what is scientific research method

Scientific Method

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The scientific method is a series of steps followed by scientific investigators to answer specific questions about the natural world. It involves making observations, formulating a hypothesis , and conducting scientific experiments . Scientific inquiry starts with an observation followed by the formulation of a question about what has been observed. The steps of the scientific method are as follows:

Observation

The first step of the scientific method involves making an observation about something that interests you. This is very important if you are doing a science project because you want your project to be focused on something that will hold your attention. Your observation can be on anything from plant movement to animal behavior, as long as it is something you really want to know more about.​ This is where you come up with the idea for your science project.

Once you've made your observation, you must formulate a question about what you have observed. Your question should tell what it is that you are trying to discover or accomplish in your experiment. When stating your question you should be as specific as possible.​ For example, if you are doing a project on plants , you may want to know how plants interact with microbes. Your question may be: Do plant spices inhibit bacterial growth ?

The hypothesis is a key component of the scientific process. A hypothesis is an idea that is suggested as an explanation for a natural event, a particular experience, or a specific condition that can be tested through definable experimentation. It states the purpose of your experiment, the variables used, and the predicted outcome of your experiment. It is important to note that a hypothesis must be testable. That means that you should be able to test your hypothesis through experimentation .​ Your hypothesis must either be supported or falsified by your experiment. An example of a good hypothesis is: If there is a relation between listening to music and heart rate, then listening to music will cause a person's resting heart rate to either increase or decrease.

Once you've developed a hypothesis, you must design and conduct an experiment that will test it. You should develop a procedure that states very clearly how you plan to conduct your experiment. It is important that you include and identify a controlled variable or dependent variable in your procedure. Controls allow us to test a single variable in an experiment because they are unchanged. We can then make observations and comparisons between our controls and our independent variables (things that change in the experiment) to develop an accurate conclusion.​

The results are where you report what happened in the experiment. That includes detailing all observations and data made during your experiment. Most people find it easier to visualize the data by charting or graphing the information.​

The final step of the scientific method is developing a conclusion. This is where all of the results from the experiment are analyzed and a determination is reached about the hypothesis. Did the experiment support or reject your hypothesis? If your hypothesis was supported, great. If not, repeat the experiment or think of ways to improve your procedure.

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Scientific Method

Science is an enormously successful human enterprise. The study of scientific method is the attempt to discern the activities by which that success is achieved. Among the activities often identified as characteristic of science are systematic observation and experimentation, inductive and deductive reasoning, and the formation and testing of hypotheses and theories. How these are carried out in detail can vary greatly, but characteristics like these have been looked to as a way of demarcating scientific activity from non-science, where only enterprises which employ some canonical form of scientific method or methods should be considered science (see also the entry on science and pseudo-science ). Others have questioned whether there is anything like a fixed toolkit of methods which is common across science and only science. Some reject privileging one view of method as part of rejecting broader views about the nature of science, such as naturalism (Dupré 2004); some reject any restriction in principle (pluralism).

Scientific method should be distinguished from the aims and products of science, such as knowledge, predictions, or control. Methods are the means by which those goals are achieved. Scientific method should also be distinguished from meta-methodology, which includes the values and justifications behind a particular characterization of scientific method (i.e., a methodology) — values such as objectivity, reproducibility, simplicity, or past successes. Methodological rules are proposed to govern method and it is a meta-methodological question whether methods obeying those rules satisfy given values. Finally, method is distinct, to some degree, from the detailed and contextual practices through which methods are implemented. The latter might range over: specific laboratory techniques; mathematical formalisms or other specialized languages used in descriptions and reasoning; technological or other material means; ways of communicating and sharing results, whether with other scientists or with the public at large; or the conventions, habits, enforced customs, and institutional controls over how and what science is carried out.

While it is important to recognize these distinctions, their boundaries are fuzzy. Hence, accounts of method cannot be entirely divorced from their methodological and meta-methodological motivations or justifications, Moreover, each aspect plays a crucial role in identifying methods. Disputes about method have therefore played out at the detail, rule, and meta-rule levels. Changes in beliefs about the certainty or fallibility of scientific knowledge, for instance (which is a meta-methodological consideration of what we can hope for methods to deliver), have meant different emphases on deductive and inductive reasoning, or on the relative importance attached to reasoning over observation (i.e., differences over particular methods.) Beliefs about the role of science in society will affect the place one gives to values in scientific method.

The issue which has shaped debates over scientific method the most in the last half century is the question of how pluralist do we need to be about method? Unificationists continue to hold out for one method essential to science; nihilism is a form of radical pluralism, which considers the effectiveness of any methodological prescription to be so context sensitive as to render it not explanatory on its own. Some middle degree of pluralism regarding the methods embodied in scientific practice seems appropriate. But the details of scientific practice vary with time and place, from institution to institution, across scientists and their subjects of investigation. How significant are the variations for understanding science and its success? How much can method be abstracted from practice? This entry describes some of the attempts to characterize scientific method or methods, as well as arguments for a more context-sensitive approach to methods embedded in actual scientific practices.

1. Overview and organizing themes

2. historical review: aristotle to mill, 3.1 logical constructionism and operationalism, 3.2. h-d as a logic of confirmation, 3.3. popper and falsificationism, 3.4 meta-methodology and the end of method, 4. statistical methods for hypothesis testing, 5.1 creative and exploratory practices.

• 5.2 Computer methods and the ‘new ways’ of doing science

6.1 “The scientific method” in science education and as seen by scientists

6.2 privileged methods and ‘gold standards’, 6.3 scientific method in the court room, 6.4 deviating practices, 7. conclusion, other internet resources, related entries.

This entry could have been given the title Scientific Methods and gone on to fill volumes, or it could have been extremely short, consisting of a brief summary rejection of the idea that there is any such thing as a unique Scientific Method at all. Both unhappy prospects are due to the fact that scientific activity varies so much across disciplines, times, places, and scientists that any account which manages to unify it all will either consist of overwhelming descriptive detail, or trivial generalizations.

The choice of scope for the present entry is more optimistic, taking a cue from the recent movement in philosophy of science toward a greater attention to practice: to what scientists actually do. This “turn to practice” can be seen as the latest form of studies of methods in science, insofar as it represents an attempt at understanding scientific activity, but through accounts that are neither meant to be universal and unified, nor singular and narrowly descriptive. To some extent, different scientists at different times and places can be said to be using the same method even though, in practice, the details are different.

Whether the context in which methods are carried out is relevant, or to what extent, will depend largely on what one takes the aims of science to be and what one’s own aims are. For most of the history of scientific methodology the assumption has been that the most important output of science is knowledge and so the aim of methodology should be to discover those methods by which scientific knowledge is generated.

Science was seen to embody the most successful form of reasoning (but which form?) to the most certain knowledge claims (but how certain?) on the basis of systematically collected evidence (but what counts as evidence, and should the evidence of the senses take precedence, or rational insight?) Section 2 surveys some of the history, pointing to two major themes. One theme is seeking the right balance between observation and reasoning (and the attendant forms of reasoning which employ them); the other is how certain scientific knowledge is or can be.

Section 3 turns to 20 th century debates on scientific method. In the second half of the 20 th century the epistemic privilege of science faced several challenges and many philosophers of science abandoned the reconstruction of the logic of scientific method. Views changed significantly regarding which functions of science ought to be captured and why. For some, the success of science was better identified with social or cultural features. Historical and sociological turns in the philosophy of science were made, with a demand that greater attention be paid to the non-epistemic aspects of science, such as sociological, institutional, material, and political factors. Even outside of those movements there was an increased specialization in the philosophy of science, with more and more focus on specific fields within science. The combined upshot was very few philosophers arguing any longer for a grand unified methodology of science. Sections 3 and 4 surveys the main positions on scientific method in 20 th century philosophy of science, focusing on where they differ in their preference for confirmation or falsification or for waiving the idea of a special scientific method altogether.

In recent decades, attention has primarily been paid to scientific activities traditionally falling under the rubric of method, such as experimental design and general laboratory practice, the use of statistics, the construction and use of models and diagrams, interdisciplinary collaboration, and science communication. Sections 4–6 attempt to construct a map of the current domains of the study of methods in science.

As these sections illustrate, the question of method is still central to the discourse about science. Scientific method remains a topic for education, for science policy, and for scientists. It arises in the public domain where the demarcation or status of science is at issue. Some philosophers have recently returned, therefore, to the question of what it is that makes science a unique cultural product. This entry will close with some of these recent attempts at discerning and encapsulating the activities by which scientific knowledge is achieved.

Attempting a history of scientific method compounds the vast scope of the topic. This section briefly surveys the background to modern methodological debates. What can be called the classical view goes back to antiquity, and represents a point of departure for later divergences. [ 1 ]

We begin with a point made by Laudan (1968) in his historical survey of scientific method:

Perhaps the most serious inhibition to the emergence of the history of theories of scientific method as a respectable area of study has been the tendency to conflate it with the general history of epistemology, thereby assuming that the narrative categories and classificatory pigeon-holes applied to the latter are also basic to the former. (1968: 5)

To see knowledge about the natural world as falling under knowledge more generally is an understandable conflation. Histories of theories of method would naturally employ the same narrative categories and classificatory pigeon holes. An important theme of the history of epistemology, for example, is the unification of knowledge, a theme reflected in the question of the unification of method in science. Those who have identified differences in kinds of knowledge have often likewise identified different methods for achieving that kind of knowledge (see the entry on the unity of science ).

Different views on what is known, how it is known, and what can be known are connected. Plato distinguished the realms of things into the visible and the intelligible ( The Republic , 510a, in Cooper 1997). Only the latter, the Forms, could be objects of knowledge. The intelligible truths could be known with the certainty of geometry and deductive reasoning. What could be observed of the material world, however, was by definition imperfect and deceptive, not ideal. The Platonic way of knowledge therefore emphasized reasoning as a method, downplaying the importance of observation. Aristotle disagreed, locating the Forms in the natural world as the fundamental principles to be discovered through the inquiry into nature ( Metaphysics Z , in Barnes 1984).

Aristotle is recognized as giving the earliest systematic treatise on the nature of scientific inquiry in the western tradition, one which embraced observation and reasoning about the natural world. In the Prior and Posterior Analytics , Aristotle reflects first on the aims and then the methods of inquiry into nature. A number of features can be found which are still considered by most to be essential to science. For Aristotle, empiricism, careful observation (but passive observation, not controlled experiment), is the starting point. The aim is not merely recording of facts, though. For Aristotle, science ( epistêmê ) is a body of properly arranged knowledge or learning—the empirical facts, but also their ordering and display are of crucial importance. The aims of discovery, ordering, and display of facts partly determine the methods required of successful scientific inquiry. Also determinant is the nature of the knowledge being sought, and the explanatory causes proper to that kind of knowledge (see the discussion of the four causes in the entry on Aristotle on causality ).

In addition to careful observation, then, scientific method requires a logic as a system of reasoning for properly arranging, but also inferring beyond, what is known by observation. Methods of reasoning may include induction, prediction, or analogy, among others. Aristotle’s system (along with his catalogue of fallacious reasoning) was collected under the title the Organon . This title would be echoed in later works on scientific reasoning, such as Novum Organon by Francis Bacon, and Novum Organon Restorum by William Whewell (see below). In Aristotle’s Organon reasoning is divided primarily into two forms, a rough division which persists into modern times. The division, known most commonly today as deductive versus inductive method, appears in other eras and methodologies as analysis/​synthesis, non-ampliative/​ampliative, or even confirmation/​verification. The basic idea is there are two “directions” to proceed in our methods of inquiry: one away from what is observed, to the more fundamental, general, and encompassing principles; the other, from the fundamental and general to instances or implications of principles.

The basic aim and method of inquiry identified here can be seen as a theme running throughout the next two millennia of reflection on the correct way to seek after knowledge: carefully observe nature and then seek rules or principles which explain or predict its operation. The Aristotelian corpus provided the framework for a commentary tradition on scientific method independent of science itself (cosmos versus physics.) During the medieval period, figures such as Albertus Magnus (1206–1280), Thomas Aquinas (1225–1274), Robert Grosseteste (1175–1253), Roger Bacon (1214/1220–1292), William of Ockham (1287–1347), Andreas Vesalius (1514–1546), Giacomo Zabarella (1533–1589) all worked to clarify the kind of knowledge obtainable by observation and induction, the source of justification of induction, and best rules for its application. [ 2 ] Many of their contributions we now think of as essential to science (see also Laudan 1968). As Aristotle and Plato had employed a framework of reasoning either “to the forms” or “away from the forms”, medieval thinkers employed directions away from the phenomena or back to the phenomena. In analysis, a phenomena was examined to discover its basic explanatory principles; in synthesis, explanations of a phenomena were constructed from first principles.

During the Scientific Revolution these various strands of argument, experiment, and reason were forged into a dominant epistemic authority. The 16 th –18 th centuries were a period of not only dramatic advance in knowledge about the operation of the natural world—advances in mechanical, medical, biological, political, economic explanations—but also of self-awareness of the revolutionary changes taking place, and intense reflection on the source and legitimation of the method by which the advances were made. The struggle to establish the new authority included methodological moves. The Book of Nature, according to the metaphor of Galileo Galilei (1564–1642) or Francis Bacon (1561–1626), was written in the language of mathematics, of geometry and number. This motivated an emphasis on mathematical description and mechanical explanation as important aspects of scientific method. Through figures such as Henry More and Ralph Cudworth, a neo-Platonic emphasis on the importance of metaphysical reflection on nature behind appearances, particularly regarding the spiritual as a complement to the purely mechanical, remained an important methodological thread of the Scientific Revolution (see the entries on Cambridge platonists ; Boyle ; Henry More ; Galileo ).

In Novum Organum (1620), Bacon was critical of the Aristotelian method for leaping from particulars to universals too quickly. The syllogistic form of reasoning readily mixed those two types of propositions. Bacon aimed at the invention of new arts, principles, and directions. His method would be grounded in methodical collection of observations, coupled with correction of our senses (and particularly, directions for the avoidance of the Idols, as he called them, kinds of systematic errors to which naïve observers are prone.) The community of scientists could then climb, by a careful, gradual and unbroken ascent, to reliable general claims.

Bacon’s method has been criticized as impractical and too inflexible for the practicing scientist. Whewell would later criticize Bacon in his System of Logic for paying too little attention to the practices of scientists. It is hard to find convincing examples of Bacon’s method being put in to practice in the history of science, but there are a few who have been held up as real examples of 16 th century scientific, inductive method, even if not in the rigid Baconian mold: figures such as Robert Boyle (1627–1691) and William Harvey (1578–1657) (see the entry on Bacon ).

It is to Isaac Newton (1642–1727), however, that historians of science and methodologists have paid greatest attention. Given the enormous success of his Principia Mathematica and Opticks , this is understandable. The study of Newton’s method has had two main thrusts: the implicit method of the experiments and reasoning presented in the Opticks, and the explicit methodological rules given as the Rules for Philosophising (the Regulae) in Book III of the Principia . [ 3 ] Newton’s law of gravitation, the linchpin of his new cosmology, broke with explanatory conventions of natural philosophy, first for apparently proposing action at a distance, but more generally for not providing “true”, physical causes. The argument for his System of the World ( Principia , Book III) was based on phenomena, not reasoned first principles. This was viewed (mainly on the continent) as insufficient for proper natural philosophy. The Regulae counter this objection, re-defining the aims of natural philosophy by re-defining the method natural philosophers should follow. (See the entry on Newton’s philosophy .)

To his list of methodological prescriptions should be added Newton’s famous phrase “ hypotheses non fingo ” (commonly translated as “I frame no hypotheses”.) The scientist was not to invent systems but infer explanations from observations, as Bacon had advocated. This would come to be known as inductivism. In the century after Newton, significant clarifications of the Newtonian method were made. Colin Maclaurin (1698–1746), for instance, reconstructed the essential structure of the method as having complementary analysis and synthesis phases, one proceeding away from the phenomena in generalization, the other from the general propositions to derive explanations of new phenomena. Denis Diderot (1713–1784) and editors of the Encyclopédie did much to consolidate and popularize Newtonianism, as did Francesco Algarotti (1721–1764). The emphasis was often the same, as much on the character of the scientist as on their process, a character which is still commonly assumed. The scientist is humble in the face of nature, not beholden to dogma, obeys only his eyes, and follows the truth wherever it leads. It was certainly Voltaire (1694–1778) and du Chatelet (1706–1749) who were most influential in propagating the latter vision of the scientist and their craft, with Newton as hero. Scientific method became a revolutionary force of the Enlightenment. (See also the entries on Newton , Leibniz , Descartes , Boyle , Hume , enlightenment , as well as Shank 2008 for a historical overview.)

Not all 18 th century reflections on scientific method were so celebratory. Famous also are George Berkeley’s (1685–1753) attack on the mathematics of the new science, as well as the over-emphasis of Newtonians on observation; and David Hume’s (1711–1776) undermining of the warrant offered for scientific claims by inductive justification (see the entries on: George Berkeley ; David Hume ; Hume’s Newtonianism and Anti-Newtonianism ). Hume’s problem of induction motivated Immanuel Kant (1724–1804) to seek new foundations for empirical method, though as an epistemic reconstruction, not as any set of practical guidelines for scientists. Both Hume and Kant influenced the methodological reflections of the next century, such as the debate between Mill and Whewell over the certainty of inductive inferences in science.

The debate between John Stuart Mill (1806–1873) and William Whewell (1794–1866) has become the canonical methodological debate of the 19 th century. Although often characterized as a debate between inductivism and hypothetico-deductivism, the role of the two methods on each side is actually more complex. On the hypothetico-deductive account, scientists work to come up with hypotheses from which true observational consequences can be deduced—hence, hypothetico-deductive. Because Whewell emphasizes both hypotheses and deduction in his account of method, he can be seen as a convenient foil to the inductivism of Mill. However, equally if not more important to Whewell’s portrayal of scientific method is what he calls the “fundamental antithesis”. Knowledge is a product of the objective (what we see in the world around us) and subjective (the contributions of our mind to how we perceive and understand what we experience, which he called the Fundamental Ideas). Both elements are essential according to Whewell, and he was therefore critical of Kant for too much focus on the subjective, and John Locke (1632–1704) and Mill for too much focus on the senses. Whewell’s fundamental ideas can be discipline relative. An idea can be fundamental even if it is necessary for knowledge only within a given scientific discipline (e.g., chemical affinity for chemistry). This distinguishes fundamental ideas from the forms and categories of intuition of Kant. (See the entry on Whewell .)

