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Empirical Research: Definition, Methods, Types and Examples

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Empirical research: Definition

Empirical research: origin, quantitative research methods, qualitative research methods, steps for conducting empirical research, empirical research methodology cycle, advantages of empirical research, disadvantages of empirical research, why is there a need for empirical research.

Empirical research is defined as any research where conclusions of the study is strictly drawn from concretely empirical evidence, and therefore “verifiable” evidence.

This empirical evidence can be gathered using quantitative market research and  qualitative market research  methods.

For example: A research is being conducted to find out if listening to happy music in the workplace while working may promote creativity? An experiment is conducted by using a music website survey on a set of audience who are exposed to happy music and another set who are not listening to music at all, and the subjects are then observed. The results derived from such a research will give empirical evidence if it does promote creativity or not.

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You must have heard the quote” I will not believe it unless I see it”. This came from the ancient empiricists, a fundamental understanding that powered the emergence of medieval science during the renaissance period and laid the foundation of modern science, as we know it today. The word itself has its roots in greek. It is derived from the greek word empeirikos which means “experienced”.

In today’s world, the word empirical refers to collection of data using evidence that is collected through observation or experience or by using calibrated scientific instruments. All of the above origins have one thing in common which is dependence of observation and experiments to collect data and test them to come up with conclusions.

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Types and methodologies of empirical research

Empirical research can be conducted and analysed using qualitative or quantitative methods.

• Quantitative research : Quantitative research methods are used to gather information through numerical data. It is used to quantify opinions, behaviors or other defined variables . These are predetermined and are in a more structured format. Some of the commonly used methods are survey, longitudinal studies, polls, etc
• Qualitative research:   Qualitative research methods are used to gather non numerical data.  It is used to find meanings, opinions, or the underlying reasons from its subjects. These methods are unstructured or semi structured. The sample size for such a research is usually small and it is a conversational type of method to provide more insight or in-depth information about the problem Some of the most popular forms of methods are focus groups, experiments, interviews, etc.

Data collected from these will need to be analysed. Empirical evidence can also be analysed either quantitatively and qualitatively. Using this, the researcher can answer empirical questions which have to be clearly defined and answerable with the findings he has got. The type of research design used will vary depending on the field in which it is going to be used. Many of them might choose to do a collective research involving quantitative and qualitative method to better answer questions which cannot be studied in a laboratory setting.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

Quantitative research methods aid in analyzing the empirical evidence gathered. By using these a researcher can find out if his hypothesis is supported or not.

• Survey research: Survey research generally involves a large audience to collect a large amount of data. This is a quantitative method having a predetermined set of closed questions which are pretty easy to answer. Because of the simplicity of such a method, high responses are achieved. It is one of the most commonly used methods for all kinds of research in today’s world.

Previously, surveys were taken face to face only with maybe a recorder. However, with advancement in technology and for ease, new mediums such as emails , or social media have emerged.

For example: Depletion of energy resources is a growing concern and hence there is a need for awareness about renewable energy. According to recent studies, fossil fuels still account for around 80% of energy consumption in the United States. Even though there is a rise in the use of green energy every year, there are certain parameters because of which the general population is still not opting for green energy. In order to understand why, a survey can be conducted to gather opinions of the general population about green energy and the factors that influence their choice of switching to renewable energy. Such a survey can help institutions or governing bodies to promote appropriate awareness and incentive schemes to push the use of greener energy.

Learn more: Renewable Energy Survey Template Descriptive Research vs Correlational Research

• Experimental research: In experimental research , an experiment is set up and a hypothesis is tested by creating a situation in which one of the variable is manipulated. This is also used to check cause and effect. It is tested to see what happens to the independent variable if the other one is removed or altered. The process for such a method is usually proposing a hypothesis, experimenting on it, analyzing the findings and reporting the findings to understand if it supports the theory or not.

For example: A particular product company is trying to find what is the reason for them to not be able to capture the market. So the organisation makes changes in each one of the processes like manufacturing, marketing, sales and operations. Through the experiment they understand that sales training directly impacts the market coverage for their product. If the person is trained well, then the product will have better coverage.

• Correlational research: Correlational research is used to find relation between two set of variables . Regression analysis is generally used to predict outcomes of such a method. It can be positive, negative or neutral correlation.

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For example: Higher educated individuals will get higher paying jobs. This means higher education enables the individual to high paying job and less education will lead to lower paying jobs.

• Longitudinal study: Longitudinal study is used to understand the traits or behavior of a subject under observation after repeatedly testing the subject over a period of time. Data collected from such a method can be qualitative or quantitative in nature.

For example: A research to find out benefits of exercise. The target is asked to exercise everyday for a particular period of time and the results show higher endurance, stamina, and muscle growth. This supports the fact that exercise benefits an individual body.

• Cross sectional: Cross sectional study is an observational type of method, in which a set of audience is observed at a given point in time. In this type, the set of people are chosen in a fashion which depicts similarity in all the variables except the one which is being researched. This type does not enable the researcher to establish a cause and effect relationship as it is not observed for a continuous time period. It is majorly used by healthcare sector or the retail industry.

For example: A medical study to find the prevalence of under-nutrition disorders in kids of a given population. This will involve looking at a wide range of parameters like age, ethnicity, location, incomes  and social backgrounds. If a significant number of kids coming from poor families show under-nutrition disorders, the researcher can further investigate into it. Usually a cross sectional study is followed by a longitudinal study to find out the exact reason.

• Causal-Comparative research : This method is based on comparison. It is mainly used to find out cause-effect relationship between two variables or even multiple variables.

For example: A researcher measured the productivity of employees in a company which gave breaks to the employees during work and compared that to the employees of the company which did not give breaks at all.

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Some research questions need to be analysed qualitatively, as quantitative methods are not applicable there. In many cases, in-depth information is needed or a researcher may need to observe a target audience behavior, hence the results needed are in a descriptive analysis form. Qualitative research results will be descriptive rather than predictive. It enables the researcher to build or support theories for future potential quantitative research. In such a situation qualitative research methods are used to derive a conclusion to support the theory or hypothesis being studied.

LEARN ABOUT: Qualitative Interview

• Case study: Case study method is used to find more information through carefully analyzing existing cases. It is very often used for business research or to gather empirical evidence for investigation purpose. It is a method to investigate a problem within its real life context through existing cases. The researcher has to carefully analyse making sure the parameter and variables in the existing case are the same as to the case that is being investigated. Using the findings from the case study, conclusions can be drawn regarding the topic that is being studied.

For example: A report mentioning the solution provided by a company to its client. The challenges they faced during initiation and deployment, the findings of the case and solutions they offered for the problems. Such case studies are used by most companies as it forms an empirical evidence for the company to promote in order to get more business.

• Observational method:   Observational method is a process to observe and gather data from its target. Since it is a qualitative method it is time consuming and very personal. It can be said that observational research method is a part of ethnographic research which is also used to gather empirical evidence. This is usually a qualitative form of research, however in some cases it can be quantitative as well depending on what is being studied.

For example: setting up a research to observe a particular animal in the rain-forests of amazon. Such a research usually take a lot of time as observation has to be done for a set amount of time to study patterns or behavior of the subject. Another example used widely nowadays is to observe people shopping in a mall to figure out buying behavior of consumers.

• One-on-one interview: Such a method is purely qualitative and one of the most widely used. The reason being it enables a researcher get precise meaningful data if the right questions are asked. It is a conversational method where in-depth data can be gathered depending on where the conversation leads.

For example: A one-on-one interview with the finance minister to gather data on financial policies of the country and its implications on the public.

• Focus groups: Focus groups are used when a researcher wants to find answers to why, what and how questions. A small group is generally chosen for such a method and it is not necessary to interact with the group in person. A moderator is generally needed in case the group is being addressed in person. This is widely used by product companies to collect data about their brands and the product.

For example: A mobile phone manufacturer wanting to have a feedback on the dimensions of one of their models which is yet to be launched. Such studies help the company meet the demand of the customer and position their model appropriately in the market.

• Text analysis: Text analysis method is a little new compared to the other types. Such a method is used to analyse social life by going through images or words used by the individual. In today’s world, with social media playing a major part of everyone’s life, such a method enables the research to follow the pattern that relates to his study.

For example: A lot of companies ask for feedback from the customer in detail mentioning how satisfied are they with their customer support team. Such data enables the researcher to take appropriate decisions to make their support team better.

Sometimes a combination of the methods is also needed for some questions that cannot be answered using only one type of method especially when a researcher needs to gain a complete understanding of complex subject matter.

We recently published a blog that talks about examples of qualitative data in education ; why don’t you check it out for more ideas?

Since empirical research is based on observation and capturing experiences, it is important to plan the steps to conduct the experiment and how to analyse it. This will enable the researcher to resolve problems or obstacles which can occur during the experiment.

Step #1: Define the purpose of the research

This is the step where the researcher has to answer questions like what exactly do I want to find out? What is the problem statement? Are there any issues in terms of the availability of knowledge, data, time or resources. Will this research be more beneficial than what it will cost.

Before going ahead, a researcher has to clearly define his purpose for the research and set up a plan to carry out further tasks.

Step #2 : Supporting theories and relevant literature

The researcher needs to find out if there are theories which can be linked to his research problem . He has to figure out if any theory can help him support his findings. All kind of relevant literature will help the researcher to find if there are others who have researched this before, or what are the problems faced during this research. The researcher will also have to set up assumptions and also find out if there is any history regarding his research problem

Step #3: Creation of Hypothesis and measurement

Before beginning the actual research he needs to provide himself a working hypothesis or guess what will be the probable result. Researcher has to set up variables, decide the environment for the research and find out how can he relate between the variables.

Researcher will also need to define the units of measurements, tolerable degree for errors, and find out if the measurement chosen will be acceptable by others.

Step #4: Methodology, research design and data collection

In this step, the researcher has to define a strategy for conducting his research. He has to set up experiments to collect data which will enable him to propose the hypothesis. The researcher will decide whether he will need experimental or non experimental method for conducting the research. The type of research design will vary depending on the field in which the research is being conducted. Last but not the least, the researcher will have to find out parameters that will affect the validity of the research design. Data collection will need to be done by choosing appropriate samples depending on the research question. To carry out the research, he can use one of the many sampling techniques. Once data collection is complete, researcher will have empirical data which needs to be analysed.

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Step #5: Data Analysis and result

Data analysis can be done in two ways, qualitatively and quantitatively. Researcher will need to find out what qualitative method or quantitative method will be needed or will he need a combination of both. Depending on the unit of analysis of his data, he will know if his hypothesis is supported or rejected. Analyzing this data is the most important part to support his hypothesis.

Step #6: Conclusion

A report will need to be made with the findings of the research. The researcher can give the theories and literature that support his research. He can make suggestions or recommendations for further research on his topic.

A.D. de Groot, a famous dutch psychologist and a chess expert conducted some of the most notable experiments using chess in the 1940’s. During his study, he came up with a cycle which is consistent and now widely used to conduct empirical research. It consists of 5 phases with each phase being as important as the next one. The empirical cycle captures the process of coming up with hypothesis about how certain subjects work or behave and then testing these hypothesis against empirical data in a systematic and rigorous approach. It can be said that it characterizes the deductive approach to science. Following is the empirical cycle.

• Observation: At this phase an idea is sparked for proposing a hypothesis. During this phase empirical data is gathered using observation. For example: a particular species of flower bloom in a different color only during a specific season.
• Induction: Inductive reasoning is then carried out to form a general conclusion from the data gathered through observation. For example: As stated above it is observed that the species of flower blooms in a different color during a specific season. A researcher may ask a question “does the temperature in the season cause the color change in the flower?” He can assume that is the case, however it is a mere conjecture and hence an experiment needs to be set up to support this hypothesis. So he tags a few set of flowers kept at a different temperature and observes if they still change the color?
• Deduction: This phase helps the researcher to deduce a conclusion out of his experiment. This has to be based on logic and rationality to come up with specific unbiased results.For example: In the experiment, if the tagged flowers in a different temperature environment do not change the color then it can be concluded that temperature plays a role in changing the color of the bloom.
• Testing: This phase involves the researcher to return to empirical methods to put his hypothesis to the test. The researcher now needs to make sense of his data and hence needs to use statistical analysis plans to determine the temperature and bloom color relationship. If the researcher finds out that most flowers bloom a different color when exposed to the certain temperature and the others do not when the temperature is different, he has found support to his hypothesis. Please note this not proof but just a support to his hypothesis.
• Evaluation: This phase is generally forgotten by most but is an important one to keep gaining knowledge. During this phase the researcher puts forth the data he has collected, the support argument and his conclusion. The researcher also states the limitations for the experiment and his hypothesis and suggests tips for others to pick it up and continue a more in-depth research for others in the future. LEARN MORE: Population vs Sample

LEARN MORE: Population vs Sample

There is a reason why empirical research is one of the most widely used method. There are a few advantages associated with it. Following are a few of them.

• It is used to authenticate traditional research through various experiments and observations.
• This research methodology makes the research being conducted more competent and authentic.
• It enables a researcher understand the dynamic changes that can happen and change his strategy accordingly.
• The level of control in such a research is high so the researcher can control multiple variables.
• It plays a vital role in increasing internal validity .

Even though empirical research makes the research more competent and authentic, it does have a few disadvantages. Following are a few of them.

• Such a research needs patience as it can be very time consuming. The researcher has to collect data from multiple sources and the parameters involved are quite a few, which will lead to a time consuming research.
• Most of the time, a researcher will need to conduct research at different locations or in different environments, this can lead to an expensive affair.
• There are a few rules in which experiments can be performed and hence permissions are needed. Many a times, it is very difficult to get certain permissions to carry out different methods of this research.
• Collection of data can be a problem sometimes, as it has to be collected from a variety of sources through different methods.

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Empirical research is important in today’s world because most people believe in something only that they can see, hear or experience. It is used to validate multiple hypothesis and increase human knowledge and continue doing it to keep advancing in various fields.

For example: Pharmaceutical companies use empirical research to try out a specific drug on controlled groups or random groups to study the effect and cause. This way, they prove certain theories they had proposed for the specific drug. Such research is very important as sometimes it can lead to finding a cure for a disease that has existed for many years. It is useful in science and many other fields like history, social sciences, business, etc.

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With the advancement in today’s world, empirical research has become critical and a norm in many fields to support their hypothesis and gain more knowledge. The methods mentioned above are very useful for carrying out such research. However, a number of new methods will keep coming up as the nature of new investigative questions keeps getting unique or changing.

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Empirical Research: Defining, Identifying, & Finding

Defining empirical research, what is empirical research, quantitative or qualitative.

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Calfee & Chambliss (2005)  (UofM login required) describe empirical research as a "systematic approach for answering certain types of questions."  Those questions are answered "[t]hrough the collection of evidence under carefully defined and replicable conditions" (p. 43). 

The evidence collected during empirical research is often referred to as "data." 

Characteristics of Empirical Research

Emerald Publishing's guide to conducting empirical research identifies a number of common elements to empirical research: 

• A  research question , which will determine research objectives.
• A particular and planned  design  for the research, which will depend on the question and which will find ways of answering it with appropriate use of resources.
• The gathering of  primary data , which is then analysed.
• A particular  methodology  for collecting and analysing the data, such as an experiment or survey.
• The limitation of the data to a particular group, area or time scale, known as a sample [emphasis added]: for example, a specific number of employees of a particular company type, or all users of a library over a given time scale. The sample should be somehow representative of a wider population.
• The ability to  recreate  the study and test the results. This is known as  reliability .
• The ability to  generalize  from the findings to a larger sample and to other situations.

If you see these elements in a research article, you can feel confident that you have found empirical research. Emerald's guide goes into more detail on each element. 

Empirical research methodologies can be described as quantitative, qualitative, or a mix of both (usually called mixed-methods).

Ruane (2016)  (UofM login required) gets at the basic differences in approach between quantitative and qualitative research:

• Quantitative research  -- an approach to documenting reality that relies heavily on numbers both for the measurement of variables and for data analysis (p. 33).
• Qualitative research  -- an approach to documenting reality that relies on words and images as the primary data source (p. 33).

Both quantitative and qualitative methods are empirical . If you can recognize that a research study is quantitative or qualitative study, then you have also recognized that it is empirical study. 

Below are information on the characteristics of quantitative and qualitative research. This video from Scribbr also offers a good overall introduction to the two approaches to research methodology: 

Characteristics of Quantitative Research 

Researchers test hypotheses, or theories, based in assumptions about causality, i.e. we expect variable X to cause variable Y. Variables have to be controlled as much as possible to ensure validity. The results explain the relationship between the variables. Measures are based in pre-defined instruments.

Examples: experimental or quasi-experimental design, pretest & post-test, survey or questionnaire with closed-ended questions. Studies that identify factors that influence an outcomes, the utility of an intervention, or understanding predictors of outcomes. 

Characteristics of Qualitative Research

Researchers explore “meaning individuals or groups ascribe to social or human problems (Creswell & Creswell, 2018, p3).” Questions and procedures emerge rather than being prescribed. Complexity, nuance, and individual meaning are valued. Research is both inductive and deductive. Data sources are multiple and varied, i.e. interviews, observations, documents, photographs, etc. The researcher is a key instrument and must be reflective of their background, culture, and experiences as influential of the research.

Examples: open question interviews and surveys, focus groups, case studies, grounded theory, ethnography, discourse analysis, narrative, phenomenology, participatory action research.

Calfee, R. C. & Chambliss, M. (2005). The design of empirical research. In J. Flood, D. Lapp, J. R. Squire, & J. Jensen (Eds.),  Methods of research on teaching the English language arts: The methodology chapters from the handbook of research on teaching the English language arts (pp. 43-78). Routledge.  http://ezproxy.memphis.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=125955&site=eds-live&scope=site .

Creswell, J. W., & Creswell, J. D. (2018).  Research design: Qualitative, quantitative, and mixed methods approaches  (5th ed.). Thousand Oaks: Sage.

How to... conduct empirical research . (n.d.). Emerald Publishing.  https://www.emeraldgrouppublishing.com/how-to/research-methods/conduct-empirical-research .

Scribbr. (2019). Quantitative vs. qualitative: The differences explained  [video]. YouTube.  https://www.youtube.com/watch?v=a-XtVF7Bofg .

Ruane, J. M. (2016).  Introducing social research methods : Essentials for getting the edge . Wiley-Blackwell.  http://ezproxy.memphis.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=1107215&site=eds-live&scope=site .  

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• Published: 01 June 2023

Data, measurement and empirical methods in the science of science

• Lu Liu 1 , 2 , 3 , 4 ,
• Benjamin F. Jones   ORCID: orcid.org/0000-0001-9697-9388 1 , 2 , 3 , 5 , 6 ,
• Brian Uzzi   ORCID: orcid.org/0000-0001-6855-2854 1 , 2 , 3 &
• Dashun Wang   ORCID: orcid.org/0000-0002-7054-2206 1 , 2 , 3 , 7  

Nature Human Behaviour volume  7 ,  pages 1046–1058 ( 2023 ) Cite this article

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The advent of large-scale datasets that trace the workings of science has encouraged researchers from many different disciplinary backgrounds to turn scientific methods into science itself, cultivating a rapidly expanding ‘science of science’. This Review considers this growing, multidisciplinary literature through the lens of data, measurement and empirical methods. We discuss the purposes, strengths and limitations of major empirical approaches, seeking to increase understanding of the field’s diverse methodologies and expand researchers’ toolkits. Overall, new empirical developments provide enormous capacity to test traditional beliefs and conceptual frameworks about science, discover factors associated with scientific productivity, predict scientific outcomes and design policies that facilitate scientific progress.

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Scientific advances are a key input to rising standards of living, health and the capacity of society to confront grand challenges, from climate change to the COVID-19 pandemic 1 , 2 , 3 . A deeper understanding of how science works and where innovation occurs can help us to more effectively design science policy and science institutions, better inform scientists’ own research choices, and create and capture enormous value for science and humanity. Building on these key premises, recent years have witnessed substantial development in the ‘science of science’ 4 , 5 , 6 , 7 , 8 , 9 , which uses large-scale datasets and diverse computational toolkits to unearth fundamental patterns behind scientific production and use.

The idea of turning scientific methods into science itself is long-standing. Since the mid-20th century, researchers from different disciplines have asked central questions about the nature of scientific progress and the practice, organization and impact of scientific research. Building on these rich historical roots, the field of the science of science draws upon many disciplines, ranging from information science to the social, physical and biological sciences to computer science, engineering and design. The science of science closely relates to several strands and communities of research, including metascience, scientometrics, the economics of science, research on research, science and technology studies, the sociology of science, metaknowledge and quantitative science studies 5 . There are noticeable differences between some of these communities, mostly around their historical origins and the initial disciplinary composition of researchers forming these communities. For example, metascience has its origins in the clinical sciences and psychology, and focuses on rigour, transparency, reproducibility and other open science-related practices and topics. The scientometrics community, born in library and information sciences, places a particular emphasis on developing robust and responsible measures and indicators for science. Science and technology studies engage the history of science and technology, the philosophy of science, and the interplay between science, technology and society. The science of science, which has its origins in physics, computer science and sociology, takes a data-driven approach and emphasizes questions on how science works. Each of these communities has made fundamental contributions to understanding science. While they differ in their origins, these differences pale in comparison to the overarching, common interest in understanding the practice of science and its societal impact.

Three major developments have encouraged rapid advances in the science of science. The first is in data 9 : modern databases include millions of research articles, grant proposals, patents and more. This windfall of data traces scientific activity in remarkable detail and at scale. The second development is in measurement: scholars have used data to develop many new measures of scientific activities and examine theories that have long been viewed as important but difficult to quantify. The third development is in empirical methods: thanks to parallel advances in data science, network science, artificial intelligence and econometrics, researchers can study relationships, make predictions and assess science policy in powerful new ways. Together, new data, measurements and methods have revealed fundamental new insights about the inner workings of science and scientific progress itself.

With multiple approaches, however, comes a key challenge. As researchers adhere to norms respected within their disciplines, their methods vary, with results often published in venues with non-overlapping readership, fragmenting research along disciplinary boundaries. This fragmentation challenges researchers’ ability to appreciate and understand the value of work outside of their own discipline, much less to build directly on it for further investigations.

Recognizing these challenges and the rapidly developing nature of the field, this paper reviews the empirical approaches that are prevalent in this literature. We aim to provide readers with an up-to-date understanding of the available datasets, measurement constructs and empirical methodologies, as well as the value and limitations of each. Owing to space constraints, this Review does not cover the full technical details of each method, referring readers to related guides to learn more. Instead, we will emphasize why a researcher might favour one method over another, depending on the research question.

Beyond a positive understanding of science, a key goal of the science of science is to inform science policy. While this Review mainly focuses on empirical approaches, with its core audience being researchers in the field, the studies reviewed are also germane to key policy questions. For example, what is the appropriate scale of scientific investment, in what directions and through what institutions 10 , 11 ? Are public investments in science aligned with public interests 12 ? What conditions produce novel or high-impact science 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ? How do the reward systems of science influence the rate and direction of progress 13 , 21 , 22 , 23 , 24 , and what governs scientific reproducibility 25 , 26 , 27 ? How do contributions evolve over a scientific career 28 , 29 , 30 , 31 , 32 , and how may diversity among scientists advance scientific progress 33 , 34 , 35 , among other questions relevant to science policy 36 , 37 .

Overall, this review aims to facilitate entry to science of science research, expand researcher toolkits and illustrate how diverse research approaches contribute to our collective understanding of science. Section 2 reviews datasets and data linkages. Section 3 reviews major measurement constructs in the science of science. Section 4 considers a range of empirical methods, focusing on one study to illustrate each method and briefly summarizing related examples and applications. Section 5 concludes with an outlook for the science of science.

Historically, data on scientific activities were difficult to collect and were available in limited quantities. Gathering data could involve manually tallying statistics from publications 38 , 39 , interviewing scientists 16 , 40 , or assembling historical anecdotes and biographies 13 , 41 . Analyses were typically limited to a specific domain or group of scientists. Today, massive datasets on scientific production and use are at researchers’ fingertips 42 , 43 , 44 . Armed with big data and advanced algorithms, researchers can now probe questions previously not amenable to quantification and with enormous increases in scope and scale, as detailed below.

Publication datasets cover papers from nearly all scientific disciplines, enabling analyses of both general and domain-specific patterns. Commonly used datasets include the Web of Science (WoS), PubMed, CrossRef, ORCID, OpenCitations, Dimensions and OpenAlex. Datasets incorporating papers’ text (CORE) 45 , 46 , 47 , data entities (DataCite) 48 , 49 and peer review reports (Publons) 33 , 50 , 51 have also become available. These datasets further enable novel measurement, for example, representations of a paper’s content 52 , 53 , novelty 15 , 54 and interdisciplinarity 55 .

Notably, databases today capture more diverse aspects of science beyond publications, offering a richer and more encompassing view of research contexts and of researchers themselves (Fig. 1 ). For example, some datasets trace research funding to the specific publications these investments support 56 , 57 , allowing high-scale studies of the impact of funding on productivity and the return on public investment. Datasets incorporating job placements 58 , 59 , curriculum vitae 21 , 59 and scientific prizes 23 offer rich quantitative evidence on the social structure of science. Combining publication profiles with mentorship genealogies 60 , 61 , dissertations 34 and course syllabi 62 , 63 provides insights on mentoring and cultivating talent.

This figure presents commonly used data types in science of science research, information contained in each data type and examples of data sources. Datasets in the science of science research have not only grown in scale but have also expanded beyond publications to integrate upstream funding investments and downstream applications that extend beyond science itself.

Finally, today’s scope of data extends beyond science to broader aspects of society. Altmetrics 64 captures news media and social media mentions of scientific articles. Other databases incorporate marketplace uses of science, including through patents 10 , pharmaceutical clinical trials and drug approvals 65 , 66 . Policy documents 67 , 68 help us to understand the role of science in the halls of government 69 and policy making 12 , 68 .

While datasets of the modern scientific enterprise have grown exponentially, they are not without limitations. As is often the case for data-driven research, drawing conclusions from specific data sources requires scrutiny and care. Datasets are typically based on published work, which may favour easy-to-publish topics over important ones (the streetlight effect) 70 , 71 . The publication of negative results is also rare (the file drawer problem) 72 , 73 . Meanwhile, English language publications account for over 90% of articles in major data sources, with limited coverage of non-English journals 74 . Publication datasets may also reflect biases in data collection across research institutions or demographic groups. Despite the open science movement, many datasets require paid subscriptions, which can create inequality in data access. Creating more open datasets for the science of science, such as OpenAlex, may not only improve the robustness and replicability of empirical claims but also increase entry to the field.

As today’s datasets become larger in scale and continue to integrate new dimensions, they offer opportunities to unveil the inner workings and external impacts of science in new ways. They can enable researchers to reach beyond previous limitations while conducting original studies of new and long-standing questions about the sciences.

