examples of a basic research

25 Basic Research Examples

Basic research is research that focuses on expanding human knowledge, without obvious practical applications.

For a scholarly definition, we can turn to Grimsgaard (2023):

“Basic research, also called pure, theoretical or fundamental research, tends to focus more on ‘big picture’ topics, such as increasing the scientific knowledge base around a particular topic.”

It is contrasted with applied research , which “seeks to solve real world problems” (Lehmann, 2023).

Generally, basis research has no clear economic or market value, meaning it tends to take place in universities rather than private organizations. Nevertheless, this blue-skies basic research can lead to enormous technological breakthroughs that forms the foundation for future applied research .

Basic Research Examples

• Physics: Understanding the properties of neutrinos.
• Medicine: Investigating the role of gut microbiota in mental health.
• Anthropology: Studying the social structures of ancient civilizations.
• Biology: Exploring the mechanism of CRISPR-Cas9 gene editing.
• Psychology: Understanding the cognitive development in infants.
• Chemistry: Researching new catalytic processes for organic synthesis.
• Astronomy: Investigating the life cycle of stars.
• Sociology: Exploring the impacts of social media on society.
• Ecology: Studying the biodiversity in rainforests.
• Computer Science: Developing new algorithms for machine learning.
• Mathematics: Exploring new approaches to number theory.
• Economics: Investigating the causes and effects of inflation.
• Linguistics: Researching the evolution of languages over time.
• Political Science: Studying the effects of political campaigns on voter behavior.
• Geology: Investigating the formation of mountain ranges.
• Architecture: Studying ancient building techniques and materials.
• Education: Researching the impact of remote learning on academic performance.
• History: Investigating trade routes in the medieval period.
• Literature: Analyzing symbolism in 19th-century novels.
• Philosophy: Exploring concepts of justice in different cultures.
• Environmental Science: Studying the impact of plastics on marine life.
• Genetics: Investigating the role of specific genes in aging.
• Engineering: Researching materials for improving battery technology.
• Art History: Investigating the influence of politics on Renaissance art.
• Agricultural Science: Studying the impact of pest management practices on crop yield.

Case Studies

1. understanding the structure of the atom.

The study of atomic structure began in the early 1800s, with John Dalton’s atomic theory suggesting that atoms were indivisible and indestructible. However, it was not until the 20th century that Ernest Rutherford’s gold foil experiment led to the discovery of the nucleus and the proposal of the planetary model of the atom, which was further refined by Niels Bohr and eventually led to the quantum mechanical model, showing that electrons move in orbital shells around the nucleus.

Research Context:

• Topic: Investigating the structure and behavior of atoms.
• Purpose: Understand the fundamental particles (protons, neutrons, and electrons) and forces that govern atomic behavior.
• Methodology: Utilize particle accelerators, theoretical models, and experimental physics.
• Significance: Fundamental understanding of atomic structures has paved the way for numerous technological and scientific breakthroughs, such as the development of nuclear energy and advancements in chemistry and materials science.

Outcomes and Further Developments:

• Discovery and exploration of subatomic particles like quarks.
• Development of quantum mechanics and quantum field theory.
• Subsequent advancements in various scientific fields, such as nuclear physics, chemistry, and nanotechnology.

2. Researching the Human Genome

The Human Genome Project, an international research effort that began in 1990, aimed to sequence and map all of the genes – collectively known as the genome – of humans. Completed in 2003, it represented a monumental achievement in science, providing researchers with powerful tools to understand the genetic factors in human disease, paving the way for new strategies for diagnosis, treatment, and prevention.

• Topic: Investigating the structure, function, and mapping of the human genome.
• Purpose: Understand the genetic makeup of humans, identify genes, and learn how they work.
• Methodology: Techniques like DNA sequencing, genetic mapping, and computational biology.
• Significance: Foundational for various advancements in genetics, medicine, and biology, providing insights into diseases, development, and evolution.
• Completion of the Human Genome Project, which mapped the entire human genome.
• Advancements in personalized medicine, genetic testing, and gene therapy.
• Development of CRISPR technology, enabling precise genetic editing.

Basic Research vs Applied Research

Basic research focuses on expanding knowledge and understanding fundamental concepts without immediate practical application, while applied research focuses on solving specific, practical problems using the knowledge gained from basic research (Akcigit, Hanley & Serrano-Velarde, 2021).

A simple comparison of definitions is below:

• Basic research seeks to gain greater knowledge or understanding of the fundamental aspects of phenomena.
• Applied research seeks to solve practical problems the researcher or their stakeholders are facing.

A researcher might choose basic research over applied if their primary motivation is to expand the boundaries of human knowledge and contribute to academic theories, whilst they might favor applied research if they are more interested in achieving immediate solutions, innovations, or enhancements impacting real-world scenarios (Akcigit, Hanley & Serrano-Velarde, 2021; Baetu, 2016).

To learn more about applied research, check out my article on applied research.

Basic Research: Disappearing in 21st Century Universities?

In the 1980s, universities increasingly came under pressure to prove their specific financial value to society. This has only intensified over the decades. So, whereas once universities were preoccupied with basic research, there’s been a big push toward academic-industry collaborations where research demonstrates its economic value, rather than its cultural or intellectual value, to society. This may, on the one hand, help make universities relevant to today’s world. But on the other hand, it may interfere with the blue skies research that could identify and solve the bigger, less financially pressing, questions and problems of our ages (Bentley, Gulbrandsen & Kyvik, 2015).

Pros and Cons of Basic Research

The primary advantage of basic research is that it generates knowledge and understanding of fundamental principles that can later serve as a foundation for technological advancement or social betterment.

It can lead to groundbreaking discoveries, stimulate creativity, and drive scientific innovation by satisfying human curiosity (Akcigit, Hanley & Serrano-Velarde, 2021; Baetu, 2016).

It is also often the catalyst for training the next generation of scientists and researchers.

However, basic research can be time-consuming, expensive, and its outcomes may not always be directly observable or immediately beneficial.

This is why it’s often left to government-funded research institutes and universities to conduct this sort of research. As Binswanger (2014) argues, “basic research constitutes, for the most part, a common good which cannot be sold profitably on markets.

Furthermore, its value is often underestimated because the applications are not immediately apparent or tangible.

Below is a summary of some advantages and disadvantages of basic research:

Abeysekera, A. (2019). Basic research and applied research.  Journal of the National Science Foundation of Sri Lanka ,  47 (3).

Akcigit, U., Hanley, D., & Serrano-Velarde, N. (2021). Back to basics: Basic research spillovers, innovation policy, and growth.  The Review of Economic Studies ,  88 (1), 1-43.

Baetu, T. M. (2016). The ‘big picture’: the problem of extrapolation in basic research. The British Journal for the Philosophy of Science.

Bentley, P. J., Gulbrandsen, M., & Kyvik, S. (2015). The relationship between basic and applied research in universities.  Higher Education ,  70 , 689-709. ( Source )

Binswanger, M. (2014). How nonsense became excellence: forcing professors to publish. In Welpe, I. M., Wollersheim, J., Osterloh, M., & Ringelhan, S. (Eds.), Incentives and Performance: Governance of Research Organizations . Springer International Publishing.

Grimsgaard, W. (2023). Design and strategy: a step by step guide . New York: Taylor & Francis.

Lehmann, W. (2023). Social Media Theory and Communications Practice . London: Taylor & Francis.

Wiid, J., & Diggines, C. (2009). Marketing Research . Juta.

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Basic Research: What it is with examples

In building knowledge, there are many stages and methodologies to generate insights that contribute to its understanding and advancement; basic research and applied research are usually the most effective on this path.

Understanding research allows us to understand all the properties of a specific science or phenomenon at a fundamental level. Some examples are branches such as sociology, humanities, and other scientific fields; below, we will tell you everything you need to know about this type of research and its possible applications.

What is Basic Research?

Basic Research is a type of research used in the scientific field to understand and extend our knowledge about a specific phenomenon or field. It is also accepted as pure investigation or fundamental research .

This type of research contributes to the intellectual body of knowledge. Basic research is concerned with the generalization of a theory in a branch of knowledge; its purpose is usually to generate data that confirm or refute the initial thesis of the study.

It can also be called foundational research; many things get built on this foundation, and more practical applications are made.

Basic Research vs. Applied Research

Basic Research finds its counterpart and complement in applied research. They are two handy research methods when generating and giving a utility to the generated data. There are very marked differences, and understanding them will allow you to understand the path followed to create new knowledge.

The most important difference between basic research and applied research lies in the objective of each. It seeks to expand the information and understanding of the object of study, while applied research aims to provide a solution to the problem studied.

The relationship between these two types of research is usually very close since the methodologies used are often quite similar; the significant change is found in the initial and final point of the investigation.

Basic Research Examples

There can be many examples of basic research; here are some of them:

• A study of how stress affects labor productivity.
• Studying the best factors of pricing strategies.
• Understand the client’s level of satisfaction before certain interactions with the company providing solutions.
• The understanding of the leadership style of a particular company.

Advantages & Disadvantages

Basic research is critical for expanding the pool of knowledge in any discipline. The introductory course usually does not have a strict period, and the researcher’s concern commonly guides them. The conclusion of the fundamental course is generally applicable in a wide range of cases and plots.

At the same time, the basic study has disadvantages as well. The findings of this type of study have limited or no constructive conclusions. In another sense, fundamental studies do not resolve complex and definite business problems, but it does help you understand them better.

Taking actions and decisions based on the results of this type of research will increase the impact these insights may have on the problem studied if that is the purpose.

LEARN ABOUT: Theoretical Research

How to do basic research?

This process follows the same steps as a standard research methodology. The most crucial point is to define a thesis or theory that involves a perfectly defined case study; this can be a phenomenon or a research problem observed in a particular place.

There are many types of research, such as longitudinal studies , observational research , and exploratory studies. So the first thing you should do is determine if you can obtain the desired result with research or if it is better to opt for another type of research.

Once you have determined your research methodology, the data collection process begins, also depending on your type of study; sometimes, you can collect the data passively through observation or experimentation. On other occasions, intervene directly and collect quantitative information with tools such as surveys.

Platforms like QuestionPro will help you have a wide range of functions and tools to carry out your research; its survey software has helped students and professionals obtain all the information necessary to generate high-value insights.

In addition, it has a data analysis suite with which you can analyze all this information using all kinds of reports for a more straightforward interpretation of the final results.

QuestionPro is much more than survey software ; we have a solution for each specific problem and industry. We also offer data management platforms such as our research data repository called Insights Hub.

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Basic Research in Psychology

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

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

Basic research—also known as fundamental or pure research—refers to study and research meant to increase our scientific knowledge base. This type of research is often purely theoretical, with the intent of increasing our understanding of certain phenomena or behavior. In contrast with applied research, basic research doesn't seek to solve or treat these problems.

Basic Research Examples

Basic research in psychology might explore:

• Whether stress levels influence how often students engage in academic cheating
• How caffeine consumption affects the brain
• Whether men or women are more likely to be diagnosed with depression
• How attachment styles among children of divorced parents compare to those raised by married parents

In all of these examples, the goal is merely to increase knowledge on a topic, not to come up with a practical solution to a problem.

The Link Between Basic and Applied Research

As Stanovich (2007) noted, many practical solutions to real-world problems have emerged directly from basic research. For this reason, the distinction between basic research and applied research is often simply a matter of time. As social psychologist Kurt Lewin once observed, "There is nothing so practical as a good theory."

For example, researchers might conduct basic research on how stress levels impact students academically, emotionally, and socially. The results of these theoretical explorations might lead to further studies designed to solve specific problems. Researchers might initially observe that students with high stress levels are more prone to dropping out of college before graduating. These first studies are examples of basic research designed to learn more about the topic.

As a result, scientists might then design research to determine what interventions might best lower these stress levels. Such studies would be examples of applied research. The purpose of applied research is specifically focused on solving a real problem that exists in the world. Thanks to the foundations established by basic research, psychologists can then design interventions that will help students effectively manage their stress levels , with the hopes of improving college retention rates.

Why Basic Research Is Important

The possible applications of basic research might not be obvious right away. During the earliest phases of basic research, scientists might not even be able to see how the information gleaned from theoretical research might ever apply to real-world problems. However, this foundational knowledge is essential. By learning as much as possible about a topic, researchers are able to gather what they need to know about an issue to fully understand the impact it may have.

"For example, early neuroscientists conducted basic research studies to understand how neurons function. The applications of this knowledge were not clear until much later when neuroscientists better understood how this neural functioning affected behavior," explained author Dawn M. McBride in her text The Process of Research in Psychology . "The understanding of the basic knowledge of neural functioning became useful in helping individuals with disorders long after this research had been completed."

Basic Research Methods

Basic research relies on many types of investigatory tools. These include observation, case studies, experiments, focus groups, surveys, interviews—anything that increases the scope of knowledge on the topic at hand.

Frequently Asked Questions

Psychologists interested in social behavior often undertake basic research. Social/community psychologists engaging in basic research are not trying to solve particular problems; rather, they want to learn more about why humans act the way they do.

Basic research is an effort to expand the scope of knowledge on a topic. Applied research uses such knowledge to solve specific problems.

An effective basic research problem statement outlines the importance of the topic; the study's significance and methods; what the research is investigating; how the results will be reported; and what the research will probably require.

Basic research might investigate, for example, the relationship between academic stress levels and cheating; how caffeine affects the brain; depression incidence in men vs. women; or attachment styles among children of divorced and married parents.

By learning as much as possible about a topic, researchers can come to fully understand the impact it may have. This knowledge can then become the basis of applied research to solve a particular problem within the topic area.

Stanovich KE.  How to Think Straight About Psychology . 8th edition. Boston, MA: Pearson Allyn and Bacon; 2007.

McCain KW. “Nothing as practical as a good theory” Does Lewin's Maxim still have salience in the applied social sciences? Proceedings of the Association for Information Science and Technology . 2015;52(1):1-4. doi:10.1002/pra2.2015.145052010077

McBride DM. The Process of Research in Psychology . 3rd edition . Thousand Oaks, CA: Sage Publications; 2015.

Committee on Department of Defense Basic Research. APPENDIX D: Definitions of basic, applied, and fundamental research . In: Assessment of Department of Defense Basic Research. Washington, D.C.: The National Academic Press; 2005.

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

• What is Pure or Basic Research? + [Examples & Method]

Sometimes, research may be aimed at expanding a field of knowledge or improving the understanding of a natural phenomenon. This type of research is known as a basic, pure or fundamental research, and it is a major means of generating new ideas, principles and theories. 

In many cases, basic research fuels scientific innovations and development because it is driven by the need to unravel the unknown. In this article, we will define what basic research is, its data collection methods and how it differs from other approaches to research. 

What is Basic Research?

Basic research is a type of research approach that is aimed at gaining a better understanding of a subject, phenomenon or basic law of nature. This type of research is primarily focused on the advancement of knowledge rather than solving a specific problem. 

Basic research is also referred to as pure research or fundamental research. The concept of basic research emerged between the late 19th century and early 20th century in an attempt to bridge the gaps existing in the societal utility of science. 

Typically, basic research can be exploratory , descriptive or explanatory; although in many cases, it is explanatory in nature. The primary aim of this research approach is to gather information in order to improve one’s understanding, and this information can then be useful in proffering solutions to a problem. 

Examples of Basic Research 

Basic research can be carried out in different fields with the primary aim of expanding the frontier of knowledge and developing the scope of these fields of study. Examples of basic research can be seen in medicine, education, psychology, technology, to mention but a few.

Basic Research Example in Education  

In education, basic research is used to develop pedagogical theories that explain teaching and learning behaviours in the classroom. Examples of basic research in education include the following:

• How does the Language Acquisition Device work  on children?
• How does the human retentive memory work?
• How do teaching methods affect student’s concentration in class?

Basic Research Example in Science

Basic research advances scientific knowledge by helping researchers understand the function of newly discovered molecules and cells, strange phenomena, or little-understood processes. As with other fields, basic research is responsible for many scientific breakthroughs; even though the knowledge gained may not seem to yield immediate benefits. 

Examples of basic research in science include: 

• A research to determine the chemical composition of organic molecules.
• A research to discover the components of the human DNA.

Basic Research Example in Psychology  

In psychology, basic research helps individuals and organisations to gain insights and better understanding into different conditions. It is entirely theoretical and allows psychologists to understand certain behaviors better without providing  solutions to these behaviours or phenomena.  

Examples of basic research in psychology include: 

• Do stress levels make individuals more aggressive?
• To what extent does caffeine consumption affect classroom concentration?
• A research on behavioral differences between children raised by separated families and children raised by married parents.
• To what extent do gender stereotypes  trigger depression?

Basic Research Example in Health   

Basic research methods improve healthcare by providing different dimensions to the understanding and interpretation of healthcare issues. For example, it allows healthcare practitioners to gain more insight into the origin of diseases which can help to provide cures to chronic medical conditions. 

Many health researchers opine that many vaccines are developed based on an understanding of the causes of the disease such as in the case of the polio vaccine. Several medical breakthroughs have been attributed to the wealth of knowledge provided through basic research. 

Examples of basic research in health include: 

• An investigation into the symptoms of Coronavirus.
• An investigation into the causative factors of malaria
• An investigation into the secondary symptoms of high blood pressure.

Basic Research Method

 An interview is a common method of data collection in basic research that involves having a one-on-one interaction with an individual in order to gather relevant information about a phenomenon. Interview can be structured, unstructured or semi-structured depending on the research process and objectives.  

In a structured interview , the researcher asks a set of premeditated questions while in an unstructured interview, the researcher does not make use of a set of premeditated questions. Rather he or she depends on spontaneity and follow-up questioning in order to gather relevant information. 

On the other hand, a semi-structured interview is a type of interview that allows the researcher to deviate from  premeditated questions in order to gather more  information about the research subject. You can conduct structured interviews online by creating and administering a survey online on Formplus .

• Observation

Observation is a type of data-gathering method that involves paying close attention to a phenomenon for a specific period of time in order to gather relevant information about its behaviors. When carrying out basic research, the researcher may need to study the research subject for a stipulated period as it interacts with its natural environment. 

Observation can be structured or unstructured depending on its procedures and approach. In structured observation, the data collection is carried out using a predefined procedure and in line with a specific schedule while unstructured observation is not restricted to a predetermined procedure. 

An experiment is a type of quantitative data-gathering method that seeks to validate or refute a hypothesis and it can also be used to test existing theories. In this method of data collection , the researcher manipulates dependent and independent variables to achieve objective research outcomes. 

Typically, in an experiment, the independent variable is modified or changed in order to determine its effects on the dependent variables in the research context. This can be done using 3 major methods; controlled experiments , field experiments, and natural experiments 

• Questionnaire

A questionnaire is a data collection tool that is made up of a series of questions to which the research subjects provide answers. It is a cost-effective method of data gathering because it allows you to collect large samples of data from the members of the group simultaneously. 

You can create and administer your pure research questionnaire online using Formplus and you can also make use of paper questionnaires; although these are  easily susceptible to damage. [

Here is a step-by-step guide of how to create and administer questionnaires for basic research using Formplus: 

• Sign in to Formplus

In the Formplus builder, you can easily create different questionnaires for applied research by dragging and dropping preferred fields into your form. To access the Formplus builder, you will need to create an account on Formplus. 

Once you do this, sign in to your account and click on “Create Form ” to begin.

Edit Form Title

Click on the field provided to input your form title, for example, “Basic Research Questionnaire”.

Click on the edit button to edit the form.

i. Add Fields: Drag and drop preferred form fields into your form from  the Formplus builder   Inputs column. There are several field input options for questionnaires in the Formplus builder. 

ii. Edit fields

iii. Click on “Save”

iv. Preview form. 

Form Customization

With the form customization options in the form builder, you can easily change the look and feel of your form and make it more unique and personalized. Formplus allows you to change your form theme, add background images and even change the font according to your brand specifications. 

Multiple Sharing Options

Formplus offers multiple form sharing options which enables you to easily share your questionnaire with respondents. You can use the direct social media sharing buttons to share your form link to your  social media pages. 

In addition, Formplus has an option to convert form links to QR codes; you can personalize and display your form QR code on your website/banners for easy access. You also can send out survey forms as email invitations to your research subjects.  

• Data Reporting

 Data reporting is a type of data collection method where the researcher gathers relevant data and turns them in for further analysis in order to arrive at specific conclusions. The crux of this method depends, almost entirely, on the validity of the data collected. 

• Case Studies

A case study is a type of data collection method that involves the detailed examination of a specific subject matter in order to gather objective information about the features and behaviors of the research subject. This method of data gathering is primarily qualitative , although it can also be quantitative or numerical in nature.  

Case studies involve a detailed contextual analysis of a limited number of events or conditions and their relationships. In carrying out a case study, the researcher must take extra care to identify the research questions, collect relevant data then evaluate and analyze the data in order to arrive at objective conclusions. 

Read More: Research Questions: Definition, Types +[Examples]

How is Basic Research Different from Applied Research? 

 Applied research is a type of research that is concerned with solving practical problems using scientific methods while basic research is a type of research that is concerned with the expansion of knowledge. 

Basic research generates new theories or improves on existing theories hence, it is theoretical in nature. On the other hand, applied research creates practical solutions to specific problems hence, it is practical in nature. 

 Basic research is knowledge-specific while applied research is solution-specific. 

• Research Purpose

The purpose of basic research is to improve on existing knowledge or to discover new knowledge while the purpose of applied research is to solve specific problems. 

The scope of basic research is universal while applied research is limited in nature. This means that while applied research addresses a specific problem and is limited to the problem which it addresses, basic research explores multiple dimensions of various fields. 

• Basic research is primarily explanatory while applied research is descriptive in nature .
• Basic research adopts an indirect approach to problem-solving while applied research adopts a direct approach to problem-solving.
• In basic research, generalizations are common while in applied research, specific problems are investigated without the aim of generalizations.

Read Also: What is Applied Research? +[Types, Examples & Methods]

Characteristics of Basic Research 

• Basic research is analytical in nature.
• It aims at theorizing concepts and not solving specific problems.
• It is primarily concerned with the expansion of knowledge and not with the applicability of the research outcomes.
• Basic research is explanatory in nature.
• Basic research is carried out without any primary focus on possible practical ends.
• It improves the general knowledge and understanding of different fields of study.

Importance of Basic Research

• Acquisition of New Knowledge: Basic research results in new knowledge. It is responsible for many research breakthroughs in different fields of study and it is often considered as the pacesetter in technological and innovative solutions.
• Basic research also enhances the understanding of different subject matters and provides multiple possible dimensions for interpretation of these subject matters.
• Findings of fundamental research are extremely useful in expanding the pool of knowledge in different disciplines.
• Basic research offers the foundation for applied research.

Disadvantages of Basic Research

• Findings from pure research have little or no immediate practical implications. However, these findings may be useful in providing solutions to different problems, in the long run.
• Fundamental research does not have strict deadlines.
• Basic research does not solve any specific problems.

Basic research is an important research method because it exposes researchers to varying dimensions within a field of study. This proves useful, not only for improving scholarship and the general knowledge-base, but for solving problems as is the concern of applied research. 

When carrying out basic research, the investigator adopts one or more qualitative and quantitative observation methods which includes case studies, experiments and observation. These data collection methods help the researcher to gather the most valid and relevant information for the research. 

In the case of using a survey or questionnaire for data collection , this can easily be done with the use of Formplus forms. Formplus allows you to create and administer different kinds of questionnaires, online and you can easily monitor and categ orise your form responses too. 

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

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The Concept of Basic Research

A nimal research is also important in another type of research, called basic research. Basic research experiments are performed to further scientific knowledge without an obvious or immediate benefit. The goal of basic research is to understand the function of newly discovered molecules and cells, strange phenomena, or little-understood processes. In spite of the fact that there may be no obvious value when the experiments are performed, many times this new knowledge leads to breakthrough methods and treatments years or decades later. For example, chemists developed a tool called a nuclear magnetic resonance (NMR) machine to determine the structure of chemicals. When it was developed, it had no obvious applications in medicine; however, scientists eventually realized that the NMR machine could be hooked up to a computer to make a magnetic resonance imagery (MRI) machine. The MRI machine takes pictures of the bone and internal tissues of the body without the use of radioactivity. Other examples of basic research that have led to important advances in medicine are the discovery of DNA (leading to cancer treatments) and neurotransmitters (leading to antidepressants and antiseizure medications). However, there are many other instances where basic research, some of which has been done on animals, has not yet resulted in any practical benefit to humans or animals.

NMR (nuclear magnetic resonance)—a machine that measures the vibration of atoms exposed to magnetic fields. Scientists use this machine to study the physical, chemical, and biological properties of matter.

MRI (magnetic resonance imaging)—a machine that produces pictures of the bone and internal tissues of the body.

• Cite this Page National Research Council (US) Committee to Update Science, Medicine, and Animals. Science, Medicine, and Animals. Washington (DC): National Academies Press (US); 2004. The Concept of Basic Research.
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Basic Research: Definition, Examples

Basic research focuses on the search for truth or the development of theory. Because of this property, basic research is fundamental. Researchers with their fundamental background knowledge “design studies that can test, refine, modify, or develop theories.”

Meaning and Definition of Basic Research

Generally, these researchers are affiliated with an academic institution and perform this research as part of their graduate or doctoral studies. Gathering knowledge for knowledge’s sake is the sole purpose of basic research .

Basic research is also called pure research. Basic research is driven by a scientist’s curiosity or interest in a scientific question.

The main motivation in basic research is to expand man’s knowledge, not to create or invent something. There is no obvious commercial value to the discoveries that result from basic research.

The term ‘basic’ indicates that, through theory generation, basic research provides the foundation for applied research . This research approach is essential for nourishing the expansion of knowledge.

It deals with questions that are intellectually interesting and challenging to the investigator. It focuses on refuting or supporting theories that operate in a changing society.

Basic research generates new ideas, principles, and theories, which may not be of immediate practical utility, though such research lays the foundations of modern progress and development in many fields.

Basic research rarely helps practitioners directly with everyday concerns but can stimulate new ways of thinking about our daily lives.

Basic researchers are more detached and academic in their approach and tend to have motives.

For example, an anthropologist may research to try and understand the physical properties, symbolic meanings, and practical qualities of things.

Such research contributes to understanding broad issues of interest to many social sciences-issues of self, family, and material culture .

Having said so, we come up with the following definition of basic research:

When the solution to the research problem has no apparent applications to any existing practical problem but serves only the scholarly interests of a community of a researcher, the research is basic.

Most scientists believe that a fundamental understanding of all branches of science is needed for progress to take place.

In other words, basic research lays the foundation for the following applied research . If basic work is done first, then this research often results from applied spin-offs.

A person wishing to do basic research in any specialized area must have studied the concepts and assumptions of that specialization enough to know what has been done in the past and what remains to be done.

For example, basic research is necessary for the health sector to generate new knowledge and technology to deal with major unsolved health problems.

Here are a few examples of questions asked in pure research:

• How did the universe begin?
• What are protons, neutrons, and electrons composed of?
• How do slime molds reproduce?
• How do the Neo-Malthusians view the Malthusian theory?
• What is the specific genetic code of the fruit fly?
• What is the relevance of the dividend theories in the capital market?

As there is no guarantee of short-term practical gain, researchers find it difficult to obtain funding for basic research.

Examples of Basic Research

The author investigated the smoothness of the solution of the degenerate Hamilton-Bellman (HJB) equation associated with a linear-quadratic regulator control.

The author established the existence of a classical solution of the degenerate HJB equation associated with this problem by the technique of viscosity solutions and hence derived an optimal control from the optimality conditions in the HJB equation.

Hasan (2009) gave a solution to linear programming problems through computer algebra. He developed a computer technique for solving such linear fractional programming problems in his paper.

At the outset, he determined all basic feasible solutions to the constraints, which are a system of linear equations.

