connectedness, nonlinearity, uncertainty, and emergence (Ives et al. 2023 ; Turner and Baker 2019 ). Instead of focusing on individual parts of systems—such as carbon emissions in transportation—a relational approach thus also requires looking into relationships, and the quality of these relationships, within and between systems, and how this influences or prevents integrative inner–outer transformation processes across individual, collective, and system levels (Wamsler et al. 2021 , 2022a , b ). In this context, “boundaries” do not define a research problem or theoretical puzzle, but “interfaces” do, which are understood as dynamic interchanges that form the edges of systems, and, are, at the same time, the focus; that is, “the appropriate center of interest in a particular system, process, or mind” (Bateson 1979 ; Charlton 2008 , p. 41).

An important aspect to consider during the first research step is the fact that paradigms form frames and language, and vice versa (Lakoff 2014 ; Ives et al. 2019 ). Reframing sustainability challenges is thus crucial for supporting transformation (Lakoff 2014 ) and must be accounted for when conducting research. While formulating the problem, it is for instance essential to consider which pre-defined concepts the problem is based upon, as moving toward a relational paradigm asks us to question established norms and understandings.

A relational paradigm also requires special attention to the wording of the research gap and associated niche, including the use of expressions that can foster or challenge dominant beliefs, values, and worldviews. Examples of wording that aims to support more relational understandings are natureculture and intra-action (Barad 2007 ; Hertz and Mancilla Garcia 2021 ), socialecological (Böhme 2023 ), thinking-with (Vu 2018 ), or the more-than-human world (Haraway 2016 ). In contrast, Hertz et al. ( 2020 ) point out that current sustainability research often employs “the environment” or “nature” and “the social” or “culture” as separate entities or phenomena, which can reinforce a reductionist paradigm. The separation between the social and the ecological also manifests in research in the so-called socio-ecological systems, a conceptualization that has strongly been influencing related research, frameworks, theories, methods, and policy insights.

In summary, identifying the problem through the lens of a relational paradigm involves a shift from focusing only on analyzing certain parts of a system, to the quality of relationships, associated meaning-making, and integrative inner–outer transformation processes. It also involves identifying and developing appropriate frames, language, and concepts that align with these characteristics.

A study on reducing carbon emissions from transportation might, for instance, be framed within a continuum and integrative understanding that links analyses at the level of behavior, at the level of systems and structures, and at the level of individual and collective mindsets. Moreover, employing a relational paradigm might involve framing emission and transportation-related challenges also around concepts of community well-being, social connectivity, and environmental justice.

In conclusion, the following questions can help in moving toward a relational paradigm:

I. How do my research problem and associated niche consider interdependencies, connectedness, nonlinearity, uncertainty, and emergence? How do they account for (the quality of) relationships and related inner–outer transformation processes across individual, collective, and system levels? For example, if my research focus and associated aims reinforce (the perception of) a separation between humans and non-humans, I might want to reframe the research.

II. Is the wording of the problem, niche and associated aims aligned with relational perspectives, or does it strengthen current mechanistic paradigms? For example, “if the words in a given language focus on shapes over function, then no wonder the speakers of that language prefer to group things according to their shape rather than their function” (Bollier and Helfrich 2019 , p. 708).

III. How can I explain relational, unfamiliar, or new concepts so that others (co-researchers, readers), who are new to this way of thinking, can understand? How can I create a bridge between the current and a potential new, more sustainable paradigm? For example, I could consider adding a glossary of newly-formed or uncommon terms.

Step 2: reviewing the literature

In general, the literature review entails identifying relevant sources and databases, and screening and selecting articles based on predetermined criteria. After extracting relevant information and data from the selected articles, researchers systematize and synthesize the findings to identify gaps, themes, and patterns.

From the perspective of the dominat mechanistic paradigm, scientific, peer-reviewed information is generally considered the key source for ensuring credibility and reliability. Adopting a relational paradigm challenges this notion. It requires questioning the dominance of the existing sensemaking frames and discussing their possible limitations, biases, and blind spots, including regarding the ontological premises underlying other epistemological and ethical considerations and emergent phenomena (Storm et al. 2019 ; Ives et al. 2023 ; Alvesson and Sandberg 2020 ).

Epistemologically, the focus shifts from privileging empiricism and positivism to embracing multiple ways of knowing. It acknowledges that different knowledge systems offer unique perspectives and understandings of the world. This may include lived experience, traditional and Indigenous wisdom, artistic expression, and other non-conventional sources that can offer valuable insights into the complexities of environmental issues, associated human–environment relationships, and esthetics (Osgood et al. 2020 ). It challenges the idea that only ‘objectifiable’ data is valid and recognizes that experiential, subjective, and transpersonal insights are equally essential in comprehending sustainability and the associated literature (Storm et al. 2019 ).

Ethically, the relational paradigm prompts critical reflection on whose knowledge is recognized and legitimized. It questions power dynamics within knowledge production, highlighting the need to amplify marginalized voices and perspectives that may have been historically excluded or undervalued within academia or the scientific discourse. It thus requires decolonizing strategies for identifying and reviewing the literature (Vu 2018 ).

Continuing with the example of carbon emissions from transport that was given in step 1, a relational paradigm would also require reviewing related, non-scientific literature and other sources and perspectives that shed light on aspects that have so far not been explored by mainstream science. This might involve considering the (limited) methodological bases and foci of the examined literature, and including additional data and voices for a more comprehensive review (e.g., examining all levels of transformation, related views, structures, and practices that might add additional context and perspectives).

Other common assumptions during step 2 are that the literature presents external, fixed knowledge, which the author has developed, and that the reader interprets the literature through a reflective process that is independent of dominant social paradigms. Accordingly, a systematic literature review should always lead to the same results and interpretations when repeated, regardless of the author(s), researcher(s), and reader(s). In contrast, a relational paradigm acknowledges the relational nature of knowledge creation, distribution, and interpretation, which arises from a process of entangled relations and associated paradigms (Barad 2007 ). The literature review is thus as much influenced by the researcher(s) themselves, as it is influenced by the perspective(s) of the respective author(s).

In the light of these observations, reviewing the literature is as much about understanding current knowledge as it is about understanding and considering how knowledge came to be. A relational paradigm thus posits that knowledge arises because it is co-produced by sociomaterial configurations and associated inner–outer change processes; it is neither fixed and permanent, nor individualized. Knowledge is a product of intra-action, “not something that someone or something has” (Barad 2007 , p. 178). As Cilliers ( 2005 , p. 609) argues, “There are facts that exist independently of the observer of those facts, but the facts do not have their meaning written on their faces. Meaning only comes to be in the process of interaction. Knowledge is interpreted data.”

Put simply, any literature review needs to recognize that (the analyzed and produced) knowledge is co-created and influenced by dominant social paradigms and associated inner–outer change processes. By conducting a literature review, we participate in a relational configuration through the entanglements of the involved agents.

The following guiding questions might thus help in moving toward a relational paradigm:

How can I integrate sources beyond scholarly articles to better understand current knowledge? Are there ways to systematically include non-human perspectives? For example, if the identified literature only represents knowledge from certain elements, communities or groups, other sources need to be considered (e.g., illustrated by Vu’s ( 2018 ) ethico-auto-ethnographies or Kuntz and Presnall’s ( 2012 ) intra-views).

What underlying or tacit ontological, epistemological, and ethical assumptions might be present within the reviewed literature? For example, how might dominant social paradigms and perspectives have influenced the presented theories of change, the exclusion of inner dimensions, or an overlooking of marginalized agents and non-human actors?

How does my perspective, subjectivity, and social–ecological position influence the interpretation and analysis of the literature, and how can I take account of this? For example, I could consider adding related considerations when discussing the limitations of the review.

Step 3: creating research hypotheses

From a mechanistic, positivist stance, a literature review is generally used to formulate hypotheses about the relationship between the independent and dependent variables. Within our dominant paradigm, these are generally expressed as testable hypotheses, and each hypothesis should be specific, concise, and presented as a statement that establishes a clear cause-and-effect relationship between the variables. They should also be falsifiable, which means that they offer supportive or neglective evidence through empirical qualitative or quantitative testing. Commonly, such hypotheses are formulated using either inductive or deductive reasoning (Smartt Gullion 2018 ).

An alternative approach, which is aligned with a relational paradigm, is abductive reasoning (Tullio 2016 ), sometimes also referred to as adductive reasoning. Abduction differs from both deduction and induction. It begins with an observed phenomenon that requires an explanation, then speculates on potential answers. Related reasoning involves a leap of the imagination and proposing hypotheses or interpretations that go beyond current evidence or knowledge. It is essentially a creative process of suggesting answers based on relational patterns, analogies, and insights from diverse sources. The researcher synthesizes information and uses speculative reasoning to suggest potential explanations, in addition to ‘obvious’ hypotheses (Nersessian 2010 ; Selg and Ventsel 2020 ; Van der Hoorn 1995 ). As Hertz et al. ( 2020 , p. 9) point out:

“Abduction reverses the order of reasoning. It focuses on a phenomenon that needs explaining and then ponders potential causes. During this speculative activity, novel conceptualizations and dynamics can be introduced to an explanatory scheme. Methods and approaches in social-ecological systems research with this potential include place-based and context-rich qualitative research methods (like narratives and participatory scenario development) and computational methods.”