Measurement

Here we discuss prominent measurement approaches in the science of science, including their purposes and limitations.

Modern publication databases typically include data on which articles and authors cite other papers and scientists. These citation linkages have been used to engage core conceptual ideas in scientific research. Here we consider two common measures based on citation information: citation counts and knowledge flows.

First, citation counts are commonly used indicators of impact. The term ‘indicator’ implies that it only approximates the concept of interest. A citation count is defined as how many times a document is cited by subsequent documents and can proxy for the importance of research papers 75 , 76 as well as patented inventions 77 , 78 , 79 . Rather than treating each citation equally, measures may further weight the importance of each citation, for example by using the citation network structure to produce centrality 80 , PageRank 81 , 82 or Eigenfactor indicators 83 , 84 .

Citation-based indicators have also faced criticism 84 , 85 . Citation indicators necessarily oversimplify the construct of impact, often ignoring heterogeneity in the meaning and use of a particular reference, the variations in citation practices across fields and institutional contexts, and the potential for reputation and power structures in science to influence citation behaviour 86 , 87 . Researchers have started to understand more nuanced citation behaviours ranging from negative citations 86 to citation context 47 , 88 , 89 . Understanding what a citation actually measures matters in interpreting and applying many research findings in the science of science. Evaluations relying on citation-based indicators rather than expert judgements raise questions regarding misuse 90 , 91 , 92 . Given the importance of developing indicators that can reliably quantify and evaluate science, the scientometrics community has been working to provide guidance for responsible citation practices and assessment 85 .

Second, scientists use citations to trace knowledge flows. Each citation in a paper is a link to specific previous work from which we can proxy how new discoveries draw upon existing ideas 76 , 93 and how knowledge flows between fields of science 94 , 95 , research institutions 96 , regions and nations 97 , 98 , 99 , and individuals 81 . Combinations of citation linkages can also approximate novelty 15 , disruptiveness 17 , 100 and interdisciplinarity 55 , 95 , 101 , 102 . A rapidly expanding body of work further examines citations to scientific articles from other domains (for example, patents, clinical drug trials and policy documents) to understand the applied value of science 10 , 12 , 65 , 66 , 103 , 104 , 105 .

Individuals

Analysing individual careers allows researchers to answer questions such as: How do we quantify individual scientific productivity? What is a typical career lifecycle? How are resources and credits allocated across individuals and careers? A scholar’s career can be examined through the papers they publish 30 , 31 , 106 , 107 , 108 , with attention to career progression and mobility, publication counts and citation impact, as well as grant funding 24 , 109 , 110 and prizes 111 , 112 , 113 ,

Studies of individual impact focus on output, typically approximated by the number of papers a researcher publishes and citation indicators. A popular measure for individual impact is the h -index 114 , which takes both volume and per-paper impact into consideration. Specifically, a scientist is assigned the largest value h such that they have h papers that were each cited at least h times. Later studies build on the idea of the h -index and propose variants to address limitations 115 , these variants ranging from emphasizing highly cited papers in a career 116 , to field differences 117 and normalizations 118 , to the relative contribution of an individual in collaborative works 119 .

To study dynamics in output over the lifecycle, individuals can be studied according to age, career age or the sequence of publications. A long-standing literature has investigated the relationship between age and the likelihood of outstanding achievement 28 , 106 , 111 , 120 , 121 . Recent studies further decouple the relationship between age, publication volume and per-paper citation, and measure the likelihood of producing highly cited papers in the sequence of works one produces 30 , 31 .

As simple as it sounds, representing careers using publication records is difficult. Collecting the full publication list of a researcher is the foundation to study individuals yet remains a key challenge, requiring name disambiguation techniques to match specific works to specific researchers. Although algorithms are increasingly capable at identifying millions of career profiles 122 , they vary in accuracy and robustness. ORCID can help to alleviate the problem by offering researchers the opportunity to create, maintain and update individual profiles themselves, and it goes beyond publications to collect broader outputs and activities 123 . A second challenge is survivorship bias. Empirical studies tend to focus on careers that are long enough to afford statistical analyses, which limits the applicability of the findings to scientific careers as a whole. A third challenge is the breadth of scientists’ activities, where focusing on publications ignores other important contributions such as mentorship and teaching, service (for example, refereeing papers, reviewing grant proposals and editing journals) or leadership within their organizations. Although researchers have begun exploring these dimensions by linking individual publication profiles with genealogical databases 61 , 124 , dissertations 34 , grants 109 , curriculum vitae 21 and acknowledgements 125 , scientific careers beyond publication records remain under-studied 126 , 127 . Lastly, citation-based indicators only serve as an approximation of individual performance with similar limitations as discussed above. The scientific community has called for more appropriate practices 85 , 128 , ranging from incorporating expert assessment of research contributions to broadening the measures of impact beyond publications.

Over many decades, science has exhibited a substantial and steady shift away from solo authorship towards coauthorship, especially among highly cited works 18 , 129 , 130 . In light of this shift, a research field, the science of team science 131 , 132 , has emerged to study the mechanisms that facilitate or hinder the effectiveness of teams. Team size can be proxied by the number of coauthors on a paper, which has been shown to predict distinctive types of advance: whereas larger teams tend to develop ideas, smaller teams tend to disrupt current ways of thinking 17 . Team characteristics can be inferred from coauthors’ backgrounds 133 , 134 , 135 , allowing quantification of a team’s diversity in terms of field, age, gender or ethnicity. Collaboration networks based on coauthorship 130 , 136 , 137 , 138 , 139 offer nuanced network-based indicators to understand individual and institutional collaborations.

However, there are limitations to using coauthorship alone to study teams 132 . First, coauthorship can obscure individual roles 140 , 141 , 142 , which has prompted institutional responses to help to allocate credit, including authorship order and individual contribution statements 56 , 143 . Second, coauthorship does not reflect the complex dynamics and interactions between team members that are often instrumental for team success 53 , 144 . Third, collaborative contributions can extend beyond coauthorship in publications to include members of a research laboratory 145 or co-principal investigators (co-PIs) on a grant 146 . Initiatives such as CRediT may help to address some of these issues by recording detailed roles for each contributor 147 .

Institutions

Research institutions, such as departments, universities, national laboratories and firms, encompass wider groups of researchers and their corresponding outputs. Institutional membership can be inferred from affiliations listed on publications or patents 148 , 149 , and the output of an institution can be aggregated over all its affiliated researchers 150 . Institutional research information systems (CRIS) contain more comprehensive research outputs and activities from employees.

Some research questions consider the institution as a whole, investigating the returns to research and development investment 104 , inequality of resource allocation 22 and the flow of scientists 21 , 148 , 149 . Other questions focus on institutional structures as sources of research productivity by looking into the role of peer effects 125 , 151 , 152 , 153 , how institutional policies impact research outcomes 154 , 155 and whether interdisciplinary efforts foster innovation 55 . Institution-oriented measurement faces similar limitations as with analyses of individuals and teams, including name disambiguation for a given institution and the limited capacity of formal publication records to characterize the full range of relevant institutional outcomes. It is also unclear how to allocate credit among multiple institutions associated with a paper. Moreover, relevant institutional employees extend beyond publishing researchers: interns, technicians and administrators all contribute to research endeavours 130 .

In sum, measurements allow researchers to quantify scientific production and use across numerous dimensions, but they also raise questions of construct validity: Does the proposed metric really reflect what we want to measure? Testing the construct’s validity is important, as is understanding a construct’s limits. Where possible, using alternative measurement approaches, or qualitative methods such as interviews and surveys, can improve measurement accuracy and the robustness of findings.

Empirical methods

In this section, we review two broad categories of empirical approaches (Table 1 ), each with distinctive goals: (1) to discover, estimate and predict empirical regularities; and (2) to identify causal mechanisms. For each method, we give a concrete example to help to explain how the method works, summarize related work for interested readers, and discuss contributions and limitations.

Descriptive and predictive approaches

Empirical regularities and generalizable facts.

The discovery of empirical regularities in science has had a key role in driving conceptual developments and the directions of future research. By observing empirical patterns at scale, researchers unveil central facts that shape science and present core features that theories of scientific progress and practice must explain. For example, consider citation distributions. de Solla Price first proposed that citation distributions are fat-tailed 39 , indicating that a few papers have extremely high citations while most papers have relatively few or even no citations at all. de Solla Price proposed that citation distribution was a power law, while researchers have since refined this view to show that the distribution appears log-normal, a nearly universal regularity across time and fields 156 , 157 . The fat-tailed nature of citation distributions and its universality across the sciences has in turn sparked substantial theoretical work that seeks to explain this key empirical regularity 20 , 156 , 158 , 159 .

Empirical regularities are often surprising and can contest previous beliefs of how science works. For example, it has been shown that the age distribution of great achievements peaks in middle age across a wide range of fields 107 , 121 , 160 , rejecting the common belief that young scientists typically drive breakthroughs in science. A closer look at the individual careers also indicates that productivity patterns vary widely across individuals 29 . Further, a scholar’s highest-impact papers come at a remarkably constant rate across the sequence of their work 30 , 31 .

The discovery of empirical regularities has had important roles in shaping beliefs about the nature of science 10 , 45 , 161 , 162 , sources of breakthrough ideas 15 , 163 , 164 , 165 , scientific careers 21 , 29 , 126 , 127 , the network structure of ideas and scientists 23 , 98 , 136 , 137 , 138 , 139 , 166 , gender inequality 57 , 108 , 126 , 135 , 143 , 167 , 168 , and many other areas of interest to scientists and science institutions 22 , 47 , 86 , 97 , 102 , 105 , 134 , 169 , 170 , 171 . At the same time, care must be taken to ensure that findings are not merely artefacts due to data selection or inherent bias. To differentiate meaningful patterns from spurious ones, it is important to stress test the findings through different selection criteria or across non-overlapping data sources.

Regression analysis

When investigating correlations among variables, a classic method is regression, which estimates how one set of variables explains variation in an outcome of interest. Regression can be used to test explicit hypotheses or predict outcomes. For example, researchers have investigated whether a paper’s novelty predicts its citation impact 172 . Adding additional control variables to the regression, one can further examine the robustness of the focal relationship.

Although regression analysis is useful for hypothesis testing, it bears substantial limitations. If the question one wishes to ask concerns a ‘causal’ rather than a correlational relationship, regression is poorly suited to the task as it is impossible to control for all the confounding factors. Failing to account for such ‘omitted variables’ can bias the regression coefficient estimates and lead to spurious interpretations. Further, regression models often have low goodness of fit (small R 2 ), indicating that the variables considered explain little of the outcome variation. As regressions typically focus on a specific relationship in simple functional forms, regressions tend to emphasize interpretability rather than overall predictability. The advent of predictive approaches powered by large-scale datasets and novel computational techniques offers new opportunities for modelling complex relationships with stronger predictive power.

Mechanistic models

Mechanistic modelling is an important approach to explaining empirical regularities, drawing from methods primarily used in physics. Such models predict macro-level regularities of a system by modelling micro-level interactions among basic elements with interpretable and modifiable formulars. While theoretical by nature, mechanistic models in the science of science are often empirically grounded, and this approach has developed together with the advent of large-scale, high-resolution data.

Simplicity is the core value of a mechanistic model. Consider for example, why citations follow a fat-tailed distribution. de Solla Price modelled the citing behaviour as a cumulative advantage process on a growing citation network 159 and found that if the probability a paper is cited grows linearly with its existing citations, the resulting distribution would follow a power law, broadly aligned with empirical observations. The model is intentionally simplified, ignoring myriad factors. Yet the simple cumulative advantage process is by itself sufficient in explaining a power law distribution of citations. In this way, mechanistic models can help to reveal key mechanisms that can explain observed patterns.

Moreover, mechanistic models can be refined as empirical evidence evolves. For example, later investigations showed that citation distributions are better characterized as log-normal 156 , 173 , prompting researchers to introduce a fitness parameter to encapsulate the inherent differences in papers’ ability to attract citations 174 , 175 . Further, older papers are less likely to be cited than expected 176 , 177 , 178 , motivating more recent models 20 to introduce an additional aging effect 179 . By combining the cumulative advantage, fitness and aging effects, one can already achieve substantial predictive power not just for the overall properties of the system but also the citation dynamics of individual papers 20 .

In addition to citations, mechanistic models have been developed to understand the formation of collaborations 136 , 180 , 181 , 182 , 183 , knowledge discovery and diffusion 184 , 185 , topic selection 186 , 187 , career dynamics 30 , 31 , 188 , 189 , the growth of scientific fields 190 and the dynamics of failure in science and other domains 178 .

At the same time, some observers have argued that mechanistic models are too simplistic to capture the essence of complex real-world problems 191 . While it has been a cornerstone for the natural sciences, representing social phenomena in a limited set of mathematical equations may miss complexities and heterogeneities that make social phenomena interesting in the first place. Such concerns are not unique to the science of science, as they represent a broader theme in computational social sciences 192 , 193 , ranging from social networks 194 , 195 to human mobility 196 , 197 to epidemics 198 , 199 . Other observers have questioned the practical utility of mechanistic models and whether they can be used to guide decisions and devise actionable policies. Nevertheless, despite these limitations, several complex phenomena in the science of science are well captured by simple mechanistic models, showing a high degree of regularity beneath complex interacting systems and providing powerful insights about the nature of science. Mixing such modelling with other methods could be particularly fruitful in future investigations.

Machine learning

The science of science seeks in part to forecast promising directions for scientific research 7 , 44 . In recent years, machine learning methods have substantially advanced predictive capabilities 200 , 201 and are playing increasingly important parts in the science of science. In contrast to the previous methods, machine learning does not emphasize hypotheses or theories. Rather, it leverages complex relationships in data and optimizes goodness of fit to make predictions and categorizations.

Traditional machine learning models include supervised, semi-supervised and unsupervised learning. The model choice depends on data availability and the research question, ranging from supervised models for citation prediction 202 , 203 to unsupervised models for community detection 204 . Take for example mappings of scientific knowledge 94 , 205 , 206 . The unsupervised method applies network clustering algorithms to map the structures of science. Related visualization tools make sense of clusters from the underlying network, allowing observers to see the organization, interactions and evolution of scientific knowledge. More recently, supervised learning, and deep neural networks in particular, have witnessed especially rapid developments 207 . Neural networks can generate high-dimensional representations of unstructured data such as images and texts, which encode complex properties difficult for human experts to perceive.

Take text analysis as an example. A recent study 52 utilizes 3.3 million paper abstracts in materials science to predict the thermoelectric properties of materials. The intuition is that the words currently used to describe a material may predict its hitherto undiscovered properties (Fig. 2 ). Compared with a random material, the materials predicted by the model are eight times more likely to be reported as thermoelectric in the next 5 years, suggesting that machine learning has the potential to substantially speed up knowledge discovery, especially as data continue to grow in scale and scope. Indeed, predicting the direction of new discoveries represents one of the most promising avenues for machine learning models, with neural networks being applied widely to biology 208 , physics 209 , 210 , mathematics 211 , chemistry 212 , medicine 213 and clinical applications 214 . Neural networks also offer a quantitative framework to probe the characteristics of creative products ranging from scientific papers 53 , journals 215 , organizations 148 , to paintings and movies 32 . Neural networks can also help to predict the reproducibility of papers from a variety of disciplines at scale 53 , 216 .

This figure illustrates the word2vec skip-gram methods 52 , where the goal is to predict useful properties of materials using previous scientific literature. a , The architecture and training process of the word2vec skip-gram model, where the 3-layer, fully connected neural network learns the 200-dimensional representation (hidden layer) from the sparse vector for each word and its context in the literature (input layer). b , The top two principal components of the word embedding. Materials with similar features are close in the 2D space, allowing prediction of a material’s properties. Different targeted words are shown in different colours. Reproduced with permission from ref. 52 , Springer Nature Ltd.

While machine learning can offer high predictive accuracy, successful applications to the science of science face challenges, particularly regarding interpretability. Researchers may value transparent and interpretable findings for how a given feature influences an outcome, rather than a black-box model. The lack of interpretability also raises concerns about bias and fairness. In predicting reproducible patterns from data, machine learning models inevitably include and reproduce biases embedded in these data, often in non-transparent ways. The fairness of machine learning 217 is heavily debated in applications ranging from the criminal justice system to hiring processes. Effective and responsible use of machine learning in the science of science therefore requires thoughtful partnership between humans and machines 53 to build a reliable system accessible to scrutiny and modification.

Causal approaches

The preceding methods can reveal core facts about the workings of science and develop predictive capacity. Yet, they fail to capture causal relationships, which are particularly useful in assessing policy interventions. For example, how can we test whether a science policy boosts or hinders the performance of individuals, teams or institutions? The overarching idea of causal approaches is to construct some counterfactual world where two groups are identical to each other except that one group experiences a treatment that the other group does not.

Towards causation

Before engaging in causal approaches, it is useful to first consider the interpretative challenges of observational data. As observational data emerge from mechanisms that are not fully known or measured, an observed correlation may be driven by underlying forces that were not accounted for in the analysis. This challenge makes causal inference fundamentally difficult in observational data. An awareness of this issue is the first step in confronting it. It further motivates intermediate empirical approaches, including the use of matching strategies and fixed effects, that can help to confront (although not fully eliminate) the inference challenge. We first consider these approaches before turning to more fully causal methods.

Matching. Matching utilizes rich information to construct a control group that is similar to the treatment group on as many observable characteristics as possible before the treatment group is exposed to the treatment. Inferences can then be made by comparing the treatment and the matched control groups. Exact matching applies to categorical values, such as country, gender, discipline or affiliation 35 , 218 . Coarsened exact matching considers percentile bins of continuous variables and matches observations in the same bin 133 . Propensity score matching estimates the probability of receiving the ‘treatment’ on the basis of the controlled variables and uses the estimates to match treatment and control groups, which reduces the matching task from comparing the values of multiple covariates to comparing a single value 24 , 219 . Dynamic matching is useful for longitudinally matching variables that change over time 220 , 221 .

Fixed effects. Fixed effects are a powerful and now standard tool in controlling for confounders. A key requirement for using fixed effects is that there are multiple observations on the same subject or entity (person, field, institution and so on) 222 , 223 , 224 . The fixed effect works as a dummy variable that accounts for the role of any fixed characteristic of that entity. Consider the finding where gender-diverse teams produce higher-impact papers than same-gender teams do 225 . A confounder may be that individuals who tend to write high-impact papers may also be more likely to work in gender-diverse teams. By including individual fixed effects, one accounts for any fixed characteristics of individuals (such as IQ, cultural background or previous education) that might drive the relationship of interest.

In sum, matching and fixed effects methods reduce potential sources of bias in interpreting relationships between variables. Yet, confounders may persist in these studies. For instance, fixed effects do not control for unobserved factors that change with time within the given entity (for example, access to funding or new skills). Identifying casual effects convincingly will then typically require distinct research methods that we turn to next.

Quasi-experiments

Researchers in economics and other fields have developed a range of quasi-experimental methods to construct treatment and control groups. The key idea here is exploiting randomness from external events that differentially expose subjects to a particular treatment. Here we review three quasi-experimental methods: difference-in-differences, instrumental variables and regression discontinuity (Fig. 3 ).

a – c , This figure presents illustrations of ( a ) differences-in-differences, ( b ) instrumental variables and ( c ) regression discontinuity methods. The solid line in b represents causal links and the dashed line represents the relationships that are not allowed, if the IV method is to produce causal inference.

Difference-in-differences. Difference-in-difference regression (DiD) investigates the effect of an unexpected event, comparing the affected group (the treated group) with an unaffected group (the control group). The control group is intended to provide the counterfactual path—what would have happened were it not for the unexpected event. Ideally, the treated and control groups are on virtually identical paths before the treatment event, but DiD can also work if the groups are on parallel paths (Fig. 3a ). For example, one study 226 examines how the premature death of superstar scientists affects the productivity of their previous collaborators. The control group are collaborators of superstars who did not die in the time frame. The two groups do not show significant differences in publications before a death event, yet upon the death of a star scientist, the treated collaborators on average experience a 5–8% decline in their quality-adjusted publication rates compared with the control group. DiD has wide applicability in the science of science, having been used to analyse the causal effects of grant design 24 , access costs to previous research 155 , 227 , university technology transfer policies 154 , intellectual property 228 , citation practices 229 , evolution of fields 221 and the impacts of paper retractions 230 , 231 , 232 . The DiD literature has grown especially rapidly in the field of economics, with substantial recent refinements 233 , 234 .

Instrumental variables. Another quasi-experimental approach utilizes ‘instrumental variables’ (IV). The goal is to determine the causal influence of some feature X on some outcome Y by using a third, instrumental variable. This instrumental variable is a quasi-random event that induces variation in X and, except for its impact through X , has no other effect on the outcome Y (Fig. 3b ). For example, consider a study of astronomy that seeks to understand how telescope time affects career advancement 235 . Here, one cannot simply look at the correlation between telescope time and career outcomes because many confounds (such as talent or grit) may influence both telescope time and career opportunities. Now consider the weather as an instrumental variable. Cloudy weather will, at random, reduce an astronomer’s observational time. Yet, the weather on particular nights is unlikely to correlate with a scientist’s innate qualities. The weather can then provide an instrumental variable to reveal a causal relationship between telescope time and career outcomes. Instrumental variables have been used to study local peer effects in research 151 , the impact of gender composition in scientific committees 236 , patents on future innovation 237 and taxes on inventor mobility 238 .

Regression discontinuity. In regression discontinuity, policies with an arbitrary threshold for receiving some benefit can be used to construct treatment and control groups (Fig. 3c ). Take the funding paylines for grant proposals as an example. Proposals with scores increasingly close to the payline are increasingly similar in their both observable and unobservable characteristics, yet only those projects with scores above the payline receive the funding. For example, a study 110 examines the effect of winning an early-career grant on the probability of winning a later, mid-career grant. The probability has a discontinuous jump across the initial grant’s payline, providing the treatment and control groups needed to estimate the causal effect of receiving a grant. This example utilizes the ‘sharp’ regression discontinuity that assumes treatment status to be fully determined by the cut-off. If we assume treatment status is only partly determined by the cut-off, we can use ‘fuzzy’ regression discontinuity designs. Here the probability of receiving a grant is used to estimate the future outcome 11 , 110 , 239 , 240 , 241 .

Although quasi-experiments are powerful tools, they face their own limitations. First, these approaches identify causal effects within a specific context and often engage small numbers of observations. How representative the samples are for broader populations or contexts is typically left as an open question. Second, the validity of the causal design is typically not ironclad. Researchers usually conduct different robustness checks to verify whether observable confounders have significant differences between the treated and control groups, before treatment. However, unobservable features may still differ between treatment and control groups. The quality of instrumental variables and the specific claim that they have no effect on the outcome except through the variable of interest, is also difficult to assess. Ultimately, researchers must rely partly on judgement to tell whether appropriate conditions are met for causal inference.

This section emphasized popular econometric approaches to causal inference. Other empirical approaches, such as graphical causal modelling 242 , 243 , also represent an important stream of work on assessing causal relationships. Such approaches usually represent causation as a directed acyclic graph, with nodes as variables and arrows between them as suspected causal relationships. In the science of science, the directed acyclic graph approach has been applied to quantify the causal effect of journal impact factor 244 and gender or racial bias 245 on citations. Graphical causal modelling has also triggered discussions on strengths and weaknesses compared to the econometrics methods 246 , 247 .

Experiments

In contrast to quasi-experimental approaches, laboratory and field experiments conduct direct randomization in assigning treatment and control groups. These methods engage explicitly in the data generation process, manipulating interventions to observe counterfactuals. These experiments are crafted to study mechanisms of specific interest and, by designing the experiment and formally randomizing, can produce especially rigorous causal inference.

Laboratory experiments. Laboratory experiments build counterfactual worlds in well-controlled laboratory environments. Researchers randomly assign participants to the treatment or control group and then manipulate the laboratory conditions to observe different outcomes in the two groups. For example, consider laboratory experiments on team performance and gender composition 144 , 248 . The researchers randomly assign participants into groups to perform tasks such as solving puzzles or brainstorming. Teams with a higher proportion of women are found to perform better on average, offering evidence that gender diversity is causally linked to team performance. Laboratory experiments can allow researchers to test forces that are otherwise hard to observe, such as how competition influences creativity 249 . Laboratory experiments have also been used to evaluate how journal impact factors shape scientists’ perceptions of rewards 250 and gender bias in hiring 251 .

Laboratory experiments allow for precise control of settings and procedures to isolate causal effects of interest. However, participants may behave differently in synthetic environments than in real-world settings, raising questions about the generalizability and replicability of the results 252 , 253 , 254 . To assess causal effects in real-world settings, researcher use randomized controlled trials.

Randomized controlled trials. A randomized controlled trial (RCT), or field experiment, is a staple for causal inference across a wide range of disciplines. RCTs randomly assign participants into the treatment and control conditions 255 and can be used not only to assess mechanisms but also to test real-world interventions such as policy change. The science of science has witnessed growing use of RCTs. For instance, a field experiment 146 investigated whether lower search costs for collaborators increased collaboration in grant applications. The authors randomly allocated principal investigators to face-to-face sessions in a medical school, and then measured participants’ chance of writing a grant proposal together. RCTs have also offered rich causal insights on peer review 256 , 257 , 258 , 259 , 260 and gender bias in science 261 , 262 , 263 .

While powerful, RCTs are difficult to conduct in the science of science, mainly for two reasons. The first concerns potential risks in a policy intervention. For instance, while randomizing funding across individuals could generate crucial causal insights for funders, it may also inadvertently harm participants’ careers 264 . Second, key questions in the science of science often require a long-time horizon to trace outcomes, which makes RCTs costly. It also raises the difficulty of replicating findings. A relative advantage of the quasi-experimental methods discussed earlier is that one can identify causal effects over potentially long periods of time in the historical record. On the other hand, quasi-experiments must be found as opposed to designed, and they often are not available for many questions of interest. While the best approaches are context dependent, a growing community of researchers is building platforms to facilitate RCTs for the science of science, aiming to lower their costs and increase their scale. Performing RCTs in partnership with science institutions can also contribute to timely, policy-relevant research that may substantially improve science decision-making and investments.

Research in the science of science has been empowered by the growth of high-scale data, new measurement approaches and an expanding range of empirical methods. These tools provide enormous capacity to test conceptual frameworks about science, discover factors impacting scientific productivity, predict key scientific outcomes and design policies that better facilitate future scientific progress. A careful appreciation of empirical techniques can help researchers to choose effective tools for questions of interest and propel the field. A better and broader understanding of these methodologies may also build bridges across diverse research communities, facilitating communication and collaboration, and better leveraging the value of diverse perspectives. The science of science is about turning scientific methods on the nature of science itself. The fruits of this work, with time, can guide researchers and research institutions to greater progress in discovery and understanding across the landscape of scientific inquiry.

Bush, V . S cience–the Endless Frontier: A Report to the President on a Program for Postwar Scientific Research (National Science Foundation, 1990).

