what is a relational research question

Research Question 101 📖

Everything you need to know to write a high-quality research question

By: Derek Jansen (MBA) | Reviewed By: Dr. Eunice Rautenbach | October 2023

If you’ve landed on this page, you’re probably asking yourself, “ What is a research question? ”. Well, you’ve come to the right place. In this post, we’ll explain what a research question is , how it’s differen t from a research aim, and how to craft a high-quality research question that sets you up for success.

Research Question 101

What is a research question.

• Research questions vs research aims
• The 4 types of research questions
• How to write a research question
• Frequently asked questions
• Examples of research questions

As the name suggests, the research question is the core question (or set of questions) that your study will (attempt to) answer .

In many ways, a research question is akin to a target in archery . Without a clear target, you won’t know where to concentrate your efforts and focus. Essentially, your research question acts as the guiding light throughout your project and informs every choice you make along the way.

Let’s look at some examples:

What impact does social media usage have on the mental health of teenagers in New York?

How does the introduction of a minimum wage affect employment levels in small businesses in outer London?

How does the portrayal of women in 19th-century American literature reflect the societal attitudes of the time?

What are the long-term effects of intermittent fasting on heart health in adults?

As you can see in these examples, research questions are clear, specific questions that can be feasibly answered within a study. These are important attributes and we’ll discuss each of them in more detail a little later . If you’d like to see more examples of research questions, you can find our RQ mega-list here .

Research Questions vs Research Aims

At this point, you might be asking yourself, “ How is a research question different from a research aim? ”. Within any given study, the research aim and research question (or questions) are tightly intertwined , but they are separate things . Let’s unpack that a little.

A research aim is typically broader in nature and outlines what you hope to achieve with your research. It doesn’t ask a specific question but rather gives a summary of what you intend to explore.

The research question, on the other hand, is much more focused . It’s the specific query you’re setting out to answer. It narrows down the research aim into a detailed, researchable question that will guide your study’s methods and analysis.

Let’s look at an example:

Research Aim: To explore the effects of climate change on marine life in Southern Africa.

Research Question: How does ocean acidification caused by climate change affect the reproduction rates of coral reefs?

As you can see, the research aim gives you a general focus , while the research question details exactly what you want to find out.

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Types of research questions

Now that we’ve defined what a research question is, let’s look at the different types of research questions that you might come across. Broadly speaking, there are (at least) four different types of research questions – descriptive , comparative , relational , and explanatory . 

Descriptive questions ask what is happening. In other words, they seek to describe a phenomena or situation . An example of a descriptive research question could be something like “What types of exercise do high-performing UK executives engage in?”. This would likely be a bit too basic to form an interesting study, but as you can see, the research question is just focused on the what – in other words, it just describes the situation.

Comparative research questions , on the other hand, look to understand the way in which two or more things differ , or how they’re similar. An example of a comparative research question might be something like “How do exercise preferences vary between middle-aged men across three American cities?”. As you can see, this question seeks to compare the differences (or similarities) in behaviour between different groups.

Next up, we’ve got exploratory research questions , which ask why or how is something happening. While the other types of questions we looked at focused on the what, exploratory research questions are interested in the why and how . As an example, an exploratory research question might ask something like “Why have bee populations declined in Germany over the last 5 years?”. As you can, this question is aimed squarely at the why, rather than the what.

Last but not least, we have relational research questions . As the name suggests, these types of research questions seek to explore the relationships between variables . Here, an example could be something like “What is the relationship between X and Y” or “Does A have an impact on B”. As you can see, these types of research questions are interested in understanding how constructs or variables are connected , and perhaps, whether one thing causes another.

Of course, depending on how fine-grained you want to get, you can argue that there are many more types of research questions , but these four categories give you a broad idea of the different flavours that exist out there. It’s also worth pointing out that a research question doesn’t need to fit perfectly into one category – in many cases, a research question might overlap into more than just one category and that’s okay.

The key takeaway here is that research questions can take many different forms , and it’s useful to understand the nature of your research question so that you can align your research methodology accordingly.

How To Write A Research Question

As we alluded earlier, a well-crafted research question needs to possess very specific attributes, including focus , clarity and feasibility . But that’s not all – a rock-solid research question also needs to be rooted and aligned . Let’s look at each of these.

A strong research question typically has a single focus. So, don’t try to cram multiple questions into one research question; rather split them up into separate questions (or even subquestions), each with their own specific focus. As a rule of thumb, narrow beats broad when it comes to research questions.

Clear and specific

A good research question is clear and specific, not vague and broad. State clearly exactly what you want to find out so that any reader can quickly understand what you’re looking to achieve with your study. Along the same vein, try to avoid using bulky language and jargon – aim for clarity.

Unfortunately, even a super tantalising and thought-provoking research question has little value if you cannot feasibly answer it. So, think about the methodological implications of your research question while you’re crafting it. Most importantly, make sure that you know exactly what data you’ll need (primary or secondary) and how you’ll analyse that data.

A good research question (and a research topic, more broadly) should be rooted in a clear research gap and research problem . Without a well-defined research gap, you risk wasting your effort pursuing a question that’s already been adequately answered (and agreed upon) by the research community. A well-argued research gap lays at the heart of a valuable study, so make sure you have your gap clearly articulated and that your research question directly links to it.

As we mentioned earlier, your research aim and research question are (or at least, should be) tightly linked. So, make sure that your research question (or set of questions) aligns with your research aim . If not, you’ll need to revise one of the two to achieve this.

FAQ: Research Questions

Research question faqs, how many research questions should i have, what should i avoid when writing a research question, can a research question be a statement.

Typically, a research question is phrased as a question, not a statement. A question clearly indicates what you’re setting out to discover.

Can a research question be too broad or too narrow?

Yes. A question that’s too broad makes your research unfocused, while a question that’s too narrow limits the scope of your study.

Here’s an example of a research question that’s too broad:

“Why is mental health important?”

Conversely, here’s an example of a research question that’s likely too narrow:

“What is the impact of sleep deprivation on the exam scores of 19-year-old males in London studying maths at The Open University?”

Can I change my research question during the research process?

How do i know if my research question is good.

A good research question is focused, specific, practical, rooted in a research gap, and aligned with the research aim. If your question meets these criteria, it’s likely a strong question.

Is a research question similar to a hypothesis?

Not quite. A hypothesis is a testable statement that predicts an outcome, while a research question is a query that you’re trying to answer through your study. Naturally, there can be linkages between a study’s research questions and hypothesis, but they serve different functions.

How are research questions and research objectives related?

The research question is a focused and specific query that your study aims to answer. It’s the central issue you’re investigating. The research objective, on the other hand, outlines the steps you’ll take to answer your research question. Research objectives are often more action-oriented and can be broken down into smaller tasks that guide your research process. In a sense, they’re something of a roadmap that helps you answer your research question.

Need some inspiration?

If you’d like to see more examples of research questions, check out our research question mega list here .  Alternatively, if you’d like 1-on-1 help developing a high-quality research question, consider our private coaching service .

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Research: Articulating Questions, Generating Hypotheses, and Choosing Study Designs

Introduction.

Articulating a clear and concise research question is fundamental to conducting a robust and useful research study. Although “getting stuck into” the data collection is the exciting part of research, this preparation stage is crucial. Clear and concise research questions are needed for a number of reasons. Initially, they are needed to enable you to search the literature effectively. They will allow you to write clear aims and generate hypotheses. They will also ensure that you can select the most appropriate research design for your study.

This paper begins by describing the process of articulating clear and concise research questions, assuming that you have minimal experience. It then describes how to choose research questions that should be answered and how to generate study aims and hypotheses from your questions. Finally, it describes briefly how your question will help you to decide on the research design and methods best suited to answering it.

TURNING CURIOSITY INTO QUESTIONS

A research question has been described as “the uncertainty that the investigator wants to resolve by performing her study” 1 or “a logical statement that progresses from what is known or believed to be true to that which is unknown and requires validation”. 2 Developing your question usually starts with having some general ideas about the areas within which you want to do your research. These might flow from your clinical work, for example. You might be interested in finding ways to improve the pharmaceutical care of patients on your wards. Alternatively, you might be interested in identifying the best antihypertensive agent for a particular subgroup of patients. Lipowski 2 described in detail how work as a practising pharmacist can be used to great advantage to generate interesting research questions and hence useful research studies. Ideas could come from questioning received wisdom within your clinical area or the rationale behind quick fixes or workarounds, or from wanting to improve the quality, safety, or efficiency of working practice.

Alternatively, your ideas could come from searching the literature to answer a query from a colleague. Perhaps you could not find a published answer to the question you were asked, and so you want to conduct some research yourself. However, just searching the literature to generate questions is not to be recommended for novices—the volume of material can feel totally overwhelming.

Use a research notebook, where you regularly write ideas for research questions as you think of them during your clinical practice or after reading other research papers. It has been said that the best way to have a great idea is to have lots of ideas and then choose the best. The same would apply to research questions!

When you first identify your area of research interest, it is likely to be either too narrow or too broad. Narrow questions (such as “How is drug X prescribed for patients with condition Y in my hospital?”) are usually of limited interest to anyone other than the researcher. Broad questions (such as “How can pharmacists provide better patient care?”) must be broken down into smaller, more manageable questions. If you are interested in how pharmacists can provide better care, for example, you might start to narrow that topic down to how pharmacists can provide better care for one condition (such as affective disorders) for a particular subgroup of patients (such as teenagers). Then you could focus it even further by considering a specific disorder (depression) and a particular type of service that pharmacists could provide (improving patient adherence). At this stage, you could write your research question as, for example, “What role, if any, can pharmacists play in improving adherence to fluoxetine used for depression in teenagers?”

TYPES OF RESEARCH QUESTIONS

Being able to consider the type of research question that you have generated is particularly useful when deciding what research methods to use. There are 3 broad categories of question: descriptive, relational, and causal.

Descriptive

One of the most basic types of question is designed to ask systematically whether a phenomenon exists. For example, we could ask “Do pharmacists ‘care’ when they deliver pharmaceutical care?” This research would initially define the key terms (i.e., describing what “pharmaceutical care” and “care” are), and then the study would set out to look for the existence of care at the same time as pharmaceutical care was being delivered.

When you know that a phenomenon exists, you can then ask description and/or classification questions. The answers to these types of questions involve describing the characteristics of the phenomenon or creating typologies of variable subtypes. In the study above, for example, you could investigate the characteristics of the “care” that pharmacists provide. Classifications usually use mutually exclusive categories, so that various subtypes of the variable will have an unambiguous category to which they can be assigned. For example, a question could be asked as to “what is a pharmacist intervention” and a definition and classification system developed for use in further research.

When seeking further detail about your phenomenon, you might ask questions about its composition. These questions necessitate deconstructing a phenomenon (such as a behaviour) into its component parts. Within hospital pharmacy practice, you might be interested in asking questions about the composition of a new behavioural intervention to improve patient adherence, for example, “What is the detailed process that the pharmacist implicitly follows during delivery of this new intervention?”