Clarifying fundamental ideas would therefore be an essential part of scientific method and scientific progress. Whewell called this process “Discoverer’s Induction”. It was induction, following Bacon or Newton, but Whewell sought to revive Bacon’s account by emphasising the role of ideas in the clear and careful formulation of inductive hypotheses. Whewell’s induction is not merely the collecting of objective facts. The subjective plays a role through what Whewell calls the Colligation of Facts, a creative act of the scientist, the invention of a theory. A theory is then confirmed by testing, where more facts are brought under the theory, called the Consilience of Inductions. Whewell felt that this was the method by which the true laws of nature could be discovered: clarification of fundamental concepts, clever invention of explanations, and careful testing. Mill, in his critique of Whewell, and others who have cast Whewell as a fore-runner of the hypothetico-deductivist view, seem to have under-estimated the importance of this discovery phase in Whewell’s understanding of method (Snyder 1997a,b, 1999). Down-playing the discovery phase would come to characterize methodology of the early 20 th century (see section 3 ).

Mill, in his System of Logic , put forward a narrower view of induction as the essence of scientific method. For Mill, induction is the search first for regularities among events. Among those regularities, some will continue to hold for further observations, eventually gaining the status of laws. One can also look for regularities among the laws discovered in a domain, i.e., for a law of laws. Which “law law” will hold is time and discipline dependent and open to revision. One example is the Law of Universal Causation, and Mill put forward specific methods for identifying causes—now commonly known as Mill’s methods. These five methods look for circumstances which are common among the phenomena of interest, those which are absent when the phenomena are, or those for which both vary together. Mill’s methods are still seen as capturing basic intuitions about experimental methods for finding the relevant explanatory factors ( System of Logic (1843), see Mill entry). The methods advocated by Whewell and Mill, in the end, look similar. Both involve inductive generalization to covering laws. They differ dramatically, however, with respect to the necessity of the knowledge arrived at; that is, at the meta-methodological level (see the entries on Whewell and Mill entries).

3. Logic of method and critical responses

The quantum and relativistic revolutions in physics in the early 20 th century had a profound effect on methodology. Conceptual foundations of both theories were taken to show the defeasibility of even the most seemingly secure intuitions about space, time and bodies. Certainty of knowledge about the natural world was therefore recognized as unattainable. Instead a renewed empiricism was sought which rendered science fallible but still rationally justifiable.

Analyses of the reasoning of scientists emerged, according to which the aspects of scientific method which were of primary importance were the means of testing and confirming of theories. A distinction in methodology was made between the contexts of discovery and justification. The distinction could be used as a wedge between the particularities of where and how theories or hypotheses are arrived at, on the one hand, and the underlying reasoning scientists use (whether or not they are aware of it) when assessing theories and judging their adequacy on the basis of the available evidence. By and large, for most of the 20 th century, philosophy of science focused on the second context, although philosophers differed on whether to focus on confirmation or refutation as well as on the many details of how confirmation or refutation could or could not be brought about. By the mid-20 th century these attempts at defining the method of justification and the context distinction itself came under pressure. During the same period, philosophy of science developed rapidly, and from section 4 this entry will therefore shift from a primarily historical treatment of the scientific method towards a primarily thematic one.

Advances in logic and probability held out promise of the possibility of elaborate reconstructions of scientific theories and empirical method, the best example being Rudolf Carnap’s The Logical Structure of the World (1928). Carnap attempted to show that a scientific theory could be reconstructed as a formal axiomatic system—that is, a logic. That system could refer to the world because some of its basic sentences could be interpreted as observations or operations which one could perform to test them. The rest of the theoretical system, including sentences using theoretical or unobservable terms (like electron or force) would then either be meaningful because they could be reduced to observations, or they had purely logical meanings (called analytic, like mathematical identities). This has been referred to as the verifiability criterion of meaning. According to the criterion, any statement not either analytic or verifiable was strictly meaningless. Although the view was endorsed by Carnap in 1928, he would later come to see it as too restrictive (Carnap 1956). Another familiar version of this idea is operationalism of Percy William Bridgman. In The Logic of Modern Physics (1927) Bridgman asserted that every physical concept could be defined in terms of the operations one would perform to verify the application of that concept. Making good on the operationalisation of a concept even as simple as length, however, can easily become enormously complex (for measuring very small lengths, for instance) or impractical (measuring large distances like light years.)

Carl Hempel’s (1950, 1951) criticisms of the verifiability criterion of meaning had enormous influence. He pointed out that universal generalizations, such as most scientific laws, were not strictly meaningful on the criterion. Verifiability and operationalism both seemed too restrictive to capture standard scientific aims and practice. The tenuous connection between these reconstructions and actual scientific practice was criticized in another way. In both approaches, scientific methods are instead recast in methodological roles. Measurements, for example, were looked to as ways of giving meanings to terms. The aim of the philosopher of science was not to understand the methods per se , but to use them to reconstruct theories, their meanings, and their relation to the world. When scientists perform these operations, however, they will not report that they are doing them to give meaning to terms in a formal axiomatic system. This disconnect between methodology and the details of actual scientific practice would seem to violate the empiricism the Logical Positivists and Bridgman were committed to. The view that methodology should correspond to practice (to some extent) has been called historicism, or intuitionism. We turn to these criticisms and responses in section 3.4 . [ 4 ]

Positivism also had to contend with the recognition that a purely inductivist approach, along the lines of Bacon-Newton-Mill, was untenable. There was no pure observation, for starters. All observation was theory laden. Theory is required to make any observation, therefore not all theory can be derived from observation alone. (See the entry on theory and observation in science .) Even granting an observational basis, Hume had already pointed out that one could not deductively justify inductive conclusions without begging the question by presuming the success of the inductive method. Likewise, positivist attempts at analyzing how a generalization can be confirmed by observations of its instances were subject to a number of criticisms. Goodman (1965) and Hempel (1965) both point to paradoxes inherent in standard accounts of confirmation. Recent attempts at explaining how observations can serve to confirm a scientific theory are discussed in section 4 below.

The standard starting point for a non-inductive analysis of the logic of confirmation is known as the Hypothetico-Deductive (H-D) method. In its simplest form, a sentence of a theory which expresses some hypothesis is confirmed by its true consequences. As noted in section 2 , this method had been advanced by Whewell in the 19 th century, as well as Nicod (1924) and others in the 20 th century. Often, Hempel’s (1966) description of the H-D method, illustrated by the case of Semmelweiss’ inferential procedures in establishing the cause of childbed fever, has been presented as a key account of H-D as well as a foil for criticism of the H-D account of confirmation (see, for example, Lipton’s (2004) discussion of inference to the best explanation; also the entry on confirmation ). Hempel described Semmelsweiss’ procedure as examining various hypotheses explaining the cause of childbed fever. Some hypotheses conflicted with observable facts and could be rejected as false immediately. Others needed to be tested experimentally by deducing which observable events should follow if the hypothesis were true (what Hempel called the test implications of the hypothesis), then conducting an experiment and observing whether or not the test implications occurred. If the experiment showed the test implication to be false, the hypothesis could be rejected. If the experiment showed the test implications to be true, however, this did not prove the hypothesis true. The confirmation of a test implication does not verify a hypothesis, though Hempel did allow that “it provides at least some support, some corroboration or confirmation for it” (Hempel 1966: 8). The degree of this support then depends on the quantity, variety and precision of the supporting evidence.

Another approach that took off from the difficulties with inductive inference was Karl Popper’s critical rationalism or falsificationism (Popper 1959, 1963). Falsification is deductive and similar to H-D in that it involves scientists deducing observational consequences from the hypothesis under test. For Popper, however, the important point was not the degree of confirmation that successful prediction offered to a hypothesis. The crucial thing was the logical asymmetry between confirmation, based on inductive inference, and falsification, which can be based on a deductive inference. (This simple opposition was later questioned, by Lakatos, among others. See the entry on historicist theories of scientific rationality. )

Popper stressed that, regardless of the amount of confirming evidence, we can never be certain that a hypothesis is true without committing the fallacy of affirming the consequent. Instead, Popper introduced the notion of corroboration as a measure for how well a theory or hypothesis has survived previous testing—but without implying that this is also a measure for the probability that it is true.

Popper was also motivated by his doubts about the scientific status of theories like the Marxist theory of history or psycho-analysis, and so wanted to demarcate between science and pseudo-science. Popper saw this as an importantly different distinction than demarcating science from metaphysics. The latter demarcation was the primary concern of many logical empiricists. Popper used the idea of falsification to draw a line instead between pseudo and proper science. Science was science because its method involved subjecting theories to rigorous tests which offered a high probability of failing and thus refuting the theory.

A commitment to the risk of failure was important. Avoiding falsification could be done all too easily. If a consequence of a theory is inconsistent with observations, an exception can be added by introducing auxiliary hypotheses designed explicitly to save the theory, so-called ad hoc modifications. This Popper saw done in pseudo-science where ad hoc theories appeared capable of explaining anything in their field of application. In contrast, science is risky. If observations showed the predictions from a theory to be wrong, the theory would be refuted. Hence, scientific hypotheses must be falsifiable. Not only must there exist some possible observation statement which could falsify the hypothesis or theory, were it observed, (Popper called these the hypothesis’ potential falsifiers) it is crucial to the Popperian scientific method that such falsifications be sincerely attempted on a regular basis.

The more potential falsifiers of a hypothesis, the more falsifiable it would be, and the more the hypothesis claimed. Conversely, hypotheses without falsifiers claimed very little or nothing at all. Originally, Popper thought that this meant the introduction of ad hoc hypotheses only to save a theory should not be countenanced as good scientific method. These would undermine the falsifiabililty of a theory. However, Popper later came to recognize that the introduction of modifications (immunizations, he called them) was often an important part of scientific development. Responding to surprising or apparently falsifying observations often generated important new scientific insights. Popper’s own example was the observed motion of Uranus which originally did not agree with Newtonian predictions. The ad hoc hypothesis of an outer planet explained the disagreement and led to further falsifiable predictions. Popper sought to reconcile the view by blurring the distinction between falsifiable and not falsifiable, and speaking instead of degrees of testability (Popper 1985: 41f.).

From the 1960s on, sustained meta-methodological criticism emerged that drove philosophical focus away from scientific method. A brief look at those criticisms follows, with recommendations for further reading at the end of the entry.

Thomas Kuhn’s The Structure of Scientific Revolutions (1962) begins with a well-known shot across the bow for philosophers of science:

History, if viewed as a repository for more than anecdote or chronology, could produce a decisive transformation in the image of science by which we are now possessed. (1962: 1)

The image Kuhn thought needed transforming was the a-historical, rational reconstruction sought by many of the Logical Positivists, though Carnap and other positivists were actually quite sympathetic to Kuhn’s views. (See the entry on the Vienna Circle .) Kuhn shares with other of his contemporaries, such as Feyerabend and Lakatos, a commitment to a more empirical approach to philosophy of science. Namely, the history of science provides important data, and necessary checks, for philosophy of science, including any theory of scientific method.

The history of science reveals, according to Kuhn, that scientific development occurs in alternating phases. During normal science, the members of the scientific community adhere to the paradigm in place. Their commitment to the paradigm means a commitment to the puzzles to be solved and the acceptable ways of solving them. Confidence in the paradigm remains so long as steady progress is made in solving the shared puzzles. Method in this normal phase operates within a disciplinary matrix (Kuhn’s later concept of a paradigm) which includes standards for problem solving, and defines the range of problems to which the method should be applied. An important part of a disciplinary matrix is the set of values which provide the norms and aims for scientific method. The main values that Kuhn identifies are prediction, problem solving, simplicity, consistency, and plausibility.

An important by-product of normal science is the accumulation of puzzles which cannot be solved with resources of the current paradigm. Once accumulation of these anomalies has reached some critical mass, it can trigger a communal shift to a new paradigm and a new phase of normal science. Importantly, the values that provide the norms and aims for scientific method may have transformed in the meantime. Method may therefore be relative to discipline, time or place

Feyerabend also identified the aims of science as progress, but argued that any methodological prescription would only stifle that progress (Feyerabend 1988). His arguments are grounded in re-examining accepted “myths” about the history of science. Heroes of science, like Galileo, are shown to be just as reliant on rhetoric and persuasion as they are on reason and demonstration. Others, like Aristotle, are shown to be far more reasonable and far-reaching in their outlooks then they are given credit for. As a consequence, the only rule that could provide what he took to be sufficient freedom was the vacuous “anything goes”. More generally, even the methodological restriction that science is the best way to pursue knowledge, and to increase knowledge, is too restrictive. Feyerabend suggested instead that science might, in fact, be a threat to a free society, because it and its myth had become so dominant (Feyerabend 1978).

An even more fundamental kind of criticism was offered by several sociologists of science from the 1970s onwards who rejected the methodology of providing philosophical accounts for the rational development of science and sociological accounts of the irrational mistakes. Instead, they adhered to a symmetry thesis on which any causal explanation of how scientific knowledge is established needs to be symmetrical in explaining truth and falsity, rationality and irrationality, success and mistakes, by the same causal factors (see, e.g., Barnes and Bloor 1982, Bloor 1991). Movements in the Sociology of Science, like the Strong Programme, or in the social dimensions and causes of knowledge more generally led to extended and close examination of detailed case studies in contemporary science and its history. (See the entries on the social dimensions of scientific knowledge and social epistemology .) Well-known examinations by Latour and Woolgar (1979/1986), Knorr-Cetina (1981), Pickering (1984), Shapin and Schaffer (1985) seem to bear out that it was social ideologies (on a macro-scale) or individual interactions and circumstances (on a micro-scale) which were the primary causal factors in determining which beliefs gained the status of scientific knowledge. As they saw it therefore, explanatory appeals to scientific method were not empirically grounded.

A late, and largely unexpected, criticism of scientific method came from within science itself. Beginning in the early 2000s, a number of scientists attempting to replicate the results of published experiments could not do so. There may be close conceptual connection between reproducibility and method. For example, if reproducibility means that the same scientific methods ought to produce the same result, and all scientific results ought to be reproducible, then whatever it takes to reproduce a scientific result ought to be called scientific method. Space limits us to the observation that, insofar as reproducibility is a desired outcome of proper scientific method, it is not strictly a part of scientific method. (See the entry on reproducibility of scientific results .)

By the close of the 20 th century the search for the scientific method was flagging. Nola and Sankey (2000b) could introduce their volume on method by remarking that “For some, the whole idea of a theory of scientific method is yester-year’s debate …”.

Despite the many difficulties that philosophers encountered in trying to providing a clear methodology of conformation (or refutation), still important progress has been made on understanding how observation can provide evidence for a given theory. Work in statistics has been crucial for understanding how theories can be tested empirically, and in recent decades a huge literature has developed that attempts to recast confirmation in Bayesian terms. Here these developments can be covered only briefly, and we refer to the entry on confirmation for further details and references.

Statistics has come to play an increasingly important role in the methodology of the experimental sciences from the 19 th century onwards. At that time, statistics and probability theory took on a methodological role as an analysis of inductive inference, and attempts to ground the rationality of induction in the axioms of probability theory have continued throughout the 20 th century and in to the present. Developments in the theory of statistics itself, meanwhile, have had a direct and immense influence on the experimental method, including methods for measuring the uncertainty of observations such as the Method of Least Squares developed by Legendre and Gauss in the early 19 th century, criteria for the rejection of outliers proposed by Peirce by the mid-19 th century, and the significance tests developed by Gosset (a.k.a. “Student”), Fisher, Neyman & Pearson and others in the 1920s and 1930s (see, e.g., Swijtink 1987 for a brief historical overview; and also the entry on C.S. Peirce ).

These developments within statistics then in turn led to a reflective discussion among both statisticians and philosophers of science on how to perceive the process of hypothesis testing: whether it was a rigorous statistical inference that could provide a numerical expression of the degree of confidence in the tested hypothesis, or if it should be seen as a decision between different courses of actions that also involved a value component. This led to a major controversy among Fisher on the one side and Neyman and Pearson on the other (see especially Fisher 1955, Neyman 1956 and Pearson 1955, and for analyses of the controversy, e.g., Howie 2002, Marks 2000, Lenhard 2006). On Fisher’s view, hypothesis testing was a methodology for when to accept or reject a statistical hypothesis, namely that a hypothesis should be rejected by evidence if this evidence would be unlikely relative to other possible outcomes, given the hypothesis were true. In contrast, on Neyman and Pearson’s view, the consequence of error also had to play a role when deciding between hypotheses. Introducing the distinction between the error of rejecting a true hypothesis (type I error) and accepting a false hypothesis (type II error), they argued that it depends on the consequences of the error to decide whether it is more important to avoid rejecting a true hypothesis or accepting a false one. Hence, Fisher aimed for a theory of inductive inference that enabled a numerical expression of confidence in a hypothesis. To him, the important point was the search for truth, not utility. In contrast, the Neyman-Pearson approach provided a strategy of inductive behaviour for deciding between different courses of action. Here, the important point was not whether a hypothesis was true, but whether one should act as if it was.

Similar discussions are found in the philosophical literature. On the one side, Churchman (1948) and Rudner (1953) argued that because scientific hypotheses can never be completely verified, a complete analysis of the methods of scientific inference includes ethical judgments in which the scientists must decide whether the evidence is sufficiently strong or that the probability is sufficiently high to warrant the acceptance of the hypothesis, which again will depend on the importance of making a mistake in accepting or rejecting the hypothesis. Others, such as Jeffrey (1956) and Levi (1960) disagreed and instead defended a value-neutral view of science on which scientists should bracket their attitudes, preferences, temperament, and values when assessing the correctness of their inferences. For more details on this value-free ideal in the philosophy of science and its historical development, see Douglas (2009) and Howard (2003). For a broad set of case studies examining the role of values in science, see e.g. Elliott & Richards 2017.