Mokyr, J. The Gifts of Athena (Princeton Univ. Press, 2011).

Jones, B. F. in Rebuilding the Post-Pandemic Economy (eds Kearney, M. S. & Ganz, A.) 272–310 (Aspen Institute Press, 2021).

Wang, D. & Barabási, A.-L. The Science of Science (Cambridge Univ. Press, 2021).

Fortunato, S. et al. Science of science. Science 359 , eaao0185 (2018).

Article   PubMed   PubMed Central   Google Scholar  

Azoulay, P. et al. Toward a more scientific science. Science 361 , 1194–1197 (2018).

Article   PubMed   Google Scholar  

Clauset, A., Larremore, D. B. & Sinatra, R. Data-driven predictions in the science of science. Science 355 , 477–480 (2017).

Article   CAS   PubMed   Google Scholar  

Zeng, A. et al. The science of science: from the perspective of complex systems. Phys. Rep. 714 , 1–73 (2017).

Article   Google Scholar  

Lin, Z., Yin. Y., Liu, L. & Wang, D. SciSciNet: a large-scale open data lake for the science of science research. Sci. Data, https://doi.org/10.1038/s41597-023-02198-9 (2023).

Ahmadpoor, M. & Jones, B. F. The dual frontier: patented inventions and prior scientific advance. Science 357 , 583–587 (2017).

Azoulay, P., Graff Zivin, J. S., Li, D. & Sampat, B. N. Public R&D investments and private-sector patenting: evidence from NIH funding rules. Rev. Econ. Stud. 86 , 117–152 (2019).

Yin, Y., Dong, Y., Wang, K., Wang, D. & Jones, B. F. Public use and public funding of science. Nat. Hum. Behav. 6 , 1344–1350 (2022).

Merton, R. K. The Sociology of Science: Theoretical and Empirical Investigations (Univ. Chicago Press, 1973).

Kuhn, T. The Structure of Scientific Revolutions (Princeton Univ. Press, 2021).

Uzzi, B., Mukherjee, S., Stringer, M. & Jones, B. Atypical combinations and scientific impact. Science 342 , 468–472 (2013).

Zuckerman, H. Scientific Elite: Nobel Laureates in the United States (Transaction Publishers, 1977).

Wu, L., Wang, D. & Evans, J. A. Large teams develop and small teams disrupt science and technology. Nature 566 , 378–382 (2019).

Wuchty, S., Jones, B. F. & Uzzi, B. The increasing dominance of teams in production of knowledge. Science 316 , 1036–1039 (2007).

Foster, J. G., Rzhetsky, A. & Evans, J. A. Tradition and innovation in scientists’ research strategies. Am. Sociol. Rev. 80 , 875–908 (2015).

Wang, D., Song, C. & Barabási, A.-L. Quantifying long-term scientific impact. Science 342 , 127–132 (2013).

Clauset, A., Arbesman, S. & Larremore, D. B. Systematic inequality and hierarchy in faculty hiring networks. Sci. Adv. 1 , e1400005 (2015).

Ma, A., Mondragón, R. J. & Latora, V. Anatomy of funded research in science. Proc. Natl Acad. Sci. USA 112 , 14760–14765 (2015).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Ma, Y. & Uzzi, B. Scientific prize network predicts who pushes the boundaries of science. Proc. Natl Acad. Sci. USA 115 , 12608–12615 (2018).

Azoulay, P., Graff Zivin, J. S. & Manso, G. Incentives and creativity: evidence from the academic life sciences. RAND J. Econ. 42 , 527–554 (2011).

Schor, S. & Karten, I. Statistical evaluation of medical journal manuscripts. JAMA 195 , 1123–1128 (1966).

Platt, J. R. Strong inference: certain systematic methods of scientific thinking may produce much more rapid progress than others. Science 146 , 347–353 (1964).

Ioannidis, J. P. Why most published research findings are false. PLoS Med. 2 , e124 (2005).

Simonton, D. K. Career landmarks in science: individual differences and interdisciplinary contrasts. Dev. Psychol. 27 , 119 (1991).

Way, S. F., Morgan, A. C., Clauset, A. & Larremore, D. B. The misleading narrative of the canonical faculty productivity trajectory. Proc. Natl Acad. Sci. USA 114 , E9216–E9223 (2017).

Sinatra, R., Wang, D., Deville, P., Song, C. & Barabási, A.-L. Quantifying the evolution of individual scientific impact. Science 354 , aaf5239 (2016).

Liu, L. et al. Hot streaks in artistic, cultural, and scientific careers. Nature 559 , 396–399 (2018).

Liu, L., Dehmamy, N., Chown, J., Giles, C. L. & Wang, D. Understanding the onset of hot streaks across artistic, cultural, and scientific careers. Nat. Commun. 12 , 5392 (2021).

Squazzoni, F. et al. Peer review and gender bias: a study on 145 scholarly journals. Sci. Adv. 7 , eabd0299 (2021).

Hofstra, B. et al. The diversity–innovation paradox in science. Proc. Natl Acad. Sci. USA 117 , 9284–9291 (2020).

Huang, J., Gates, A. J., Sinatra, R. & Barabási, A.-L. Historical comparison of gender inequality in scientific careers across countries and disciplines. Proc. Natl Acad. Sci. USA 117 , 4609–4616 (2020).

Gläser, J. & Laudel, G. Governing science: how science policy shapes research content. Eur. J. Sociol. 57 , 117–168 (2016).

Stephan, P. E. How Economics Shapes Science (Harvard Univ. Press, 2012).

Garfield, E. & Sher, I. H. New factors in the evaluation of scientific literature through citation indexing. Am. Doc. 14 , 195–201 (1963).

Article   CAS   Google Scholar  

de Solla Price, D. J. Networks of scientific papers. Science 149 , 510–515 (1965).

Etzkowitz, H., Kemelgor, C. & Uzzi, B. Athena Unbound: The Advancement of Women in Science and Technology (Cambridge Univ. Press, 2000).

Simonton, D. K. Scientific Genius: A Psychology of Science (Cambridge Univ. Press, 1988).

Khabsa, M. & Giles, C. L. The number of scholarly documents on the public web. PLoS ONE 9 , e93949 (2014).

Xia, F., Wang, W., Bekele, T. M. & Liu, H. Big scholarly data: a survey. IEEE Trans. Big Data 3 , 18–35 (2017).

Evans, J. A. & Foster, J. G. Metaknowledge. Science 331 , 721–725 (2011).

Milojević, S. Quantifying the cognitive extent of science. J. Informetr. 9 , 962–973 (2015).

Rzhetsky, A., Foster, J. G., Foster, I. T. & Evans, J. A. Choosing experiments to accelerate collective discovery. Proc. Natl Acad. Sci. USA 112 , 14569–14574 (2015).

Poncela-Casasnovas, J., Gerlach, M., Aguirre, N. & Amaral, L. A. Large-scale analysis of micro-level citation patterns reveals nuanced selection criteria. Nat. Hum. Behav. 3 , 568–575 (2019).

Hardwicke, T. E. et al. Data availability, reusability, and analytic reproducibility: evaluating the impact of a mandatory open data policy at the journal Cognition. R. Soc. Open Sci. 5 , 180448 (2018).

Nagaraj, A., Shears, E. & de Vaan, M. Improving data access democratizes and diversifies science. Proc. Natl Acad. Sci. USA 117 , 23490–23498 (2020).

Bravo, G., Grimaldo, F., López-Iñesta, E., Mehmani, B. & Squazzoni, F. The effect of publishing peer review reports on referee behavior in five scholarly journals. Nat. Commun. 10 , 322 (2019).

Tran, D. et al. An open review of open review: a critical analysis of the machine learning conference review process. Preprint at https://doi.org/10.48550/arXiv.2010.05137 (2020).

Tshitoyan, V. et al. Unsupervised word embeddings capture latent knowledge from materials science literature. Nature 571 , 95–98 (2019).

Yang, Y., Wu, Y. & Uzzi, B. Estimating the deep replicability of scientific findings using human and artificial intelligence. Proc. Natl Acad. Sci. USA 117 , 10762–10768 (2020).

Mukherjee, S., Uzzi, B., Jones, B. & Stringer, M. A new method for identifying recombinations of existing knowledge associated with high‐impact innovation. J. Prod. Innov. Manage. 33 , 224–236 (2016).

Leahey, E., Beckman, C. M. & Stanko, T. L. Prominent but less productive: the impact of interdisciplinarity on scientists’ research. Adm. Sci. Q. 62 , 105–139 (2017).

Sauermann, H. & Haeussler, C. Authorship and contribution disclosures. Sci. Adv. 3 , e1700404 (2017).

Oliveira, D. F. M., Ma, Y., Woodruff, T. K. & Uzzi, B. Comparison of National Institutes of Health grant amounts to first-time male and female principal investigators. JAMA 321 , 898–900 (2019).

Yang, Y., Chawla, N. V. & Uzzi, B. A network’s gender composition and communication pattern predict women’s leadership success. Proc. Natl Acad. Sci. USA 116 , 2033–2038 (2019).

Way, S. F., Larremore, D. B. & Clauset, A. Gender, productivity, and prestige in computer science faculty hiring networks. In Proc. 25th International Conference on World Wide Web 1169–1179. (ACM 2016)

Malmgren, R. D., Ottino, J. M. & Amaral, L. A. N. The role of mentorship in protege performance. Nature 465 , 622–626 (2010).

Ma, Y., Mukherjee, S. & Uzzi, B. Mentorship and protégé success in STEM fields. Proc. Natl Acad. Sci. USA 117 , 14077–14083 (2020).

Börner, K. et al. Skill discrepancies between research, education, and jobs reveal the critical need to supply soft skills for the data economy. Proc. Natl Acad. Sci. USA 115 , 12630–12637 (2018).

Biasi, B. & Ma, S. The Education-Innovation Gap (National Bureau of Economic Research Working papers, 2020).

Bornmann, L. Do altmetrics point to the broader impact of research? An overview of benefits and disadvantages of altmetrics. J. Informetr. 8 , 895–903 (2014).

Cleary, E. G., Beierlein, J. M., Khanuja, N. S., McNamee, L. M. & Ledley, F. D. Contribution of NIH funding to new drug approvals 2010–2016. Proc. Natl Acad. Sci. USA 115 , 2329–2334 (2018).

Spector, J. M., Harrison, R. S. & Fishman, M. C. Fundamental science behind today’s important medicines. Sci. Transl. Med. 10 , eaaq1787 (2018).

Haunschild, R. & Bornmann, L. How many scientific papers are mentioned in policy-related documents? An empirical investigation using Web of Science and Altmetric data. Scientometrics 110 , 1209–1216 (2017).

Yin, Y., Gao, J., Jones, B. F. & Wang, D. Coevolution of policy and science during the pandemic. Science 371 , 128–130 (2021).

Sugimoto, C. R., Work, S., Larivière, V. & Haustein, S. Scholarly use of social media and altmetrics: a review of the literature. J. Assoc. Inf. Sci. Technol. 68 , 2037–2062 (2017).

Dunham, I. Human genes: time to follow the roads less traveled? PLoS Biol. 16 , e3000034 (2018).

Kustatscher, G. et al. Understudied proteins: opportunities and challenges for functional proteomics. Nat. Methods 19 , 774–779 (2022).

Rosenthal, R. The file drawer problem and tolerance for null results. Psychol. Bull. 86 , 638 (1979).

Franco, A., Malhotra, N. & Simonovits, G. Publication bias in the social sciences: unlocking the file drawer. Science 345 , 1502–1505 (2014).

Vera-Baceta, M.-A., Thelwall, M. & Kousha, K. Web of Science and Scopus language coverage. Scientometrics 121 , 1803–1813 (2019).

Waltman, L. A review of the literature on citation impact indicators. J. Informetr. 10 , 365–391 (2016).

Garfield, E. & Merton, R. K. Citation Indexing: Its Theory and Application in Science, Technology, and Humanities (Wiley, 1979).

Kelly, B., Papanikolaou, D., Seru, A. & Taddy, M. Measuring Technological Innovation Over the Long Run Report No. 0898-2937 (National Bureau of Economic Research, 2018).

Kogan, L., Papanikolaou, D., Seru, A. & Stoffman, N. Technological innovation, resource allocation, and growth. Q. J. Econ. 132 , 665–712 (2017).

Hall, B. H., Jaffe, A. & Trajtenberg, M. Market value and patent citations. RAND J. Econ. 36 , 16–38 (2005).

Google Scholar  

Yan, E. & Ding, Y. Applying centrality measures to impact analysis: a coauthorship network analysis. J. Am. Soc. Inf. Sci. Technol. 60 , 2107–2118 (2009).

Radicchi, F., Fortunato, S., Markines, B. & Vespignani, A. Diffusion of scientific credits and the ranking of scientists. Phys. Rev. E 80 , 056103 (2009).

Bollen, J., Rodriquez, M. A. & Van de Sompel, H. Journal status. Scientometrics 69 , 669–687 (2006).

Bergstrom, C. T., West, J. D. & Wiseman, M. A. The eigenfactor™ metrics. J. Neurosci. 28 , 11433–11434 (2008).

Cronin, B. & Sugimoto, C. R. Beyond Bibliometrics: Harnessing Multidimensional Indicators of Scholarly Impact (MIT Press, 2014).

Hicks, D., Wouters, P., Waltman, L., De Rijcke, S. & Rafols, I. Bibliometrics: the Leiden Manifesto for research metrics. Nature 520 , 429–431 (2015).

Catalini, C., Lacetera, N. & Oettl, A. The incidence and role of negative citations in science. Proc. Natl Acad. Sci. USA 112 , 13823–13826 (2015).

Alcacer, J. & Gittelman, M. Patent citations as a measure of knowledge flows: the influence of examiner citations. Rev. Econ. Stat. 88 , 774–779 (2006).

Ding, Y. et al. Content‐based citation analysis: the next generation of citation analysis. J. Assoc. Inf. Sci. Technol. 65 , 1820–1833 (2014).

Teufel, S., Siddharthan, A. & Tidhar, D. Automatic classification of citation function. In Proc. 2006 Conference on Empirical Methods in Natural Language Processing, 103–110 (Association for Computational Linguistics 2006)

Seeber, M., Cattaneo, M., Meoli, M. & Malighetti, P. Self-citations as strategic response to the use of metrics for career decisions. Res. Policy 48 , 478–491 (2019).

Pendlebury, D. A. The use and misuse of journal metrics and other citation indicators. Arch. Immunol. Ther. Exp. 57 , 1–11 (2009).

Biagioli, M. Watch out for cheats in citation game. Nature 535 , 201 (2016).

Jo, W. S., Liu, L. & Wang, D. See further upon the giants: quantifying intellectual lineage in science. Quant. Sci. Stud. 3 , 319–330 (2022).

Boyack, K. W., Klavans, R. & Börner, K. Mapping the backbone of science. Scientometrics 64 , 351–374 (2005).

Gates, A. J., Ke, Q., Varol, O. & Barabási, A.-L. Nature’s reach: narrow work has broad impact. Nature 575 , 32–34 (2019).

Börner, K., Penumarthy, S., Meiss, M. & Ke, W. Mapping the diffusion of scholarly knowledge among major US research institutions. Scientometrics 68 , 415–426 (2006).

King, D. A. The scientific impact of nations. Nature 430 , 311–316 (2004).

Pan, R. K., Kaski, K. & Fortunato, S. World citation and collaboration networks: uncovering the role of geography in science. Sci. Rep. 2 , 902 (2012).

Jaffe, A. B., Trajtenberg, M. & Henderson, R. Geographic localization of knowledge spillovers as evidenced by patent citations. Q. J. Econ. 108 , 577–598 (1993).

Funk, R. J. & Owen-Smith, J. A dynamic network measure of technological change. Manage. Sci. 63 , 791–817 (2017).

Yegros-Yegros, A., Rafols, I. & D’este, P. Does interdisciplinary research lead to higher citation impact? The different effect of proximal and distal interdisciplinarity. PLoS ONE 10 , e0135095 (2015).

Larivière, V., Haustein, S. & Börner, K. Long-distance interdisciplinarity leads to higher scientific impact. PLoS ONE 10 , e0122565 (2015).

Fleming, L., Greene, H., Li, G., Marx, M. & Yao, D. Government-funded research increasingly fuels innovation. Science 364 , 1139–1141 (2019).

Bowen, A. & Casadevall, A. Increasing disparities between resource inputs and outcomes, as measured by certain health deliverables, in biomedical research. Proc. Natl Acad. Sci. USA 112 , 11335–11340 (2015).

Li, D., Azoulay, P. & Sampat, B. N. The applied value of public investments in biomedical research. Science 356 , 78–81 (2017).

Lehman, H. C. Age and Achievement (Princeton Univ. Press, 2017).

Simonton, D. K. Creative productivity: a predictive and explanatory model of career trajectories and landmarks. Psychol. Rev. 104 , 66 (1997).

Duch, J. et al. The possible role of resource requirements and academic career-choice risk on gender differences in publication rate and impact. PLoS ONE 7 , e51332 (2012).

Wang, Y., Jones, B. F. & Wang, D. Early-career setback and future career impact. Nat. Commun. 10 , 4331 (2019).

Bol, T., de Vaan, M. & van de Rijt, A. The Matthew effect in science funding. Proc. Natl Acad. Sci. USA 115 , 4887–4890 (2018).

Jones, B. F. Age and great invention. Rev. Econ. Stat. 92 , 1–14 (2010).

Newman, M. Networks (Oxford Univ. Press, 2018).

Mazloumian, A., Eom, Y.-H., Helbing, D., Lozano, S. & Fortunato, S. How citation boosts promote scientific paradigm shifts and nobel prizes. PLoS ONE 6 , e18975 (2011).

Hirsch, J. E. An index to quantify an individual’s scientific research output. Proc. Natl Acad. Sci. USA 102 , 16569–16572 (2005).

Alonso, S., Cabrerizo, F. J., Herrera-Viedma, E. & Herrera, F. h-index: a review focused in its variants, computation and standardization for different scientific fields. J. Informetr. 3 , 273–289 (2009).

Egghe, L. An improvement of the h-index: the g-index. ISSI Newsl. 2 , 8–9 (2006).

Kaur, J., Radicchi, F. & Menczer, F. Universality of scholarly impact metrics. J. Informetr. 7 , 924–932 (2013).

Majeti, D. et al. Scholar plot: design and evaluation of an information interface for faculty research performance. Front. Res. Metr. Anal. 4 , 6 (2020).

Sidiropoulos, A., Katsaros, D. & Manolopoulos, Y. Generalized Hirsch h-index for disclosing latent facts in citation networks. Scientometrics 72 , 253–280 (2007).

Jones, B. F. & Weinberg, B. A. Age dynamics in scientific creativity. Proc. Natl Acad. Sci. USA 108 , 18910–18914 (2011).

Dennis, W. Age and productivity among scientists. Science 123 , 724–725 (1956).

Sanyal, D. K., Bhowmick, P. K. & Das, P. P. A review of author name disambiguation techniques for the PubMed bibliographic database. J. Inf. Sci. 47 , 227–254 (2021).

Haak, L. L., Fenner, M., Paglione, L., Pentz, E. & Ratner, H. ORCID: a system to uniquely identify researchers. Learn. Publ. 25 , 259–264 (2012).

Malmgren, R. D., Ottino, J. M. & Amaral, L. A. N. The role of mentorship in protégé performance. Nature 465 , 662–667 (2010).

Oettl, A. Reconceptualizing stars: scientist helpfulness and peer performance. Manage. Sci. 58 , 1122–1140 (2012).

Morgan, A. C. et al. The unequal impact of parenthood in academia. Sci. Adv. 7 , eabd1996 (2021).

Morgan, A. C. et al. Socioeconomic roots of academic faculty. Nat. Hum. Behav. 6 , 1625–1633 (2022).

San Francisco Declaration on Research Assessment (DORA) (American Society for Cell Biology, 2012).

Falk‐Krzesinski, H. J. et al. Advancing the science of team science. Clin. Transl. Sci. 3 , 263–266 (2010).

Cooke, N. J. et al. Enhancing the Effectiveness of Team Science (National Academies Press, 2015).

Börner, K. et al. A multi-level systems perspective for the science of team science. Sci. Transl. Med. 2 , 49cm24 (2010).

Leahey, E. From sole investigator to team scientist: trends in the practice and study of research collaboration. Annu. Rev. Sociol. 42 , 81–100 (2016).

AlShebli, B. K., Rahwan, T. & Woon, W. L. The preeminence of ethnic diversity in scientific collaboration. Nat. Commun. 9 , 5163 (2018).

Hsiehchen, D., Espinoza, M. & Hsieh, A. Multinational teams and diseconomies of scale in collaborative research. Sci. Adv. 1 , e1500211 (2015).

Koning, R., Samila, S. & Ferguson, J.-P. Who do we invent for? Patents by women focus more on women’s health, but few women get to invent. Science 372 , 1345–1348 (2021).

Barabâsi, A.-L. et al. Evolution of the social network of scientific collaborations. Physica A 311 , 590–614 (2002).

Newman, M. E. Scientific collaboration networks. I. Network construction and fundamental results. Phys. Rev. E 64 , 016131 (2001).

Newman, M. E. Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality. Phys. Rev. E 64 , 016132 (2001).

Palla, G., Barabási, A.-L. & Vicsek, T. Quantifying social group evolution. Nature 446 , 664–667 (2007).

Ross, M. B. et al. Women are credited less in science than men. Nature 608 , 135–145 (2022).

Shen, H.-W. & Barabási, A.-L. Collective credit allocation in science. Proc. Natl Acad. Sci. USA 111 , 12325–12330 (2014).

Merton, R. K. Matthew effect in science. Science 159 , 56–63 (1968).

Ni, C., Smith, E., Yuan, H., Larivière, V. & Sugimoto, C. R. The gendered nature of authorship. Sci. Adv. 7 , eabe4639 (2021).

Woolley, A. W., Chabris, C. F., Pentland, A., Hashmi, N. & Malone, T. W. Evidence for a collective intelligence factor in the performance of human groups. Science 330 , 686–688 (2010).

Feldon, D. F. et al. Postdocs’ lab engagement predicts trajectories of PhD students’ skill development. Proc. Natl Acad. Sci. USA 116 , 20910–20916 (2019).

Boudreau, K. J. et al. A field experiment on search costs and the formation of scientific collaborations. Rev. Econ. Stat. 99 , 565–576 (2017).

Holcombe, A. O. Contributorship, not authorship: use CRediT to indicate who did what. Publications 7 , 48 (2019).

Murray, D. et al. Unsupervised embedding of trajectories captures the latent structure of mobility. Preprint at https://doi.org/10.48550/arXiv.2012.02785 (2020).

Deville, P. et al. Career on the move: geography, stratification, and scientific impact. Sci. Rep. 4 , 4770 (2014).

Edmunds, L. D. et al. Why do women choose or reject careers in academic medicine? A narrative review of empirical evidence. Lancet 388 , 2948–2958 (2016).

Waldinger, F. Peer effects in science: evidence from the dismissal of scientists in Nazi Germany. Rev. Econ. Stud. 79 , 838–861 (2012).

Agrawal, A., McHale, J. & Oettl, A. How stars matter: recruiting and peer effects in evolutionary biology. Res. Policy 46 , 853–867 (2017).

Fiore, S. M. Interdisciplinarity as teamwork: how the science of teams can inform team science. Small Group Res. 39 , 251–277 (2008).

Hvide, H. K. & Jones, B. F. University innovation and the professor’s privilege. Am. Econ. Rev. 108 , 1860–1898 (2018).

Murray, F., Aghion, P., Dewatripont, M., Kolev, J. & Stern, S. Of mice and academics: examining the effect of openness on innovation. Am. Econ. J. Econ. Policy 8 , 212–252 (2016).

Radicchi, F., Fortunato, S. & Castellano, C. Universality of citation distributions: toward an objective measure of scientific impact. Proc. Natl Acad. Sci. USA 105 , 17268–17272 (2008).

Waltman, L., van Eck, N. J. & van Raan, A. F. Universality of citation distributions revisited. J. Am. Soc. Inf. Sci. Technol. 63 , 72–77 (2012).

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

de Solla Price, D. A general theory of bibliometric and other cumulative advantage processes. J. Am. Soc. Inf. Sci. 27 , 292–306 (1976).

Cole, S. Age and scientific performance. Am. J. Sociol. 84 , 958–977 (1979).

Ke, Q., Ferrara, E., Radicchi, F. & Flammini, A. Defining and identifying sleeping beauties in science. Proc. Natl Acad. Sci. USA 112 , 7426–7431 (2015).

Bornmann, L., de Moya Anegón, F. & Leydesdorff, L. Do scientific advancements lean on the shoulders of giants? A bibliometric investigation of the Ortega hypothesis. PLoS ONE 5 , e13327 (2010).

Mukherjee, S., Romero, D. M., Jones, B. & Uzzi, B. The nearly universal link between the age of past knowledge and tomorrow’s breakthroughs in science and technology: the hotspot. Sci. Adv. 3 , e1601315 (2017).

Packalen, M. & Bhattacharya, J. NIH funding and the pursuit of edge science. Proc. Natl Acad. Sci. USA 117 , 12011–12016 (2020).

Zeng, A., Fan, Y., Di, Z., Wang, Y. & Havlin, S. Fresh teams are associated with original and multidisciplinary research. Nat. Hum. Behav. 5 , 1314–1322 (2021).

Newman, M. E. The structure of scientific collaboration networks. Proc. Natl Acad. Sci. USA 98 , 404–409 (2001).

Larivière, V., Ni, C., Gingras, Y., Cronin, B. & Sugimoto, C. R. Bibliometrics: global gender disparities in science. Nature 504 , 211–213 (2013).

West, J. D., Jacquet, J., King, M. M., Correll, S. J. & Bergstrom, C. T. The role of gender in scholarly authorship. PLoS ONE 8 , e66212 (2013).

Gao, J., Yin, Y., Myers, K. R., Lakhani, K. R. & Wang, D. Potentially long-lasting effects of the pandemic on scientists. Nat. Commun. 12 , 6188 (2021).

Jones, B. F., Wuchty, S. & Uzzi, B. Multi-university research teams: shifting impact, geography, and stratification in science. Science 322 , 1259–1262 (2008).

Chu, J. S. & Evans, J. A. Slowed canonical progress in large fields of science. Proc. Natl Acad. Sci. USA 118 , e2021636118 (2021).

Wang, J., Veugelers, R. & Stephan, P. Bias against novelty in science: a cautionary tale for users of bibliometric indicators. Res. Policy 46 , 1416–1436 (2017).

Stringer, M. J., Sales-Pardo, M. & Amaral, L. A. Statistical validation of a global model for the distribution of the ultimate number of citations accrued by papers published in a scientific journal. J. Assoc. Inf. Sci. Technol. 61 , 1377–1385 (2010).