The author then computed and compared the objective function values and obtained the optimal objective function value and optimal solutions. The method was then illustrated with a few numerical examples.

What is the primary focus of basic research?

Basic research primarily focuses on the search for truth or the development of theory. It is fundamental in nature and aims to design studies that test, refine, modify, or develop theories.

How does basic research differ from applied research in terms of its purpose?

The sole purpose of basic research is to gather knowledge for knowledge’s sake. It is driven by a scientist’s curiosity or interest in a scientific question without any immediate commercial value to the discoveries, whereas applied research has practical applications.

What is the significance of the term “basic” in basic research?

The term “basic” indicates that the research provides the foundation for applied research through theory generation. It lays the groundwork for modern progress and development in various fields.

Why might researchers face challenges in obtaining funding for basic research?

Since there is no guarantee of short-term practical gain from basic research, researchers often find it difficult to secure funding for such endeavors.

With a clear understanding of basic research; for more learning use our complete guideline on research and research methodology concepts .

• Exploratory Research: Definition, Types, Examples
• Monitoring and Evaluation: Process, Design, Methods
• Non-Probability Sampling
• Personal Interview Method: Definition, Advantages, Disadvantages, Techniques
• Theory: Meaning, Concepts, Theoretical Framework
• Questionnaire: Definition, Characteristics, Contents, Types
• Parallel Forms Method: Definition, Example
• Telephone Interviewing: Advantages, Disadvantages,
• Participatory Rural Appraisal (PRA)
• Descriptive Research: Definition, 7 Types, Examples
• Research Paradigm: Key Concepts & Perspectives
• Types of Analytical Procedures
• Systematic Sampling: Definition, Examples
• Social Research: Definition, Examples
• Guttman Scale (Cumulative Scale): Definition, Example

What is Basic Research?

Introduction

What is the meaning of basic research, examples of basic research, how do i perform basic research.

Basic science research is an essential pillar of scientific knowledge, because it extends understanding, provides new insights, and contributes to the advancement of science and fundamental knowledge across disciplines. In contrast, applied research aims for the discovery of practical solutions, which can involve using a technology or innovation that stems from existing knowledge. Basic science research potentially allows for generating ideas on which applied science can build novel inquiry and useful applications.

The process for conducting basic research is essentially the same as in an applied research orientation, but a better understanding of the distinction may prove increasingly important when crafting your research inquiry. In this article, we'll detail the characteristics and importance of basic research.

One of the key distinctions in science is the divide between basic and applied research . Applied research is directly associated with practical applications such as:

• career development
• program evaluation
• policy reform
• community action

In inquiries regarding each of these applications, researchers identify a specific problem to be solved and design a study intentionally aimed at developing solutions to that problem. Basic research is less concerned about specific problems and more focused on the nature of understanding.

Characteristics of basic research

Research that advances understanding of knowledge has distinguishing characteristics and important considerations.

• Focus on theoretical development . Rather than focus on practical applications, scholars in basic science research are more interested in ordering data and understanding in a scientific manner. This means expanding the consensus understanding of theory and the proposal of new theoretical frameworks that ultimately further research.
• Exploratory research questions . Basic research tends to look at areas where there is insufficient theoretical coherence to empirically understand phenomena. In other words, basic research often employs research questions that seek greater definition of knowledge.
• Funding for basic science . The nature of the support available for research depends on whether the science is basic or applied . Government agencies, national institutes, and private organizations all have different objectives, making some more appropriate for basic research than others.
• Writing for research dissemination . Academic journals exist on a continuum between theoretical and practical orientations. Journals that are more interested in theoretical and methodological discussions are more appropriate for basic research than are journals that look for more practical implications arising from research.

The brief survey of these characteristics should guide researchers about how they should approach research design in terms of feasibility, methods, and execution. This discussion shouldn't preclude you from pursuing basic research if it is more appropriate to your research inquiry. Instead, it should inform you of the opportunities, advantages, and challenges of basic research.

Importance of basic research

Fundamental research may seem aimless and unfocused if it doesn't yield any direct practical implications. However, its contribution to scholarly discussion cannot be overstated as it guides the development of theories and facilitates critical discussion about what applied studies to pursue next.

Basic science has guided fields such as microbiology, engineering, and chemistry. Scientists ultimately use its findings to develop new methods in treating disease and innovating on new technology.

Its contribution to the social sciences through observation and longitudinal study is also immeasurable. While basic research is often a precursor to more applied science, the theories it generates spur further study that ultimately leads to professional development programs and policy reform in social institutions.

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Different fields rely on both applied and basic science for generating new knowledge. While applied research looks to yield direct benefits through real-world applications, fundamental research provides the necessary theoretical foundation for practical research in various fields.

Basic research example in education

Basic research in schooling contexts focuses on understanding the nature of teaching and learning or the processes within educational environments before any focused investigation can be designed, let alone conducted. Basic research is necessary in this case because of the various situated differences across learners who come from different cultures and backgrounds.

Basic research in education looks at various inquiries such as how teachers and students interact with each other and how alternative assessments can create positive learning outcomes. Ultimately, this may lead to applied research that can facilitate the creation of teacher education and professional development programs.

Basic research example in psychology

Psychology is a field that is under constant development. Basic research is essential to developing theories related to human behavior and mental processes. The subfield of cognition is a significant benefactor of basic research as it relies on novel theoretical frameworks relating to memory and learning.

Basic research example in health

A great deal of health research that reaches public consciousness is undoubtedly applied research. The development of vaccines and other medicine to combat the COVID-19 pandemic was one such line of inquiry that addressed a practical need.

That said, scientists will undoubtedly credit basic research as a precursor to medical breakthroughs in applied science research. The knowledge gained through basic research laid the foundation for genomic sequencing of the COVID-19 virus, while experiments on living systems created knowledge about how to safely vaccinate the human body.

The National Institute of Health sponsors such basic research and research in other areas such as human DNA, while the National Science Foundation funds basic research on topics such as gender stereotypes and stress levels.

At its core, all scientific inquiry seeks to identify causal factors, relationships, and distinguishing characteristics among concepts and phenomena. As a result, the process is essentially the same for basic or applied science. Nonetheless, it is worth reviewing the process.

• Research design . Identify gaps in existing research that novel inquiry can address. A rigorous literature review can help identify theoretical or methodological gaps that a new study with an exploratory research question can address.
• Data collection . Exploratory research questions tend to prioritize data collection methods such as interviews , focus groups , and observations . Basic research, as a result, casts a wide net for any and all potential data that can facilitate generation of theoretical developments.
• Data analysis . At this stage, the goal is to organize and view your data in such a way that facilitates the identification of key insights. Analysis in basic research serves the dual purpose of filtering data through existing theoretical frameworks and generating new theory.
• Research dissemination . Once you determine your findings, you will want to present your insights in an empirical and rigorous manner. Visualizing data in your papers and presentations is useful for pointing out the most relevant data and analysis in your study.

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Fundamental Research

Fundamental research , also known as  basic research  or  pure research  does not usually generate findings that have immediate applications in a practical level. Fundamental research is driven by curiosity and the desire to expand knowledge in specific research area. This type of research makes a specific contribution to the academic body of knowledge in the research area.

Fundamental studies tend to make generalizations about the phenomenon, and the philosophy of this type of studies can be explained as ‘gathering knowledge for the sake of knowledge’. Fundamental researches mainly aim to answer the questions of why, what or how and they tend to contribute the pool of fundamental knowledge in the research area .

Opposite to fundamental research is  applied research  that aims to solve specific problems, thus findings of applied research do have immediate practical implications.

Differences between Fundamental and Applied Research

Differences between applied and fundamental research have been specified in a way that fundamental research studies individual cases without generalizing, and recognizes that other variables are in constant change.

Applied research, on the contrary, seeks generalizations and assumes that other variables do not change. The table below summarizes the differences between the two types of research in terms of purpose and context:

Differences between fundamental and applied research [1]

It is important to note that although fundamental studies do not pursue immediate commercial objectives, nevertheless, findings of fundamental studies may result in innovations, as well as, generating solutions to practical problems. For example, a study entitled “A critical assessment of the role of organizational culture in facilitating management-employee communications” is a fundamental study, but findings of this study may be used to increase the levels of effectiveness of management-employee communications, thus resulting in practical implications.

Examples of Fundamental Research

The following are examples for fundamental researches in business:

• A critical analysis of product placement as an effective marketing strategy
• An investigation into the main elements of brands and branding
• A study of factors impacting each stage of product life cycle

Advantages and Disadvantages of Fundamental Research

Advantages of fundamental research are considered as disadvantages of applied research and vice versa. Fundamental researches are important to expand the pool of knowledge in any discipline. Findings of fundamental studies are usually applicable in a wide range of cases and scenarios. Fundamental studies usually do not have strict deadlines and they are usually driven by the curiosity of the researcher.

At the same time, fundamental studies have disadvantages as well. Findings of this type of studies have little or no practical implications. In other words, fundamental studies do not resolve concrete and specific business problems.

My e-book,  The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step assistance   contains discussions of research types and application of research methods in practice. The e-book also explains all stages of the  research process  starting from the  selection of the research area  to writing personal reflection. Important elements of dissertations such as  research philosophy ,  research approach ,  research design ,  methods of data collection  and  data analysis , sampling and others are explained in this e-book in simple words.

John Dudovskiy

[1] Table adapted from Saunders, M., Lewis, P. & Thornhill, A. (2012) “Research Methods for Business Students” 6 th  edition, Pearson Education Limited

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Basic vs. applied research: what’s the difference?

Last updated

27 February 2023

Reviewed by

Cathy Heath

Research can be used to learn new facts, create new products, and solve various problems. Yet, there are different ways to undertake research to meet a desired goal. 

The method you choose to conduct research will most likely be based on what question you want to answer, plus other factors that will help you accurately get the answer you need. 

Research falls into two main categories: basic research and applied research. Both types of research have distinct purposes and varied benefits. 

This guide will help you understand the differences and similarities between basic and applied research and how they're used. It also answers common questions about the two types of research, including:

Why is it called basic research?

What is more important, basic research or applied research?

What are examples of pure (basic) research and applied research?

Analyze basic and applied research

Dovetail streamlines analysis to help you uncover and share actionable insights

• What is basic research?

Basic research (sometimes called fundamental or pure) advances scientific knowledge to completely understand a subject, topic, or phenomenon. It's conducted to satisfy curiosity or develop a full body of knowledge on a specific subject.

Basic research is used to bring about a fundamental understanding of the world, different behaviors, and is the foundation of knowledge in the scientific disciplines. It is usually conducted based on developing and testing theories.

While there is no apparent commercial value to the discoveries that result from basic research, it is the foundation of research used for other projects like developing solutions to solve problems. 

Examples of basic research

Basic research has always been used to give humans a better understanding of all branches of science and knowledge. However, it's not specifically based on identifying new things about the universe.

Basic research has a wide range of uses, as shown in the following examples:

Investigation into how the universe began

A study searching for the causes of cancer

Understanding the components that make up human DNA

An examination into whether a vegetarian diet is healthier than one with meat

A study to learn more about which areas in the world get the most precipitation

Benefits of conducting basic research

Called basic research because it is performed without an immediate or obvious benefit, this type of research often leads to vital solutions in the future. While basic research isn't technically solution-driven, it develops the underlying knowledge used for additional learning and research. 

There are many benefits derived from basic research, including:

Gaining an understanding of living systems and the environment

Gathering information that can help society prepare for the future

Expanding knowledge that can lead to medical advances

Providing a foundation for applied research

• What is applied research?

Applied research studies particular circumstances to apply the information to real-life situations. It helps improve the human condition by finding practical solutions for existing problems.

Applied research builds off facts derived from basic research and other data to address challenges in all facets of life. Instead of exploring theories of the unknown, applied research requires researchers to use existing knowledge, facts, and discoveries to generate new knowledge. 

Solutions derived from applied research are used in situations ranging from medical treatments or product development to new laws or regulations.

Examples of applied research

Applied research is designed to solve practical problems that exist under current conditions. However, it's not only used for consumer-based products and decisions.

Applied research can be used in a variety of ways, as illustrated by the following examples:

The investigation of ways to improve agricultural crop production

A study to improve methods to market products for Gen Z consumers

Examination of how technology can t make car tires last longer

Exploration of how to cook healthy meals with a limited budget

A study on how to treat patients with insomnia

Benefits of using applied research

Although applied research expands upon a foundation of existing knowledge, it also brings about new ideas. Applied research provides many benefits in various circumstances, including:

Designing new products and services

Creating new objectives

Providing unbiased data through the testing of verifiable evidence

• Basic research vs. applied research: the differences

Both basic and applied research are tactics for discovering specific information. However, they differ significantly in the way research is conducted and the objectives they achieve. 

Some of the most notable differences between basic and applied research include the following:

Research outcomes: curiosity-driven vs. solution-driven

Basic research is generally conducted to learn more about a specific subject. It is usually self-initiated to gain knowledge to satisfy curiosity or confirm a theory. 

Conversely, applied knowledge is directed toward finding a solution to a specific problem. It is often conducted to assist a client in improving products, services, or issues.

Research scope: universal scope vs. specific scope

Basic research uses a broad scope to apply various concepts to gain more knowledge. Research methods may include studying different subjects to add more information that connects evidence points in a greater body of data.

Meanwhile, applied research depends on a specific or narrow scope to gather specific evidence to address a certain problem.

Research approaches: expanding existing knowledge vs. finding new knowledge

Researchers conduct basic research to fill in gaps between existing information points. Basic knowledge is an expansion of existing knowledge to gain a deeper understanding. It is often based on how, what, or why something is the way it is. Although applied research may be based on information derived from basic research, it's not designed to expand the knowledge. Instead, the research is conducted to find new knowledge, usually in the form of a solution.

Research commercialization: Informational vs. commercial gain

The main basis of product development is to solve a problem for consumers.

Basic research might lead to solutions and commercial products in the future to help with this. Since applied research is used to develop solutions, it's often used for commercial gain.

Theory formulation: theoretical vs. practical nature

Basic research is usually based on a theory about a specific subject. Researchers may develop a theory that grows and changes as more information is discovered during the research process. Conversely, applied research is practical in nature since the goal is to solve a specific problem.

• Are there similarities between applied and basic research?

While some obvious differences exist, applied and basic research methods have similarities. For example, researchers may use the same methods to collect data (like interviews, surveys , and focus groups ) for both types of research. 

Both types of research require researchers to use inductive and deductive reasoning to develop and prove hypotheses . The two types of research frequently intersect when basic research serves as the foundation for applied research.

While applied research is solution-based, basic research is equally important because it yields information used to develop solutions to many types of problems. 

• Methods used in basic research and applied research

While basic and applied research have different approaches and goals, they require researchers or scientists to gather data. Basic and applied research makes use of many of the same methods to gather and study information, including the following:

Observations: Studying research subjects for an extended time allows researchers to gather information about how subjects behave under different conditions.

Interviews: Surveys and one-to-one discussions help researchers gain information from other subjects and validate data.

Experiments: Researchers conduct experiments to prove or disprove certain hypotheses based on information that has been gathered.

Questionnaires: A series of questions related to the research context helps researchers gather quantitative information applicable to both basic and applied research.

• How do you determine when to use basic research vs. applied research?

Basic and applied research are both helpful in obtaining knowledge. However, they aren't usually used in the same settings or under the same circumstances. 

When you're trying to determine which type of research to use for a particular project, it's essential to consider your product goals. Basic research seeks answers to universal, theoretical questions. While it works to uncover specific knowledge, it's generally not used to develop a solution. Conversely, applied research discovers answers to specific questions. It should be used to find out new knowledge to solve a problem.

• Bottom line

Both basic and applied research are methods used to gather information and analyze facts that help build knowledge around a subject. However, basic research is used to gain understanding and satisfy curiosity, while applied research is used to solve specific problems. Both types of research depend on gathering information to prove a hypothesis or create a product, service, or valuable process. 

By learning more about the similarities and differences between basic and applied research, you'll be prepared to gather and use data efficiently to meet your needs.

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What is Basic Research? Insights from Historical Semantics

• Open access
• Published: 24 June 2014
• Volume 52 , pages 273–328, ( 2014 )

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• Désirée Schauz 1  

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For some years now, the concept of basic research has been under attack. Yet although the significance of the concept is in doubt, basic research continues to be used as an analytical category in science studies. But what exactly is basic research? What is the difference between basic and applied research? This article seeks to answer these questions by applying historical semantics. I argue that the concept of basic research did not arise out of the tradition of pure science. On the contrary, this new concept emerged in the late 19th and early 20th centuries, a time when scientists were being confronted with rising expectations regarding the societal utility of science. Scientists used the concept in order to try to bridge the gap between the promise of utility and the uncertainty of scientific endeavour. Only after 1945, when United States science policy shaped the notion of basic research, did the concept revert to the older ideals of pure science. This revival of the purity discourse was caused by the specific historical situation in the US at that time: the need to reform federal research policy after the Second World War, the new dimension of ethical dilemmas in science and technology during the atomic era, and the tense political climate during the Cold War.

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Basic Research

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For some years now, the concept of basic research has been under attack. Its relevance has been questioned empirically as a result of changes in academic research, normatively with respect to science policy, and even theoretically in science and technology studies. Yet while the significance of the concept is in doubt, basic research is still a very common analytical category, deployed not least as a means of distinguishing the new future science policy from the old ideal of basic research. But what exactly is basic research? What is the difference between basic and applied research? Aside from a few exceptional studies (Calvert 2006 ; Godin 2005a ; Pielke 2012 ), science studies have only just begun to seriously reflect upon these questions. When and why did the concept of basic research emerge in the first place? Is the ideal of basic research nothing more than a relaunch of the older pure-science discourse? Historical semantics appears to be a useful approach for answering these questions because its historical perspective provides the conceptual clarity required both in current debates in science and technology studies and public debates on science policy.

In the 1990s, sociological studies claimed that science was undergoing profound changes. Since then, prominent labels such as “Mode 2” or “triple helix” have come to signify a new way of organizing science and technology that transgresses institutional boundaries between universities, industry, and governmental research. According to the alleged paradigm shift from Mode 1 to Mode 2, application-oriented research programmes with cooperative and transdisciplinary project teams have replaced the former university-centred basic research mode. Proponents of this new way of comprehending knowledge production even call for science policy to be modified in order to reflect the altered research mode (Gibbons et al. 1994 ; Etzkowitz and Leydesdorff 1997 ). Our “Leonardo world”, as portrayed by Jürgen Mittelstraß, is ruled by the imperative of technology. The interplay of science and technology raises society’s expectations of research applications, even when the outcomes sometimes turn out to be risky (Mittelstraß 1994 ). These arguments have certainly shaped the debates in science and technology studies and science policy in recent years, although discussions about the degree of change and how to evaluate it remain controversial (Weingart 2008 ; Greenberg 2007 ).

According to studies addressing these historical shifts in science, basic research determined the status quo ante. These studies describe basic research as an application-disinterested mode of research embedded in a disciplinary and academic setting that contrasts, in respect of every analytical feature, to Mode 2. The concept of Mode 1, however, is not based upon profound historical analysis; it rather appears to represent the previously prevailing sociological perspective on science in the tradition of Robert Merton, who emphasized disinterestedness and universalism as central characteristics of modern science. Yet historical studies suggest that the way in which science was organized had already undergone significant change in the early 20th century, as politicians, scientists, and industry formed a new alliance from which all three groups hoped to benefit (Ash 2002 ; Mowery and Rosenberg 1993 ).

Moreover, although recent debates in science studies have demonstrated high levels of discontent with the notion of basic research, producing instead new analytic labels like triple helix or Mode 2, the term “basic research” and its antonym “applied research” continue to frame the discourse about science, without any awareness of both terms’ historical conditionality as discursive strategies in research policy. The semantic dichotomy merely gives way to a continuum between basic and applied research in which the favourite mode, the “use-inspired basic research” (in German “ anwendungsorientierte Grundlagenforschung ”), is located somewhere in the middle of the continuum (Stokes 1997 ; Mittelstraß 1994 ). However, aside from the motif of application, we lack an explicit set of distinctive criteria because studies persist in assuming basic research to be a given category.

In other studies, categories such as basic and applied research no longer play a major role. Research grounded in approaches such as actor-network theory, that is studies emphasizing the societal context of science, anthropological studies focusing on day-to-day laboratory work, and the new – although still vague – concept of technoscience are united in their critique of discursive boundaries, which they claim to obstruct the view on the reality of research. While research dealing with Mode 2 indicates the change within the historical development of science, those supporting these new approaches call for a change in theoretical perspective. Bruno Latour, one of the most famous proponents of this idea, identifies demarcations such as nature/society or science/technology as a typically modern delusion covering, albeit quite successfully, the hybrid character of research (Latour 1993 ). Claiming an overall paradigm shift for the social sciences, Latour suspects that traditional sociology has frozen thought within boundaries and institutional separations in its studies for quite a long time and levels his criticisms at a static display of society blind to the dynamics of interactions (Latour 2005 ). For Latour, the distinction between basic and applied research is supposed to be part of these delusive demarcations: such a simple dichotomous order cannot represent the “complicated and unpredictable relations between scientists and other agencies” (Latour 1987 : 117). Latour argues that the high esteem in which basic science is held does not correspond with the reality of technoscience. In his early call for the concept of technoscience, he even argued statistically, interpreting the high proportion of spending on development and applied research in contrast to that spent on basic research evident in research and development statistics as indicative of the real importance of technology and the level of overall support it receives within society (Latour 1987 : esp. 168–173).

Latour’s argument about modern delusions and his opposition to a basic-research-centred perspective on science have found resonance among some historians of science. For instance, Peter Dear identifies the ideology of modern science as misrepresenting the reality of research in the natural sciences. Although, according to Dear, some effort has been made to integrate the instrumental and useful character of the natural sciences in the tradition of science since Francis Bacon, natural philosophy, with its ideal of contemplative understanding, has retained the upper hand (Dear 2005 : 404). From an historical point of view, this discrepancy between the philosophical notion of science and research practice appears as an anachronism requiring explanation. In general, criticism levelled at the long-prevailing ideal of pure science has led to a reorientation in the history of science that includes the applied side of science and opens up the field to the history of technology (Forman 2010 ). Recent studies look beyond the academic core – the universities – and into industrial laboratories, where the majority of researchers have worked throughout the 20th century (Shapin 2008 ).

Despite this growing awareness of the ideological or normative character of basic research, the majority of historians still use the concept as a given, analytical category without questioning its relationship to varying historical contexts. Studies on German war-time science, for instance, try to determine to which extreme of the basic-applied continuum the examined research projects tended. Footnote 1 As to the history of US science and innovation policy, the concept of basic research seems to be inevitably associated with the name of Vannevar Bush and the reorganization of US science after the Second World War. The basic-applied taxonomy is therefore primarily regarded as representation of the institutional logic of modern research organization: the so-called linear model which coined the idea of innovation process for so many years. Investigating the negotiation of science policy in the 1940s, historical studies have revealed dissenting political preferences and conflictive institutional interests, demonstrating that the post-war order in science policy had initially been highly contested. However, although the historical contingency of the concept has thus become more and more apparent, many historians still do not reflect on the meanings and functions of the concept of basic research. Even the meteoric career of this relatively young term does not seem to be puzzling historians. They rather interpret the concept as additional part of an existing taxonomy, “supplementing” the former “language of pure and applied science” (Dennis 2004 : 225). As a result, the concept of basic research has been locked up in a black box next to “pure science” whose meaning is also still enigmatic (Galison 2008 ). It is only recently that the investigation of shifting functions, varying meanings and symbolic dimensions of the concept of basic research – beyond the institutional level of research organization and funding – have become an object of interest in the history of science (Krige 2006 ). Footnote 2

What remains of the current debates in science and technology studies is the question as to why demarcations such as basic and applied research have occurred at all. If Latour is right in pointing out that the concepts of basic and applied research do not represent actual research practices, why have these terms become so important? Which (other) functions have they fulfilled? More precisely, what has “basic research” meant for the identity of science and for its relationship to technology? Which role has the concept of basic research played in science policy, that is in the negotiations between science and society about aims and values of research? And how has the concept affected the public image of science?

This article therefore seeks to analyze the genesis of the concept of basic research up until the early 1960s, by which time it had become a common concept in science policy in the West. It will also take a brief look at discourses on pure science prevalent in the 19th century as a means of establishing the effects of historical legacy and variation over time. This study has two central aims. Firstly, it intends to detect the different semantic dimensions of basic research – its institutional, epistemic, ethical, social, and political attributions. Secondly, it discusses the significance of the concept of basic research in the natural sciences, in research policy, and in science studies: to which historical challenges faced by research in the 20th century did the concept of basic research respond?

I argue that the concepts of basic research and fundamental research did not arise out of the 19th-century tradition of pure science, which had idealized research as an intrinsically philosophical search for eternal truth. On the contrary, these new concepts emerged in the late 19th and early 20th centuries at a time when society’s expectations regarding the utility of science were rising sharply. In the knowledge that research output is hard to predict, scientists used these concepts to bridge the gap between the promise of utility and the uncertainty of scientific endeavour. Only after 1945, when US policy strongly shaped the notion of basic research, did these concepts revert to the older ideals of pure science. In order to understand this revival of the purity discourse, we need to take the specific historical situation of the post-war US into account, in particular the new plans for federal funding of research, the new dimension of ethical dilemmas faced by science and technology following Hiroshima, and the overall political climate of the Cold-War era. The insights gained from historical semantics show that basic research was not – and cannot be – considered a clearly distinguishable analytical mode of research. After 1945, the concept of basic research formed part of a discursive strategy that adjusted scientific research to complex and even contradictory societal requirements; it was for these socio-political reasons that the concept became so important. Consequently, moral and ideological attributions were and still are inseparably tied to the concept of basic research.

American and German discourses provide the empirical basis of this study. Yet this article is not intended as a fully-fledged comparative study of two countries. Rather, I analyze Germany and the US because these countries were considered best-practice models in science at varying points in time and they both share a long history of mutual exchange and learning. At different points in time, each of the two countries allows us to trace the emergence and evolution of specific understandings of the role of science in society. The first section on the older pure-science ideals of the 19th century revolves mainly around Germany, which had become a leading science nation at that time. In the following section, which discusses how the concept of basic research emerged and evolved until 1945, the German experience also takes centre stage. The third section covers US science policy from the Second World War until the early 1960s, when the term basic research had become established as a key concept in science policy. The article ends, on a more comparative note, with a short history of the concept of basic research in post-war Germany. The second and the third sections overlap in time because the Second World War and the post-war period require a more comparative perspective. For a long time, scientific research during the Nazi period was thought to represent a turning away from all fundamental principles of science. The war, however, confronted both US and German scientists with similar political demands and requirements. After 1945, US policy became a role model for the Federal Republic of Germany (West Germany). Before the empirical analysis commences, however, the next section will introduce readers to historical semantics and discuss how I will use this approach to structure the empirical discussion.

Some Remarks on Historical Semantics

This study resorts to approaches in conceptual history and discourse analysis. Discourse analysis fits with the research questions for several reasons. Firstly, it is designed to make visible what is taken for granted when people think or talk about social phenomena and the implicit rules that apply in the practice of framing topics. Secondly, discourse analysis identifies classifications and demarcations, such as the distinction between basic and applied research, as essential strategies in discursive practice. Thirdly, it is based on the assumption that discursive production is historically contingent. Whereas discourse analysis strives, in the main, to analyze patterns of assertions, conceptual history focuses on semantics and key concepts. Especially the latter takes the polysemy of language and communication into account. Moreover, conceptual history’s foundation in the philosophy of history means that it offers us assumptions about semantic shifts over time.