Bateson ( 1982 ) argues that abductive reasoning is particularly pertinent for studying complex systems, such as ecosystems, social systems, and associated mental processes. Engaging in abductive reasoning allows researchers to extend their understanding beyond existing knowledge, potentially revealing deeper insights (ibid).

This can be illustrated by studying community resilience in the face of natural disasters. From a positivist perspective, the focus might be on testing specific hypotheses that predict the relationship between factors like socioeconomic status and disaster preparedness. Each hypothesis would be clearly defined and testable, aiming to establish a cause-and-effect relationship between independent and dependent variables. For instance, a hypothesis could propose that higher socioeconomic status correlates with better disaster preparedness measures. In contrast, adopting a relational paradigm would also involve abductive reasoning, which allows for additional exploration of the phenomenon and associated inner–outer transformation processes, enabling the researchers to identify and explore further hypotheses.

In summary, formulating hypotheses from a relational perspective requires their anchoring in the above-described steps 1–2. In addition, it should not only involve inductive and deductive, but also abductive reasoning. Deductive reasoning starts with a general rule, and inductive reasoning begins with a specific observation. In contrast, abductive reasoning assumes that observations are incomplete. Abductive reasoning embraces the idea that phenomena are unpredictable, contingent, dynamic, and emerge through open-ended intra-actions and relationships.

To explore this alternative path and move toward a relational paradigm, the following guiding questions might assist:

I. Do my hypotheses reflect the dominant social paradigm and related ontological assumptions? For example, are they based on a ‘fix-it’ and ‘fix-others’ mindset that reinforces current, unsustainable paradigms? Do they only focus on apparent external problems and solutions without due consideration of related inner dimensions of transformation? Or do they presuppose a division between nature and culture? If yes, I might need to reconsider or make explicit related biases and effects.

II. How do my hypotheses adequately consider the role of relationships (to self, others, nature, and the world at large)? For example, if they examine values without considering the relationships from which these values are co-created and emerge across individual, collective, and system levels, I might consider redirecting their focus.

III. How might abductive reasoning enhance my hypotheses? For example, I might speculate on potential explanations through the lens of different disciplines and sources, including Indigenous and local knowledge systems.

Step 4: defining the overall research design

During the design process, the research object is further defined, and an overall methodology is chosen to investigate it. Within the mechanistic paradigm, the boundaries of the object are clearly drawn. Complex phenomena are broken down into simpler components. The prevailing thought is that all complexity can be reduced to manageable parts and then understood through discrete analyses, measurements, or computational simulation (Smartt Gullion 2018 ).

It is clear that reductionist approaches are necessary in all scientific approaches to study some ‘thing’ or some ‘one’. At the same time, reduction has to be handled with particular care to include relational, ever-moving, and changing processes and aspects of systems that are key for understanding and transformation. For example, Bateson ( 2021 ) argues that common research approaches alone cannot answer questions regarding what and how autopoietic cycles of adaption within complexity are learning (Bateson 2021 ). In other words, overly mechanistic reduction might result in overlooking, or not engaging enough with so-called ‘warm data’, which is information about the interrelationships that form complexity, and thus the foundation of living systems and life itself. Warm data capture qualitative dynamics and offer another dimension of understanding to what is learned through “living data” (Bateson 2021 , 2022 ).

The overall research design has to take account of this relational living systems information and associated knowledge creation processes. It requires consideration of constantly emerging inner–outer learning processes of experiences, cultural beliefs, and perspectives. Unreflected simplification might lead to unintended or even harmful outcomes and consequences that support unsustainable paradigms.

At the same time, as the relational paradigm builds on the ontological premise that everything is related to everything else, the challenge is to design research in a way that stays true to its ideas, while not becoming too diffuse or abstract. A view that attempts to encompass all relations risks losing the distinction between the system and its environment. Researchers can then fall into two traps—either a radical openness systems view that leads to relativism, or an approach that relies on measurement and computational simulation (Morin 2008 ). The former is criticized for being a reaction to reductionism and promoting a kind of holism that negates the need for ontology. The latter fails to recognize the intangible nature of emergent properties (Preiser 2012 ). Therefore, both views have limitations: they either neglect the need for a reliable ontology, or oversimplify the intangible nature of ever-moving and emergent properties. A rigorous understanding of complexity denies total holism and total reductionism simultaneously, resulting in what Cilliers ( 2005 , p. 261) describes as “performative tension”.

In practice, this performative tension can be addressed by drawing boundaries, while simultaneously redirecting attention to related interfaces and being aware of, and making explicit, the fact that these boundaries are artificial. This is also referred to as “critical complexity” (Audouin et al. 2013 ), which transcends and incorporates mechanistic strategies while recognizing the need for reduction and transparency. Critical complexity can bring value-based choices to the forefront, if the reduction itself is a conscious value-based choice, where the researcher chooses which aspects to focus on, while staying aware that the research and the researcher(s) themselves are part of the living system of engaging with knowledge creation (and thus are constantly changing and are changed through responsively relating with the emergent character of this process). It is not either the researcher(s) or the research outcome that independently creates knowledge; instead, the overall design process can be regarded as learning and potentially transformation on all levels (Bateson 2021 ; Preiser 2012 ; Wamsler et al. 2022a , b ). This differs from the mechanistic approach, which often overlooks the consequences of reductionist practices, especially when defining the overall research design.

The critical complexity rationale recognizes that reductionism, under specific conditions, can by itself effectively enhance understanding. For instance, Cilliers ( 2005 ) argues that although reduction is unavoidable in our efforts to comprehend socialecological systems, we can shift our focus toward framing the strategies that are employed during the process of reduction. This change promotes a more relational standpoint, fostered through self-reflection.

Overall, finding an appropriate methodology can be a challenge and requires the careful consideration of relationships and engagements regarding both external and internal research stakeholders. Although several relational methodologies exist, such as intra-views (Kuntz and Presnall 2012 ), diffractive ethnography (Smartt Gullion 2018 ), ethico-auto-ethnography (Vu 2018 ), phenomenology, integral and narrative-based methodologies (Snowden and Greenberg 2021 ; Van der Merwe et al. 2019 ; Wilber 2021 ), the relational paradigm does not advocate prescriptive methodologies.

In summary, the challenge is to maintain a relational perspective without becoming overly abstract and risking relativist holism. This requires explicitly: (1) acknowledging the limitations of reductionist strategies; (2) accounting for relationships and associated inner–outer change processes (individual, collective, system levels) that are relevant for understanding the research object; and (3) considering how the overall design can itself support transformation, both regarding its object and stakeholders.

For example, when investigating the impact of a city’s electric vehicle adoption program on reducing carbon emissions, the researcher might consciously adopt a design that avoids falling into the trap of exhausting and exploiting oneself, others, and the planet (e.g., through explicit consideration of wellbeing, equity issues, the research’s inherent CO 2 emissions, time management, and meeting formats). At the same time, methodologies can be applied in ways such that they, themselves, can support individual, cultural, and system transformation toward post-carbon behaviors (e.g., Osberg et al. 2024 ; Wamsler et al. 2022b ).

To navigate alternative pathways for designing an overall research methodology, the following guiding questions might be thus helpful:

I. How can I explicitly integrate a relational perspective when using reductionist methodologies? For instance, would it be beneficial to develop a research process that pursues a reductionist approach, while critically highlighting its limitations?

II. How can I design the overall research approach in a way that accounts for relationships and associated inner–outer change processes (individual, collective, and system levels) that are relevant for understanding the selected object? For instance, how might I employ a hybrid methodology that integrates qualitative, quantitative, and related innovative approaches to ensure a comprehensive understanding (e.g., contemplative and creative approaches)?

III. How can the overall design support transformation, for example, a change toward a more relational paradigm (both regarding the research object and stakeholders)? For instance, what relational approaches exist, and how might I combine them in my overall research design?

Step 5: data collection and analysis

Data collection aims to gather relevant information and answer the research questions and/or hypotheses. Diverse methods and techniques are used to systematically collect, record, organize, examine, and interpret related data and draw meaningful conclusions.

Within the mechanistic approach, new scientific knowledge and theory is usually built on the collection and analysis of credible sources of data. In this context, focus tends to be on certain (but not all) dimensions of reality and associated methods for data collection, and, consequently, certain (but not all) ways of generating knowledge about the world (Ives et al. 2019 ). Footnote 6

The relational paradigm questions this fragmented approach (cf. Steps 1–4). In a context where all parts (e.g., culture, institutions, individual and collective behavior and views) are colored by the dominant social paradigm, the combination of scientific, philosophical, Footnote 7 and other methods of enquiry is particularly important to support both an integrated understanding of existing ways of knowing and innovative pathways for new knowledge generation. It requires introspection, contemplative, esthetic, visual, sensory, and embodied forms of sensemaking, and it also demands that we decolonize current methods, for instance, to avoid undermining local knowledge and the experiences of marginalized populations.