Bianconi, G. & Barabási, A.-L. Bose-Einstein condensation in complex networks. Phys. Rev. Lett. 86 , 5632 (2001).

Bianconi, G. & Barabási, A.-L. Competition and multiscaling in evolving networks. Europhys. Lett. 54 , 436 (2001).

Yin, Y. & Wang, D. The time dimension of science: connecting the past to the future. J. Informetr. 11 , 608–621 (2017).

Pan, R. K., Petersen, A. M., Pammolli, F. & Fortunato, S. The memory of science: Inflation, myopia, and the knowledge network. J. Informetr. 12 , 656–678 (2018).

Yin, Y., Wang, Y., Evans, J. A. & Wang, D. Quantifying the dynamics of failure across science, startups and security. Nature 575 , 190–194 (2019).

Candia, C. & Uzzi, B. Quantifying the selective forgetting and integration of ideas in science and technology. Am. Psychol. 76 , 1067 (2021).

Milojević, S. Principles of scientific research team formation and evolution. Proc. Natl Acad. Sci. USA 111 , 3984–3989 (2014).

Guimera, R., Uzzi, B., Spiro, J. & Amaral, L. A. N. Team assembly mechanisms determine collaboration network structure and team performance. Science 308 , 697–702 (2005).

Newman, M. E. Coauthorship networks and patterns of scientific collaboration. Proc. Natl Acad. Sci. USA 101 , 5200–5205 (2004).

Newman, M. E. Clustering and preferential attachment in growing networks. Phys. Rev. E 64 , 025102 (2001).

Iacopini, I., Milojević, S. & Latora, V. Network dynamics of innovation processes. Phys. Rev. Lett. 120 , 048301 (2018).

Kuhn, T., Perc, M. & Helbing, D. Inheritance patterns in citation networks reveal scientific memes. Phys. Rev. 4 , 041036 (2014).

Jia, T., Wang, D. & Szymanski, B. K. Quantifying patterns of research-interest evolution. Nat. Hum. Behav. 1 , 0078 (2017).

Zeng, A. et al. Increasing trend of scientists to switch between topics. Nat. Commun. https://doi.org/10.1038/s41467-019-11401-8 (2019).

Siudem, G., Żogała-Siudem, B., Cena, A. & Gagolewski, M. Three dimensions of scientific impact. Proc. Natl Acad. Sci. USA 117 , 13896–13900 (2020).

Petersen, A. M. et al. Reputation and impact in academic careers. Proc. Natl Acad. Sci. USA 111 , 15316–15321 (2014).

Jin, C., Song, C., Bjelland, J., Canright, G. & Wang, D. Emergence of scaling in complex substitutive systems. Nat. Hum. Behav. 3 , 837–846 (2019).

Hofman, J. M. et al. Integrating explanation and prediction in computational social science. Nature 595 , 181–188 (2021).

Lazer, D. et al. Computational social science. Science 323 , 721–723 (2009).

Lazer, D. M. et al. Computational social science: obstacles and opportunities. Science 369 , 1060–1062 (2020).

Albert, R. & Barabási, A.-L. Statistical mechanics of complex networks. Rev. Mod. Phys. 74 , 47 (2002).

Newman, M. E. The structure and function of complex networks. SIAM Rev. 45 , 167–256 (2003).

Song, C., Qu, Z., Blumm, N. & Barabási, A.-L. Limits of predictability in human mobility. Science 327 , 1018–1021 (2010).

Alessandretti, L., Aslak, U. & Lehmann, S. The scales of human mobility. Nature 587 , 402–407 (2020).

Pastor-Satorras, R. & Vespignani, A. Epidemic spreading in scale-free networks. Phys. Rev. Lett. 86 , 3200 (2001).

Pastor-Satorras, R., Castellano, C., Van Mieghem, P. & Vespignani, A. Epidemic processes in complex networks. Rev. Mod. Phys. 87 , 925 (2015).

Goodfellow, I., Bengio, Y. & Courville, A. Deep Learning (MIT Press, 2016).

Bishop, C. M. Pattern Recognition and Machine Learning (Springer, 2006).

Dong, Y., Johnson, R. A. & Chawla, N. V. Will this paper increase your h-index? Scientific impact prediction. In Proc. 8th ACM International Conference on Web Search and Data Mining, 149–158 (ACM 2015)

Xiao, S. et al. On modeling and predicting individual paper citation count over time. In IJCAI, 2676–2682 (IJCAI, 2016)

Fortunato, S. Community detection in graphs. Phys. Rep. 486 , 75–174 (2010).

Chen, C. Science mapping: a systematic review of the literature. J. Data Inf. Sci. 2 , 1–40 (2017).

CAS   Google Scholar  

Van Eck, N. J. & Waltman, L. Citation-based clustering of publications using CitNetExplorer and VOSviewer. Scientometrics 111 , 1053–1070 (2017).

LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521 , 436–444 (2015).

Senior, A. W. et al. Improved protein structure prediction using potentials from deep learning. Nature 577 , 706–710 (2020).

Krenn, M. & Zeilinger, A. Predicting research trends with semantic and neural networks with an application in quantum physics. Proc. Natl Acad. Sci. USA 117 , 1910–1916 (2020).

Iten, R., Metger, T., Wilming, H., Del Rio, L. & Renner, R. Discovering physical concepts with neural networks. Phys. Rev. Lett. 124 , 010508 (2020).

Guimerà, R. et al. A Bayesian machine scientist to aid in the solution of challenging scientific problems. Sci. Adv. 6 , eaav6971 (2020).

Segler, M. H., Preuss, M. & Waller, M. P. Planning chemical syntheses with deep neural networks and symbolic AI. Nature 555 , 604–610 (2018).

Ryu, J. Y., Kim, H. U. & Lee, S. Y. Deep learning improves prediction of drug–drug and drug–food interactions. Proc. Natl Acad. Sci. USA 115 , E4304–E4311 (2018).

Kermany, D. S. et al. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172 , 1122–1131.e9 (2018).

Peng, H., Ke, Q., Budak, C., Romero, D. M. & Ahn, Y.-Y. Neural embeddings of scholarly periodicals reveal complex disciplinary organizations. Sci. Adv. 7 , eabb9004 (2021).

Youyou, W., Yang, Y. & Uzzi, B. A discipline-wide investigation of the replicability of psychology papers over the past two decades. Proc. Natl Acad. Sci. USA 120 , e2208863120 (2023).

Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K. & Galstyan, A. A survey on bias and fairness in machine learning. ACM Computing Surveys (CSUR) 54 , 1–35 (2021).

Way, S. F., Morgan, A. C., Larremore, D. B. & Clauset, A. Productivity, prominence, and the effects of academic environment. Proc. Natl Acad. Sci. USA 116 , 10729–10733 (2019).

Li, W., Aste, T., Caccioli, F. & Livan, G. Early coauthorship with top scientists predicts success in academic careers. Nat. Commun. 10 , 5170 (2019).

Hendry, D. F., Pagan, A. R. & Sargan, J. D. Dynamic specification. Handb. Econ. 2 , 1023–1100 (1984).

Jin, C., Ma, Y. & Uzzi, B. Scientific prizes and the extraordinary growth of scientific topics. Nat. Commun. 12 , 5619 (2021).

Azoulay, P., Ganguli, I. & Zivin, J. G. The mobility of elite life scientists: professional and personal determinants. Res. Policy 46 , 573–590 (2017).

Slavova, K., Fosfuri, A. & De Castro, J. O. Learning by hiring: the effects of scientists’ inbound mobility on research performance in academia. Organ. Sci. 27 , 72–89 (2016).

Sarsons, H. Recognition for group work: gender differences in academia. Am. Econ. Rev. 107 , 141–145 (2017).

Campbell, L. G., Mehtani, S., Dozier, M. E. & Rinehart, J. Gender-heterogeneous working groups produce higher quality science. PLoS ONE 8 , e79147 (2013).

Azoulay, P., Graff Zivin, J. S. & Wang, J. Superstar extinction. Q. J. Econ. 125 , 549–589 (2010).

Furman, J. L. & Stern, S. Climbing atop the shoulders of giants: the impact of institutions on cumulative research. Am. Econ. Rev. 101 , 1933–1963 (2011).

Williams, H. L. Intellectual property rights and innovation: evidence from the human genome. J. Polit. Econ. 121 , 1–27 (2013).

Rubin, A. & Rubin, E. Systematic Bias in the Progress of Research. J. Polit. Econ. 129 , 2666–2719 (2021).

Lu, S. F., Jin, G. Z., Uzzi, B. & Jones, B. The retraction penalty: evidence from the Web of Science. Sci. Rep. 3 , 3146 (2013).

Jin, G. Z., Jones, B., Lu, S. F. & Uzzi, B. The reverse Matthew effect: consequences of retraction in scientific teams. Rev. Econ. Stat. 101 , 492–506 (2019).

Azoulay, P., Bonatti, A. & Krieger, J. L. The career effects of scandal: evidence from scientific retractions. Res. Policy 46 , 1552–1569 (2017).

Goodman-Bacon, A. Difference-in-differences with variation in treatment timing. J. Econ. 225 , 254–277 (2021).

Callaway, B. & Sant’Anna, P. H. Difference-in-differences with multiple time periods. J. Econ. 225 , 200–230 (2021).

Hill, R. Searching for Superstars: Research Risk and Talent Discovery in Astronomy Working Paper (Massachusetts Institute of Technology, 2019).

Bagues, M., Sylos-Labini, M. & Zinovyeva, N. Does the gender composition of scientific committees matter? Am. Econ. Rev. 107 , 1207–1238 (2017).

Sampat, B. & Williams, H. L. How do patents affect follow-on innovation? Evidence from the human genome. Am. Econ. Rev. 109 , 203–236 (2019).

Moretti, E. & Wilson, D. J. The effect of state taxes on the geographical location of top earners: evidence from star scientists. Am. Econ. Rev. 107 , 1858–1903 (2017).

Jacob, B. A. & Lefgren, L. The impact of research grant funding on scientific productivity. J. Public Econ. 95 , 1168–1177 (2011).

Li, D. Expertise versus bias in evaluation: evidence from the NIH. Am. Econ. J. Appl. Econ. 9 , 60–92 (2017).

Pearl, J. Causal diagrams for empirical research. Biometrika 82 , 669–688 (1995).

Pearl, J. & Mackenzie, D. The Book of Why: The New Science of Cause and Effect (Basic Books, 2018).

Traag, V. A. Inferring the causal effect of journals on citations. Quant. Sci. Stud. 2 , 496–504 (2021).

Traag, V. & Waltman, L. Causal foundations of bias, disparity and fairness. Preprint at https://doi.org/10.48550/arXiv.2207.13665 (2022).

Imbens, G. W. Potential outcome and directed acyclic graph approaches to causality: relevance for empirical practice in economics. J. Econ. Lit. 58 , 1129–1179 (2020).

Heckman, J. J. & Pinto, R. Causality and Econometrics (National Bureau of Economic Research, 2022).

Aggarwal, I., Woolley, A. W., Chabris, C. F. & Malone, T. W. The impact of cognitive style diversity on implicit learning in teams. Front. Psychol. 10 , 112 (2019).

Balietti, S., Goldstone, R. L. & Helbing, D. Peer review and competition in the Art Exhibition Game. Proc. Natl Acad. Sci. USA 113 , 8414–8419 (2016).

Paulus, F. M., Rademacher, L., Schäfer, T. A. J., Müller-Pinzler, L. & Krach, S. Journal impact factor shapes scientists’ reward signal in the prospect of publication. PLoS ONE 10 , e0142537 (2015).

Williams, W. M. & Ceci, S. J. National hiring experiments reveal 2:1 faculty preference for women on STEM tenure track. Proc. Natl Acad. Sci. USA 112 , 5360–5365 (2015).

Collaboration, O. S. Estimating the reproducibility of psychological science. Science 349 , aac4716 (2015).

Camerer, C. F. et al. Evaluating replicability of laboratory experiments in economics. Science 351 , 1433–1436 (2016).

Camerer, C. F. et al. Evaluating the replicability of social science experiments in Nature and Science between 2010 and 2015. Nat. Hum. Behav. 2 , 637–644 (2018).

Duflo, E. & Banerjee, A. Handbook of Field Experiments (Elsevier, 2017).

Tomkins, A., Zhang, M. & Heavlin, W. D. Reviewer bias in single versus double-blind peer review. Proc. Natl Acad. Sci. USA 114 , 12708–12713 (2017).

Blank, R. M. The effects of double-blind versus single-blind reviewing: experimental evidence from the American Economic Review. Am. Econ. Rev. 81 , 1041–1067 (1991).

Boudreau, K. J., Guinan, E. C., Lakhani, K. R. & Riedl, C. Looking across and looking beyond the knowledge frontier: intellectual distance, novelty, and resource allocation in science. Manage. Sci. 62 , 2765–2783 (2016).

Lane, J. et al. When Do Experts Listen to Other Experts? The Role of Negative Information in Expert Evaluations for Novel Projects Working Paper #21-007 (Harvard Business School, 2020).

Teplitskiy, M. et al. Do Experts Listen to Other Experts? Field Experimental Evidence from Scientific Peer Review (Harvard Business School, 2019).

Moss-Racusin, C. A., Dovidio, J. F., Brescoll, V. L., Graham, M. J. & Handelsman, J. Science faculty’s subtle gender biases favor male students. Proc. Natl Acad. Sci. USA 109 , 16474–16479 (2012).

Forscher, P. S., Cox, W. T., Brauer, M. & Devine, P. G. Little race or gender bias in an experiment of initial review of NIH R01 grant proposals. Nat. Hum. Behav. 3 , 257–264 (2019).

Dennehy, T. C. & Dasgupta, N. Female peer mentors early in college increase women’s positive academic experiences and retention in engineering. Proc. Natl Acad. Sci. USA 114 , 5964–5969 (2017).

Azoulay, P. Turn the scientific method on ourselves. Nature 484 , 31–32 (2012).

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Acknowledgements

The authors thank all members of the Center for Science of Science and Innovation (CSSI) for invaluable comments. This work was supported by the Air Force Office of Scientific Research under award number FA9550-19-1-0354, National Science Foundation grant SBE 1829344, and the Alfred P. Sloan Foundation G-2019-12485.

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Liu, L., Jones, B.F., Uzzi, B. et al. Data, measurement and empirical methods in the science of science. Nat Hum Behav 7 , 1046–1058 (2023). https://doi.org/10.1038/s41562-023-01562-4

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Quantitative and Empirical Research vs. Other Types of Research: Quantitative Research

• Quantitative Research
• Other Types of Research
• What are Scholarly Journals?

     P rofessors often want you to use scholarly journal articles for your assignments.

     Sometimes, they will require you to use scholarly journal articles that contain quantitative research .

DEFINITIONS

QUANTITATIVE

     Quantitative research looks at factors that can actually be measured in some way, in other words, quantified . It produces numerical results that can be analyzed statistically.

     Quantitative research commonly involves experimentation, surveys, or questionnaires in the context of a large, randomly selected group.

     The term  empirical research  is often used as a synonym for quantitative research, but strictly speaking, empirical research is simply any form of research based upon direct observation. It might also be quantitative, but it might not.

PLEASE NOTE: Some professors use these two terms interchangeably.  When this occurs, they are usually referring to articles that fit the quantitative description above.

HINT: Don't use the words "quantitative" or "empirical" in your keyword searches.  They usually do not appear in article titles, abstracts, or subject words.  Instead, check the articles you find to see if some sort of numerical measuring and statistical analysis is present along with the characteristics listed on the right.

CHARACTERISTICS OF QUANTITATIVE RESEARCH

      W atch for these features when determining if an article has quantitative research. They may appear in the abstract, or you may need to skim the text of the article to find them.

• Introduction : a statement of background or purpose (what was being studied and why). May review prior studies on the same topic.
• Description of the design and/or method of the study (the experimental group or sample , control, variables, number of test subjects, test conditions, etc.)
• Results , or report of the findings (in numeric form as tables, charts, or graphs, etc., often with statistical analysis)
• Conclusions that can be drawn from the results (may be labeled  discussion or significance )
• Footnotes and/or a bibliography
• Author credentials (degrees earned, where they work, etc.)  

SAMPLE QUANTITATIVE RESEARCH ARTICLES

• Relations Between Trait Impulsivity, Behavioral Impulsivity, Physiological Arousal, and Risky Sexual Behavior Among Young Men
• Nocturnal Heart Rate Variability in Patients Treated with Cognitive–Behavioral Therapy for Insomnia.
• Characterisation of Mainstream and Passive Vapors Emitted by Selected Electronic Cigarettes

• Next: Other Types of Research >>
• Last Updated: Apr 6, 2023 8:16 AM
• URL: https://libguides.csusb.edu/quantitative

Get science-backed answers as you write with Paperpal's Research feature

Empirical Research: A Comprehensive Guide for Academics 

Empirical research relies on gathering and studying real, observable data. The term ’empirical’ comes from the Greek word ’empeirikos,’ meaning ‘experienced’ or ‘based on experience.’ So, what is empirical research? Instead of using theories or opinions, empirical research depends on real data obtained through direct observation or experimentation. 

Why Empirical Research?

Empirical research plays a key role in checking or improving current theories, providing a systematic way to grow knowledge across different areas. By focusing on objectivity, it makes research findings more trustworthy, which is critical in research fields like medicine, psychology, economics, and public policy. In the end, the strengths of empirical research lie in deepening our awareness of the world and improving our capacity to tackle problems wisely. 1,2  

Qualitative and Quantitative Methods

There are two main types of empirical research methods – qualitative and quantitative. 3,4 Qualitative research delves into intricate phenomena using non-numerical data, such as interviews or observations, to offer in-depth insights into human experiences. In contrast, quantitative research analyzes numerical data to spot patterns and relationships, aiming for objectivity and the ability to apply findings to a wider context. 

Steps for Conducting Empirical Research

When it comes to conducting research, there are some simple steps that researchers can follow. 5,6  

• Create Research Hypothesis:  Clearly state the specific question you want to answer or the hypothesis you want to explore in your study. 
• Examine Existing Research:  Read and study existing research on your topic. Understand what’s already known, identify existing gaps in knowledge, and create a framework for your own study based on what you learn. 
• Plan Your Study:  Decide how you’ll conduct your research—whether through qualitative methods, quantitative methods, or a mix of both. Choose suitable techniques like surveys, experiments, interviews, or observations based on your research question. 
• Develop Research Instruments:  Create reliable research collection tools, such as surveys or questionnaires, to help you collate data. Ensure these tools are well-designed and effective. 
• Collect Data:  Systematically gather the information you need for your research according to your study design and protocols using the chosen research methods. 
• Data Analysis:  Analyze the collected data using suitable statistical or qualitative methods that align with your research question and objectives. 
• Interpret Results:  Understand and explain the significance of your analysis results in the context of your research question or hypothesis. 
• Draw Conclusions:  Summarize your findings and draw conclusions based on the evidence. Acknowledge any study limitations and propose areas for future research. 

Advantages of Empirical Research

Empirical research is valuable because it stays objective by relying on observable data, lessening the impact of personal biases. This objectivity boosts the trustworthiness of research findings. Also, using precise quantitative methods helps in accurate measurement and statistical analysis. This precision ensures researchers can draw reliable conclusions from numerical data, strengthening our understanding of the studied phenomena. 4  

Disadvantages of Empirical Research

While empirical research has notable strengths, researchers must also be aware of its limitations when deciding on the right research method for their study.4 One significant drawback of empirical research is the risk of oversimplifying complex phenomena, especially when relying solely on quantitative methods. These methods may struggle to capture the richness and nuances present in certain social, cultural, or psychological contexts. Another challenge is the potential for confounding variables or biases during data collection, impacting result accuracy.  

Tips for Empirical Writing

In empirical research, the writing is usually done in research papers, articles, or reports. The empirical writing follows a set structure, and each section has a specific role. Here are some tips for your empirical writing. 7   

• Define Your Objectives:  When you write about your research, start by making your goals clear. Explain what you want to find out or prove in a simple and direct way. This helps guide your research and lets others know what you have set out to achieve. 
• Be Specific in Your Literature Review:  In the part where you talk about what others have studied before you, focus on research that directly relates to your research question. Keep it short and pick studies that help explain why your research is important. This part sets the stage for your work. 
• Explain Your Methods Clearly : When you talk about how you did your research (Methods), explain it in detail. Be clear about your research plan, who took part, and what you did; this helps others understand and trust your study. Also, be honest about any rules you follow to make sure your study is ethical and reproducible. 
• Share Your Results Clearly : After doing your empirical research, share what you found in a simple way. Use tables or graphs to make it easier for your audience to understand your research. Also, talk about any numbers you found and clearly state if they are important or not. Ensure that others can see why your research findings matter. 
• Talk About What Your Findings Mean:  In the part where you discuss your research results, explain what they mean. Discuss why your findings are important and if they connect to what others have found before. Be honest about any problems with your study and suggest ideas for more research in the future. 
• Wrap It Up Clearly:  Finally, end your empirical research paper by summarizing what you found and why it’s important. Remind everyone why your study matters. Keep your writing clear and fix any mistakes before you share it. Ask someone you trust to read it and give you feedback before you finish. 

References:  

• Empirical Research in the Social Sciences and Education, Penn State University Libraries. Available online at  https://guides.libraries.psu.edu/emp  
• How to conduct empirical research, Emerald Publishing. Available online at  https://www.emeraldgrouppublishing.com/how-to/research-methods/conduct-empirical-research  
• Empirical Research: Quantitative & Qualitative, Arrendale Library, Piedmont University. Available online at  https://library.piedmont.edu/empirical-research  
• Bouchrika, I.  What Is Empirical Research? Definition, Types & Samples  in 2024. Research.com, January 2024. Available online at  https://research.com/research/what-is-empirical-research  
• Quantitative and Empirical Research vs. Other Types of Research. California State University, April 2023. Available online at  https://libguides.csusb.edu/quantitative  
• Empirical Research, Definitions, Methods, Types and Examples, Studocu.com website. Available online at  https://www.studocu.com/row/document/uganda-christian-university/it-research-methods/emperical-research-definitions-methods-types-and-examples/55333816  
• Writing an Empirical Paper in APA Style. Psychology Writing Center, University of Washington. Available online at  https://psych.uw.edu/storage/writing_center/APApaper.pdf  

Paperpal is an AI writing assistant that help academics write better, faster with real-time suggestions for in-depth language and grammar correction. Trained on millions of research manuscripts enhanced by professional academic editors, Paperpal delivers human precision at machine speed.  

Try it for free or upgrade to  Paperpal Prime , which unlocks unlimited access to premium features like academic translation, paraphrasing, contextual synonyms, consistency checks and more. It’s like always having a professional academic editor by your side! Go beyond limitations and experience the future of academic writing.  Get Paperpal Prime now at just US$19 a month!  

Related Reads:

• How to Write a Scientific Paper in 10 Steps 
• What is a Literature Review? How to Write It (with Examples)
• What is an Argumentative Essay? How to Write It (With Examples)
• Ethical Research Practices For Research with Human Subjects

Ethics in Science: Importance, Principles & Guidelines 

Presenting research data effectively through tables and figures, you may also like, how to write a high-quality conference paper, how paperpal’s research feature helps you develop and..., how paperpal is enhancing academic productivity and accelerating..., how to write a successful book chapter for..., academic editing: how to self-edit academic text with..., 4 ways paperpal encourages responsible writing with ai, what are scholarly sources and where can you..., how to write a hypothesis types and examples , measuring academic success: definition & strategies for excellence, what is academic writing: tips for students.

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

Research Methods

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Research Design

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Quantitative vs. Qualitative Research: The Differences Explained

From Scribbr 

Empirical Research

What is empirical research.

"Empirical research is research that is based on observation and measurement of phenomena, as directly experienced by the researcher. The data thus gathered may be compared against a theory or hypothesis, but the results are still based on real life experience. The data gathered is all primary data, although secondary data from a literature review may form the theoretical background."

Characteristics of Empirical Research

Emerald Publishing's  guide to conducting empirical research  identifies a number of common elements to empirical research: 

A  research question , which will determine research objectives.

A particular and planned  design  for the research, which will depend on the question and which will find ways of answering it with appropriate use of resources.

The gathering of  primary data , which is then analysed.

A particular  methodology  for collecting and analysing the data, such as an experiment or survey.

The limitation of the data to a particular group, area or time scale, known as a  sample  [emphasis added]: for example, a specific number of employees of a particular company type, or all users of a library over a given time scale. The sample should be somehow representative of a wider population.

The ability to  recreate  the study and test the results. This is known as  reliability .

The ability to  generalize  from the findings to a larger sample and to other situations.

If you see these elements in a research article, you can feel confident that you have found empirical research. Emerald's guide goes into more detail on each element. 

Emerald Publishing (n.d.). How to... conduct empirical research. https://www.emeraldgrouppublishing.com/how-to/research-methods/conduct-empirical-research-l 

• Quantitative vs. Qualitative
• Data Collection Methods
• Analyzing Data

When collecting and analyzing data,  quantitative research  deals with numbers and statistics, while  qualitative research  deals with words and meanings. Both are important for gaining different kinds of knowledge.

Quantitative research

Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions.

Qualitative research

Common qualitative methods include interviews with open-ended questions, observations described in words, and literature reviews that explore concepts and theories.

Streefkerk, R. (2022, February 7). Qualitative vs. quantitative research: Differences, examples & methods. Scibbr. https://www.scribbr.com/methodology/qualitative-quantitative-research/ 

Quantitative and qualitative data can be collected using various methods. It is important to use a  data collection  method that will help answer your research question(s).

Many data collection methods can be either qualitative or quantitative. For example, in surveys, observations or  case studies , your data can be represented as numbers (e.g. using rating scales or counting frequencies) or as words (e.g. with open-ended questions or descriptions of what you observe).

However, some methods are more commonly used in one type or the other.

Quantitative data collection methods

• Surveys :  List of closed or multiple choice questions that is distributed to a  sample  (online, in person, or over the phone).
• Experiments :  Situation in which  variables  are controlled and manipulated to establish cause-and-effect relationships.
• Observations:  Observing subjects in a natural environment where variables can’t be controlled.

Qualitative data collection methods

• Interviews : Asking open-ended questions verbally to respondents.
• Focus groups:  Discussion among a group of people about a topic to gather opinions that can be used for further research.
• Ethnography : Participating in a community or organization for an extended period of time to closely observe culture and behavior.
• Literature review :  Survey of published works by other authors.

When to use qualitative vs. quantitative research

A rule of thumb for deciding whether to use qualitative or quantitative data is:

• Use quantitative research if you want to  confirm or test something  (a theory or hypothesis)
• Use qualitative research if you want to  understand something  (concepts, thoughts, experiences)

For most  research topics  you can choose a qualitative, quantitative or  mixed methods approach . Which type you choose depends on, among other things, whether you’re taking an  inductive vs. deductive research approach ; your  research question(s) ; whether you’re doing  experimental ,  correlational , or  descriptive research ; and practical considerations such as time, money, availability of data, and access to respondents.