In contrast to the tradition of semantic analyses in the philosophy of science, which is mainly interested in the epistemic impact of metaphors (Blumenberg 2010 ), my study is based on a strand of historical semantics rooted in historical studies on the dawn of modernity. It focuses on key concepts in social and political language. If we assume that basic research is largely a concept of science policy or of negotiations between the scientific community and the public, then this approach seems more suitable for this study. Moreover, conceptual history is embedded in reflections about the philosophy of history. According to Reinhart Koselleck, the major proponent of the German school of conceptual history, a shifting societal dictionary – the emergence of neologisms or changes in semantic attributions – indicates historical upheaval. Key concepts and parts of their meanings, however, may persist, so that old and new semantic dimensions coexist. Koselleck’s approach thus corresponds with approaches in the philosophy of history that take different layers of time into account. Koselleck clearly demonstrates that language is not an epiphenomenon of reality, but rather that it frames both human experience and the way in which society perceives the world. He conceives key concepts as cognitive strategies designed to deal with reality, especially in situations where expectation and experience diverge. Ideologies, in particular, are supposed to compensate semantically for a lack of convergence between expectations and experiences (Koselleck 2006 : 85).

Whereas Koselleck’s conceptual history defines key concepts primarily as cognitive strategies of the human that deal with reality, discourse analysis goes further in assuming that discursive strategies might serve various societal functions. In his commentary on the concept of the dispositive, Michel Foucault emphasized that discourses, non-discursive practices, institutions, and objects are linked by common strategic functions. This does not mean, however, that the outcome of such a strategic dispositive necessarily corresponds to the initial function. On the one hand, novel discourses have the power to set new practices or different forms of institutional organization. On the other hand, it is also possible that emerging discourses provide existing institutions or operations with new legitimacy. The history of dispositives also turns out to be quite complex. Taking Foucault’s remarks on the philosophy of history into account, the concept of the dispositive is quite similar to Koselleck’s idea of a complex history of different layers of time lying upon one another (Schauz 2010 ).

Since discourse analysis has progressed by adapting aspects of polysemy, the combination with conceptual historical approaches has become more obvious. One approach appears to be particularly fruitful for investigating the history of basic research: Jürgen Link’s idea of “collective symbols”, which came about when Link dealt with the problem of interdiscursive processes. Link believes that multiple meanings of metaphors and symbols are capable of linking different discourses demonstrating diverse patterns of assertions (Link 1986 ). In other words, metaphors can bridge discursive gaps. With regard to this study, science policy may be described as one such interdiscursive process in which scientific expectations encounter society’s expectations. And, without anticipating the detailed analysis of the concept of basic research below, it is obvious that “basic” as the first part of the compound offers a variety of possible interpretations.

Of course, discourse analysis also has a tradition in science studies, in particular regarding demarcation discourses. Most relevant in this context is Thomas F. Gieryn’s study ( 1999 ) on the cultural boundaries of science, which he identifies as resulting from professional boundary work. According to Gieryn, boundary work does not represent fixed or institutional demarcations, but is rather a dynamic process of negotiations with contested boarders and regenerated situations of uncertainty. Gieryn stresses that boundaries linked to key concepts such as pure science vary according to special situations and social circumstances. Unlike Gieryn, however, I do not expect that discursive practices revolving around basic research are strategies exclusively used by scientists to protect their professional interests. Moreover, I doubt that the discursive function of basic research can be restricted to boundary work.

In summary, this study is based upon the followings assumptions derived from conceptual history, discourse analysis, and studies on scientific boundary work. The attributions and linked demarcations of basic research are expected to vary according to space and time. Prior semantic dimensions, however, might persist or experience revival. The emergence of basic research as a new term may at least indicate an historical shift in either science or its role in society. The abstractness of the term basic research offers a wide range of meanings and discursive strategies. The concept has the potential to function as a collective symbol for science policy that links different discourses within society. Given its variability, this key concept of science policy, together with its antonyms, cannot be interpreted as representing fixed institutional boundaries. Rather, the concepts seem to emerge in situations of uncertainty or cognitive dissonance. Yet they may legitimize the institutional organization of research or define operative goals. Moreover, the discourses revolving around basic research communicate a wide range of ideals, expectations, promises, as well on professional and public claims.

Finally, there are some preliminary methodological remarks that need to be addressed. Although the study focuses on the concept of basic research, it also has to detect conceptual variations and alternative or concurrent terms, not to mention antonyms. Relevant terms for the US case are basic research, fundamental research, pure science and basic science. Antonyms and concurrent terms like applied research, applied science, contract research and mission-oriented research are included as far as they are needed to analyze the meanings of basic research, but their own conceptual histories will not be analyzed at full length. For the German case, these terms are Grundlagenforschung , reine Wissenschaft , reine Forschung , angewandte Forschung , angewandte Wissenschaft and Zweckforschung .

With regard to conducting the discourse analysis, it was most relevant to compile a broad sample of documents enabling me to identify prevalent, repeated patterns of assertions. Footnote 3 Besides key texts from scientists well-established in research organization, the sample also covers texts produced for normal-science communication. Footnote 4 The study is thus based on published documents relating to science policy as well as on scientific articles and books. Especially the volumes of the American journal Science and its German counterpart Die Naturwissenschaften have been subjected to systematic analysis. Furthermore, electronic search functions, in particular those enabling full-text searches with the keywords listed above, have been most useful for periods in which concepts were not yet commonplace. The digital library of Google Books is an important tool for historical semantics because it enables us to detect texts which might otherwise be overlooked by more traditional research strategies based on library holdings and cross references. As such, Google Books provides a unique tool for tracing both the emergence and diffusion of concepts. However, given that text acquisition in Google Books is dynamic and not entirely transparent to the user, it is difficult to delineate the corpus of books actually contained within its database. Thus Google Books may not be easy to use for scholars interested in exact bibliometric analysis, but it can help researchers gain a rough idea of when certain concepts began to be used and how use of these concepts became more or less common across different periods of time and within different language communities. This is how the current article uses the information derived from Google Books.

Pure Science in the 19th Century: The Natural Sciences and the Philosophical Tradition of Academia

As studies have so far located the concept of basic research in the tradition of pure-science ideals, the following section will deal with the term’s prehistory as a means of tracking continuities and breaks in the way science perceived itself. The notion of pure science and the conceptual opposition between “pure” and “applied” in science can be traced back to the 18th century. The attributes of “pure” and “applied” referred in turn to the much older, classical distinction between theory and practice that had undergone reinterpretation during the Scientific Revolution. Back then, Francis Bacon and his contemporaries had tried to conflate the new empirical and instrumental form of knowledge of nature with the older tradition of natural philosophy and its idea of contemplative understanding (Dear 2005 : 393–397). In the late 18th century, these attributes became important once again for natural scientists positioning themselves within the academic community for the purposes of finding a way into the university system. Although states such as Prussia demanded ever more instrumental knowledge and technical education for their mining industries or other state-owned enterprises (Klein 2010 ), natural scientists had to adjust to the predominant philosophical understanding of science Footnote 5 at universities, which, even then, consisted only of philosophical, theological, legal, and medical faculties.

In the case of chemistry, Christoph Meinel has already demonstrated that, in the Age of Enlightenment, chemists labelled their discipline as “pure and applied” so that chemistry could become an acceptable subject at universities, shedding its older status as an auxiliary science of medicine (Meinel 1985 ). Due to its empirical approach and its utilitarian orientation, chemistry was still classified as an “art” rather than as a “science” in the 18th century. Academic teaching had hitherto focused on imparting theoretical knowledge and established theorems, that is pure science. In contrast, the applied sciences represented experience-based knowledge on the epistemic level; at the same time “applied” denoted research with a practical purpose. Both aspects of these so-called applied sciences did not (yet) fit into the philosophical tradition of universities. By striving to become a part of this academic institution, chemists had to stress both the pure scientific and applied aspects of their discipline (Meinel 1985 ; Bud and Roberts 1984 ).

At the very same time, philosophy was engaged in reviving the controversy between rationalism and empiricism that solidified a hierarchical concept of knowledge. As a consequence of the philosophical longing for the wholeness and absoluteness of ideas, a posteriori approaches continually played a subordinate role in contrast to a priori and metaphysical ways of knowing (Ross 1962 : 68–69). The concept of cognition process in science turned out to be one-way: from the general to the particular. This concept of scientific progress implied the possibility of deducing endless applications and specific, context-linked knowledge from universal principles such as the laws of nature. The advancement of knowledge, however, was not supposed to take place the other way around. This distinction between pure and applied science thus corresponded to institutional and epistemic settings in the scientific community of the late 18th and 19th centuries.

The Natural Sciences Face Challenges from Engineering and Technological Success

In the mid-19th century the pure/applied boundary started focusing on the distinction between the natural sciences and technology. The common definition of technology as applied natural sciences represented a special version of this one-way concept of knowledge. This definition was widespread – even economists believed in the one-way relationship between science and technology. They assumed that only scientific discoveries and theories paved the way for innovations: “Technical science may stimulate pure science to a certain extent, but, on the whole, technology is much more at the receiving end. Pure science is always further ahead of applied science, and never the other way round. However, technology finally turns science into a common good” (Rössler 1857 : 179, translation by DS).

It was above all the community of natural scientists that wanted to preserve the hierarchical distinction between science and technology. The scientific foundation and the aspiring academic status of engineering in the second half of the 19th century challenged the scientific profession, in particular physicists (Gieryn 1999 : 51–62). As the natural sciences had only recently assumed their place within the university, the legacy of natural philosophy and its epistemic and moral ideals, such as the unrewarded dedication to science for its own sake, was even stronger than the century before (Dear 2005 : 401–404). Having scarcely ascended to the league of the pure sciences, the natural sciences even adopted the idea of an eternal truth defined by the discovery of natural laws.

The words of German physician Rudolf Virchow represent this adapted concept of pure science, but, more importantly, they also show that this purity discourse was not without contradictions. With the economic success of technical innovations and the growing appreciation of engineers within society throughout the German Empire, Virchow and his colleagues increasingly forged a link between themselves and the promise of technical progress in order to promote the idea of indispensable scientific endeavour:

All the benefits that have emerged from the steam engine, from telegraphy, photography, chemical discoveries, the production of colours and so on and so forth, all these benefits are based on scientific theorems that we men of science have unveiled, and not until we are absolutely sure that they are laws of nature, we pass these truths on to the general public so that others can work with them and create new things that nobody could imagine before, that no one has ever dreamt of, that see the light of day for the first time and transform the character of society and the state. (Virchow 1877 : 8–9, translation by DS)

Compared with the great engineering inventions of the 19th century and their noticeable effects on everyday life and society as a whole, scientific progress was less visible. In a way, this poor visibility was one aspect of the ideal of the pure scientist in its philosophical tradition: a scholar who, in solitude, dedicates life and work to science, driven by the sole motive of finding the truth – or at least contributing his tiny part to the scientific community’s joint effort – even without any prospect of public acknowledgement. In fact, as Peter Dear put it, “the authority of science in the modern world rests to a considerable extent on the idea that it is powerful, that it can do things” (Dear 2005 : 404). Yet, the scientific strategy of technological promise in order to gain greater visibility, support, and acknowledgment appeared risky; the scientific pledge to technological progress needed a show of confidence. Given the uncertainty and contingency of scientific advancement, it seemed even harder to predict if or when discoveries would lead to new technologies. Scientists thus defined their work as a long-term endeavour in contrast to engineering, which they classified as a medium-term project aimed at satisfying immediate need. In any case, the fact that researchers such as the chemist Justus von Liebig felt it necessary to defend the scientific profession reflects the growing pressure the scientific community faced from societal expectations in the course of the 19th century:

Even the most powerful effect of science on the life and spirit of men is so slow, noiseless, creeping and barely perceptible that a superficial observer would be hard pressed to assess its impact. The expert, however, knows that no real progress in this world is currently achieved without science and that the accusation whereby it is not of public benefit preoccupies the general public and not the men of science, who each in their own way, unwaveringly follow their goals. Indeed, they remain untroubled about the future benefits of their work since these accrue neither to them nor to an individual country but to the whole of mankind. (Liebig 1862 : 33, translation by DS)

Blurring Boundaries in the Late 19th and Early 20th Centuries: Scientists in Transition

The fact that scientists felt compelled to do boundary work indicates that scientific practice had already begun to change and that the hierarchical epistemic order no longer applied across the board. It was the birth of engineering as an academic discipline that set off this dynamic process of boundary work. By acquiring the right to award doctorates in the late 19th and early 20th centuries, the German technical colleges enhanced their academic status (König 1999 ). Leading figures of this new group of aspiring engineers such as Alois Riedler, a mechanical engineer and rector of the Technische Universität Berlin-Charlottenburg from 1899 to 1900, persistently stressed that the relationship between science and technology was a two-way process:

Technology has its natural share in the progress of the natural sciences; in many areas technology has even run ahead of the natural sciences until deeper scientific insights in turn paved the way for perfecting technical development; … [T]hrough the magnificence of its tangible achievements, technology has raised the public’s awareness of the natural sciences and has contributed enormously to making science, in general, more popular. (Riedler 1900 : 12, translation by DS)

Conversely, scientists themselves began to overcome the gap between (pure) science and technology. Related distinctions, for instance, between discovery and invention were also blurring. Within the expanding field of the natural sciences in the late 19th century, researchers had to transcend the limits of both established disciplines and methods in order to find out something new. The development of instruments became, more than ever before, an integral part of scientific work; the act of designing new techniques became as relevant as discovering new elements or laws of nature. The instrumentality of science, not only in terms of its methodological role of confirming theories but also in terms of its effectiveness, had finally become part of the image of the truthfulness of science in the modern world (Wilhelm Ostwald 1929 : 21; Dear 2005 : 404; Joerges and Shinn 2001 ).

Scientists such as the Nobel Prize winner and pioneer of physical chemistry Wilhelm Ostwald campaigned for closer cooperation between scientists and engineers. While criticizing the old supremacy of natural philosophy, he emphasized the similarities of scientific and technological endeavour, in particular a systematic approach to research and to the desire to venture into the unknown (Wilhelm Ostwald 1908 : 20). As far as Ostwald was concerned, scientists and engineers nonetheless differed in terms of their motivations (or goals) and their temporal perspective; having discovered a new technology, engineers abandoned scientific questioning, whereas scientists followed the path to its very end, hoping to find definitive explanations to their questions. Although this notion of the advancement of knowledge was less asymmetric than it had been a few decades earlier, the emphasis Ostwald placed on science’s long-term orientation and the continued ideal of human curiosity as a scientific value in itself demonstrated that a sense of the moral superiority of science endured. (Wilhelm Ostwald 1905 , 1911 ).

While the ideals of pure science were in the process of dissolving, by 1900, both the institutional settings of research and research practices in the natural sciences had already undergone significant change. The emergence of professional industrial laboratories with salaried researchers (initially in the chemical and electrical industry), the establishment of special research institutes outside of the universities (both national laboratories in the service of the state and research centres for specific research fields with mixed funding), the beginning of special funding programmes for science, and the more extensive involvement of the administration in science policy issues were some of the developments in science and in the attitudes within society towards science observable in different countries.

Studies into German science emphasize that two new types of institutes, the Notgemeinschaft der deutschen Wissenschaft (Emergency Association of German Science) and the Kaiser-Wilhelm-Gesellschaft (Kaiser Wilhelm Society), concluded an ongoing process of change in science at an institutional level that had come about in response to the limitations of the former university-centred organization of research and to the new expectations of industrialized mass society (Szöllösi-Janze 2005 ; Ash 2002 : 35–38). Footnote 6 The Kaiser Wilhelm Society, established in 1911 to promote the natural sciences in Germany, was a reaction to the increased requirements of disciplines such as chemistry and physics as well as a response to increasing industrial demand for scientific knowledge and growing international competition. With the financial support of both the state and influential entrepreneurs, scientists in the institutes on material research belonging to the Kaiser Wilhelm Society were able to concentrate their entire efforts on research, that is “pure science”, without needing to undertake teaching duties. The Emergency Association of German Science largely sponsored research projects at the universities. This fund, derived from a variety of sources and governed by academics, had been initiated by scientists after the First World War.

The funding programme Gemeinschaftsforschung (Collaborative Research), which sought to further public health, the economy, and the greater public good, together with the research areas pursued by several institutes belonging to the Kaiser Wilhelm Society provide evidence that the pure-science ideal was becoming less important. These self-governed academic institutions promoted research that responded directly to industrial and political demands. Collaborative Research, for example, financed projects which promised to either secure the production of raw materials or develop substitute materials, to improve material processing or technological development, and to increase food production.

To sum up the whole section, the historical overview from the 19th to the early 20th century shows that the pure-science ideal prevailed until the late 19th century when the cooperation between university scientists and industry started to become closer. The pure-science ideal was a legacy of the long-standing domination of philosophy in academic culture. Having worked hard to earn the status of academic disciplines, it was difficult for the natural sciences to overturn a notion of science that strove for eternal truth while ignoring the technical and economic fruitfulness of research. The fact that natural scientists continued to cling to the philosophical tradition, however, became a point of conflict in the late nineteenth century because the high social esteem enjoyed by the natural sciences was based primarily on their significance for technological innovation and economic success. German science had already begun to adjust to the new role of science in society on an institutional level, the conceptual distinctions between pure and applied science and between science and technology were set to blur in the early 20th century.

Science in the First Half of the 20th Century: Fundamental Research and the Promise of Utility

The scientific purity discourses lost importance around 1900 and new terms began to reshape the notion of science. This semantic shift suggests that the role of science in society had already changed. The German composite noun Grundlagenforschung (fundamental research), Footnote 7 is a relatively young term that first emerged in the early 20th century within a very specific context in the discipline of mathematics (Dingler 1911 : 35; Rulf 1913 ). In the late 19th century, mathematics underwent a disciplinary realignment known as mathematical modernism (Mehrtens 1990 ). German mathematicians played a leading role in this scientific movement, the main goal of which was disciplinary autonomy. The movement’s proponents created a special, self-referential language by freeing the discipline from any metaphysical grounds and providing mathematics with a theoretical framework that denied any reference to reality or other concepts in science and technology and favoured instead an intrinsic, formal logic. Journal articles such as “Mathematische Probleme” by David Hilbert ( 1901 ) delineated a future research programme for mathematics revolving around principal epistemic questions of proof. In summary, modern mathematicians created a new epistemic foundation for their discipline.

Although the role of applied mathematics was an issue for dispute within this reform movement, the term fundamental research was not actually used as an antonym that contrasted to applied mathematics. Within the particular context of mathematics, fundamental research denoted studies that contributed to solving fundamental logical problems like those Hilbert had put on the agenda. Herbert Mehrtens ( 1990 : 149) thus classifies fundamental research as a specific subdiscipline (“ Spezialdisziplin ”) within mathematics. Because this specific meaning was confined to mathematics, the term fundamental research first spread to adjacent disciplines such as philosophy and, in particular, the philosophy of science (Lewin 1922 ). In fact, the German version of fundamental research was not common throughout the 1920s and early 1930s, and the few times the term emerged, it referred mostly to fundamental epistemic questions within disciplines.

In contrast to the German scientific discourse, the English term “fundamental research” emerged slightly earlier and, more importantly, within a different context than in Germany. The English term basic research was initially less prevalent. Roger Pielke has detected a New York Times article from 1919 in which “basic research” emerged in the context of a Congressional hearing on agricultural policy. According to him, the concept was an offspring of the political discourse since its use was restricted to the political arena until the late 1930s (Pielke 2012 : 343). It must be added that “fundamental” and “basic” were, among other things, used as attributes to denote the core academic disciplines, such as physics, mathematics, or chemistry, upon which other disciplines were founded. Thus, fundamental science and basic science meant something completely different to fundamental research or basic research in the English/American context.

The initial use of fundamental research in fields such as plant breeding and technological or industrial research indicates that the term did not emerge from the 19th-century purity discourse. In the 1890s, scientists of agronomy at the American land-grant colleges called for more fundamental research in general aspects of plant physiology in order to continue making progress in plant breeding (Arthur 1895 : 360). Problem- and application-oriented research led them to new questions that “pure” botany had not yet raised. The land-grant colleges were the result of a federal initiative to foster education in agronomy and technology, and to offer higher education to the wider public. As a result of their agricultural focus, these colleges were provided with federally controlled land to establish agricultural experiment stations. Similar to the German technical colleges, the land-grant colleges were not originally on an equal footing with the universities in terms of scientific prestige (Thelin 2004 : 135–137). Yet researchers in these experimental centres faced high public expectations to provide results that could improve farming practices and increase crop yields (Marcus 1985 ).

The demand for more fundamental research expounded one problem: the uncertainty of scientific outcomes, even if a project had a clear task to fulfil right from the start. Given this uncertainty, doing fundamental research meant at least promising to lay a cornerstone for future technologies, new products, or new materials. If research failed to produce new knowledge proving useful, scientists could still legitimise their work via the ideal of pure science, that is the advancement of knowledge as a value in itself. As any reference to the intrinsic ideal of pure science was secondary, it served primarily as a back-up means of legitimisation and only secondarily as a way to claim recognition for applied botany among “pure” scientists. In the end, similar to the German example in engineering, scientists in applied botany declared the distinction between pure and applied science to be invalid: “All science is one. Pure science is often immensely practical, applied science is often very pure science, and between the two there is no dividing line” (Coulter 1917 : 228). Applied botanists called upon science to remain open to everyday needs and problems (Coulter 1919 : 366). Alongside these examples from botany, the term fundamental research can be found very early on in the context of technological and industrial research. Fundamental research denoted any scientific research revolving around basic technical problems with the goal of improving existing technology or, hopefully, developing new technology (Nutting 1917 : 250).

The fact that the concept of fundamental research arose in research fields with an explicit application-orientation reveals that the new term was not a synonym for pure science. Rather, it conveyed the promise that science would produce, sooner or later, useful knowledge. This semantic shift was a response to the growing expectations of science within society and the increasing number of possibilities that scientific research had been able to offer in the development of technology and other societal improvements since the late 19th century. However, researchers and scientists phrased their promise of utility very cautiously; the metaphorical meanings of “fundamental” express the idea that research is the first, but not the only step in a complex process. Hence, the strategic use of the term can be described as twofold: to promise utility and, at the same time, to confine expectations that may be far too high.

With respect to British science policy in the first half of the 20th century, Sabine Clarke ( 2010 ) has already pointed out that fundamental research did not emerge as a synonym for pure science. She shows that in Britain, the new Department of Scientific and Industrial Research, established in 1916, used the term first and foremost to stimulate industrial research. The new ministry was supposed to coordinate and support research that promised economic and social improvement. At first, manufacturers and scientists scarcely welcomed the new grants offered by the Department; according to Clarke, both parties wanted to avoid any kind of governmental interference. Confronted by this industrial opposition, the Department of Scientific and Industrial Research advertised long-term research projects dealing with the basic properties of materials or with technical processes with the new term “fundamental research”. In this particular context, the label pure science would have evoked the image of curiosity-driven research without any practical end.

As Clarke demonstrates, the new term can only be understood within its specific institutional and national setting; thus, we should not be too rash to conclude that the findings of the British study also apply to the German case. Furthermore, Robert Kline’s older study ( 1995 ) on the boundary discourse of pure and applied science in the US, which focuses on engineering and its relationship to the natural sciences, suggests that, even in the English-speaking world, the meaning of the term fundamental research varied greatly. According to Kline, the distinction between “pure” and “applied” had only become common in the 1870s, and so the ideal of pure science was a relatively recent phenomenon in the US. Although the demarcation between pure and applied science was becoming blurred in the interwar period, Kline argues that the majority of researchers in engineering eventually adopted the pure-science ideal in order to underscore their scientific capabilities and their growing professional status. Kline’s main argument is that because engineering was unable to assert an autonomous ideal of itself, technological knowledge continued to be subordinated to scientific knowledge in the 20th century. For Kline the new term fundamental research represented a modified ideal of pure science which could also apply to technology. Where engineering is concerned, Kline admits that he is unable to identify a clear strategy of autonomy forming an essential aspect of the traditional notion of pure science.

Nazi Opposition to the Notion of Pure Science

In Germany, the term Grundlagenforschung only became common in the sciences during the late 1930s. Its meanings certainly deviated from the original use of the concept within the context of German mathematics, as well as from the old semantics of pure science. After the scientific purity discourse ran out of steam in the 1920s, the National Socialist German University Lecturers’ League ( Nationalsozialistischer Deutscher Dozentenbund ), which represented the younger generation of lecturers attempting to bring the universities into line with Nazi ideology in particular, fought against the institutional, epistemic, and normative concepts that characterised the ideals of pure science (Nagel 2008 ). The Nazi discourse denounced the 19th-century humanistic notion of academia as a liberal bourgeois ideal that had permanently estranged science and scholarship from the German people.

On a detailed scale, the Nazi discourse criticized the older concept of science as being a selfish project pursued by scientists. This criticism was levelled at the epistemic norm of objective neutrality and the assumption that the natural sciences were unconditional – in particular in terms of the choice of research subjects – thus exposing the notion of pure science as a concept contrived by the ivory tower. Furthermore, Nazi critics blamed the self-referential concept of pure science for causing institutional fragmentation and disciplinary differentiation in science. Continuing the Weimar policy of collaborative research, the Nazi scientific ideal entailed joint efforts by researchers from different institutional and disciplinary backgrounds aimed at solving the problems of the day; problems that were, of course, defined by the politics of the Nazi regime. It is no surprise that the Nazi counter-concept of science quite openly called for a politicization of the academic world – in particular with regard to staff and research policy – and reinterpreted the ideals of universalism, academic freedom, and unity of science in light of the Volksgemeinschaft ideology (the ideology of the community of German people): academic universalism transformed into social universalism, which sought to overcome individual, institutional, and disciplinary interests. The political interpretation of freedom meant that science was in a position to contribute to the German people’s independence from foreign raw materials, in accordance with the Nazi quest for autarky. And lastly, by invoking the older ideal of the unity of science, they legitimized collaborative science, its different disciplines, and its various institutions in order to fulfil national tasks (Henkel 1933 ; Krieck 1933 ; Löhr 1938 ; W. Schultze 1938 ).

Research in Nazi Germany: Between Four-Year Plans and Long-Term Science Policy

In light of the official campaign against the old pure-science ideal at the beginning of the Nazi regime, the use of fundamental research in the late 1930s can hardly be understood as a new version of pure science presenting the search for knowledge of nature and truth both as an a priori goal of research and a value in itself. The terms Grundlagenforschung and Zweckforschung (goal-oriented research) gained hold as political efforts to acquire control over academic and industrial research increased. In 1937, the Nazi regime established a research council, the Reichsforschungsrat (Reich Research Council), which was responsible for funding research. During the war, the Research Council was directly responsible to the Army Ordnance Office (Flachowsky 2008 : 232–462).

The Research Council’s first president, military general and professor of army technology Karl Becker, defined fundamental research as science that could not be “commanded and accelerated”. He guaranteed, therefore, that “as far as researchers and facilities in the institutions [for fundamental research] in question offer even some guarantee of success”, there would be no interference from the Research Council (Becker 1937 : 26). Becker made particular mention of the various institutions for aeronautical research and the institutes of the Kaiser Wilhelm Society, promising to abstain from exerting any control over these institutions in light of their close relationships to industry. Goal-oriented research, which was meant to be built on fundamental research, was to fit into the schedule of the four-year plan. In this context, goal-oriented research denoted first and foremost industrial research leading to the development of advanced technology. Against the backdrop of the four-year plan, the Nazi regime demanded that industry give complete insight into its research activities (Becker 1937 : 25, 27).