After you have described your phenomena, you may then be interested in asking questions about the relationships between several phenomena. If you work on a renal ward, for example, you may be interested in looking at the relationship between hemoglobin levels and renal function, so your question would look something like this: “Are hemoglobin levels related to level of renal function?” Alternatively, you may have a categorical variable such as grade of doctor and be interested in the differences between them with regard to prescribing errors, so your research question would be “Do junior doctors make more prescribing errors than senior doctors?” Relational questions could also be asked within qualitative research, where a detailed understanding of the nature of the relationship between, for example, the gender and career aspirations of clinical pharmacists could be sought.

Once you have described your phenomena and have identified a relationship between them, you could ask about the causes of that relationship. You may be interested to know whether an intervention or some other activity has caused a change in your variable, and your research question would be about causality. For example, you may be interested in asking, “Does captopril treatment reduce blood pressure?” Generally, however, if you ask a causality question about a medication or any other health care intervention, it ought to be rephrased as a causality–comparative question. Without comparing what happens in the presence of an intervention with what happens in the absence of the intervention, it is impossible to attribute causality to the intervention. Although a causality question would usually be answered using a comparative research design, asking a causality–comparative question makes the research design much more explicit. So the above question could be rephrased as, “Is captopril better than placebo at reducing blood pressure?”

The acronym PICO has been used to describe the components of well-crafted causality–comparative research questions. 3 The letters in this acronym stand for Population, Intervention, Comparison, and Outcome. They remind the researcher that the research question should specify the type of participant to be recruited, the type of exposure involved, the type of control group with which participants are to be compared, and the type of outcome to be measured. Using the PICO approach, the above research question could be written as “Does captopril [ intervention ] decrease rates of cardiovascular events [ outcome ] in patients with essential hypertension [ population ] compared with patients receiving no treatment [ comparison ]?”

DECIDING WHETHER TO ANSWER A RESEARCH QUESTION

Just because a question can be asked does not mean that it needs to be answered. Not all research questions deserve to have time spent on them. One useful set of criteria is to ask whether your research question is feasible, interesting, novel, ethical, and relevant. 1 The need for research to be ethical will be covered in a later paper in the series, so is not discussed here. The literature review is crucial to finding out whether the research question fulfils the remaining 4 criteria.

Conducting a comprehensive literature review will allow you to find out what is already known about the subject and any gaps that need further exploration. You may find that your research question has already been answered. However, that does not mean that you should abandon the question altogether. It may be necessary to confirm those findings using an alternative method or to translate them to another setting. If your research question has no novelty, however, and is not interesting or relevant to your peers or potential funders, you are probably better finding an alternative.

The literature will also help you learn about the research designs and methods that have been used previously and hence to decide whether your potential study is feasible. As a novice researcher, it is particularly important to ask if your planned study is feasible for you to conduct. Do you or your collaborators have the necessary technical expertise? Do you have the other resources that will be needed? If you are just starting out with research, it is likely that you will have a limited budget, in terms of both time and money. Therefore, even if the question is novel, interesting, and relevant, it may not be one that is feasible for you to answer.

GENERATING AIMS AND HYPOTHESES

All research studies should have at least one research question, and they should also have at least one aim. As a rule of thumb, a small research study should not have more than 2 aims as an absolute maximum. The aim of the study is a broad statement of intention and aspiration; it is the overall goal that you intend to achieve. The wording of this broad statement of intent is derived from the research question. If it is a descriptive research question, the aim will be, for example, “to investigate” or “to explore”. If it is a relational research question, then the aim should state the phenomena being correlated, such as “to ascertain the impact of gender on career aspirations”. If it is a causal research question, then the aim should include the direction of the relationship being tested, such as “to investigate whether captopril decreases rates of cardiovascular events in patients with essential hypertension, relative to patients receiving no treatment”.

The hypothesis is a tentative prediction of the nature and direction of relationships between sets of data, phrased as a declarative statement. Therefore, hypotheses are really only required for studies that address relational or causal research questions. For the study above, the hypothesis being tested would be “Captopril decreases rates of cardiovascular events in patients with essential hypertension, relative to patients receiving no treatment”. Studies that seek to answer descriptive research questions do not test hypotheses, but they can be used for hypothesis generation. Those hypotheses would then be tested in subsequent studies.

CHOOSING THE STUDY DESIGN

The research question is paramount in deciding what research design and methods you are going to use. There are no inherently bad research designs. The rightness or wrongness of the decision about the research design is based simply on whether it is suitable for answering the research question that you have posed.

It is possible to select completely the wrong research design to answer a specific question. For example, you may want to answer one of the research questions outlined above: “Do pharmacists ‘care’ when they deliver pharmaceutical care?” Although a randomized controlled study is considered by many as a “gold standard” research design, such a study would just not be capable of generating data to answer the question posed. Similarly, if your question was, “Is captopril better than placebo at reducing blood pressure?”, conducting a series of in-depth qualitative interviews would be equally incapable of generating the necessary data. However, if these designs are swapped around, we have 2 combinations (pharmaceutical care investigated using interviews; captopril investigated using a randomized controlled study) that are more likely to produce robust answers to the questions.

The language of the research question can be helpful in deciding what research design and methods to use. Subsequent papers in this series will cover these topics in detail. For example, if the question starts with “how many” or “how often”, it is probably a descriptive question to assess the prevalence or incidence of a phenomenon. An epidemiological research design would be appropriate, perhaps using a postal survey or structured interviews to collect the data. If the question starts with “why” or “how”, then it is a descriptive question to gain an in-depth understanding of a phenomenon. A qualitative research design, using in-depth interviews or focus groups, would collect the data needed. Finally, the term “what is the impact of” suggests a causal question, which would require comparison of data collected with and without the intervention (i.e., a before–after or randomized controlled study).

CONCLUSIONS

This paper has briefly outlined how to articulate research questions, formulate your aims, and choose your research methods. It is crucial to realize that articulating a good research question involves considerable iteration through the stages described above. It is very common that the first research question generated bears little resemblance to the final question used in the study. The language is changed several times, for example, because the first question turned out not to be feasible and the second question was a descriptive question when what was really wanted was a causality question. The books listed in the “Further Reading” section provide greater detail on the material described here, as well as a wealth of other information to ensure that your first foray into conducting research is successful.

This article is the second in the CJHP Research Primer Series, an initiative of the CJHP Editorial Board and the CSHP Research Committee. The planned 2-year series is intended to appeal to relatively inexperienced researchers, with the goal of building research capacity among practising pharmacists. The articles, presenting simple but rigorous guidance to encourage and support novice researchers, are being solicited from authors with appropriate expertise.

Previous article in this series:

Bond CM. The research jigsaw: how to get started. Can J Hosp Pharm . 2014;67(1):28–30.

Competing interests: Mary Tully has received personal fees from the UK Renal Pharmacy Group to present a conference workshop on writing research questions and nonfinancial support (in the form of travel and accommodation) from the Dubai International Pharmaceuticals and Technologies Conference and Exhibition (DUPHAT) to present a workshop on conducting pharmacy practice research.

Further Reading

• Cresswell J. Research design: qualitative, quantitative and mixed methods approaches. London (UK): Sage; 2009. [ Google Scholar ]
• Haynes RB, Sackett DL, Guyatt GH, Tugwell P. Clinical epidemiology: how to do clinical practice research. 3rd ed. Philadelphia (PA): Lippincott, Williams & Wilkins; 2006. [ Google Scholar ]
• Kumar R. Research methodology: a step-by-step guide for beginners. 3rd ed. London (UK): Sage; 2010. [ Google Scholar ]
• Smith FJ. Conducting your pharmacy practice research project. London (UK): Pharmaceutical Press; 2005. [ Google Scholar ]

Scientific Research and Methodology

2.4 types of rqs.

All RQs have a population (P) and an outcome (O). However, different types of RQ emerge depending on whether the RQ also has an comparison/connection (C) or intervention (I).

This section studies different types of research questions:

• Descriptive RQs ;
• Relational RQs ;
• Interventional RQs .

These are compared in Sect. 2.4.4 .

2.4.1 Descriptive RQs (PO)

Descriptive RQs are the most basic RQs, and identify:

• The P opulation to be studied.
• The O utcome of interest about this population.

Typically, descriptive RQs look like this:

Among {the population}, what is {the outcome}?

Example 2.13 (Descriptive RQ) Consider this RQ:

Among Australian males between 18 and 35 years of age, what is the average heart rate?

In this RQ, the Population is ‘Australians males between 18 and 35 years of age,’ and the Outcome is ‘ Average heart rate.’ Notice that the Outcome is a numerical summary of the Outcome across the population (the average heart rate).

Think 2.3 (POCI) Consider this RQ:

Among Australian adults, what proportion are coeliacs?

For this RQ, identify the P opulation and the O utcome.

2.4.2 Relational RQs (POC)

Usually, relationships are more interesting than just descriptions; relational RQs explore existing relationships. Relational RQs identify:

• The P opulation.
• The O utcome.
• The C omparison or C onnection.

Relational RQs have no intervention; the connection or comparison is not imposed by the researchers.

Typically, relational RQs based on a comparison look like this:

Among {the population}, is {the outcome} the same for {the groups being compared}?

Example 2.14 (Relational RQ) Consider this RQ:

Among Australians between 18 and 35 years of age, is the average heart rate the same for females and males?

In this RQ, the Population is ‘Australians between 18 and 35 years of age,’ the Outcome is ‘average heart rate,’ and the Comparison is ‘between females and males.’

This is a relational RQ based on a comparison . Notice that the average heart rate (Outcome) is a numerical summary across the two population sub-groups being compared (females; males).

Typically, relational RQs based on a connection look like this:

Among {the population}, is {the outcome} related to {something else}?

Example 2.15 (Relational RQ) Consider this RQ:

Among Australians between 18 and 35 years of age, is the average heart rate related to age?

In this RQ, the Population is ‘Australians between 18 and 35 years of age,’ the Outcome is ‘average heart rate,’ and the Connection is with ‘age.’

Think 2.4 (POCI) Consider this RQ (based on Brown et al. ( 2000 ) ):

In the Queensland Ambulance Service last year, what was the difference between the average response time to emergency calls between weekdays and weekends?

Think 2.5 (POCI) Consider this RQ (based on Maron ( 2007 ) ):

In Queensland state forests, is there a relationship between the average number of noisy miners and the number of eucalypts, in general?

Example 2.16 (Descriptive and relational RQs) Consider a study of blood pressure in Australians (the Population), comparing right- and left-arm blood pressures.

This is a descriptive RQ. There is no comparison, since there are not two subsets of the population being compared.

The blood pressure is measured twice on each member of the population: every member of the population is treated in the same way. The outcome is ‘the average difference between right- and left-arm blood pressure.’ This is a descriptive RQ.

2.4.3 Interventional RQs (POCI)

Interventional RQs explore relationships where the comparison/connection is determined or allocated by the researchers. They identify:

• The I ntervention.

Interventional RQs may look like relational RQs, except that the comparison or connection is determined or allocated (i.e., imposed) by the researchers.

Sometimes it is not clear if the comparison or connection has been imposed by the researchers in an interventional RQ. When writing interventional RQs, make efforts to make it clear, if possible, when the RQ is interventional.

Example 2.17 (Interventional RQ) Consider this RQ:

Among Australians between 18 and 35 years of age, is the average heart rate for people allocated to receive a new pill the same as for people allocated to receive an existing pill?

In this RQ, the Population is ‘Australians between 18 and 35 years of age,’ the Outcome is ‘average heart rate,’ and the Comparison is ‘between those taking the new pill, and those taking the existing pill.’