From a relational perspective, data that can be used to construct and test ideas can be empirical, but can also take theoretical, conceptual, or other forms (Bhaskar et al. 2016 ). For instance, viewing first-person enquiry or embodiment as a way of perceiving and understanding the world distinguishes it from the dominant mode of knowledge (Frank et al. 2024 ), known as propositional knowing. Propositional knowing primarily relies on creating conceptual maps, which, although helpful, can sometimes be deceptive as they oversimplify reality (the map is not the territory). According to systems theorist Nassim Nicholas Taleb, phenomenological knowledge is often more resilient and adaptable than propositional knowledge (Taleb 2013 ). This does not mean that propositional knowledge should be disregarded entirely; rather, when enriched by phenomenological knowledge, it creates space for the emergence of more imaginative and practical ideas (Pöllänen et al. 2023 ).

Purely objective data does not exist, as pointed out by post-structuralists (Kirby 2011 ). Accordingly, St. Pierre ( 2013 , p. 226) states that “if being is always already entangled, then something called data cannot be separate from me, out there for me to collect.” Denzin ( 2013 , p. 35) therefore suggests thinking about data in terms of “empirical materials”. Data selection and interpretation thus always have material consequences (Barad 2007 ; Smartt Gullion 2018 ). Based on this understanding, data are phenomena that “cannot be engineered by human subjects but are differential patterns of mattering produced by neither the material nor the cultural but the material–cultural” (Vu 2018 , p. 85) or naturecultural (Haraway 2016 ). Phenomenological and narrative-based methods explicitly account for this perspective (see related studies by Pöllänen et al. 2023 ; Wamsler et al. 2022b ).

Furthermore, a relational paradigm involves acknowledging the potential relevance of data that are generally dismissed (Smartt Gullion 2018 ). For example, in statistical modeling, deviation from the mean is often dismissed as noise. To streamline the analysis, ‘noisy’ data undergo various manipulations including outlier removal, logarithmic transformation, or smoothing, ultimately resulting in a linear form (ibid.). While reductionist approaches are necessary (cf. Step 4), noise might conceal significant insights, for instance from non-human or marginalized groups (West 2006 ).

West ( 2006 , p. 72) asserts that “smoothing or filtering the time series might eliminate the very thing we want to know.” Such processing tends to neglect the unique variability that characterizes individuals and emphasizes commonalities. Additionally, the understanding that large sample sizes are good undermines individual variability. As sample sizes grow, models tend to produce statistically significant results. However, this significance is purely a statistical concept and does not always reflect substantial relationships between variables. Even random correlations can appear statistically significant with large sample sizes (Smartt Gullion 2019). For certain studies, it might thus be beneficial to scrutinize the noise.

Building on the previous arguments, it is crucial to employ methods that can investigate all, also today’s ‘hidden’ dimensions, of reality and their inherent relationships. This requires combining traditional methods with other techniques and data sources, such as introspection, contemplative, esthetic, visual, sensory, and embodied forms of sensemaking.

For example, instead of merely using a statistical analysis of the number of bikes rented daily, and the corresponding decrease in individual car usage and emissions, researchers who are studying the impact of a city’s new bike-sharing program on reducing carbon emissions might also consider data from users about underlying (shifts in) values, beliefs, emotions, and paradigms, inter-group variations, and obstacles and enablers for inner–outer change, which can take different forms (e.g., collected stories, constellations, or drawings).

When collecting and analyzing data from a relational perspective, the following questions should be considered to move toward a relational paradigm:

I. How can I critically examine my role as a researcher during the data collection and analysis process? For example, how might my perspectives, assumptions, and values shape my data selection and interpretation?

II. How can I embrace a broad range of methods, data types, and formats beyond traditional textual or numerical approaches? For example, maybe I can incorporate experiential, visual, or sensory forms of data to capture relevant human and non-human interactions.

III. What is the noise that I might be overlooking? For example, if I have smoothed or filtered data, it might be relevant to revisit those data points (if possible) for a closer examination.

Step 6: the writing process

The end product of research is some form of representation of the findings. Commonly, findings are reported in written form in an international journal, a poster, a book, or a monograph. The underlying assumption is that the results—through the use of language—can reflect and influence reality.

This is based on a certain understanding of objectivity and the role of information. From a mechanistic paradigm, research results represent an objective truth that was discovered. Epistemologically, the common understanding is that a knowing subject (the researcher) can objectively study objects (things in the world) to understand them.

As described above, relational epistemology questions the idea of an objective observer (Ngunjiri et al. 2010 ). This understanding is by no means original in its attempt to expose the limitations of reductionist practices. “Philosophers of science, such as Popper ( 1963 ), Feyerabend ( 1975 ), and Kuhn ( 1996 ), are well known for their arguments against false claims of objectivity and scientific autonomy” (Audouin et al. 2013 , p. 17).

The challenge that arises from this understanding is how to represent this subjectivity when reporting results, sometimes referred to as a crisis of representation (Smartt Gullion 2018 ). The crisis of representation comes from asking whether the final product represents reality. Is it accurate? Trustworthy? Ethical? It results from speaking for others—in sustainability science, this is often marginalized humans or non-humans—and the adequacy of their representation (ibid).

Within the relational paradigm, the crisis of representation could be addressed by explicitly acknowledging related challenges, choosing alternative or additional forms of representation (art, stories, music), and portraying the self as performative (Verlie 2018 ). The latter can for instance involve moving away from a first-person scholarly narrator who is self-referential and unavailable to criticism or revision (Pollock 2007 ).

In contrast to representationalism, performativism focuses on “understanding thinking, observing, and theorizing as practices of engagement with, and as part of, the world in which we have our being” (Barad 2007 , p. 133). This understanding of self represents identity and experience as uncertain, fluid, and open to interpretation and revision (Jones and Adams 2010 ).

Although this last research step makes the performative ‘I’ visible, related considerations are relevant for all steps. In the latter case, it relates to: inquiries about one’s role and entanglements; actively engaging with the subjects of research, for example, through dialog; making deliberate methodological choices; considering potential power dynamics, informed consent, confidentiality, and the well-being of participants and oneself; and being transparent about the role of the performative ‘I’ in shaping outcomes.

Another important aspect to consider during the writing process is the fact that writing itself can (and should) be understood as a relational process that, in turn, can foster or hamper relationality in real life (Barad 2007 ; Puig de la Bellacasa 2017 ). For example, the process can be constrained by project schedules, power structures, or other external pressures. This scenario tends to result in a more mechanical, instrumental, and task-oriented approach to crafting or ‘fitting’ content, scope, and form. Conversely, when writing emanates from an integrated self and an embodied, deeper connection to one’s thoughts, emotions, body, and creativity, words can flow more organically. In these instances, the writing process becomes an expressive act, which allows the person to tap into their full potential, rather than fulfilling external demands. Hawkins ( 2015 ) points out that writing is not merely a cognitive or linguistic activity, but is deeply entwined with social, emotional, and spatial contexts and relationships. Thus, writing itself is affected by relational influences, and the way of writing can support or hamper engagement in transformational change (ibid.).

In summary, the writing process requires addressing relational aspects of representation. It involves explicitly addressing related limitations (such as power dynamics and ethical considerations), portraying the self as performative, and using alternative or additional forms of representation where relevant (art, stories, music).

To integrate relational perspectives into the writing process, the following questions can thus be helpful:

I. How might my perspectives and assumptions shape the interpretation and representation of my results, and how can I make them explicit in my writing? For instance, do I acknowledge related limitations in the description of research outcomes?

II. Who do I speak for? Am I contributing to empowerment and justice, or am I disempowering certain individuals, groups, or other agents? For example, how can I give voice to non-human actors and consider their perspectives and interactions? How can I make my writings widely accessible for diverse audiences?

III. What kind of world or other ways of representation can I use to support integrative understanding and transformation? For example, do my research results contribute to, or challenge, existing paradigms and practices? How can I reach people’s minds and hearts, and foster individual and collective agency, hope, and courage to act?

Step 7: dissemination of the results

Lastly, the research process involves the dissemination of its results. Especially in sustainability science, the transfer of knowledge is a crucial step for fostering transformation, and results are disseminated through publishing in academic journals (cf. step 6).

From a relational perspective, relationships also play a key role in dissemination. As relational approaches require the consideration of the perspectives, needs, and relationships of human and non-human stakeholders, it is important to involve stakeholders in different forms in all research steps. In the context of dissemination, this relates to the sharing and application of research findings. Science communication increasingly uses dissemination formats beyond academic papers, such as podcasts, books, or policy briefs that aim to reach different societal groups. However, to support transformation, more relational communication and implementation strategies might also be needed, for instance, the creation of reflection and generative dialog spaces, community workshops, communities of practice, or other interactive formats (Mar et al. 2023 ). What makes these formats particularly relevant is their co-creative approach, placing researchers within a learning ecosystem, field, or network, as learning subjects themselves. Moreover, dissemination could place greater emphasis on the relationships and contexts in which the results were generated. This could involve storytelling, case study illustrations, or imaginary narratives as part of the dissemination strategy that highlight the interconnectedness of the findings within specific social, cultural, or environmental contexts.