In recent decades, philosophical discussions of the evaluation of probabilistic hypotheses by statistical inference have largely focused on Bayesianism that understands probability as a measure of a person’s degree of belief in an event, given the available information, and frequentism that instead understands probability as a long-run frequency of a repeatable event. Hence, for Bayesians probabilities refer to a state of knowledge, whereas for frequentists probabilities refer to frequencies of events (see, e.g., Sober 2008, chapter 1 for a detailed introduction to Bayesianism and frequentism as well as to likelihoodism). Bayesianism aims at providing a quantifiable, algorithmic representation of belief revision, where belief revision is a function of prior beliefs (i.e., background knowledge) and incoming evidence. Bayesianism employs a rule based on Bayes’ theorem, a theorem of the probability calculus which relates conditional probabilities. The probability that a particular hypothesis is true is interpreted as a degree of belief, or credence, of the scientist. There will also be a probability and a degree of belief that a hypothesis will be true conditional on a piece of evidence (an observation, say) being true. Bayesianism proscribes that it is rational for the scientist to update their belief in the hypothesis to that conditional probability should it turn out that the evidence is, in fact, observed (see, e.g., Sprenger & Hartmann 2019 for a comprehensive treatment of Bayesian philosophy of science). Originating in the work of Neyman and Person, frequentism aims at providing the tools for reducing long-run error rates, such as the error-statistical approach developed by Mayo (1996) that focuses on how experimenters can avoid both type I and type II errors by building up a repertoire of procedures that detect errors if and only if they are present. Both Bayesianism and frequentism have developed over time, they are interpreted in different ways by its various proponents, and their relations to previous criticism to attempts at defining scientific method are seen differently by proponents and critics. The literature, surveys, reviews and criticism in this area are vast and the reader is referred to the entries on Bayesian epistemology and confirmation .

5. Method in Practice

Attention to scientific practice, as we have seen, is not itself new. However, the turn to practice in the philosophy of science of late can be seen as a correction to the pessimism with respect to method in philosophy of science in later parts of the 20 th century, and as an attempted reconciliation between sociological and rationalist explanations of scientific knowledge. Much of this work sees method as detailed and context specific problem-solving procedures, and methodological analyses to be at the same time descriptive, critical and advisory (see Nickles 1987 for an exposition of this view). The following section contains a survey of some of the practice focuses. In this section we turn fully to topics rather than chronology.

A problem with the distinction between the contexts of discovery and justification that figured so prominently in philosophy of science in the first half of the 20 th century (see section 2 ) is that no such distinction can be clearly seen in scientific activity (see Arabatzis 2006). Thus, in recent decades, it has been recognized that study of conceptual innovation and change should not be confined to psychology and sociology of science, but are also important aspects of scientific practice which philosophy of science should address (see also the entry on scientific discovery ). Looking for the practices that drive conceptual innovation has led philosophers to examine both the reasoning practices of scientists and the wide realm of experimental practices that are not directed narrowly at testing hypotheses, that is, exploratory experimentation.

Examining the reasoning practices of historical and contemporary scientists, Nersessian (2008) has argued that new scientific concepts are constructed as solutions to specific problems by systematic reasoning, and that of analogy, visual representation and thought-experimentation are among the important reasoning practices employed. These ubiquitous forms of reasoning are reliable—but also fallible—methods of conceptual development and change. On her account, model-based reasoning consists of cycles of construction, simulation, evaluation and adaption of models that serve as interim interpretations of the target problem to be solved. Often, this process will lead to modifications or extensions, and a new cycle of simulation and evaluation. However, Nersessian also emphasizes that

creative model-based reasoning cannot be applied as a simple recipe, is not always productive of solutions, and even its most exemplary usages can lead to incorrect solutions. (Nersessian 2008: 11)

Thus, while on the one hand she agrees with many previous philosophers that there is no logic of discovery, discoveries can derive from reasoned processes, such that a large and integral part of scientific practice is

the creation of concepts through which to comprehend, structure, and communicate about physical phenomena …. (Nersessian 1987: 11)

Similarly, work on heuristics for discovery and theory construction by scholars such as Darden (1991) and Bechtel & Richardson (1993) present science as problem solving and investigate scientific problem solving as a special case of problem-solving in general. Drawing largely on cases from the biological sciences, much of their focus has been on reasoning strategies for the generation, evaluation, and revision of mechanistic explanations of complex systems.

Addressing another aspect of the context distinction, namely the traditional view that the primary role of experiments is to test theoretical hypotheses according to the H-D model, other philosophers of science have argued for additional roles that experiments can play. The notion of exploratory experimentation was introduced to describe experiments driven by the desire to obtain empirical regularities and to develop concepts and classifications in which these regularities can be described (Steinle 1997, 2002; Burian 1997; Waters 2007)). However the difference between theory driven experimentation and exploratory experimentation should not be seen as a sharp distinction. Theory driven experiments are not always directed at testing hypothesis, but may also be directed at various kinds of fact-gathering, such as determining numerical parameters. Vice versa , exploratory experiments are usually informed by theory in various ways and are therefore not theory-free. Instead, in exploratory experiments phenomena are investigated without first limiting the possible outcomes of the experiment on the basis of extant theory about the phenomena.

The development of high throughput instrumentation in molecular biology and neighbouring fields has given rise to a special type of exploratory experimentation that collects and analyses very large amounts of data, and these new ‘omics’ disciplines are often said to represent a break with the ideal of hypothesis-driven science (Burian 2007; Elliott 2007; Waters 2007; O’Malley 2007) and instead described as data-driven research (Leonelli 2012; Strasser 2012) or as a special kind of “convenience experimentation” in which many experiments are done simply because they are extraordinarily convenient to perform (Krohs 2012).

5.2 Computer methods and ‘new ways’ of doing science

The field of omics just described is possible because of the ability of computers to process, in a reasonable amount of time, the huge quantities of data required. Computers allow for more elaborate experimentation (higher speed, better filtering, more variables, sophisticated coordination and control), but also, through modelling and simulations, might constitute a form of experimentation themselves. Here, too, we can pose a version of the general question of method versus practice: does the practice of using computers fundamentally change scientific method, or merely provide a more efficient means of implementing standard methods?

Because computers can be used to automate measurements, quantifications, calculations, and statistical analyses where, for practical reasons, these operations cannot be otherwise carried out, many of the steps involved in reaching a conclusion on the basis of an experiment are now made inside a “black box”, without the direct involvement or awareness of a human. This has epistemological implications, regarding what we can know, and how we can know it. To have confidence in the results, computer methods are therefore subjected to tests of verification and validation.

The distinction between verification and validation is easiest to characterize in the case of computer simulations. In a typical computer simulation scenario computers are used to numerically integrate differential equations for which no analytic solution is available. The equations are part of the model the scientist uses to represent a phenomenon or system under investigation. Verifying a computer simulation means checking that the equations of the model are being correctly approximated. Validating a simulation means checking that the equations of the model are adequate for the inferences one wants to make on the basis of that model.

A number of issues related to computer simulations have been raised. The identification of validity and verification as the testing methods has been criticized. Oreskes et al. (1994) raise concerns that “validiation”, because it suggests deductive inference, might lead to over-confidence in the results of simulations. The distinction itself is probably too clean, since actual practice in the testing of simulations mixes and moves back and forth between the two (Weissart 1997; Parker 2008a; Winsberg 2010). Computer simulations do seem to have a non-inductive character, given that the principles by which they operate are built in by the programmers, and any results of the simulation follow from those in-built principles in such a way that those results could, in principle, be deduced from the program code and its inputs. The status of simulations as experiments has therefore been examined (Kaufmann and Smarr 1993; Humphreys 1995; Hughes 1999; Norton and Suppe 2001). This literature considers the epistemology of these experiments: what we can learn by simulation, and also the kinds of justifications which can be given in applying that knowledge to the “real” world. (Mayo 1996; Parker 2008b). As pointed out, part of the advantage of computer simulation derives from the fact that huge numbers of calculations can be carried out without requiring direct observation by the experimenter/​simulator. At the same time, many of these calculations are approximations to the calculations which would be performed first-hand in an ideal situation. Both factors introduce uncertainties into the inferences drawn from what is observed in the simulation.

For many of the reasons described above, computer simulations do not seem to belong clearly to either the experimental or theoretical domain. Rather, they seem to crucially involve aspects of both. This has led some authors, such as Fox Keller (2003: 200) to argue that we ought to consider computer simulation a “qualitatively different way of doing science”. The literature in general tends to follow Kaufmann and Smarr (1993) in referring to computer simulation as a “third way” for scientific methodology (theoretical reasoning and experimental practice are the first two ways.). It should also be noted that the debates around these issues have tended to focus on the form of computer simulation typical in the physical sciences, where models are based on dynamical equations. Other forms of simulation might not have the same problems, or have problems of their own (see the entry on computer simulations in science ).

In recent years, the rapid development of machine learning techniques has prompted some scholars to suggest that the scientific method has become “obsolete” (Anderson 2008, Carrol and Goodstein 2009). This has resulted in an intense debate on the relative merit of data-driven and hypothesis-driven research (for samples, see e.g. Mazzocchi 2015 or Succi and Coveney 2018). For a detailed treatment of this topic, we refer to the entry scientific research and big data .

6. Discourse on scientific method

Despite philosophical disagreements, the idea of the scientific method still figures prominently in contemporary discourse on many different topics, both within science and in society at large. Often, reference to scientific method is used in ways that convey either the legend of a single, universal method characteristic of all science, or grants to a particular method or set of methods privilege as a special ‘gold standard’, often with reference to particular philosophers to vindicate the claims. Discourse on scientific method also typically arises when there is a need to distinguish between science and other activities, or for justifying the special status conveyed to science. In these areas, the philosophical attempts at identifying a set of methods characteristic for scientific endeavors are closely related to the philosophy of science’s classical problem of demarcation (see the entry on science and pseudo-science ) and to the philosophical analysis of the social dimension of scientific knowledge and the role of science in democratic society.

One of the settings in which the legend of a single, universal scientific method has been particularly strong is science education (see, e.g., Bauer 1992; McComas 1996; Wivagg & Allchin 2002). [ 5 ] Often, ‘the scientific method’ is presented in textbooks and educational web pages as a fixed four or five step procedure starting from observations and description of a phenomenon and progressing over formulation of a hypothesis which explains the phenomenon, designing and conducting experiments to test the hypothesis, analyzing the results, and ending with drawing a conclusion. Such references to a universal scientific method can be found in educational material at all levels of science education (Blachowicz 2009), and numerous studies have shown that the idea of a general and universal scientific method often form part of both students’ and teachers’ conception of science (see, e.g., Aikenhead 1987; Osborne et al. 2003). In response, it has been argued that science education need to focus more on teaching about the nature of science, although views have differed on whether this is best done through student-led investigations, contemporary cases, or historical cases (Allchin, Andersen & Nielsen 2014)

Although occasionally phrased with reference to the H-D method, important historical roots of the legend in science education of a single, universal scientific method are the American philosopher and psychologist Dewey’s account of inquiry in How We Think (1910) and the British mathematician Karl Pearson’s account of science in Grammar of Science (1892). On Dewey’s account, inquiry is divided into the five steps of

(i) a felt difficulty, (ii) its location and definition, (iii) suggestion of a possible solution, (iv) development by reasoning of the bearing of the suggestions, (v) further observation and experiment leading to its acceptance or rejection. (Dewey 1910: 72)

Similarly, on Pearson’s account, scientific investigations start with measurement of data and observation of their correction and sequence from which scientific laws can be discovered with the aid of creative imagination. These laws have to be subject to criticism, and their final acceptance will have equal validity for “all normally constituted minds”. Both Dewey’s and Pearson’s accounts should be seen as generalized abstractions of inquiry and not restricted to the realm of science—although both Dewey and Pearson referred to their respective accounts as ‘the scientific method’.

Occasionally, scientists make sweeping statements about a simple and distinct scientific method, as exemplified by Feynman’s simplified version of a conjectures and refutations method presented, for example, in the last of his 1964 Cornell Messenger lectures. [ 6 ] However, just as often scientists have come to the same conclusion as recent philosophy of science that there is not any unique, easily described scientific method. For example, the physicist and Nobel Laureate Weinberg described in the paper “The Methods of Science … And Those By Which We Live” (1995) how

The fact that the standards of scientific success shift with time does not only make the philosophy of science difficult; it also raises problems for the public understanding of science. We do not have a fixed scientific method to rally around and defend. (1995: 8)

Interview studies with scientists on their conception of method shows that scientists often find it hard to figure out whether available evidence confirms their hypothesis, and that there are no direct translations between general ideas about method and specific strategies to guide how research is conducted (Schickore & Hangel 2019, Hangel & Schickore 2017)

Reference to the scientific method has also often been used to argue for the scientific nature or special status of a particular activity. Philosophical positions that argue for a simple and unique scientific method as a criterion of demarcation, such as Popperian falsification, have often attracted practitioners who felt that they had a need to defend their domain of practice. For example, references to conjectures and refutation as the scientific method are abundant in much of the literature on complementary and alternative medicine (CAM)—alongside the competing position that CAM, as an alternative to conventional biomedicine, needs to develop its own methodology different from that of science.

Also within mainstream science, reference to the scientific method is used in arguments regarding the internal hierarchy of disciplines and domains. A frequently seen argument is that research based on the H-D method is superior to research based on induction from observations because in deductive inferences the conclusion follows necessarily from the premises. (See, e.g., Parascandola 1998 for an analysis of how this argument has been made to downgrade epidemiology compared to the laboratory sciences.) Similarly, based on an examination of the practices of major funding institutions such as the National Institutes of Health (NIH), the National Science Foundation (NSF) and the Biomedical Sciences Research Practices (BBSRC) in the UK, O’Malley et al. (2009) have argued that funding agencies seem to have a tendency to adhere to the view that the primary activity of science is to test hypotheses, while descriptive and exploratory research is seen as merely preparatory activities that are valuable only insofar as they fuel hypothesis-driven research.

In some areas of science, scholarly publications are structured in a way that may convey the impression of a neat and linear process of inquiry from stating a question, devising the methods by which to answer it, collecting the data, to drawing a conclusion from the analysis of data. For example, the codified format of publications in most biomedical journals known as the IMRAD format (Introduction, Method, Results, Analysis, Discussion) is explicitly described by the journal editors as “not an arbitrary publication format but rather a direct reflection of the process of scientific discovery” (see the so-called “Vancouver Recommendations”, ICMJE 2013: 11). However, scientific publications do not in general reflect the process by which the reported scientific results were produced. For example, under the provocative title “Is the scientific paper a fraud?”, Medawar argued that scientific papers generally misrepresent how the results have been produced (Medawar 1963/1996). Similar views have been advanced by philosophers, historians and sociologists of science (Gilbert 1976; Holmes 1987; Knorr-Cetina 1981; Schickore 2008; Suppe 1998) who have argued that scientists’ experimental practices are messy and often do not follow any recognizable pattern. Publications of research results, they argue, are retrospective reconstructions of these activities that often do not preserve the temporal order or the logic of these activities, but are instead often constructed in order to screen off potential criticism (see Schickore 2008 for a review of this work).

Philosophical positions on the scientific method have also made it into the court room, especially in the US where judges have drawn on philosophy of science in deciding when to confer special status to scientific expert testimony. A key case is Daubert vs Merrell Dow Pharmaceuticals (92–102, 509 U.S. 579, 1993). In this case, the Supreme Court argued in its 1993 ruling that trial judges must ensure that expert testimony is reliable, and that in doing this the court must look at the expert’s methodology to determine whether the proffered evidence is actually scientific knowledge. Further, referring to works of Popper and Hempel the court stated that

ordinarily, a key question to be answered in determining whether a theory or technique is scientific knowledge … is whether it can be (and has been) tested. (Justice Blackmun, Daubert v. Merrell Dow Pharmaceuticals; see Other Internet Resources for a link to the opinion)

But as argued by Haack (2005a,b, 2010) and by Foster & Hubner (1999), by equating the question of whether a piece of testimony is reliable with the question whether it is scientific as indicated by a special methodology, the court was producing an inconsistent mixture of Popper’s and Hempel’s philosophies, and this has later led to considerable confusion in subsequent case rulings that drew on the Daubert case (see Haack 2010 for a detailed exposition).

The difficulties around identifying the methods of science are also reflected in the difficulties of identifying scientific misconduct in the form of improper application of the method or methods of science. One of the first and most influential attempts at defining misconduct in science was the US definition from 1989 that defined misconduct as

fabrication, falsification, plagiarism, or other practices that seriously deviate from those that are commonly accepted within the scientific community . (Code of Federal Regulations, part 50, subpart A., August 8, 1989, italics added)

However, the “other practices that seriously deviate” clause was heavily criticized because it could be used to suppress creative or novel science. For example, the National Academy of Science stated in their report Responsible Science (1992) that it

wishes to discourage the possibility that a misconduct complaint could be lodged against scientists based solely on their use of novel or unorthodox research methods. (NAS: 27)

This clause was therefore later removed from the definition. For an entry into the key philosophical literature on conduct in science, see Shamoo & Resnick (2009).

The question of the source of the success of science has been at the core of philosophy since the beginning of modern science. If viewed as a matter of epistemology more generally, scientific method is a part of the entire history of philosophy. Over that time, science and whatever methods its practitioners may employ have changed dramatically. Today, many philosophers have taken up the banners of pluralism or of practice to focus on what are, in effect, fine-grained and contextually limited examinations of scientific method. Others hope to shift perspectives in order to provide a renewed general account of what characterizes the activity we call science.

One such perspective has been offered recently by Hoyningen-Huene (2008, 2013), who argues from the history of philosophy of science that after three lengthy phases of characterizing science by its method, we are now in a phase where the belief in the existence of a positive scientific method has eroded and what has been left to characterize science is only its fallibility. First was a phase from Plato and Aristotle up until the 17 th century where the specificity of scientific knowledge was seen in its absolute certainty established by proof from evident axioms; next was a phase up to the mid-19 th century in which the means to establish the certainty of scientific knowledge had been generalized to include inductive procedures as well. In the third phase, which lasted until the last decades of the 20 th century, it was recognized that empirical knowledge was fallible, but it was still granted a special status due to its distinctive mode of production. But now in the fourth phase, according to Hoyningen-Huene, historical and philosophical studies have shown how “scientific methods with the characteristics as posited in the second and third phase do not exist” (2008: 168) and there is no longer any consensus among philosophers and historians of science about the nature of science. For Hoyningen-Huene, this is too negative a stance, and he therefore urges the question about the nature of science anew. His own answer to this question is that “scientific knowledge differs from other kinds of knowledge, especially everyday knowledge, primarily by being more systematic” (Hoyningen-Huene 2013: 14). Systematicity can have several different dimensions: among them are more systematic descriptions, explanations, predictions, defense of knowledge claims, epistemic connectedness, ideal of completeness, knowledge generation, representation of knowledge and critical discourse. Hence, what characterizes science is the greater care in excluding possible alternative explanations, the more detailed elaboration with respect to data on which predictions are based, the greater care in detecting and eliminating sources of error, the more articulate connections to other pieces of knowledge, etc. On this position, what characterizes science is not that the methods employed are unique to science, but that the methods are more carefully employed.