In 1940, the Illustrierte Zeitung , a well-established illustrated magazine published in Leipzig, devoted an entire issue to the topic of German research in the service of the people in order to present Nazi science policy. The magazine included articles from leading scientists such as the biochemist and Noble Prize winner Adolf Butenandt, journalists specializing in scientific topics such as Hans Hartmann Footnote 8 , and ministry officials (No. 4956, 22 August 1940). To some extent, the issue was a response to continuing foreign criticism of the way the Nazis had incorporated German academia into National Socialism (Rust 1940 ; Hartmann 1940 ). Completely ignoring criticism of racist staffing policy, the articles presented a concept of science that responded to the needs of society without compromising scientists’ research freedom. “The freedom of research would not be endangered when the state ensures that state-funded institutes are given the task of conducting fundamental research in order to solve problems within the national economy” (Krauch 1940 : 122, translated by DS).

The articles, however, also addressed German scientists on the question of how a more utility-oriented research affected its institutional setting. The issue of organizing science in order to quickly achieve societal and technological progress without duplicating efforts in both academic and industrial research had already been under discussion within the paradigm of rationalisation prior to the Nazi’s seizure of power. From the late 19th century onwards, industry conducted more and more research in its own laboratories, and the good salaries attracted talented researchers. The future role of universities as training and research institutions and the initial division of labour between academic and industrial research thus became a vital question of science policy. Furthermore, the changing research practices also led to an organizational discussion about individual or team research. The terms fundamental research and goal-oriented research were part of these ongoing negotiations (Krauch 1941 : 2; Brüche 1944 : 114–115; Stadlinger 1944 : 227, 229; Verein Deutscher Chemiker 1943 ; Drescher-Kaden 1941 : 10, 16–17).

Overall, the articles in this special issue sought mainly to demonstrate to the public how German scientists, whose work was less visible, contributed to the nation during war time. Authors such as Butenandt tried to explain their ongoing experimental work in terms of both its meaning for society and its potential impact to a wider lay audience (Butenandt 1940 ). Following the initial hostility demonstrated by Nazi ideology towards the academic elite and elitist institutions such as the academies of sciences, this issue of the Illustrierte Zeitung promoted science wholesale by emphasizing that it was necessary for society to support research.

Within the natural sciences up to 1939, the new term “fundamental research” was rarely used and did not yet have an established, fixed set of meanings. In physics, for example, fundamental research could denote theoretical physics or, alternatively, it referred to the older distinction between the natural sciences and technology (Reichenbächer 1937 : 285; Hiedemann 1939 : V, 1). Despite this semantic variation, the strategic uses of the new term in most of the disciplines bore some resemblance to one another when it came down to combining the term with goal-oriented research. It is striking that as the term fundamental research became more widespread after 1939, scientists tended to mention Grundlagenforschung and Zweckforschung in the same breath (Witzell 1944 : 212–217). In fact, the term fundamental research emerged in the natural and technical sciences mainly when the individual field of research was close to application or demonstrated promise for military, economic, and political aims. This was the case, for instance, with innovations in weaponry and military strategy, maintaining public health, ensuring food supply, rationalizing the production and use of raw materials, inventing substitute materials, and encouraging industrial production. In the humanities, the term fundamental research was still less common. This observation leads us to the question of whether the use of the two terms really worked as a boundary discourse.

Interdisciplinary research fields, such as forestry, represented a utility-oriented notion of science in the first place. In the case of forestry, research promised more profitable cultivation and effective technical treatment of the raw material wood. Germany’s rise as a colonial power in the late nineteenth century had already transformed forestry into a politically and economically significant discipline, fostered since by the German state. In the Nazi war economy, the issue of raw materials, and with it the supply of wood, gained even greater importance (Steinsiek 2008 ). In this disciplinary context, fundamental research and goal-oriented research represented two equivalent sub-areas of forestry: one that studied the nature of the substance wood, and one that analyzed its material properties and the effects of technical treatment. The overall goal of both research fields was to acquire knowledge about the optimal use for the raw material wood (Runkel 1942 : 305–306). Footnote 9

The majority of scientists defined fundamental research as pursuing fundamental questions of nature, its substances, and its processes. This contrast to goal-oriented research still adhered to the old demarcation between nature, on the one hand, and society and its relationship to natural resources, on the other. But questions about nature, labelled by scientists as fundamental research in the late 1930s and 1940s, arose within the context of technical and practical problems (Kaiser Wilhelm-Gesellschaft zur Förderung der Wissenschaften 1939 : 322; Hoffmann and Suhr 1944 : 550), that is in applied science disciplines such as aeronautics, armament, forestry, plant breeding, and nutrition.

In the majority of cases, scientists simply stressed the necessity of both fundamental research and goal-oriented research, in other words, the general necessity of research for any kind of progress. This is where the views of the scientific community converged with the goals of Nazi economic policymakers, who were aware that the US and British governments were providing massive support to research for economic and military purposes (Krauch 1939 ). When it came to clearly defining terminology in this period, scientists surprisingly described fundamental research as the study of nature, devoid of any concrete notion of how it might be applied in terms of technology or societal utility. Yet having just drawn a distinction between utility-oriented research and research driven simply by the urge for knowledge, scientists immediately strove to emphasize that limiting fundamental research was not possible in terms of research practice and its institutional settings, whether in industry, in universities, or in other research institutions (Bauermeister 1938 ; Wolfgang Ostwald 1942 : 130–131; Niemeier 1944 : 106–107). Moreover, the distinction between fundamental research and goal-oriented research was often criticized as misleading because it suggested that fundamental research was far removed from any notion of useful application (Zenneck 1944 : 10; Endell 1942 : 113; Wolfgang Ostwald 1942 : 130–131).

These definitions must be seen as a vestige of patterns characterizing scientists’ former understanding of science. However, one question remains unanswered: if this differentiation of research types appeared to have little consequence for the scientific community, why did scientists introduce new terminology that could be understood as part of a dichotomy and that, moreover, was reminiscent of former boundary discourses? As the use of the term fundamental research was prevalent in engineering as well as in those research fields in chemistry, physics, biology, and geography that responded, in particular, to the concrete needs of the economy and the political regime, the intention was hardly to reactivate either the old demarcation between science and technology or the ideal of science for its own sake. The fact that new terms emerged reveals two things. Firstly, under the Nazi regime the scientific community felt the need to renegotiate the conditions under which science and research were conducted. Secondly, the old concepts of science no longer fit with existing practices in science.

The term fundamental research was fresh; Zweckforschung , which was highly unusual in the natural sciences until the mid 1930s, was even more so. Footnote 10 In fact, the latter only gained importance during the Nazi period. Some scientists explicitly considered goal-oriented research as a temporary focus of science responding to a situation of national emergency. In 1936, the chemist Wolfgang Ostwald, son of Wilhelm Ostwald and former president of the Kolloid-Gesellschaft (Colloid Society), stated that “[o]ver the last years, much has been said about so-called ‘Zweckforschung’. It means the entirety of efforts to draw more extensively than usual on scientific research for solving major economic problems” (Kolloid-Gesellschaft 1936 : 159, translation by DS). To be precise, the term goal-oriented research was spreading at the very same time that the creation of the new government administration in 1937, the Reich Research Council, institutionalised the four-year plan. This new authority and the second four-year plan that ensued broadened the field of activities in which science henceforth was understood as an important prerequisite for economic progress. Thus, in contrast to free research, goal-oriented research meant target-oriented research according to the goals of the four-year plan (Bachér 1937 ; Willing 1937 ). Footnote 11

In order to explain the emergence of this new scientific nomenclature, it is most revealing to look at the chronology governing the spread of new terms. With the foundation of the new Reich Research Council ( 1937 ), which claimed to bring German research efficiently into line with Nazi policies, some scientists were concerned about the future funding of fundamental research (Bauermeister 1938 : 476). It would be misleading to interpret this plea for fundamental research as a struggle for freedom of science that ignored the expectations of society in favour of absolute professional independence; the concerns expressed do not reveal an objection to the idea that science should serve political aims or national tasks. Yet the scientists’ worries certainly revolved around the question of how to govern science. The concern was, in fact, that scientific knowledge as a resource for innovation might dwindle in the long run. It can be described as an argument of knowledge sustainability meaning that knowledge will run short if scientists and policymakers align knowledge production exclusively with immediate needs. Within this sustainability discourse, fundamental research represented the experiences that, firstly, scientific progress was often unexpected and, secondly, that even the research output that sought practical solutions was unpredictable and needed time before its application was possible. According to these researchers, science had to conquer new ground deemed necessary for the long-term advancement of technology. Only a few scientists actually recognized the semantic shift in scientific nomenclature and criticized the new term fundamental research for constraining science to technological ends (Richter 1943 : 207).

The reference to the long-term and unpredictable nature of scientific research was, of course, not new. Back in the 19th century, this had already served as an argument in the science-technology boundary discourse. Yet it was not until the 20th century that this aspect of scientific and technological progress became an everyday experience in many fields of research. The problem of how to find the right balance between venturing into the unknown and, at the same time, abiding by a research policy that sought to keep the aims of research in its sights had been under discussion in the 1920s, in particular within the context of industrial research. Faced by the Nazi Regime’s four-year plan and the increasing pressure of the expectations on science during the war, it became even more important for researchers to communicate to the regime that their work contributed to political aims, even if they were unable to guarantee any immediate success.

The argument that research had its own temporal logic was also present in research fields devoid of a science-technology nexus. In 1943, Joachim H. Schultze, professor of geography in Jena, expressed the belief that science ought to be one step ahead of the demands of the day. He defined fundamental research as “general research regardless of its practical application and regardless of the benefits of everyday life” (J. H. Schultze 1943 : 197). He described research in geography as the task of depicting the overall research areas in the discipline, which included topics as diverse as the earth’s surface, landscapes, and the cultural and demographic depiction of countries. The central aim of Schultze’s article was not, however, to protect a self-referential concept of science, but rather to praise the utility of geography in general as well as the research carried out thus far for the purpose of warfare. Referring to historical examples of the huge political and economic interest in geography, Schultze argued that science, rather than being left to its own devices, needed both a societal mission and interest from the public. He advocated the idea of a central German geographical institute which would carry out fundamental and goal-oriented research for the state and for economic purposes. Schultze called for the combination of fundamental and goal-oriented research for an epistemic reason: research needs time and the future utility of scientific outcomes is not foreseeable as readily as future societal needs (J. H. Schultze 1943 : 201). Thus, the term fundamental research stood for sustainable knowledge with potential benefit, or a sort of stock of knowledge (Ziegelmayer 1936 : 253; Stock 1938 : 150–151; Brüche 1944 : 113).

The Discursive Strategy of Fundamental Research and the Reassessment of German Science in the Nazi Period

Over the last decade, German science and its research endeavours under National Socialism have undergone a historical re-evaluation, namely within two major projects on the history of the Kaiser Wilhelm Society and the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation). The focus has shifted to some extent from the effects of Nazi ideology and the participation of the humanities, anthropology, and medicine in racist and eugenic policies, to the hard and technical sciences that contributed to the military and economic goals of the Nazi regime. Whereas former studies stressed the negative effects of Nazi science policy, such as, the international isolation of the German scientific community, the experience of being cut off from raw materials required by the experimental sciences and the focus on substitute research as a result of a policy of autarky, and, since 1933, the incredible loss of excellent researchers as a result of racist science policy, recent studies present a more differentiated picture of science under the Nazi regime when focusing on research output and technical innovation.

Despite the regulatory claims of the Reich Research Council and the German Research Foundation’s loss of autonomy, recent studies show that researchers were still able to shape research policy according to their own interests. Provided that researchers showed a political affinity to the Nazi regime, scientific reputation and peer review continued to define the allocation of research funding (Flachowsky 2010 ). In particular after 1942, the year in which the Reich Research Council was reorganized and military technical equipment assumed greater importance in the German war effort, it appears that the regulatory claims of German research policy finally gave way to a more efficiency-oriented policy. As Mitchell Ash puts it, normal science existed throughout the Nazi period (Ash 2006 : 34–35).

In this reassessment of German science and scholarship, the question of whether Nazi science policy led to a shift in focus from Grundlagenforschung (fundamental research) to angewandte Forschung (applied research) plays a crucial role. Recent studies provide evidence that fundamental research and applied research did not work as clearly demarcated, transdisciplinary, and supertemporal categories. Current studies on the history of the natural sciences during the Nazi period attest to a continuity of – what they call – fundamental research. Some studies suggest that German professors adjusted to the new conditions by combining applied research that accorded to political and economic requirements with fundamental research that earned greater appreciation within the scientific community in their projects. Although full professors apparently still honoured the ideal of pure science, most of them had contact with industry as individual consultants and/or via collaboration. Other studies identify fundamental research in especially applied contexts such as armament and defence research, but also in economically promising research fields such as metals research and polymer chemistry (Luxbacher 2010 ; Erker 2010 ; Flachowsky 2010 ; Epple 2002 : 318–322). In the case of metals research, Günter Luxbach differentiates between research on the composition of metal, which was labelled as fundamental research, and research that tested the technical properties of metals, which was known as applied research. In contrast to this classical distinction between the quest for knowledge of nature and the quest for technological progress, Paul Erker describes polymer chemistry as a discipline that strove to combine these two motives. Erker employs the label of basic research for a heterogeneous and innovative research policy. Thus the meaning of fundamental research differs in historical studies on the natural sciences, not least because these studies investigate different disciplines.

By countering older historical interpretations that see German science in decline since its political instrumentalization in 1933, the main thrust of these recent contributions is, of course, that the Nazi’s war and policy of autonomy did not cause the profile of academic research to change overall. The insight that fundamental research went hand-in-hand with goal-oriented research is a novelty only if we analyse science on the premise that basic and applied research constitute two fundamentally different forms of research. Most of the historians quoted above still do not question the distinction between basic and applied research. The long-established categories still appear to be so self-evident that these authors do not feel obliged to define them explicitly for the specific research fields upon which they focus. Moreover, most of them still fail to reflect on how scientists employed terms such as Grundlagenforschung and Zweckforschung during the Nazi period.

Only a few of these historians have reconsidered their analytic vocabulary in light of new evaluations of the Nazi period. Moritz Epple, for instance, no longer believes in the opposition of the terms basic and applied. As in recent propositions in the philosophy of science, he suggests that we should speak of application-oriented fundamental research within the context of Nazi science (Epple 2010 : 213). Another interpretation suggests that as German professors were increasingly involved in applied research, the use of the term fundamental research was merely symbolic, for the purpose of scientific reputation (Wagner 2010 : 26–27, 33). Surprisingly, semantic sensibility is on the rise when it comes to discussing the aftermath of the Second World War. Within this context, the use of the term fundamental research is more often identified as a simply rhetorical strategy deployed by German scientists in order to retrospectively downplay their involvement in the Nazi system. Carola Sachse argues that this strategy of moral relief also worked in the American context: it was supposed to dispel fear of German post-war science (Sachse 2010 : 480).

So far, this analysis of the first half of the 20th century has shown that the new terms fundamental and basic research initially emerged in mission-oriented or technical research fields. In Germany the concept only gained importance since the 1930s when research had to meet high political expectations. With regard to the historical context of the Nazi regime, the results suggest that the interpretation of a simply rhetorical strategy, whether as a strategy of individual moral relief or as a professional strategy for protecting a scientist’s guaranteed freedoms, is not entirely convincing. Because many German scientists demonstrated their commitment to the Nazi regime by offering their research services, the terms fundamental research did not serve to protect the old intrinsic ideal of science. In a period when the political expectations placed on science were high, the terms expounded instead the experience that scientific progress and procedures leading to exploitable results were difficult to predict.

From Knowledge Sustainability to Purity Discourse: US Science Policy Between the Second World War and the Cold War Period

As the rise of basic research as a pivotal keyword in science policy during the post-war era was not peculiar to Germany, it is now time for a more comparative perspective. Although the two terms fundamental and basic research had gained greater currency throughout the 1930s in US science and, more generally, in science throughout the English-speaking world than had the term Grundlagenforschung in German science, they had not yet spilled over into all the different disciplines. Footnote 12 Analysis of the journal Science demonstrates that, at that time, the use of these terms was still limited to biology (agriculture as well as studies on vitamins and proteins, which attracted pharmaceutical companies, also employed these terms), industrial research, and engineering. Once again, the terms denoted long-term studies focusing on fundamental problems in biology, chemistry, or physics emerging within the context of technical and application-related questions. The term fundamental research did not constitute an antonym to applied research; it was not part of a boundary discourse. In 1942, the research administrator of the US Department of Agriculture described basic research as follows:

In all these cases, either basic research precedes the practical applications of science, or a certain amount of this kind of research is found to be necessary somewhere along the line to clear-up obscurities that block further progress. … the point I am making is that in research there is no single road to practical results. If we keep our eyes constantly and exclusively on what seem to be immediate needs, we miss some of the richest fruits of scientific work – the fruits that grow from the discovery of important fundamental facts. … The emphasis I have given to basic research and freedom of inquiry does not mean that we should pay any less attention than we do to homely experimentation directed toward solving everyday problems. (Auchter 1942 : 287, 288)

In the case of engineering, the concept of fundamental research largely represented the ongoing process of the scientification of technology (Gibb 1937 : 233–234; Jewett 1944 ). Institutes such as the Mellon Institute of Industrial Research at the University of Philadelphia, which defined itself as a link between science and technology (or in the words of Edward Weidlein “between the world of science and the industry”), used the term fundamental research as a general label for their projects and training (Weidlein 1935 : 562).

In light of these examples, the argument that the new concepts reflected the increasing expectation that science should be beneficial to the economy and to society as a whole also applies to the US case. Scientists were aware of the epistemic and institutional challenges to research that the 20th century brought forth. As a result of the increasing commercial potential of fundamental research, patents became a major issue at US universities quite early on. In contrast to the German universities, where the right to hold patents appeared to be considered part of the individual academic freedom of German professors (at least until the rise of the Nazi reign), Footnote 13 the American land-grant colleges introduced patent regulations as early as the 1920s. Nevertheless, the administrative, legal, and ethical problems of patenting within institutions of higher education remained a controversial issue over the next few years (Potter 1940 ).

After dealing bit-by-bit with the shifting situation of science in the early 20th century, the Second World War marked an incisive and formative experience for the scientific community. When the US entered the war, the national mobilisation of science acquired the same level of importance there as in the other warring countries. In the early 1940s, the US debate on the effects of wartime revolved first and foremost around financial redistribution in science. The US universities, which depended mostly on private donations, were considered to be the losers in this process. In 1941, the long-standing idea of a federal fund that aimed to guarantee research funding on a regular and permanent basis was reignited. Although the financial crisis of the American universities had begun earlier during the Great Depression and bore several failed attempts to secure federal support for academic research (K. T. Compton 1934 ; Geiger 1986 : 246–255), proponents of this initiative blamed the war for worsening the financial situation of the universities and diagnosed a crisis in fundamental research (Blakeslee 1941 ).

Those advocating federal support argued that a new form of funding was necessary because research in the basic sciences, that is in basic disciplines such as physics or chemistry, laid the indispensable foundation for future benefits: “We are all familiar with the material conveniences and comforts which science has given us, but we often forget the original patient, fundamental research which made them possible and will be the basis for future advances” (Robbins 1941 : 8). As the concept of fundamental research had thus far denoted only research with a clear reference to application, the novelty here was the fact that the supporters of such a fund classified the entire endeavour of academic research at universities as fundamental research. Moreover, the lack of financial support for the universities was in opposition to the better funding of industrial and governmental research, which was only supposed to favour research that could demonstrate the prospect of immediate benefits (Robbins 1941 ).

From Wartime to Peacetime: Vannevar Bush’s Plans for Transforming Science Policy

During the war, scientists had discussed the future conditions of science (Science 1942 ). At the end of the war, plans for a new science policy were already on the table. In the literature on research and development policy, funding for basic research and the dissociative model of basic and applied research in the post-war era are still inseparably linked to the name Vannevar Bush (Braun-Thürmann et al. 2010 : 17). The MIT professor for electrical engineering served as presidential science adviser and, in particular, as chairman of the National Defense Research Committee and director of its successor organization, the Office of Scientific Research and Development. While coordinating the American military research programmes, including the Manhattan Project (the project devoted to constructing the atomic bomb), he began to make plans for a federal peacetime science policy. Based on the negotiations of four scientific committees (a Medical Advisory Committee, a Committee on Science and the Public Welfare, a Committee on Discovery and Development of Scientific Talent, and a Committee on Publication of scientific Information), in July 1945, Bush presented guidelines for future governmental promotion of scientific activity in the natural sciences and in medicine to the public. As well as providing financial support for academic research and junior scientists in the natural sciences, the proposals encompassed a reform of patent law and tax incentives for industrial research, the promotion of medical research, the plea for open science by fostering international exchange and strategies of declassification, and, finally, the sponsorship of basic research on military matters. Bush’s report “Science—The Endless Frontier” essentially sought to institutionalize federal science policy on a permanent basis (Bush 1945 ).

We should interpret his draft against the background of the war experience. The Second World War had demonstrated, once again, the importance of research for society and the fast-growing need for scientific knowledge. During the war, scientists and engineers had found that the search for technical innovation in the service of national defence spawned new questions and new problems for the natural sciences, the implication of which was long-term research. Given the immense expectations concerning immediate results within the context of warfare, some scientists feared that researchers would no longer be able to meet the demand of new knowledge for technical development (Simons 1943 : 391). Despite the achievements made during the war, researchers warned of an exhaustion and future shortage of scientific knowledge: only by exploiting existing knowledge, they claimed, had it been possible to invent penicillin and radar, two of research’s major wartime success stories. In other words, there was a fear that the equilibrium between the production of scientific knowledge and its application would be disturbed (Bush 1945 : 5, 8). The argument of knowledge sustainability thus became also important within the US community of scientists facing the war-time conditions of research.

This scarcity anxiety also applied to personnel resources in science (Barton and Burnham 1943 : 176; H. S. Taylor 1944 : 250). Bush’s report criticized the fact that, due to radical recruitment practices, the shortage of scientific personnel in the US was greater than in other countries (Bush 1945 : 19). Bush’s colleagues, such as the Nobel Prize winner Arthur H. Compton, believed that the training situation and the support afforded to fundamental research Footnote 14 at the universities were even worse in the US than in Germany (A. H. Compton 1945 : 208). A lack of scientifically trained researchers also posed a problem for science-based industry. Thus the four committees suggested programmes for fostering scientific talent that included the generation in uniform returning from the war, particularly through doctoral fellowships for basic research.

In spite of this crisis and the discourse of epistemic and personnel shortage, wartime research efforts had, after all, strengthened the position of science in society. As the US government had spent more money on science throughout the course of the Second World War than ever before (Bush 1945 : 82), scientists had a particular interest in perpetuating this federal commitment to science in peacetime. Since the US were traditionally characterized by less state intervention and a scientific infrastructure based largely on philanthropy and private donations, the federal support for academic research and training had been much lower than in Germany or in other European countries. It thus became necessary to legitimize the regular government funding envisioned through science’s role in the overall welfare of the nation. The Bush report justified the government’s obligation to support basic research in three ways. Firstly, medical research would improve public health. Secondly, research would advance the overall public welfare, which was almost synonymous with economic growth and job security due to innovations and new products. And finally, long-term civilian research promised to give the US a technological edge in armaments which was supposed to guarantee national security. Only then did basic research become a real keyword in research funding. And the metaphor of “basic” did the trick; by laying the basics for all kinds of future benefits, the federal government financed basic research as for the common good.

Bush’s proposal also reacted to the organizational conditions of wartime research, in particular with regard to security restrictions. The problem of secrecy policy had already been discussed openly during the war (K. T. Compton 1942 : 28). Bush’s report called for the prompt release of classified research after the war. This request also involved a secrecy strategy in which projects were split up into small, isolated research groups, each of which worked on a specialised problem without the opportunity for any kind of exchange between them. Footnote 15 It is worth mentioning that the work on these specialized problems within isolated research groups was sometimes denoted as fundamental or basic research during the war (Simons 1943 : 392), which indicates that Bush’s use of the term deviated from the former understanding. In order to near the ideal of open science once again, Bush believed that the federal government was also obliged to encourage publication, international communication, and cooperation following the war. In general, the report restricted the role of federal science policy to financial support and the provision of coordinating infrastructure (Bush 1945 : 22–24). Bush sought to prevent the government and the military from continuing to pursue the managerial approach to science policy they had applied in wartime. Bush criticized the military leadership for being too narrow-minded, a characteristic that did not fit with his understanding of the speculative and multidimensional nature of research (Reingold 1987 : 338–341). Failed attempts to establish federal research funding had already demonstrated that the majority of the scientific community disapproved of any governmental intervention in science (Geiger 1986 : 255).

Other scientists shared with Bush the rising concerns over free scientific exchange toward the end of the war (H. S. Taylor 1944 : 255; Jewett 1944 : 3), but Bush’s report was the first to link the relatively new notion of basic research with an institutional guarantee of scientific autonomy in such a close fashion. This also included his contrasting juxtaposition of basic research with applied research and development. By then, applied research had not been an antonym to basic research. Basic research thus not only meant that science should be freed from the burden of high expectations tied to immediately exploitable results; it also entailed the freedom of both inquiry and scientific communication. The different agendas and arguments – the strengthening of US universities as research and training institutions, the switch from war to peace, and attracting federal support for science in the name of national welfare – converged in the report’s recommendation to enhance the universities and non-profit research institutions as centres of basic research.

Following the release of the report, US scientists, politicians, and industry representatives entered into controversial discussions on various aspects of Bush’s proposals, which delayed the establishment of the National Science Foundation for nearly five years. The patent issue, the suggestion that the social sciences be excluded from federal support, the uneven distribution of excellent research universities in the individual states coupled with the corresponding problem of how to fairly allocate federal funding, and, finally, the envisioned scientific expertocracy within the federal foundation proved to be particularly delicate subjects. Bush’s proposal faced opposition, in particular from the military, liberal and democrat activists, and even from scientific colleagues. At the universities, which stood to benefit most from the funding, some scientists considered the plea for basic research as restricting their funding and research habits, which included contract research for industry or the army. There is no need to go into the details of this debate here since a mass of literature has already revealed these conflicting institutional interests and the political dimension of the controversy on science policy in the early years following the Second World War (Kevles 1977 ; Reingold 1987 ; Owens 1994 ; Zachary 1997 : 218–239, 249–260; Guston 2000 ; Dennis 2004 ). Most interpretations allude to Bush’s political conservatism aiming at the restoration of the pre-war political order. More generally speaking, studies on post-war research policy have so far presented a mainly political reading of these debates, which essentially revolved around the issue of more or of less intervention of the federal government into research. Even Roger Pielke’s ( 2012 ) current interpretation confines the concept basic research to this political dimension: in his view, the symbolic capacity of the term accommodated the conflicting parties, striving for the organization of science by the federal government on the one side and the autonomous organization of research by scientists on the other side, by promising potential utility.

What this analysis can add to the previous literature is a more nuanced interpretation of the conflicts within the scientific community. I argue that the scientific controversy over Bush’s “Endless frontier” partly stemmed from differences or even misunderstandings in the semantics of basic research. Although Bush developed the concept from the common discourse on knowledge sustainability, he added new semantic dimensions that had to meet multiple requirements of a new funding programme. In what follows, I demonstrate how the various problems in research organization and the overall political climate during the Cold-War period were turning this sustainability discourse by and by into a purity discourse.

Bush’s Definition of Basic Research: The Beginning of a History of Misconceptions?

Bush’s report marked a semantic shift in basic research that made a clear break with existing practices and notions in order to reorganize research in the post-war period. His specific use of the concept of basic research thus gave rise to misunderstandings and confusion. Moreover, the history of basic research in the second half of the 20th century has been characterized, in part, by these misunderstandings, which in the long run evoked anachronisms over which science and technology studies are still puzzling today. Bush’s short definition of basic research as “research performed without thought of practical ends” (Bush 1945 : 13), still singled out by most studies (Stokes 1997 : 116; Godin 2005b : 265; Popp Berman 2012 : 21), definitely contradicted the original understanding of basic/fundamental research in the context of application. Even more so, this reduction fails to represent the conceptual range of basic research in the report and the wider debates that took place right after the war.