Streefkerk, R. (2022, February 7).  Qualitative vs. quantitative research: Differences, examples & methods.  Scibbr. https://www.scribbr.com/methodology/qualitative-quantitative-research/ 

Qualitative or quantitative data by itself can’t prove or demonstrate anything, but has to be analyzed to show its meaning in relation to the research questions. The method of analysis differs for each type of data.

Analyzing quantitative data

Quantitative data is based on numbers. Simple math or more advanced  statistical analysis  is used to discover commonalities or patterns in the data. The results are often reported in graphs and tables.

Applications such as Excel, SPSS, or R can be used to calculate things like:

• Average scores
• The number of times a particular answer was given
• The  correlation or causation  between two or more variables
• The  reliability and validity  of the results

Analyzing qualitative data

Qualitative data is more difficult to analyze than quantitative data. It consists of text, images or videos instead of numbers.

Some common approaches to analyzing qualitative data include:

• Qualitative content analysis : Tracking the occurrence, position and meaning of words or phrases
• Thematic analysis : Closely examining the data to identify the main themes and patterns
• Discourse analysis : Studying how communication works in social contexts

Comparison of Research Processes

Creswell, J. W., & Creswell, J. D. (2018).  Research design : qualitative, quantitative, and mixed methods approaches  (Fifth). SAGE Publications.

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Quantitative methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques . Quantitative research focuses on gathering numerical data and generalizing it across groups of people or to explain a particular phenomenon.

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Muijs, Daniel. Doing Quantitative Research in Education with SPSS . 2nd edition. London: SAGE Publications, 2010.

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Statistics & Data Research Guide

Characteristics of Quantitative Research

Your goal in conducting quantitative research study is to determine the relationship between one thing [an independent variable] and another [a dependent or outcome variable] within a population. Quantitative research designs are either descriptive [subjects usually measured once] or experimental [subjects measured before and after a treatment]. A descriptive study establishes only associations between variables; an experimental study establishes causality.

Quantitative research deals in numbers, logic, and an objective stance. Quantitative research focuses on numeric and unchanging data and detailed, convergent reasoning rather than divergent reasoning [i.e., the generation of a variety of ideas about a research problem in a spontaneous, free-flowing manner].

Its main characteristics are :

• The data is usually gathered using structured research instruments.
• The results are based on larger sample sizes that are representative of the population.
• The research study can usually be replicated or repeated, given its high reliability.
• Researcher has a clearly defined research question to which objective answers are sought.
• All aspects of the study are carefully designed before data is collected.
• Data are in the form of numbers and statistics, often arranged in tables, charts, figures, or other non-textual forms.
• Project can be used to generalize concepts more widely, predict future results, or investigate causal relationships.
• Researcher uses tools, such as questionnaires or computer software, to collect numerical data.

The overarching aim of a quantitative research study is to classify features, count them, and construct statistical models in an attempt to explain what is observed.

  Things to keep in mind when reporting the results of a study using quantitative methods :

• Explain the data collected and their statistical treatment as well as all relevant results in relation to the research problem you are investigating. Interpretation of results is not appropriate in this section.
• Report unanticipated events that occurred during your data collection. Explain how the actual analysis differs from the planned analysis. Explain your handling of missing data and why any missing data does not undermine the validity of your analysis.
• Explain the techniques you used to "clean" your data set.
• Choose a minimally sufficient statistical procedure ; provide a rationale for its use and a reference for it. Specify any computer programs used.
• Describe the assumptions for each procedure and the steps you took to ensure that they were not violated.
• When using inferential statistics , provide the descriptive statistics, confidence intervals, and sample sizes for each variable as well as the value of the test statistic, its direction, the degrees of freedom, and the significance level [report the actual p value].
• Avoid inferring causality , particularly in nonrandomized designs or without further experimentation.
• Use tables to provide exact values ; use figures to convey global effects. Keep figures small in size; include graphic representations of confidence intervals whenever possible.
• Always tell the reader what to look for in tables and figures .

NOTE:   When using pre-existing statistical data gathered and made available by anyone other than yourself [e.g., government agency], you still must report on the methods that were used to gather the data and describe any missing data that exists and, if there is any, provide a clear explanation why the missing data does not undermine the validity of your final analysis.

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Brians, Craig Leonard et al. Empirical Political Analysis: Quantitative and Qualitative Research Methods . 8th ed. Boston, MA: Longman, 2011; McNabb, David E. Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches . 2nd ed. Armonk, NY: M.E. Sharpe, 2008; Quantitative Research Methods. Writing@CSU. Colorado State University; Singh, Kultar. Quantitative Social Research Methods . Los Angeles, CA: Sage, 2007.

Basic Research Design for Quantitative Studies

Before designing a quantitative research study, you must decide whether it will be descriptive or experimental because this will dictate how you gather, analyze, and interpret the results. A descriptive study is governed by the following rules: subjects are generally measured once; the intention is to only establish associations between variables; and, the study may include a sample population of hundreds or thousands of subjects to ensure that a valid estimate of a generalized relationship between variables has been obtained. An experimental design includes subjects measured before and after a particular treatment, the sample population may be very small and purposefully chosen, and it is intended to establish causality between variables. Introduction The introduction to a quantitative study is usually written in the present tense and from the third person point of view. It covers the following information:

• Identifies the research problem -- as with any academic study, you must state clearly and concisely the research problem being investigated.
• Reviews the literature -- review scholarship on the topic, synthesizing key themes and, if necessary, noting studies that have used similar methods of inquiry and analysis. Note where key gaps exist and how your study helps to fill these gaps or clarifies existing knowledge.
• Describes the theoretical framework -- provide an outline of the theory or hypothesis underpinning your study. If necessary, define unfamiliar or complex terms, concepts, or ideas and provide the appropriate background information to place the research problem in proper context [e.g., historical, cultural, economic, etc.].

Methodology The methods section of a quantitative study should describe how each objective of your study will be achieved. Be sure to provide enough detail to enable the reader can make an informed assessment of the methods being used to obtain results associated with the research problem. The methods section should be presented in the past tense.

• Study population and sampling -- where did the data come from; how robust is it; note where gaps exist or what was excluded. Note the procedures used for their selection;
• Data collection – describe the tools and methods used to collect information and identify the variables being measured; describe the methods used to obtain the data; and, note if the data was pre-existing [i.e., government data] or you gathered it yourself. If you gathered it yourself, describe what type of instrument you used and why. Note that no data set is perfect--describe any limitations in methods of gathering data.
• Data analysis -- describe the procedures for processing and analyzing the data. If appropriate, describe the specific instruments of analysis used to study each research objective, including mathematical techniques and the type of computer software used to manipulate the data.

Results The finding of your study should be written objectively and in a succinct and precise format. In quantitative studies, it is common to use graphs, tables, charts, and other non-textual elements to help the reader understand the data. Make sure that non-textual elements do not stand in isolation from the text but are being used to supplement the overall description of the results and to help clarify key points being made. Further information about how to effectively present data using charts and graphs can be found here .

• Statistical analysis -- how did you analyze the data? What were the key findings from the data? The findings should be present in a logical, sequential order. Describe but do not interpret these trends or negative results; save that for the discussion section. The results should be presented in the past tense.

Discussion Discussions should be analytic, logical, and comprehensive. The discussion should meld together your findings in relation to those identified in the literature review, and placed within the context of the theoretical framework underpinning the study. The discussion should be presented in the present tense.

• Interpretation of results -- reiterate the research problem being investigated and compare and contrast the findings with the research questions underlying the study. Did they affirm predicted outcomes or did the data refute it?
• Description of trends, comparison of groups, or relationships among variables -- describe any trends that emerged from your analysis and explain all unanticipated and statistical insignificant findings.
• Discussion of implications – what is the meaning of your results? Highlight key findings based on the overall results and note findings that you believe are important. How have the results helped fill gaps in understanding the research problem?
• Limitations -- describe any limitations or unavoidable bias in your study and, if necessary, note why these limitations did not inhibit effective interpretation of the results.

Conclusion End your study by to summarizing the topic and provide a final comment and assessment of the study.

• Summary of findings – synthesize the answers to your research questions. Do not report any statistical data here; just provide a narrative summary of the key findings and describe what was learned that you did not know before conducting the study.
• Recommendations – if appropriate to the aim of the assignment, tie key findings with policy recommendations or actions to be taken in practice.
• Future research – note the need for future research linked to your study’s limitations or to any remaining gaps in the literature that were not addressed in your study.

Black, Thomas R. Doing Quantitative Research in the Social Sciences: An Integrated Approach to Research Design, Measurement and Statistics . London: Sage, 1999; Gay,L. R. and Peter Airasain. Educational Research: Competencies for Analysis and Applications . 7th edition. Upper Saddle River, NJ: Merril Prentice Hall, 2003; Hector, Anestine. An Overview of Quantitative Research in Composition and TESOL . Department of English, Indiana University of Pennsylvania; Hopkins, Will G. “Quantitative Research Design.” Sportscience 4, 1 (2000); "A Strategy for Writing Up Research Results. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper." Department of Biology. Bates College; Nenty, H. Johnson. "Writing a Quantitative Research Thesis." International Journal of Educational Science 1 (2009): 19-32; Ouyang, Ronghua (John). Basic Inquiry of Quantitative Research . Kennesaw State University.

Strengths of Using Quantitative Methods

Quantitative researchers try to recognize and isolate specific variables contained within the study framework, seek correlation, relationships and causality, and attempt to control the environment in which the data is collected to avoid the risk of variables, other than the one being studied, accounting for the relationships identified.

Among the specific strengths of using quantitative methods to study social science research problems:

• Allows for a broader study, involving a greater number of subjects, and enhancing the generalization of the results;
• Allows for greater objectivity and accuracy of results. Generally, quantitative methods are designed to provide summaries of data that support generalizations about the phenomenon under study. In order to accomplish this, quantitative research usually involves few variables and many cases, and employs prescribed procedures to ensure validity and reliability;
• Applying well established standards means that the research can be replicated, and then analyzed and compared with similar studies;
• You can summarize vast sources of information and make comparisons across categories and over time; and,
• Personal bias can be avoided by keeping a 'distance' from participating subjects and using accepted computational techniques .

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Brians, Craig Leonard et al. Empirical Political Analysis: Quantitative and Qualitative Research Methods . 8th ed. Boston, MA: Longman, 2011; McNabb, David E. Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches . 2nd ed. Armonk, NY: M.E. Sharpe, 2008; Singh, Kultar. Quantitative Social Research Methods . Los Angeles, CA: Sage, 2007.

Limitations of Using Quantitative Methods

Quantitative methods presume to have an objective approach to studying research problems, where data is controlled and measured, to address the accumulation of facts, and to determine the causes of behavior. As a consequence, the results of quantitative research may be statistically significant but are often humanly insignificant.

Some specific limitations associated with using quantitative methods to study research problems in the social sciences include:

• Quantitative data is more efficient and able to test hypotheses, but may miss contextual detail;
• Uses a static and rigid approach and so employs an inflexible process of discovery;
• The development of standard questions by researchers can lead to "structural bias" and false representation, where the data actually reflects the view of the researcher instead of the participating subject;
• Results provide less detail on behavior, attitudes, and motivation;
• Researcher may collect a much narrower and sometimes superficial dataset;
• Results are limited as they provide numerical descriptions rather than detailed narrative and generally provide less elaborate accounts of human perception;
• The research is often carried out in an unnatural, artificial environment so that a level of control can be applied to the exercise. This level of control might not normally be in place in the real world thus yielding "laboratory results" as opposed to "real world results"; and,
• Preset answers will not necessarily reflect how people really feel about a subject and, in some cases, might just be the closest match to the preconceived hypothesis.

Research Tip

Finding Examples of How to Apply Different Types of Research Methods

SAGE publications is a major publisher of studies about how to design and conduct research in the social and behavioral sciences. Their SAGE Research Methods Online and Cases database includes contents from books, articles, encyclopedias, handbooks, and videos covering social science research design and methods including the complete Little Green Book Series of Quantitative Applications in the Social Sciences and the Little Blue Book Series of Qualitative Research techniques. The database also includes case studies outlining the research methods used in real research projects. This is an excellent source for finding definitions of key terms and descriptions of research design and practice, techniques of data gathering, analysis, and reporting, and information about theories of research [e.g., grounded theory]. The database covers both qualitative and quantitative research methods as well as mixed methods approaches to conducting research.

SAGE Research Methods Online and Cases

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What is Empirical Research? Definition, Methods, Examples

Appinio Research · 09.02.2024 · 36min read

Ever wondered how we gather the facts, unveil hidden truths, and make informed decisions in a world filled with questions? Empirical research holds the key.

In this guide, we'll delve deep into the art and science of empirical research, unraveling its methods, mysteries, and manifold applications. From defining the core principles to mastering data analysis and reporting findings, we're here to equip you with the knowledge and tools to navigate the empirical landscape.

What is Empirical Research?

Empirical research is the cornerstone of scientific inquiry, providing a systematic and structured approach to investigating the world around us. It is the process of gathering and analyzing empirical or observable data to test hypotheses, answer research questions, or gain insights into various phenomena. This form of research relies on evidence derived from direct observation or experimentation, allowing researchers to draw conclusions based on real-world data rather than purely theoretical or speculative reasoning.

Characteristics of Empirical Research

Empirical research is characterized by several key features:

• Observation and Measurement : It involves the systematic observation or measurement of variables, events, or behaviors.
• Data Collection : Researchers collect data through various methods, such as surveys, experiments, observations, or interviews.
• Testable Hypotheses : Empirical research often starts with testable hypotheses that are evaluated using collected data.
• Quantitative or Qualitative Data : Data can be quantitative (numerical) or qualitative (non-numerical), depending on the research design.
• Statistical Analysis : Quantitative data often undergo statistical analysis to determine patterns , relationships, or significance.
• Objectivity and Replicability : Empirical research strives for objectivity, minimizing researcher bias . It should be replicable, allowing other researchers to conduct the same study to verify results.
• Conclusions and Generalizations : Empirical research generates findings based on data and aims to make generalizations about larger populations or phenomena.

Importance of Empirical Research

Empirical research plays a pivotal role in advancing knowledge across various disciplines. Its importance extends to academia, industry, and society as a whole. Here are several reasons why empirical research is essential:

• Evidence-Based Knowledge : Empirical research provides a solid foundation of evidence-based knowledge. It enables us to test hypotheses, confirm or refute theories, and build a robust understanding of the world.
• Scientific Progress : In the scientific community, empirical research fuels progress by expanding the boundaries of existing knowledge. It contributes to the development of theories and the formulation of new research questions.
• Problem Solving : Empirical research is instrumental in addressing real-world problems and challenges. It offers insights and data-driven solutions to complex issues in fields like healthcare, economics, and environmental science.
• Informed Decision-Making : In policymaking, business, and healthcare, empirical research informs decision-makers by providing data-driven insights. It guides strategies, investments, and policies for optimal outcomes.
• Quality Assurance : Empirical research is essential for quality assurance and validation in various industries, including pharmaceuticals, manufacturing, and technology. It ensures that products and processes meet established standards.
• Continuous Improvement : Businesses and organizations use empirical research to evaluate performance, customer satisfaction, and product effectiveness. This data-driven approach fosters continuous improvement and innovation.
• Human Advancement : Empirical research in fields like medicine and psychology contributes to the betterment of human health and well-being. It leads to medical breakthroughs, improved therapies, and enhanced psychological interventions.
• Critical Thinking and Problem Solving : Engaging in empirical research fosters critical thinking skills, problem-solving abilities, and a deep appreciation for evidence-based decision-making.

Empirical research empowers us to explore, understand, and improve the world around us. It forms the bedrock of scientific inquiry and drives progress in countless domains, shaping our understanding of both the natural and social sciences.

How to Conduct Empirical Research?

So, you've decided to dive into the world of empirical research. Let's begin by exploring the crucial steps involved in getting started with your research project.

1. Select a Research Topic

Selecting the right research topic is the cornerstone of a successful empirical study. It's essential to choose a topic that not only piques your interest but also aligns with your research goals and objectives. Here's how to go about it:

• Identify Your Interests : Start by reflecting on your passions and interests. What topics fascinate you the most? Your enthusiasm will be your driving force throughout the research process.
• Brainstorm Ideas : Engage in brainstorming sessions to generate potential research topics. Consider the questions you've always wanted to answer or the issues that intrigue you.
• Relevance and Significance : Assess the relevance and significance of your chosen topic. Does it contribute to existing knowledge? Is it a pressing issue in your field of study or the broader community?
• Feasibility : Evaluate the feasibility of your research topic. Do you have access to the necessary resources, data, and participants (if applicable)?

2. Formulate Research Questions

Once you've narrowed down your research topic, the next step is to formulate clear and precise research questions . These questions will guide your entire research process and shape your study's direction. To create effective research questions:

• Specificity : Ensure that your research questions are specific and focused. Vague or overly broad questions can lead to inconclusive results.
• Relevance : Your research questions should directly relate to your chosen topic. They should address gaps in knowledge or contribute to solving a particular problem.
• Testability : Ensure that your questions are testable through empirical methods. You should be able to gather data and analyze it to answer these questions.
• Avoid Bias : Craft your questions in a way that avoids leading or biased language. Maintain neutrality to uphold the integrity of your research.

3. Review Existing Literature

Before you embark on your empirical research journey, it's essential to immerse yourself in the existing body of literature related to your chosen topic. This step, often referred to as a literature review, serves several purposes:

• Contextualization : Understand the historical context and current state of research in your field. What have previous studies found, and what questions remain unanswered?
• Identifying Gaps : Identify gaps or areas where existing research falls short. These gaps will help you formulate meaningful research questions and hypotheses.
• Theory Development : If your study is theoretical, consider how existing theories apply to your topic. If it's empirical, understand how previous studies have approached data collection and analysis.
• Methodological Insights : Learn from the methodologies employed in previous research. What methods were successful, and what challenges did researchers face?

4. Define Variables

Variables are fundamental components of empirical research. They are the factors or characteristics that can change or be manipulated during your study. Properly defining and categorizing variables is crucial for the clarity and validity of your research. Here's what you need to know:

• Independent Variables : These are the variables that you, as the researcher, manipulate or control. They are the "cause" in cause-and-effect relationships.
• Dependent Variables : Dependent variables are the outcomes or responses that you measure or observe. They are the "effect" influenced by changes in independent variables.
• Operational Definitions : To ensure consistency and clarity, provide operational definitions for your variables. Specify how you will measure or manipulate each variable.
• Control Variables : In some studies, controlling for other variables that may influence your dependent variable is essential. These are known as control variables.

Understanding these foundational aspects of empirical research will set a solid foundation for the rest of your journey. Now that you've grasped the essentials of getting started, let's delve deeper into the intricacies of research design.

Empirical Research Design

Now that you've selected your research topic, formulated research questions, and defined your variables, it's time to delve into the heart of your empirical research journey – research design . This pivotal step determines how you will collect data and what methods you'll employ to answer your research questions. Let's explore the various facets of research design in detail.

Types of Empirical Research

Empirical research can take on several forms, each with its own unique approach and methodologies. Understanding the different types of empirical research will help you choose the most suitable design for your study. Here are some common types:

• Experimental Research : In this type, researchers manipulate one or more independent variables to observe their impact on dependent variables. It's highly controlled and often conducted in a laboratory setting.
• Observational Research : Observational research involves the systematic observation of subjects or phenomena without intervention. Researchers are passive observers, documenting behaviors, events, or patterns.
• Survey Research : Surveys are used to collect data through structured questionnaires or interviews. This method is efficient for gathering information from a large number of participants.
• Case Study Research : Case studies focus on in-depth exploration of one or a few cases. Researchers gather detailed information through various sources such as interviews, documents, and observations.
• Qualitative Research : Qualitative research aims to understand behaviors, experiences, and opinions in depth. It often involves open-ended questions, interviews, and thematic analysis.
• Quantitative Research : Quantitative research collects numerical data and relies on statistical analysis to draw conclusions. It involves structured questionnaires, experiments, and surveys.

Your choice of research type should align with your research questions and objectives. Experimental research, for example, is ideal for testing cause-and-effect relationships, while qualitative research is more suitable for exploring complex phenomena.

Experimental Design

Experimental research is a systematic approach to studying causal relationships. It's characterized by the manipulation of one or more independent variables while controlling for other factors. Here are some key aspects of experimental design:

• Control and Experimental Groups : Participants are randomly assigned to either a control group or an experimental group. The independent variable is manipulated for the experimental group but not for the control group.
• Randomization : Randomization is crucial to eliminate bias in group assignment. It ensures that each participant has an equal chance of being in either group.
• Hypothesis Testing : Experimental research often involves hypothesis testing. Researchers formulate hypotheses about the expected effects of the independent variable and use statistical analysis to test these hypotheses.

Observational Design

Observational research entails careful and systematic observation of subjects or phenomena. It's advantageous when you want to understand natural behaviors or events. Key aspects of observational design include:

• Participant Observation : Researchers immerse themselves in the environment they are studying. They become part of the group being observed, allowing for a deep understanding of behaviors.
• Non-Participant Observation : In non-participant observation, researchers remain separate from the subjects. They observe and document behaviors without direct involvement.
• Data Collection Methods : Observational research can involve various data collection methods, such as field notes, video recordings, photographs, or coding of observed behaviors.

Survey Design

Surveys are a popular choice for collecting data from a large number of participants. Effective survey design is essential to ensure the validity and reliability of your data. Consider the following:

• Questionnaire Design : Create clear and concise questions that are easy for participants to understand. Avoid leading or biased questions.
• Sampling Methods : Decide on the appropriate sampling method for your study, whether it's random, stratified, or convenience sampling.
• Data Collection Tools : Choose the right tools for data collection, whether it's paper surveys, online questionnaires, or face-to-face interviews.

Case Study Design

Case studies are an in-depth exploration of one or a few cases to gain a deep understanding of a particular phenomenon. Key aspects of case study design include:

• Single Case vs. Multiple Case Studies : Decide whether you'll focus on a single case or multiple cases. Single case studies are intensive and allow for detailed examination, while multiple case studies provide comparative insights.
• Data Collection Methods : Gather data through interviews, observations, document analysis, or a combination of these methods.

Qualitative vs. Quantitative Research

In empirical research, you'll often encounter the distinction between qualitative and quantitative research . Here's a closer look at these two approaches:

• Qualitative Research : Qualitative research seeks an in-depth understanding of human behavior, experiences, and perspectives. It involves open-ended questions, interviews, and the analysis of textual or narrative data. Qualitative research is exploratory and often used when the research question is complex and requires a nuanced understanding.
• Quantitative Research : Quantitative research collects numerical data and employs statistical analysis to draw conclusions. It involves structured questionnaires, experiments, and surveys. Quantitative research is ideal for testing hypotheses and establishing cause-and-effect relationships.

Understanding the various research design options is crucial in determining the most appropriate approach for your study. Your choice should align with your research questions, objectives, and the nature of the phenomenon you're investigating.

Data Collection for Empirical Research

Now that you've established your research design, it's time to roll up your sleeves and collect the data that will fuel your empirical research. Effective data collection is essential for obtaining accurate and reliable results.

Sampling Methods

Sampling methods are critical in empirical research, as they determine the subset of individuals or elements from your target population that you will study. Here are some standard sampling methods:

• Random Sampling : Random sampling ensures that every member of the population has an equal chance of being selected. It minimizes bias and is often used in quantitative research.
• Stratified Sampling : Stratified sampling involves dividing the population into subgroups or strata based on specific characteristics (e.g., age, gender, location). Samples are then randomly selected from each stratum, ensuring representation of all subgroups.
• Convenience Sampling : Convenience sampling involves selecting participants who are readily available or easily accessible. While it's convenient, it may introduce bias and limit the generalizability of results.
• Snowball Sampling : Snowball sampling is instrumental when studying hard-to-reach or hidden populations. One participant leads you to another, creating a "snowball" effect. This method is common in qualitative research.
• Purposive Sampling : In purposive sampling, researchers deliberately select participants who meet specific criteria relevant to their research questions. It's often used in qualitative studies to gather in-depth information.

The choice of sampling method depends on the nature of your research, available resources, and the degree of precision required. It's crucial to carefully consider your sampling strategy to ensure that your sample accurately represents your target population.

Data Collection Instruments

Data collection instruments are the tools you use to gather information from your participants or sources. These instruments should be designed to capture the data you need accurately. Here are some popular data collection instruments:

• Questionnaires : Questionnaires consist of structured questions with predefined response options. When designing questionnaires, consider the clarity of questions, the order of questions, and the response format (e.g., Likert scale , multiple-choice).
• Interviews : Interviews involve direct communication between the researcher and participants. They can be structured (with predetermined questions) or unstructured (open-ended). Effective interviews require active listening and probing for deeper insights.
• Observations : Observations entail systematically and objectively recording behaviors, events, or phenomena. Researchers must establish clear criteria for what to observe, how to record observations, and when to observe.
• Surveys : Surveys are a common data collection instrument for quantitative research. They can be administered through various means, including online surveys, paper surveys, and telephone surveys.
• Documents and Archives : In some cases, data may be collected from existing documents, records, or archives. Ensure that the sources are reliable, relevant, and properly documented.

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Data Collection Procedures

Data collection procedures outline the step-by-step process for gathering data. These procedures should be meticulously planned and executed to maintain the integrity of your research.

• Training : If you have a research team, ensure that they are trained in data collection methods and protocols. Consistency in data collection is crucial.
• Pilot Testing : Before launching your data collection, conduct a pilot test with a small group to identify any potential problems with your instruments or procedures. Make necessary adjustments based on feedback.
• Data Recording : Establish a systematic method for recording data. This may include timestamps, codes, or identifiers for each data point.
• Data Security : Safeguard the confidentiality and security of collected data. Ensure that only authorized individuals have access to the data.
• Data Storage : Properly organize and store your data in a secure location, whether in physical or digital form. Back up data to prevent loss.

Ethical Considerations

Ethical considerations are paramount in empirical research, as they ensure the well-being and rights of participants are protected.

• Informed Consent : Obtain informed consent from participants, providing clear information about the research purpose, procedures, risks, and their right to withdraw at any time.
• Privacy and Confidentiality : Protect the privacy and confidentiality of participants. Ensure that data is anonymized and sensitive information is kept confidential.
• Beneficence : Ensure that your research benefits participants and society while minimizing harm. Consider the potential risks and benefits of your study.
• Honesty and Integrity : Conduct research with honesty and integrity. Report findings accurately and transparently, even if they are not what you expected.
• Respect for Participants : Treat participants with respect, dignity, and sensitivity to cultural differences. Avoid any form of coercion or manipulation.
• Institutional Review Board (IRB) : If required, seek approval from an IRB or ethics committee before conducting your research, particularly when working with human participants.