Another key aspect of the relational paradigm that is relevant for dissemination is epistemic justice (Fricker 2007 ; Puig de la Bellacasa 2017 ; Whyte 2020 ). Epistemic justice calls for the recognition and amplification of marginalized or underrepresented voices in knowledge production and learning. In the context of dissemination, this translates into actively seeking out, addressing, and including diverse perspectives and knowledge holders in the communication and sharing of research findings. It involves sharing results beyond the scientific community, both with humans and other agents, where possible. It also includes the use of diverse communication channels and formats that cater to different audiences, languages, and accessibility needs. Such an approach embraces tangible actions and accessibility, to have a more inclusive impact that integrates different ways of learning and understanding.

In summary, dissemination requires actively seeking out and addressing relationships and diverse perspectives, and making research outcomes widely accessible in ways that integrate cognitive, social, emotional, ethical, and embodied learning.

For example, when disseminating outcomes, the researcher might also want to represent and ‘let speak’ other voices—such as birds or trees—through videos, photographs, or exhibitions, as an addition to the dissemination of written material.

To integrate relational perspectives into the dissemination process, the following questions can be helpful:

I. In what forms can I best share these research results to account for, and address diverse stakeholders, needs, and perspectives? For example, are videos, exhibitions, networks, or communities of practice relevant channels for dissemination and implementation?

II. Am I conveying information accurately, respectfully, and in ways that honor diverse contributions and contexts, particularly those of marginalized groups? For example, have I critically examined and reframed narratives that perpetuate injustices or exclude certain perspectives?

III. How do I engage with relevant stakeholders during the dissemination process to support integrative understanding and transformation? For example, how can I move from traditional communication formats to more relational approaches that challenge current paradigms?

Our assessment of the different research steps has shown that some characteristics of a relational paradigm apply to several, or all, steps. For example, it is important to consider that sustainability science, by nature, is intertwined with human values, societal norms, and ethics throughout the process. It is inherently subjective and normative, which makes the idea of “total” objectivity obsolete (Ngunjiri et al. 2010 ). Consequently, inner dimensions, including people’s individual and collective mindsets, beliefs, values, worldviews, and associated inner qualities/capacities are key for defining, pursuing, and achieving sustainability goals across all levels (individual, collective, system). Embracing a relational approach in sustainability science therefore necessitates an explicit consideration of related inner–outer transformation processes, which, in turn, requires conscious inter- and intrarelating through introspection and reflexivity. This shift broadens the scope of sustainability science and poses epistemological, ontological, ethical, and praxis-related questions regarding (1) how we see the world, (2) how we get to know, (3) how we engage, and (4) how we ensure equity considerations across all aspects (Ives et al. 2023 ; Wamsler et al. 2024 ). The relational paradigm thus decenters the human in the production of knowledge. We have explained related aspects in detail in the previous sections, and in those research steps in which their influence is greatest.

New pathways for sustainability science: toward a relational approach in research

Given the challenges of the anthropocene, scholars are increasingly calling for a relational turn to address the root causes of today’s polycrisis. At the same time, little is known about the associated challenges, and there is little advice regarding how to operationalize the approach in sustainability science.

Against this background, this paper explored how we can break out of modern, unsustainable paradigms and approaches, and instead apply more relational thinking, being, and acting in the way we conduct research. To achieve this, we systematically list all major research phases and assess possible pathways for integrating a relational paradigm (see Table  1 for an overview and suppl. material).

We show that moving toward a relational paradigm requires us to methodically question and redefine existing theories of change, concepts, and approaches. However, transitioning from a mechanistic to a relational paradigm in the domain of sustainability science and beyond does not involve a straightforward substitution.

Instead of viewing paradigm shifts as abrupt replacements, our analyses highlight the evolutionary and emergent nature of such changes. Contrary to Kuhn’s ( 1996 ) concept of successive paradigms, our approach recognizes the value of integrating and acknowledging the partial validity of multiple, preceding, and mutually informing paradigms. It is about taking small steps and creating bridges between the current and a potential new paradigm, by exploring how best to be in relationship, with ourselves, our fellow humans, and the other-than-human in a regenerative way.

Yet, as Raymond et al. ( 2021 ) point out, methodological challenges and pragmatic decisions to move toward more relational thinking must be addressed, such as the need for setting certain systems boundaries or interfaces. As suggested by the concept of critical complexity, it is possible to transcend the limitations of our dominant mechanistic approaches, while acknowledging the necessity for reduction in research. It embraces the nuanced understanding that some reductionist practices are indispensable, while advocating for a broader framework that encompasses the complexity of entangled socio-ecological systems. Moreover, as Walsh et al. ( 2020 ) point out, applying a differentiated relational ontology acknowledges both the separate as well as the relational reality. For instance, dealing with challenges such as identifying leverage points in research—which stems from a bifurcation—means that we acknowledge paradoxes. We might apply the leverage points model to identify where to intervene in the system, while at the same time acknowledging that the model is limited and not fully aligned with relational thinking (Raymond et al. 2021 ). The need to embrace paradoxes is, in fact, part of moving toward a relational approach (e.g., Kulundu-Bolus 2023 ): it requires a humble and thus relational attitude and understanding of the research process and the results in themselves.

A key challenge for moving toward a relational paradigm is the current landscape within which sustainability science operates, as it is in itself an expression of the dominant modern paradigm. The field operates within a larger context that is characterized by constant acceleration, a high-speed society, exponential technological development, and continuous social change, all of which affect our own relationships and those involved in any research object (Rosa 2019 ). Tensions thus arise from the clash between the inherent qualities of a relational approach—which emphasizes interdependencies, connectedness, nonlinearity, uncertainty, and emergence—and systemic pressures that prioritize rapid outputs, quantifiable outcomes, and often individualistic gains. We therefore acknowledge that a paradigm shift needs to go hand in hand with an overall reevaluation of how systems, institutions, policies, and practices are structured and incentivized within sustainability science.

To integrate a relational paradigm into the researcher’s work, we suggest developing processes and practices of reflexive praxis, such as interrupting existing conversations, listening deeply to overlooked, marginalized, or suppressed perspectives, and daring to ask difficult and new questions that support mutual learning toward the emergence of a more relational being, understanding, and acting upon the world (Spreitzer 2021 ). Moving toward a more relational paradigm is thus not just about adopting a different framework, but is about cultivating individual and collective capabilities and capacities that allow us to challenge conventional norms, structures, and institutions, and encourage exploration and creation from diverse viewpoints toward potential alternatives (Wamsler et al. 2024 ).

Challenging mainstream thought and daring to ask different questions in each research step are crucial to shifting current scientific norms and systems. Hence, we offer a catalog of questions that allows us to systematically integrate relational being, thinking, and acting into the research process (see Table  1 , as well as suppl. material for an overview of the questions and examples). Each question encapsulates underlying assumptions and implications for the research process and can thus serve as a catalyst for embracing a more relational perspective.

Many of the characteristics of a relational paradigm have an impact across multiple research steps. These aspects include the need to decenter the human perspective, account for the role of relationships, support integrative inner–outer transformation processes across individual, collective, and system levels, and encourage deep reflection on one’s positionality. While these characteristics influence the entire research process, their significance becomes more pronounced in certain steps, which we therefore explored in more detail in the previous sections.

Although we offer some concrete ideas regarding how to move toward a relational paradigm, further research is required to test our theoretical and conceptual considerations and generate further measures and pathways. As the relational paradigm focuses on the (quality of) relationships within systems, and associated inner–outer transformation processes, one key aspect to consider is whether, and how, changes in relationships can be best addressed. Research on the human–nature connection, such as the Connectedness to Nature Scale (Mayer and McPherson Frantz 2004 ), already exists. However, this only addresses a small part of the story, and related work is generally not linked to sustainability outcomes across individual, collective, and system levels. Other ways to study changes in relationships and their link to sustainability outcomes have been tested, for instance, in the context of leadership training for the European Commission, the UNDP Conscious Food Systems Alliance, and the Inner Development Goals (IDG) initiative (Janss et al. 2023 ; Jordan 2021 ; Ramstetter et al. 2023 ; Rupprecht and Wamsler 2023 ; Wamsler et al. 2024 ). Based on the inner–outer transformation model, the change in the relationship to self, others, nature, and the world at large is here applied as a proxy for inner–outer transformation and associated sustainability outcomes (Wamsler et al. 2021 ). Research is needed to further assess related aspects, for instance to account for intergenerational trauma and power dynamics, and identify whether the latter might be transactional or a means-in-itself, as transactional relationships often lead to overexploitation and injustice (Rosa 2019 ).

To conclude, we must dare to question our questions, and dare to ask new questions—relational, existential questions about our identity, our role, and our responsibility in the world in more reflexive and thus transformative ways. It is about developing sustainability and regeneration as a capacity, and as a foundation for pursuing research not as only a form of ‘about-ing’ and ‘enact-ing’, but also as a ‘within-ing’ and thus ‘be-ing’. The suggested guiding questions may appear to be small, individual acts. However, these small choices can have profound impacts, as they can help to initiate deeper changes, to let go of mental habits, decolonize our minds, and, ultimately, challenge the cultural, institutional, and political landscape that maintains the story of separation of humans and nature, and the story of human dominance and superiority over the “living” that underlies both our current research approaches and today’s sustainability crises.