Bush’s definitions of research, science, and applied science confused his peers and even one of his closest companions James Conant, who reflected critically on the new conceptual boundaries (Conant 1948 ). Representatives of national or military laboratories felt particularly compelled to argue against an institutional separation of basic and applied research. A member of the Naval Ordnance Laboratory, for instance, argued that “the naval laboratory programmes make it necessary for us to carry on basic research in certain parts of certain fields simply because no other agency is interested in, or has the facilities for, doing this work” (Bennett 1946 ). Bush’s peers in engineering were certainly confused by the different ways of denoting basic research. Universities specializing in the applied sciences and engineering, such as the California Institute of Technology (Caltech), understood basic research – inseparable from the overall pragmatic goal of inventing new technology – as an integral part of modern engineering (DuBridge 1959 : 109–110).

Following the publication of “Endless frontier”, scientists and other policymakers tried to differentiate and redefine the concept of basic research, which indicates that Bush’s understanding of basic research was not taken for granted and that researchers struggled with it because it did not fit the existing research landscape. John Steelman, science adviser to President Truman, for instance, divided basic research into two subcategories: firstly, fundamental research defined as “theoretical analysis … directed to the extension of knowledge of the general principles governing natural or social phenomena”, and secondly, “background research” defined as “systematic observation, collection, organization, and presentation of facts using known principles to reach objectives that are clearly defined before the research is undertaken to provide a foundation for subsequent research” (Steelman 1947a : 6). Others tried to introduce a distinction between “fundamental research, which leads to an understanding of the laws of nature, the discovery of new facts and laws, and the theoretical development”, and “basic research as it applies to industrial or military development involving basic studies of the fruits of fundamental work to determine their potentialities antecedent to application” (Leob 1946 : 540). An industrial researcher defined basic research as an intermediate category, which he located between pure research as “inquiry after knowledge for its own sake” and applied research as “the investigation carried out in response to immediate, direct, and obvious needs” (Spaght 1955 : 785). The gradual emergence of new variations such as “mission-oriented basic research” indicates, at least, that the criterion of intention, whether utility-oriented or not, became problematic in the long run (Tuve 1959 : 174; Kistiakowsky 1966 : 18).

While all these redefinitions can be interpreted as a claim to reintegrate application goals into the concept of basic research, Bush himself actually did not exclude the idea of mission-oriented research. Nathan Reingold sees “the pursuit of new knowledge” – and not the pursuit of knowledge for its own sake – as the real core of Bush’s notion of basic research. Reingold further refines his interpretation by quoting Bush’s argument that “there is no specification as to whether the knowledge is or is not of direct utility” (Reingold 1987 : 305). That sheds a very different light on the story of basic research. The importance of new scientific knowledge becomes even clearer if we take into account Bush’s metaphor of “the endless frontier”, which emphasized the cutting-edge role of scientific research. It thus placed scientific problems at the border of the unknown, reflecting the uncertainty of scientific outcomes and their long lead time in a world increasingly reliant upon scientific progress. Not least, Bush’s outline for a federal funding programme entailed financial support for basic research (long-range scientific research) on military matters.

Nevertheless, some of Bush’s contemporaries interpreted the report as an attempt to return to the old intrinsic ideal of pure science. They criticized the report for favouring a selfish notion of scientific autonomy that did not respond to any societal or economic needs (Shepard 1946 ). Footnote 16 My analysis thus far reveals that the discursive strategy of basic research initially aimed to acquire regular financial support from the government by promising utility in spite of the uncertainty of scientific research. This happened to conform to the interests of the scientific profession, so long as this support did not affect the ideal of open science. In the long run, however, the allegation of a return to an ancient pure-science ideal proved to be true. As the promotion of basic research continued, the sustainability discourse was transformed into a purity discourse, which revealed aspects of a past notion of science deemed to have been superseded in the 20th century.

After having become the spearhead of scientific endeavour, surprisingly enough American researchers looked back towards continental Europe. Post-war proposals for higher education in the US idealized the European university system and its humanistic tradition by associating it with democracy (Bender 1997 : 4–5). The old model of the European research university and its success story in basic disciplines such as physics and chemistry became a role model. According to Bush’s report, the arguments for reinvigorating the university within an increasingly pluralistic research landscape were twofold: immense need for scientifically trained researchers and the demand for scientific autonomy.

American scientists had repeatedly commented on the short-sighted focus of armaments research and on the threat Hitler’s regime implied for the freedom of science, presenting the liberal democratic order as the only safeguard for the fundamental principles of science (Fosdick 1934 : 380; Simons 1943 : 392; Goudsmit 1947 : XI). The debates on lessons to be drawn from the war experience, however, demonstrate that American scientists were chiefly worried about the threat to scientific freedom with regard to their own national conditions (H. S. Taylor 1944 : 255; Goudsmit 1947 : 232–246). Bush’s report blamed previous federal policy – from the Morill Land-Grant Colleges Act to the more recent practice of contract research – for being primarily interested in immediate benefits. The report argued that, due to an alleged inclination towards more utility-based research, the American nation depended entirely on the European production of new scientific knowledge (Bush 1945 : 2; see also Astin 1959 : 146–147).

By reproaching the societal and governmental focus on the utility of science, Bush’s sustainability argument was just about to tilt over towards the purity ideal of science. This explains why some scientists initially reclaimed the application aspect of research. The related aspect of academic autonomy, however, met with the approval of most scientists. Aside from the politicians supporting a science policy agency like the Democrat Harley M. Kilgore ( 1945 : 636), only a few scientists argued frankly against the anxiety about governmental interference by pointing out that the increasing social and economic demand for scientific research and the necessary political coordination of research in response to these needs were facts which scientists in the 20th century had to accept (Dunn 1945 ). Although the final establishment of the National Science Foundation turned out to be a compromise for all parties having negotiated this new form of federal science funding, Bush’s altered definition of basic research, in the end, became accepted.

At the end of the 1950s, after the National Science Foundation had been operating for several years, scientists continued to criticize the low federal base rate for basic research in comparison to that of contract research in the Department of Defense, which was twice as high (Elvehjem 1959 : 94; Waterman 1959 : 26–27). Some deployed the sophisticated argument that many projects were not truly basic research, but actually mission-directed basic research. In fact, the Korean War had meanwhile intensified the Cold War conflict and the Soviet’s launch of the Sputnik satellite turned the ideological competition between West and East into a science and technology race (Tuve 1959 : 173–176). As a consequence, basic research stood primarily for federally financed academic research – with or without any concept of practical use.

From the outset, the various drafts of the documents formally establishing the National Science Foundation included fellowships for graduates and junior scientists, so that the concept of basic research was closely linked to training scientific talent (Steelman 1947b : 29–30). From the late 19th century onwards, the modern research university inevitably moved further and further away from the traditional concept of a university as a specialized institution of higher education that excluded any notion of material benefit or practical aims. Yet the post-war debate on support for basic research led to a new version of the old boundary discourse of pure versus applied and theory versus practice. At a major symposium on basic research in May 1959, one representative of a private technical university reasoned that the

most difficult questions arise as to what is fundamental research, what is practical development, and which projects could be more appropriately done in commercial laboratories. … One useful criterion which helps many decisions in this field is that to be acceptable in any area a research program must be one which is consistent with and contributes to the educational program. This means it must be one in which graduate students can participate. This means, among other things, it must not be ‘classified’, either for reasons of trade secrecy or military security. (DuBridge 1959 : 109–110)

In the discourse among academic teachers, the ideal of training “good scientists” was not compatible with military or other contract research (Elvehjem 1959 : 94). Even engineering sciences felt compelled to adopt pure-science ideals whereby profit and research projects with self-serving interests should be taboo in institutions of higher education as long as they were part of scientific training. Given the fact that the growing number of military-related research projects at universities during the Cold-War years often included doctoral students, these statements certainly did not mirror the actual practice in the higher education of engineers (Dennis 1994 ). They rather seem to reflect the increasing uneasiness with the security guidelines related to contract research for the armed forces and the increasing number of military-related research projects.

The call for new knowledge through basic research in the post-war era also reached industry. Big companies such as DuPont or the Bell Telephone Company, which could afford their own well-equipped laboratories, intended to expand their participation in basic research after the war had ended (Fisk 1959 ). However, since economic rationales entailed selecting projects that were most likely to lead to innovation, these companies welcomed the idea of the federal government funding riskier projects to be carried out at the universities (Greenewalt 1959 : 130). After all, failures and deadlocks – all more or less inevitable parts of the scientific production of knowledge – would cause costs they wanted to avoid. Furthermore, industry representatives appreciated federal support for training the future generation of researchers they needed. This division of labour was financially promising for companies as “a technological savings account” (Greenewalt 1959 ).

While the amount of research carried out in direct response to economic and military demands had increased tremendously since the Korean War (Killian 1959a : 122), the university was meant to become a sort of reservation for long-term basic research within a changing research landscape. Academic freedom in the second half of the 20th century largely sought to liberate science from over-the-top societal expectations. Protecting scientific research “from the insistent demands of applied research” became a central argument deployed by scientists as well as industry and politics (Weaver 1959 : XIV; see also Greenewalt 1959 : 128). Yet what was initially intended to protect scarce knowledge resources could, in the long run, transform into an ideal of purity. The university was granted the status of a reservation in the midst of a rapidly changing research landscape in order to protect science against excessive expectations and thus guarantee the open development of scientific knowledge. With the status of reservation, however, also came the danger that research conditions be artificially conserved, making it difficult to respond to changes in scientific practices.

The Revival of 19th-Century Epistemic Norms and Virtues

The shift from a discourse of knowledge sustainability to a discourse of purity affected the epistemic concepts of science in particular and, in so doing, appeared to hark back to ideas coursing in the 18th and 19th centuries. First and foremost, this shift concerned the relationship between the natural sciences and technology. Although Vannevar Bush himself dealt with basic questions in mathematics – a central basic discipline in engineering – as well as with construction design in his own research, his proposals ended up reviving the old distinction between nature and technology because they made the distinction between engineering, on the one hand, and the natural sciences, on the other.

Historians have explained this distinctive position on the natural sciences with Bush’s personal concepts of administration and his ideas about achieving excellence in science through specialized researchers, based, of course, on the premise that the rationale of open science would guarantee the unhindered diffusion of knowledge for the benefit of technical progress (Reingold 1987 : 306–307). This relapse into outdated concepts of science, however, cannot be reduced to the personal preferences of Vannevar Bush. It should instead be seen as a broader academic phenomenon, which began as a move to counter the increasing demand on science for immediate benefits that reached its height during the Second World War, before finally turning into a political programme in the West, nestled within the ideological competition of the Cold War.

In order to protect basic research in the natural sciences, academic experts wanted these disciplines to steer clear of any kind of technical development. As Alan T. Waterman ( 1959 : 28) proclaimed in 1959, “the growing applications of physics, chemistry, and mathematics should be shifted to engineering departments and kept out of the regular science departments”. In other words, from the point of view of the natural sciences, applied research primarily meant research that sought to yield future technology.

An oceanographical study carried out within the context of naval research in the late 1940s and early 1950s reconfirms this one-dimensional understanding of applied research in contrast to basic research. The US Office of Naval Research was a staunch supporter of basic research in oceanography, yet the question of secrecy revealed that the Navy and scientists differed in their classification of basic and applied research and in their notion of utility. Oceanographers defined their investigations of the topographical features or meteorological conditions of the ocean as basic research as long as it did not expressly serve the development of technology destined for use by the Navy. The Navy, however, developed “a more sophisticated definition of basic research that would take its operational nature into account” and demonstrated strategic utility of geography for military purposes (Hamblin 2002 : 27).

This purification of the natural sciences even affected the existing research vocabulary. Science policy experts tried to find new labels for research fields in engineering formerly classified as fundamental or basic research. The term “analytical engineering” is a good example of this renaming practice (Killian 1959a : 122). Moreover, in the debates revolving around basic research in the post-war era, the whole attitude towards technology appeared to become more ambivalent. In the 1950s, the National Science Foundation still justified the support for basic research primarily by the goal of enhancing technical progress. At the same time, it became ever more common for statements on science to conclude with a declaration bearing the motivating force behind scientific endeavour; the pursuit of knowledge for its own sake and the quest for truth became the appendix of federal science policy (Waterman 1959 : 37–40; Astin 1959 : 154).

Researchers in innovation studies have associated post-war research policy with the “linear model”, that is with a linear trajectory from basic research in the natural sciences to technology (Edgerton 2004 ). Implicit in the new policy of basic research was a renaissance of the older epistemic notion of an asymmetry of knowledge and, by association, the scientific preference for research led by theoretical questions. Particular support for basic research in the natural sciences was grounded in the hope that a few basic discoveries would be sufficient to significantly broaden the potential for technological application (Elvehjem 1959 : 98). In the process of striving for the endless frontier of the unknown, the idea of major theories in the natural sciences came to be the ultimate driving force of scientific progress and thus a further argument for supporting basic research.

Even representatives of industrial research endorsed the orientation of academic research towards theory in order to provide mutual benefit:

[T]he existence of even a crude and preliminary physical theory and the heeding of it in the expectations and patterns of operation of scientific work would permit coupling of the individual, uncommitted, undirected researcher to the general objectives of economic and social programs. … In the still regrettably small list of findings from basic scientific research which have been quickly and directly connected with large advances in technology and useful operations are several important examples. In these, the really new idea came out because a unifying theory had displaced the true possibilities – the wide range of means rather than simply the ends themselves … (W. O. Baker 1959 : 54).

This hierarchical and linear notion of knowledge production contrasted with a more dynamic understanding of the relationship between fundamentally theoretical questions and approaches that started out from a concrete problem of application. Although the professional self-image of academic superiority certainly continued to have an effect on epistemic ideas and norms in the late 19th and early 20th centuries, shifting research practices had already begun breaking up this static epistemic model. As the special support of basic research and its distinctive position within the different research activities was beyond dispute in the late 1950s, representatives of industrial research or national laboratories only casually mentioned the mutual reinforcement of theoretical and application problems they encountered (Astin 1959 : 145, 151; Fisk 1959 : 160–161).

Debates on basic research eventually revealed another old epistemic ideal referring to the intellectual qualities of researchers and to research conditions that encouraged scientific creativity. New (federal) support for basic research initially focused on individual researchers in order to foster “the development of the individual scientist” (Waterman 1959 : 34; see also Weaver 1959 : XI; Greenewalt 1959 : 128–131; Morison 1959 : 230). Experts esteemed individual creativity as the main property of outstanding scientists, enabling them to move forward into the unknown. The free flow of unconstrained intellectual creativity was thus defined as basic research. Not least, the financial relief stemming from regular federal funding was well received as a guarantee of intellectual freedom (Tuve 1959 ).

This particular position was backed up by the revival of old academic virtues. “[T]ruly ‘basic research’ was driven by a passionate love for knowledge. Basic research thus meant ‘support for ideas’ in the first place” (Tuve 1959 : 174, 175; see also Waterman 1959 ). This definition of basic research tended to be averse to technology. Furthermore, the hierarchy of basic and applied research implied the moral superiority of academic research over benefit-oriented industrial research, even on the personal level of researchers (Elvehjem 1959 : 94–96). In the end, the epistemic virtue of disinterestedness – according to Robert Merton one of four imperatives of modern science – got mixed up with social and moral values.

This deep appreciation of individuality was partly a reaction to the growing experience of scientific teamwork, which had become common within large military or industrial research projects. Individual creativity contrasted with the conservative atmosphere of research groups, which tended to object to fresh, radical ideas (Waterman 1959 : 30; Tuve 1959 : 176). Even those involved in industrial research highlighted the advantage of academic research because companies were only able to offer limited space for the individuality of their researchers. Furthermore, the freedom of investigation was supposed to be a special incentive for academic research – an incentive that had to compete with the high salaries and the technologically well-equipped laboratories in industrial research (Elvehjem 1959 : 96–97). Praise for individuality in science, however, derived partly from the ideological value of individualism in Western civilization. The first director of the National Science Foundation, Alan T. Waterman, put it like this: “Surely one of the great assets of democracy is the encouragement of individual initiative” (Waterman 1959 : 25).

Democracy at Risk: The Ideological Role of Basic Research in the Cold-War US

The ideological potential of the basic-research concept contributed significantly to the shift from a discourse of sustainability to one of purity. Politicians, for example US President Dwight D. Eisenhower, translated the new science policy directly into political slogans such as “Science: Handmaiden of Freedom” (Eisenhower 1959 ). Politicians still placed great hopes and expectations on science as the pacemaker of technical progress, capable of securing national security, national welfare, and prosperity. At the same time, their support of basic research enabled politicians to praise academic freedom as an overall value of liberal Western society. In addition to this, federal funding for basic research, defined as support for individual initiative and creativity, symbolized the individualism within democracy (Waterman 1959 : 25). As a collective symbol bridging the gap between scientific and public discourse by the polysemy of metaphors, basic research offered a true ideological surplus. Politicians further contrasted the “limited or local application” within mission-directed research with the universality of basic research designed to “benefit all mankind” (Eisenhower 1959 : 137). Leading the technological race with the launch of its Sputnik satellite, the Soviet Union then stood for an application-oriented understanding of science in the service of communist goals, whereas the Western argument pertaining to the universality and openness of basic research claimed ethical superiority.

During the 1950s, this high praise for free basic research stood in opposition to the high percentage of projects funded by the military and the increased demands for secrecy imposed on large areas of research in physics or other fields relevant to military projects by US security policy. It is telling that, in 1951, Alan Waterman, first director of the National Science Foundation and former technical director of the Office of Naval Research, emphasized the role of science in the situation of national emergency in the wake of conflict with the communist world; in spite of the National Science Foundation’s basic research programme, he underlined the need for science to focus on urgent application problems (Waterman 1951 ). According to the literature (Forman 1987 ; Westwick 2000 ), patriotic mobilization among scientists was still high. Many classified their research voluntarily, or adjusted to political pressure for security by compartmentalizing research and forming classified communities. Although these strategies were supposed to guarantee as much scientific exchange as possible, secrecy meant that research largely took place within a national context.

Moreover, the debates in Science during the 1950s demonstrate that the secrecy policy and the effects of a dominating military grip on science gave more and more cause for concern within the scientific community. Scientists criticized the idea that the military had a “sophisticated understanding of the needs of basic research”, arguing, moreover, that “those branches of pure science that lack military appeal are as badly off financially as they ever were” (Phillips 1952 : 440). In the early 1960s, military or military-related institutes, such as the Office of Naval Research, were still financing most academic research, in particular at prestigious universities (Leslie 1993 ). Against this backdrop, the political reading of basic research was not merely an aspect of portraying the US as a liberal society to the outside world. The debate on basic research also reflected, more controversially, the internal effects of the cold war on research. The debate was embedded in a more general intellectual discourse on the consequences of the predominant security policy and the growing power of the military for democratic society (see, for example, Shils 1956 : 176–191).

Eisenhower’s statements demonstrated this growing ambiguity. In his well-known “Farewell Address” from 1961, the departing president, former supreme allied commander and president of Columbia University, warned against the growing power of a “military-industrial complex”:

[W]e must guard against the acquisition of unwarranted influence, whether sought or unsought, by the military-industrial complex. The potential for the disastrous rise of misplaced power exists and will persist. We must never let the weight of this combination endanger our liberties or democratic processes. We should take nothing for granted. Only an alert and knowledgeable citizenry can compel the proper meshing of the huge industrial and military machinery of defense with our peaceful methods and goals, so that security and liberty may prosper together. (Eisenhower 2003 : 414)

Eisenhower construed financially attractive contract research as a threat to the academic “fountainhead of free ideas”. More importantly, he warned against the menace to public policy and civil society of a new “scientific-technological elite” (Eisenhower 2003 : 414–415). Although Robert Merton had already stressed the similarity or affinity between open science and Western democracy, in the late 1950s and early 1960s Eisenhower and other politicians identified science as a threat to democracy when a close connection between science, the military, and the economy remained intact (Wang 1999b ).

Along with the attribute of universality, another of Merton’s four imperatives of modern science, the notion of truth also gained importance in this ideological discourse (Waterman 1959 : 39). The ideal of truth had already been part of the ideological fight against fascism during the Second World War when researchers emphasized that science offered more than technical applications: “American science therefore has an especial duty to keep aflame the torch of free research for truth, which is dimmed or gone out in so many lands” (Blakeslee 1940 : 592).

As the natural sciences had needed a long time to set themselves apart from an understanding of science dominated by natural philosophy, the revitalization of the idea of universal truth appears anachronistic. In the 19th century, the natural sciences developed a mechanical and structural understanding of objectivity based on methodological processes that sometimes even stood in contradiction to the quest for truth and certitude (Daston 2000 : 32–34). At the beginning of the 20th century, the quest for truth had something old-fashioned about it in a scientific era in which research was constantly doing away with established certainties.

Coping with Ethical Dilemmas in the Cold-War Era

During the Cold War, however, the attributes of truth and universality were revitalized and became part of an effort to present science as a politically and ideologically independent authority in society. From the viewpoint of politics, science was able to act as a neutral authority upon which decision-makers could rely (Price 1962 : 1105). Scientists themselves praised the idea “that science has something more valuable than its material gifts to offer. … Science can have no dogma, no arbitrary authority, no ‘party line’” (Sinnott 1950 : 125). Scientific virtues of “objectivity, tolerance, reluctance to distort or suppress evidence, and willingness to accept sound logic and demonstrable fact” were transformed into political virtues (W. P. Taylor 1953 : 449). At the same time, however, the position of impartial experts tended to be morally overloaded when scientists were meant to become missionaries of “reason and good will” in the fight against “falsehood and hate” (Sinnott 1950 : 126; see also Szent-Györgyi 1957 ; Rapoport 1957 ; Weaver 1961 : 259). In fact, the democratic framing of basic research and the revival of knowledge ideals in the tradition of Humanism led to a politicization of science and, as a result, basic research itself became part of ideology, namely Western ideology.

Historians have already pointed to the various ideological dimensions of science in the post-war period (Wang 1999a ; Ash 2006 : 30; for the social sciences and humanities, see Bender 1997 ). Some scholars from science and technology studies blame Robert Merton’s comparison of science in democracy with science in fascist and communist regimes for the misconception of scientific ideals such as autonomy and universality, a misconception that they have been trying to correct ever since (see the overview in Daston 2000 : 18–20). But the societal, political, and ethical implications of the basic-research concept were embraced by the scientific community, even without sociological mediation.

After the atomic bomb was dropped on Hiroshima, the role of science in society certainly became more contradictory (Conant 1961 : 6–13). While researchers had wholeheartedly praised the salutary benefits of science before Hiroshima (A. H. Compton 1940 : 56), contemporaries noted afterwards that the “atom bomb once and for all explodes the ‘neutrality’ of technology” (Shepard 1946 : 66). The promise of progress was only one side of the coin. Scientists became increasingly aware of the burden of responsibility in their own research. Some of them hoped to avoid this problem by pursuing more theoretical research topics. Others tried to take political action, such as the atomic physicists’ movement, which fought for civilian use of scientific knowledge and technological invention. But the anxious atmosphere during the Cold War period – anti-communist harassment and the increasing public fear of a new scientific-technological elite – aggravated the ethical dilemmas of post-war science.

A statement made by the physicist Julius Robert Oppenheimer, a leading figure in the Manhattan Project, about the debate on basic research indicates scientists’ uneasiness when they were faced with these dilemmas: “The argument that the quest for new knowledge, which is basic science, is ennobling, and the argument that the quest for new knowledge produces new knowledge which is useful to technology and thus to practice, are disturbingly separate and unrelated arguments. … Yet science and technology are symbiotic” (Oppenheimer 1959 : 9; for a similar argument, see W. O. Baker 1959 : 43–47). Oppenheimer seemed to suspect that the debate on basic research simply reflected these modern dilemmas. It is striking, but also telling, that he tried hard to avoid the dualistic semantics that characterized science policy at this time. Oppenheimer explicitly raised the political problems brought about by the powerful scientific culture of the 20th century. Taking the growing criticism toward scientists into account, the physicist believed that making the public understand research goals had become difficult. While the impact of science on society had increased tremendously, the fast growth of scientific knowledge and technical innovations made it hard for laypersons to judge issues in science policy. Oppenheimer feared that this asymmetry of knowledge between experts and the lay public weakened democratic political decision-making (Oppenheimer 1959 : 12–13). Footnote 17

The charges brought by the McCarthy Committee in 1954 against Oppenheimer relating to his opposition to the hydrogen bomb illustrate that scientists who were willing to assume responsibility for their research by taking political action had to learn the hard way that there was little room in the political climate of the Cold War to deal openly with these dilemmas of modern science (Bird and Sherwin 2005 : 462–550). With regard to scientists of the progressive left advocating a more utility-oriented notion of science, Jessica Wang notes that “[a]lthough their views on the structure of postwar science were not directly responsible for their political difficulties in every case, these scientists and others who embraced a liberal-left politics of science were likely to hold other views that made them vulnerable to anti-communist attacks and excluded them from political influence” (Wang 1995 : 166). In the mid 1950s, the National Science Foundation and the Academy of Science included the criterion of national loyalty into their peer-review system for unclassified research. Both organisations thus sought to avoid allegations of supporting researchers who were suspected of sympathising with communist ideas (Waterman 1960 : 127; Committee on Loyalty in Relation to Government Support of Unclassified Research 1956 ).

The question of loyalty arose especially when it came to discussing technological application, as an official statement by the President of Associated Universities addressed to the Committee on Government Operations confirmed:

If a scientist expresses a strong view on some technological matter that may be contrary to the application of technology to current or to subsequent policy, he is open to the accusation of taking this view with the intent of deliberate subversion. … Moreover, secrecy prevents him from stating the essential technical grounds on which his view is based. Therefore, in the simple process of doing his job for his country well, he is open to damaging criticism against which he is permitted to produce little defense. (Berkner 1956 : 784–785)

Given this pitfall, the discursive separation of science from technology provided a strategy to avoid the risk of being forced to go “politicking”, which gradually came to be considered as the “disease” of the project research dominating American universities at that time (Gates 1958 : 234).

In this particular situation (the ethical dilemmas of the techno-scientific world, the fragile relationship between science and the public in democracy, and the ideological antagonism during the Cold War), the dissociation of the natural sciences from applied research and any practical application of scientific knowledge was thought to offer a strategy of individual, professional, and institutional relief: Firstly, a strategy that avoids assuming ethical responsibility for the changes caused by scientific knowledge. Secondly, a sort of self-protecting strategy that sought to avoid the direct line of political fire in a society entirely concerned with national security, the latter which produced an atmosphere of suspicion. And thirdly, a strategy of political neutrality and independence from any self-serving interests as a means of guaranteeing the institutional freedom of academic science and a self-regulating scientific community which, from a scientific point of view, was best capable of dealing with the open and often unpredictable process of epistemic progress. The scientific community retreated into a “satisfactory philosophy of ignorance”; as long as science was defined as institutionalized scepticism, it was still possible to maintain the belief in science or scientific knowledge as an indispensable value of modern civilization (Feynman 1955 : 15).

Conflicting Promises and Their Effects on the Public Image of Science

This neutral position secured the federal funding of research at universities in the US – something the universities had longed for since the 1920s. In return, academic researchers promised simply that science would lay the foundation for progress. They also offered their expertise to politics, thus acting as an independent authority over truth in a pluralistic, democratic society. The certainty academic scientists offered appeared to be especially welcome at a time in which society was driven by great anxiety. With regard to the outside image of the US during the Cold War, the universities’ role as reservations devoted to autonomous science served as a symbol for Western liberal society in the tradition of Humanism amid the great ideological competition, while simultaneously providing fig-leaf camouflage for the technology-based arms race. The post-war understanding of scientific autonomy was, in fact, the result of a broad process of the politicization of science arising from the growing importance of scientific knowledge for society.