Another, similar approach has been offered by Haack (2003). She sets off, similar to Hoyningen-Huene, from a dissatisfaction with the recent clash between what she calls Old Deferentialism and New Cynicism. The Old Deferentialist position is that science progressed inductively by accumulating true theories confirmed by empirical evidence or deductively by testing conjectures against basic statements; while the New Cynics position is that science has no epistemic authority and no uniquely rational method and is merely just politics. Haack insists that contrary to the views of the New Cynics, there are objective epistemic standards, and there is something epistemologically special about science, even though the Old Deferentialists pictured this in a wrong way. Instead, she offers a new Critical Commonsensist account on which standards of good, strong, supportive evidence and well-conducted, honest, thorough and imaginative inquiry are not exclusive to the sciences, but the standards by which we judge all inquirers. In this sense, science does not differ in kind from other kinds of inquiry, but it may differ in the degree to which it requires broad and detailed background knowledge and a familiarity with a technical vocabulary that only specialists may possess.

• Aikenhead, G.S., 1987, “High-school graduates’ beliefs about science-technology-society. III. Characteristics and limitations of scientific knowledge”, Science Education , 71(4): 459–487.
• Allchin, D., H.M. Andersen and K. Nielsen, 2014, “Complementary Approaches to Teaching Nature of Science: Integrating Student Inquiry, Historical Cases, and Contemporary Cases in Classroom Practice”, Science Education , 98: 461–486.
• Anderson, C., 2008, “The end of theory: The data deluge makes the scientific method obsolete”, Wired magazine , 16(7): 16–07
• Arabatzis, T., 2006, “On the inextricability of the context of discovery and the context of justification”, in Revisiting Discovery and Justification , J. Schickore and F. Steinle (eds.), Dordrecht: Springer, pp. 215–230.
• Barnes, J. (ed.), 1984, The Complete Works of Aristotle, Vols I and II , Princeton: Princeton University Press.
• Barnes, B. and D. Bloor, 1982, “Relativism, Rationalism, and the Sociology of Knowledge”, in Rationality and Relativism , M. Hollis and S. Lukes (eds.), Cambridge: MIT Press, pp. 1–20.
• Bauer, H.H., 1992, Scientific Literacy and the Myth of the Scientific Method , Urbana: University of Illinois Press.
• Bechtel, W. and R.C. Richardson, 1993, Discovering complexity , Princeton, NJ: Princeton University Press.
• Berkeley, G., 1734, The Analyst in De Motu and The Analyst: A Modern Edition with Introductions and Commentary , D. Jesseph (trans. and ed.), Dordrecht: Kluwer Academic Publishers, 1992.
• Blachowicz, J., 2009, “How science textbooks treat scientific method: A philosopher’s perspective”, The British Journal for the Philosophy of Science , 60(2): 303–344.
• Bloor, D., 1991, Knowledge and Social Imagery , Chicago: University of Chicago Press, 2 nd edition.
• Boyle, R., 1682, New experiments physico-mechanical, touching the air , Printed by Miles Flesher for Richard Davis, bookseller in Oxford.
• Bridgman, P.W., 1927, The Logic of Modern Physics , New York: Macmillan.
• –––, 1956, “The Methodological Character of Theoretical Concepts”, in The Foundations of Science and the Concepts of Science and Psychology , Herbert Feigl and Michael Scriven (eds.), Minnesota: University of Minneapolis Press, pp. 38–76.
• Burian, R., 1997, “Exploratory Experimentation and the Role of Histochemical Techniques in the Work of Jean Brachet, 1938–1952”, History and Philosophy of the Life Sciences , 19(1): 27–45.
• –––, 2007, “On microRNA and the need for exploratory experimentation in post-genomic molecular biology”, History and Philosophy of the Life Sciences , 29(3): 285–311.
• Carnap, R., 1928, Der logische Aufbau der Welt , Berlin: Bernary, transl. by R.A. George, The Logical Structure of the World , Berkeley: University of California Press, 1967.
• –––, 1956, “The methodological character of theoretical concepts”, Minnesota studies in the philosophy of science , 1: 38–76.
• Carrol, S., and D. Goodstein, 2009, “Defining the scientific method”, Nature Methods , 6: 237.
• Churchman, C.W., 1948, “Science, Pragmatics, Induction”, Philosophy of Science , 15(3): 249–268.
• Cooper, J. (ed.), 1997, Plato: Complete Works , Indianapolis: Hackett.
• Darden, L., 1991, Theory Change in Science: Strategies from Mendelian Genetics , Oxford: Oxford University Press
• Dewey, J., 1910, How we think , New York: Dover Publications (reprinted 1997).
• Douglas, H., 2009, Science, Policy, and the Value-Free Ideal , Pittsburgh: University of Pittsburgh Press.
• Dupré, J., 2004, “Miracle of Monism ”, in Naturalism in Question , Mario De Caro and David Macarthur (eds.), Cambridge, MA: Harvard University Press, pp. 36–58.
• Elliott, K.C., 2007, “Varieties of exploratory experimentation in nanotoxicology”, History and Philosophy of the Life Sciences , 29(3): 311–334.
• Elliott, K. C., and T. Richards (eds.), 2017, Exploring inductive risk: Case studies of values in science , Oxford: Oxford University Press.
• Falcon, Andrea, 2005, Aristotle and the science of nature: Unity without uniformity , Cambridge: Cambridge University Press.
• Feyerabend, P., 1978, Science in a Free Society , London: New Left Books
• –––, 1988, Against Method , London: Verso, 2 nd edition.
• Fisher, R.A., 1955, “Statistical Methods and Scientific Induction”, Journal of The Royal Statistical Society. Series B (Methodological) , 17(1): 69–78.
• Foster, K. and P.W. Huber, 1999, Judging Science. Scientific Knowledge and the Federal Courts , Cambridge: MIT Press.
• Fox Keller, E., 2003, “Models, Simulation, and ‘computer experiments’”, in The Philosophy of Scientific Experimentation , H. Radder (ed.), Pittsburgh: Pittsburgh University Press, 198–215.
• Gilbert, G., 1976, “The transformation of research findings into scientific knowledge”, Social Studies of Science , 6: 281–306.
• Gimbel, S., 2011, Exploring the Scientific Method , Chicago: University of Chicago Press.
• Goodman, N., 1965, Fact , Fiction, and Forecast , Indianapolis: Bobbs-Merrill.
• Haack, S., 1995, “Science is neither sacred nor a confidence trick”, Foundations of Science , 1(3): 323–335.
• –––, 2003, Defending science—within reason , Amherst: Prometheus.
• –––, 2005a, “Disentangling Daubert: an epistemological study in theory and practice”, Journal of Philosophy, Science and Law , 5, Haack 2005a available online . doi:10.5840/jpsl2005513
• –––, 2005b, “Trial and error: The Supreme Court’s philosophy of science”, American Journal of Public Health , 95: S66-S73.
• –––, 2010, “Federal Philosophy of Science: A Deconstruction-and a Reconstruction”, NYUJL & Liberty , 5: 394.
• Hangel, N. and J. Schickore, 2017, “Scientists’ conceptions of good research practice”, Perspectives on Science , 25(6): 766–791
• Harper, W.L., 2011, Isaac Newton’s Scientific Method: Turning Data into Evidence about Gravity and Cosmology , Oxford: Oxford University Press.
• Hempel, C., 1950, “Problems and Changes in the Empiricist Criterion of Meaning”, Revue Internationale de Philosophie , 41(11): 41–63.
• –––, 1951, “The Concept of Cognitive Significance: A Reconsideration”, Proceedings of the American Academy of Arts and Sciences , 80(1): 61–77.
• –––, 1965, Aspects of scientific explanation and other essays in the philosophy of science , New York–London: Free Press.
• –––, 1966, Philosophy of Natural Science , Englewood Cliffs: Prentice-Hall.
• Holmes, F.L., 1987, “Scientific writing and scientific discovery”, Isis , 78(2): 220–235.
• Howard, D., 2003, “Two left turns make a right: On the curious political career of North American philosophy of science at midcentury”, in Logical Empiricism in North America , G.L. Hardcastle & A.W. Richardson (eds.), Minneapolis: University of Minnesota Press, pp. 25–93.
• Hoyningen-Huene, P., 2008, “Systematicity: The nature of science”, Philosophia , 36(2): 167–180.
• –––, 2013, Systematicity. The Nature of Science , Oxford: Oxford University Press.
• Howie, D., 2002, Interpreting probability: Controversies and developments in the early twentieth century , Cambridge: Cambridge University Press.
• Hughes, R., 1999, “The Ising Model, Computer Simulation, and Universal Physics”, in Models as Mediators , M. Morgan and M. Morrison (eds.), Cambridge: Cambridge University Press, pp. 97–145
• Hume, D., 1739, A Treatise of Human Nature , D. Fate Norton and M.J. Norton (eds.), Oxford: Oxford University Press, 2000.
• Humphreys, P., 1995, “Computational science and scientific method”, Minds and Machines , 5(1): 499–512.
• ICMJE, 2013, “Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals”, International Committee of Medical Journal Editors, available online , accessed August 13 2014
• Jeffrey, R.C., 1956, “Valuation and Acceptance of Scientific Hypotheses”, Philosophy of Science , 23(3): 237–246.
• Kaufmann, W.J., and L.L. Smarr, 1993, Supercomputing and the Transformation of Science , New York: Scientific American Library.
• Knorr-Cetina, K., 1981, The Manufacture of Knowledge , Oxford: Pergamon Press.
• Krohs, U., 2012, “Convenience experimentation”, Studies in History and Philosophy of Biological and BiomedicalSciences , 43: 52–57.
• Kuhn, T.S., 1962, The Structure of Scientific Revolutions , Chicago: University of Chicago Press
• Latour, B. and S. Woolgar, 1986, Laboratory Life: The Construction of Scientific Facts , Princeton: Princeton University Press, 2 nd edition.
• Laudan, L., 1968, “Theories of scientific method from Plato to Mach”, History of Science , 7(1): 1–63.
• Lenhard, J., 2006, “Models and statistical inference: The controversy between Fisher and Neyman-Pearson”, The British Journal for the Philosophy of Science , 57(1): 69–91.
• Leonelli, S., 2012, “Making Sense of Data-Driven Research in the Biological and the Biomedical Sciences”, Studies in the History and Philosophy of the Biological and Biomedical Sciences , 43(1): 1–3.
• Levi, I., 1960, “Must the scientist make value judgments?”, Philosophy of Science , 57(11): 345–357
• Lindley, D., 1991, Theory Change in Science: Strategies from Mendelian Genetics , Oxford: Oxford University Press.
• Lipton, P., 2004, Inference to the Best Explanation , London: Routledge, 2 nd edition.
• Marks, H.M., 2000, The progress of experiment: science and therapeutic reform in the United States, 1900–1990 , Cambridge: Cambridge University Press.
• Mazzochi, F., 2015, “Could Big Data be the end of theory in science?”, EMBO reports , 16: 1250–1255.
• Mayo, D.G., 1996, Error and the Growth of Experimental Knowledge , Chicago: University of Chicago Press.
• McComas, W.F., 1996, “Ten myths of science: Reexamining what we think we know about the nature of science”, School Science and Mathematics , 96(1): 10–16.
• Medawar, P.B., 1963/1996, “Is the scientific paper a fraud”, in The Strange Case of the Spotted Mouse and Other Classic Essays on Science , Oxford: Oxford University Press, 33–39.
• Mill, J.S., 1963, Collected Works of John Stuart Mill , J. M. Robson (ed.), Toronto: University of Toronto Press
• NAS, 1992, Responsible Science: Ensuring the integrity of the research process , Washington DC: National Academy Press.
• Nersessian, N.J., 1987, “A cognitive-historical approach to meaning in scientific theories”, in The process of science , N. Nersessian (ed.), Berlin: Springer, pp. 161–177.
• –––, 2008, Creating Scientific Concepts , Cambridge: MIT Press.
• Newton, I., 1726, Philosophiae naturalis Principia Mathematica (3 rd edition), in The Principia: Mathematical Principles of Natural Philosophy: A New Translation , I.B. Cohen and A. Whitman (trans.), Berkeley: University of California Press, 1999.
• –––, 1704, Opticks or A Treatise of the Reflections, Refractions, Inflections & Colors of Light , New York: Dover Publications, 1952.
• Neyman, J., 1956, “Note on an Article by Sir Ronald Fisher”, Journal of the Royal Statistical Society. Series B (Methodological) , 18: 288–294.
• Nickles, T., 1987, “Methodology, heuristics, and rationality”, in Rational changes in science: Essays on Scientific Reasoning , J.C. Pitt (ed.), Berlin: Springer, pp. 103–132.
• Nicod, J., 1924, Le problème logique de l’induction , Paris: Alcan. (Engl. transl. “The Logical Problem of Induction”, in Foundations of Geometry and Induction , London: Routledge, 2000.)
• Nola, R. and H. Sankey, 2000a, “A selective survey of theories of scientific method”, in Nola and Sankey 2000b: 1–65.
• –––, 2000b, After Popper, Kuhn and Feyerabend. Recent Issues in Theories of Scientific Method , London: Springer.
• –––, 2007, Theories of Scientific Method , Stocksfield: Acumen.
• Norton, S., and F. Suppe, 2001, “Why atmospheric modeling is good science”, in Changing the Atmosphere: Expert Knowledge and Environmental Governance , C. Miller and P. Edwards (eds.), Cambridge, MA: MIT Press, 88–133.
• O’Malley, M., 2007, “Exploratory experimentation and scientific practice: Metagenomics and the proteorhodopsin case”, History and Philosophy of the Life Sciences , 29(3): 337–360.
• O’Malley, M., C. Haufe, K. Elliot, and R. Burian, 2009, “Philosophies of Funding”, Cell , 138: 611–615.
• Oreskes, N., K. Shrader-Frechette, and K. Belitz, 1994, “Verification, Validation and Confirmation of Numerical Models in the Earth Sciences”, Science , 263(5147): 641–646.
• Osborne, J., S. Simon, and S. Collins, 2003, “Attitudes towards science: a review of the literature and its implications”, International Journal of Science Education , 25(9): 1049–1079.
• Parascandola, M., 1998, “Epidemiology—2 nd -Rate Science”, Public Health Reports , 113(4): 312–320.
• Parker, W., 2008a, “Franklin, Holmes and the Epistemology of Computer Simulation”, International Studies in the Philosophy of Science , 22(2): 165–83.
• –––, 2008b, “Computer Simulation through an Error-Statistical Lens”, Synthese , 163(3): 371–84.
• Pearson, K. 1892, The Grammar of Science , London: J.M. Dents and Sons, 1951
• Pearson, E.S., 1955, “Statistical Concepts in Their Relation to Reality”, Journal of the Royal Statistical Society , B, 17: 204–207.
• Pickering, A., 1984, Constructing Quarks: A Sociological History of Particle Physics , Edinburgh: Edinburgh University Press.
• Popper, K.R., 1959, The Logic of Scientific Discovery , London: Routledge, 2002
• –––, 1963, Conjectures and Refutations , London: Routledge, 2002.
• –––, 1985, Unended Quest: An Intellectual Autobiography , La Salle: Open Court Publishing Co..
• Rudner, R., 1953, “The Scientist Qua Scientist Making Value Judgments”, Philosophy of Science , 20(1): 1–6.
• Rudolph, J.L., 2005, “Epistemology for the masses: The origin of ‘The Scientific Method’ in American Schools”, History of Education Quarterly , 45(3): 341–376
• Schickore, J., 2008, “Doing science, writing science”, Philosophy of Science , 75: 323–343.
• Schickore, J. and N. Hangel, 2019, “‘It might be this, it should be that…’ uncertainty and doubt in day-to-day science practice”, European Journal for Philosophy of Science , 9(2): 31. doi:10.1007/s13194-019-0253-9
• Shamoo, A.E. and D.B. Resnik, 2009, Responsible Conduct of Research , Oxford: Oxford University Press.
• Shank, J.B., 2008, The Newton Wars and the Beginning of the French Enlightenment , Chicago: The University of Chicago Press.
• Shapin, S. and S. Schaffer, 1985, Leviathan and the air-pump , Princeton: Princeton University Press.
• Smith, G.E., 2002, “The Methodology of the Principia”, in The Cambridge Companion to Newton , I.B. Cohen and G.E. Smith (eds.), Cambridge: Cambridge University Press, 138–173.
• Snyder, L.J., 1997a, “Discoverers’ Induction”, Philosophy of Science , 64: 580–604.
• –––, 1997b, “The Mill-Whewell Debate: Much Ado About Induction”, Perspectives on Science , 5: 159–198.
• –––, 1999, “Renovating the Novum Organum: Bacon, Whewell and Induction”, Studies in History and Philosophy of Science , 30: 531–557.
• Sober, E., 2008, Evidence and Evolution. The logic behind the science , Cambridge: Cambridge University Press
• Sprenger, J. and S. Hartmann, 2019, Bayesian philosophy of science , Oxford: Oxford University Press.
• Steinle, F., 1997, “Entering New Fields: Exploratory Uses of Experimentation”, Philosophy of Science (Proceedings), 64: S65–S74.
• –––, 2002, “Experiments in History and Philosophy of Science”, Perspectives on Science , 10(4): 408–432.
• Strasser, B.J., 2012, “Data-driven sciences: From wonder cabinets to electronic databases”, Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences , 43(1): 85–87.
• Succi, S. and P.V. Coveney, 2018, “Big data: the end of the scientific method?”, Philosophical Transactions of the Royal Society A , 377: 20180145. doi:10.1098/rsta.2018.0145
• Suppe, F., 1998, “The Structure of a Scientific Paper”, Philosophy of Science , 65(3): 381–405.
• Swijtink, Z.G., 1987, “The objectification of observation: Measurement and statistical methods in the nineteenth century”, in The probabilistic revolution. Ideas in History, Vol. 1 , L. Kruger (ed.), Cambridge MA: MIT Press, pp. 261–285.
• Waters, C.K., 2007, “The nature and context of exploratory experimentation: An introduction to three case studies of exploratory research”, History and Philosophy of the Life Sciences , 29(3): 275–284.
• Weinberg, S., 1995, “The methods of science… and those by which we live”, Academic Questions , 8(2): 7–13.
• Weissert, T., 1997, The Genesis of Simulation in Dynamics: Pursuing the Fermi-Pasta-Ulam Problem , New York: Springer Verlag.
• William H., 1628, Exercitatio Anatomica de Motu Cordis et Sanguinis in Animalibus , in On the Motion of the Heart and Blood in Animals , R. Willis (trans.), Buffalo: Prometheus Books, 1993.
• Winsberg, E., 2010, Science in the Age of Computer Simulation , Chicago: University of Chicago Press.
• Wivagg, D. & D. Allchin, 2002, “The Dogma of the Scientific Method”, The American Biology Teacher , 64(9): 645–646

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• Blackmun opinion , in Daubert v. Merrell Dow Pharmaceuticals (92–102), 509 U.S. 579 (1993).
• Scientific Method at philpapers. Darrell Rowbottom (ed.).
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Scientific Method Steps in Psychology Research

Steps, Uses, and Key Terms

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.