Paradigms shape our ways of knowing, being, and acting in the world (Walsh et al. 2020 ) and can thus be both a critical barrier and driver for sustainability. They not only influence us personally (i.e., our motivation, values, attitudes, and psychological makeup), but also shape our systems (social, economic, political, technical, ecological) and cultural associations (i.e., narrative frames and cultural norms) (Escobar 2017 ; Lakoff 2014 ; Orr 2002 ; Wahl 2017 ). Paradigms represent the dominant thought patterns in societies, and thus underlie the theories and methods we use in science (O’Brien 2016 ; Walsh et al. 2020 ). This is also true for sustainability, climate science, and any other related field (Kuhn 1996 ). As a result, they hold significant potential as catalysts for transforming systems (Meadows 1999 ).

In the context of research, related norms are characterized by rationalism, reductionism, empiricism, dualism, and determinism (Redclift and Sage 1994 ; Rees 1999 ; Capra and Luisi 2014 ; Böhme et al. 2022 ).

Despite a rich discourse on relationality, there is no single, comprehensive definition of a relational paradigm. It can rather be seen as an umbrella term that encompasses various strands of thoughts (Walsh et al. 2020 ), as presented in our article.

Transformation literacy is the skill to steward transformative change collectively across the boundaries of institutions, nations, sectors, and cultures (Künkel and Ragnarsdottir 2022 ).

Our work draws heavily on a literature review that explores relational ontologies, epistemologies, and ethics by Walsh et al. ( 2020 ). We also included recent research papers that specifically address sustainability science and relational perspectives. Examples include Hertz et al. ( 2020 ) and Mancilla Garcia et al. ( 2020 ), who look at socio-ecological systems research from a process-relational perspective, and West et al. ( 2020 ), who look at the relational turn in sustainability science in general. These key sources led us to further papers dealing with the relational research approaches relevant for our review.

According to integral theory (Wilber 2021 ), there are two dimensions of reality: an internally versus externally experienced dimension; and an individually versus collectively experienced dimension. Combining these two dimensions yields four domains of human experience, or ways of generating knowledge about the world. These four dimensions involve: (1) ‘it’—knowledge of exterior and individual phenomena; (2) ‘they’—knowledge of exterior and collective phenomena and their interactions; 3) ‘we’—knowledge of internal and collective phenomena and their interactions’ and 4) ‘I’—knowledge of internal and individual phenomena and experiences (Esbjörn-Hargens 2010 ). In sustainability science, the fourth dimension—‘I’— and the in-depth assessment of the relationship between the different dimensions has been largely neglected (Ives et al. 2019 , 2023 ).

For a philosophical theory to be valid, it must be internally consistent within its self-referential axioms and core assumptions. Philosophy makes reasoned arguments based on systems of logic, while science is focused on the systematic collection of evidence (Esbjörn-Hargens 2010 ).

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Acknowledgements

This article is a co-creation, encompassing the more-than-human-world, along with our cultural heritage and predecessors who have formed our knowledge and understanding, and the cultural and technological tools such as networks and laptops and people involved in their development.

The research was supported by the Existential Resilience project funded by Lund University and two projects funded by the Swedish Research Council Formas: (1) Mind4Change (grant number 2019-00390; full title: Agents of Change: Mind, Cognitive Bias and Decision-Making in a Context of Social and Climate Change), and (2) TransVision (grant number 2019-01969; full title: Transition Visions: Coupling Society, Well-being and Energy Systems for Transitioning to a Fossil-free Society).

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Preparedness for a first clinical placement in nursing: a descriptive qualitative study

• Philippa H. M. Marriott 1 ,
• Jennifer M. Weller-Newton 2   nAff3 &
• Katharine J. Reid 4  

BMC Nursing volume  23 , Article number:  345 ( 2024 ) Cite this article

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A first clinical placement for nursing students is a challenging period involving translation of theoretical knowledge and development of an identity within the healthcare setting; it is often a time of emotional vulnerability. It can be a pivotal moment for ambivalent nursing students to decide whether to continue their professional training. To date, student expectations prior to their first clinical placement have been explored in advance of the experience or gathered following the placement experience. However, there is a significant gap in understanding how nursing students’ perspectives about their first clinical placement might change or remain consistent following their placement experiences. Thus, the study aimed to explore first-year nursing students’ emotional responses towards and perceptions of their preparedness for their first clinical placement and to examine whether initial perceptions remain consistent or change during the placement experience.

The research utilised a pre-post qualitative descriptive design. Six focus groups were undertaken before the first clinical placement (with up to four participants in each group) and follow-up individual interviews ( n  = 10) were undertaken towards the end of the first clinical placement with first-year entry-to-practice postgraduate nursing students. Data were analysed thematically.

Three main themes emerged: (1) adjusting and managing a raft of feelings, encapsulating participants’ feelings about learning in a new environment and progressing from academia to clinical practice; (2) sinking or swimming, comprising students’ expectations before their first clinical placement and how these perceptions are altered through their clinical placement experience; and (3) navigating placement, describing relationships between healthcare staff, patients, and peers.

Conclusions

This unique study of first-year postgraduate entry-to-practice nursing students’ perspectives of their first clinical placement adds to the extant knowledge. By examining student experience prior to and during their first clinical placement experience, it is possible to explore the consistency and change in students’ narratives over the course of an impactful experience. Researching the narratives of nursing students embarking on their first clinical placement provides tertiary education institutions with insights into preparing students for this critical experience.

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First clinical placements enable nursing students to develop their professional identity through initial socialisation, and where successful, first clinical placement experiences can motivate nursing students to persist with their studies [ 1 , 2 , 3 , 4 ]. Where the transition from the tertiary environment to learning in the healthcare workplace is turbulent, it may impact nursing students’ learning, their confidence and potentially increase attrition rates from educational programs [ 2 , 5 , 6 ]. Attrition from preregistration nursing courses is a global concern, with the COVID-19 pandemic further straining the nursing workforce; thus, the supply of nursing professionals is unlikely to meet demand [ 7 ]. The COVID-19 pandemic has also impacted nursing education, with student nurses augmenting the diminishing nursing workforce [ 7 , 8 ].

The first clinical placement often triggers immense anxiety and fear for nursing students [ 9 , 10 ]. Research suggests that among nursing students, anxiety arises from perceived knowledge deficiencies, role ambiguity, the working environment, caring for ‘real’ people, potentially causing harm, exposure to nudity and death, and ‘not fitting in’ [ 2 , 3 , 11 ]. These stressors are reported internationally and often relate to inadequate preparation for entering the clinical environment [ 2 , 10 , 12 ]. Previous research suggests that high anxiety before the first clinical placement can be related to factors likely to affect patient outcomes, such as self-confidence and efficacy [ 13 ]. High anxiety during clinical placement may impair students’ capacity to learn, thus compromising the value of the clinical environment for learning [ 10 ].

The first clinical placement often occurs soon after commencing nursing training and can challenge students’ beliefs, philosophies, and preconceived ideas about nursing. An experience of cultural or ‘reality’ shock often arises when entering the healthcare setting, creating dissonance between reality and expectations [ 6 , 14 ]. These experiences may be exacerbated by tertiary education providers teaching of ‘ideal’ clinical practice [ 2 , 6 ]. The perceived distance between theoretical knowledge and what is expected in a healthcare placement, as opposed to what occurs on clinical placement, has been well documented as the theory-practice gap or an experience of cognitive dissonance [ 2 , 3 ].

Given the pivotal role of the first clinical placement in nursing students’ trajectories to nursing practice, it is important to understand students’ experiences and to explore how the placement experience shapes initial perceptions. Existing research focusses almost entirely either on describing nursing students’ projected emotions and perceptions prior to undertaking a first clinical placement [ 3 ] or examines student perceptions of reflecting on a completed first placement [ 15 ]. We wished to examine consistency and change in student perception of their first clinical placement by tracking their experiences longitudinally. We focused on a first clinical placement undertaken in a Master of Nursing Science. This two-year postgraduate qualification provides entry-to-practice nursing training for students who have completed any undergraduate qualification. The first clinical placement component of the course aimed to orient students to the clinical environment, support students to acquire skills and develop their clinical reasoning through experiential learning with experienced nursing mentors.

This paper makes a significant contribution to understanding how nursing students’ perceptions might develop over time because of their clinical placement experiences. Our research addresses a further gap in the existing literature, by focusing on students completing an accelerated postgraduate two-year entry-to-practice degree open to students with any prior undergraduate degree. Thus, the current research aimed to understand nursing students’ emotional responses and expectations and their perceptions of preparedness before attending their first clinical placement and to contrast these initial perceptions with their end-of-placement perspectives.

Study design

A descriptive qualitative study was undertaken, utilising a pre- and post-design for data collection. Focus groups with first-year postgraduate entry-to-practice nursing students were conducted before the first clinical placement, with individual semi-structured interviews undertaken during the first clinical placement.