Since there is, by definition, no clear solution for dilemmas, the strategy of basic research inevitably caused problems for the relationship between science and the public in the long run. Articles on this relationship and on topics such as the responsibility of science in the late 1950s show that public mediation between the needs of science and those of society became increasingly problematic (Killian 1959b : 136; Sayre 1961 ; Price 1962 ). According to Bender, this understanding of the autonomy of science, in particular the position of elitist experts and how they neglected their responsibilities, alienated science from society, evoked the impression of an academic ivory tower, and, finally, ended in federal budget cuts for academic research (Bender 1997 : 8–12).

Moreover, I argue that the simple promises of truth and progress scientists had avowed to society covered the complexity and uncertainty of research dynamics as well as the tentativeness of contested scientific knowledge. Moreover, the authority of scientific objectivity and methodologically certified knowledge revealed its limitations during political negotiations on values and societal goals; the position of moral neutrality might bewilder the public. It could thus lead to disappointment, misunderstanding, and even to the loss of science’s integrity in the public sphere. Furthermore, the increasing interlocking of technology and the natural sciences was also hidden behind praise for basic research. Since technological innovation had become part of the natural sciences, questions of risk and utility had inevitably arisen and transformed themselves into political and ethical issues: Who will profit from the results? How do we manage risks?

Only few researchers at that time anticipated that the excessive expectations of and contradictory demands on research might turn the public against science (W. O. Baker 1959 : 48; Dryden 1954 ). The shift from a discourse of knowledge sustainability to one of purity meant that the concept of basic research itself sent contradictory signals to the public: “The uneasiness of scientists on this score is revealed by the observation that, whereas they claim among themselves that their primary interest is in the conceptual, not in the applied, aspects of science, in public they justify basic research by asserting that it always leads to ‘useful’ results” (Dubos 1961 : 1209; see also Daniels 1967 ).

In fact, the concept of basic research and the underlying linear model of innovation had already come under attack in the late 1960s and early 1970s. The long-term and highly speculative nature of scientific research was difficult to communicate to a public that expected economic prosperity and welfare here and now. Society’s disappointment backfired on the scientific community and stimulated a debate about the appropriateness of dissociating basic from applied research (Abelson 1966 ; Reagan 1967 ). Yet this crisis is another chapter in the conceptual history of basic research and goes beyond the scope of this paper.

Despite recurring crises, the concept of basic research functioned as a collective symbol for science policy over quite a long period of time. Moreover, the semantics of the new US science policy spread across the entire Western world. Ever since the National Science Foundation established a periodical survey of overall research in the US based on the categories basic research, applied research, and development (the final stage of innovation, when technologies or ideas are turned into marketable products), nearly all countries in the Organisation for Economic Co-operation and Development (OECD) adopted this classification (OECD 1976 ). Basic research and its corresponding categories were converted into enduring statistical realities that played a crucial role in budget planning within industry and in funding allocation undertaken by government bodies (Godin 2005b ).

Fundamental Research in the Federal Republic of Germany: A Brief Overview

In the Federal Republic of Germany (hereafter referred to as West Germany), fundamental research also became a key concept in science policy. The impact of the American role model on West Germany is quite obvious. Within the context of re-education and development programmes, those representing US science promoted their concept of science in democracy with its special focus on fundamental research in West Germany (Conant 1953 ; Bush 1954 ). However, the national characteristics of the German research landscape coupled with the historical burden of the Nazi past meant that the way fundamental research and its corresponding discourses were implemented differed to a certain degree from the American experience. I will briefly mention some of these Germany-specific characteristics in order to maintain a balance between the two national perspectives.

After the Second World War, the Allies assumed control of science in Germany with the intention of suppressing all further research activities relevant to the development of armaments. Allied Control Council Acts and the ensuing executive regulations specified by each of the Western occupation zones forbade any fundamental or applied scientific research with military relevance (Frowein 1949 , 1950 ). Footnote 18 It is remarkable that the crucial criterion for prohibition was the military potential of research projects rather than the difference between fundamental and applied research.

Similar to the American reaction to Bush’s proposals, discussions within the German scientific community over the dissociation of basic from applied research were quite controversial in the initial post-war years. Those from engineering or the applied sciences were particularly confused by this distinction and felt insecure about their future position and status within academia (Vieweg 1950 : 731–732; Sörensen 1952 : 158). The creation of compounds such as “applied fundamental research” ( angewandte Grundlagenforschung ) was a further German strategy designed to overcome this confusion in engineering (Heiss 1950 : 121, 127; Wever 1952 : 1053).

In order to cope with the Nazi past, the concept of pure science was initially more attractive because of its moral connotation in the sense of innocence. Many scientists labelled their research activities during the Nazi period retrospectively as pure science in order to avoid being accused of complying with and supporting the former fascist regime (Mehrtens 1994 ). In general, the revival of ideals belonging to the 19th-century concept of pure science was more extensive than in the US. Reference to the Humanist notion of education became part of the programme to democratise society. This notion thus shaped the self-understanding of German universities, which culminated in a re-glorification of the Prussian university reformer Wilhelm von Humboldt, who had emphasized the educational function of science. Footnote 19 German professors embraced the older scientific ideal of truth-seeking as the ultimate motive for research. Footnote 20

Right after the war, German academics tried hard to avoid the impression that their research was driven by any political or economic interests. The US occupying forces certainly wanted to keep science at a great distance to politics, but they did not seek to suppress economically and technically promising research (Cassidy 1996 : 200–206). In fact, the growing tension with the Soviet Union meant that the Marshall Plan’s aim was speedy economic recovery in both Germany and Western Europe. John Krige has already pointed out that the basic-research concept played a key role in reconstructing European science under “American hegemony”. Firstly, the concept was important for communicating the US financial support for the former wartime enemies towards the American public. Secondly, it transported the Western ideology and was therefore part of the envisioned democratization process in central Europe. Finally, the US promoted basic research as unclassified research in the allied countries in order to increase its stock of scientific knowledge and thus to secure the American technological leadership (Krige 2010 ).

The German discourse on the general role of science in society defined scientific knowledge primarily as a cultural good in order to strip off the Nazi past: science was given a religious appeal (Walden 1946 ; Rein 1946 ; Reppe 1950 : 1; Erbe 1954 ). However, even scientists such as the physicist Otto Hahn, who argued strongly that research in the natural sciences should abstain from any economic or technological considerations, advocating instead that science ought to be driven by the thirst for knowledge, campaigned for research funding by highlighting examples of scientific discoveries that eventually led to successful products or innovative technology (Hahn 1949 , 1954 ).

Overall, the German concept of fundamental research resembled the US one in many ways. It encompassed the idea of the long-term perspective and that of the scientific knowledge reservoir or resource, the demand for scientific talent, individual creativity in research, and the belief in disciplinary specialisation (see, for instance, Reppe 1950 ). Eventually, fundamental research also became the key concept in the public funding of research in West Germany. However, the Germans’ attempt to institutionally dissociate academic research from research promising primarily economic utility was much more radical than in the US. West Germany founded the German Research Foundation in order to fund academic research and the Fraunhofer-Gesellschaft (Fraunhofer Society) as the funding body responsible for economically relevant research.

The German Research Foundation’s crucial criterion for defining fundamental research was the institutional autonomy of academic research. According to a number of historical studies, this particular focus on fundamental research implied a restoration of the power held by full professors (known in Germany as the Ordinariensystem ). As the demands for the applied and the technical sciences were growing continually, in 1956, the German Research Foundation also established a special commission for funding applied research. However, this commission failed to gain importance as an instrument for promoting research in the technical sciences (Deutsche Forschungsgemeinschaft 1956 ). Technical universities therefore had to look for financial support from another quarter. In the end, the ideal of fundamental research in West Germany seemed to slow down the institutional emancipation of technical colleges from universities. Furthermore, academic research lost contact with expensive, major scientific projects carried out in publicly funded research institutes (Orth 2011 ).

All in all, the shift from a discourse of sustainability to one of purity after the Second World War appears to have been a transnational process, although both the background and the intensity of the purity ideals in the US and in West Germany differed from one another. In both nations, the purity discourse implied a revival of scientific ideals dating back to the 19th century. West Germany adopted the American imperative of basic research, but German scientists referred more extensively to the Humanist tradition of academia because they had to dissociate themselves from their Nazi past. As universities were supposed to play an important role in Germany’s effort to progress towards democracy, academic science was defined by primarily educational ideals. The call for basic research after the Second World War in the US initially sought to maintain federal funding for academic research in order to enable scientific talent to flourish without it being subject to pressure from the expectation of benefit held by society. Basic research became a key concept in US federal science policy because the latter defined science as a common good with a long-term perspective. However, the fact that this key concept became crucial in the long run can be explained only by the fact that it functioned as a discursive strategy designed to cope with the political and ethical dilemmas of science during the Cold War.

Conclusions

This article has sought to demonstrate the importance of an historical approach in order to, firstly, understand the complex meanings of basic research and, secondly, answer the two questions of why science policy revolved around the concept of basic research and its dissociation from applied research for such a long time, and why this is still such a hot topic in science and technology studies today. If we continue to describe basic research as a timeless, clearly definable mode, even as an ideal type of research in contrast to applied research, we completely overlook the reason why this key concept in modern science policy emerged at all. In fact, this study has shown that the term basic research cannot be seen as a simple synonym for the older notion of pure science. As a consequence, the assumption made in social-scientific studies that the ideal of basic research structured modern science continuously up until the postmodern era, when application-oriented research was thought to gain predominance, needs to be corrected.

As the term basic research emerged in the early 20th century and became more common only in the late 1930s, it is actually quite young. Basic research is best described as a collective symbol of science policy designed to bridge the gap between the desire to support research, despite the fact that scientific output is unpredictable and that the expectations placed upon science by society have been growing constantly during the 20th century. For the history of basic research, it is crucial to note that the concept itself (as well as similar terms such as fundamental research) initially emerged in both the natural sciences within research fields that pursued explicitly practical ends and subdisciplines of engineering that targeted technological innovation and improvement.

While science profited financially from society’s growing demand for research, researchers simultaneously faced pressure from society’s expectation that science should produce immediately exploitable knowledge. In deploying the concept of basic research, scientists promised the public that research would lay the ultimate foundation for all sorts of progress and innovation, while at the same time conveying the experience that scientific research was time-consuming and its outcome and technical applications were hard to predict. Until 1945, basic research primarily meant long-term research in the natural sciences that was ultimately expected to solve practical problems.

After the Second World War, basic research became a central concept of US science policy, which particularly promoted research at universities and non-profit research institutes. Although the scientific promise of progress remained an important message in this concept after 1945, the discourse revolving around basic research shifted considerably in the post-war period from a discourse of knowledge sustainability to a discourse of purity. During the war, scientists had learnt to value massive governmental support of research, but they were concerned that the short-term planning of war-related research and its security restrictions would put the sustainability of both scientific knowledge and manpower at risk in the long run. Believing that scientists knew best when it came down to making science flourish and knowing what it took to explore the unknown, the challenge for scientists was legitimizing the continuance of federal science funding while at the same time advocating the institutional autonomy of science.

To this end, science policy advisers such as Vannevar Bush revived a long-lasting semantic reservoir of scientific ideals. By dissociating scientific knowledge from its potential applications, it became possible to define academic research as a common good capable of laying claim to federal protection, just as the older concept of pure science had done before. Bush’s proposal focused on the natural sciences, whose studies in the fundamental principles of nature were thought to offer nearly endless possibilities for technical innovation. Moreover, this new definition responded to the educational tasks undertaken by universities in which research projects were part of scientific qualification. The self-concept of higher education institutions traditionally kept their distance from any utilitarian aspects of scientific knowledge.

The distinction between basic and applied research thus served, first and foremost, as a criterion governing the allocation of federal funding, implemented through the newly founded National Science Foundation. Although the majority of researchers were grateful for the new federal support for research, the concept of basic research became the subject of controversies in the late 1940s because it reanimated ideals and norms of the older, European discourse of pure science. With these semantic references, basic research evoked older epistemic and social hierarchies. Research was seen to be more theory- than problem-oriented, the natural sciences assumed moral superiority over the technical sciences, and academic researchers were considered morally superior to industrial researchers. The individual pursuit of knowledge ennobled academic researchers, who became detached from immediate demands so that scientific creativity was given free rein.

To a certain extent, the re-establishment of older scientific ideals was a reaction to the exceptional conditions of wartime research. In many research fields, however, the ideals belonging to a former notion of science contradicted the changed practices in and demands placed on research in the 20th century. In particular, the idea of keeping technology apart from the natural sciences, which derived from an artificial funding demarcation, appeared anachronistic. These social and epistemic attributions of basic research looked like a cultural lag in modern science.

The reason why the concept of basic research, with all its reminiscences to former purity discourses, finally prevailed was that it functioned as a discursive strategy to cope with the difficult relationship between science and the public, the ideologically charged atmosphere of the Cold War, and the ethical dilemmas in science during the second half of the 20th century. When it comes to the political dimension of the concept of basic research, there are usually references to Robert Merton or Michael Polanyi, who stated that only democracy guaranteed full scientific autonomy and that, vice versa, scientific independence was a prerequisite of democratic pluralism because it presented a disinterested authority of truth (Merton 1942 ; Polanyi 1962 ). This self-image of science as being autonomous and disinterested was partly a result of Western ideology competing with the Soviet Union during the Cold War.

The effects of the Cold War on domestic politics were a major challenge to the scientific community. The first use of the atomic bomb rendered discussion of the goals of science unavoidable. Scientists who took part in the debate about the application of scientific knowledge for good or bad discovered that there was little room for negotiation in Cold-War America. At the same time, the relationship between the scientific community and the public became ever tenser as cooperation between science and the military increased. American intellectuals perceived this military-science nexus as a threat to US democratic culture. As a consequence, resorting to basic research was part of a strategy of relief – not only relief from society’s expectation of science to produce immediate benefits, but also from political controversies that might affect a researcher’s reputation and put his or her chances of acquiring funding at risk.

The success of this key concept in science policy lay in the polysemy of “basic”, which functioned as a kind of self-reassurance within the scientific community and could be used to signal societal utility when communicating to the wider public. The concept of basic research thus worked as collective symbol linking the public discourse to the scientific discourse. The label “basic” signified that research was a precondition for future scientific progress. At the same time, it communicated the fundamental importance of research for societal or economic and technological progress.

In the end, the concept of basic research could not solve the dilemmas of science in 20th-century societies. In fact, it produced its own confusion and misleading expectations. The simplified promise of progress depended on society’s confidence, which dwindled during the economic crisis of the 1970s. Particularly after the discourse on basic research referred to the intrinsic ideal of science for its own sake, society’s trust was put at risk. In addition, the simplified promise of scientific objectivity – the alleged neutrality of facts – obscured the actual complexity of research, where scientific truth is always contested.

Which lessons can we draw from this analysis for the current theoretical debate in science and technology studies? Bruno Latour’s argument against basic research is well taken in so far as the semantics of the concept do not represent the actual research practices and their institutional settings. As the above analysis has shown, the same type of criticism was already voiced by contemporaries of Vannevar Bush. Bush’s definition of basic research, especially his dissociation of the natural sciences from the technical sciences and its purity ideals, however, were more than just a simple misrepresentation: they had a long-lasting effect on the Western notion of science and research policy.

The resort to purity ideals can be blamed for retarding or interrupting this reflective process within the natural sciences. Moreover, there are hints that the reference to older scientific ideals led to taboos being placed on research topics leading to technical innovations, at least in some disciplines within the natural sciences. Eric J. Vettel has demonstrated how the revival of the policy of pure science in the 1950s altered research topics and institutional organization in microbiology and how the turn toward an application-oriented research policy during the 1960s led, in the long run, to biotechnology (Vettel 2006 ). The discipline of biology is thus a good example for demonstrating that parts of this purity discourse have, once again, faded away. The self-image of researchers like Craig Venter, geneticist and entrepreneur, no longer corresponds to the old image of the quiet academic scholar (Venter 2007 ). These recent historical shifts have indeed been noted by science studies. But we need more long-term historical studies on individual research fields and disciplines – studies tracing the historical development of research topics as far back as the late 19th and early 20th centuries – in order to understand the effects of the return of the purity discourse after 1945.

Bruno Latour has described purity discourses as a typically modern phenomenon that has since become less important. The results of this analysis, however, show that the concept of basic research had many functions. Aside from its role as a criterion for distributing research funding, the concept of basic research mainly served as a strategy for coping, firstly, with society’s increasing expectations of science, secondly, with the ethical dilemmas associated with the debate on the overall purpose of science, and, thirdly, with the political implications of science’s role as an increasingly powerful force in society. The case of the US shows that, despite the initial criticism of anachronism, the revival of the purity discourse succeeded because the concept of basic research became a strategy to cope with the uncertainties and dilemmas of the Cold-War period.

In fact, many of these dilemmas will continue to challenge science policy in the 21st century. As science and technology have become powerful forces in our societies, they will be subject to both conflicts of interest and political and ethical controversies. Whether the strategy of dealing with these controversies will continue to characterize basic research is, however, an open question. On the one hand, some representatives of science and technology studies believe that previous strategies have failed in coping with these dilemmas (Jasanoff 2005 : 6; Shapin 2010 : 387–391). On the other hand, new concepts like the European Research Council’s “frontier research” seem to revive the basic-research concept – at least with regard to its original function as part of a discourse on knowledge sustainability, which the current article has sought to reveal (High-Level Expert Group 2005 : 16). Footnote 21

Analysis of these discourses provides us with insights into expectations placed upon future research and into societal and scientific experiences from the past, which, among other things, frame decisions about what kind of research society wants and what kind of research should be funded by the public purse. Historical semantics can help to elucidate scientific taboos, which are taken for granted because they are the outcome of specific political or societal situations. All in all, historical semantics could be one useful approach among many in science and technology studies. It provides a critical perspective on the complex relationship between science and society. Moreover, it helps to reveal the historical legacy of our notions of science and technology, including their multiple attributes, which are still very present, although this seems to have gone unnoticed by many contemporary observers. This is why the analysis of concepts such as basis research is (still) an interesting, worthwhile subject for science studies. However, with regard to the legacy of the concept of basic research, this article suggests that it should not be used as a technical term whose meanings can be taken for granted. Scholars in the field of science and technology studies are thus well advised to explicate which of the many facets of the term they allude to when using the concept of basic research.

See the subsection “The discursive strategy of ‘fundamental research’ and the reassessment of German science in the Nazi period” below for more details.

Given the recently growing interest in the relationship between science and technology, the reflection on terms like “applied science” seems to have started a little bit earlier (Bud 2012a , b ; Gooday 2012 ; Karns Alexander 2012 ; Lucier 2012 ; Schatzberg 2012 ). This literature, however, focusses primarily on the 19th century.

In contrast to older approaches of historical semantics like intellectual history or the history of ideas, which cling to the idea of authorship and reception theory, discourse analysis does not aim at identifying the pedigree of new concepts or arguments in detail. Instead of singling out individual statements or discussing personal rationales, discourse analysis rather tries to find common patters of arguments that are shared by many discourse participants.

The cited sources and documents do not represent the whole sample of texts. The texts I refer to in this article were chosen because they exemplify broader discursive strategies revolving around the concept of basic research in a particularly typical way.

In the German-speaking context, the term science is not restricted to natural sciences. This also applied to the English-speaking academia in early modern times.

The history of both scientific organizations was the subject of two major research projects, which have recently been concluded. See the wealth of research published in the academic series Beiträge zur Geschichte der deutschen Forschungsgemeinschaft (Stuttgart: Franz Steiner Verlag, four volumes published so far) and Geschichte der Kaiser-Wilhelm-Gesellschaft im Nationalsozialismus (Göttingen: Wallstein Verlag, seventeen volumes published so far).

In this article, I will consistently translate the German term Grundlagenforschung as “fundamental research”. Unlike the Anglo-American science discourse, the German science discourse does not accommodate conceptual variations such as that between “basic research” and “fundamental research”.

Trained as a theologian, Hans Hartmann worked as an author specializing in writing about scientists and science policy issues.

In other disciplinary contexts, the study of material properties was also denoted as goal-oriented research (Bauermeister 1938 : 479).

In the natural sciences during this period, the term only appeared in a very specific context. In the 1920s, a group of biologists and psychologists discussed the question of the functional usefulness of organs or mental processes within organisms, an approach they also labelled as Zweckforschung (E. Becher 1921 : 296–304; H. Becher 1923 ).

It took a while before the new term Zweckforschung (goal-oriented research) was established. Confusingly, some Nazi partisans used the term goal-oriented research as a synonym for pure science, criticizing the idea of research as an end in itself (W. Schultze 1938 : 5; Löhr 1938 : 20).

In order to get a better idea of conceptual dissemination, I provide here some results of a statistical analysis of the electronically accessible journal Science (including the supplement The Scientific Monthly ). Between 1921 and 1930, the term basic research was used 14 times and fundamental research was used 121 times. Between 1931 and 1940, the term basic research showed up 51 times and fundamental research 154 times. Just five years later, the term basic research had been employed 69 times and fundamental research 182 times.

See, for instance, the decision of the Senate of the Ludwig Maximilian University in Munich in the early 1920s (Archive of the University, Sen. 523).

Bush consistently referred to “basic research” in his report. At the time, however, the term “fundamental research” was more widespread.

As the broader research context was hidden to scientific peers and to the researchers themselves, this strategy at least allowed the publication of detailed results.

In 1944 and 1945 there was already an ongoing debate in letters to the editor of the journal Science that critically reflected the re-idealization of the notion of pure science (Stern 1944 ; Pearson 1944 ; Robin 1944 ; Feibleman 1944 ; Alexander 1945 ; Stern 1945 ; Moore 1945 ; J. R. Baker 1945 ).

See also James C. Connant’s contribution to the debate on the relationship between science and the public ( 1951 ). For more details, see Hershberg ( 1993 : 554–577).

The exact German terms deployed in the two key laws, the Kontrollratsgesetz no. 25 from 29.04.1946 and the Militärregierungsgesetz, no. 23 from 12.09.1949 were “grundlegende wissenschaftliche Forschung” and “angewandte wissenschaftliche Forschung”. The regulations defined military relevance on the basis of several classified research fields. For more details of how the allies controlled research, see Heinemann ( 2001 ).

In the German university tradition, Humboldt represents the link between education and research. For the current state of research on Humboldt’s role in university discourse, see Eichler ( 2012 ), Jarausch ( 1999 ).

The semantic differences between the two versions of the speech held by philosopher Karl Jaspers in 1923 and 1946 are significant when defining the role of universities in the wake of changes to the respective political systems: while Jaspers emphasized the objectivity of scientific knowledge in 1923, he replaced the term “objectivity” with “truth” in 1946 (Jaspers 1923 ; 1946 ).

The definition of frontier research relies again on the basic-applied nomenclature. In contrast to recent trends of promoting more applied forms of research, it shifts the balance further towards the basic-research pole of the continuum. See also the mission statement on the ERC website: http://erc.europa.eu/mission .

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This article is part of a larger research project on changing notions of science in modern history funded by the VolkswagenStiftung. I would like to thank Isabelle Huber and Johannes Wittlinger for their competent research assistance. I am also grateful for the valuable comments and suggestions of Oliver Treib, Claudia Stein, Ulrich Wengenroth, Peter Weingart, Florian Schmaltz, Anne Sudrow and the two anonymous referees. Finally, I am much obliged to Gabrielle Robilliard for her careful language editing.

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Schauz, D. What is Basic Research? Insights from Historical Semantics. Minerva 52 , 273–328 (2014). https://doi.org/10.1007/s11024-014-9255-0

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Univariate Analysis: basic theory and example

Univariate analysis: this article explains univariate analysis in a practical way. The article begins with a general explanation and an explanation of the reasons for applying this method in research, followed by the definition of the term and a graphical representation of the different ways of representing univariate statistics. Enjoy the read!

Introduction

Research is a dynamic process that carefully uses different techniques and methods to gain insights, validate hypotheses and make informed decisions.

Using a variety of analytical methods, researchers can gain a thorough understanding of their data, revealing patterns, trends, and relationships.

One of the main approaches or methods for research is the univariate analysis, which provides valuable insights into individual variables and their characteristics.

In this article, we dive into the world of univariate analysis, its definition, importance and applications in research.

Techniques and methods in research

Research methodologies encompass a wide variety of techniques and methods that help researchers extract meaningful information from their data. Some common approaches are:

Descriptive statistics

Summarizing data using measures such as mean, median, mode, variance, and standard deviation.

Inferential statistics

Drawing conclusions about a broader population based on a sample. Methods such as hypothesis testing and confidence intervals are used for this.

Multivariate analysis

Exploring relationships between multiple variables simultaneously, allowing researchers to explore complex interactions and dependencies. A bivariate analysis is when the relationship between two variables is explored.

Qualitative analysis

Discovering insights and trying to understand subjective type of data, such as interviews, observations and case studies.

Quantitative analysis

Analyzing numerical data using statistical methods to reveal patterns and trends.

What is univariate analysis?

Univariate analysis focuses on the study and interpretation of only one variable on its own, without considering possible relationships with other variables.

The method aims to understand the characteristics and behavior of that specific variable. Univariate analysis is the simplest form of analyzing data.

Definition of univariate

The term univariate consists of two elements: uni, which means one, and variate, which refers to a statistical variable. Therefore, univariate analysis focuses on exploring and summarizing the properties of one variable independently.

Importance of univariate analysis

Univariate analysis serves as an important first step in many research projects, as it provides essential insights and lays a foundation for further research. It offers researchers the following benefits:

Data exploration

Univariate analysis allows researchers to understand the distribution, central tendency, and variability of a variable.

Identification of outliers

By detecting anomalous values, univariate analysis helps identify outliers that require further investigation or treatment during the data analysis phase.

Data cleaning

Univariate analysis helps identify missing data, inconsistencies or errors within a variable, allowing researchers to refine and optimize their data set before moving on to more complex analyses.

Variable selection

Researchers can use the univariate analysis to determine which variables are most promising for further research. This enables efficient allocation of resources and hypothesis testing.

Reporting and visualization

Summarizing and visualizing univariate statistics facilitates clear and concise reporting of research results. This makes complex data more accessible to a wider audience.

Research Methods For Business Students Course A-Z guide to writing a rockstar Research Paper with a bulletproof Research Methodology!   More information

Applications of univariate analysis

Univariate analysis is used in various research areas and disciplines. It is often used in:

• Epidemiological studies to analyze risk factors
• Social science research to investigate attitudes, behaviors or socio-economic variables
• Market research to understand consumer preferences, buying patterns or market trends
• Environmental studies to investigate pollution, climate data or species distributions

By using univariate analysis, researchers can uncover valuable insights, detect trends, and lay the groundwork for more comprehensive statistical analysis.

Types of univariate analyses

The most common method of performing univariate analysis is summary statistics. The correct statistics are determined by the level of measurement or the nature of the information in the variabels. The following are the most common types of summary statistics:

• Measures of dispersion: these numbers describe how evenly the values are distributed in a dataset. The range, standard deviation, interquartile range, and variance are some examples.
• Range: the difference between the highest and lowest value in a data set.
• Standard deviation: an average measure of the spread.
• Interquartile range: the spread of the middle 50% of the values.
• Measures of central tendency: these numbers describe the location of the center point of a data set or the middle value of the data set. The mean, median and mode are the three main measures of central tendency.

Figure 1. Univariate Analysis – Types

Frequency table

Frequency indicates how often something occurs. The frequency of observation thus indicates the number of times an event occurs.

The frequency distribution table can display qualitative and numerical or quantitative variables. The distribution provides an overview of the data and allows you to spot patterns.

The bar chart is displayed in the form of rectangular bars. The chart compares different categories. The chart can be plotted vertically or horizontally.

In most cases, the bar is plotted vertically.

The horizontal or x-axis represents the category and the vertical y-axis represents the value of the category.