Adhering to ethical guidelines is not only essential for the ethical conduct of research but also crucial for the credibility and validity of your study. Ethical research practices build trust between researchers and participants and contribute to the advancement of knowledge with integrity.

With a solid understanding of data collection, including sampling methods, instruments, procedures, and ethical considerations, you are now well-equipped to gather the data needed to answer your research questions.

Empirical Research Data Analysis

Now comes the exciting phase of data analysis, where the raw data you've diligently collected starts to yield insights and answers to your research questions. We will explore the various aspects of data analysis, from preparing your data to drawing meaningful conclusions through statistics and visualization.

Data Preparation

Data preparation is the crucial first step in data analysis. It involves cleaning, organizing, and transforming your raw data into a format that is ready for analysis. Effective data preparation ensures the accuracy and reliability of your results.

• Data Cleaning : Identify and rectify errors, missing values, and inconsistencies in your dataset. This may involve correcting typos, removing outliers, and imputing missing data.
• Data Coding : Assign numerical values or codes to categorical variables to make them suitable for statistical analysis. For example, converting "Yes" and "No" to 1 and 0.
• Data Transformation : Transform variables as needed to meet the assumptions of the statistical tests you plan to use. Common transformations include logarithmic or square root transformations.
• Data Integration : If your data comes from multiple sources, integrate it into a unified dataset, ensuring that variables match and align.
• Data Documentation : Maintain clear documentation of all data preparation steps, as well as the rationale behind each decision. This transparency is essential for replicability.

Effective data preparation lays the foundation for accurate and meaningful analysis. It allows you to trust the results that will follow in the subsequent stages.

Descriptive Statistics

Descriptive statistics help you summarize and make sense of your data by providing a clear overview of its key characteristics. These statistics are essential for understanding the central tendencies, variability, and distribution of your variables. Descriptive statistics include:

• Measures of Central Tendency : These include the mean (average), median (middle value), and mode (most frequent value). They help you understand the typical or central value of your data.
• Measures of Dispersion : Measures like the range, variance, and standard deviation provide insights into the spread or variability of your data points.
• Frequency Distributions : Creating frequency distributions or histograms allows you to visualize the distribution of your data across different values or categories.

Descriptive statistics provide the initial insights needed to understand your data's basic characteristics, which can inform further analysis.

Inferential Statistics

Inferential statistics take your analysis to the next level by allowing you to make inferences or predictions about a larger population based on your sample data. These methods help you test hypotheses and draw meaningful conclusions. Key concepts in inferential statistics include:

• Hypothesis Testing : Hypothesis tests (e.g., t-tests, chi-squared tests) help you determine whether observed differences or associations in your data are statistically significant or occurred by chance.
• Confidence Intervals : Confidence intervals provide a range within which population parameters (e.g., population mean) are likely to fall based on your sample data.
• Regression Analysis : Regression models (linear, logistic, etc.) help you explore relationships between variables and make predictions.
• Analysis of Variance (ANOVA) : ANOVA tests are used to compare means between multiple groups, allowing you to assess whether differences are statistically significant.

Inferential statistics are powerful tools for drawing conclusions from your data and assessing the generalizability of your findings to the broader population.

Qualitative Data Analysis

Qualitative data analysis is employed when working with non-numerical data, such as text, interviews, or open-ended survey responses. It focuses on understanding the underlying themes, patterns, and meanings within qualitative data. Qualitative analysis techniques include:

• Thematic Analysis : Identifying and analyzing recurring themes or patterns within textual data.
• Content Analysis : Categorizing and coding qualitative data to extract meaningful insights.
• Grounded Theory : Developing theories or frameworks based on emergent themes from the data.
• Narrative Analysis : Examining the structure and content of narratives to uncover meaning.

Qualitative data analysis provides a rich and nuanced understanding of complex phenomena and human experiences.

Data Visualization

Data visualization is the art of representing data graphically to make complex information more understandable and accessible. Effective data visualization can reveal patterns, trends, and outliers in your data. Common types of data visualization include:

• Bar Charts and Histograms : Used to display the distribution of categorical data or discrete data .
• Line Charts : Ideal for showing trends and changes in data over time.
• Scatter Plots : Visualize relationships and correlations between two variables.
• Pie Charts : Display the composition of a whole in terms of its parts.
• Heatmaps : Depict patterns and relationships in multidimensional data through color-coding.
• Box Plots : Provide a summary of the data distribution, including outliers.
• Interactive Dashboards : Create dynamic visualizations that allow users to explore data interactively.

Data visualization not only enhances your understanding of the data but also serves as a powerful communication tool to convey your findings to others.

As you embark on the data analysis phase of your empirical research, remember that the specific methods and techniques you choose will depend on your research questions, data type, and objectives. Effective data analysis transforms raw data into valuable insights, bringing you closer to the answers you seek.

How to Report Empirical Research Results?

At this stage, you get to share your empirical research findings with the world. Effective reporting and presentation of your results are crucial for communicating your research's impact and insights.

1. Write the Research Paper

Writing a research paper is the culmination of your empirical research journey. It's where you synthesize your findings, provide context, and contribute to the body of knowledge in your field.

• Title and Abstract : Craft a clear and concise title that reflects your research's essence. The abstract should provide a brief summary of your research objectives, methods, findings, and implications.
• Introduction : In the introduction, introduce your research topic, state your research questions or hypotheses, and explain the significance of your study. Provide context by discussing relevant literature.
• Methods : Describe your research design, data collection methods, and sampling procedures. Be precise and transparent, allowing readers to understand how you conducted your study.
• Results : Present your findings in a clear and organized manner. Use tables, graphs, and statistical analyses to support your results. Avoid interpreting your findings in this section; focus on the presentation of raw data.
• Discussion : Interpret your findings and discuss their implications. Relate your results to your research questions and the existing literature. Address any limitations of your study and suggest avenues for future research.
• Conclusion : Summarize the key points of your research and its significance. Restate your main findings and their implications.
• References : Cite all sources used in your research following a specific citation style (e.g., APA, MLA, Chicago). Ensure accuracy and consistency in your citations.
• Appendices : Include any supplementary material, such as questionnaires, data coding sheets, or additional analyses, in the appendices.

Writing a research paper is a skill that improves with practice. Ensure clarity, coherence, and conciseness in your writing to make your research accessible to a broader audience.

2. Create Visuals and Tables

Visuals and tables are powerful tools for presenting complex data in an accessible and understandable manner.

• Clarity : Ensure that your visuals and tables are clear and easy to interpret. Use descriptive titles and labels.
• Consistency : Maintain consistency in formatting, such as font size and style, across all visuals and tables.
• Appropriateness : Choose the most suitable visual representation for your data. Bar charts, line graphs, and scatter plots work well for different types of data.
• Simplicity : Avoid clutter and unnecessary details. Focus on conveying the main points.
• Accessibility : Make sure your visuals and tables are accessible to a broad audience, including those with visual impairments.
• Captions : Include informative captions that explain the significance of each visual or table.

Compelling visuals and tables enhance the reader's understanding of your research and can be the key to conveying complex information efficiently.

3. Interpret Findings

Interpreting your findings is where you bridge the gap between data and meaning. It's your opportunity to provide context, discuss implications, and offer insights. When interpreting your findings:

• Relate to Research Questions : Discuss how your findings directly address your research questions or hypotheses.
• Compare with Literature : Analyze how your results align with or deviate from previous research in your field. What insights can you draw from these comparisons?
• Discuss Limitations : Be transparent about the limitations of your study. Address any constraints, biases, or potential sources of error.
• Practical Implications : Explore the real-world implications of your findings. How can they be applied or inform decision-making?
• Future Research Directions : Suggest areas for future research based on the gaps or unanswered questions that emerged from your study.

Interpreting findings goes beyond simply presenting data; it's about weaving a narrative that helps readers grasp the significance of your research in the broader context.

With your research paper written, structured, and enriched with visuals, and your findings expertly interpreted, you are now prepared to communicate your research effectively. Sharing your insights and contributing to the body of knowledge in your field is a significant accomplishment in empirical research.

Examples of Empirical Research

To solidify your understanding of empirical research, let's delve into some real-world examples across different fields. These examples will illustrate how empirical research is applied to gather data, analyze findings, and draw conclusions.

Social Sciences

In the realm of social sciences, consider a sociological study exploring the impact of socioeconomic status on educational attainment. Researchers gather data from a diverse group of individuals, including their family backgrounds, income levels, and academic achievements.

Through statistical analysis, they can identify correlations and trends, revealing whether individuals from lower socioeconomic backgrounds are less likely to attain higher levels of education. This empirical research helps shed light on societal inequalities and informs policymakers on potential interventions to address disparities in educational access.

Environmental Science

Environmental scientists often employ empirical research to assess the effects of environmental changes. For instance, researchers studying the impact of climate change on wildlife might collect data on animal populations, weather patterns, and habitat conditions over an extended period.

By analyzing this empirical data, they can identify correlations between climate fluctuations and changes in wildlife behavior, migration patterns, or population sizes. This empirical research is crucial for understanding the ecological consequences of climate change and informing conservation efforts.

Business and Economics

In the business world, empirical research is essential for making data-driven decisions. Consider a market research study conducted by a business seeking to launch a new product. They collect data through surveys , focus groups , and consumer behavior analysis.

By examining this empirical data, the company can gauge consumer preferences, demand, and potential market size. Empirical research in business helps guide product development, pricing strategies, and marketing campaigns, increasing the likelihood of a successful product launch.

Psychological studies frequently rely on empirical research to understand human behavior and cognition. For instance, a psychologist interested in examining the impact of stress on memory might design an experiment. Participants are exposed to stress-inducing situations, and their memory performance is assessed through various tasks.

By analyzing the data collected, the psychologist can determine whether stress has a significant effect on memory recall. This empirical research contributes to our understanding of the complex interplay between psychological factors and cognitive processes.

These examples highlight the versatility and applicability of empirical research across diverse fields. Whether in medicine, social sciences, environmental science, business, or psychology, empirical research serves as a fundamental tool for gaining insights, testing hypotheses, and driving advancements in knowledge and practice.

Conclusion for Empirical Research

Empirical research is a powerful tool for gaining insights, testing hypotheses, and making informed decisions. By following the steps outlined in this guide, you've learned how to select research topics, collect data, analyze findings, and effectively communicate your research to the world. Remember, empirical research is a journey of discovery, and each step you take brings you closer to a deeper understanding of the world around you. Whether you're a scientist, a student, or someone curious about the process, the principles of empirical research empower you to explore, learn, and contribute to the ever-expanding realm of knowledge.

How to Collect Data for Empirical Research?

Introducing Appinio , the real-time market research platform revolutionizing how companies gather consumer insights for their empirical research endeavors. With Appinio, you can conduct your own market research in minutes, gaining valuable data to fuel your data-driven decisions.

Appinio is more than just a market research platform; it's a catalyst for transforming the way you approach empirical research, making it exciting, intuitive, and seamlessly integrated into your decision-making process.

Here's why Appinio is the go-to solution for empirical research:

• From Questions to Insights in Minutes : With Appinio's streamlined process, you can go from formulating your research questions to obtaining actionable insights in a matter of minutes, saving you time and effort.
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Empirical research  is based on observed and measured phenomena and derives knowledge from actual experience rather than from theory or belief. 

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From: Suter, W. N. (2012). Qualitative Data, Analysis, and Design. In  Introduction to educational research: A critical thinking approach . SAGE Publications, Inc., www.galileo.usg.edu/redirect?inst=pie1&url=https://dx.doi.org/10.4135/9781483384443

The words in this table can be used to evaluate whether an article tends more toward the quantitative or qualitative domain. Well-written article abstracts will contain words like these to succinctly characterize the article's content.

Adapted from: McMillan, J. H. (2012).  Educational research: Fundamentals for the consumer  (6th ed.). Boston, MA: Pearson.

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Access, acceptance and adherence to cancer prehabilitation: a mixed-methods systematic review

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• Tessa Watts 1 ,
• Nicholas Courtier 1 ,
• Sarah Fry 1 ,
• Nichola Gale 1 ,
• Elizabeth Gillen 1 ,
• Grace McCutchan 1 ,
• Manasi Patil 1 ,
• Tracy Rees 1 ,
• Dominic Roche 1 ,
• Sally Wheelwright 2 &
• Jane Hopkinson 1  

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The purpose of this systematic review is to better understand access to, acceptance of and adherence to cancer prehabilitation.

MEDLINE, CINAHL, PsychINFO, Embase, Physiotherapy Evidence Database, ProQuest Medical Library, Cochrane Library, Web of Science and grey literature were systematically searched for quantitative, qualitative and mixed-methods studies published in English between January 2017 and June 2023. Screening, data extraction and critical appraisal were conducted by two reviewers independently using Covidence™ systematic review software. Data were analysed and synthesised thematically to address the question ‘What do we know about access, acceptance and adherence to cancer prehabilitation, particularly among socially deprived and minority ethnic groups?’

The protocol is published on PROSPERO CRD42023403776

Searches identified 11,715 records, and 56 studies of variable methodological quality were included: 32 quantitative, 15 qualitative and nine mixed-methods. Analysis identified facilitators and barriers at individual and structural levels, and with interpersonal connections important for prehabilitation access, acceptance and adherence. No study reported analysis of facilitators and barriers to prehabilitation specific to people from ethnic minority communities. One study described health literacy as a barrier to access for people from socioeconomically deprived communities.

Conclusions

There is limited empirical research of barriers and facilitators to inform improvement in equity of access to cancer prehabilitation.

Implications for Cancer Survivors

To enhance the inclusivity of cancer prehabilitation, adjustments may be needed to accommodate individual characteristics and attention given to structural factors, such as staff training. Interpersonal connections are proposed as a fundamental ingredient for successful prehabilitation.

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Introduction

Prehabilitation is a core component of supportive care for health and well-being during cancer survivorship. It aims to improve cancer treatment outcomes and long-term health by preparing people awaiting cancer treatments, not only surgery, through support for physical activity, nutrition and emotional well-being either alone or in combination, and from the point of diagnosis [ 1 ]. Growing international evidence indicates that, in specific cancers, engagement with either uni or multimodal prehabilitation interventions can improve individuals’ pre-treatment functional capacity [ 2 , 3 ], reduce treatment-related complications [ 4 , 5 , 6 ], ease anxiety [ 7 ] and enhance post-treatment recovery [ 8 , 9 ]. As the evidence base develops and momentum for prehabilitation grows, the need to embed prehabilitation as the standard of care across different cancers has been recognised [ 10 , 11 , 12 ]. In some regions, multimodal prehabilitation is now offered as the standard of care in certain cancers, particularly lung [ 13 ] and colorectal [ 14 ].

Internationally, there are persistent health disparities following cancer treatment. Treatment and survival outcomes are poor among people from socioeconomically deprived communities and some minority ethnic groups compared to socioeconomically advantaged and majority groups [ 15 , 16 , 17 ]. To ease the overall social and economic impact of cancer on individuals and society, and to reduce the societal and healthcare costs of suboptimal treatment outcomes, it is important to identify the facilitators of and barriers to individuals’ engagement with interventions. People from socioeconomically deprived communities and some minority ethnic groups are known to be underserved in prehabilitation interventions [ 1 , 18 ]. Accordingly, to better understand reasons for informed action, this mixed-methods systematic review aims to identify, critically appraise and synthesise international empirical evidence of the facilitators of and barriers to access, acceptance  and adherence of cancer prehabilitation. For this review, prehabilitation is defined as proactive and preventative for all cancer treatments (not only surgery and including neoadjuvant) and includes interventions to support physical activity, nutritional intake or psychological well-being, alone or together, carried out at any time before a course of treatment begins.

Review question

What is known about access, acceptance and adherence to cancer prehabilitation, particularly among socially deprived and minority ethnic groups?

The systematic review was informed by the Joanna Briggs Institute (JBI) mixed-methods systematic reviews (MMSR) methodology [ 19 ]. A convergent, integrated approach to data synthesis and integration was adopted [ 19 , 20 ]. The review was registered in PROSPERO CRD42023403776) on 3 March 2023 and is reported in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines [ 21 ]. Ethical approval was not required.

Database searches

In collaboration with a specialist health service systematic review librarian, the search strategy was developed using medical subject headings (MeSH) and keywords including and relating to cancer, prehabilitation, inequity, inequality, socioeconomic deprivation, ethnic groups and health services accessibility, and then tested and refined. The electronic databases Ovid SP MEDLINE, CINAHL via EBSCO host, PsycINFO, Ovid SP EMBASE, Ovid Emcare, Allied and Complementary Medicine (AMED), Physiotherapy Evidence Database (PEDRo) and Cochrane Central were systematically searched by EG for studies published in English between January 2017 and May 2023. The search strategy was tailored for each database and detailed in online resource (Supplementary information 1 ). Supplementary searches of grey literature using the Overton, Dimensions and Proquest dissertation and theses databases (PQDT), and relevant organisational websites were conducted. Reference lists of papers retrieved for full review were scrutinised for potentially useful papers not identified through the database searches.

Selection criteria

The PICO framework was used to guide inclusion criteria on population (P), Intervention (I), comparators (C) and outcomes (O) and context (Co). It enabled identification of primary qualitative, quantitative and mixed-methods research studies about prehabilitation, published in peer-reviewed journals. Eligibility criteria were used during study selection to screen this body of literature for empirical data about barriers and facilitators of prehabilitation. Non-empirical, opinion pieces, theoretical and methodological articles, reviews and editorials were excluded, as were studies involving children, adolescents and focusing on end-of-life care.

Study selection

All search results were stored in Endnote™. Following deduplication, results were imported into Covidence™ systematic review management software. For study selection, standardised systematic review methods [ 22 ] were used. All project team members were involved in study screening and selection. Firstly, two reviewers independently screened all returned titles and abstracts. Based on eligibility and relevance, these were sifted into ‘yes’, ‘no’ or ‘maybe’ categories. Disagreements were resolved by a third reviewer. Where a definite decision could not be made, full text was retrieved and assessed. Secondly, full text of all potentially relevant abstracts was retrieved and independently assessed for inclusion by two reviewers against the eligibility criteria. Arbitration by an independent reviewer in the event of disagreement was not required at this stage. Reasons for exclusion at full text review were recorded.

Quality assessment

Two reviewers independently assessed the quality of included studies via Covidence ™ using the Mixed Methods Appraisal Tool (MMAT) version 18 [ 23 ]. The MMAT was constructed specifically for quality appraisal in mixed studies reviews and is widely used [ 23 , 24 ]. Within a single tool, Version 18 of the MMAT can be used to appraise the methodological quality of five broad categories of study design, namely qualitative, randomised controlled trials, non-randomised, quantitative descriptive and mixed methods studies. The MMAT comprises two screening questions to establish whether or not the quality appraisal should proceed and 25 core questions: five criteria which mostly relate to the appropriateness of study design and approaches to sampling, data collection and analysis relevant to each of the five study designs [ 23 ]. Each criterion is assessed as being met (Yes) or not (No). There is also scope to indicate uncertainty. A third reviewer independently moderated all quality assessments for accuracy.

Data extraction

Two reviewers independently extracted data systematically via Covidence™ using an adapted, piloted JBI mixed-methods data extraction form. Information extracted included study author, aim, year and country of publication, setting, intervention type, design, sample, data collection, analysis, data relating to prehabilitation facilitators and barriers and, as relevant, data on intervention for support of access, acceptance or adherence to prehabilitation. A third reviewer cross-checked the data extraction tables independently for accuracy and completeness.

Data synthesis and integration

All extracted findings were imported into Microsoft Excel. Quantitative data were ‘qualitised’ into textual descriptions of quantitative results to enable assimilation with qualitative data [ 25 ]. To analyse and synthesise all findings, thematic synthesis [ 26 , 27 ] was used. Thematic analysis is an established process involving the identification and development of patterns and analytic themes in primary research data. Two reviewers coded the findings and then grouped related codes into preliminary descriptive themes which captured patterns across the data describing barriers to and facilitators of cancer prehabilitation [ 26 ]. Preliminary themes were discussed with a third reviewer. Themes were then further combined and synthesised to generate three overarching analytical themes relative to the review question [ 26 ].

Figure 1 shows the PRISMA flow chart of search results. Following the first and second round screening, 56 papers published between 2017 and 2023 were included: 33 quantitative; 14 qualitative and nine mixed methods.

PRISMA 2020 flow diagram for new systematic reviews which included searches of databases, registers and other sources. *Consider, if feasible to do so, reporting the number of records identified from each database or register searched (rather than the total number across all databases/registers). **If automation tools were used, indicate how many records were excluded by a human and how many were excluded by automation tools. From: Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71. 10.1136/bmj.n71. For more information, visit: http://www.prisma-statement.org/

A synopsis of study characteristics and the quality appraisal outcomes is found in Table 1 . Brief narrative summaries of the included papers’ findings of relevance to the review question, namely access, acceptance and adherence of prehabilitation interventions, are provided in the online supplementary information (supplementary information 2 ).

Study characteristics

Of the 32 quantitative studies reviewed, there were eight randomised controlled trials, two single-arm multi-centre trials, seven cohort studies and one cross-sectional survey. Others were pilot ( n = 3), feasibility ( n = 7), observational ( n = 1) and prevalence ( n = 1) studies, with one non-randomised trial and one audit. Qualitative studies ( n = 15) mainly used a broad qualitative approach ( n = 12), one used phenomenology, one participatory action research and one used a cross-sectional survey. Nine studies used mixed methods.

Study populations

The majority of included studies were conducted in Europe ( n = 33) (UK ( n = 19), Netherlands ( n = 4), Denmark ( n = 3), Spain ( n = 1), France ( n =1), Portugal ( n = 1), Belgium ( n = 1), Slovenia ( n = 1), Norway ( n = 1) and Sweden ( n = 1)). Eleven were conducted in North America (Canada ( n = 8), United States ( n =3)), and eight were from Australia. The remaining studies were from Japan ( n = 1) and China ( n =1), and two studies were conducted across two countries, Australia and New Zealand and the UK and Norway. Studies focused on prehabilitation in different settings including hospitals ( n = 12), local communities (including universities and local gymnasiums), individuals’ homes ( n = 14) and outdoors ( n = 1). Ten studies reported a hybrid, home and hospital approach to prehabilitation, whilst digital prehabilitation was reported in nine studies. Fifty-three studies were conducted in a range of cancers. Of these, 41 reported data for a single cancer site: colorectal ( n = 11); gastrointestinal ( n = 9); lung ( n = 7); haematology ( n= 4); breast ( n = 3); head and neck ( n =2); bladder ( n = 2) prostate ( n =1) and a range of abdominal surgeries ( n = 3). In 12 studies, cancer sites were pooled. Three studies focused on healthcare professionals ( n = 2) and key stakeholders ( n = 1).

Methodological quality

There was considerable variation in the methodological quality of the 56 studies included. Twelve studies, 10 qualitative and two quantitative, satisfied all the MMAT criteria [ 23 ]. Fourteen studies, nine mixed methods, two qualitative and three quantitative, satisfied just one or two criteria. Thus, data were extracted from a body of literature where one-fifth (21%) of publications were about research of the highest quality, defined as having met 100% of the MMAT criteria [ 23 ]. Detailed results of the MMAT quality assessments are found in supplementary information (supplementary information 3 ).

Thematic synthesis

The thematic synthesis identified three cross-cutting analytic themes. As illustrated in Figure 2 , these themes reflected individual, structural and interpersonal facilitators of and barriers to access, acceptability and adherence of cancer prehabilitation:

Overarching themes

Theme 1 The influence of individual drivers of cancer prehabilitation engagement

Theme 2 Providing acceptable cancer prehabilitation service and interventions

Theme 3 Interpersonal support – the unifying golden thread

Interpersonal support was the unifying golden thread as it facilitated the fit between the individual and the structural for access to, acceptance of and adherence to prehabilitation.

Theme 1. The influence of individual drivers of cancer prehabilitation engagement

Factors at the level of the individual were found to shape prehabilitation access, acceptance and adherence. These included perceived need and benefits, motivations, health status and everyday practicalities.

The perceived need for and potential benefits of prehabilitation

A key stimulus for accessing and adhering to cancer prehabilitation was a belief that engagement might confer benefit. Influences included clinicians’ prehabilitation endorsement and encouragement [ 12 , 13 , 42 , 52 , 55 , 59 , 60 , 65 , 66 , 71 ], positive prior personal experiences of routine physical activities [ 60 , 69 , 70 , 77 ] and weight loss programmes [ 77 ], other patients’ support [ 12 , 71 ] and the perceived need to improve personal fitness [ 60 , 63 ]. Some participants in UK-based studies believed they had a social responsibility to engage in prehabilitation [ 63 , 64 ] as enhanced fitness would benefit healthcare services financially [ 12 , 64 ].

The money, the cost per night in the hospital, goodness knows how much that costs and the follow-up with all the doctors, the dieticians and everyone else behind (….). It’s (prehabilitation) saving the NHS thousands and thousands of pounds of money ([ 64 ] p.4).

Several studies indicated some individuals perceived prehabilitation to be beneficial in that interventions provided a welcome distraction from their illness and situation [ 64 , 72 , 74 ]. Benefit was understood in terms of being psychologically and physically prepared for cancer treatments, potentially enhancing post-treatment recovery and survival [ 12 , 55 , 60 , 63 , 64 , 66 , 67 , 68 , 70 , 71 , 74 ].

I benefited a lot from it because it caught me in that time just after diagnosis when things were pretty scary and pretty awful and I felt like it was one of the key pieces of my plan for positivity during this whole thing, because it was setting a tone for recovery ([ 74 ] p. 8)

Yet, it was also clear that some individuals were disinterested in engaging with prehabilitation [ 56 , 58 , 66 , 74 , 80 ]. Some studies suggested a connection between imminent surgery and patients’ perceptions of little benefit of prehabilitation in the short timescales [ 47 , 54 , 63 , 69 , 77 , 79 ]. Some individuals felt that making additional hospital visits for prehabilitation was onerous [ 54 ]. Others were unaccustomed to or did not want to exercise [ 36 , 70 ] or perceived exercise as demanding [ 41 ], particularly when combined with cancer treatment [ 51 ]. Some considered their existing fitness levels [ 61 , 63 ] and diet [ 61 ] sufficient. A sense of low perceived benefit of or need for prehabilitation meant it was considered a low priority [ 36 ].

Personal motivators

A cancer diagnosis [ 71 , 77 ] conjoined with the desire to improve fitness [ 63 , 64 , 72 ], survive surgery [ 63 , 64 ] and to be present for and enjoy their families [ 64 ] were influential motivators for individuals’ proactively effecting lifestyle change and thus engagement with prehabilitation. Having accessed prehabilitation, exercise logs and diaries [ 64 , 68 , 74 ], personal goal setting [ 61 , 64 , 71 ], progress self-monitoring [ 61 , 64 , 68 , 71 , 77 ], activity tracking and objective feedback [ 56 , 60 ] motivated individuals to maintain participation. They inspired them to remain on track, enabled them to realise their progress, build self-efficacy for prehabilitation adherence [ 60 , 70 , 73 , 76 , 77 ] and, through a process of cognitive reframing, regain a sense of control [ 71 ].