Consider this RQ ( McLinn et al. 1994 ) :

In children with acute otitis media, what is the difference in the average duration of symptoms when treated with cefuroxime compared to amoxicillin?

In this RQ, the Population is ‘children with acute atitis media,’ the Outcome is ‘average duration of symptoms,’ and the Connection is between the types of drug (comparing ‘cefuroxime’ and ‘amoxicillin’).

It is not clear if there is an Intervention.

If the drugs are given to the children by the researchers, there is an intervention (giving the drug).

If the researchers just find children who are already taking the two drugs and measure the outcome (‘average duration of symptoms’), there is no intervention.

2.4.4 Comparing the three levels of RQs

Descriptive RQs are the most basic and are usually used when a research topic is in its infancy; descriptive RQs set the platform for asking relational questions.

Relational RQs explore relationships, and provide an understanding of how the outcome of interest is related to certain sub-groups of the population; they may set the platform for asking interventional questions.

Interventional RQs (when possible to answer) are the most interesting: they can be used to test theories or models, or to establish cause-and-effect relationships (Table 2.1 ).

Research often develops through these stages of RQs as knowledge grows and develops. For example:

• Descriptive : What proportion of Australian adults are coeliacs ( Cook et al. 2000 ) ?
• Relational : Among Australian adults, is the proportion of females who are coeliacs the same as the proportion of males who are coeliacs ( Cook et al. 2000 ) ?
• Interventional : Among Australian adult coeliacs, is the percentage with adverse symptoms the same for those given a diet without oats and those given a diet with oats? ( Janatuinen et al. 2002 ; Lundin et al. 2003 ) ?

Think 2.6 (RQ types) What type of RQs are the following: Descriptive, Relational, or Interventional?

• Among Australian upper-limb amputees, is the percentage wearing prosthesis ‘all the time’ the same for transradial and transhumeral amputations? ( Davidson 2002 )
• In New York, what is the difference between the average height of oaks trees ten weeks after planting, comparing trees planted in a concrete sidewalk and a grassed sidewalk? ( Grabosky and Bassuk 2016 )
• What is the average response time of paramedics to emergency calls? ( Pons et al. 2005 )
• Is there a relationship between the average weekly hours of physical activity in children and the weekly maximum temperature? ( Edwards et al. 2015 )

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There are three basic types of questions that research projects can address:

• Descriptive. When a study is designed primarily to describe what is going on or what exists. Public opinion polls that seek only to describe the proportion of people who hold various opinions are primarily descriptive in nature. For instance, if we want to know what percent of the population would vote for a Democratic or a Republican in the next presidential election, we are simply interested in describing something.
• Relational. When a study is designed to look at the relationships between two or more variables. A public opinion poll that compares what proportion of males and females say they would vote for a Democratic or a Republican candidate in the next presidential election is essentially studying the relationship between gender and voting preference.
• Causal. When a study is designed to determine whether one or more variables (e.g. a program or treatment variable) causes or affects one or more outcome variables. If we did a public opinion poll to try to determine whether a recent political advertising campaign changed voter preferences, we would essentially be studying whether the campaign (cause) changed the proportion of voters who would vote Democratic or Republican (effect).

The three question types can be viewed as cumulative. That is, a relational study assumes that you can first describe (by measuring or observing) each of the variables you are trying to relate. And, a causal study assumes that you can describe both the cause and effect variables and that you can show that they are related to each other. Causal studies are probably the most demanding of the three.

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• Writing Strong Research Questions | Criteria & Examples

Writing Strong Research Questions | Criteria & Examples

Published on 30 October 2022 by Shona McCombes . Revised on 12 December 2023.

A research question pinpoints exactly what you want to find out in your work. A good research question is essential to guide your research paper , dissertation , or thesis .

All research questions should be:

• Focused on a single problem or issue
• Researchable using primary and/or secondary sources
• Feasible to answer within the timeframe and practical constraints
• Specific enough to answer thoroughly
• Complex enough to develop the answer over the space of a paper or thesis
• Relevant to your field of study and/or society more broadly

Table of contents

How to write a research question, what makes a strong research question, research questions quiz, frequently asked questions.

You can follow these steps to develop a strong research question:

• Choose your topic
• Do some preliminary reading about the current state of the field
• Narrow your focus to a specific niche
• Identify the research problem that you will address

The way you frame your question depends on what your research aims to achieve. The table below shows some examples of how you might formulate questions for different purposes.

Using your research problem to develop your research question

Note that while most research questions can be answered with various types of research , the way you frame your question should help determine your choices.

Prevent plagiarism, run a free check.

Research questions anchor your whole project, so it’s important to spend some time refining them. The criteria below can help you evaluate the strength of your research question.

Focused and researchable

Feasible and specific, complex and arguable, relevant and original.

The way you present your research problem in your introduction varies depending on the nature of your research paper . A research paper that presents a sustained argument will usually encapsulate this argument in a thesis statement .

A research paper designed to present the results of empirical research tends to present a research question that it seeks to answer. It may also include a hypothesis – a prediction that will be confirmed or disproved by your research.

As you cannot possibly read every source related to your topic, it’s important to evaluate sources to assess their relevance. Use preliminary evaluation to determine whether a source is worth examining in more depth.

This involves:

• Reading abstracts , prefaces, introductions , and conclusions
• Looking at the table of contents to determine the scope of the work
• Consulting the index for key terms or the names of important scholars

An essay isn’t just a loose collection of facts and ideas. Instead, it should be centered on an overarching argument (summarised in your thesis statement ) that every part of the essay relates to.

The way you structure your essay is crucial to presenting your argument coherently. A well-structured essay helps your reader follow the logic of your ideas and understand your overall point.

A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (‘ x affects y because …’).

A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses. In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.

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Types of Research Questions

Research questions can be categorized into different types, depending on the type of research to be undertaken.

Qualitative questions concern broad areas or more specific areas of research and focus on discovering, explaining and exploring.  Types of qualitative questions include:

• Exploratory Questions, which seeks to understand without influencing the results.  The objective is to learn more about a topic without bias or preconceived notions.
• Predictive Questions, which seek to understand the intent or future outcome around a topic.
• Interpretive Questions, which tries to understand people’s behavior in a natural setting.  The objective is to understand how a group makes sense of shared experiences with regards to various phenomena.

Quantitative questions prove or disprove a  researcher’s hypothesis and are constructed to express the relationship between variables  and whether this relationship is significant.  Types of quantitative questions include:

• Descriptive questions , which are the most basic type of quantitative research question and seeks to explain the when, where, why or how something occurred. 
• Comparative questions are helpful when studying groups with dependent variables where one variable is compared with another.
• Relationship-based questions try to answer whether or not one variable has an influence on another.  These types of question are generally used in experimental research questions.

References/Additional Resources

Lipowski, E. E. (2008). Developing great research questions . American Journal of Health-System Pharmacy, 65(17), 1667–1670.

Ratan, S. K., Anand, T., & Ratan, J. (2019). Formulation of Research Question - Stepwise Approach .  Journal of Indian Association of Pediatric Surgeons ,  24 (1), 15–20.

Fandino W.(2019). Formulating a good research question: Pearls and pitfalls . I ndian J Anaesth. 63(8) :611-616. 

Beck, L. L. (2023). The question: types of research questions and how to develop them . In Translational Surgery: Handbook for Designing and Conducting Clinical and Translational Research (pp. 111-120). Academic Press. 

Doody, O., & Bailey, M. E. (2016). Setting a research question, aim and objective. Nurse Researcher, 23(4), 19–23.

Plano Clark, V., & Badiee, M. (2010). Research questions in mixed methods research . In: SAGE Handbook of Mixed Methods in Social & Behavioral Research .  SAGE Publications, Inc.,

Agee, J. (2009). Developing qualitative research questions: A reflective process .  International journal of qualitative studies in education ,  22 (4), 431-447. 

Flemming, K., & Noyes, J. (2021). Qualitative Evidence Synthesis: Where Are We at? I nternational Journal of Qualitative Methods, 20.  

Research Question Frameworks

Research question frameworks have been designed to help structure research questions and clarify the main concepts. Not every question can fit perfectly into a framework, but using even just parts of a framework can help develop a well-defined research question. The framework to use depends on the type of question to be researched.   There are over 25 research question frameworks available.  The University of Maryland has a nice table listing out several of these research question frameworks, along with what the acronyms mean and what types of questions/disciplines that may be used for.

The process of developing a good research question involves taking your topic and breaking each aspect of it down into its component parts.

Booth, A., Noyes, J., Flemming, K., Moore, G., Tunçalp, Ö., & Shakibazadeh, E. (2019). Formulating questions to explore complex interventions within qualitative evidence synthesis.   BMJ global health ,  4 (Suppl 1), e001107. (See supplementary data#1)

The "Well-Built Clinical Question“: PICO(T)

One well-established framework that can be used both for refining questions and developing strategies is known as PICO(T). The PICO framework was designed primarily for questions that include interventions and comparisons, however other types of questions may also be able to follow its principles.  If the PICO(T) framework does not precisely fit your question, using its principles (see alternative component suggestions) can help you to think about what you want to explore even if you do not end up with a true PICO question.

A PICO(T) question has the following components:

• P : The patient’s disorder or disease or problem of interest / research object
• I: The intervention, exposure or finding under review / Application of a theory or method
• C: A comparison intervention or control (if applicable- not always present)/ Alternative theories or methods (or, in their absence, the null hypothesis)
• O : The outcome(s) (desired or of interest) / Knowledge generation
• T : (The time factor or period)

Keep in mind that solely using a tool will not enable you to design a good question. What is required is for you to think, carefully, about exactly what you want to study and precisely what you mean by each of the things that you think you want to study.

Rzany, & Bigby, M. (n.d.). Formulating Well-Built Clinical Questions. In Evidence-based dermatology / (pp. 27–30). Blackwell Pub/BMJ Books.  

Nishikawa-Pacher, A. (2022). Research questions with PICO: a universal mnemonic.   Publications ,  10 (3), 21.

• << Previous: Characteristics of a good research question
• Next: Choosing the Search Terms >>

• Research Questions: Definitions, Types + [Examples]

Research questions lie at the core of systematic investigation and this is because recording accurate research outcomes is tied to asking the right questions. Asking the right questions when conducting research can help you collect relevant and insightful information that ultimately influences your work, positively. 

The right research questions are typically easy to understand, straight to the point, and engaging. In this article, we will share tips on how to create the right research questions and also show you how to create and administer an online questionnaire with Formplus . 

What is a Research Question? 

A research question is a specific inquiry which the research seeks to provide a response to. It resides at the core of systematic investigation and it helps you to clearly define a path for the research process. 

A research question is usually the first step in any research project. Basically, it is the primary interrogation point of your research and it sets the pace for your work.  

Typically, a research question focuses on the research, determines the methodology and hypothesis, and guides all stages of inquiry, analysis, and reporting. With the right research questions, you will be able to gather useful information for your investigation. 

Types of Research Questions 

Research questions are broadly categorized into 2; that is, qualitative research questions and quantitative research questions. Qualitative and quantitative research questions can be used independently and co-dependently in line with the overall focus and objectives of your research. 