This diagram can be used, for example, to see which part of a budget is the largest.

A histogram is a graph that shows how often certain values occur in a data set. It consists of bars whose height indicates how often a certain value occurs.

Frequency polygon

The frequency polygon is very similar to the histogram. It is used to compare data sets or to display the cumulative frequency distribution.

The frequency polygon is displayed as a line graph.

The pie chart displays the data in a circular format. The diagram is divided into pieces where each piece is proportional to its part of the complete category. So each “pie slice” in the pie chart is a portion of the total. The total of the pieces should always be 100.

Example situation of an Univariate Analysis

An example of univariate analysis might be examining the age of employees in a company.

Data is collected on the age of all employees and then a univariate analysis is performed to understand the characteristics and distribution of this single variable.

We can calculate summary statistics, such as the mean, median, and standard deviation, to get an idea of the central tendency and range of ages.

Histograms can also be used to visualize the frequency of different age groups and to identify any patterns or outliers.

Now it’s your turn

What do you think? Do you recognize the explanation about the univariate analysis? Have you ever heard of univariate analysis? Have you applied it yourself during any of the studies you have conducted? Do you know of any other methods or techniques used in conjunction with univariate analysis? Are you familiar with the visual graphs used in univariate analysis?

Share your experience and knowledge in the comments box below.

More information about the Univariate Analysis

• Barick, R. (2021). Research Methods For Business Students . Retrieved 02/16/2024 from Udemy.
• Dowdy, S., Wearden, S., & Chilko, D. (2011). Statistics for research . John Wiley & Sons.
• Garfield, J., & Ben‐Zvi, D. (2007). How students learn statistics revisited: A current review of research on teaching and learning statistics . International statistical review, 75(3), 372-396.
• Ostle, B. (1963). Statistics in research . Statistics in research., (2nd Ed).
• Wagner III, W. E. (2019). Using IBM® SPSS® statistics for research methods and social science statistics . Sage Publications .

How to cite this article: Janse, B. (2024). Univariate Analysis . Retrieved [insert date] from Toolshero: https://www.toolshero.com/research/univariate-analysis/

Original publication date: 03/22/2024 | Last update: 03/22/2024

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Ben Janse is a young professional working at ToolsHero as Content Manager. He is also an International Business student at Rotterdam Business School where he focusses on analyzing and developing management models. Thanks to his theoretical and practical knowledge, he knows how to distinguish main- and side issues and to make the essence of each article clearly visible.

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Original research article, learning scientific observation with worked examples in a digital learning environment.

• 1 Department Educational Sciences, Chair for Formal and Informal Learning, Technical University Munich School of Social Sciences and Technology, Munich, Germany
• 2 Aquatic Systems Biology Unit, TUM School of Life Sciences, Technical University of Munich, Freising, Germany

Science education often aims to increase learners’ acquisition of fundamental principles, such as learning the basic steps of scientific methods. Worked examples (WE) have proven particularly useful for supporting the development of such cognitive schemas and successive actions in order to avoid using up more cognitive resources than are necessary. Therefore, we investigated the extent to which heuristic WE are beneficial for supporting the acquisition of a basic scientific methodological skill—conducting scientific observation. The current study has a one-factorial, quasi-experimental, comparative research design and was conducted as a field experiment. Sixty two students of a German University learned about scientific observation steps during a course on applying a fluvial audit, in which several sections of a river were classified based on specific morphological characteristics. In the two experimental groups scientific observation was supported either via faded WE or via non-faded WE both presented as short videos. The control group did not receive support via WE. We assessed factual and applied knowledge acquisition regarding scientific observation, motivational aspects and cognitive load. The results suggest that WE promoted knowledge application: Learners from both experimental groups were able to perform the individual steps of scientific observation more accurately. Fading of WE did not show any additional advantage compared to the non-faded version in this regard. Furthermore, the descriptive results reveal higher motivation and reduced extraneous cognitive load within the experimental groups, but none of these differences were statistically significant. Our findings add to existing evidence that WE may be useful to establish scientific competences.

1 Introduction

Learning in science education frequently involves the acquisition of basic principles or generalities, whether of domain-specific topics (e.g., applying a mathematical multiplication rule) or of rather universal scientific methodologies (e.g., performing the steps of scientific observation) ( Lunetta et al., 2007 ). Previous research has shown that worked examples (WE) can be considered particularly useful for developing such cognitive schemata during learning to avoid using more cognitive resources than necessary for learning successive actions ( Renkl et al., 2004 ; Renkl, 2017 ). WE consist of the presentation of a problem, consecutive solution steps and the solution itself. This is especially advantageous in initial cognitive skill acquisition, i.e., for novice learners with low prior knowledge ( Kalyuga et al., 2001 ). With growing knowledge, fading WE can lead from example-based learning to independent problem-solving ( Renkl et al., 2002 ). Preliminary work has shown the advantage of WE in specific STEM domains like mathematics ( Booth et al., 2015 ; Barbieri et al., 2021 ), but less studies have investigated their impact on the acquisition of basic scientific competencies that involve heuristic problem-solving processes (scientific argumentation, Schworm and Renkl, 2007 ; Hefter et al., 2014 ; Koenen et al., 2017 ). In the realm of natural sciences, various basic scientific methodologies are employed to acquire knowledge, such as experimentation or scientific observation ( Wellnitz and Mayer, 2013 ). During the pursuit of knowledge through scientific inquiry activities, learners may encounter several challenges and difficulties. Similar to the hurdles faced in experimentation, where understanding the criteria for appropriate experimental design, including the development, measurement, and evaluation of results, is crucial ( Sirum and Humburg, 2011 ; Brownell et al., 2014 ; Dasgupta et al., 2014 ; Deane et al., 2014 ), scientific observation additionally presents its own set of issues. In scientific observation, e.g., the acquisition of new insights may be somewhat incidental due to spontaneous and uncoordinated observations ( Jensen, 2014 ). To address these challenges, it is crucial to provide instructional support, including the use of WE, particularly when observations are carried out in a more self-directed manner.

For this reason, the aim of the present study was to determine the usefulness of digitally presented WE to support the acquisition of a basic scientific methodological skill—conducting scientific observations—using a digital learning environment. In this regard, this study examined the effects of different forms of digitally presented WE (non-faded vs. faded) on students’ cognitive and motivational outcomes and compared them to a control group without WE. Furthermore, the combined perspective of factual and applied knowledge, as well as motivational and cognitive aspects, represent further value added to the study.

2 Theoretical background

2.1 worked examples.

WE have been commonly used in the fields of STEM education (science, technology, engineering, and mathematics) ( Booth et al., 2015 ). They consist of a problem statement, the steps to solve the problem, and the solution itself ( Atkinson et al., 2000 ; Renkl et al., 2002 ; Renkl, 2014 ). The success of WE can be explained by their impact on cognitive load (CL) during learning, based on assumptions from Cognitive Load Theory ( Sweller, 2006 ).

Learning with WE is considered time-efficient, effective, and superior to problem-based learning (presentation of the problem without demonstration of solution steps) when it comes to knowledge acquisition and transfer (WE-effect, Atkinson et al., 2000 ; Van Gog et al., 2011 ). Especially WE can help by reducing the extraneous load (presentation and design of the learning material) and, in turn, can lead to an increase in germane load (effort of the learner to understand the learning material) ( Paas et al., 2003 ; Renkl, 2014 ). With regard to intrinsic load (difficulty and complexity of the learning material), it is still controversially discussed if it can be altered by instructional design, e.g., WE ( Gerjets et al., 2004 ). WE have a positive effect on learning and knowledge transfer, especially for novices, as the step-by-step presentation of the solution requires less extraneous mental effort compared to problem-based learning ( Sweller et al., 1998 ; Atkinson et al., 2000 ; Bokosmaty et al., 2015 ). With growing knowledge, WE can lose their advantages (due to the expertise-reversal effect), and scaffolding learning via faded WE might be more successful for knowledge gain and transfer ( Renkl, 2014 ). Faded WE are similar to complete WE, but fade out solution steps as knowledge and competencies grow. Faded WE enhance near-knowledge transfer and reduce errors compared to non-faded WE ( Renkl et al., 2000 ).

In addition, the reduction of intrinsic and extraneous CL by WE also has an impact on learner motivation, such as interest ( Van Gog and Paas, 2006 ). Um et al. (2012) showed that there is a strong positive correlation between germane CL and the motivational aspects of learning, like satisfaction and emotion. Gupta (2019) mentions a positive correlation between CL and interest. Van Harsel et al. (2019) found that WE positively affect learning motivation, while no such effect was found for problem-solving. Furthermore, learning with WE increases the learners’ belief in their competence in completing a task. In addition, fading WE can lead to higher motivation for more experienced learners, while non-faded WE can be particularly motivating for learners without prior knowledge ( Paas et al., 2005 ). In general, fundamental motivational aspects during the learning process, such as situational interest ( Lewalter and Knogler, 2014 ) or motivation-relevant experiences, like basic needs, are influenced by learning environments. At the same time, their use also depends on motivational characteristics of the learning process, such as self-determined motivation ( Deci and Ryan, 2012 ). Therefore, we assume that learning with WE as a relevant component of a learning environment might also influence situational interest and basic needs.

2.1.1 Presentation of worked examples

WE are frequently used in digital learning scenarios ( Renkl, 2014 ). When designing WE, the application via digital learning media can be helpful, as their content can be presented in different ways (video, audio, text, and images), tailored to the needs of the learners, so that individual use is possible according to their own prior knowledge or learning pace ( Mayer, 2001 ). Also, digital media can present relevant information in a timely, motivating, appealing and individualized way and support learning in an effective and needs-oriented way ( Mayer, 2001 ). The advantages of using digital media in designing WE have already been shown in previous studies. Dart et al. (2020) presented WE as short videos (WEV). They report that the use of WEV leads to increased student satisfaction and more positive attitudes. Approximately 90% of the students indicated an active learning approach when learning with the WEV. Furthermore, the results show that students improved their content knowledge through WEV and that they found WEV useful for other courses as well.

Another study ( Kay and Edwards, 2012 ) presented WE as video podcasts. Here, the advantages of WE regarding self-determined learning in terms of learning location, learning time, and learning speed were shown. Learning performance improved significantly after use. The step-by-step, easy-to-understand explanations, the diagrams, and the ability to determine the learning pace by oneself were seen as beneficial.

Multimedia WE can also be enhanced with self-explanation prompts ( Berthold et al., 2009 ). Learning from WE with self-explanation prompts was shown to be superior to other learning methods, such as hypertext learning and observational learning.

In addition to presenting WE in different medial ways, WE can also comprise different content domains.

2.1.2 Content and context of worked examples

Regarding the content of WE, algorithmic and heuristic WE, as well as single-content and double-content WE, can be distinguished ( Reiss et al., 2008 ; Koenen et al., 2017 ; Renkl, 2017 ). Algorithmic WE are traditionally used in the very structured mathematical–physical field. Here, an algorithm with very specific solution steps is to learn, for example, in probability calculation ( Koenen et al., 2017 ). In this study, however, we focus on heuristic double-content WE. Heuristic WE in science education comprise fundamental scientific working methods, e.g., conducting experiments ( Koenen et al., 2017 ). Furthermore, double-content WE contain two learning domains that are relevant for the learning process: (1) the learning domain describes the primarily to be learned abstract process or concept, e.g., scientific methodologies like observation (see section 2.2), while (2) the exemplifying domain consists of the content that is necessary to teach this process or concept, e.g., mapping of river structure ( Renkl et al., 2009 ).

Depending on the WE content to be learned, it may be necessary for learning to take place in different settings. This can be in a formal or informal learning setting or a non-formal field setting. In this study, the focus is on learning scientific observation (learning domain) through river structure mapping (exemplary domain), which takes place with the support of digital media in a formal (university) setting, but in an informal context (nature).

2.2 Scientific observation

Scientific observation is fundamental to all scientific activities and disciplines ( Kohlhauf et al., 2011 ). Scientific observation must be clearly distinguished from everyday observation, where observation is purely a matter of noticing and describing specific characteristics ( Chinn and Malhotra, 2001 ). In contrast to this everyday observation, scientific observation as a method of knowledge acquisition can be described as a rather complex activity, defined as the theory-based, systematic and selective perception of concrete systems and processes without any fundamental manipulation ( Wellnitz and Mayer, 2013 ). Wellnitz and Mayer (2013) described the scientific observation process via six steps: (1) formulation of the research question (s), (2) deduction of the null hypothesis and the alternative hypothesis, (3) planning of the research design, (4) conducting the observation, (5) analyzing the data, and (6) answering the research question(s) on this basis. Only through reliable and qualified observation, valid data can be obtained that provide solid scientific evidence ( Wellnitz and Mayer, 2013 ).

Since observation activities are not trivial and learners often observe without generating new knowledge or connecting their observations to scientific explanations and thoughts, it is important to provide support at the related cognitive level, so that observation activities can be conducted in a structured way according to pre-defined criteria ( Ford, 2005 ; Eberbach and Crowley, 2009 ). Especially during field-learning experiences, scientific observation is often spontaneous and uncoordinated, whereby random discoveries result in knowledge gain ( Jensen, 2014 ).

To promote successful observing in rather unstructured settings like field trips, instructional support for the observation process seems useful. To guide observation activities, digitally presented WE seem to be an appropriate way to introduce learners to the individual steps of scientific observation using concrete examples.

2.3 Research questions and hypothesis

The present study investigates the effect of digitally presented double-content WE that supports the mapping of a small Bavarian river by demonstrating the steps of scientific observation. In this analysis, we focus on the learning domain of the WE and do not investigate the exemplifying domain in detail. Distinct ways of integrating WE in the digital learning environment (faded WE vs. non-faded WE) are compared with each other and with a control group (no WE). The aim is to examine to what extent differences between those conditions exist with regard to (RQ1) learners’ competence acquisition [acquisition of factual knowledge about the scientific observation method (quantitative data) and practical application of the scientific observation method (quantified qualitative data)], (RQ2) learners’ motivation (situational interest and basic needs), and (RQ3) CL. It is assumed that (Hypothesis 1), the integration of WE (faded and non-faded) leads to significantly higher competence acquisition (factual and applied knowledge), significantly higher motivation and significantly lower extraneous CL as well as higher germane CL during the learning process compared to a learning environment without WE. No differences between the conditions are expected regarding intrinsic CL. Furthermore, it is assumed (Hypothesis 2) that the integration of faded WE leads to significantly higher competence acquisition, significantly higher motivation, and lower extraneous CL as well as higher germane CL during the learning processes compared to non-faded WE. No differences between the conditions are expected with regard to intrinsic CL.

The study took place during the field trips of a university course on the application of a fluvial audit (FA) using the German working aid for mapping the morphology of rivers and their floodplains ( Bayerisches Landesamt für Umwelt, 2019 ). FA is the leading fluvial geomorphological tool for application to data collection contiguously along all watercourses of interest ( Walker et al., 2007 ). It is widely used because it is a key example of environmental conservation and monitoring that needs to be taught to students of selected study programs; thus, knowing about the most effective ways of learning is of high practical relevance.

3.1 Sample and design

3.1.1 sample.

The study was conducted with 62 science students and doctoral students of a German University (age M  = 24.03 years; SD  = 4.20; 36 females; 26 males). A total of 37 participants had already conducted a scientific observation and would rate their knowledge in this regard at a medium level ( M  = 3.32 out of 5; SD  = 0.88). Seven participants had already conducted an FA and would rate their knowledge in this regard at a medium level ( M  = 3.14 out of 5; SD  = 0.90). A total of 25 participants had no experience at all. Two participants had to be excluded from the sample afterward because no posttest results were available.

3.1.2 Design

The study has a 1-factorial quasi-experimental comparative research design and is conducted as a field experiment using a pre/posttest design. Participants were randomly assigned to one of three conditions: no WE ( n  = 20), faded WE ( n  = 20), and non-faded WE ( n  = 20).

3.2 Implementation and material

3.2.1 implementation.

The study started with an online kick-off meeting where two lecturers informed all students within an hour about the basics regarding the assessment of the structural integrity of the study river and the course of the field trip days to conduct an FA. Afterward, within 2 weeks, students self-studied via Moodle the FA following the German standard method according to the scoresheets of Bayerisches Landesamt für Umwelt (2019) . This independent preparation using the online presented documents was a necessary prerequisite for participation in the field days and was checked in the pre-testing. The preparatory online documents included six short videos and four PDF files on the content, guidance on the German protocol of the FA, general information on river landscapes, information about anthropogenic changes in stream morphology and the scoresheets for applying the FA. In these sheets, the river and its floodplain are subdivided into sections of 100 m in length. Each of these sections is evaluated by assessing 21 habitat factors related to flow characteristics and structural variability. The findings are then transferred into a scoring system for the description of structural integrity from 1 (natural) to 7 (highly modified). Habitat factors have a decisive influence on the living conditions of animals and plants in and around rivers. They included, e.g., variability in water depth, stream width, substratum diversity, or diversity of flow velocities.

3.2.2 Materials

On the field trip days, participants were handed a tablet and a paper-based FA worksheet (last accessed 21st September 2022). 1 This four-page assessment sheet was accompanied by a digital learning environment presented on Moodle that instructed the participants on mapping the water body structure and guided the scientific observation method. All three Moodle courses were identical in structure and design; the only difference was the implementation of the WE. Below, the course without WE are described first. The other two courses have an identical structure, but contain additional WE in the form of learning videos.

3.2.3 No worked example

After a short welcome and introduction to the course navigation, the FA started with the description of a short hypothetical scenario: Participants should take the role of an employee of an urban planning office that assesses the ecomorphological status of a small river near a Bavarian city. The river was divided into five sections that had to be mapped separately. The course was structured accordingly. At the beginning of each section, participants had to formulate and write down a research question, and according to hypotheses regarding the ecomorphological status of the river’s section, they had to collect data in this regard via the mapping sheet and then evaluate their data and draw a conclusion. Since this course serves as a control group, no WE videos supporting the scientific observation method were integrated. The layout of the course is structured like a book, where it is not possible to scroll back. This is important insofar as the participants do not have the possibility to revisit information in order to keep the conditions comparable as well as distinguishable.

3.2.4 Non-faded worked example

In the course with no-faded WE, three instructional videos are shown for each of the five sections. In each of the three videos, two steps of the scientific observation method are presented so that, finally, all six steps of scientific observation are demonstrated. The mapping of the first section starts after the general introduction (as described above) with the instruction to work on the first two steps of scientific observation: the formulation of a research question and hypotheses. To support this, a video of about 4 min explains the features of scientific sound research questions and hypotheses. To this aim, a practical example, including explanations and tips, is given regarding the formulation of research questions and hypotheses for this section (e.g., “To what extent does the building development and the closeness of the path to the water body have an influence on the structure of the water body?” Alternative hypothesis: It is assumed that the housing development and the closeness of the path to the water body have a negative influence on the water body structure. Null hypothesis: It is assumed that the housing development and the closeness of the path to the watercourse have no negative influence on the watercourse structure.). Participants should now formulate their own research questions and hypotheses, write them down in a text field at the end of the page, and then skip to the next page. The next two steps of scientific observation, planning and conducting, are explained in a short 4-min video. To this aim, a practical example including explanations and tips is given regarding planning and conducting scientific for this section (e.g., “It’s best to go through each evaluation category carefully one by one that way you are sure not to forget anything!”). Now, participants were asked to collect data for the first section using their paper-based FA worksheet. Participants individually surveyed the river and reported their results in the mapping sheet by ticking the respective boxes in it. After collecting this data, they returned to the digital learning environment to learn how to use these data by studying the last two steps of scientific observation, evaluation, and conclusion. The third 4-min video explained how to evaluate and interpret collected data. For this purpose, a practical example with explanations and tips is given regarding evaluating and interpreting data for this section (e.g., “What were the individual points that led to the assessment? Have there been points that were weighted more than others? Remember the introduction video!”). At the end of the page, participants could answer their before-stated research questions and hypotheses by evaluating their collected data and drawing a conclusion. This brings participants to the end of the first mapping section. Afterward, the cycle begins again with the second section of the river that has to be mapped. Again, participants had to conduct the steps of scientific observation, guided by WE videos, explaining the steps in slightly different wording or with different examples. A total of five sections are mapped, in which the structure of the learning environment and the videos follow the same procedure.

3.2.5 Faded worked example

The digital learning environment with the faded WE follow the same structure as the version with the non-faded WE. However, in this version, the information in the WE videos is successively reduced. In the first section, all three videos are identical to the version with the non-faded WE. In the second section, faded content was presented as follows: the tip at the end was omitted in all three videos. In the third section, the tip and the practical example were omitted. In the fourth and fifth sections, no more videos were presented, only the work instructions.

3.3 Procedure

The data collection took place on four continuous days on the university campus, with a maximum group size of 15 participants on each day. The students were randomly assigned to one of the three conditions (no WE vs. faded WE vs. non-faded WE). After a short introduction to the procedure, the participants were handed the paper-based FA worksheet and one tablet per person. Students scanned the QR code on the first page of the worksheet that opened the pretest questionnaire, which took about 20 min to complete. After completing the questionnaire, the group walked for about 15 min to the nearby small river that was to be mapped. Upon arrival, there was first a short introduction to the digital learning environment and a check that the login (via university account on Moodle) worked. During the next 4 h, the participants individually mapped five segments of the river using the cartography worksheet. They were guided through the steps of scientific observation using the digital learning environment on the tablet. The results of their scientific observation were logged within the digital learning environment. At the end of the digital learning environment, participants were directed to the posttest via a link. After completing the test, the tablets and mapping sheets were returned. Overall, the study took about 5 h per group each day.

3.4 Instruments

In the pretest, sociodemographic data (age and gender), the study domain and the number of study semesters were collected. Additionally, the previous scientific observation experience and the estimation of one’s own ability in this regard were assessed. For example, it was asked whether scientific observation had already been conducted and, if so, how the abilities were rated on a 5-point scale from very low to very high. Preparation for the FA on the basis of the learning material was assessed: Participants were asked whether they had studied all six videos and all four PDF documents, with the response options not at all, partially, and completely. Furthermore, a factual knowledge test about scientific observation and questions about self-determination theory was administered. The posttest used the same knowledge test, and additional questions on basic needs, situational interest, measures of CL and questions about the usefulness of the WE. All scales were presented online, and participants reached the questionnaire via QR code.

3.4.1 Scientific observation competence acquisition

For the factual knowledge (quantitative assessment of the scientific observation competence), a single-choice knowledge test with 12 questions was developed and used as pre- and posttest with a maximum score of 12 points. It assesses the learners’ knowledge of the scientific observation method regarding the steps of scientific observation, e.g., formulating research questions and hypotheses or developing a research design. The questions are based on Wahser (2008 , adapted by Koenen, 2014 ) and adapted to scientific observation: “Although you are sure that you have conducted the scientific observation correctly, an unexpected result turns up. What conclusion can you draw?” Each question has four answer options (one of which is correct) and, in addition, one “I do not know” option.

For the applied knowledge (quantified qualitative assessment of the scientific observation competence), students’ scientific observations written in the digital learning environment were analyzed. A coding scheme was used with the following codes: 0 = insufficient (text field is empty or includes only insufficient key points), 1 = sufficient (a research question and no hypotheses or research question and inappropriate hypotheses are stated), 2 = comprehensive (research question and appropriate hypothesis or research question and hypotheses are stated, but, e.g., incorrect null hypothesis), 3 = very comprehensive (correct research question, hypothesis and null hypothesis are stated). One example of a very comprehensive answer regarding the research question and hypothesis is: To what extent does the lack of riparian vegetation have an impact on water body structure? Hypothesis: The lack of shore vegetation has a negative influence on the water body structure. Null hypothesis: The lack of shore vegetation has no influence on the water body structure. Afterward, a sum score was calculated for each participant. Five times, a research question and hypotheses (steps 1 and 2 in the observation process) had to be formulated (5 × max. 3 points = 15 points), and five times, the research questions and hypotheses had to be answered (steps 5 and 6 in the observation process: evaluation and conclusion) (5 × max. 3 points = 15 points). Overall, participants could reach up to 30 points. Since the observation and evaluation criteria in data collection and analysis were strongly predetermined by the scoresheet, steps 3 and 4 of the observation process (planning and conducting) were not included in the analysis.

All 600 cases (60 participants, each 10 responses to code) were coded by the first author. For verification, 240 cases (24 randomly selected participants, eight from each course) were cross-coded by an external coder. In 206 of the coded cases, the raters agreed. The cases in which the raters did not agree were discussed together, and a solution was found. This results in Cohen’s κ = 0.858, indicating a high to very high level of agreement. This indicates that the category system is clearly formulated and that the individual units of analysis could be correctly assigned.

3.4.2 Self-determination index

For the calculation of the self-determination index (SDI-index), Thomas and Müller (2011) scale for self-determination was used in the pretest. The scale consists of four subscales: intrinsic motivation (five items; e.g., I engage with the workshop content because I enjoy it; reliability of alpha = 0.87), identified motivation (four items; e.g., I engage with the workshop content because it gives me more options when choosing a career; alpha = 0.84), introjected motivation (five items; e.g., I engage with the workshop content because otherwise I would have a guilty feeling; alpha = 0.79), and external motivation (three items, e.g., I engage with the workshop content because I simply have to learn it; alpha = 0.74). Participants could indicate their answers on a 5-point Likert scale ranging from 1 = completely disagree to 5 = completely agree. To calculate the SDI-index, the sum of the self-determined regulation styles (intrinsic and identified) is subtracted from the sum of the external regulation styles (introjected and external), where intrinsic and external regulation are scored two times ( Thomas and Müller, 2011 ).

3.4.3 Motivation

Basic needs were measured in the posttest with the scale by Willems and Lewalter (2011) . The scale consists of three subscales: perceived competence (four items; e.g., during the workshop, I felt that I could meet the requirements; alpha = 0.90), perceived autonomy (five items; e.g., during the workshop, I felt that I had a lot of freedom; alpha = 0.75), and perceived autonomy regarding personal wishes and goals (APWG) (four items; e.g., during the workshop, I felt that the workshop was how I wish it would be; alpha = 0.93). We added all three subscales to one overall basic needs scale (alpha = 0.90). Participants could indicate their answers on a 5-point Likert scale ranging from 1 = completely disagree to 5 = completely agree.

Situational interest was measured in the posttest with the 12-item scale by Lewalter and Knogler (2014 ; Knogler et al., 2015 ; Lewalter, 2020 ; alpha = 0.84). The scale consists of two subscales: catch (six items; e.g., I found the workshop exciting; alpha = 0.81) and hold (six items; e.g., I would like to learn more about parts of the workshop; alpha = 0.80). Participants could indicate their answers on a 5-point Likert scale ranging from 1 = completely disagree to 5 = completely agree.

3.4.4 Cognitive load

In the posttest, CL was used to examine the mental load during the learning process. The intrinsic CL (three items; e.g., this task was very complex; alpha = 0.70) and extraneous CL (three items; e.g., in this task, it is difficult to identify the most important information; alpha = 0.61) are measured with the scales from Klepsch et al. (2017) . The germane CL (two items; e.g., the learning session contained elements that supported me to better understand the learning material; alpha = 0.72) is measured with the scale from Leppink et al. (2013) . Participants could indicate their answers on a 5-point Likert scale ranging from 1 = completely disagree to 5 = completely agree.

3.4.5 Attitudes toward worked examples

To measure how effective participants rated the WE, we used two scales related to the WE videos as instructional support. The first scale from Renkl (2001) relates to the usefulness of WE. The scale consists of four items (e.g., the explanations were helpful; alpha = 0.71). Two items were recoded because they were formulated negatively. The second scale is from Wachsmuth (2020) and relates to the participant’s evaluation of the WE. The scale consists of nine items (e.g., I always did what was explained in the learning videos; alpha = 0.76). Four items were recoded because they were formulated negatively. Participants could indicate their answers on a 5-point Likert scale ranging from 1 = completely disagree to 5 = completely agree.