Setting and participants

All first-year students enrolled in the two-year Master of Nursing Science program ( n  = 190) at a tertiary institution in Melbourne, Australia, were eligible to participate. There were no exclusion criteria. At the time of this study, students were enrolled in a semester-long subject focused on nursing assessment and care. They studied the theoretical underpinnings of nursing and science, theoretical and practical nursing clinical skills and Indigenous health over the first six weeks of the course. Students completed a preclinical assessment as a hurdle before commencing a three-week clinical placement in a hospital setting, a subacute or acute environment. Overall, the clinical placement aimed to provide opportunities for experiential learning, skill acquisition, development of clinical reasoning skills and professional socialisation [ 16 , 17 ].

In total, sixteen students participated voluntarily in a focus group of between 60 and 90 min duration; ten of these students also participated in individual interviews of between 30 and 60 min duration, a number sufficient to reach data saturation. Table  1 shows the questions used in the focus groups conducted before clinical placement commenced and the questions for the semi-structured interview questions conducted during clinical placement. Study participants’ undergraduate qualifications included bachelor’s degrees in science, arts and business. A small number of participants had previous healthcare experience (e.g. as healthcare assistants). The participants attended clinical placement in the Melbourne metropolitan, Victorian regional and rural hospital locations.

Data collection

The study comprised two phases. The first phase comprised six focus groups prior to the first clinical placement, and the second phase comprised ten individual semi-structured interviews towards the end of the first clinical placement. Focus groups (with a maximum of four participants) and individual interviews were conducted by the lead author online via Zoom and were audio-recorded. Capping group size to a relatively small number considered diversity of perceptions and opportunities for participants to share their insights and to confirm or contradict their peers, particularly in the online environment [ 18 , 19 ].

Focus groups and interview questions were developed with reference to relevant literature, piloted with volunteer final-year nursing students, and then verified with the coauthors. All focus groups and interviewees received the same structured questions (Table  1 ) to ensure consistency and to facilitate comparison across the placement experience in the development of themes. Selective probing of interviewees’ responses for clarification to gain in-depth responses was undertaken. Nonverbal cues, impressions, or observations were noted.

The lead author was a registered nurse who had a clinical teaching role within the nursing department and was responsible for coordinating clinical placement experiences. To ensure rigour during the data collection process, the lead author maintained a reflective account, exploring her experiences of the discussions, reflecting on her interactions with participants as a researcher and as a clinical educator, and identifying areas for improvement (for instance allowing participants to tell their stories with fewer prompts). These reflections in conjunction with regular discussion with the other authors throughout the data collection period, aided in identifying any researcher biases, feelings and thoughts that possibly influenced the research [ 20 ].

To maintain rigour during the data analysis phase, we adhered to a systematic process involving input from all authors to code the data and to identify, refine and describe the themes and subthemes reported in this work. This comprehensive analytic process, reported in detail in the following section, was designed to ensure that the findings arising from this research were derived from a rigorous approach to analysing the data.

Data analysis

Focus groups and interviews were transcribed using the online transcription service Otter ( https://otter.ai/ ) and then checked and anonymised by the first author. Preliminary data analysis was carried out simultaneously by the first author using thematic content analysis proposed by Braun and Clarke [ 21 ] using NVivo 12 software [ 22 ]. All three authors undertook a detailed reading of the first three transcripts from both the focus groups and interviews and independently identified major themes. This preliminary coding was used as the basis of a discussion session to identify common themes between authors, to clarify sources of disagreement and to establish guidelines for further coding. Subsequent coding of the complete data set by the lead author identified a total of 533 descriptive codes; no descriptive code was duplicated across the themes. Initially, the descriptive codes were grouped into major themes identified from the literature, but with further analysis, themes emerged that were unique to the current study.

The research team met frequently during data analysis to discuss the initial descriptive codes, to confirm the major themes and subthemes, to revise themes on which there was disagreement and to identify any additional themes. Samples of quotes were reviewed by the second and third authors to decide whether these quotes were representative of the identified themes. The process occurred iteratively to refine the thematic categories, to discuss the definitions of each theme and to identify exemplar quotes.

Ethical considerations

The lead author was a clinical teacher and the clinical placement coordinator in the nursing department at the time of the study. Potential risks of perceived coercion and power imbalances were identified because of the lead author’s dual roles as an academic and as a researcher. To manage these potential risks, an academic staff member who was not part of the research study informed students about the study during a face-to-face lecture and ensured that all participants received a plain language statement identifying the lead author’s role and how perceived conflicts of interest would be managed. These included the lead author not undertaking any teaching or assessment role for the duration of the study and ensuring that placement allocations were completed prior to undertaking recruitment for the study. All students who participated in the study provided informed written consent. No financial or other incentives were offered. Approval to conduct the study was granted by the University of Melbourne Human Research Ethics Committee (Ethics ID 1955997.1).

Three main themes emerged describing students’ feelings and perceptions of their first clinical placement. In presenting the findings, before or during has been assigned to participants’ quotes to clarify the timing of students’ perspectives related to the clinical placement.

Major theme 1: Adjusting and managing a raft of feelings

The first theme encompassed the many positive and negative feelings about work-integrated learning expressed by participants before and during their clinical placement. Positive feelings before clinical placement were expressed by participants who were comfortable with the unknown and cautiously optimistic.

I am ready to just go with the flow, roll with the punches (Participant [P]1 before).

Overwhelmingly, however, the majority of feelings and thoughts anticipating the first clinical placement were negatively oriented. Students who expressed feelings of fear, anxiety, lack of knowledge, lack of preparedness, uncertainty about nursing as a career, or strong concerns about being a burden were all classified as conveying negative feelings. These negative feelings were categorised into four subthemes.

Subtheme 1.1 I don’t have enough knowledge

All participants expressed some concerns and anxiety before their first clinical placement. These encompassed concerns about knowledge inadequacy and were linked to a perception of under preparedness. Participants’ fears related to harming patients, responsibility for managing ‘real’ people, medication administration, and incomplete understanding of the language and communication skills within a healthcare setting. Anxiety for many participants merged with the logistics and management of their life during the clinical placement.

I’m scared that they will assume that I have more knowledge than I do (P3 before). I feel quite similar with P10, especially when she said fear of unknown and fear that she might do something wrong (P9 before).

Subtheme 1.2 Worry about judgment, being seen through that lens

Participants voiced concerns that they would be judged negatively by patients or healthcare staff because they perceived that the student nurse belonged to specific social groups related to their cultural background, ethnicity or gender. Affiliation with these groups contributed to students’ sense of self or identity, with students often describing such groups as a community. Before the clinical placement, participants worried that such judgements would impact the support they received on placement and their ability to deliver patient care.

Some older patients might prefer to have nurses from their own background, their own ethnicity, how they would react to me, or if racism is involved (P10 before). I just don’t want to reinforce like, whatever negative perceptions people might have of that community (P16 before).

Participants’ concerns prior to the first clinical placement about judgement or poor treatment because of patients’ preconceived ideas about specific ethnic groups did not eventuate.

I mean, it didn’t really feel like very much of a thing once I was actually there. It is one of those things you stress about, and it does not really amount to anything (P16 during).

Some students’ placement experiences revealed the positive benefits of their cultural background to enhancing patient care. One student affirmed that the placement experience reinforced their commitment to nursing and that this was related to their ability to communicate with patients whose first language was not English.

Yeah, definitely. Like, I can speak a few dialects. You know, I can actually see a difference with a lot of the non-English speaking background people. As soon as you, as soon as they’re aware that you’re trying and you’re trying to speak your language, they, they just open up. Yeah, yes. And it improves the care (P10 during).

However, a perceived lack of judgement was sometimes attributed to wearing the full personal protective equipment required during the COVID-19 pandemic, which meant that their personal features were largely obscured. For this reason, it was more difficult for patients to make assumptions or attributions about students’ ethnic or gender identity based on their appearance.

People tend to assume and call us all girls, which was irritating. It was mostly just because all of us were so covered up, no one could see anyone’s faces (P16 during).

Subtheme 1.3 Is nursing really for me?

Prior to their first clinical placement experience, many participants expressed ambivalence about a nursing career and anticipated that undertaking clinical placement could determine their suitability for the profession. Once exposed to clinical placement, the majority of students were completely committed to their chosen profession, with a minority remaining ambivalent or, in rare cases, choosing to leave the course. Not yet achieving full commitment to a nursing career was related to not wishing to work in the ward they had for their clinical placement, while remaining open to trying different specialities.

I didn’t have an actual idea of what I wanted to do after arts, this wasn’t something that I was aiming towards specifically (P14 before). I think I’m still not 100%, but enough to go on, that I’m happy to continue the course as best as I can (P11 during).

Subtheme 1.4 Being a burden

Before clinical placement, participants had concerns about being burdensome and how this would affect their clinical placement experiences.

If we end up being a burden to them, an extra responsibility for them on top of their day, then we might not be treated as well (P10 before).

A sense of burden remained a theme during the clinical placement for participants for the first five to seven days, after which most participants acknowledged that their role became more active. As students contributed more productively to their placement, their feelings of being a burden reduced.