If your research aims at collecting quantifiable data , you will need to make use of quantitative research questions. On the other hand, qualitative questions help you to gather qualitative data bothering on the perceptions and observations of your research subjects. 

Qualitative Research Questions  

A qualitative research question is a type of systematic inquiry that aims at collecting qualitative data from research subjects. The aim of qualitative research questions is to gather non-statistical information pertaining to the experiences, observations, and perceptions of the research subjects in line with the objectives of the investigation. 

Types of Qualitative Research Questions  

• Ethnographic Research Questions

As the name clearly suggests, ethnographic research questions are inquiries presented in ethnographic research. Ethnographic research is a qualitative research approach that involves observing variables in their natural environments or habitats in order to arrive at objective research outcomes. 

These research questions help the researcher to gather insights into the habits, dispositions, perceptions, and behaviors of research subjects as they interact in specific environments. 

Ethnographic research questions can be used in education, business, medicine, and other fields of study, and they are very useful in contexts aimed at collecting in-depth and specific information that are peculiar to research variables. For instance, asking educational ethnographic research questions can help you understand how pedagogy affects classroom relations and behaviors. 

This type of research question can be administered physically through one-on-one interviews, naturalism (live and work), and participant observation methods. Alternatively, the researcher can ask ethnographic research questions via online surveys and questionnaires created with Formplus.  

Examples of Ethnographic Research Questions

• Why do you use this product?
• Have you noticed any side effects since you started using this drug?
• Does this product meet your needs?

• Case Studies

A case study is a qualitative research approach that involves carrying out a detailed investigation into a research subject(s) or variable(s). In the course of a case study, the researcher gathers a range of data from multiple sources of information via different data collection methods, and over a period of time. 

The aim of a case study is to analyze specific issues within definite contexts and arrive at detailed research subject analyses by asking the right questions. This research method can be explanatory, descriptive , or exploratory depending on the focus of your systematic investigation or research. 

An explanatory case study is one that seeks to gather information on the causes of real-life occurrences. This type of case study uses “how” and “why” questions in order to gather valid information about the causative factors of an event. 

Descriptive case studies are typically used in business researches, and they aim at analyzing the impact of changing market dynamics on businesses. On the other hand, exploratory case studies aim at providing answers to “who” and “what” questions using data collection tools like interviews and questionnaires. 

Some questions you can include in your case studies are: 

• Why did you choose our services?
• How has this policy affected your business output?
• What benefits have you recorded since you started using our product?

An interview is a qualitative research method that involves asking respondents a series of questions in order to gather information about a research subject. Interview questions can be close-ended or open-ended , and they prompt participants to provide valid information that is useful to the research. 

An interview may also be structured, semi-structured , or unstructured , and this further influences the types of questions they include. Structured interviews are made up of more close-ended questions because they aim at gathering quantitative data while unstructured interviews consist, primarily, of open-ended questions that allow the researcher to collect qualitative information from respondents. 

You can conduct interview research by scheduling a physical meeting with respondents, through a telephone conversation, and via digital media and video conferencing platforms like Skype and Zoom. Alternatively, you can use Formplus surveys and questionnaires for your interview. 

Examples of interview questions include: 

• What challenges did you face while using our product?
• What specific needs did our product meet?
• What would you like us to improve our service delivery?

Quantitative Research Questions

Quantitative research questions are questions that are used to gather quantifiable data from research subjects. These types of research questions are usually more specific and direct because they aim at collecting information that can be measured; that is, statistical information. 

Types of Quantitative Research Questions

• Descriptive Research Questions

Descriptive research questions are inquiries that researchers use to gather quantifiable data about the attributes and characteristics of research subjects. These types of questions primarily seek responses that reveal existing patterns in the nature of the research subjects. 

It is important to note that descriptive research questions are not concerned with the causative factors of the discovered attributes and characteristics. Rather, they focus on the “what”; that is, describing the subject of the research without paying attention to the reasons for its occurrence. 

Descriptive research questions are typically closed-ended because they aim at gathering definite and specific responses from research participants. Also, they can be used in customer experience surveys and market research to collect information about target markets and consumer behaviors. 

Descriptive Research Question Examples

• How often do you make use of our fitness application?
• How much would you be willing to pay for this product?

• Comparative Research Questions

A comparative research question is a type of quantitative research question that is used to gather information about the differences between two or more research subjects across different variables. These types of questions help the researcher to identify distinct features that mark one research subject from the other while highlighting existing similarities. 

Asking comparative research questions in market research surveys can provide insights on how your product or service matches its competitors. In addition, it can help you to identify the strengths and weaknesses of your product for a better competitive advantage.  

The 5 steps involved in the framing of comparative research questions are: 

• Choose your starting phrase
• Identify and name the dependent variable
• Identify the groups you are interested in
• Identify the appropriate adjoining text
• Write out the comparative research question

Comparative Research Question Samples 

• What are the differences between a landline telephone and a smartphone?
• What are the differences between work-from-home and on-site operations?

• Relationship-based Research Questions  

Just like the name suggests, a relationship-based research question is one that inquires into the nature of the association between two research subjects within the same demographic. These types of research questions help you to gather information pertaining to the nature of the association between two research variables. 

Relationship-based research questions are also known as correlational research questions because they seek to clearly identify the link between 2 variables. 

Read: Correlational Research Designs: Types, Examples & Methods

Examples of relationship-based research questions include: 

• What is the relationship between purchasing power and the business site?
• What is the relationship between the work environment and workforce turnover?

Examples of a Good Research Question

Since research questions lie at the core of any systematic investigations, it is important to know how to frame a good research question. The right research questions will help you to gather the most objective responses that are useful to your systematic investigation. 

A good research question is one that requires impartial responses and can be answered via existing sources of information. Also, a good research question seeks answers that actively contribute to a body of knowledge; hence, it is a question that is yet to be answered in your specific research context.

• Open-Ended Questions

 An open-ended question is a type of research question that does not restrict respondents to a set of premeditated answer options. In other words, it is a question that allows the respondent to freely express his or her perceptions and feelings towards the research subject. 

Examples of Open-ended Questions

• How do you deal with stress in the workplace?
• What is a typical day at work like for you?
• Close-ended Questions

A close-ended question is a type of survey question that restricts respondents to a set of predetermined answers such as multiple-choice questions . Close-ended questions typically require yes or no answers and are commonly used in quantitative research to gather numerical data from research participants. 

Examples of Close-ended Questions

• Did you enjoy this event?
• How likely are you to recommend our services?
• Very Likely
• Somewhat Likely
• Likert Scale Questions

A Likert scale question is a type of close-ended question that is structured as a 3-point, 5-point, or 7-point psychometric scale . This type of question is used to measure the survey respondent’s disposition towards multiple variables and it can be unipolar or bipolar in nature. 

Example of Likert Scale Questions

• How satisfied are you with our service delivery?
• Very dissatisfied
• Not satisfied
• Very satisfied
• Rating Scale Questions

A rating scale question is a type of close-ended question that seeks to associate a specific qualitative measure (rating) with the different variables in research. It is commonly used in customer experience surveys, market research surveys, employee reviews, and product evaluations. 

Example of Rating Questions

• How would you rate our service delivery?

  Examples of a Bad Research Question

Knowing what bad research questions are would help you avoid them in the course of your systematic investigation. These types of questions are usually unfocused and often result in research biases that can negatively impact the outcomes of your systematic investigation. 

• Loaded Questions

A loaded question is a question that subtly presupposes one or more unverified assumptions about the research subject or participant. This type of question typically boxes the respondent in a corner because it suggests implicit and explicit biases that prevent objective responses. 

Example of Loaded Questions

• Have you stopped smoking?
• Where did you hide the money?
• Negative Questions

A negative question is a type of question that is structured with an implicit or explicit negator. Negative questions can be misleading because they upturn the typical yes/no response order by requiring a negative answer for affirmation and an affirmative answer for negation. 

Examples of Negative Questions

• Would you mind dropping by my office later today?
• Didn’t you visit last week?
• Leading Questions  

A l eading question is a type of survey question that nudges the respondent towards an already-determined answer. It is highly suggestive in nature and typically consists of biases and unverified assumptions that point toward its premeditated responses. 

Examples of Leading Questions

• If you enjoyed this service, would you be willing to try out our other packages?
• Our product met your needs, didn’t it?

Read More: Leading Questions: Definition, Types, and Examples

How to Use Formplus as Online Research Questionnaire Tool  

With Formplus, you can create and administer your online research questionnaire easily. In the form builder, you can add different form fields to your questionnaire and edit these fields to reflect specific research questions for your systematic investigation. 

Here is a step-by-step guide on how to create an online research questionnaire with Formplus: 

• Sign in to your Formplus accoun t, then click on the “create new form” button in your dashboard to access the Form builder.

• In the form builder, add preferred form fields to your online research questionnaire by dragging and dropping them into the form. Add a title to your form in the title block. You can edit form fields by clicking on the “pencil” icon on the right corner of each form field.

• Save the form to access the customization section of the builder. Here, you can tweak the appearance of your online research questionnaire by adding background images, changing the form font, and adding your organization’s logo.

• Finally, copy your form link and share it with respondents. You can also use any of the multiple sharing options available.

Conclusion  

The success of your research starts with framing the right questions to help you collect the most valid and objective responses. Be sure to avoid bad research questions like loaded and negative questions that can be misleading and adversely affect your research data and outcomes. 

Your research questions should clearly reflect the aims and objectives of your systematic investigation while laying emphasis on specific contexts. To help you seamlessly gather responses for your research questions, you can create an online research questionnaire on Formplus.  

Connect to Formplus, Get Started Now - It's Free!

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Introduction to Research Methods in Psychology

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

There are several different research methods in psychology , each of which can help researchers learn more about the way people think, feel, and behave. If you're a psychology student or just want to know the types of research in psychology, here are the main ones as well as how they work.

Three Main Types of Research in Psychology

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Psychology research can usually be classified as one of three major types.

1. Causal or Experimental Research

When most people think of scientific experimentation, research on cause and effect is most often brought to mind. Experiments on causal relationships investigate the effect of one or more variables on one or more outcome variables. This type of research also determines if one variable causes another variable to occur or change.

An example of this type of research in psychology would be changing the length of a specific mental health treatment and measuring the effect on study participants.

2. Descriptive Research

Descriptive research seeks to depict what already exists in a group or population. Three types of psychology research utilizing this method are:

• Case studies
• Observational studies

An example of this psychology research method would be an opinion poll to determine which presidential candidate people plan to vote for in the next election. Descriptive studies don't try to measure the effect of a variable; they seek only to describe it.

3. Relational or Correlational Research

A study that investigates the connection between two or more variables is considered relational research. The variables compared are generally already present in the group or population.

For example, a study that looks at the proportion of males and females that would purchase either a classical CD or a jazz CD would be studying the relationship between gender and music preference.

Theory vs. Hypothesis in Psychology Research

People often confuse the terms theory and hypothesis or are not quite sure of the distinctions between the two concepts. If you're a psychology student, it's essential to understand what each term means, how they differ, and how they're used in psychology research.

A theory is a well-established principle that has been developed to explain some aspect of the natural world. A theory arises from repeated observation and testing and incorporates facts, laws, predictions, and tested hypotheses that are widely accepted.