3.5 Data analysis

An ANOVA was used to calculate if the variable’s prior knowledge and SDI index differed between the three groups. However, as no significant differences between the conditions were found [prior factual knowledge: F (2, 59) = 0.15, p  = 0.865, η 2  = 0.00 self-determination index: F (2, 59) = 0.19, p  = 0.829, η 2  = 0.00], they were not included as covariates in subsequent analyses.

Furthermore, a repeated measure, one-way analysis of variance (ANOVA), was conducted to compare the three treatment groups (no WE vs. faded WE vs. non-faded WE) regarding the increase in factual knowledge about the scientific observation method from pretest to posttest.

A MANOVA (multivariate analysis) was calculated with the three groups (no WE vs. non-faded WE vs. faded WE) as a fixed factor and the dependent variables being the practical application of the scientific observation method (first research question), situational interest, basic needs (second research question), and CL (third research question).

Additionally, to determine differences in applied knowledge even among the three groups, Bonferroni-adjusted post-hoc analyses were conducted.

The descriptive statistics between the three groups in terms of prior factual knowledge about the scientific observation method and the self-determination index are shown in Table 1 . The descriptive statistics revealed only small, non-significant differences between the three groups in terms of factual knowledge.

Table 1 . Means (standard deviations) of factual knowledge tests (pre- and posttest) and self-determination index for the three different groups.

The results of the ANOVA revealed that the overall increase in factual knowledge from pre- to posttest just misses significance [ F (1, 57) = 3.68, p  = 0.060, η 2  = 0 0.06]. Furthermore, no significant differences between the groups were found regarding the acquisition of factual knowledge from pre- to posttest [ F (2, 57) = 2.93, p  = 0.062, η 2  = 0.09].

An analysis of the descriptive statistics showed that the largest differences between the groups were found in applied knowledge (qualitative evaluation) and extraneous load (see Table 2 ).

Table 2 . Means (standard deviations) of dependent variables with the three different groups.

Results of the MANOVA revealed significant overall differences between the three groups [ F (12, 106) = 2.59, p  = 0.005, η 2  = 0.23]. Significant effects were found for the application of knowledge [ F (2, 57) = 13.26, p  = <0.001, η 2  = 0.32]. Extraneous CL just missed significance [ F (2, 57) = 2.68, p  = 0.065, η 2  = 0.09]. There were no significant effects for situational interest [ F (2, 57) = 0.44, p  = 0.644, η 2  = 0.02], basic needs [ F (2, 57) = 1.22, p  = 0.302, η 2  = 0.04], germane CL [ F (2, 57) = 2.68, p  = 0.077, η 2  = 0.09], and intrinsic CL [ F (2, 57) = 0.28, p  = 0.757, η 2  = 0.01].

Bonferroni-adjusted post hoc analysis revealed that the group without WE had significantly lower scores in the evaluation of the applied knowledge than the group with non-faded WE ( p  = <0.001, M diff  = −8.90, 95% CI [−13.47, −4.33]) and then the group with faded WE ( p  = <0.001, M diff  = −7.40, 95% CI [−11.97, −2.83]). No difference was found between the groups with faded and non-faded WE ( p  = 1.00, M diff  = −1.50, 95% CI [−6.07, 3.07]).

The descriptive statistics regarding the perceived usefulness of WE and participants’ evaluation of the WE revealed that the group with the faded WE rated usefulness slightly higher than the participants with non-faded WE and also reported a more positive evaluation. However, the results of a MANOVA revealed no significant overall differences [ F (2, 37) = 0.32, p  = 0.732, η 2  = 0 0.02] (see Table 3 ).

Table 3 . Means (standard deviations) of dependent variables with the three different groups.

5 Discussion

This study investigated the use of WE to support students’ acquisition of science observation. Below, the research questions are answered, and the implications and limitations of the study are discussed.

5.1 Results on factual and applied knowledge

In terms of knowledge gain (RQ1), our findings revealed no significant differences in participants’ results of the factual knowledge test both across all three groups and specifically between the two experimental groups. These results are in contradiction with related literature where WE had a positive impact on knowledge acquisition ( Renkl, 2014 ) and faded WE are considered to be more effective in knowledge acquisition and transfer, in contrast to non-faded WE ( Renkl et al., 2000 ; Renkl, 2014 ). A limitation of the study is the fact that the participants already scored very high on the pretest, so participation in the intervention would likely not yield significant knowledge gains due to ceiling effects ( Staus et al., 2021 ). Yet, nearly half of the students reported being novices in the field prior to the study, suggesting that the difficulty of some test items might have been too low. Here, it would be important to revise the factual knowledge test, e.g., the difficulty of the distractors in further study.

Nevertheless, with regard to application knowledge, the results revealed large significant differences: Participants of the two experimental groups performed better in conducting scientific observation steps than participants of the control group. In the experimental groups, the non-faded WE group performed better than the faded WE group. However, the absence of significant differences between the two experimental groups suggests that faded and non-faded WE used as double-content WE are suitable to teach applied knowledge about scientific observation in the learning domain ( Koenen, 2014 ). Furthermore, our results differ from the findings of Renkl et al. (2000) , in which the faded version led to the highest knowledge transfer. Despite the fact that the non-faded WE performed best in our study, the faded version of the WE was also appropriate to improve learning, confirming the findings of Renkl (2014) and Hesser and Gregory (2015) .

5.2 Results on learners’ motivation

Regarding participants’ motivation (RQ2; situational interest and basic needs), no significant differences were found across all three groups or between the two experimental groups. However, descriptive results reveal slightly higher motivation in the two experimental groups than in the control group. In this regard, our results confirm existing literature on a descriptive level showing that WE lead to higher learning-relevant motivation ( Paas et al., 2005 ; Van Harsel et al., 2019 ). Additionally, both experimental groups rated the usefulness of the WE as high and reported a positive evaluation of the WE. Therefore, we assume that even non-faded WE do not lead to over-instruction. Regarding the descriptive tendency, a larger sample might yield significant results and detect even small effects in future investigations. However, because this study also focused on comprehensive qualitative data analysis, it was not possible to evaluate a larger sample in this study.

5.3 Results on cognitive load

Finally, CL did not vary significantly across all three groups (RQ3). However, differences in extraneous CL just slightly missed significance. In descriptive values, the control group reported the highest extrinsic and lowest germane CL. The faded WE group showed the lowest extrinsic CL and a similar germane CL as the non-faded WE group. These results are consistent with Paas et al. (2003) and Renkl (2014) , reporting that WE can help to reduce the extraneous CL and, in return, lead to an increase in germane CL. Again, these differences were just above the significance level, and it would be advantageous to retest with a larger sample to detect even small effects.

Taken together, our results only partially confirm H1: the integration of WE (both faded and non-faded WE) led to a higher acquisition of application knowledge than the control group without WE, but higher factual knowledge was not found. Furthermore, higher motivation or different CL was found on a descriptive level only. The control group provided the basis for comparison with the treatment in order to investigate if there is an effect at all and, if so, how large the effect is. This is an important point to assess whether the effort of implementing WE is justified. Additionally, regarding H2, our results reveal no significant differences between the two WE conditions. We assume that the high complexity of the FA could play a role in this regard, which might be hard to handle, especially for beginners, so learners could benefit from support throughout (i.e., non-faded WE).

In addition to the limitations already mentioned, it must be noted that only one exemplary topic was investigated, and the sample only consisted of students. Since only the learning domain of the double-content WE was investigated, the exemplifying domain could also be analyzed, or further variables like motivation could be included in further studies. Furthermore, the influence of learners’ prior knowledge on learning with WE could be investigated, as studies have found that WE are particularly beneficial in the initial acquisition of cognitive skills ( Kalyuga et al., 2001 ).

6 Conclusion

Overall, the results of the current study suggest a beneficial role for WE in supporting the application of scientific observation steps. A major implication of these findings is that both faded and non-faded WE should be considered, as no general advantage of faded WE over non-faded WE was found. This information can be used to develop targeted interventions aimed at the support of scientific observation skills.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

Ethical approval was not required for the study involving human participants in accordance with the local legislation and institutional requirements. Written informed consent to participate in this study was not required from the participants in accordance with the national legislation and the institutional requirements.

Author contributions

ML: Writing – original draft. SM: Writing – review & editing. JP: Writing – review & editing. JG: Writing – review & editing. DL: Writing – review & editing.

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/feduc.2024.1293516/full#supplementary-material

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Keywords: digital media, worked examples, scientific observation, motivation, cognitive load

Citation: Lechner M, Moser S, Pander J, Geist J and Lewalter D (2024) Learning scientific observation with worked examples in a digital learning environment. Front. Educ . 9:1293516. doi: 10.3389/feduc.2024.1293516

Received: 13 September 2023; Accepted: 29 February 2024; Published: 18 March 2024.

Reviewed by:

Copyright © 2024 Lechner, Moser, Pander, Geist and Lewalter. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Miriam Lechner, [email protected]

Four of the biggest problems facing education—and four trends that could make a difference

Eduardo velez bustillo, harry a. patrinos.

In 2022, we published, Lessons for the education sector from the COVID-19 pandemic , which was a follow up to,  Four Education Trends that Countries Everywhere Should Know About , which summarized views of education experts around the world on how to handle the most pressing issues facing the education sector then. We focused on neuroscience, the role of the private sector, education technology, inequality, and pedagogy.

Unfortunately, we think the four biggest problems facing education today in developing countries are the same ones we have identified in the last decades .

1. The learning crisis was made worse by COVID-19 school closures

Low quality instruction is a major constraint and prior to COVID-19, the learning poverty rate in low- and middle-income countries was 57% (6 out of 10 children could not read and understand basic texts by age 10). More dramatic is the case of Sub-Saharan Africa with a rate even higher at 86%. Several analyses show that the impact of the pandemic on student learning was significant, leaving students in low- and middle-income countries way behind in mathematics, reading and other subjects.  Some argue that learning poverty may be close to 70% after the pandemic , with a substantial long-term negative effect in future earnings. This generation could lose around $21 trillion in future salaries, with the vulnerable students affected the most.

2. Countries are not paying enough attention to early childhood care and education (ECCE)

At the pre-school level about two-thirds of countries do not have a proper legal framework to provide free and compulsory pre-primary education. According to UNESCO, only a minority of countries, mostly high-income, were making timely progress towards SDG4 benchmarks on early childhood indicators prior to the onset of COVID-19. And remember that ECCE is not only preparation for primary school. It can be the foundation for emotional wellbeing and learning throughout life; one of the best investments a country can make.

3. There is an inadequate supply of high-quality teachers

Low quality teaching is a huge problem and getting worse in many low- and middle-income countries.  In Sub-Saharan Africa, for example, the percentage of trained teachers fell from 84% in 2000 to 69% in 2019 . In addition, in many countries teachers are formally trained and as such qualified, but do not have the minimum pedagogical training. Globally, teachers for science, technology, engineering, and mathematics (STEM) subjects are the biggest shortfalls.

4. Decision-makers are not implementing evidence-based or pro-equity policies that guarantee solid foundations

It is difficult to understand the continued focus on non-evidence-based policies when there is so much that we know now about what works. Two factors contribute to this problem. One is the short tenure that top officials have when leading education systems. Examples of countries where ministers last less than one year on average are plentiful. The second and more worrisome deals with the fact that there is little attention given to empirical evidence when designing education policies.

To help improve on these four fronts, we see four supporting trends:

1. Neuroscience should be integrated into education policies

Policies considering neuroscience can help ensure that students get proper attention early to support brain development in the first 2-3 years of life. It can also help ensure that children learn to read at the proper age so that they will be able to acquire foundational skills to learn during the primary education cycle and from there on. Inputs like micronutrients, early child stimulation for gross and fine motor skills, speech and language and playing with other children before the age of three are cost-effective ways to get proper development. Early grade reading, using the pedagogical suggestion by the Early Grade Reading Assessment model, has improved learning outcomes in many low- and middle-income countries. We now have the tools to incorporate these advances into the teaching and learning system with AI , ChatGPT , MOOCs and online tutoring.

2. Reversing learning losses at home and at school

There is a real need to address the remaining and lingering losses due to school closures because of COVID-19.  Most students living in households with incomes under the poverty line in the developing world, roughly the bottom 80% in low-income countries and the bottom 50% in middle-income countries, do not have the minimum conditions to learn at home . These students do not have access to the internet, and, often, their parents or guardians do not have the necessary schooling level or the time to help them in their learning process. Connectivity for poor households is a priority. But learning continuity also requires the presence of an adult as a facilitator—a parent, guardian, instructor, or community worker assisting the student during the learning process while schools are closed or e-learning is used.

To recover from the negative impact of the pandemic, the school system will need to develop at the student level: (i) active and reflective learning; (ii) analytical and applied skills; (iii) strong self-esteem; (iv) attitudes supportive of cooperation and solidarity; and (v) a good knowledge of the curriculum areas. At the teacher (instructor, facilitator, parent) level, the system should aim to develop a new disposition toward the role of teacher as a guide and facilitator. And finally, the system also needs to increase parental involvement in the education of their children and be active part in the solution of the children’s problems. The Escuela Nueva Learning Circles or the Pratham Teaching at the Right Level (TaRL) are models that can be used.

3. Use of evidence to improve teaching and learning

We now know more about what works at scale to address the learning crisis. To help countries improve teaching and learning and make teaching an attractive profession, based on available empirical world-wide evidence , we need to improve its status, compensation policies and career progression structures; ensure pre-service education includes a strong practicum component so teachers are well equipped to transition and perform effectively in the classroom; and provide high-quality in-service professional development to ensure they keep teaching in an effective way. We also have the tools to address learning issues cost-effectively. The returns to schooling are high and increasing post-pandemic. But we also have the cost-benefit tools to make good decisions, and these suggest that structured pedagogy, teaching according to learning levels (with and without technology use) are proven effective and cost-effective .

4. The role of the private sector

When properly regulated the private sector can be an effective education provider, and it can help address the specific needs of countries. Most of the pedagogical models that have received international recognition come from the private sector. For example, the recipients of the Yidan Prize on education development are from the non-state sector experiences (Escuela Nueva, BRAC, edX, Pratham, CAMFED and New Education Initiative). In the context of the Artificial Intelligence movement, most of the tools that will revolutionize teaching and learning come from the private sector (i.e., big data, machine learning, electronic pedagogies like OER-Open Educational Resources, MOOCs, etc.). Around the world education technology start-ups are developing AI tools that may have a good potential to help improve quality of education .

After decades asking the same questions on how to improve the education systems of countries, we, finally, are finding answers that are very promising.  Governments need to be aware of this fact.

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Consultant, Education Sector, World Bank

Senior Adviser, Education

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How Do We Know Climate Change Is Real?

There is unequivocal evidence that Earth is warming at an unprecedented rate. Human activity is the principal cause.

• While Earth’s climate has changed throughout its history , the current warming is happening at a rate not seen in the past 10,000 years.
• According to the Intergovernmental Panel on Climate Change ( IPCC ), "Since systematic scientific assessments began in the 1970s, the influence of human activity on the warming of the climate system has evolved from theory to established fact." 1
• Scientific information taken from natural sources (such as ice cores, rocks, and tree rings) and from modern equipment (like satellites and instruments) all show the signs of a changing climate.
• From global temperature rise to melting ice sheets, the evidence of a warming planet abounds.

The rate of change since the mid-20th century is unprecedented over millennia.

Earth's climate has changed throughout history. Just in the last 800,000 years, there have been eight cycles of ice ages and warmer periods, with the end of the last ice age about 11,700 years ago marking the beginning of the modern climate era — and of human civilization. Most of these climate changes are attributed to very small variations in Earth’s orbit that change the amount of solar energy our planet receives.

The current warming trend is different because it is clearly the result of human activities since the mid-1800s, and is proceeding at a rate not seen over many recent millennia. 1 It is undeniable that human activities have produced the atmospheric gases that have trapped more of the Sun’s energy in the Earth system. This extra energy has warmed the atmosphere, ocean, and land, and widespread and rapid changes in the atmosphere, ocean, cryosphere, and biosphere have occurred.

Related Reading

Do scientists agree on climate change?

Yes, the vast majority of actively publishing climate scientists – 97 percent – agree that humans are causing global warming and climate change.

Earth-orbiting satellites and new technologies have helped scientists see the big picture, collecting many different types of information about our planet and its climate all over the world. These data, collected over many years, reveal the signs and patterns of a changing climate.

Scientists demonstrated the heat-trapping nature of carbon dioxide and other gases in the mid-19th century. 2 Many of the science instruments NASA uses to study our climate focus on how these gases affect the movement of infrared radiation through the atmosphere. From the measured impacts of increases in these gases, there is no question that increased greenhouse gas levels warm Earth in response.

"Scientific evidence for warming of the climate system is unequivocal." — Intergovernmental Panel on Climate Change

Ice cores drawn from Greenland, Antarctica, and tropical mountain glaciers show that Earth’s climate responds to changes in greenhouse gas levels. Ancient evidence can also be found in tree rings, ocean sediments, coral reefs, and layers of sedimentary rocks. This ancient, or paleoclimate, evidence reveals that current warming is occurring roughly 10 times faster than the average rate of warming after an ice age. Carbon dioxide from human activities is increasing about 250 times faster than it did from natural sources after the last Ice Age. 3

The Evidence for Rapid Climate Change Is Compelling:

Global temperature is rising.

The planet's average surface temperature has risen about 2 degrees Fahrenheit (1 degrees Celsius) since the late 19th century, a change driven largely by increased carbon dioxide emissions into the atmosphere and other human activities. 4 Most of the warming occurred in the past 40 years, with the seven most recent years being the warmest. The years 2016 and 2020 are tied for the warmest year on record. 5

The Ocean Is Getting Warmer

The ocean has absorbed much of this increased heat, with the top 100 meters (about 328 feet) of ocean showing warming of 0.67 degrees Fahrenheit (0.33 degrees Celsius) since 1969. 6 Earth stores 90% of the extra energy in the ocean.

The Ice Sheets Are Shrinking

The Greenland and Antarctic ice sheets have decreased in mass. Data from NASA's Gravity Recovery and Climate Experiment show Greenland lost an average of 279 billion tons of ice per year between 1993 and 2019, while Antarctica lost about 148 billion tons of ice per year. 7

Glaciers Are Retreating

Glaciers are retreating almost everywhere around the world — including in the Alps, Himalayas, Andes, Rockies, Alaska, and Africa. 8

Snow Cover Is Decreasing

Satellite observations reveal that the amount of spring snow cover in the Northern Hemisphere has decreased over the past five decades and the snow is melting earlier. 9

Sea Level Is Rising

Global sea level rose about 8 inches (20 centimeters) in the last century. The rate in the last two decades, however, is nearly double that of the last century and accelerating slightly every year. 10

Arctic Sea Ice Is Declining

Both the extent and thickness of Arctic sea ice has declined rapidly over the last several decades. 11

Extreme Events Are Increasing in Frequency

The number of record high temperature events in the United States has been increasing, while the number of record low temperature events has been decreasing, since 1950. The U.S. has also witnessed increasing numbers of intense rainfall events. 12

Ocean Acidification Is Increasing

Since the beginning of the Industrial Revolution, the acidity of surface ocean waters has increased by about 30%. 13 , 14 This increase is due to humans emitting more carbon dioxide into the atmosphere and hence more being absorbed into the ocean. The ocean has absorbed between 20% and 30% of total anthropogenic carbon dioxide emissions in recent decades (7.2 to 10.8 billion metric tons per year). 1 5 , 16

1. IPCC Sixth Assessment Report, WGI, Technical Summary . B.D. Santer et.al., “A search for human influences on the thermal structure of the atmosphere.” Nature 382 (04 July 1996): 39-46. https://doi.org/10.1038/382039a0. Gabriele C. Hegerl et al., “Detecting Greenhouse-Gas-Induced Climate Change with an Optimal Fingerprint Method.” Journal of Climate 9 (October 1996): 2281-2306. https://doi.org/10.1175/1520-0442(1996)009<2281:DGGICC>2.0.CO;2. V. Ramaswamy, et al., “Anthropogenic and Natural Influences in the Evolution of Lower Stratospheric Cooling.” Science 311 (24 February 2006): 1138-1141. https://doi.org/10.1126/science.1122587. B.D. Santer et al., “Contributions of Anthropogenic and Natural Forcing to Recent Tropopause Height Changes.” Science 301 (25 July 2003): 479-483. https://doi.org/10.1126/science.1084123. T. Westerhold et al., "An astronomically dated record of Earth’s climate and its predictability over the last 66 million years." Science 369 (11 Sept. 2020): 1383-1387. https://doi.org/10.1126/science.1094123

2. In 1824, Joseph Fourier calculated that an Earth-sized planet, at our distance from the Sun, ought to be much colder. He suggested something in the atmosphere must be acting like an insulating blanket. In 1856, Eunice Foote discovered that blanket, showing that carbon dioxide and water vapor in Earth's atmosphere trap escaping infrared (heat) radiation. In the 1860s, physicist John Tyndall recognized Earth's natural greenhouse effect and suggested that slight changes in the atmospheric composition could bring about climatic variations. In 1896, a seminal paper by Swedish scientist Svante Arrhenius first predicted that changes in atmospheric carbon dioxide levels could substantially alter the surface temperature through the greenhouse effect. In 1938, Guy Callendar connected carbon dioxide increases in Earth’s atmosphere to global warming. In 1941, Milutin Milankovic linked ice ages to Earth’s orbital characteristics. Gilbert Plass formulated the Carbon Dioxide Theory of Climate Change in 1956.

3. IPCC Sixth Assessment Report, WG1, Chapter 2 Vostok ice core data; NOAA Mauna Loa CO2 record O. Gaffney, W. Steffen, "The Anthropocene Equation." The Anthropocene Review 4, issue 1 (April 2017): 53-61. https://doi.org/abs/10.1177/2053019616688022.

4. https://www.ncei.noaa.gov/monitoring https://crudata.uea.ac.uk/cru/data/temperature/ http://data.giss.nasa.gov/gistemp

5. https://www.giss.nasa.gov/research/news/20170118/

6. S. Levitus, J. Antonov, T. Boyer, O Baranova, H. Garcia, R. Locarnini, A. Mishonov, J. Reagan, D. Seidov, E. Yarosh, M. Zweng, " NCEI ocean heat content, temperature anomalies, salinity anomalies, thermosteric sea level anomalies, halosteric sea level anomalies, and total steric sea level anomalies from 1955 to present calculated from in situ oceanographic subsurface profile data (NCEI Accession 0164586), Version 4.4. (2017) NOAA National Centers for Environmental Information. https://www.nodc.noaa.gov/OC5/3M_HEAT_CONTENT/index3.html K. von Schuckmann, L. Cheng, L,. D. Palmer, J. Hansen, C. Tassone, V. Aich, S. Adusumilli, H. Beltrami, H., T. Boyer, F. Cuesta-Valero, D. Desbruyeres, C. Domingues, A. Garcia-Garcia, P. Gentine, J. Gilson, M. Gorfer, L. Haimberger, M. Ishii, M., G. Johnson, R. Killick, B. King, G. Kirchengast, N. Kolodziejczyk, J. Lyman, B. Marzeion, M. Mayer, M. Monier, D. Monselesan, S. Purkey, D. Roemmich, A. Schweiger, S. Seneviratne, A. Shepherd, D. Slater, A. Steiner, F. Straneo, M.L. Timmermans, S. Wijffels. "Heat stored in the Earth system: where does the energy go?" Earth System Science Data 12, Issue 3 (07 September 2020): 2013-2041. https://doi.org/10.5194/essd-12-2013-2020.

7. I. Velicogna, Yara Mohajerani, A. Geruo, F. Landerer, J. Mouginot, B. Noel, E. Rignot, T. Sutterly, M. van den Broeke, M. Wessem, D. Wiese, "Continuity of Ice Sheet Mass Loss in Greenland and Antarctica From the GRACE and GRACE Follow-On Missions." Geophysical Research Letters 47, Issue 8 (28 April 2020): e2020GL087291. https://doi.org/10.1029/2020GL087291.

8. National Snow and Ice Data Center World Glacier Monitoring Service

9. National Snow and Ice Data Center D.A. Robinson, D. K. Hall, and T. L. Mote, "MEaSUREs Northern Hemisphere Terrestrial Snow Cover Extent Daily 25km EASE-Grid 2.0, Version 1 (2017). Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: https://doi.org/10.5067/MEASURES/CRYOSPHERE/nsidc-0530.001 . http://nsidc.org/cryosphere/sotc/snow_extent.html Rutgers University Global Snow Lab. Data History

10. R.S. Nerem, B.D. Beckley, J. T. Fasullo, B.D. Hamlington, D. Masters, and G.T. Mitchum, "Climate-change–driven accelerated sea-level rise detected in the altimeter era." PNAS 15, no. 9 (12 Feb. 2018): 2022-2025. https://doi.org/10.1073/pnas.1717312115.

11. https://nsidc.org/cryosphere/sotc/sea_ice.html Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS, Zhang and Rothrock, 2003) http://psc.apl.washington.edu/research/projects/arctic-sea-ice-volume-anomaly/ http://psc.apl.uw.edu/research/projects/projections-of-an-ice-diminished-arctic-ocean/

12. USGCRP, 2017: Climate Science Special Report: Fourth National Climate Assessment, Volume I [Wuebbles, D.J., D.W. Fahey, K.A. Hibbard, D.J. Dokken, B.C. Stewart, and T.K. Maycock (eds.)]. U.S. Global Change Research Program, Washington, DC, USA, 470 pp, https://doi.org/10.7930/j0j964j6 .

13. http://www.pmel.noaa.gov/co2/story/What+is+Ocean+Acidification%3F

14. http://www.pmel.noaa.gov/co2/story/Ocean+Acidification

15. C.L. Sabine, et al., “The Oceanic Sink for Anthropogenic CO2.” Science 305 (16 July 2004): 367-371. https://doi.org/10.1126/science.1097403.

16. Special Report on the Ocean and Cryosphere in a Changing Climate , Technical Summary, Chapter TS.5, Changing Ocean, Marine Ecosystems, and Dependent Communities, Section 5.2.2.3. https://www.ipcc.ch/srocc/chapter/technical-summary/

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Methodology

• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on June 19, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analyzing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

• How does social media shape body image in teenagers?
• How do children and adults interpret healthy eating in the UK?
• What factors influence employee retention in a large organization?
• How is anxiety experienced around the world?
• How can teachers integrate social issues into science curriculums?

Table of contents

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, other interesting articles, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography , action research , phenomenological research, and narrative research. They share some similarities, but emphasize different aims and perspectives.

Note that qualitative research is at risk for certain research biases including the Hawthorne effect , observer bias , recall bias , and social desirability bias . While not always totally avoidable, awareness of potential biases as you collect and analyze your data can prevent them from impacting your work too much.

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Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

• Observations: recording what you have seen, heard, or encountered in detailed field notes.
• Interviews:  personally asking people questions in one-on-one conversations.
• Focus groups: asking questions and generating discussion among a group of people.
• Surveys : distributing questionnaires with open-ended questions.
• Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
• You take field notes with observations and reflect on your own experiences of the company culture.
• You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
• You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves “instruments” in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analyzing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

• Prepare and organize your data. This may mean transcribing interviews or typing up fieldnotes.
• Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
• Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorize your data.
• Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
• Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analyzing qualitative data. Although these methods share similar processes, they emphasize different concepts.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

• Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

• Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

• Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

• Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

Researchers must consider practical and theoretical limitations in analyzing and interpreting their data. Qualitative research suffers from:

• Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

• Subjectivity

Due to the researcher’s primary role in analyzing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

• Limited generalizability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalizable conclusions because the data may be biased and unrepresentative of the wider population .

• Labor-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

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

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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