Verywell / Theresa Chiechi

How do researchers investigate psychological phenomena? They utilize a process known as the scientific method to study different aspects of how people think and behave.

When conducting research, the scientific method steps to follow are:

• Observe what you want to investigate
• Ask a research question and make predictions
• Test the hypothesis and collect data
• Examine the results and draw conclusions
• Report and share the results 

This process not only allows scientists to investigate and understand different psychological phenomena but also provides researchers and others a way to share and discuss the results of their studies.

Generally, there are five main steps in the scientific method, although some may break down this process into six or seven steps. An additional step in the process can also include developing new research questions based on your findings.

What Is the Scientific Method?

What is the scientific method and how is it used in psychology?

The scientific method consists of five steps. It is essentially a step-by-step process that researchers can follow to determine if there is some type of relationship between two or more variables.

By knowing the steps of the scientific method, you can better understand the process researchers go through to arrive at conclusions about human behavior.

Scientific Method Steps

While research studies can vary, these are the basic steps that psychologists and scientists use when investigating human behavior.

The following are the scientific method steps:

Step 1. Make an Observation

Before a researcher can begin, they must choose a topic to study. Once an area of interest has been chosen, the researchers must then conduct a thorough review of the existing literature on the subject. This review will provide valuable information about what has already been learned about the topic and what questions remain to be answered.

A literature review might involve looking at a considerable amount of written material from both books and academic journals dating back decades.

The relevant information collected by the researcher will be presented in the introduction section of the final published study results. This background material will also help the researcher with the first major step in conducting a psychology study: formulating a hypothesis.

Step 2. Ask a Question

Once a researcher has observed something and gained some background information on the topic, the next step is to ask a question. The researcher will form a hypothesis, which is an educated guess about the relationship between two or more variables

For example, a researcher might ask a question about the relationship between sleep and academic performance: Do students who get more sleep perform better on tests at school?

In order to formulate a good hypothesis, it is important to think about different questions you might have about a particular topic.

You should also consider how you could investigate the causes. Falsifiability is an important part of any valid hypothesis. In other words, if a hypothesis was false, there needs to be a way for scientists to demonstrate that it is false.

Step 3. Test Your Hypothesis and Collect Data

Once you have a solid hypothesis, the next step of the scientific method is to put this hunch to the test by collecting data. The exact methods used to investigate a hypothesis depend on exactly what is being studied. There are two basic forms of research that a psychologist might utilize: descriptive research or experimental research.

Descriptive research is typically used when it would be difficult or even impossible to manipulate the variables in question. Examples of descriptive research include case studies, naturalistic observation , and correlation studies. Phone surveys that are often used by marketers are one example of descriptive research.

Correlational studies are quite common in psychology research. While they do not allow researchers to determine cause-and-effect, they do make it possible to spot relationships between different variables and to measure the strength of those relationships. 

Experimental research is used to explore cause-and-effect relationships between two or more variables. This type of research involves systematically manipulating an independent variable and then measuring the effect that it has on a defined dependent variable .

One of the major advantages of this method is that it allows researchers to actually determine if changes in one variable actually cause changes in another.

While psychology experiments are often quite complex, a simple experiment is fairly basic but does allow researchers to determine cause-and-effect relationships between variables. Most simple experiments use a control group (those who do not receive the treatment) and an experimental group (those who do receive the treatment).

Step 4. Examine the Results and Draw Conclusions

Once a researcher has designed the study and collected the data, it is time to examine this information and draw conclusions about what has been found.  Using statistics , researchers can summarize the data, analyze the results, and draw conclusions based on this evidence.

So how does a researcher decide what the results of a study mean? Not only can statistical analysis support (or refute) the researcher’s hypothesis; it can also be used to determine if the findings are statistically significant.

When results are said to be statistically significant, it means that it is unlikely that these results are due to chance.

Based on these observations, researchers must then determine what the results mean. In some cases, an experiment will support a hypothesis, but in other cases, it will fail to support the hypothesis.

So what happens if the results of a psychology experiment do not support the researcher's hypothesis? Does this mean that the study was worthless?

Just because the findings fail to support the hypothesis does not mean that the research is not useful or informative. In fact, such research plays an important role in helping scientists develop new questions and hypotheses to explore in the future.

After conclusions have been drawn, the next step is to share the results with the rest of the scientific community. This is an important part of the process because it contributes to the overall knowledge base and can help other scientists find new research avenues to explore.

Step 5. Report the Results

The final step in a psychology study is to report the findings. This is often done by writing up a description of the study and publishing the article in an academic or professional journal. The results of psychological studies can be seen in peer-reviewed journals such as  Psychological Bulletin , the  Journal of Social Psychology ,  Developmental Psychology , and many others.

The structure of a journal article follows a specified format that has been outlined by the  American Psychological Association (APA) . In these articles, researchers:

• Provide a brief history and background on previous research
• Present their hypothesis
• Identify who participated in the study and how they were selected
• Provide operational definitions for each variable
• Describe the measures and procedures that were used to collect data
• Explain how the information collected was analyzed
• Discuss what the results mean

Why is such a detailed record of a psychological study so important? By clearly explaining the steps and procedures used throughout the study, other researchers can then replicate the results. The editorial process employed by academic and professional journals ensures that each article that is submitted undergoes a thorough peer review, which helps ensure that the study is scientifically sound.

Once published, the study becomes another piece of the existing puzzle of our knowledge base on that topic.

Before you begin exploring the scientific method steps, here's a review of some key terms and definitions that you should be familiar with:

• Falsifiable : The variables can be measured so that if a hypothesis is false, it can be proven false
• Hypothesis : An educated guess about the possible relationship between two or more variables
• Variable : A factor or element that can change in observable and measurable ways
• Operational definition : A full description of exactly how variables are defined, how they will be manipulated, and how they will be measured

Uses for the Scientific Method

The  goals of psychological studies  are to describe, explain, predict and perhaps influence mental processes or behaviors. In order to do this, psychologists utilize the scientific method to conduct psychological research. The scientific method is a set of principles and procedures that are used by researchers to develop questions, collect data, and reach conclusions.

Goals of Scientific Research in Psychology

Researchers seek not only to describe behaviors and explain why these behaviors occur; they also strive to create research that can be used to predict and even change human behavior.

Psychologists and other social scientists regularly propose explanations for human behavior. On a more informal level, people make judgments about the intentions, motivations , and actions of others on a daily basis.

While the everyday judgments we make about human behavior are subjective and anecdotal, researchers use the scientific method to study psychology in an objective and systematic way. The results of these studies are often reported in popular media, which leads many to wonder just how or why researchers arrived at the conclusions they did.

Examples of the Scientific Method

Now that you're familiar with the scientific method steps, it's useful to see how each step could work with a real-life example.

Say, for instance, that researchers set out to discover what the relationship is between psychotherapy and anxiety .

• Step 1. Make an observation : The researchers choose to focus their study on adults ages 25 to 40 with generalized anxiety disorder.
• Step 2. Ask a question : The question they want to answer in their study is: Do weekly psychotherapy sessions reduce symptoms in adults ages 25 to 40 with generalized anxiety disorder?
• Step 3. Test your hypothesis : Researchers collect data on participants' anxiety symptoms . They work with therapists to create a consistent program that all participants undergo. Group 1 may attend therapy once per week, whereas group 2 does not attend therapy.
• Step 4. Examine the results : Participants record their symptoms and any changes over a period of three months. After this period, people in group 1 report significant improvements in their anxiety symptoms, whereas those in group 2 report no significant changes.
• Step 5. Report the results : Researchers write a report that includes their hypothesis, information on participants, variables, procedure, and conclusions drawn from the study. In this case, they say that "Weekly therapy sessions are shown to reduce anxiety symptoms in adults ages 25 to 40."

Of course, there are many details that go into planning and executing a study such as this. But this general outline gives you an idea of how an idea is formulated and tested, and how researchers arrive at results using the scientific method.

Erol A. How to conduct scientific research ? Noro Psikiyatr Ars . 2017;54(2):97-98. doi:10.5152/npa.2017.0120102

University of Minnesota. Psychologists use the scientific method to guide their research .

Shaughnessy, JJ, Zechmeister, EB, & Zechmeister, JS. Research Methods In Psychology . New York: McGraw Hill Education; 2015.

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

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How to Conduct Scientific Research?

United Nations Educational, Scientific and Cultural Organization (UNESCO) defines research as systematic and creative actions taken to increase knowledge about humans, culture, and society and to apply it in new areas of interest. Scientific research is the research performed by applying systematic and constructed scientific methods to obtain, analyze, and interpret data.

Scientific research is the neutral, systematic, planned, and multiple-step process that uses previously discovered facts to advance knowledge that does not exist in the literature. It can be classified as observational or experimental with respect to data collection techniques, descriptive or analytical with respect to causality, and prospective, retrospective, or cross-sectional with respect to time ( 1 ).

All scientific investigations start with a specific research question and the formulation of a hypothesis to answer this question. Hypothesis should be clear, specific, and directly aim to answer the research question. A strong and testable hypothesis is the fundamental part of the scientific research. The next step is testing the hypothesis using scientific method to approve or disapprove it.

Scientific method should be neutral, objective, rational, and as a result, should be able to approve or disapprove the hypothesis. The research plan should include the procedure to obtain data and evaluate the variables. It should ensure that analyzable data are obtained. It should also include plans on the statistical analysis to be performed. The number of subjects and controls needed to get valid statistical results should be calculated, and data should be obtained in appropriate numbers and methods. The researcher should be continuously observing and recording all data obtained.

Data should be analyzed with the most appropriate statistical methods and be rearranged to make more sense if needed. Unfortunately, results obtained via analyses are not always sufficiently clear. Multiple reevaluations of data, review of the literature, and interpretation of results in light of previous research are required. Only after the completion of these stages can a research be written and presented to the scientific society. A well-conducted and precisely written research should always be open to scientific criticism. It should also be kept in mind that research should be in line with ethical rules all through its stages.

Actually, psychiatric research has been developing rapidly, possibly even more than any other medical field, thus reflecting the utilization of new research methods and advanced treatment technologies. Nevertheless, basic research principles and ethical considerations keep their importance.

Ethics are standards used to differentiate acceptable and unacceptable behavior. Adhering to ethical standards in scientific research is noteworthy because of many different reasons. First, these standards promote the aims of research, such as knowledge, truth, and avoidance of error. For example, prohibitions against fabricating, falsifying, or misrepresenting research data promote truth and minimize error. In addition, ethical standards promote values that are essential to collaborative work, such as trust, accountability, mutual respect, and fairness. Many ethical standards in research, such as guidelines for authorship, copyright and patenting policies, data-sharing policies, and confidentiality rules in peer review, are designed to protect intellectual property interests while encouraging collaboration. Many ethical standards such as policies on research misconduct and conflicts of interest are necessary to ensure that researchers can be held accountable to the public. Last but not the least, ethical standards of research promote a variety of other important moral and social values, such as social responsibility, human rights, animal welfare, compliance with the law, and public health and safety ( 2 ). In conclusion, for the good of science and humanity, research has the inevitable responsibility of precisely transferring the knowledge to new generations ( 3 ).

In medical research, all clinical investigations are obliged to comply with some ethical principles. These principles could be summarized as respect to humans, respect to the society, benefit, harmlessness, autonomy, and justice. Respect to humans indicates that all humans have the right to refuse to participate in an investigation or to withdraw their consent any time without any repercussions. Respect to society indicates that clinical research should seek answers to scientific questions using scientific methods and should benefit the society. Benefit indicates that research outcomes are supposed to provide solutions to a health problem. Harmlessness describes all necessary precautions that are taken to protect volunteers from potential harm. Autonomy indicates that participating in research is voluntary and with freewill. Justice indicates that subject selection is based on justice and special care is taken for special groups that could be easily traumatized ( 4 ).

In psychiatric studies, if the patient is not capable of giving consent, the relatives have the right to consent on behalf of the patient. This is based on the idea of providing benefit to the patient with discovery of new treatment methods via research. However, the relatives’ consent rights are under debate from an ethical point of view. On the other hand, research on those patients aim to directly get new knowledge about them, and it looks like an inevitable necessity. The only precaution that could be taken to overcome this ambivalence has been the scrupulous audit of the Research Ethic Committees. Still, there are many examples that show that this method is not always able to prevent patient abuse ( 5 ). Therefore, it is difficult to claim autonomy when psychiatric patients are studied, and psychiatric patients are considered among patients to require special care.

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Science and the scientific method: Definitions and examples

Here's a look at the foundation of doing science — the scientific method.

The scientific method

Hypothesis, theory and law, a brief history of science, additional resources, bibliography.

Science is a systematic and logical approach to discovering how things in the universe work. It is also the body of knowledge accumulated through the discoveries about all the things in the universe. 

The word "science" is derived from the Latin word "scientia," which means knowledge based on demonstrable and reproducible data, according to the Merriam-Webster dictionary . True to this definition, science aims for measurable results through testing and analysis, a process known as the scientific method. Science is based on fact, not opinion or preferences. The process of science is designed to challenge ideas through research. One important aspect of the scientific process is that it focuses only on the natural world, according to the University of California, Berkeley . Anything that is considered supernatural, or beyond physical reality, does not fit into the definition of science.

When conducting research, scientists use the scientific method to collect measurable, empirical evidence in an experiment related to a hypothesis (often in the form of an if/then statement) that is designed to support or contradict a scientific theory .

"As a field biologist, my favorite part of the scientific method is being in the field collecting the data," Jaime Tanner, a professor of biology at Marlboro College, told Live Science. "But what really makes that fun is knowing that you are trying to answer an interesting question. So the first step in identifying questions and generating possible answers (hypotheses) is also very important and is a creative process. Then once you collect the data you analyze it to see if your hypothesis is supported or not."

The steps of the scientific method go something like this, according to Highline College :

• Make an observation or observations.
• Form a hypothesis — a tentative description of what's been observed, and make predictions based on that hypothesis.
• Test the hypothesis and predictions in an experiment that can be reproduced.
• Analyze the data and draw conclusions; accept or reject the hypothesis or modify the hypothesis if necessary.
• Reproduce the experiment until there are no discrepancies between observations and theory. "Replication of methods and results is my favorite step in the scientific method," Moshe Pritsker, a former post-doctoral researcher at Harvard Medical School and CEO of JoVE, told Live Science. "The reproducibility of published experiments is the foundation of science. No reproducibility — no science."

Some key underpinnings to the scientific method:

• The hypothesis must be testable and falsifiable, according to North Carolina State University . Falsifiable means that there must be a possible negative answer to the hypothesis.
• Research must involve deductive reasoning and inductive reasoning . Deductive reasoning is the process of using true premises to reach a logical true conclusion while inductive reasoning uses observations to infer an explanation for those observations.
• An experiment should include a dependent variable (which does not change) and an independent variable (which does change), according to the University of California, Santa Barbara .
• An experiment should include an experimental group and a control group. The control group is what the experimental group is compared against, according to Britannica .

The process of generating and testing a hypothesis forms the backbone of the scientific method. When an idea has been confirmed over many experiments, it can be called a scientific theory. While a theory provides an explanation for a phenomenon, a scientific law provides a description of a phenomenon, according to The University of Waikato . One example would be the law of conservation of energy, which is the first law of thermodynamics that says that energy can neither be created nor destroyed. 

A law describes an observed phenomenon, but it doesn't explain why the phenomenon exists or what causes it. "In science, laws are a starting place," said Peter Coppinger, an associate professor of biology and biomedical engineering at the Rose-Hulman Institute of Technology. "From there, scientists can then ask the questions, 'Why and how?'"

Laws are generally considered to be without exception, though some laws have been modified over time after further testing found discrepancies. For instance, Newton's laws of motion describe everything we've observed in the macroscopic world, but they break down at the subatomic level.

This does not mean theories are not meaningful. For a hypothesis to become a theory, scientists must conduct rigorous testing, typically across multiple disciplines by separate groups of scientists. Saying something is "just a theory" confuses the scientific definition of "theory" with the layperson's definition. To most people a theory is a hunch. In science, a theory is the framework for observations and facts, Tanner told Live Science.

The earliest evidence of science can be found as far back as records exist. Early tablets contain numerals and information about the solar system , which were derived by using careful observation, prediction and testing of those predictions. Science became decidedly more "scientific" over time, however.

1200s: Robert Grosseteste developed the framework for the proper methods of modern scientific experimentation, according to the Stanford Encyclopedia of Philosophy. His works included the principle that an inquiry must be based on measurable evidence that is confirmed through testing.

1400s: Leonardo da Vinci began his notebooks in pursuit of evidence that the human body is microcosmic. The artist, scientist and mathematician also gathered information about optics and hydrodynamics.

1500s: Nicolaus Copernicus advanced the understanding of the solar system with his discovery of heliocentrism. This is a model in which Earth and the other planets revolve around the sun, which is the center of the solar system.

1600s: Johannes Kepler built upon those observations with his laws of planetary motion. Galileo Galilei improved on a new invention, the telescope, and used it to study the sun and planets. The 1600s also saw advancements in the study of physics as Isaac Newton developed his laws of motion.

1700s: Benjamin Franklin discovered that lightning is electrical. He also contributed to the study of oceanography and meteorology. The understanding of chemistry also evolved during this century as Antoine Lavoisier, dubbed the father of modern chemistry , developed the law of conservation of mass.