Major theme 2: Sinking or swimming

The second major theme, sinking or swimming, described participants’ expectations about a successful placement experience and identified themes related to students’ successes (‘swimming’) or difficulties (‘sinking’) during their placement experience. Prior to clinical placement, without a realistic preview of what the experience might entail, participants were uncertain of their role, hoped for ‘nice’ supervising nurses and anticipated an observational role that would keep them afloat.

I will focus on what I want to learn and see if that coincides with what is expected, I guess (P15 before).

During the clinical placement, the reality was very different, with a sense of sinking. Participants discovered, some with shock, that they were expected to participate actively in the healthcare team.

I got the sense that they were not going to muck around, and, you know, they’re ‘gonna’ use the free labour that came with me (P1 during).

Adding to the confusion about the expected placement experience, participants believed that healthcare staff were unclear about students’ scope of practice for a postgraduate entry-to-practice degree, creating misalignment between students’ and supervising nurses’ expectations.

It seems to me like the educators don’t really seem to have a clear picture of what the scope is, and what is actually required or expected of us (P10 during).

In exploring perceived expectations of the clinical placement and the modifying effect of placement on initial expectations, three subthemes were identified: translation to practice is overwhelming, trying to find the rhythm or jigsaw pieces, and individual agency.

Subtheme 2.1 Translation to practice is overwhelming

Before clinical placement, participants described concerns about insufficient knowledge to enable them to engage effectively with the placement experience.

If I am doing an assessment understanding what are those indications and why I would be doing it or not doing it at a certain time (P1 before).

Integrating and applying theoretical content while navigating an unfamiliar clinical environment created a significant gap between theory and practice during clinical placement. As the clinical placement experience proceeded and initial fears dissipated, students became more aware of applying their theoretical knowledge in the clinical context.

We’re learning all this theory and clinical stuff, but then we don’t really have a realistic idea of what it’s like until we’re kind of thrown into it for three weeks (P10 during).

Subtheme 2.2 Trying to find the rhythm or the jigsaw pieces

Before clinical placement, participants described learning theory and clinical skills with contextual unfamiliarity. They had the jigsaw pieces but did not know how to assemble it; they had the music but did not know the final song. When discussing their expectations about clinical placement, the small number of participants with a healthcare background (e.g. as healthcare assistants) proposed realistic answers, whereas others struggled to answer or cited stories from friends or television. With a lack of context, feelings of unpreparedness were exacerbated. Once in the clinical environment, participants further emphasised that they could not identify what they needed to know to successfully prepare for clinical placement.

It was never really pieced together. We’ve learned bits and pieces, and then we’re putting it together ourselves (P8 during). On this course I feel it was this is how you do it, but I did not know how it was supposed to be played, I did not know the rhythm (P4 during).

Subtheme 2.3 Individual agency

Participants’ individual agency, their attitude, self-efficacy, and self-motivation affected their clinical placement experiences. Participant perceptions in advance of the clinical placement experience remained consistent with their perspectives following clinical placement. Before clinical placement, participants who were highly motivated to learn exhibited a growth mindset [ 23 ] and planned to be proactive in delivering patient care. During their clinical placement, initially positive students remained positive and optimistic about their future. Participants who believed that their first clinical placement role would be largely observational and were less proactive about applying their knowledge and skills identified boredom and a lack of learning opportunities on clinical placement.

A shadowing position, we don’t have enough skills and authority to do any work, not do any worthwhile skills (P3 before). I thought it would be a lot busier, because we’re limited with our scope, so there’s not much we can do, it’s just a bit slower than I thought (P12 during).

Individual agency appears to influence a successful first clinical placement; other factors may also be implicated but were not the focus of this study. Further research exploring the relationships between students’ age, life experience, resilience, individual agency, and the use of coping strategies during a first clinical placement would be useful.

Major theme 3: The reality of navigating placement relationships

The third main theme emphasised the reality of navigating clinical placement relationships and explored students’ relationships with healthcare staff, patients, and peers. Before clinical placement, many participants, especially those with healthcare backgrounds, expressed fears about relationships with supervising nurses. They perceived that the dynamics of the team and the healthcare workplace might influence the support they received. Several participants were nervous about attending placement on their own without peers for support, especially if the experience was challenging. Participants identified expectations of being mistreated, believing that it was unavoidable, and prepared themselves to not take it personally.

For me it’s where we’re going to land, are we going to be in a supportive, kind of nurturing environment, or is it just kind of sink or swim? (P5 before). If you don’t really trust them, you’re nervous the entire time and you’ll be like what if I get it wrong (P16 before).

Despite these concerns, students strongly emphasised the value of relationships during their first clinical placement, with these perceptions unchanged by their clinical placement experience. Where relationships were positive, participants felt empowered to be autonomous, and their self-confidence increased.

You get that that instant reaction from the patients. And that makes you feel more confident. So that really got me through the first week (P14 during). I felt like I was intruding, then as I started to build a bit of rapport with the people, and they saw that I was around, I don’t feel that as much now (P1 during).

Such development hinged on the receptiveness and support of supervising nurses, the team on the ward, and patients and could be hindered by poor relationships.

He was the old-style buddy nurse in his fifties, every time I questioned him, he would go ssshh, just listen, no questions, it was very stressful (P10 during). It depends whether the buddy sees us as an extra pair of hands, or we’re learners (P11 during).

Where students experienced poor behaviour from supervising nurses, they described a range of emotional responses to these interactions and also coping strategies including avoiding unfriendly staff and actively seeking out those who were more inclusive.

If they weren’t very nice, it wouldn’t be very enjoyable and if they didn’t trust you, then it would be a bit frustrating, that like I can do this, but you won’t let me (P12 during). If another nurse was not nice to me, and I was their buddy, I would literally just not buddy with them and go and follow whoever was nice to me (P4 during).

Relationships with peers were equally important; students on clinical placement with peers valued the shared experience. In contrast, students who attended clinical placement alone at a regional or rural hospital felt disconnected from the opportunities that learning with peers afforded.

Our research explored the emotional responses and perceptions of preparedness of postgraduate entry-to-practice nursing students prior to and during their first clinical placement. In this study, we described how the perceptions of nursing students remained consistent or were modified by their clinical placement experiences. Our analysis of students’ experiences identified three major themes: adjusting and managing a raft of feelings; sinking or swimming; and the reality of navigating placement relationships. We captured similar themes identified in the literature; however, our study also identified novel aspects of nursing students’ experiences of their first clinical placement.

The key theme, adjusting and managing a raft of feelings, which encapsulates anxiety before clinical placement, is consistent with previous research. This theme included concerns in communicating with healthcare staff and managing registered nurses’ negative attitudes and expectations, in addition to an academic workload [ 11 , 24 ]. Concerns not previously identified in the literature included a fear of judgement or discrimination by healthcare staff or patients that might impact the reputation of marginalised communities. Fortunately, these initial fears largely dissipated during clinical placement. Some students discovered that a diverse cultural background was an asset during their clinical placement. Although these initial fears were ameliorated by clinical placement experiences, evidence of such fears before clinical placement is concerning. Further research to identify appropriate support for nursing students from culturally diverse or marginalised communities is warranted. For example, a Finnish study highlighted the importance of mentoring culturally diverse students, creating a pedagogical atmosphere during clinical placement and integrating cultural diversity into nursing education [ 25 ].

Preclinical expectations of being mistreated can be viewed as an unavoidable phenomenon for nursing students [ 26 ]. The existing literature highlights power imbalances and hierarchical differences within the healthcare system, where student nurses may be marginalised, disrespected, and ignored [ 9 , 27 , 28 ]. During their clinical placement, students in our study reported unintentional incivility by supervising nurses: feeling not wanted, ignored, or asked to remain quiet by supervising nurses who were unfriendly or highly critical. These findings were similar to those of Thomas et al.’s [ 29 ] UK study and were particularly heightened at the beginning of clinical placement. Several students acknowledged that nursing staff fatigue from a high turnover of students on their ward and the COVID-19 pandemic could be contributing factors. In response to such incivility, students reported decreased self-confidence and described becoming quiet and withdrawing from active participation with their patients. Students oriented their behaviour towards repetitive low-level tasks, aiming to please and help their supervising nurse, to the detriment of learning opportunities. Fortunately, these incidents did not appear to impact nursing students’ overall experience of clinical placement. Indeed, students found positive experiences with different supervising nurses and their own self-reflection assisted with coping. Other active strategies to combat incivility identified in the current study that were also identified by Thomas et al. [ 29 ] included avoiding nurses who were uncivil, asking to work with nurses who were ‘nice’ to them, and seeking out support from other staff as a coping strategy. The nursing students in our study were undertaking a postgraduate entry-to-practice qualification and already had an undergraduate degree. The likely greater levels of experience and maturity of this cohort may influence their resilience when working with unsupportive supervising nurses and identifying strategies to manage challenging situations.