Now I have a feeling of control over my body . . . I don’t want cancer to define me. [ 71 ]

Nonetheless, one study reported that motivation to access prehabilitation may be negatively affected by low levels of health literacy, which is associated with socioeconomic deprivation [ 46 ]. Furthermore, sustaining motivation to continue prehabilitation could be challenging [ 43 , 45 , 58 , 64 , 70 , 74 ], especially when faced with unanticipated setbacks such as delayed surgery [ 57 ] or insufficient peer support [ 64 ].

The enduring problems of health limitations

Individuals’ physical and psychological health status influenced prehabilitation access and adherence, particularly when there was a perception of insufficient on-going professional [ 61 , 72 , 73 ] and family support [ 31 ], and interventions were located away from home. Pancreatic cancer [ 33 ] adversely affected individuals’ access to prehabilitation. Furthermore, physical health problems limited some individuals’ ability to travel and thus access hospital-based prehabilitation [ 54 , 59 , 71 ]. Symptoms experienced and perceived health status influenced individuals’ prehabilitation adherence. Reported adherence barriers included physical symptoms [ 61 , 67 , 70 , 72 , 73 , 81 ] such as fatigue [ 45 , 50 , 57 , 70 , 73 ], pain [ 40 , 45 , 57 , 59 , 70 , 71 , 73 ], digestive problems [ 30 , 35 , 39 , 47 , 55 , 67 ] and feeling unwell [ 40 , 43 , 64 , 79 ]. In addition, functional limitations [ 63 , 70 ] associated with comorbidities [ 31 , 37 , 40 , 49 , 51 , 57 , 64 , 70 , 77 ], disease status [ 37 , 41 ], pre-surgery neoadjuvant treatments [ 37 , 53 , 64 , 70 , 81 ] and mental health problems [ 35 , 39 ] were all reported to negatively affect individuals’ ability to engage with and adhere to prehabilitation, particularly in terms of physical activities.

Several studies reported that psychological distress had a negative effect on prehabilitation access and adherence [ 59 , 61 , 70 , 73 ]. Described by a participant in one study [ 63 ] as ‘dark moments’, as anxiety and stress were often connected with attending hospitals [ 71 ]. In addition, several studies reported that individuals felt overwhelmed, both generally [ 42 , 57 , 74 ] and emotionally [ 12 , 70 ], in advance of their treatments. Information overload [ 62 ] and competing personal matters which required their attention pre-treatment [ 70 , 80 ] contributed to the sense of feeling overwhelmed.

The challenges of everyday life

Across studies, insufficient time for prehabilitation was frequently reported [ 40 , 50 , 51 , 55 , 58 , 66 , 71 , 72 , 74 , 77 , 78 ]. Some individuals described competing priorities in the short space of time between diagnosis and treatment [ 49 , 57 , 59 , 70 , 79 ]. This was partly due to putting affairs in order, prioritising family time [ 61 ] or treatments being scheduled earlier than originally planned [ 35 , 54 , 55 ]. Others were constrained by their employment [ 51 , 70 , 73 , 80 ] and family responsibilities, including caring for other family members [ 55 , 58 , 70 ]. Additional barriers to prehabilitation engagement included geographical distance to hospitals delivering prehabilitation [ 28 , 32 , 41 , 51 , 54 , 57 , 63 , 74 ]; transport difficulties [ 29 , 49 , 51 , 54 , 58 , 60 , 66 , 79 ] and associated financial costs [ 51 , 66 , 71 ]; inclement weather, particularly in relation to prehabilitation with outdoor exercise components [ 45 , 57 , 64 , 70 , 73 , 74 ]; low digital literacy [ 34 , 42 , 76 ]; restricted or limited access to and problems with technology [ 42 , 56 , 76 , 80 ], notably broadband [ 45 , 79 ] and experiencing physical discomfort with exercise equipment [ 60 , 64 ].

Theme 2. Providing acceptable cancer prehabilitation service and interventions

The prehabilitation environment, mode of delivery (which might be technological) and the perceived utility of interventions were important facilitators of access [ 34 , 48 , 57 , 66 , 71 , 75 , 80 ] and adherence [ 36 , 45 , 48 , 61 ] and influenced acceptance [ 36 , 52 , 61 , 64 , 69 , 71 , 77 , 80 , 81 ].

The value of home-based prehabilitation

Home-based prehabilitation interventions with remote professional supervision and support were accepted for their convenience [ 38 , 74 ], capacity to motivate [ 38 , 61 , 64 , 73 ] and build self-efficacy [ 40 , 61 , 64 , 73 ] and perceived benefit [ 40 , 69 , 74 ]. Specifically, individuals reported that home-based prehabilitation enabled them to integrate interventions into their everyday lives [ 61 , 64 ]. Exercising in the safe, private, space of home was enjoyable [ 36 , 66 ], could help with overcoming self-consciousness and engendered a sense of control [ 61 , 64 ].

I couldn’t go to the gym any longer. I can’t very well be running out to the toilet the whole time. So, I had to find something else, so it was that [static bike at home]. ([ 61 ] p. 206) …I don’t want to do it [prehabilitation] in a hospital because I think it then becomes really competitive. And people are, like, if they can’t do it, they feel…. They would feel like, ‘Oh, I’m not strong enough…’ you know what I mean. It might depress them. Whereas if you do it in the house, you can do it at your own pace, there’s nobody watching over you and everything. [ 64 ]

Home-based prehabilitation interventions were important facilitators of access [ 48 , 66 ] and adherence [ 36 , 48 , 61 ]. The provision of portable exercise equipment such as resistance bands enabled sustained adherence, particularly when individuals were temporarily away from home [ 74 ]. Some individuals welcomed the freedom and flexibility of home-based prehabilitation [ 72 ]. Yet despite being provided with resources to monitor [ 34 , 42 , 52 , 64 , 66 , 76 ], supplement and continue physical activity at home [ 48 , 63 , 66 , 74 , 77 ], insufficient in-person healthcare professional engagement and encouragement could mean adherence was often difficult to monitor [ 69 , 81 ] and sustained intervention adherence could be challenging [ 28 , 63 , 64 ] and afforded a low priority by individuals [ 61 , 72 , 73 ].

There had to be real pressure, there really had! And then if suddenly they were not around (the health professionals), then I’m not sure I’d finish it. That’s how I am. You have to keep an eye on me. [ 72 ]

Navigating the technological space of tele-prehabilitation

Sometimes referred to as ‘tele’ or ‘digital’-prehabilitation, technology-based uni and multimodal home-based prehabilitation capitalised on internet and/or telephone communication services and was delivered using smartphones, videos, wearable technology, tablets, mobile applications, video platforms and secure video conferencing [ 34 , 36 , 42 , 45 , 56 , 70 , 71 , 76 , 80 ]. In terms of acceptability, individuals perceived home-based, tele-prehabilitation programmes as accessible, particularly during the SARS-CoV-2 pandemic [ 34 , 71 , 80 ]:

Having prehabilitation outside of the hospital setting made things easier. I wasn’t feeling good with the pain and couldn’t travel too far. Could also do it in my own time ([ 71 ] p. 646)

Home-based tele-rehabilitation was also perceived as motivating [ 36 , 45 , 56 , 76 ], conferred benefit [ 34 , 36 , 45 , 56 , 80 ], particularly when personalised [ 34 , 45 , 56 , 71 ] and reduced transport-associated costs [ 80 ].

Sustained tele-prehabilitation engagement was aided by the provision of smartphones [ 56 , 76 ], tablets with relevant applications and content downloaded [ 34 ], training watches [ 34 , 56 , 76 ], supplementary information and alternate web browser pathways for those without access to or with low digital literacy [ 42 ] and integrated digital training and support during the intervention’s implementation [ 34 , 36 , 42 ].

I would not have been able to endure the treatments and the surgery thereafter had it not been for the continuous support I was receiving through the digital platform. [ 34 ]

Reported barriers were primarily intervention specific. They included technical [ 45 , 80 ] and device connectivity issues [ 34 , 76 ], broadband and website interface problems, particularly for individuals unaccustomed to using technology [ 45 ]. Negative views of mobile mindfulness apps [ 56 ] and equipment aesthetics [ 76 ] were also described.

The perceived utility of prehabilitation interventions

Interventions that were perceived as being accessible in terms of their user-friendliness [ 34 , 56 , 74 , 76 ] and appropriately designed to meet individuals’ needs, preferences and capabilities in terms of their structure [ 40 , 52 , 60 , 68 , 74 , 77 , 78 ], notably coherence [ 36 , 38 , 45 , 75 , 76 ] and components [ 38 , 54 , 55 , 64 , 69 , 74 ], including nutritional supplements [ 44 , 54 , 55 , 67 ], enhanced acceptability. The acceptability of prehabilitation interventions was reflected in the expressions of gratitude [ 12 ] and the positive ways in which interventions were variously described by individuals in some studies [ 12 , 38 , 58 , 64 , 74 ] as ‘excellent’, ‘very good’, ‘great’, ‘brilliant’, ‘hugely beneficial’ and ‘fun’. Some would even recommend home-based prehabilitation to people preparing for cancer treatments [ 52 , 63 , 68 , 74 ]. However, one study [ 42 ] reported that unfamiliarity with the English language had a negative impact on access, whilst in another study [ 56 ], individuals reported adhering to protein targets challenging.

At an individual level, the availability [ 61 ] and extent of integrated healthcare professional supervision and support was perceived to enable intervention access [ 75 ] and adherence [ 42 , 60 , 61 , 64 , 66 , 68 , 69 , 74 , 78 ], particularly when this was personalised [ 34 , 45 , 56 , 65 , 68 , 71 , 78 ]. Unpalatable nutritional interventions had a negative effect on intervention adherence [ 30 , 50 ], and it was reported that inspiratory muscle training devices could be difficult for individuals to use [ 38 ].

Healthcare professionals reported organisational barriers to implementation, and thus individuals’ access to, acceptance of and adherence with prehabilitation. These barriers included workforce capacity limitations [ 12 , 65 , 75 , 79 , 81 ], including insufficient embedded specialist prehabilitation professionals [ 69 , 81 ], delayed or insufficient referral to prehabilitation [ 33 , 44 , 63 ], disconnect in cross-boundary systematic service delivery and communication [ 12 , 28 , 75 , 81 ], inadequate funding [ 12 , 65 , 79 , 81 ] and awareness of local prehabilitation provision, uncertainty regarding what constitutes prehabilitation among some healthcare professionals [ 28 , 79 , 81 ] and space and time constraints [ 69 , 81 ] together with insufficient equipment [ 28 ] in hospital settings to deliver interventions [ 81 ].

Theme 3. Interpersonal support: the unifying golden thread

Across the studies reviewed, the unifying golden thread was interpersonal support, for this was an important, valued enabler of prehabilitation access [ 64 ] acceptance and adherence. It was reported that interpersonal support was derived from family and friends [ 12 , 45 , 60 , 61 , 64 , 70 , 73 ], prehabilitation healthcare professionals [ 42 , 51 , 55 , 60 , 61 , 63 , 64 , 66 , 69 , 71 , 75 , 78 ], prehabilitation peers [ 51 , 59 ], volunteers [ 79 ] and in-person and online peer support groups [ 71 , 79 ]. When embedded within interventions, a network of interpersonal support helped to sustain prehabilitation adherence, particularly in relation to physical activity [ 59 , 60 , 68 , 72 , 79 ]. During what could be challenging times, the interpersonal support experienced during prehabilitation enhanced interventions’ acceptability [ 52 , 60 , 63 , 68 ].

The active involvement of family during physical activities such as walking and exercise routines was reported to generate a sense of companionship, encouragement and motivational and psychological support [ 34 , 60 , 61 , 64 , 70 , 71 , 77 ]. In these ways, prehabilitation interventions with embedded family support enhanced their acceptability [ 52 ].

My wife did the same ones with me so there were two of us doing the same stuff. We did the walks together. Then we would both do the exercises. So that was good company. [ 64 ]

Findings reported in one study [ 31 ] indicated that living alone could have a negative effect on prehabilitation adherence.

The acceptability of prehabilitation interventions was enhanced by relevant healthcare professionals’ supportive dialogue in the shape of information, personalised encouragement, validation and timely, constructive feedback on individuals’ engagement, progress and performance [ 69 , 77 ], signposting to other support services [ 63 ] and broader emotional support [ 77 ]. In addition to sustaining prehabilitation behaviours through collaboration, activation and motivational support [ 60 , 61 , 71 , 72 , 77 , 78 ], healthcare professionals’ presence instilled a sense of trust [ 71 ], comfort [ 51 ] and safety [ 38 , 62 , 63 ] and reduced feelings of social isolation [ 71 ]. The need for and importance of supportive dialogue with healthcare professionals during prehabilitation was identified by participants in one study investigating individuals’ experiences of multimodal prehabilitation delivered via a leaflet and with no embedded healthcare professional support [ 73 ].

I have only been a number. Like I was a garden shovel with a barcode that you scanned at the cash register. There is no one who thinks about what this means for one’s self-understanding–- just to be regarded as a disease [...] There is no one asking about the human being behind it. It is insane [ 73 ]

For some participants, peer support in the shape of information sharing was beneficial and enabled prehabilitation access [ 63 , 71 ]. Integrated group or one to one peer support was reported to enhance an intervention’s acceptability [ 12 , 63 ]. In part, this was because individuals did not always want to engage their families, and peer support reduced their sense of isolation [ 71 ]. Peer support was reported to be beneficial in terms of interaction with others in a similar situation, thereby lending individuals’ social, emotional and motivational support, enabling them to remain on track with their prehabilitation programme [ 51 , 59 , 64 , 66 , 71 ].

Exercising in a group motivates. Let new patients exercise with other patients who are further along and have more experience exercising. They (experienced patients) can then tell them, Yes, you will get muscle aches, but they will subside too. [ 59 ]

It was clear from some studies that the absence of peer support in prehabilitation interventions was lamented [ 64 , 71 ], with some participants exercising agency and accessing online patient forums to derive required support [ 71 ].

This review reports findings from across the globe regarding facilitators of and barriers to access, acceptance and adherence of cancer prehabilitation. The findings draw attention to cross-cutting themes at individual and structural levels and interpersonal factors that connect the levels. As illuminated in Fig. 2 , the multifaceted facilitators and barriers underscore the complexity of cancer prehabilitation access, acceptance and adherence.

This review found interpersonal connections, support either directly obtained from peers, family, healthcare professionals or via digital connectivity, can facilitate a fit between the individual factors and structural factors that affect engagement with prehabilitation. Examples include encouragement from a spouse willing to engage in a recommended physical activity with the patient, practical help with digital technology, peer support during group prehabilitation and health professional supervision. Support through these interpersonal connections may be a core ingredient for successful access, acceptance and adherence. This proposition should now be explored and tested. There may be sub-groups with need or preference for certain sources of interpersonal support. Our review was designed to find out ‘what is known about access, acceptance and adherence to cancer prehabilitation, particularly among socially deprived and minority ethnic groups’ because of the known benefits from prehab for post treatment recovery [ 8 , 9 ]. It found no empirically based analysis of prehabilitation access, acceptance or adherence by people from these groups.

The individual and structural context

This review revealed individual factors enabling or impeding prehabilitation access, acceptance and adherence include personal beliefs and understandings about potential harms or benefits; motivations, for example finding enjoyment in participation; health status and everyday practicalities such as time and transport availability. Structural factors identified included the availability of knowledgeable and supportive health professionals and/or people affected by cancer’ service organisation, such as the availability of a prehabilitation multidisciplinary team and the place and space of service delivery, for example, if it was available in the community.

Individual and structural level factors affecting access to cancer treatment and care are widely reported [ 82 , 83 , 84 , 85 ]. Some are proposed to be modifiable for improved health outcomes in groups at risk of poor health because of poverty and/or discrimination based on age, race, ethnicity or gender [ 84 ]. The findings of the review are consistent with this wider literature on service access, acceptance and adherence. It is notable that although our search was designed to identify all literature about access, acceptance and adherence to cancer prehabilitation from 2017 to 2023, we found no analysis of structural differences. The differential experience of people from structurally vulnerable groups, for example, those who are socioeconomically deprived or from minority communities, had not been considered. Yet, evidence indicates that cancer rehabilitation services are underutilised by people from socioeconomically deprived communities [ 86 , 87 ] and ethnic minorities [ 88 ]. We also know patient engagement with prehabilitation is variable [ 89 ], and third sector organisations claim people from socioeconomically deprived communities, which include people from some ethnic minorities, are underserved by prehabilitation services [ 1 ]. Exploration and understanding of difference in prehabilitation experiences across social groups is needed if support for access, acceptance and adherence is to achieve equity in health outcomes.

Interpersonal connections linking individual experience and structural context

This review identified that it was people, namely peers, family members and friends, who, through their support, influenced the extent to which individual and structural level factors were obstacles or enablers of prehabilitation. In the relational space between individual experience and the infrastructure in place to enable prehabilitation, these people were supportive actors, influencing individuals’ access to, acceptance of and adherence to prehabilitation.

International studies have revealed that interpersonal support is related to mental and physical health. Low perceived social support has been shown to be associated with mental and physical health problems [ 90 ]. In the USA, a high level of perceived social support was found more likely in women and young people and low level of perceived social support more likely for those living in poverty [ 90 ]. Loneliness has been proposed the mediating factor between socioeconomic status and health in a Norwegian population-based study of people aged over 40 years [ 91 ]. Two explanations were suggested. Firstly, people with few social contacts have low levels of physical activity. Secondly, people with poor physical or emotional health are more likely to have low self-esteem and self-efficacy in self-care, which is associated with less successful occupational career and low socioeconomic status and thus fewer social contact resources to manage health [ 91 ].

This review supports an argument that interpersonal connections can be important for prehabilitation access, acceptance and adherence. It found evidence of relationships with family, peers and cancer care staff influencing access to, acceptance of, and adherence to prehabilitation. Perceived social support may have a key role in successful prehabilitation. This proposition should be further explored, paying attention to the known relationship between social support and socioeconomic status in other contexts and the potential for this to be an explanation of any observed difference in access across socioeconomic groups.

Technology as interpersonal connection?

An interesting finding is of data showing some people find web-based resources and/or online help to satisfy their prehabilitation information and support needs. These people experienced interpersonal connection through technology. An online survey among 1037 adults (18+) in the UK found that 80% of those with a long-term condition used technology for managing their health, a majority for seeking information whilst a third used wearable technology or apps. Those most likely to use technologies were younger and/or of high socioeconomic status, leading the authors to caution completely digital approaches because of the potential to exclude some groups from the care they need [ 92 ]. Arguably, technology may provide a partial solution to enabling successful prehabilitation.

What this review adds

Our finding of structural and individual level factors affecting access to, acceptance of and adherence to prehabilitation is consistent with Levesque et al.’s [ 93 ] socioecological model of access to health services. Levesque et al.’s [ 93 ] model sets out access as a process with five dimensions of accessibility (approachability; acceptability; availability and accommodation; affordability; appropriateness) and five corresponding abilities of populations (ability to perceive; ability to seek; ability to reach; ability to pay; ability to engage). The model enables attention to social, service organisation and person-centred factors that influence access. However, the model does not address the relational dimensions derived from our data analysis, i.e. how person-centred and structural factors interrelate for better or poorer service access. Based on our findings, an important ingredient for improving access to prehabilitation may be attention to what happens in the relational space connecting these factors. Voorhees et al. [ 94 ] interpreted findings of participatory research about access to general practice and claimed it is the human abilities of workforce and clients that are an important yet absent consideration in Levesque’s model. They argued that staff training and support for human interaction were needed. We agree. In addition, and based on our analysis, we also consider important the network of interactions between patient and others. Understanding the nature and mechanisms of these interactions may be important for health equity in prehabilitation.

Strengths and limitations

A strength of this review is that established, rigorous systematic review processes were followed to identify and select relevant peer-reviewed literature. Methods and thematic synthesis procedures were reported explicitly, providing an audit trail for dependability. To maximise study identification, the detailed and comprehensive search strategy was developed with the assistance of an expert information specialist, and the review was conducted by a multidisciplinary team with a minimum of two reviewers engaged in the screening and extracting process. Searches were limited from 2017 to 2023 and published in the English language. By limiting the search dates in this way, we have ensured that the evidence assessed has context and relevance to current policy and practices. This systematic review, as a result, provides an overarching picture and holistic understanding of access, acceptance and adherence to cancer prehabilitation. However, this review is not without its limitations. It is possible that some potentially useful studies, notably those not published in the English language have been omitted. Furthermore, we did not take account of study quality in our analysis. To reduce the risk of selection bias, studies were included irrespective of their methodological quality assessment. However, this means that some low quality evidence has been included, and this is a limitation to the credibility of the analysis. Nevertheless, there is some consistency between studies and across international healthcare settings. This does indicate a level of trustworthiness in the review findings. The review was of mixed cancer sites. Cancer site along with its symptoms and treatment-related problems may affect access, acceptance and adherence to prehabilitation. As the body of literature about engagement with prehabilitation grows, further work will be warranted to investigate cancer site–specific factors affecting inclusion in prehabilitation.

ThQueryere is limited empirical study of barriers and facilitators to inform improvement in equity of access to cancer prehabilitation. To enhance the inclusivity of cancer prehabilitation, adjustments may be needed to accommodate individual preferences and characteristics, such as comorbidity, and attention given to structural factors, such as staff training. Based on our findings, we propose interpersonal connections as a fundamental core ingredient for facilitation of prehabilitation access, acceptance and adherence.

Systematic review registration

This systematic review was registered in PROSPERO (CRD42023403776)

Data Availability

All data generated for this review are included in the manuscript and/or the supplementary files.

Macmillan Cancer Support. Prehabilitation for people with cancer: Principles and guidance for prehabilitation within the management and support of people with cancer. Macmillan Cancer Support. 2020. http://www.macmillan.org.uk/healthcare-professionals/news-and-resources/guides/principles-and-guidance-forprehabilitation . Accessed 03 Mar 2023.

Waterland JL, McCourt O, Edbrooke L, Granger CL, Ismail H, Riedel B, Denehy L. Efficacy of prehabilitation including exercise on postoperative outcomes following abdominal cancer surgery: a systematic review and meta-analysis Front Surg. 2021;19(8):628848. https://doi.org/10.3389/fsurg.2021.628848 .

Falz R, Bischoff C, Thieme R, Lässing J, Mehdorn M, Stelzner S, Busse M, Gockel I. Effects and duration of exercise-based prehabilitation in surgical therapy of colon and rectal cancer: a systematic review and meta-analysis. J Cancer Res Clin Oncol. 2022;148(9):2187–213. https://doi.org/10.1007/s00432-022-04088-w .

Article   PubMed   PubMed Central   Google Scholar  

Moran J, Guinan E, McCormick P, Larkin J, Mockler D, Hussey J, Moriarty J, Wilson F. The ability of prehabilitation to influence postoperative outcome after intra-abdominal operation: a systematic review and meta-analysis. Surgery. 2016;160(5):1189–201. https://doi.org/10.1016/j.surg.2016.05.014 .

Article   PubMed   Google Scholar  

Halliday LJ, Doganay E, Wynter-Blyth VA, Hanna GB, Moorthy K. The impact of prehabilitation on post-operative outcomes in oesophageal cancer surgery: a propensity score matched comparison. J Gastrointest Surg. 2021;25(11):2733–41. https://doi.org/10.1007/s11605-020-04881-3 .

Heil TC, Verdaasdonk EGG, Maas HAAM, van Munster BC, Rikkert MGMO, de Wilt JHW, Melis RJF. Improved postoperative outcomes after prehabilitation for colorectal cancer surgery in older patients: an emulated target trial. Ann Surg Oncol. 2023;30(1):244–54. https://doi.org/10.1245/s10434-022-12623-9 .

Scriney A, Russell A, Loughney L, Gallagher P, Boran L. The impact of prehabilitation interventions on affective and functional outcomes for young to midlife adult cancer patients: a systematic review. Psychooncology. 2022;31(12):2050–62. https://doi.org/10.1002/pon.6029 .

Michael CM, Lehrer EJ, Schmitz KH, Zaorsky NG. Prehabilitation exercise therapy for cancer: a systematic review and meta-analysis. Cancer Med. 2021;10(13):4195–205. https://doi.org/10.1002/cam4.4021 .

Minnella EM, Awasthi R, Bousquet-Dion G, Ferreira V, Austin B, Audi C, Tanguay S, Aprikian A, Carli F, Kassouf W. Multimodal prehabilitation to enhance functional capacity following radical cystectomy: a randomized controlled trial. Eur Urol Focus. 2021;7(1):132–8. https://doi.org/10.1016/j.euf.2019.05.016 .

Stout NL, Silver JK, Baima J, Knowlton SE, Hu X. Prehabilitation: an emerging standard in exercise Oncology. In: Schmitz K, editor. Exercise Oncology. Switzerland AG: Springer International Publishing; 2020. p. 111–43.

Chapter   Google Scholar  

Moore J, Merchant Z, Rowlinson K, McEwan K, Evison M, Faulkner G, Sultan J, McPhee JS, Steele J. Implementing a system-wide cancer prehabilitation programme: the journey of Greater Manchester's Prehab4cancer. Eur J Surg Oncol. 2021; 47(3 Pt A):524-532 https://doi.org/10.1016/j.ejso.2020.04.042

Bingham SL, Small S, Sempl CJ. A qualitative evaluation of a multi-modal cancer prehabilitation programme for colorectal, head and neck and lung cancer patients. PLoS One. 2023;18(10):e0277589. https://doi.org/10.1371/journal.pone.0277589 .

Article   CAS   PubMed   PubMed Central   Google Scholar  

Bradley P, Marchant Z, Rowlinson-Groves K, Taylor M, Moore J, Evison M. Feasibility and outcomes of a real-world regional lung prehabilitation programme in the UK. Br J Anaesth. 2023;130(1):e47-e55 https://doi.org/10.1016/j.bja.2022.05.034

Sabajo CR, Ten Cate DWG, Heijmans MHM, Koot CTG, van Leeuwen LVL, Slooter GD. Prehabilitation in colorectal cancer surgery improves outcome and reduces hospital costs. Eur J Surg Oncol. 2024;50(1):107302. https://doi.org/10.1016/j.ejso.2023.107302 .

Ammitzbøll G, Levinsen AKG, Kjær TK, Ebbestad FE, Horsbøl TA, Saltbæk L, Badre-Esfahani SK, Joensen A, Kjeldsted E, Halgren Olsen M, Dalton SO. Socioeconomic inequality in cancer in the Nordic countries. A systematic review. Acta Oncol. 2022;61(11):1317–31. https://doi.org/10.1080/0284186X.2022.2143278 .