1800s: Milestones included Alessandro Volta's discoveries regarding electrochemical series, which led to the invention of the battery. John Dalton also introduced atomic theory, which stated that all matter is composed of atoms that combine to form molecules. The basis of modern study of genetics advanced as Gregor Mendel unveiled his laws of inheritance. Later in the century, Wilhelm Conrad Röntgen discovered X-rays , while George Ohm's law provided the basis for understanding how to harness electrical charges.

1900s: The discoveries of Albert Einstein , who is best known for his theory of relativity, dominated the beginning of the 20th century. Einstein's theory of relativity is actually two separate theories. His special theory of relativity, which he outlined in a 1905 paper, " The Electrodynamics of Moving Bodies ," concluded that time must change according to the speed of a moving object relative to the frame of reference of an observer. His second theory of general relativity, which he published as " The Foundation of the General Theory of Relativity ," advanced the idea that matter causes space to curve.

In 1952, Jonas Salk developed the polio vaccine , which reduced the incidence of polio in the United States by nearly 90%, according to Britannica . The following year, James D. Watson and Francis Crick discovered the structure of DNA , which is a double helix formed by base pairs attached to a sugar-phosphate backbone, according to the National Human Genome Research Institute .

2000s: The 21st century saw the first draft of the human genome completed, leading to a greater understanding of DNA. This advanced the study of genetics, its role in human biology and its use as a predictor of diseases and other disorders, according to the National Human Genome Research Institute .

• This video from City University of New York delves into the basics of what defines science.
• Learn about what makes science science in this book excerpt from Washington State University .
• This resource from the University of Michigan — Flint explains how to design your own scientific study.

Merriam-Webster Dictionary, Scientia. 2022. https://www.merriam-webster.com/dictionary/scientia

University of California, Berkeley, "Understanding Science: An Overview." 2022. ​​ https://undsci.berkeley.edu/article/0_0_0/intro_01  

Highline College, "Scientific method." July 12, 2015. https://people.highline.edu/iglozman/classes/astronotes/scimeth.htm  

North Carolina State University, "Science Scripts." https://projects.ncsu.edu/project/bio183de/Black/science/science_scripts.html  

University of California, Santa Barbara. "What is an Independent variable?" October 31,2017. http://scienceline.ucsb.edu/getkey.php?key=6045  

Encyclopedia Britannica, "Control group." May 14, 2020. https://www.britannica.com/science/control-group  

The University of Waikato, "Scientific Hypothesis, Theories and Laws." https://sci.waikato.ac.nz/evolution/Theories.shtml  

Stanford Encyclopedia of Philosophy, Robert Grosseteste. May 3, 2019. https://plato.stanford.edu/entries/grosseteste/  

Encyclopedia Britannica, "Jonas Salk." October 21, 2021. https://www.britannica.com/ biography /Jonas-Salk

National Human Genome Research Institute, "​Phosphate Backbone." https://www.genome.gov/genetics-glossary/Phosphate-Backbone  

National Human Genome Research Institute, "What is the Human Genome Project?" https://www.genome.gov/human-genome-project/What  

‌ Live Science contributor Ashley Hamer updated this article on Jan. 16, 2022.

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The Scientific Method

Introduction.

There are many scientific disciplines that address topics from medicine and astrophysics to agriculture and zoology. In each discipline, modern scientists use a process called the "Scientific Method" to advance their knowledge and understanding. This publication describes the method scientists use to conduct research and describe and explain nature, ultimately trying prove or disprove theories.

Scientists all over the world conduct research using the Scientific Method. The University of Nevada Cooperative Extension exists to provide unbiased, research-based information on topics important and relevant to society. The scientific research efforts, analyses, and subsequent information disseminated by Cooperative Extension is driven by careful review and synthesis of relevant scientific research. Cooperative Extension presents useful information based on the best science available, and today that science is based on knowledge obtained by application of the Scientific Method.

The Scientific Method – What it’s Not

The Scientific Method is a process for explaining the world we see. It is:

• Not a formula

The Scientific Method – What is it?

The Scientific Method is a process used to validate observations while minimizing observer bias. Its goal is for research to be conducted in a fair, unbiased and repeatable manner.

Long ago, people viewed the workings of nature and believed that the events and phenomena they observed were associated with the intrinsic nature of the beings or things being observed (Ackoff 1962, Wilson 1937). Today we view events and phenomena as having been caused , and science has evolved as a process to ask how and why things and events happen. Scientists seek to understand the relationships and intricacies between cause and effect in order to predict outcomes of future or similar events. To answer these questions and to help predict future happenings, scientists use the Scientific Method - a series of steps that lead to answers that accurately describe the things we observe, or at least improve our understanding of them.

The Scientific Method is not the only way, but is the best-known way to discover how and why the world works, without our knowledge being tainted by religious, political, or philosophical values. This method provides a means to formulate questions about general observations and devise theories of explanation. The approach lends itself to answering questions in fair and unbiased statements, as long as questions are posed correctly, in a hypothetical form that can be tested.

Definitions

It is important to understand three important terms before describing the Scientific Method.

This is a statement made by a researcher that is a working assumption to be tested and proven. It is something "considered true for the purpose of investigation" (Webster’s Dictionary 1995). An example might be “The earth is round.”

general principles drawn from facts that explain observations and can be used to predict new events. An example would be Newton’s theory of gravitation or Einstein’s theory of relativity. Each is based on falsifiable hypotheses of phenomenon we observe.

Falsifiable/ Null Hypothesis

to prove to be false (Webster’s Dictionary 1995). The hypothesis that is generated must be able to be tested, and either accepted or rejected. Scientists make hypotheses that they want to disprove in order that they may prove the working assumption describing the observed phenomena. This is done by declaring the statement or hypothesis as falsifiable . So, we would state the above hypothesis as “the earth is not round,” or “the earth is square” making it a working statement to be disproved.

The Scientific Method is not a formula, but rather a process with a number of sequential steps designed to create an explainable outcome that increases our knowledge base. This process is as follows:

STEP 1. Make an OBSERVATION

gather and assimilate information about an event, phenomenon, process, or an exception to a previous observation, etc.

STEP 2. Define the PROBLEM

ask questions about the observation that are relevant and testable. Define the null hypothesis to provide unbiased results.

STEP 3: Form the HYPOTHESIS

create an explanation, or educated guess, for the observation that is testable and falsifiable.

STEP 4: Conduct the EXPERIMENT

devise and perform an experiment to test the hypothesis.

STEP 5: Derive a THEORY

create a statement based in the outcome of the experiment that explains the observation(s) and predicts the likelihood of future observations.

Replication

Using the Scientific Method to answer questions about events or phenomena we observe can be repeated to fine-tune our theories. For example, if we conduct research using the Scientific Method and think we have answered a question, but different results occur the next time we make an observation, we may have to ask new questions and formulate new hypotheses that are tested by another experiment. Sometimes scientists must perform many experiments over many years or even decades using the Scientific Method to prove or disprove theories that are generated from one initial question. Numerous studies are often necessary to fully test the broad range of results that occur in order that scientists can formulate theories that truly account for the variation we see in our natural environment.

The Scientific Method – Is it worth all the effort?

Scientific knowledge can only advance when all scientists systematically use the same process to discover and disseminate new information. The advantage of all scientific research using the Scientific Method is that the experiments are repeatable by anyone, anywhere. When similar results occur in each experiment, these facts make the case for the theory stronger. If the same experiment is performed many times in many different locations, under a broad range of conditions, then the theory derived from these experiments is considered strong and widely applicable. If the questions are posed as testable hypotheses that rely on inductive reasoning and empiricism – that is, observations and data collection – then experiments can be devised to generate logical theories that explain the things we see. If we understand why the observed results occur, then we can accurately apply concepts derived from the experiment to other situations.

What do we need to consider when using the Scientific Method?

The Scientific Method requires that we ask questions and perform experiments to prove or disprove questions in ways that will lead to unbiased answers. Experiments must be well designed to provide accurate and repeatable (precise) results. If we test hypotheses correctly, then we can prove the cause of a phenomenon and determine the likelihood (probability) of the events to happen again. This provides predictive power. The Scientific Method enables us to test a hypothesis and distinguish between the correlation of two or more things happening in association with each other and the actual cause of the phenomenon we observe.

Correlation of two variables cannot explain the cause and effect of their relationship. Scientists design experiments using a number of methods to ensure the results reveal the likelihood of the observation happening (probability). Controlled experiments are used to analyze these relationships and develop cause and effect relationships. Statistical analysis is used to determine whether differences between treatments can be attributed to the treatment applied, if they are artifacts of the experimental design, or of natural variation.

In summary, the Scientific Method produces answers to questions posed in the form of a working hypothesis that enables us to derive theories about what we observe in the world around us. Its power lies in its ability to be repeated, providing unbiased answers to questions to derive theories. This information is powerful and offers opportunity to predict future events and phenomena.

Bibliography

• Ackoff, R. 1962. Scientific Method, Optimizing Applied Research Decisions. Wiley and Sons, New York, NY.
• Wilson, F. 1937. The Logic and Methodology of Science in Early Modern Thought. University of Toronto Press. Buffalo, NY.
• Committee on Science, Engineering, and Public Policy. Experimental Error. 1995. From: On Being a Scientist: Responsible Conduct in Research. Second Edition.
• The Gale Group. The Scientific Method. 2001. Gale Encyclopedia of Psychology. Second Edition.

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Research Process

• Select a Topic
• Find Background Info
• Focus Topic
• List Keywords
• Search for Sources
• Evaluate & Integrate Sources
• Cite and Track Sources

What is Scientific Research?

Research study design, natural vs. social science, qualitative vs. quantitative research, more information on qualitative research in the social sciences, acknowledgements.

Thank you to Julie Miller, reference intern, for helping to create this page.

Some people use the term research loosely, for example:

• People will say they are researching different online websites to find the best place to buy a new appliance or locate a lawn care service.
• TV news may talk about conducting research when they conduct a viewer poll on current event topic such as an upcoming election.
• Undergraduate students working on a term paper or project may say they are researching the internet to find information.
• Private sector companies may say they are conducting research to find a solution for a supply chain holdup.

However, none of the above is considered “scientific research” unless:

• The research contributes to a body of science by providing new information through ethical study design or
• The research follows the scientific method, an iterative process of observation and inquiry.

The Scientific Method

• Make an observation: notice a phenomenon in your life or in society or find a gap in the already published literature.
• Ask a question about what you have observed.
• Hypothesize about a potential answer or explanation.
• Make predictions if our hypothesis is correct.
• Design an experiment or study that will test your prediction.
• Test the prediction by conducting an experiment or study; report the outcomes of your study.
• Iterate! Was your prediction correct? Was the outcome unexpected? Did it lead to new observations?

The scientific method is not separate from the Research Process as described in the rest of this guide, in fact the Research Process is directly related to the observation stage of the scientific method. Understanding what other scientists and researchers have already studied will help you focus your area of study and build on their knowledge.

Designing your experiment or study is important for both natural and social scientists. Sage Research Methods (SRM) has an excellent "Project Planner" that guides you through the basic stages of research design. SRM also has excellent explanations of qualitative and quantitative research methods for the social sciences.

For the natural sciences, Springer Nature Experiments and Protocol Exchange have guidance on quantitative research methods.

Books, journals, reference books, videos, podcasts, data-sets, and case studies on social science research methods.

Sage Research Methods includes over 2,000 books, reference books, journal articles, videos, datasets, and case studies on all aspects of social science research methodology. Browse the methods map or the list of methods to identify a social science method to pursue further. Includes a project planning tool and the "Which Stats Test" tool to identify the best statistical method for your project. Includes the notable "little green book" series (Quantitative Applications in the Social Sciences) and the "little blue book" series (Qualitative Research Methods).

Platform connecting researchers with protocols and methods.

Springer Nature Experiments has been designed to help users/researchers find and evaluate relevant protocols and methods across the whole Springer Nature protocols and methods portfolio using one search. This database includes:

• Nature Protocols
• Nature Reviews Methods Primers
• Nature Methods
• Springer Protocols

Open repository for sharing scientific research protocols. These protocols are posted directly on the Protocol Exchange by authors and are made freely available to the scientific community for use and comment.

Includes these topics:

• Biochemistry
• Biological techniques
• Chemical biology
• Chemical engineering
• Cheminformatics
• Climate science
• Computational biology and bioinformatics
• Drug discovery
• Electronics
• Energy sciences
• Environmental sciences
• Materials science
• Molecular biology
• Molecular medicine
• Neuroscience
• Organic chemistry
• Planetary science

Qualitative research is primarily exploratory. It is used to gain an understanding of underlying reasons, opinions, and motivations. Qualitative research is also used to uncover trends in thought and opinions and to dive deeper into a problem by studying an individual or a group.

Qualitative methods usually use unstructured or semi-structured techniques. The sample size is typically smaller than in quantitative research.

Example: interviews and focus groups.

Quantitative research is characterized by the gathering of data with the aim of testing a hypothesis. The data generated are numerical, or, if not numerical, can be transformed into useable statistics.

Quantitative data collection methods are more structured than qualitative data collection methods and sample sizes are usually larger.

Example: survey

Note: The above descriptions of qualitative and quantitative research are mainly for research in the Social Sciences, rather than for Natural Sciences as most natural sciences rely on quantitative methods for their experiments.

Qualitative research is approaching the world in its natural setting and in a way that reveals the particularities rather than doing studies in a controlled setting. It aims to understand, describe, and sometimes explain social phenomena in a number of different ways:

• Experiences of individuals or groups
• Interactions and communications
• Documents (texts, images, film, or sounds, and digital documents)
• Experiences or interactions

Qualitative researchers seek to understand how people conceptualize the world around them, what they are doing, how they are doing it or what is happening to them in terms that are significant and that offer meaningful learnings.

Qualitative researchers develop and refine concepts (or hypotheses, if they are used) in the process of research and of collecting data. Cases (its history and complexity) are an important context for understanding the issue that is studied. A major part of qualitative research is based on text and writing – from field notes and transcripts to descriptions and interpretations and finally to the presentation of the findings and of the research as a whole.

For more information, see:

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Home » What is Science – Definition, Methods, Types

What is Science – Definition, Methods, Types

Table of Contents

Definition:

Science is the study of the natural world. It is a process of observing, investigating, and experimenting to find out how things in the universe work. Science is all around us in the way plants grow, the weather changes, and even our bodies function.

Science is not just a body of knowledge; it is a way of thinking . Scientists are constantly asking questions, testing hypotheses, and looking for patterns. This process helps them to better understand how the world works and make predictions about what will happen next.

History of Science

The study of science is often thought to be a relatively recent pursuit, but its origins date back thousands of years. The early scientists were mostly interested in understanding the natural world and how it worked, but their work was often hindered by a lack of reliable evidence and the limitations of their observations.

As time went on, however, scientific advances began to accumulate at an ever-increasing pace. In the 16th and 17th centuries, astronomers such as Galileo and Johannes Kepler made groundbreaking discoveries about the solar system. In the 18th century, scientists like Isaac Newton and Charles Darwin began to develop theories that would change our understanding of the universe forever.

And in the 19th and 20th centuries, scientists made incredible strides in fields as diverse as physics, chemistry, biology, and anthropology. As a result, many of the scientific theories that were once considered controversial have become universally accepted. In some cases, these theories have even transformed our understanding of the world and overturned previously held beliefs. Like in the early 19th century, most people believed that the Earth was flat. In fact, this belief had been widely held for centuries.

Branches of Science

branches of Science are divided into five types:

Natural science

Social science, formal science, applied science, interdisciplinary science.

Natural science is the study of the physical world. It covers a wide range of topics, from the structure of the atom to the origins of the universe.

Natural science is divided into two main branches:

Physics is the study of matter and energy, while chemistry is the study of the composition and properties of matter.

Natural science is essential for understanding how the world works. It helps us to develop new technologies and find solutions to problems like climate change.

Social science is the study of human behavior. It covers the following topics:

• Anthropology
• Political science

Social science aims to understand how people interact with each other and how they make choices.

Formal science is the branch of knowledge that is formalized and can be expressed in the form of mathematical equations or other logical constructs. It includes branches such as:

• Mathematics
• Computer science

It is distinct from natural science, which deals with the empirical study of the natural world. The two are often seen as complementary, with formal science providing a theoretical framework for natural science.

Formal science has important applications in many fields, including engineering, computer science and economics. It helps us to understand complex systems and to make predictions about their behavior.

Applied science is a discipline of science that applies existing scientific knowledge to develop more practical applications, like technology or inventions. It is often considered a bridge between basic research (pure science) and applied research (which seeks to solve specific problems).

Some common examples of applied sciences include:

• Medical Science
• Agricultural Science
• Industrial Design
• Information Technology

One of the main goals of applied science is to improve the quality of human life. For example, medical science can be used to develop new treatments for diseases, while agricultural science can be used to improve crop yields.

Interdisciplinary science is a branch of science that brings together multiple disciplines in order to study complex systems . It integrates knowledge from different fields in order to build a more comprehensive understanding of the system under study.

Scientific Method

The scientific method is a process for experimentation that is used to explore observations and answer questions. It is important to use the scientific method because it is a systematic way to test ideas and find answers to questions. The steps of the scientific method are:

• Ask a Question
• Do background research
• Formulate a Hypothesis
• Test the Hypothesis
• Analyze the Data
• Draw a conclusion

The scientific method is important because it helps scientists to design experiments that accurately test their hypotheses.

Purpose of Science

The purpose of science is to provide explanations for natural phenomena. It is also to find patterns in those phenomena and to develop theories that can be tested and verified. Science is a systematic way of acquiring knowledge about the natural world. It is based on observation, experimentation, and the application of scientific methods.

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Chapter 1 Science and Scientific Research

What is research? Depending on who you ask, you will likely get very different answers to this seemingly innocuous question. Some people will say that they routinely research different online websites to find the best place to buy goods or services they want. Television news channels supposedly conduct research in the form of viewer polls on topics of public interest such as forthcoming elections or government-funded projects. Undergraduate students research the Internet to find the information they need to complete assigned projects or term papers. Graduate students working on research projects for a professor may see research as collecting or analyzing data related to their project. Businesses and consultants research different potential solutions to remedy organizational problems such as a supply chain bottleneck or to identify customer purchase patterns. However, none of the above can be considered “scientific research” unless: (1) it contributes to a body of science, and (2) it follows the scientific method. This chapter will examine what these terms mean.