The theory-practice gap emerged in the theme of sinking or swimming. A theory-practice gap describes the perceived dissonance between theoretical knowledge and expectations for the first clinical placement, as opposed to the reality of the experience, and has been reported in previous studies (see, for instance, 24 , 30 , 31 , 32 ). Existing research has shown that when the first clinical placement does not meet inexperienced student nurses’ expectations, a disconnect between theory and practice occurs, creating feelings of being lost and insecure within the new environment, potentially impacting students’ motivation and risk of attrition [ 19 , 33 ]. The current study identified further areas exacerbating the theory-practice gap. Before the clinical placement, students without a healthcare background lacked context for their learning. They lacked understanding of nurses’ shift work and were apprehensive about applying clinical skills learned in the classroom. Hence, some students were uncertain if they were prepared for their first clinical placement or even how to prepare, which increased their anxiety. Prior research has demonstrated that applying theoretical knowledge more seamlessly during clinical placement was supported when students knew what to expect [ 6 ]. For instance, a Canadian study exposed students as observers to the healthcare setting before starting clinical placement, enabling early theory to practice connections that minimised misconceptions and false assumptions during clinical placement [ 34 ].

In the current study, the theory-practice gap was further exacerbated during clinical placement, where healthcare staff were confused about students’ scope of practice and the course learning objectives and expectations in a postgraduate entry-to-practice nursing qualification. The central booking system for clinical placements classifies first-year nursing students who participated in this study as equivalent to second-year undergraduate nursing students. Such a classification could create a misalignment between clinical educators’ expectations and their delivery of education versus students’ actual learning needs and capacity [ 3 , 31 ]. Additional communication to healthcare partners is warranted to enhance understanding of the scope of practice and expectations of a first-year postgraduate entry-to-practice nursing student. Educating and empowering students to communicate their learning needs within their scope of practice is also required.

Our research identified a link between students’ personality traits or individual agency and their first clinical placement experience. The importance of a positive orientation towards learning and the nursing profession in preparedness for clinical placement has been highlighted in previous studies [ 31 ]. Students’ experience of their first clinical placement in our study appeared to be strongly influenced by their mindset [ 23 ]. Some students demonstrated motivation to learn, were happy to ‘roll with the punches’, yet remain active in their learning requirements, whereas others perceived their role as observational and expected supervising nurses to provide learning opportunities. Students who anticipated a passive learning approach prior to their first clinical placement reported boredom, limited activity, and lack of opportunities during their first clinical placement. These students could have a lowered sense of self-efficacy, which may lead to a greater risk of doubt, stress, and reduced commitment to the profession [ 35 ]. Self-efficacy theory explores self-perceived confidence and competence around people’s beliefs in their ability to influence events, which is associated with motivation and is key to nursing students progressing in their career path confidently [ 35 , 36 ]. In the current study, students who actively engaged in their learning process used strategies such as self-reflection and sought support from clinical educators, peers and family. Such active approaches to learning appeared to increase their resilience and motivation to learn as they progressed in their first clinical placement.

Important relationships with supervising nurses, peers, or patients were highlighted in the theme of the reality of navigating placement relationships. This theme links with previous research findings about belongingness. Belongingness is a fundamental human need and impacts students’ behaviour, emotions, cognitive processes, overall well-being, and socialisation into the profession [ 37 , 38 ]. Nursing students who experience belongingness feel part of a team and are more likely to report positive experiences. Several students in the current study described how feeling part of a team improved self-confidence and empowered work-integrated learning. Nonetheless, compared with previous literature (see for instance, 2), working as a team and belongingness were infrequent themes. Such infrequency could be related to the short duration of the clinical placement. In shorter clinical placements, nursing students learn a range of technical skills but have less time to develop teamwork skills and experience socialisation to the profession [ 29 , 39 ].

Positive relationships with supervising nurses appeared fundamental to students’ experiences. Previous research has shown that in wards with safe psycho-social climates, where the culture tolerates mistakes, regarding them as learning opportunities, a pedagogical atmosphere prevails [ 25 , 39 ]. Whereas, if nursing students experience insolent behaviours or incivility, this not only impacts learning it can also affect career progression [ 26 ]. Participants who felt safe asking questions were given responsibility, had autonomy to conduct skills within their scope of practice and thrived in their learning. This finding aligns with previous research affirming that a welcoming and supportive clinical placement environment, where staff are caring, approachable and helpful, enables student nurses to flourish [ 36 , 40 , 41 , 42 ]. Related research highlights that students’ perception of a good clinical placement is linked to participation within the community and instructor behaviour over the quality of the clinical environment and opportunities [ 27 , 28 ]. Over a decade ago, a large European study found that the single most important element for students’ clinical learning was the supervisory relationship [ 39 ]. In our study, students identified how supervising nurses impacted their emotions and this was critical to their experience of clinical placement, rather than how effective they were in their teaching, delivery of feedback, or their knowledge base.

Students’ relationships with patients were similarly important for a successful clinical placement. Before the clinical placement, students expressed anxiety and fears in communicating and interacting with patients, particularly if they were dying or acutely unwell, which is reflective of the literature [ 2 , 10 , 11 ]. However, during clinical placement, relationships with patients positively impacted nursing students’ experiences, especially at the beginning when they felt particularly vulnerable in a new environment. Towards the end of clinical placement, feelings of incompetence, nervousness and uncertainty had subsided. Students were more active in patient care, which increased self-confidence, empowerment, and independence, in turn further improving relationships with patients and creating a positive feedback loop [ 36 , 42 , 43 ].

Limitations

This study involved participants from one university and a single course, thus limiting the generalisability of the results. Thus, verification of the major themes identified in this research in future studies is needed. Nonetheless, the purpose of this study was to explore in detail the way in which the experiences of clinical placement for student nurses modified initial emotional responses towards undertaking placement and their perceptions of preparedness. Participants in this study undertook their clinical placement in a variety of different hospital wards in different specialties, which contributed to the rigour of the study in identifying similar themes in nursing students’ experiences across diverse placement contexts.

This study explored the narratives of first-year nursing students undertaking a postgraduate entry-to-practice qualification on their preparedness for clinical placement. Exploring students’ changing perspectives before and during the clinical placement adds to extant knowledge about nursing students’ emotional responses and perceptions of preparedness. Our research highlighted the role that preplacement emotions and expectations may have in shaping nursing students’ clinical placement experiences. Emerging themes from this study highlighted the importance students placed on relationships with peers, patients, and supervising nurses. Significant anxiety and other negative emotions experienced by nursing students prior to the first clinical placement suggests that further research is needed to explore the impact of contextual learning to scaffold students’ transition to the clinical environment. The findings of this research also have significant implications for educational practice. Additional educational support for nursing students prior to entering the clinical environment for the first time might include developing students’ understanding of the clinical environment, such as through increasing students’ understanding of the different roles of nurses in the clinical context through pre-recorded interviews with nurses. Modified approaches to simulated teaching prior to the first clinical placement would also be useful to increase the emphasis on students applying their learning in a team-based, student-led context, rather than emphasising discrete clinical skill competencies. Finally, increasing contact between students and university-based educators throughout the placement would provide further opportunities for students to debrief, to receive support and to manage some of the negative emotions identified in this study. Further supporting the transition to the first clinical placement could be fundamental to reducing the theory-practice gap and allaying anxiety. Such support is crucial during their first clinical placement to reduce attrition and boost the nursing workforce.

Data availability

The datasets generated and/or analysed during the current study are not publicly available due to the conditions of our ethics approval but may be available from the corresponding author on reasonable request and subject to permission from the Human Research Ethics Committee.

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Acknowledgements

The authors wish to thank the first-year nursing students who participated in this study and generously shared their experiences of undertaking their first clinical placement.

No funding was received for this study.

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Jennifer M. Weller-Newton

Present address: School of Nursing and Midwifery, University of Canberra, Kirinari Drive, Bruce, Canberra, ACT, 2617, Australia

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Department of Nursing, The University of Melbourne, Grattan St, Parkville, VIC, 3010, Australia

Philippa H. M. Marriott

Department of Rural Health, The University of Melbourne, Grattan St, Shepparton, VIC, 3630, Australia

Present address: Department of Medical Education, Melbourne Medical School, The University of Melbourne, Grattan St, Parkville, VIC, 3010, Australia

Katharine J. Reid

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All authors made a substantial contribution to conducting the research and preparing the manuscript for publication. P.M., J.W-N. and K.R. conceptualised the research and designed the study. P.M. undertook the data collection, and all authors were involved in thematic analysis and interpretation. P.M. wrote the first draft of the manuscript, K.R. undertook a further revision and all authors contributed to subsequent versions. All authors approved the final version for submission. Each author is prepared to take public responsibility for the research.

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The research was undertaken in accordance with the National Health and Medical Research Council of Australia’s National Statement on Ethical Conduct in Human Research and the Australian Code for the Responsible Conduct of Research. Ethical approval to conduct the study was obtained from the University of Melbourne Human Research Ethics Committee (Ethics ID 1955997.1). All participants received a plain language statement that described the requirements of the study. All participants provided informed written consent to participate, which was affirmed verbally at the beginning of focus groups and interviews.

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Marriott, P.H.M., Weller-Newton, J.M. & Reid, K.J. Preparedness for a first clinical placement in nursing: a descriptive qualitative study. BMC Nurs 23 , 345 (2024). https://doi.org/10.1186/s12912-024-01916-x

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