A hypothesis is a specific, testable prediction about what you expect to happen in your study. For example, an experiment designed to look at the relationship between study habits and test anxiety might have a hypothesis that states, "We predict that students with better study habits will suffer less test anxiety." Unless your study is exploratory in nature, your hypothesis should always explain what you expect to happen during the course of your experiment or research.

While the terms are sometimes used interchangeably in everyday use, the difference between a theory and a hypothesis is important when studying experimental design.

Some other important distinctions to note include:

• A theory predicts events in general terms, while a hypothesis makes a specific prediction about a specified set of circumstances.
• A theory has been extensively tested and is generally accepted, while a hypothesis is a speculative guess that has yet to be tested.

The Effect of Time on Research Methods in Psychology

There are two types of time dimensions that can be used in designing a research study:

• Cross-sectional research takes place at a single point in time. All tests, measures, or variables are administered to participants on one occasion. This type of research seeks to gather data on present conditions instead of looking at the effects of a variable over a period of time.
• Longitudinal research is a study that takes place over a period of time. Data is first collected at the beginning of the study, and may then be gathered repeatedly throughout the length of the study. Some longitudinal studies may occur over a short period of time, such as a few days, while others may take place over a period of months, years, or even decades.

The effects of aging are often investigated using longitudinal research.

Causal Relationships Between Psychology Research Variables

What do we mean when we talk about a “relationship” between variables? In psychological research, we're referring to a connection between two or more factors that we can measure or systematically vary.

One of the most important distinctions to make when discussing the relationship between variables is the meaning of causation.

A causal relationship is when one variable causes a change in another variable. These types of relationships are investigated by experimental research to determine if changes in one variable actually result in changes in another variable.

Correlational Relationships Between Psychology Research Variables

A correlation is the measurement of the relationship between two variables. These variables already occur in the group or population and are not controlled by the experimenter.

• A positive correlation is a direct relationship where, as the amount of one variable increases, the amount of a second variable also increases.
• In a negative correlation , as the amount of one variable goes up, the levels of another variable go down.

In both types of correlation, there is no evidence or proof that changes in one variable cause changes in the other variable. A correlation simply indicates that there is a relationship between the two variables.

The most important concept is that correlation does not equal causation. Many popular media sources make the mistake of assuming that simply because two variables are related, a causal relationship exists.

Psychologists use descriptive, correlational, and experimental research designs to understand behavior . In:  Introduction to Psychology . Minneapolis, MN: University of Minnesota Libraries Publishing; 2010.

Caruana EJ, Roman M, Herandez-Sanchez J, Solli P. Longitudinal studies . Journal of Thoracic Disease. 2015;7(11):E537-E540. doi:10.3978/j.issn.2072-1439.2015.10.63

University of Berkeley. Science at multiple levels . Understanding Science 101 . Published 2012.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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Organizing Your Social Sciences Research Paper

• The Research Problem/Question
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A research problem is a definite or clear expression [statement] about an area of concern, a condition to be improved upon, a difficulty to be eliminated, or a troubling question that exists in scholarly literature, in theory, or within existing practice that points to a need for meaningful understanding and deliberate investigation. A research problem does not state how to do something, offer a vague or broad proposition, or present a value question. In the social and behavioral sciences, studies are most often framed around examining a problem that needs to be understood and resolved in order to improve society and the human condition.

Bryman, Alan. “The Research Question in Social Research: What is its Role?” International Journal of Social Research Methodology 10 (2007): 5-20; Guba, Egon G., and Yvonna S. Lincoln. “Competing Paradigms in Qualitative Research.” In Handbook of Qualitative Research . Norman K. Denzin and Yvonna S. Lincoln, editors. (Thousand Oaks, CA: Sage, 1994), pp. 105-117; Pardede, Parlindungan. “Identifying and Formulating the Research Problem." Research in ELT: Module 4 (October 2018): 1-13; Li, Yanmei, and Sumei Zhang. "Identifying the Research Problem." In Applied Research Methods in Urban and Regional Planning . (Cham, Switzerland: Springer International Publishing, 2022), pp. 13-21.

Importance of...

The purpose of a problem statement is to:

• Introduce the reader to the importance of the topic being studied . The reader is oriented to the significance of the study.
• Anchors the research questions, hypotheses, or assumptions to follow . It offers a concise statement about the purpose of your paper.
• Place the topic into a particular context that defines the parameters of what is to be investigated.
• Provide the framework for reporting the results and indicates what is probably necessary to conduct the study and explain how the findings will present this information.

In the social sciences, the research problem establishes the means by which you must answer the "So What?" question. This declarative question refers to a research problem surviving the relevancy test [the quality of a measurement procedure that provides repeatability and accuracy]. Note that answering the "So What?" question requires a commitment on your part to not only show that you have reviewed the literature, but that you have thoroughly considered the significance of the research problem and its implications applied to creating new knowledge and understanding or informing practice.

To survive the "So What" question, problem statements should possess the following attributes:

• Clarity and precision [a well-written statement does not make sweeping generalizations and irresponsible pronouncements; it also does include unspecific determinates like "very" or "giant"],
• Demonstrate a researchable topic or issue [i.e., feasibility of conducting the study is based upon access to information that can be effectively acquired, gathered, interpreted, synthesized, and understood],
• Identification of what would be studied, while avoiding the use of value-laden words and terms,
• Identification of an overarching question or small set of questions accompanied by key factors or variables,
• Identification of key concepts and terms,
• Articulation of the study's conceptual boundaries or parameters or limitations,
• Some generalizability in regards to applicability and bringing results into general use,
• Conveyance of the study's importance, benefits, and justification [i.e., regardless of the type of research, it is important to demonstrate that the research is not trivial],
• Does not have unnecessary jargon or overly complex sentence constructions; and,
• Conveyance of more than the mere gathering of descriptive data providing only a snapshot of the issue or phenomenon under investigation.

Bryman, Alan. “The Research Question in Social Research: What is its Role?” International Journal of Social Research Methodology 10 (2007): 5-20; Brown, Perry J., Allen Dyer, and Ross S. Whaley. "Recreation Research—So What?" Journal of Leisure Research 5 (1973): 16-24; Castellanos, Susie. Critical Writing and Thinking. The Writing Center. Dean of the College. Brown University; Ellis, Timothy J. and Yair Levy Nova. "Framework of Problem-Based Research: A Guide for Novice Researchers on the Development of a Research-Worthy Problem." Informing Science: the International Journal of an Emerging Transdiscipline 11 (2008); Thesis and Purpose Statements. The Writer’s Handbook. Writing Center. University of Wisconsin, Madison; Thesis Statements. The Writing Center. University of North Carolina; Tips and Examples for Writing Thesis Statements. The Writing Lab and The OWL. Purdue University; Selwyn, Neil. "‘So What?’…A Question that Every Journal Article Needs to Answer." Learning, Media, and Technology 39 (2014): 1-5; Shoket, Mohd. "Research Problem: Identification and Formulation." International Journal of Research 1 (May 2014): 512-518.

Structure and Writing Style

I.  Types and Content

There are four general conceptualizations of a research problem in the social sciences:

• Casuist Research Problem -- this type of problem relates to the determination of right and wrong in questions of conduct or conscience by analyzing moral dilemmas through the application of general rules and the careful distinction of special cases.
• Difference Research Problem -- typically asks the question, “Is there a difference between two or more groups or treatments?” This type of problem statement is used when the researcher compares or contrasts two or more phenomena. This a common approach to defining a problem in the clinical social sciences or behavioral sciences.
• Descriptive Research Problem -- typically asks the question, "what is...?" with the underlying purpose to describe the significance of a situation, state, or existence of a specific phenomenon. This problem is often associated with revealing hidden or understudied issues.
• Relational Research Problem -- suggests a relationship of some sort between two or more variables to be investigated. The underlying purpose is to investigate specific qualities or characteristics that may be connected in some way.

A problem statement in the social sciences should contain :

• A lead-in that helps ensure the reader will maintain interest over the study,
• A declaration of originality [e.g., mentioning a knowledge void or a lack of clarity about a topic that will be revealed in the literature review of prior research],
• An indication of the central focus of the study [establishing the boundaries of analysis], and
• An explanation of the study's significance or the benefits to be derived from investigating the research problem.

NOTE:   A statement describing the research problem of your paper should not be viewed as a thesis statement that you may be familiar with from high school. Given the content listed above, a description of the research problem is usually a short paragraph in length.

II.  Sources of Problems for Investigation

The identification of a problem to study can be challenging, not because there's a lack of issues that could be investigated, but due to the challenge of formulating an academically relevant and researchable problem which is unique and does not simply duplicate the work of others. To facilitate how you might select a problem from which to build a research study, consider these sources of inspiration:

Deductions from Theory This relates to deductions made from social philosophy or generalizations embodied in life and in society that the researcher is familiar with. These deductions from human behavior are then placed within an empirical frame of reference through research. From a theory, the researcher can formulate a research problem or hypothesis stating the expected findings in certain empirical situations. The research asks the question: “What relationship between variables will be observed if theory aptly summarizes the state of affairs?” One can then design and carry out a systematic investigation to assess whether empirical data confirm or reject the hypothesis, and hence, the theory.

Interdisciplinary Perspectives Identifying a problem that forms the basis for a research study can come from academic movements and scholarship originating in disciplines outside of your primary area of study. This can be an intellectually stimulating exercise. A review of pertinent literature should include examining research from related disciplines that can reveal new avenues of exploration and analysis. An interdisciplinary approach to selecting a research problem offers an opportunity to construct a more comprehensive understanding of a very complex issue that any single discipline may be able to provide.

Interviewing Practitioners The identification of research problems about particular topics can arise from formal interviews or informal discussions with practitioners who provide insight into new directions for future research and how to make research findings more relevant to practice. Discussions with experts in the field, such as, teachers, social workers, health care providers, lawyers, business leaders, etc., offers the chance to identify practical, “real world” problems that may be understudied or ignored within academic circles. This approach also provides some practical knowledge which may help in the process of designing and conducting your study.

Personal Experience Don't undervalue your everyday experiences or encounters as worthwhile problems for investigation. Think critically about your own experiences and/or frustrations with an issue facing society or related to your community, your neighborhood, your family, or your personal life. This can be derived, for example, from deliberate observations of certain relationships for which there is no clear explanation or witnessing an event that appears harmful to a person or group or that is out of the ordinary.

Relevant Literature The selection of a research problem can be derived from a thorough review of pertinent research associated with your overall area of interest. This may reveal where gaps exist in understanding a topic or where an issue has been understudied. Research may be conducted to: 1) fill such gaps in knowledge; 2) evaluate if the methodologies employed in prior studies can be adapted to solve other problems; or, 3) determine if a similar study could be conducted in a different subject area or applied in a different context or to different study sample [i.e., different setting or different group of people]. Also, authors frequently conclude their studies by noting implications for further research; read the conclusion of pertinent studies because statements about further research can be a valuable source for identifying new problems to investigate. The fact that a researcher has identified a topic worthy of further exploration validates the fact it is worth pursuing.