What is science? To some, science refers to difficult high school or college-level courses such as physics, chemistry, and biology meant only for the brightest students. To others, science is a craft practiced by scientists in white coats using specialized equipment in their laboratories. Etymologically, the word “science” is derived from the Latin word scientia meaning knowledge. Science refers to a systematic and organized body of knowledge in any area of inquiry that is acquired using “the scientific method” (the scientific method is described further below). Science can be grouped into two broad categories: natural science and social science. Natural science is the science of naturally occurring objects or phenomena, such as light, objects, matter, earth, celestial bodies, or the human body. Natural sciences can be further classified into physical sciences, earth sciences, life sciences, and others. Physical sciences consist of disciplines such as physics (the science of physical objects), chemistry (the science of matter), and astronomy (the science of celestial objects). Earth sciences consist of disciplines such as geology (the science of the earth). Life sciences include disciplines such as biology (the science of human bodies) and botany (the science of plants). In contrast, social science is the science of people or collections of people, such as groups, firms, societies, or economies, and their individual or collective behaviors. Social sciences can be classified into disciplines such as psychology (the science of human behaviors), sociology (the science of social groups), and economics (the science of firms, markets, and economies).

The natural sciences are different from the social sciences in several respects. The natural sciences are very precise, accurate, deterministic, and independent of the person m aking the scientific observations. For instance, a scientific experiment in physics, such as measuring the speed of sound through a certain media or the refractive index of water, should always yield the exact same results, irrespective of the time or place of the experiment, or the person conducting the experiment. If two students conducting the same physics experiment obtain two different values of these physical properties, then it generally means that one or both of those students must be in error. However, the same cannot be said for the social sciences, which tend to be less accurate, deterministic, or unambiguous. For instance, if you measure a person’s happiness using a hypothetical instrument, you may find that the same person is more happy or less happy (or sad) on different days and sometimes, at different times on the same day. One’s happiness may vary depending on the news that person received that day or on the events that transpired earlier during that day. Furthermore, there is not a single instrument or metric that can accurately measure a person’s happiness. Hence, one instrument may calibrate a person as being “more happy” while a second instrument may find that the same person is “less happy” at the same instant in time. In other words, there is a high degree of measurement error in the social sciences and there is considerable uncertainty and little agreement on social science policy decisions. For instance, you will not find many disagreements among natural scientists on the speed of light or the speed of the earth around the sun, but you will find numerous disagreements among social scientists on how to solve a social problem such as reduce global terrorism or rescue an economy from a recession. Any student studying the social sciences must be cognizant of and comfortable with handling higher levels of ambiguity, uncertainty, and error that come with such sciences, which merely reflects the high variability of social objects.

Sciences can also be classified based on their purpose. Basic sciences , also called pure sciences, are those that explain the most basic objects and forces, relationships between them, and laws governing them. Examples include physics, mathematics, and biology. Applied sciences , also called practical sciences, are sciences that apply scientific knowledge from basic sciences in a physical environment. For instance, engineering is an applied science that applies the laws of physics and chemistry for practical applications such as building stronger bridges or fuel efficient combustion engines, while medicine is an applied science that applies the laws of biology for solving human ailments. Both basic and applied sciences are required for human development. However, applied sciences cannot stand on their own right, but instead relies on basic sciences for its progress. Of course, the industry and private enterprises tend to focus more on applied sciences given their practical value, while universities study both basic and applied sciences.

Scientific Knowledge

The purpose of science is to create scientific knowledge. Scientific knowledge refers to a generalized body of laws and theories to explain a phenomenon or behavior of interest that are acquired using the scientific method. Laws are observed patterns of phenomena or behaviors, while theories are systematic explanations of the underlying phenomenon or behavior. For instance, in physics, the Newtonian Laws of Motion describe what happens when an object is in a state of rest or motion (Newton’s First Law), what force is needed to move a stationary object or stop a moving object (Newton’s Second Law), and what happens when two objects collide (Newton’s Third Law). Collectively, the three laws constitute the basis of classical mechanics – a theory of moving objects. Likewise, the theory of optics explains the properties of light and how it behaves in different media, electromagnetic theory explains the properties of electricity and how to generate it, quantum mechanics explains the properties of subatomic \particles, and thermodynamics explains the properties of energy and mechanical work. An introductory college level text book in physics will likely contain separate chapters devoted to each of these theories. Similar theories are also available in social sciences. For instance, cognitive dissonance theory in psychology explains how people react when their observations of an event is different from what they expected of that event, general deterrence theory explains why some people engage in improper or criminal behaviors, such as illegally download music or commit software piracy, and the theory of planned behavior explains how people make conscious reasoned choices in their everyday lives.

The goal of scientific research is to discover laws and postulate theories that can explain natural or social phenomena, or in other words, build scientific knowledge. It is important to understand that this knowledge may be imperfect or even quite far from the truth. Sometimes, there may not be a single universal truth, but rather an equilibrium of “multiple truths.” We must understand that the theories, upon which scientific knowledge is based, are only explanations of a particular phenomenon, as suggested by a scientist. As such, there may be good or poor explanations, depending on the extent to which those explanations fit well with reality, and consequently, there may be good or poor theories. The progress of science is marked by our progression over time from poorer theories to better theories, through better observations using more accurate instruments and more informed logical reasoning.

We arrive at scientific laws or theories through a process of logic and evidence. Logic (theory) and evidence (observations) are the two, and only two, pillars upon which scientific knowledge is based. In science, theories and observations are interrelated and cannot exist without each other. Theories provide meaning and significance to what we observe, and observations help validate or refine existing theory or construct new theory. Any other means of knowledge acquisition, such as faith or authority cannot be considered science.

Scientific Research

Given that theories and observations are the two pillars of science, scientific research operates at two levels: a theoretical level and an empirical level. The theoretical level is concerned with developing abstract concepts about a natural or social phenomenon and relationships between those concepts (i.e., build “theories”), while the empirical level is concerned with testing the theoretical concepts and relationships to see how well they reflect our observations of reality, with the goal of ultimately building better theories. Over time, a theory becomes more and more refined (i.e., fits the observed reality better), and the science gains maturity. Scientific research involves continually moving back and forth between theory and observations. Both theory and observations are essential components of scientific research. For instance, relying solely on observations for making inferences and ignoring theory is not considered valid scientific research.

Depending on a researcher’s training and interest, scientific inquiry may take one of two possible forms: inductive or deductive. In inductive research , the goal of a researcher is to infer theoretical concepts and patterns from observed data. In deductive research , the goal of the researcher is to test concepts and patterns known from theory using new empirical data. Hence, inductive research is also called theory-building research, and deductive research is theory-testing research. Note here that the goal of theory-testing is not just to test a theory, but possibly to refine, improve, and extend it. Figure 1.1 depicts the complementary nature of inductive and deductive research. Note that inductive and deductive research are two halves of the research cycle that constantly iterates between theory and observations. You cannot do inductive or deductive research if you are not familiar with both the theory and data components of research. Naturally, a complete researcher is one who can traverse the entire research cycle and can handle both inductive and deductive research.

It is important to understand that theory-building (inductive research) and theory-testing (deductive research) are both critical for the advancement of science. Elegant theories are not valuable if they do not match with reality. Likewise, mountains of data are also useless until they can contribute to the construction to meaningful theories. Rather than viewing these two processes in a circular relationship, as shown in Figure 1.1, perhaps they can be better viewed as a helix, with each iteration between theory and data contributing to better explanations of the phenomenon of interest and better theories. Though both inductive and deductive research are important for the advancement of science, it appears that inductive (theory-building) research is more valuable when there are few prior theories or explanations, while deductive (theory-testing) research is more productive when there are many competing theories of the same phenomenon and researchers are interested in knowing which theory works best and under what circumstances.

Figure 1.1. The Cycle of Research

Theory building and theory testing are particularly difficult in the social sciences, given the imprecise nature of the theoretical concepts, inadequate tools to measure them, and the presence of many unaccounted factors that can also influence the phenomenon of interest. It is also very difficult to refute theories that do not work. For instance, Karl Marx’s theory of communism as an effective means of economic production withstood for decades, before it was finally discredited as being inferior to capitalism in promoting economic growth and social welfare. Erstwhile communist economies like the Soviet Union and China eventually moved toward more capitalistic economies characterized by profit-maximizing private enterprises. However, the recent collapse of the mortgage and financial industries in the United States demonstrates that capitalism also has its flaws and is not as effective in fostering economic growth and social welfare as previously presumed. Unlike theories in the natural sciences, social science theories are rarely perfect, which provides numerous opportunities for researchers to improve those theories or build their own alternative theories.

Conducting scientific research, therefore, requires two sets of skills – theoretical and methodological – needed to operate in the theoretical and empirical levels respectively. Methodological skills (“know-how”) are relatively standard, invariant across disciplines, and easily acquired through doctoral programs. However, theoretical skills (“know-what”) is considerably harder to master, requires years of observation and reflection, and are tacit skills that cannot be “taught” but rather learned though experience. All of the greatest scientists in the history of mankind, such as Galileo, Newton, Einstein, Neils Bohr, Adam Smith, Charles Darwin, and Herbert Simon, were master theoreticians, and they are remembered for the theories they postulated that transformed the course of science. Methodological skills are needed to be an ordinary researcher, but theoretical skills are needed to be an extraordinary researcher!

Scientific Method

In the preceding sections, we described science as knowledge acquired through a scientific method. So what exactly is the “scientific method”? Scientific method refers to a standardized set of techniques for building scientific knowledge, such as how to make valid observations, how to interpret results, and how to generalize those results. The scientific method allows researchers to independently and impartially test preexisting theories and prior findings, and subject them to open debate, modifications, or enhancements. The scientific method must satisfy four characteristics:

• Replicability: Others should be able to independently replicate or repeat a scientific study and obtain similar, if not identical, results.
• Precision: Theoretical concepts, which are often hard to measure, must be defined with such precision that others can use those definitions to measure those concepts and test that theory.
• Falsifiability: A theory must be stated in a way that it can be disproven. Theories that cannot be tested or falsified are not scientific theories and any such knowledge is not scientific knowledge. A theory that is specified in imprecise terms or whose concepts are not accurately measurable cannot be tested, and is therefore not scientific. Sigmund Freud’s ideas on psychoanalysis fall into this category and is therefore not considered a

“theory”, even though psychoanalysis may have practical utility in treating certain types of ailments.

• Parsimony: When there are multiple explanations of a phenomenon, scientists must always accept the simplest or logically most economical explanation. This concept is called parsimony or “Occam’s razor.” Parsimony prevents scientists from pursuing overly complex or outlandish theories with endless number of concepts and relationships that may explain a little bit of everything but nothing in particular.

Any branch of inquiry that does not allow the scientific method to test its basic laws or theories cannot be called “science.” For instance, theology (the study of religion) is not science because theological ideas (such as the presence of God) cannot be tested by independent observers using a replicable, precise, falsifiable, and parsimonious method. Similarly, arts, music, literature, humanities, and law are also not considered science, even though they are creative and worthwhile endeavors in their own right.

The scientific method, as applied to social sciences, includes a variety of research approaches, tools, and techniques, such as qualitative and quantitative data, statistical analysis, experiments, field surveys, case research, and so forth. Most of this book is devoted to learning about these different methods. However, recognize that the scientific method operates primarily at the empirical level of research, i.e., how to make observations and analyze and interpret these observations. Very little of this method is directly pertinent to the theoretical level, which is really the more challenging part of scientific research.

Types of Scientific Research

Depending on the purpose of research, scientific research projects can be grouped into three types: exploratory, descriptive, and explanatory. Exploratory research is often conducted in new areas of inquiry, where the goals of the research are: (1) to scope out the magnitude or extent of a particular phenomenon, problem, or behavior, (2) to generate some initial ideas (or “hunches”) about that phenomenon, or (3) to test the feasibility of undertaking a more extensive study regarding that phenomenon. For instance, if the citizens of a country are generally dissatisfied with governmental policies regarding during an economic recession, exploratory research may be directed at measuring the extent of citizens’ dissatisfaction, understanding how such dissatisfaction is manifested, such as the frequency of public protests, and the presumed causes of such dissatisfaction, such as ineffective government policies in dealing with inflation, interest rates, unemployment, or higher taxes. Such research may include examination of publicly reported figures, such as estimates of economic indicators, such as gross domestic product (GDP), unemployment, and consumer price index, as archived by third-party sources, obtained through interviews of experts, eminent economists, or key government officials, and/or derived from studying historical examples of dealing with similar problems. This research may not lead to a very accurate understanding of the target problem, but may be worthwhile in scoping out the nature and extent of the problem and serve as a useful precursor to more in-depth research.

Descriptive research is directed at making careful observations and detailed documentation of a phenomenon of interest. These observations must be based on the scientific method (i.e., must be replicable, precise, etc.), and therefore, are more reliable than casual observations by untrained people. Examples of descriptive research are tabulation of demographic statistics by the United States Census Bureau or employment statistics by the Bureau of Labor, who use the same or similar instruments for estimating employment by sector or population growth by ethnicity over multiple employment surveys or censuses. If any changes are made to the measuring instruments, estimates are provided with and without the changed instrumentation to allow the readers to make a fair before-and-after comparison regarding population or employment trends. Other descriptive research may include chronicling ethnographic reports of gang activities among adolescent youth in urban populations, the persistence or evolution of religious, cultural, or ethnic practices in select communities, and the role of technologies such as Twitter and instant messaging in the spread of democracy movements in Middle Eastern countries.

Explanatory research seeks explanations of observed phenomena, problems, or behaviors. While descriptive research examines the what, where, and when of a phenomenon, explanatory research seeks answers to why and how types of questions. It attempts to “connect the dots” in research, by identifying causal factors and outcomes of the target phenomenon. Examples include understanding the reasons behind adolescent crime or gang violence, with the goal of prescribing strategies to overcome such societal ailments. Most academic or doctoral research belongs to the explanation category, though some amount of exploratory and/or descriptive research may also be needed during initial phases of academic research. Seeking explanations for observed events requires strong theoretical and interpretation skills, along with intuition, insights, and personal experience. Those who can do it well are also the most prized scientists in their disciplines.

History of Scientific Thought

Before closing this chapter, it may be interesting to go back in history and see how science has evolved over time and identify the key scientific minds in this evolution. Although instances of scientific progress have been documented over many centuries, the terms “science,” “scientists,” and the “scientific method” were coined only in the 19 th century. Prior to this time, science was viewed as a part of philosophy, and coexisted with other branches of philosophy such as logic, metaphysics, ethics, and aesthetics, although the boundaries between some of these branches were blurred.

In the earliest days of human inquiry, knowledge was usually recognized in terms of theological precepts based on faith. This was challenged by Greek philosophers such as Plato, Aristotle, and Socrates during the 3 rd century BC, who suggested that the fundamental nature of being and the world can be understood more accurately through a process of systematic logical reasoning called rationalism . In particular, Aristotle’s classic work Metaphysics (literally meaning “beyond physical [existence]”) separated theology (the study of Gods) from ontology (the study of being and existence) and universal science (the study of first principles, upon which logic is based). Rationalism (not to be confused with “rationality”) views reason as the source of knowledge or justification, and suggests that the criterion of truth is not sensory but rather intellectual and deductive, often derived from a set of first principles or axioms (such as Aristotle’s “law of non-contradiction”).

The next major shift in scientific thought occurred during the 16 th century, when British philosopher Francis Bacon (1561-1626) suggested that knowledge can only be derived from observations in the real world. Based on this premise, Bacon emphasized knowledge acquisition as an empirical activity (rather than as a reasoning activity), and developed empiricism as an influential branch of philosophy. Bacon’s works led to the popularization of inductive methods of scientific inquiry, the development of the “scientific method” (originally called the “Baconian method”), consisting of systematic observation, measurement, and experimentation, and may have even sowed the seeds of atheism or the rejection of theological precepts as “unobservable.”

Empiricism continued to clash with rationalism throughout the Middle Ages, as philosophers sought the most effective way of gaining valid knowledge. French philosopher Rene Descartes sided with the rationalists, while British philosophers John Locke and David Hume sided with the empiricists. Other scientists, such as Galileo Galilei and Sir Issac Newton, attempted to fuse the two ideas into natural philosophy (the philosophy of nature), to focus specifically on understanding nature and the physical universe, which is considered to be the precursor of the natural sciences. Galileo (1564-1642) was perhaps the first to state that the laws of nature are mathematical, and contributed to the field of astronomy through an innovative combination of experimentation and mathematics.

In the 18 th century, German philosopher Immanuel Kant sought to resolve the dispute between empiricism and rationalism in his book Critique of Pure Reason , by arguing that experience is purely subjective and processing them using pure reason without first delving into the subjective nature of experiences will lead to theoretical illusions. Kant’s ideas led to the development of German idealism , which inspired later development of interpretive techniques such as phenomenology, hermeneutics, and critical social theory.

At about the same time, French philosopher Auguste Comte (1798–1857), founder of the discipline of sociology, attempted to blend rationalism and empiricism in a new doctrine called positivism . He suggested that theory and observations have circular dependence on each other. While theories may be created via reasoning, they are only authentic if they can be verified through observations. The emphasis on verification started the separation of modern science from philosophy and metaphysics and further development of the “scientific method” as the primary means of validating scientific claims. Comte’s ideas were expanded by Emile Durkheim in his development of sociological positivism (positivism as a foundation for social research) and Ludwig Wittgenstein in logical positivism.

In the early 20 th century, strong accounts of positivism were rejected by interpretive sociologists (antipositivists) belonging to the German idealism school of thought. Positivism was typically equated with quantitative research methods such as experiments and surveys and without any explicit philosophical commitments, while antipositivism employed qualitative methods such as unstructured interviews and participant observation. Even practitioners of positivism, such as American sociologist Paul Lazarsfield who pioneered large-scale survey research and statistical techniques for analyzing survey data, acknowledged potential problems of observer bias and structural limitations in positivist inquiry. In response, antipositivists emphasized that social actions must be studied though interpretive means based upon an understanding the meaning and purpose that individuals attach to their personal actions, which inspired Georg Simmel’s work on symbolic interactionism, Max Weber’s work on ideal types, and Edmund Husserl’s work on phenomenology.

In the mid-to-late 20 th century, both positivist and antipositivist schools of thought were subjected to criticisms and modifications. British philosopher Sir Karl Popper suggested that human knowledge is based not on unchallengeable, rock solid foundations, but rather on a set of tentative conjectures that can never be proven conclusively, but only disproven. Empirical evidence is the basis for disproving these conjectures or “theories.” This metatheoretical stance, called postpositivism (or postempiricism), amends positivism by suggesting that it is impossible to verify the truth although it is possible to reject false beliefs, though it retains the positivist notion of an objective truth and its emphasis on the scientific method.