Boujibar F, Bonnevie T, Debeaumont D, Bubenheim M, Cuvellier A, Peillon C, Gravier FE, Baste JM. Impact of prehabilitation on morbidity and mortality after pulmonary lobectomy by minimally invasive surgery: a cohort study. J Thorac Dis. 2018; 10(4):2240-2248. https://doi.org/10.21037/jtd.2018.03.161

Giaquinto AN, Miller KD, Tossas KY, Winn RA, Jemal A, Siegel RL. Cancer statistics for African American/Black People. CA Cancer J Clin. 2022;72(3):202-229. https://doi.org/10.3322/caac.21718

Lee D, Wang A, Augustin B, Buajitti E, Tahasildar B, Carli F, Gillis C. Socioeconomic status influences participation in cancer prehabilitation and preparation for surgical recovery: a pooled retrospective analysis using a validated area-level socioeconomic status metric. Eur J Surg Oncol. 2023;49(2):512–20. https://doi.org/10.1016/j.ejso.2022.10.023 .

Lizorando L, Stern C, Carrier J, Godfrey C, Rieger K, Salmond S, Apostolo J, Kirkpatrick P, Loveday H (2020) Mixed methods systematic reviews. In: Aromataris E, Munn Z, editors. JBI manual for evidence synthesis. JBI. 2020. https://doi.org/10.46658/JBIMES-20-09

Hong QN, Pluye P, Bujold M, Wassef M. Convergent and sequential synthesis designs: implications for conducting and reporting systematic reviews of qualitative and quantitative evidence. Syst Rev. 2017;6(1):61. https://doi.org/10.1186/s13643-017-0454-2 .

Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE, Chou R, Glanville J, Grimshaw JM, Hróbjartsson A, Lalu MM, Li T, Loder EW, Mayo-Wilson E, McDonald S, McGuinness LA, Stewart LA, Thomas J, Tricco AC, Welch VA, Whiting P, Moher D. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;29(372):n71. https://doi.org/10.1136/bmj.n71 .

Article   Google Scholar  

Centre for Reviews & Dissemination. CRD’s guidance on undertaking reviews in health care. York: University of York; 2009.

Google Scholar  

Hong QN, Fàbregues S, Bartlett G, Boardman F, Cargo M, Dagenais P, Gagnon MP, Griffiths F, Nicolau B, O’Cathain A, Rousseau MC, Vedel I, Pluye P. The mixed methods appraisal tool (MMAT) version 2018 for information professionals and researchers. Education for Information. 2018;34:285–91. https://doi.org/10.3233/EFI-180221 .

Pace R, Pluye P, Bartlett G, Macaulay AC, Salsberg J, Jagosh J, Seller R. Testing the reliability and efficiency of the pilot Mixed Methods Appraisal Tool (MMAT) for systematic mixed studies review. Int J Nurs Stud. 2012;49(1):47–53. https://doi.org/10.1016/j.ijnurstu.2011.07.002 .

Stern C, Lizarondo L, Carrier J, Godfrey C, Rieger K, Salmond S, Apóstolo J, Kirkpatrick P, Loveday H. Methodological guidance for the conduct of mixed methods systematic reviews. JBI Evid Synth. 2020;18(10):2108–18.

Thomas J, Harden A. Methods for the thematic synthesis of qualitative research in systematic reviews. BMC Med Res Methodol. 2008;8:45. https://doi.org/10.1186/1471-2288-8-45 .

Thomas J, O’Mara-Eves A, Harden A, Newman M. Synthesis methods for combining and configuring textual or mixed methods data. In: Gough D, Oliver S, Thomas J, editors. An introduction to systematic reviews. London: Sage Publishing; 2017. p. 181–209.

PREPARE-ABC Trial Collaborative SupPoRtive Exercise Programmes for Accelerating REcovery after major ABdominal Cancer surgery trial (PREPARE-ABC). Pilot phase of a multicentre randomised controlled trial. Colorectal Dis. 2021;23(11):3008-3022. https://doi.org/10.1111/codi.15856

Argudo N, Rodó-Pin A, Martínez-Llorens J, Marco E, Visa L, Messaggi-Sartor M, Balañá-Corberó A, Ramón JM, Rodríguez-Chiaradía DA, Grande L, Pera M. Feasibility, tolerability, and effects of exercise-based prehabilitation after neoadjuvant therapy in esophagogastric cancer patients undergoing surgery: an interventional pilot study. Dis Esophagus. 2021; 34(4):doaa086. https://doi.org/10.1093/dote/doaa086

Burden ST, Gibson DJ, Lal S, Hill J, Pilling M, Soop M, Ramesh A, Todd C. Pre-operative oral nutritional supplementation with dietary advice versus dietary advice alone in weight-losing patients with colorectal cancer: single-blind randomized controlled trial. J Cachexia Sarcopenia Muscle. 2017;8(3):437–46. https://doi.org/10.1002/jcsm.12170 .

Catho H, Guigard S, Toffart AC, Frey G, Chollier T, Brichon PY, Roux JF, Sakhri L, Bertrand D, Aguirre C, Gorain S, Wuyam B, Arbib F, Borel JC. What are the barriers to the completion of a home-based rehabilitation programme for patients awaiting surgery for lung cancer: a prospective observational study. BMJ Open. 2021;11(2):e041907. https://doi.org/10.1136/bmjopen-2020-041907 .

Crowe J, Francis JJ, Edbrooke L, Loeliger J, Joyce T, Prickett C, Martin A, Khot A, Denehy L; Centre for Prehabilitation Peri-operative Care (CPPOC). Impact of an allied health prehabilitation service for haematologic patients receiving high-dose chemotherapy in a large cancer centre. Support Care Cancer. 2022; 30(2):1841-1852. https://doi.org/10.1007/s00520-021-06607-w

Deftereos I, Yeung JM, Arslan J, Carter VM, Isenring E, Kiss N, On Behalf Of The Nourish Point Prevalence Study Group. Preoperative nutrition intervention in patients undergoing resection for upper gastrointestinal cancer: results from the Multi-Centre NOURISH Point Prevalence Study Nutrients. 2021;13(9):3205. https://doi.org/10.3390/nu13093205

Drummond K, Lambert G, Tahasildar B, Carli F. Successes and challenges of implementing teleprehabilitation for onco-surgical candidates and patients’ experience: a retrospective pilot-cohort study. Sci Rep. 2022;12(1):6775. https://doi.org/10.1038/s41598-022-10810-y .

Ferreira V, Lawson C, Carli F, Scheede-Bergdahl C, Chevalier S. Feasibility of a novel mixed-nutrient supplement in a multimodal prehabilitation intervention for lung cancer patients awaiting surgery: a randomized controlled pilot trial. Int J Surg. 2021;93:106079. https://doi.org/10.1016/j.ijsu.2021.106079 .

Franssen RFW, Bongers BC, Vogelaar FJ, Janssen-Heijnen MLG. Feasibility of a tele-prehabilitation program in high-risk patients with colon or rectal cancer undergoing elective surgery: a feasibility study. Perioper Med (Lond). 2022;11(1):28. https://doi.org/10.1186/s13741-022-00260-5 .

Halliday LJ, Doganay E, Wynter-Blyth V, Osborn H, Buckley J, Moorthy K. Adherence to pre-operative exercise and the response to prehabilitation in oesophageal cancer patients. J Gastrointest Surg. 2021;25(4):890–9. https://doi.org/10.1007/s11605-020-04561-2 .

Karlsson E, Farahnak P, Franzén E, Nygren-Bonnier M, Dronkers J, van Meeteren N, Rydwik E. Feasibility of preoperative supervised home-based exercise in older adults undergoing colorectal cancer surgery - a randomized controlled design. PLoS One. 2019;14(7):e0219158. https://doi.org/10.1371/journal.pone.0219158 .

Lawson C, Ferreira V, Carli F, Chevalier S. Effects of multimodal prehabilitation on muscle size, myosteatosis, and dietary intake of surgical patients with lung cancer - a randomized feasibility study. Appl Physiol Nutr Metab. 2021;46(11):1407–16. https://doi.org/10.1139/apnm-2021-0249 .

Article   CAS   PubMed   Google Scholar  

Machado P, Pimenta S, Garcia AL, Nogueira T, Silva S, Oliveiros B, Martins RA, Cruz J. Home-based preoperative exercise training for lung cancer patients undergoing surgery: a feasibility trial. J Clin Med. 2023;12(8):2971. https://doi.org/10.3390/jcm12082971 .

Minnella EM, Baldini G, Quang ATL, Bessissow A, Spicer J, Carli F. Prehabilitation in thoracic cancer surgery: from research to standard of care. J Cardiothorac Vasc Anesth. 2021;35(11):3255–64. https://doi.org/10.1053/j.jvca.2021.02.049 .

Moorthy K, Halliday LJ, Noor N, Peters CJ, Wynter-Blyth V, Urch CE. Feasibility of implementation and the impact of a digital prehabilitation service in patients undergoing treatment for oesophago-gastric cancer. Curr Oncol. 2023;30(2):1673–82. https://doi.org/10.3390/curroncol30020128 .

Naito T, Mitsunaga S, Miura S, Tatematsu N, Inano T, Mouri T, Tsuji T, Higashiguchi T, Inui A, Okayama T, Yamaguchi T, Morikawa A, Mori N, Takahashi T, Strasser F, Omae K, Mori K, Takayama K. Feasibility of early multimodal interventions for elderly patients with advanced pancreatic and non-small-cell lung cancer. J Cachexia Sarcopenia Muscle. 2019;10(1):73–83. https://doi.org/10.1002/jcsm.12351 .

Paynter E, Whelan E, Curnuck C, Dhaliwal S, Sherriff J. Pre-operative immunonutrition therapy in upper gastrointestinal cancer patients: postoperative outcomes and patient acceptance. AMJ. 2017;10(6):466–473. https://doi.org/10.21767/AMJ.2017.2962

Piraux E, Caty G, Reychler G, Forget P, Deswysen Y. Feasibility and preliminary effectiveness of a tele-prehabilitation program in esophagogastric cancer patients. J Clin Med. 2020;9(7):2176. https://doi.org/10.3390/jcm9072176 .

Qin PP, Jin JY, Min S, Wang WJ, Shen YW. Association between health literacy and enhanced recovery after surgery protocol adherence and postoperative outcomes among patients undergoing colorectal cancer surgery: a prospective cohort study. Anesth Analg. 2022;134(2):330–40. https://doi.org/10.1213/ANE.0000000000005829 .

Rupnik E, Skerget M, Sever M, Zupan IP, Ogrinec M, Ursic B, Kos N, Cernelc P, Zver S. Feasibility and safety of exercise training and nutritional support prior to haematopoietic stem cell transplantation in patients with haematologic malignancies. BMC Cancer. 2020;20(1):1142. https://doi.org/10.1186/s12885-020-07637-z .

Santa Mina D, Hilton WJ, Matthew AG, Awasthi R, Bousquet-Dion G, Alibhai SMH, Au D, Fleshner NE, Finelli A, Clarke H, Aprikian A, Tanguay S, Carli F. Prehabilitation for radical prostatectomy: a multicentre randomized controlled trial. Surg Oncol. 2018;27(2):289–98. https://doi.org/10.1016/j.suronc.2018.05.010 .

Shukla A, Granger CL, Wright GM, Edbrooke L, Denehy L. Attitudes and perceptions to prehabilitation in lung cancer. Integr Cancer Ther. 2020;19:1534735420924466. https://doi.org/10.1177/1534735420924466 .

Solheim TS, Laird BJA, Balstad TR, Stene GB, Bye A, Johns N, Pettersen CH, Fallon M, Fayers P, Fearon K, Kaasa S. A randomized phase II feasibility trial of a multimodal intervention for the management of cachexia in lung and pancreatic cancer. J Cachexia Sarcopenia Muscle. 2017;8(5):778–88. https://doi.org/10.1002/jcsm.12201 .

Stalsberg R, Bertheussen GF, Børset H, Thomsen SN, Husøy A, Flote VG, Thune I, Lundgren S. Do breast cancer patients manage to participate in an outdoor, tailored, physical activity program during adjuvant breast cancer treatment, independent of health and socio-demographic characteristics? J Clin Med. 2022;11(3):843. https://doi.org/10.3390/jcm11030843 .

Steffens D, Young J, Beckenkamp PR, Ratcliffe J, Rubie F, Ansari N, Pillinger N, Koh C, Munoz PA, Solomon M. Feasibility and acceptability of a preoperative exercise program for patients undergoing major cancer surgery: results from a pilot randomized controlled trial. Pilot Feasibility Stud. 2021;7(1):27. https://doi.org/10.1186/s40814-021-00765-8 .

Thoft-Jensen B, Jensen JB, Love-Retinger N, Bowker M, Retinger C, Dalbagni G. Implementing a multimodal prehabilitation program to radical cystectomy in a comprehensive cancer center: a pilot study to assess feasibility and outcomes. Urol Nurs. 2019;39(6):303–13. https://doi.org/10.7257/1053-816x.2019.39.6.303 .

Tweed TTT, Sier MAT, Van Bodegraven AA, Van Nie NC, Sipers WMWH, Boerma EG, Stoot JHMB. Feasibility and efficiency of the BEFORE (Better Exercise and Food, Better Recovery) prehabilitation program. Nutrients. 2021;13(10):3493. https://doi.org/10.3390/nu13103493 .

van Rooijen SJ, Molenaar CJL, Schep G, van Lieshout RHMA, Beijer S, Dubbers R, Rademakers N, Papen-Botterhuis NE, van Kempen S, Carli F, Roumen RMH, Slooter GD. Making patients fit for surgery: introducing a four pillar multimodal prehabilitation program in colorectal cancer. Am J Phys Med Rehabil. 2019;98(10):888–96. https://doi.org/10.1097/PHM.0000000000001221 .

Waller E, Sutton P, Rahman S, Allen J, Saxton J, Aziz O. Prehabilitation with wearables versus standard of care before major abdominal cancer surgery: a randomised controlled pilot study (trial registration: NCT04047524). Surg Endosc. 2022;36(2):1008–17. https://doi.org/10.1007/s00464-021-08365-6 .

Waterland JL, Ismail H, Granger CL, Patrick C, Denehy L, Riedel B; Centre for Prehabilitation and Perioperative Care. Prehabilitation in high-risk patients scheduled for major abdominal cancer surgery: a feasibility study. Perioper Med.2022;11(1):32. https://doi.org/10.1186/s13741-022-00263-2

Wu F, Laza-Cagigas R, Pagarkar A, Olaoke A, El Gammal M, Rampal T. The feasibility of prehabilitation as part of the breast cancer treatment pathway. PM R. 2021;13(11):1237–46. https://doi.org/10.1002/pmrj.12543 .

Agasi-Idenburg CS, Zuilen MK, Westerman MJ, Punt CJA, Aaronson NK, Stuiver MM. “I am busy surviving” - views about physical exercise in older adults scheduled for colorectal cancer surgery. J Geriatr Oncol. 2020;11(3):444–50. https://doi.org/10.1016/j.jgo.2019.05.001 .

Banerjee S, Semper K, Skarparis K, Naisby J, Lewis L, Cucato G, Mills R, Rochester M, Saxton J. Patient perspectives of vigorous intensity aerobic interval exercise prehabilitation prior to radical cystectomy: a qualitative focus group study. Disabil Rehabil. 2021;43(8):1084–91. https://doi.org/10.1080/09638288.2019.1651907 .

Beck A, Thaysen HV, Soegaard CH, Blaakaer J, Seibaek L. Investigating the experiences, thoughts, and feelings underlying and influencing prehabilitation among cancer patients: a qualitative perspective on the what, when, where, who, and why. Disabil Rehabil. 2022;44(2):202–9. https://doi.org/10.1080/09638288.2020.1762770 .

Brady GC, Goodrich J, Roe JWG. Using experience-based co-design to improve the pre-treatment care pathway for people diagnosed with head and neck cancer. Support Care Cancer. 2020;28(2):739–45. https://doi.org/10.1007/s00520-019-04877-z .

Collaço N, Henshall C, Belcher E, Canavan J, Merriman C, Mitchell J, Watson E. Patients’ and healthcare professionals’ views on a pre- and post-operative rehabilitation programme (SOLACE) for lung cancer: a qualitative study. J Clin Nurs. 2022;31(1–2):283–93. https://doi.org/10.1111/jocn.15907 .

Cooper M, Chmelo J, Sinclair RCF, Charman S, Hallsworth K, Welford J, Phillips AW, Greystoke A, Avery L. Exploring factors influencing uptake and adherence to a home-based prehabilitation physical activity and exercise intervention for patients undergoing chemotherapy before major surgery (ChemoFit): a qualitative study. BMJ Open. 2022;12(9):e062526. https://doi.org/10.1136/bmjopen-2022-062526 .

Daun JT, Twomey R, Dort JC, Capozzi LC, Crump T, Francis GJ, Matthews TW, Chandarana SP, Hart RD, Schrag C, Matthews J, McKenzie CD, Lau H, Culos-Reed SN. A Qualitative study of patient and healthcare provider perspectives on building multiphasic exercise prehabilitation into the surgical care pathway for head and neck cancer. Curr Oncol. 2022;29(8):5942–54. https://doi.org/10.3390/curroncol29080469 .

Ferreira V, Agnihotram RV, Bergdahl A, van Rooijen SJ, Awasthi R, Carli F, Scheede-Bergdahl C. Maximizing patient adherence to prehabilitation: what do the patients say? Support Care Cancer. 2018;26(8):2717–23. https://doi.org/10.1007/s00520-018-4109-1 .

Hogan SE, Solomon MJ, Carey SK. Exploring reasons behind patient compliance with nutrition supplements before pelvic exenteration surgery. Support Care Cancer. 2019;27(5):1853–60. https://doi.org/10.1007/s00520-018-4445-1 .

McCourt O, Fisher A, Land J, Ramdharry G, Roberts AL, Bekris G, Yong. “What I wanted to do was build myself back up and prepare”: qualitative findings from the PERCEPT trial of prehabilitation during autologous stem cell transplantation in myeloma. BMC Cancer. 2023;23(1):348. https://doi.org/10.1186/s12885-023-10799-1

Murdoch J, Varley A, McCulloch J, Jones M, Thomas LB, Clark A, Stirling S, Turner D, Swart AM, Dresser K, Howard G, Saxton J, Hernon J. Implementing supportive exercise interventions in the colorectal cancer care pathway: a process evaluation of the PREPARE-ABC randomised controlled trial. BMC Cancer. 2021;21(1):1137. https://doi.org/10.1186/s12885-021-08880-8 .

Sun V, Raz DJ, Kim JY, Melstrom L, Hite S, Varatkar G, Fong Y. Barriers and facilitators of adherence to a perioperative physical activity intervention for older adults with cancer and their family caregivers. J Geriatr Oncol. 2020;11(2):256–62. https://doi.org/10.1016/j.jgo.2019.06.003 .

Wu F, Laza-Cagigas R, Rampal T. Understanding patients’ experiences and perspectives of tele-prehabilitation: a qualitative study to inform service design and delivery. Clin Pract. 2022;12(4):640–52. https://doi.org/10.3390/clinpract12040067 .

Beck A, Vind Thaysen H, Hasselholt Soegaard C, Blaakaer J, Seibaek L. What matters to you? An investigation of patients’ perspectives on and acceptability of prehabilitation in major cancer surgery. Eur J Cancer Care. 2021;30(6): e13475. https://doi.org/10.1111/ecc.13475 .

Beck A, Vind Thaysen H, Hasselholt Soegaard C, Blaakaer J, Seibaek L. Prehabilitation in cancer care: patients’ ability to prepare for major abdominal surgery. Scand J Caring Sci. 2021;35(1):143–55. https://doi.org/10.1111/scs .

Brahmbhatt P, Sabiston CM, Lopez C, Chang E, Goodman J, Jones J, McCready D, Randall I, Rotstein S, Santa Mina D. Feasibility of prehabilitation prior to breast cancer surgery: a mixed-methods study. Front Oncol. 2020;10:571091. https://doi.org/10.3389/fonc.2020.571091 .

Deftereos I, Hitch D, Butzkueven S, Carter V, Fetterplace K, Fox K, Ottaway A, Pierce K, Steer B, Varghese J, Kiss N, Yeung JM. Implementing a standardised perioperative nutrition care pathway in upper gastrointestinal cancer surgery: a mixed-methods analysis of implementation using the Consolidated Framework for Implementation Research. BMC Health Serv Res. 2022;22(1):256. https://doi.org/10.1186/s12913-022-07466-9 .

Low CA, Danko M, Durica KC, Kunta AR, Mulukutla R, Ren Y, Bartlett DL, Bovbjerg DH, Dey AK, Jakicic JM. A real-time mobile intervention to reduce sedentary behavior before and after cancer surgery: usability and feasibility study. JMIR Perioper Med. 2020;3(1):e17292. https://doi.org/10.2196/17292 .

Macleod M, Steele RJC, O’Carroll RE, Wells M, Campbell A, Sugden JA, Rodger J, Stead M, McKell J, Anderson AS. Feasibility study to assess the delivery of a lifestyle intervention (TreatWELL) for patients with colorectal cancer undergoing potentially curative treatment. BMJ Open. 2018;8(6):e021117. https://doi.org/10.1136/bmjopen-2017-021117 .

Mawson S, Keen C, Skilbeck J, Ross H, Smith L, Dixey J, Walters SJ, Simpson R, Greenfield DM, Snowden JA. Feasibility and benefits of a structured prehabilitation programme prior to autologous stem cell transplantation (ASCT) in patients with myeloma; a prospective feasibility study. Physiotherapy. 2021;113:88–99. https://doi.org/10.1016/j.physio.2021.08.001 .

Provan D, McLean G, Moug SJ, Phillips I, Anderson AS. Prehabilitation services for people diagnosed with cancer in Scotland-Current practice, barriers and challenges to implementation. Surgeon. 2022;20(5):284–90. https://doi.org/10.1016/j.surge.2021.08.005 .

Waterland JL, Chahal R, Ismail H, Sinton C, Riedel B, Francis JJ, Denehy L. Implementing a telehealth prehabilitation education session for patients preparing for major cancer surgery. BMC Health Serv Res. 2021;21(1):443. https://doi.org/10.1186/s12913-021-06437-w .

Robinson R, Crank H, Humphreys H, Fisher P, Greenfield DM. Time to embed physical activity within usual care in cancer services: a qualitative study of cancer healthcare professionals’ views at a single centre in England. Disabil Rehabil. 2023;45(21):3484–92. https://doi.org/10.1080/09638288.2022.2134468 .

Hewitt M, Simone JC. Ensuring access to cancer care. In: Hewit M, Simone JC, editors. Ensuring Quality Cancer Care. Washingon: National Academy Press; 1999. p. 46–78.

Dunn JG, Garvey PC, Valery D, Ball KM, Fong S, Vinod DL, O’Connell SK, Chambers. Barriers to lung cancer care: health professionals’ perspectives. Support Care Cancer. 2017;25:497–504. https://doi.org/10.1007/s00520-016-3428-3

Bourgeois A, Horrill TC, Mollison A, Lambert LK, Stajduhar KI. Barriers to cancer treatment and care for people experiencing structural vulnerability: a secondary analysis of ethnographic data. Int J Equity in Health. 2023;22:58 https://doi.org/10.1186/s12939-023-01860-3

Pearson SA, Taylor S, Marsden A, Dalton O’Reilly J, Krishan A, Howell S, Yorke J. Geographic and sociodemographic access to systemic anticancer therapies for secondary breast cancer: a systematic review. Systematic Reviews. 2024;13:35 https://doi.org/10.1186/s13643-023-02382-3

Oksbjerg Dalton S, Halgren Olsen M, Moustsen IR, Wedell Andersen C, Vibe-Petersen J, Johansen C. Socioeconomic position, referral and attendance to rehabilitation after a cancer diagnosis: a population-based study in Copenhagen, Denmark 2010-2015. Acta Oncol. 201;58(5):730-736. https://doi.org/10.1080/0284186X.2019.1582800

Ross L, Petersen MA, Johnsen AT, Lundstrøm LH, Groenvold M. Are different groups of cancer patients offered rehabilitation to the same extent? A report from the population-based study “The Cancer Patient’s World.” Support Care Cancer. 2012;20(5):1089–100. https://doi.org/10.1007/s00520-011-1189-6 .

Kristiansen M, Adamsen L, Piil K, Halvorsen I, Nyholm N, Hendriksen C. A three-year national follow-up study on the development of community-level cancer rehabilitation in Denmark. Scand J Public Health. 2019;47(5):511–8. https://doi.org/10.1177/1403494817746535 .

Provan D, McLean G, Moug SJ, Phillips I, Anderson AS. Prehabilitation services for people diagnosed with cancer in Scotland - current practice, barriers and challenges to implementation. Surgeon. 2022;20(5):284–90. https://doi.org/10.1016/j.surge.2021.08.005 .

Moak ZB, Agrawal A. The association between perceived interpersonal social support and physical and mental health: results from the National Epidemiological Survey on Alcohol and Related Conditions. J Public Health (Oxf). 2010;32(2):191–201. https://doi.org/10.1093/pubmed/fdp093 .

Aartsen M, Veenstra M, Hansen T. Social pathways to health: On the mediating role of the social network in the relation between socio-economic position and health. SSM Popul Health. 2017;3:419–26. https://doi.org/10.1016/j.ssmph.2017.05.006 .

NHS Confederation in partnership with Google Health. Patient empowerment: what is the role of technology in transforming care? NHS Confederation. 2023.  http://www.nhsconfed.org/publications/patientempowerment-what-role-technology-transforming-care#:~:text=Ensuring%20digital%20access%20and%20inclusion,-There%20are%20disparitiestext=Adopting%20a%20digitalfirst%20approach,and%20centre%20of%20the%20design . Accessed 25 Jan 2024.

Levesque JF, Harris MF, Russell G. Patient-centred access to health care: conceptualising access at the interface of health systems and populations. Int J Equity Health. 2013;12:18. https://doi.org/10.1186/1475-9276-12-18 .

Voorhees J, Bailey S, Waterman H, Checkland K. Accessing primary care and the importance of ‘human fit’: a qualitative participatory case study. Br J Gen Pract. 2022;72(718):e342–50. https://doi.org/10.3399/BJGP.2021.0375 .

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T.W. and JH wrote the main manuscript. E.G. Designed and tested the search strategy, ran all the searches and prepared supplementary file 1 E.G. and TW prepared figure 1 JH prepared figure 2 All authors contributed to study selection, quality assessment and data extraction TW and MP prepared supplementary file 2 TW prepared supplementary file 3 TW, MP and JH analysed data. All authors reviewed the manuscript.

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Watts, T., Courtier, N., Fry, S. et al. Access, acceptance and adherence to cancer prehabilitation: a mixed-methods systematic review. J Cancer Surviv (2024). https://doi.org/10.1007/s11764-024-01605-3

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