III.  What Makes a Good Research Statement?

A good problem statement begins by introducing the broad area in which your research is centered, gradually leading the reader to the more specific issues you are investigating. The statement need not be lengthy, but a good research problem should incorporate the following features:

1.  Compelling Topic The problem chosen should be one that motivates you to address it but simple curiosity is not a good enough reason to pursue a research study because this does not indicate significance. The problem that you choose to explore must be important to you, but it must also be viewed as important by your readers and to a the larger academic and/or social community that could be impacted by the results of your study. 2.  Supports Multiple Perspectives The problem must be phrased in a way that avoids dichotomies and instead supports the generation and exploration of multiple perspectives. A general rule of thumb in the social sciences is that a good research problem is one that would generate a variety of viewpoints from a composite audience made up of reasonable people. 3.  Researchability This isn't a real word but it represents an important aspect of creating a good research statement. It seems a bit obvious, but you don't want to find yourself in the midst of investigating a complex research project and realize that you don't have enough prior research to draw from for your analysis. There's nothing inherently wrong with original research, but you must choose research problems that can be supported, in some way, by the resources available to you. If you are not sure if something is researchable, don't assume that it isn't if you don't find information right away--seek help from a librarian !

NOTE:   Do not confuse a research problem with a research topic. A topic is something to read and obtain information about, whereas a problem is something to be solved or framed as a question raised for inquiry, consideration, or solution, or explained as a source of perplexity, distress, or vexation. In short, a research topic is something to be understood; a research problem is something that needs to be investigated.

IV.  Asking Analytical Questions about the Research Problem

Research problems in the social and behavioral sciences are often analyzed around critical questions that must be investigated. These questions can be explicitly listed in the introduction [i.e., "This study addresses three research questions about women's psychological recovery from domestic abuse in multi-generational home settings..."], or, the questions are implied in the text as specific areas of study related to the research problem. Explicitly listing your research questions at the end of your introduction can help in designing a clear roadmap of what you plan to address in your study, whereas, implicitly integrating them into the text of the introduction allows you to create a more compelling narrative around the key issues under investigation. Either approach is appropriate.

The number of questions you attempt to address should be based on the complexity of the problem you are investigating and what areas of inquiry you find most critical to study. Practical considerations, such as, the length of the paper you are writing or the availability of resources to analyze the issue can also factor in how many questions to ask. In general, however, there should be no more than four research questions underpinning a single research problem.

Given this, well-developed analytical questions can focus on any of the following:

• Highlights a genuine dilemma, area of ambiguity, or point of confusion about a topic open to interpretation by your readers;
• Yields an answer that is unexpected and not obvious rather than inevitable and self-evident;
• Provokes meaningful thought or discussion;
• Raises the visibility of the key ideas or concepts that may be understudied or hidden;
• Suggests the need for complex analysis or argument rather than a basic description or summary; and,
• Offers a specific path of inquiry that avoids eliciting generalizations about the problem.

NOTE:   Questions of how and why concerning a research problem often require more analysis than questions about who, what, where, and when. You should still ask yourself these latter questions, however. Thinking introspectively about the who, what, where, and when of a research problem can help ensure that you have thoroughly considered all aspects of the problem under investigation and helps define the scope of the study in relation to the problem.

V.  Mistakes to Avoid

Beware of circular reasoning! Do not state the research problem as simply the absence of the thing you are suggesting. For example, if you propose the following, "The problem in this community is that there is no hospital," this only leads to a research problem where:

• The need is for a hospital
• The objective is to create a hospital
• The method is to plan for building a hospital, and
• The evaluation is to measure if there is a hospital or not.

This is an example of a research problem that fails the "So What?" test . In this example, the problem does not reveal the relevance of why you are investigating the fact there is no hospital in the community [e.g., perhaps there's a hospital in the community ten miles away]; it does not elucidate the significance of why one should study the fact there is no hospital in the community [e.g., that hospital in the community ten miles away has no emergency room]; the research problem does not offer an intellectual pathway towards adding new knowledge or clarifying prior knowledge [e.g., the county in which there is no hospital already conducted a study about the need for a hospital, but it was conducted ten years ago]; and, the problem does not offer meaningful outcomes that lead to recommendations that can be generalized for other situations or that could suggest areas for further research [e.g., the challenges of building a new hospital serves as a case study for other communities].

Alvesson, Mats and Jörgen Sandberg. “Generating Research Questions Through Problematization.” Academy of Management Review 36 (April 2011): 247-271 ; Choosing and Refining Topics. Writing@CSU. Colorado State University; D'Souza, Victor S. "Use of Induction and Deduction in Research in Social Sciences: An Illustration." Journal of the Indian Law Institute 24 (1982): 655-661; Ellis, Timothy J. and Yair Levy Nova. "Framework of Problem-Based Research: A Guide for Novice Researchers on the Development of a Research-Worthy Problem." Informing Science: the International Journal of an Emerging Transdiscipline 11 (2008); How to Write a Research Question. The Writing Center. George Mason University; Invention: Developing a Thesis Statement. The Reading/Writing Center. Hunter College; Problem Statements PowerPoint Presentation. The Writing Lab and The OWL. Purdue University; Procter, Margaret. Using Thesis Statements. University College Writing Centre. University of Toronto; Shoket, Mohd. "Research Problem: Identification and Formulation." International Journal of Research 1 (May 2014): 512-518; Trochim, William M.K. Problem Formulation. Research Methods Knowledge Base. 2006; Thesis and Purpose Statements. The Writer’s Handbook. Writing Center. University of Wisconsin, Madison; Thesis Statements. The Writing Center. University of North Carolina; Tips and Examples for Writing Thesis Statements. The Writing Lab and The OWL. Purdue University; Pardede, Parlindungan. “Identifying and Formulating the Research Problem." Research in ELT: Module 4 (October 2018): 1-13; Walk, Kerry. Asking an Analytical Question. [Class handout or worksheet]. Princeton University; White, Patrick. Developing Research Questions: A Guide for Social Scientists . New York: Palgrave McMillan, 2009; Li, Yanmei, and Sumei Zhang. "Identifying the Research Problem." In Applied Research Methods in Urban and Regional Planning . (Cham, Switzerland: Springer International Publishing, 2022), pp. 13-21.

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Content Analysis

Content analysis is a research tool used to determine the presence of certain words, themes, or concepts within some given qualitative data (i.e. text). Using content analysis, researchers can quantify and analyze the presence, meanings, and relationships of such certain words, themes, or concepts. As an example, researchers can evaluate language used within a news article to search for bias or partiality. Researchers can then make inferences about the messages within the texts, the writer(s), the audience, and even the culture and time of surrounding the text.

Description

Sources of data could be from interviews, open-ended questions, field research notes, conversations, or literally any occurrence of communicative language (such as books, essays, discussions, newspaper headlines, speeches, media, historical documents). A single study may analyze various forms of text in its analysis. To analyze the text using content analysis, the text must be coded, or broken down, into manageable code categories for analysis (i.e. “codes”). Once the text is coded into code categories, the codes can then be further categorized into “code categories” to summarize data even further.

Three different definitions of content analysis are provided below.

Definition 1: “Any technique for making inferences by systematically and objectively identifying special characteristics of messages.” (from Holsti, 1968)

Definition 2: “An interpretive and naturalistic approach. It is both observational and narrative in nature and relies less on the experimental elements normally associated with scientific research (reliability, validity, and generalizability) (from Ethnography, Observational Research, and Narrative Inquiry, 1994-2012).

Definition 3: “A research technique for the objective, systematic and quantitative description of the manifest content of communication.” (from Berelson, 1952)

Uses of Content Analysis

Identify the intentions, focus or communication trends of an individual, group or institution

Describe attitudinal and behavioral responses to communications

Determine the psychological or emotional state of persons or groups

Reveal international differences in communication content

Reveal patterns in communication content

Pre-test and improve an intervention or survey prior to launch

Analyze focus group interviews and open-ended questions to complement quantitative data

Types of Content Analysis

There are two general types of content analysis: conceptual analysis and relational analysis. Conceptual analysis determines the existence and frequency of concepts in a text. Relational analysis develops the conceptual analysis further by examining the relationships among concepts in a text. Each type of analysis may lead to different results, conclusions, interpretations and meanings.

Conceptual Analysis

Typically people think of conceptual analysis when they think of content analysis. In conceptual analysis, a concept is chosen for examination and the analysis involves quantifying and counting its presence. The main goal is to examine the occurrence of selected terms in the data. Terms may be explicit or implicit. Explicit terms are easy to identify. Coding of implicit terms is more complicated: you need to decide the level of implication and base judgments on subjectivity (an issue for reliability and validity). Therefore, coding of implicit terms involves using a dictionary or contextual translation rules or both.

To begin a conceptual content analysis, first identify the research question and choose a sample or samples for analysis. Next, the text must be coded into manageable content categories. This is basically a process of selective reduction. By reducing the text to categories, the researcher can focus on and code for specific words or patterns that inform the research question.

General steps for conducting a conceptual content analysis:

1. Decide the level of analysis: word, word sense, phrase, sentence, themes

2. Decide how many concepts to code for: develop a pre-defined or interactive set of categories or concepts. Decide either: A. to allow flexibility to add categories through the coding process, or B. to stick with the pre-defined set of categories.

Option A allows for the introduction and analysis of new and important material that could have significant implications to one’s research question.

Option B allows the researcher to stay focused and examine the data for specific concepts.

3. Decide whether to code for existence or frequency of a concept. The decision changes the coding process.

When coding for the existence of a concept, the researcher would count a concept only once if it appeared at least once in the data and no matter how many times it appeared.

When coding for the frequency of a concept, the researcher would count the number of times a concept appears in a text.

4. Decide on how you will distinguish among concepts:

Should text be coded exactly as they appear or coded as the same when they appear in different forms? For example, “dangerous” vs. “dangerousness”. The point here is to create coding rules so that these word segments are transparently categorized in a logical fashion. The rules could make all of these word segments fall into the same category, or perhaps the rules can be formulated so that the researcher can distinguish these word segments into separate codes.

What level of implication is to be allowed? Words that imply the concept or words that explicitly state the concept? For example, “dangerous” vs. “the person is scary” vs. “that person could cause harm to me”. These word segments may not merit separate categories, due the implicit meaning of “dangerous”.

5. Develop rules for coding your texts. After decisions of steps 1-4 are complete, a researcher can begin developing rules for translation of text into codes. This will keep the coding process organized and consistent. The researcher can code for exactly what he/she wants to code. Validity of the coding process is ensured when the researcher is consistent and coherent in their codes, meaning that they follow their translation rules. In content analysis, obeying by the translation rules is equivalent to validity.

6. Decide what to do with irrelevant information: should this be ignored (e.g. common English words like “the” and “and”), or used to reexamine the coding scheme in the case that it would add to the outcome of coding?

7. Code the text: This can be done by hand or by using software. By using software, researchers can input categories and have coding done automatically, quickly and efficiently, by the software program. When coding is done by hand, a researcher can recognize errors far more easily (e.g. typos, misspelling). If using computer coding, text could be cleaned of errors to include all available data. This decision of hand vs. computer coding is most relevant for implicit information where category preparation is essential for accurate coding.

8. Analyze your results: Draw conclusions and generalizations where possible. Determine what to do with irrelevant, unwanted, or unused text: reexamine, ignore, or reassess the coding scheme. Interpret results carefully as conceptual content analysis can only quantify the information. Typically, general trends and patterns can be identified.

Relational Analysis

Relational analysis begins like conceptual analysis, where a concept is chosen for examination. However, the analysis involves exploring the relationships between concepts. Individual concepts are viewed as having no inherent meaning and rather the meaning is a product of the relationships among concepts.

To begin a relational content analysis, first identify a research question and choose a sample or samples for analysis. The research question must be focused so the concept types are not open to interpretation and can be summarized. Next, select text for analysis. Select text for analysis carefully by balancing having enough information for a thorough analysis so results are not limited with having information that is too extensive so that the coding process becomes too arduous and heavy to supply meaningful and worthwhile results.

There are three subcategories of relational analysis to choose from prior to going on to the general steps.

Affect extraction: an emotional evaluation of concepts explicit in a text. A challenge to this method is that emotions can vary across time, populations, and space. However, it could be effective at capturing the emotional and psychological state of the speaker or writer of the text.

Proximity analysis: an evaluation of the co-occurrence of explicit concepts in the text. Text is defined as a string of words called a “window” that is scanned for the co-occurrence of concepts. The result is the creation of a “concept matrix”, or a group of interrelated co-occurring concepts that would suggest an overall meaning.

Cognitive mapping: a visualization technique for either affect extraction or proximity analysis. Cognitive mapping attempts to create a model of the overall meaning of the text such as a graphic map that represents the relationships between concepts.

General steps for conducting a relational content analysis:

1. Determine the type of analysis: Once the sample has been selected, the researcher needs to determine what types of relationships to examine and the level of analysis: word, word sense, phrase, sentence, themes. 2. Reduce the text to categories and code for words or patterns. A researcher can code for existence of meanings or words. 3. Explore the relationship between concepts: once the words are coded, the text can be analyzed for the following:

Strength of relationship: degree to which two or more concepts are related.

Sign of relationship: are concepts positively or negatively related to each other?

Direction of relationship: the types of relationship that categories exhibit. For example, “X implies Y” or “X occurs before Y” or “if X then Y” or if X is the primary motivator of Y.

4. Code the relationships: a difference between conceptual and relational analysis is that the statements or relationships between concepts are coded. 5. Perform statistical analyses: explore differences or look for relationships among the identified variables during coding. 6. Map out representations: such as decision mapping and mental models.

Reliability and Validity

Reliability : Because of the human nature of researchers, coding errors can never be eliminated but only minimized. Generally, 80% is an acceptable margin for reliability. Three criteria comprise the reliability of a content analysis:

Stability: the tendency for coders to consistently re-code the same data in the same way over a period of time.

Reproducibility: tendency for a group of coders to classify categories membership in the same way.

Accuracy: extent to which the classification of text corresponds to a standard or norm statistically.

Validity : Three criteria comprise the validity of a content analysis:

Closeness of categories: this can be achieved by utilizing multiple classifiers to arrive at an agreed upon definition of each specific category. Using multiple classifiers, a concept category that may be an explicit variable can be broadened to include synonyms or implicit variables.

Conclusions: What level of implication is allowable? Do conclusions correctly follow the data? Are results explainable by other phenomena? This becomes especially problematic when using computer software for analysis and distinguishing between synonyms. For example, the word “mine,” variously denotes a personal pronoun, an explosive device, and a deep hole in the ground from which ore is extracted. Software can obtain an accurate count of that word’s occurrence and frequency, but not be able to produce an accurate accounting of the meaning inherent in each particular usage. This problem could throw off one’s results and make any conclusion invalid.

Generalizability of the results to a theory: dependent on the clear definitions of concept categories, how they are determined and how reliable they are at measuring the idea one is seeking to measure. Generalizability parallels reliability as much of it depends on the three criteria for reliability.

Advantages of Content Analysis

Directly examines communication using text

Allows for both qualitative and quantitative analysis

Provides valuable historical and cultural insights over time

Allows a closeness to data

Coded form of the text can be statistically analyzed

Unobtrusive means of analyzing interactions

Provides insight into complex models of human thought and language use

When done well, is considered a relatively “exact” research method

Content analysis is a readily-understood and an inexpensive research method

A more powerful tool when combined with other research methods such as interviews, observation, and use of archival records. It is very useful for analyzing historical material, especially for documenting trends over time.

Disadvantages of Content Analysis

Can be extremely time consuming

Is subject to increased error, particularly when relational analysis is used to attain a higher level of interpretation

Is often devoid of theoretical base, or attempts too liberally to draw meaningful inferences about the relationships and impacts implied in a study

Is inherently reductive, particularly when dealing with complex texts

Tends too often to simply consist of word counts

Often disregards the context that produced the text, as well as the state of things after the text is produced

Can be difficult to automate or computerize

Textbooks & Chapters  

Berelson, Bernard. Content Analysis in Communication Research.New York: Free Press, 1952.

Busha, Charles H. and Stephen P. Harter. Research Methods in Librarianship: Techniques and Interpretation.New York: Academic Press, 1980.

de Sola Pool, Ithiel. Trends in Content Analysis. Urbana: University of Illinois Press, 1959.

Krippendorff, Klaus. Content Analysis: An Introduction to its Methodology. Beverly Hills: Sage Publications, 1980.

Fielding, NG & Lee, RM. Using Computers in Qualitative Research. SAGE Publications, 1991. (Refer to Chapter by Seidel, J. ‘Method and Madness in the Application of Computer Technology to Qualitative Data Analysis’.)

Methodological Articles  

Hsieh HF & Shannon SE. (2005). Three Approaches to Qualitative Content Analysis.Qualitative Health Research. 15(9): 1277-1288.

Elo S, Kaarianinen M, Kanste O, Polkki R, Utriainen K, & Kyngas H. (2014). Qualitative Content Analysis: A focus on trustworthiness. Sage Open. 4:1-10.

Application Articles  

Abroms LC, Padmanabhan N, Thaweethai L, & Phillips T. (2011). iPhone Apps for Smoking Cessation: A content analysis. American Journal of Preventive Medicine. 40(3):279-285.

Ullstrom S. Sachs MA, Hansson J, Ovretveit J, & Brommels M. (2014). Suffering in Silence: a qualitative study of second victims of adverse events. British Medical Journal, Quality & Safety Issue. 23:325-331.

Owen P. (2012).Portrayals of Schizophrenia by Entertainment Media: A Content Analysis of Contemporary Movies. Psychiatric Services. 63:655-659.

Choosing whether to conduct a content analysis by hand or by using computer software can be difficult. Refer to ‘Method and Madness in the Application of Computer Technology to Qualitative Data Analysis’ listed above in “Textbooks and Chapters” for a discussion of the issue.

QSR NVivo:  http://www.qsrinternational.com/products.aspx

Atlas.ti:  http://www.atlasti.com/webinars.html

R- RQDA package:  http://rqda.r-forge.r-project.org/

Rolly Constable, Marla Cowell, Sarita Zornek Crawford, David Golden, Jake Hartvigsen, Kathryn Morgan, Anne Mudgett, Kris Parrish, Laura Thomas, Erika Yolanda Thompson, Rosie Turner, and Mike Palmquist. (1994-2012). Ethnography, Observational Research, and Narrative Inquiry. Writing@CSU. Colorado State University. Available at: https://writing.colostate.edu/guides/guide.cfm?guideid=63 .

As an introduction to Content Analysis by Michael Palmquist, this is the main resource on Content Analysis on the Web. It is comprehensive, yet succinct. It includes examples and an annotated bibliography. The information contained in the narrative above draws heavily from and summarizes Michael Palmquist’s excellent resource on Content Analysis but was streamlined for the purpose of doctoral students and junior researchers in epidemiology.

At Columbia University Mailman School of Public Health, more detailed training is available through the Department of Sociomedical Sciences- P8785 Qualitative Research Methods.

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Home » Correlational Research – Methods, Types and Examples

Correlational Research – Methods, Types and Examples

Table of Contents

Correlational Research

Correlational Research is a type of research that examines the statistical relationship between two or more variables without manipulating them. It is a non-experimental research design that seeks to establish the degree of association or correlation between two or more variables.

Types of Correlational Research

There are three types of correlational research:

Positive Correlation

A positive correlation occurs when two variables increase or decrease together. This means that as one variable increases, the other variable also tends to increase. Similarly, as one variable decreases, the other variable also tends to decrease. For example, there is a positive correlation between the amount of time spent studying and academic performance. The more time a student spends studying, the higher their academic performance is likely to be. Similarly, there is a positive correlation between a person’s age and their income level. As a person gets older, they tend to earn more money.

Negative Correlation

A negative correlation occurs when one variable increases while the other decreases. This means that as one variable increases, the other variable tends to decrease. Similarly, as one variable decreases, the other variable tends to increase. For example, there is a negative correlation between the number of hours spent watching TV and physical activity level. The more time a person spends watching TV, the less physically active they are likely to be. Similarly, there is a negative correlation between the amount of stress a person experiences and their overall happiness. As stress levels increase, happiness levels tend to decrease.

Zero Correlation

A zero correlation occurs when there is no relationship between two variables. This means that the variables are unrelated and do not affect each other. For example, there is zero correlation between a person’s shoe size and their IQ score. The size of a person’s feet has no relationship to their level of intelligence. Similarly, there is zero correlation between a person’s height and their favorite color. The two variables are unrelated to each other.

Correlational Research Methods

Correlational research can be conducted using different methods, including:

Surveys are a common method used in correlational research. Researchers collect data by asking participants to complete questionnaires or surveys that measure different variables of interest. Surveys are useful for exploring the relationships between variables such as personality traits, attitudes, and behaviors.

Observational Studies

Observational studies involve observing and recording the behavior of participants in natural settings. Researchers can use observational studies to examine the relationships between variables such as social interactions, group dynamics, and communication patterns.

Archival Data

Archival data involves using existing data sources such as historical records, census data, or medical records to explore the relationships between variables. Archival data is useful for investigating the relationships between variables that cannot be manipulated or controlled.

Experimental Design

While correlational research does not involve manipulating variables, researchers can use experimental design to establish cause-and-effect relationships between variables. Experimental design involves manipulating one variable while holding other variables constant to determine the effect on the dependent variable.

Meta-Analysis

Meta-analysis involves combining and analyzing the results of multiple studies to explore the relationships between variables across different contexts and populations. Meta-analysis is useful for identifying patterns and inconsistencies in the literature and can provide insights into the strength and direction of relationships between variables.

Data Analysis Methods

Correlational research data analysis methods depend on the type of data collected and the research questions being investigated. Here are some common data analysis methods used in correlational research:

Correlation Coefficient

A correlation coefficient is a statistical measure that quantifies the strength and direction of the relationship between two variables. The correlation coefficient ranges from -1 to +1, with -1 indicating a perfect negative correlation, +1 indicating a perfect positive correlation, and 0 indicating no correlation. Researchers use correlation coefficients to determine the degree to which two variables are related.

Scatterplots

A scatterplot is a graphical representation of the relationship between two variables. Each data point on the plot represents a single observation. The x-axis represents one variable, and the y-axis represents the other variable. The pattern of data points on the plot can provide insights into the strength and direction of the relationship between the two variables.

Regression Analysis

Regression analysis is a statistical method used to model the relationship between two or more variables. Researchers use regression analysis to predict the value of one variable based on the value of another variable. Regression analysis can help identify the strength and direction of the relationship between variables, as well as the degree to which one variable can be used to predict the other.

Factor Analysis

Factor analysis is a statistical method used to identify patterns among variables. Researchers use factor analysis to group variables into factors that are related to each other. Factor analysis can help identify underlying factors that influence the relationship between two variables.

Path Analysis

Path analysis is a statistical method used to model the relationship between multiple variables. Researchers use path analysis to test causal models and identify direct and indirect effects between variables.

Applications of Correlational Research

Correlational research has many practical applications in various fields, including:

• Psychology : Correlational research is commonly used in psychology to explore the relationships between variables such as personality traits, behaviors, and mental health outcomes. For example, researchers may use correlational research to examine the relationship between anxiety and depression, or the relationship between self-esteem and academic achievement.
• Education : Correlational research is useful in educational research to explore the relationships between variables such as teaching methods, student motivation, and academic performance. For example, researchers may use correlational research to examine the relationship between student engagement and academic success, or the relationship between teacher feedback and student learning outcomes.
• Business : Correlational research can be used in business to explore the relationships between variables such as consumer behavior, marketing strategies, and sales outcomes. For example, marketers may use correlational research to examine the relationship between advertising spending and sales revenue, or the relationship between customer satisfaction and brand loyalty.
• Medicine : Correlational research is useful in medical research to explore the relationships between variables such as risk factors, disease outcomes, and treatment effectiveness. For example, researchers may use correlational research to examine the relationship between smoking and lung cancer, or the relationship between exercise and heart health.
• Social Science : Correlational research is commonly used in social science research to explore the relationships between variables such as socioeconomic status, cultural factors, and social behavior. For example, researchers may use correlational research to examine the relationship between income and voting behavior, or the relationship between cultural values and attitudes towards immigration.

Examples of Correlational Research

• Psychology : Researchers might be interested in exploring the relationship between two variables, such as parental attachment and anxiety levels in young adults. The study could involve measuring levels of attachment and anxiety using established scales or questionnaires, and then analyzing the data to determine if there is a correlation between the two variables. This information could be useful in identifying potential risk factors for anxiety in young adults, and in developing interventions that could help improve attachment and reduce anxiety.
• Education : In a correlational study in education, researchers might investigate the relationship between two variables, such as teacher engagement and student motivation in a classroom setting. The study could involve measuring levels of teacher engagement and student motivation using established scales or questionnaires, and then analyzing the data to determine if there is a correlation between the two variables. This information could be useful in identifying strategies that teachers could use to improve student motivation and engagement in the classroom.
• Business : Researchers might explore the relationship between two variables, such as employee satisfaction and productivity levels in a company. The study could involve measuring levels of employee satisfaction and productivity using established scales or questionnaires, and then analyzing the data to determine if there is a correlation between the two variables. This information could be useful in identifying factors that could help increase productivity and improve job satisfaction among employees.
• Medicine : Researchers might examine the relationship between two variables, such as smoking and the risk of developing lung cancer. The study could involve collecting data on smoking habits and lung cancer diagnoses, and then analyzing the data to determine if there is a correlation between the two variables. This information could be useful in identifying risk factors for lung cancer and in developing interventions that could help reduce smoking rates.
• Sociology : Researchers might investigate the relationship between two variables, such as income levels and political attitudes. The study could involve measuring income levels and political attitudes using established scales or questionnaires, and then analyzing the data to determine if there is a correlation between the two variables. This information could be useful in understanding how socioeconomic factors can influence political beliefs and attitudes.

How to Conduct Correlational Research

Here are the general steps to conduct correlational research:

• Identify the Research Question : Start by identifying the research question that you want to explore. It should involve two or more variables that you want to investigate for a correlation.
• Choose the research method: Decide on the research method that will be most appropriate for your research question. The most common methods for correlational research are surveys, archival research, and naturalistic observation.
• Choose the Sample: Select the participants or data sources that you will use in your study. Your sample should be representative of the population you want to generalize the results to.
• Measure the variables: Choose the measures that will be used to assess the variables of interest. Ensure that the measures are reliable and valid.
• Collect the Data: Collect the data from your sample using the chosen research method. Be sure to maintain ethical standards and obtain informed consent from your participants.
• Analyze the data: Use statistical software to analyze the data and compute the correlation coefficient. This will help you determine the strength and direction of the correlation between the variables.
• Interpret the results: Interpret the results and draw conclusions based on the findings. Consider any limitations or alternative explanations for the results.
• Report the findings: Report the findings of your study in a research report or manuscript. Be sure to include the research question, methods, results, and conclusions.

Purpose of Correlational Research

The purpose of correlational research is to examine the relationship between two or more variables. Correlational research allows researchers to identify whether there is a relationship between variables, and if so, the strength and direction of that relationship. This information can be useful for predicting and explaining behavior, and for identifying potential risk factors or areas for intervention.

Correlational research can be used in a variety of fields, including psychology, education, medicine, business, and sociology. For example, in psychology, correlational research can be used to explore the relationship between personality traits and behavior, or between early life experiences and later mental health outcomes. In education, correlational research can be used to examine the relationship between teaching practices and student achievement. In medicine, correlational research can be used to investigate the relationship between lifestyle factors and disease outcomes.

Overall, the purpose of correlational research is to provide insight into the relationship between variables, which can be used to inform further research, interventions, or policy decisions.

When to use Correlational Research

Here are some situations when correlational research can be particularly useful:

• When experimental research is not possible or ethical: In some situations, it may not be possible or ethical to manipulate variables in an experimental design. In these cases, correlational research can be used to explore the relationship between variables without manipulating them.
• When exploring new areas of research: Correlational research can be useful when exploring new areas of research or when researchers are unsure of the direction of the relationship between variables. Correlational research can help identify potential areas for further investigation.
• When testing theories: Correlational research can be useful for testing theories about the relationship between variables. Researchers can use correlational research to examine the relationship between variables predicted by a theory, and to determine whether the theory is supported by the data.
• When making predictions: Correlational research can be used to make predictions about future behavior or outcomes. For example, if there is a strong positive correlation between education level and income, one could predict that individuals with higher levels of education will have higher incomes.
• When identifying risk factors: Correlational research can be useful for identifying potential risk factors for negative outcomes. For example, a study might find a positive correlation between drug use and depression, indicating that drug use could be a risk factor for depression.

Characteristics of Correlational Research

Here are some common characteristics of correlational research:

• Examines the relationship between two or more variables: Correlational research is designed to examine the relationship between two or more variables. It seeks to determine if there is a relationship between the variables, and if so, the strength and direction of that relationship.
• Non-experimental design: Correlational research is typically non-experimental in design, meaning that the researcher does not manipulate any variables. Instead, the researcher observes and measures the variables as they naturally occur.
• Cannot establish causation : Correlational research cannot establish causation, meaning that it cannot determine whether one variable causes changes in another variable. Instead, it only provides information about the relationship between the variables.
• Uses statistical analysis: Correlational research relies on statistical analysis to determine the strength and direction of the relationship between variables. This may include calculating correlation coefficients, regression analysis, or other statistical tests.
• Observes real-world phenomena : Correlational research is often used to observe real-world phenomena, such as the relationship between education and income or the relationship between stress and physical health.
• Can be conducted in a variety of fields : Correlational research can be conducted in a variety of fields, including psychology, sociology, education, and medicine.
• Can be conducted using different methods: Correlational research can be conducted using a variety of methods, including surveys, observational studies, and archival studies.

Advantages of Correlational Research

There are several advantages of using correlational research in a study:

• Allows for the exploration of relationships: Correlational research allows researchers to explore the relationships between variables in a natural setting without manipulating any variables. This can help identify possible relationships between variables that may not have been previously considered.
• Useful for predicting behavior: Correlational research can be useful for predicting future behavior. If a strong correlation is found between two variables, researchers can use this information to predict how changes in one variable may affect the other.
• Can be conducted in real-world settings: Correlational research can be conducted in real-world settings, which allows for the collection of data that is representative of real-world phenomena.
• Can be less expensive and time-consuming than experimental research: Correlational research is often less expensive and time-consuming than experimental research, as it does not involve manipulating variables or creating controlled conditions.
• Useful in identifying risk factors: Correlational research can be used to identify potential risk factors for negative outcomes. By identifying variables that are correlated with negative outcomes, researchers can develop interventions or policies to reduce the risk of negative outcomes.
• Useful in exploring new areas of research: Correlational research can be useful in exploring new areas of research, particularly when researchers are unsure of the direction of the relationship between variables. By conducting correlational research, researchers can identify potential areas for further investigation.

Limitation of Correlational Research

Correlational research also has several limitations that should be taken into account:

• Cannot establish causation: Correlational research cannot establish causation, meaning that it cannot determine whether one variable causes changes in another variable. This is because it is not possible to control all possible confounding variables that could affect the relationship between the variables being studied.
• Directionality problem: The directionality problem refers to the difficulty of determining which variable is influencing the other. For example, a correlation may exist between happiness and social support, but it is not clear whether social support causes happiness, or whether happy people are more likely to have social support.
• Third variable problem: The third variable problem refers to the possibility that a third variable, not included in the study, is responsible for the observed relationship between the two variables being studied.
• Limited generalizability: Correlational research is often limited in terms of its generalizability to other populations or settings. This is because the sample studied may not be representative of the larger population, or because the variables studied may behave differently in different contexts.
• Relies on self-reported data: Correlational research often relies on self-reported data, which can be subject to social desirability bias or other forms of response bias.
• Limited in explaining complex behaviors: Correlational research is limited in explaining complex behaviors that are influenced by multiple factors, such as personality traits, situational factors, and social context.

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Conducting sustainability research in the anthropocene: toward a relational approach

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• Published: 24 May 2024

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• 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  

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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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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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Böhme, J., Spreitzer, EM. & Wamsler, C. Conducting sustainability research in the anthropocene: toward a relational approach. Sustain Sci (2024). https://doi.org/10.1007/s11625-024-01510-9

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May 29, 2024

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A multi-relational graph perspective on semantic similarity in program retrieval

by Frontiers Journals

Program retrieval remains a cornerstone of software development, crucial for boosting productivity throughout the development lifecycle. Amidst diverse program retrieval models, many have ignored the disparities between natural language queries and code, resulting in a prominent semantic gap.

Moreover, programs and queries carry rich structural and semantic information. Yet, prevailing approaches often overlook the cohesion among different aspects of source code and treat queries as sequences, neglecting their inherent structural characteristics.

To solve these problems, a research team led by Yunwei Dong published their research in Frontiers of Computer Science .

The team proposed a framework that formulates program retrieval as a multi-relational graph similarity problem. Furthermore, a dual-level attention is applied to assign weights to nodes in multi-relational graphs by intra-relation and inter-relation level attention.

To begin, the multi-relational graph construction module focuses on representing programs and queries using code property graphs (CPG) and abstract meaning representations (AMR). This strategic approach facilitates a more comprehensive and nuanced portrayal of program and query semantics.

Then the dual-level attention graph neural network is leveraged to learn semantic information for AMR and CPG. Finally, a semantic similarity calculation module is designed to calculate the similarity of query-program pairs. Compared with the existing research results, the proposed method performs relatively well among all baselines.

Future research endeavors could concentrate on optimizing multi-relational graphs by minimizing extraneous information, thereby diminishing graph complexity. Additionally, a promising avenue lies in the deliberate integration of external knowledge, such as knowledge graphs, aiming to enhance the representation of program semantics.

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