Likewise, antipositivists have also been criticized for trying only to understand society but not critiquing and changing society for the better. The roots of this thought lie in Das Capital , written by German philosophers Karl Marx and Friedrich Engels, which critiqued capitalistic societies as being social inequitable and inefficient, and recommended resolving this inequity through class conflict and proletarian revolutions. Marxism inspired social revolutions in countries such as Germany, Italy, Russia, and China, but generally failed to accomplish the social equality that it aspired. Critical research (also called critical theory) propounded by Max Horkheimer and Jurgen Habermas in the 20 th century, retains similar ideas of critiquing and resolving social inequality, and adds that people can and should consciously act to change their social and economic circumstances, although their ability to do so is constrained by various forms of social, cultural and political domination. Critical research attempts to uncover and critique the restrictive and alienating conditions of the status quo by analyzing the oppositions, conflicts and contradictions in contemporary society, and seeks to eliminate the causes of alienation and domination (i.e., emancipate the oppressed class). More on these different research philosophies and approaches will be covered in future chapters of this book.

• Social Science Research: Principles, Methods, and Practices. Authored by : Anol Bhattacherjee. Provided by : University of South Florida. Located at : http://scholarcommons.usf.edu/oa_textbooks/3/ . License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike

Scientific Methods

What is scientific method.

The Scientific method is a process with the help of which scientists try to investigate, verify, or construct an accurate and reliable version of any natural phenomena. They are done by creating an objective framework for the purpose of scientific inquiry and analysing the results scientifically to come to a conclusion that either supports or contradicts the observation made at the beginning.

Scientific Method Steps

The aim of all scientific methods is the same, that is, to analyse the observation made at the beginning. Still, various steps are adopted per the requirement of any given observation. However, there is a generally accepted sequence of steps in scientific methods.

• Observation and formulation of a question:  This is the first step of a scientific method. To start one, an observation has to be made into any observable aspect or phenomena of the universe, and a question needs to be asked about that aspect. For example, you can ask, “Why is the sky black at night? or “Why is air invisible?”
• Data Collection and Hypothesis:  The next step involved in the scientific method is to collect all related data and formulate a hypothesis based on the observation. The hypothesis could be the cause of the phenomena, its effect, or its relation to any other phenomena.
• Testing the hypothesis:  After the hypothesis is made, it needs to be tested scientifically. Scientists do this by conducting experiments. The aim of these experiments is to determine whether the hypothesis agrees with or contradicts the observations made in the real world. The confidence in the hypothesis increases or decreases based on the result of the experiments.
• Analysis and Conclusion:  This step involves the use of proper mathematical and other scientific procedures to determine the results of the experiment. Based on the analysis, the future course of action can be determined. If the data found in the analysis is consistent with the hypothesis, it is accepted. If not, then it is rejected or modified and analysed again.

It must be remembered that a hypothesis cannot be proved or disproved by doing one experiment. It needs to be done repeatedly until there are no discrepancies in the data and the result. When there are no discrepancies and the hypothesis is proved, it is accepted as a ‘theory’.

Scientific Method Examples

Following is an example of the scientific method:

Growing bean plants:

• What is the purpose: The main purpose of this experiment is to know where the bean plant should be kept inside or outside to check the growth rate and also set the time frame as four weeks.
• Construction of hypothesis: The hypothesis used is that the bean plant can grow anywhere if the scientific methods are used.
• Executing the hypothesis and collecting the data: Four bean plants are planted in identical pots using the same soil. Two are placed inside, and the other two are placed outside. Parameters like the amount of exposure to sunlight, and amount of water all are the same. After the completion of four weeks, all four plant sizes are measured.
• Analyse the data:  While analysing the data, the average height of plants should be taken into account from both places to determine which environment is more suitable for growing the bean plants.
• Conclusion:  The conclusion is drawn after analyzing the data.
• Results:  Results can be reported in the form of a tabular form.

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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

1. ^ https://www.lfu.bayern.de/wasser/gewaesserstrukturkartierung/index.htm

Atkinson, R. K., Derry, S. J., Renkl, A., and Wortham, D. (2000). Learning from examples: instructional principles from the worked examples research. Rev. Educ. Res. 70, 181–214. doi: 10.3102/00346543070002181

Crossref Full Text | Google Scholar

Barbieri, C. A., Booth, J. L., Begolli, K. N., and McCann, N. (2021). The effect of worked examples on student learning and error anticipation in algebra. Instr. Sci. 49, 419–439. doi: 10.1007/s11251-021-09545-6

Bayerisches Landesamt für Umwelt. (2019). Gewässerstrukturkartierung von Fließgewässern in Bayern – Erläuterungen zur Erfassung und Bewertung. (Water structure mapping of flowing waters in Bavaria - Explanations for recording and assessment) . Available at: https://www.bestellen.bayern.de/application/eshop_app000005?SID=1020555825&ACTIONxSESSxSHOWPIC(BILDxKEY:%27lfu_was_00152%27,BILDxCLASS:%27Artikel%27,BILDxTYPE:%27PDF%27)

Google Scholar

Berthold, K., Eysink, T. H., and Renkl, A. (2009). Assisting self-explanation prompts are more effective than open prompts when learning with multiple representations. Instr. Sci. 37, 345–363. doi: 10.1007/s11251-008-9051-z

Bokosmaty, S., Sweller, J., and Kalyuga, S. (2015). Learning geometry problem solving by studying worked examples: effects of learner guidance and expertise. Am. Educ. Res. J. 52, 307–333. doi: 10.3102/0002831214549450

Booth, J. L., McGinn, K., Young, L. K., and Barbieri, C. A. (2015). Simple practice doesn’t always make perfect. Policy Insights Behav. Brain Sci. 2, 24–32. doi: 10.1177/2372732215601691

Brownell, S. E., Wenderoth, M. P., Theobald, R., Okoroafor, N., Koval, M., Freeman, S., et al. (2014). How students think about experimental design: novel conceptions revealed by in-class activities. Bioscience 64, 125–137. doi: 10.1093/biosci/bit016

Chinn, C. A., and Malhotra, B. A. (2001). “Epistemologically authentic scientific reasoning” in Designing for science: implications from everyday, classroom, and professional settings . eds. K. Crowley, C. D. Schunn, and T. Okada (Mahwah, NJ: Lawrence Erlbaum), 351–392.

Dart, S., Pickering, E., and Dawes, L. (2020). Worked example videos for blended learning in undergraduate engineering. AEE J. 8, 1–22. doi: 10.18260/3-1-1153-36021

Dasgupta, A., Anderson, T. R., and Pelaez, N. J. (2014). Development and validation of a rubric for diagnosing students’ experimental design knowledge and difficulties. CBE Life Sci. Educ. 13, 265–284. doi: 10.1187/cbe.13-09-0192

PubMed Abstract | Crossref Full Text | Google Scholar

Deane, T., Nomme, K. M., Jeffery, E., Pollock, C. A., and Birol, G. (2014). Development of the biological experimental design concept inventory (BEDCI). CBE Life Sci. Educ. 13, 540–551. doi: 10.1187/cbe.13-11-0218

Deci, E. L., and Ryan, R. M. (2012). Self-determination theory. In P. A. M. LangeVan, A. W. Kruglanski, and E. T. Higgins (Eds.), Handbook of theories of social psychology , 416–436.

Eberbach, C., and Crowley, K. (2009). From everyday to scientific observation: how children learn to observe the Biologist’s world. Rev. Educ. Res. 79, 39–68. doi: 10.3102/0034654308325899

Ford, D. (2005). The challenges of observing geologically: third graders’ descriptions of rock and mineral properties. Sci. Educ. 89, 276–295. doi: 10.1002/sce.20049

Gerjets, P., Scheiter, K., and Catrambone, R. (2004). Designing instructional examples to reduce intrinsic cognitive load: molar versus modular presentation of solution procedures. Instr. Sci. 32, 33–58. doi: 10.1023/B:TRUC.0000021809.10236.71

Gupta, U. (2019). Interplay of germane load and motivation during math problem solving using worked examples. Educ. Res. Theory Pract. 30, 67–71.

Hefter, M. H., Berthold, K., Renkl, A., Riess, W., Schmid, S., and Fries, S. (2014). Effects of a training intervention to foster argumentation skills while processing conflicting scientific positions. Instr. Sci. 42, 929–947. doi: 10.1007/s11251-014-9320-y

Hesser, T. L., and Gregory, J. L. (2015). Exploring the Use of Faded Worked Examples as a Problem Solving Approach for Underprepared Students. High. Educ. Stud. 5, 36–46.

Jensen, E. (2014). Evaluating children’s conservation biology learning at the zoo. Conserv. Biol. 28, 1004–1011. doi: 10.1111/cobi.12263

Kalyuga, S., Chandler, P., Tuovinen, J., and Sweller, J. (2001). When problem solving is superior to studying worked examples. J. Educ. Psychol. 93, 579–588. doi: 10.1037/0022-0663.93.3.579

Kay, R. H., and Edwards, J. (2012). Examining the use of worked example video podcasts in middle school mathematics classrooms: a formative analysis. Can. J. Learn. Technol. 38, 1–20. doi: 10.21432/T2PK5Z

Klepsch, M., Schmitz, F., and Seufert, T. (2017). Development and validation of two instruments measuring intrinsic, extraneous, and germane cognitive load. Front. Psychol. 8:1997. doi: 10.3389/fpsyg.2017.01997

Knogler, M., Harackiewicz, J. M., Gegenfurtner, A., and Lewalter, D. (2015). How situational is situational interest? Investigating the longitudinal structure of situational interest. Contemp. Educ. Psychol. 43, 39–50. doi: 10.1016/j.cedpsych.2015.08.004

Koenen, J. (2014). Entwicklung und Evaluation von experimentunterstützten Lösungsbeispielen zur Förderung naturwissenschaftlich experimenteller Arbeitsweisen . Dissertation.

Koenen, J., Emden, M., and Sumfleth, E. (2017). Naturwissenschaftlich-experimentelles Arbeiten. Potenziale des Lernens mit Lösungsbeispielen und Experimentierboxen. (scientific-experimental work. Potentials of learning with solution examples and experimentation boxes). Zeitschrift für Didaktik der Naturwissenschaften 23, 81–98. doi: 10.1007/s40573-017-0056-5

Kohlhauf, L., Rutke, U., and Neuhaus, B. J. (2011). Influence of previous knowledge, language skills and domain-specific interest on observation competency. J. Sci. Educ. Technol. 20, 667–678. doi: 10.1007/s10956-011-9322-3

Leppink, J., Paas, F., Van der Vleuten, C. P., Van Gog, T., and Van Merriënboer, J. J. (2013). Development of an instrument for measuring different types of cognitive load. Behav. Res. Methods 45, 1058–1072. doi: 10.3758/s13428-013-0334-1

Lewalter, D. (2020). “Schülerlaborbesuche aus motivationaler Sicht unter besonderer Berücksichtigung des Interesses. (Student laboratory visits from a motivational perspective with special attention to interest)” in Handbuch Forschen im Schülerlabor – theoretische Grundlagen, empirische Forschungsmethoden und aktuelle Anwendungsgebiete . eds. K. Sommer, J. Wirth, and M. Vanderbeke (Münster: Waxmann-Verlag), 62–70.

Lewalter, D., and Knogler, M. (2014). “A questionnaire to assess situational interest – theoretical considerations and findings” in Poster Presented at the 50th Annual Meeting of the American Educational Research Association (AERA) (Philadelphia, PA)

Lunetta, V., Hofstein, A., and Clough, M. P. (2007). Learning and teaching in the school science laboratory: an analysis of research, theory, and practice. In N. Lederman and S. Abel (Eds.). Handbook of research on science education , Mahwah, NJ: Lawrence Erlbaum, 393–441.

Mayer, R. E. (2001). Multimedia learning. Cambridge University Press.

Paas, F., Renkl, A., and Sweller, J. (2003). Cognitive load theory and instructional design: recent developments. Educ. Psychol. 38, 1–4. doi: 10.1207/S15326985EP3801_1

Paas, F., Tuovinen, J., van Merriënboer, J. J. G., and Darabi, A. (2005). A motivational perspective on the relation between mental effort and performance: optimizing learner involvement in instruction. Educ. Technol. Res. Dev. 53, 25–34. doi: 10.1007/BF02504795

Reiss, K., Heinze, A., Renkl, A., and Groß, C. (2008). Reasoning and proof in geometry: effects of a learning environment based on heuristic worked-out examples. ZDM Int. J. Math. Educ. 40, 455–467. doi: 10.1007/s11858-008-0105-0

Renkl, A. (2001). Explorative Analysen zur effektiven Nutzung von instruktionalen Erklärungen beim Lernen aus Lösungsbeispielen. (Exploratory analyses of the effective use of instructional explanations in learning from worked examples). Unterrichtswissenschaft 29, 41–63. doi: 10.25656/01:7677

Renkl, A. (2014). “The worked examples principle in multimedia learning” in Cambridge handbook of multimedia learning . ed. R. E. Mayer (Cambridge University Press), 391–412.

Renkl, A. (2017). Learning from worked-examples in mathematics: students relate procedures to principles. ZDM 49, 571–584. doi: 10.1007/s11858-017-0859-3

Renkl, A., Atkinson, R. K., and Große, C. S. (2004). How fading worked solution steps works. A cognitive load perspective. Instr. Sci. 32, 59–82. doi: 10.1023/B:TRUC.0000021815.74806.f6

Renkl, A., Atkinson, R. K., and Maier, U. H. (2000). “From studying examples to solving problems: fading worked-out solution steps helps learning” in Proceeding of the 22nd Annual Conference of the Cognitive Science Society . eds. L. Gleitman and A. K. Joshi (Mahwah, NJ: Erlbaum), 393–398.

Renkl, A., Atkinson, R. K., Maier, U. H., and Staley, R. (2002). From example study to problem solving: smooth transitions help learning. J. Exp. Educ. 70, 293–315. doi: 10.1080/00220970209599510

Renkl, A., Hilbert, T., and Schworm, S. (2009). Example-based learning in heuristic domains: a cognitive load theory account. Educ. Psychol. Rev. 21, 67–78. doi: 10.1007/s10648-008-9093-4

Schworm, S., and Renkl, A. (2007). Learning argumentation skills through the use of prompts for self-explaining examples. J. Educ. Psychol. 99, 285–296. doi: 10.1037/0022-0663.99.2.285

Sirum, K., and Humburg, J. (2011). The experimental design ability test (EDAT). Bioscene 37, 8–16.

Staus, N. L., O’Connell, K., and Storksdieck, M. (2021). Addressing the ceiling effect when assessing STEM out-of-school time experiences. Front. Educ. 6:690431. doi: 10.3389/feduc.2021.690431

Sweller, J. (2006). The worked example effect and human cognition. Learn. Instr. 16, 165–169. doi: 10.1016/j.learninstruc.2006.02.005

Sweller, J., Van Merriënboer, J. J. G., and Paas, F. (1998). Cognitive architecture and instructional design. Educ. Psychol. Rev. 10, 251–295. doi: 10.1023/A:1022193728205

Thomas, A. E., and Müller, F. H. (2011). “Skalen zur motivationalen Regulation beim Lernen von Schülerinnen und Schülern. Skalen zur akademischen Selbstregulation von Schüler/innen SRQ-A [G] (überarbeitete Fassung)” in Scales of motivational regulation in student learning. Student academic self-regulation scales SRQ-A [G] (revised version). Wissenschaftliche Beiträge aus dem Institut für Unterrichts- und Schulentwicklung Nr. 5 (Klagenfurt: Alpen-Adria-Universität)

Um, E., Plass, J. L., Hayward, E. O., and Homer, B. D. (2012). Emotional design in multimedia learning. J. Educ. Psychol. 104, 485–498. doi: 10.1037/a0026609

Van Gog, T., Kester, L., and Paas, F. (2011). Effects of worked examples, example-problem, and problem- example pairs on novices’ learning. Contemp. Educ. Psychol. 36, 212–218. doi: 10.1016/j.cedpsych.2010.10.004

Van Gog, T., and Paas, G. W. C. (2006). Optimising worked example instruction: different ways to increase germane cognitive load. Learn. Instr. 16, 87–91. doi: 10.1016/j.learninstruc.2006.02.004

Van Harsel, M., Hoogerheide, V., Verkoeijen, P., and van Gog, T. (2019). Effects of different sequences of examples and problems on motivation and learning. Contemp. Educ. Psychol. 58, 260–275. doi: 10.1002/acp.3649

Wachsmuth, C. (2020). Computerbasiertes Lernen mit Aufmerksamkeitsdefizit: Unterstützung des selbstregulierten Lernens durch metakognitive prompts. (Computer-based learning with attention deficit: supporting self-regulated learning through metacognitive prompts) . Chemnitz: Dissertation Technische Universität Chemnitz.

Wahser, I. (2008). Training von naturwissenschaftlichen Arbeitsweisen zur Unterstützung experimenteller Kleingruppenarbeit im Fach Chemie (Training of scientific working methods to support experimental small group work in chemistry) . Dissertation

Walker, J., Gibson, J., and Brown, D. (2007). Selecting fluvial geomorphological methods for river management including catchment scale restoration within the environment agency of England and Wales. Int. J. River Basin Manag. 5, 131–141. doi: 10.1080/15715124.2007.9635313

Wellnitz, N., and Mayer, J. (2013). Erkenntnismethoden in der Biologie – Entwicklung und evaluation eines Kompetenzmodells. (Methods of knowledge in biology - development and evaluation of a competence model). Z. Didaktik Naturwissensch. 19, 315–345.

Willems, A. S., and Lewalter, D. (2011). “Welche Rolle spielt das motivationsrelevante Erleben von Schülern für ihr situationales Interesse im Mathematikunterricht? (What role does students’ motivational experience play in their situational interest in mathematics classrooms?). Befunde aus der SIGMA-Studie” in Erziehungswissenschaftliche Forschung – nachhaltige Bildung. Beiträge zur 5. DGfE-Sektionstagung “Empirische Bildungsforschung”/AEPF-KBBB im Frühjahr 2009 . eds. B. Schwarz, P. Nenninger, and R. S. Jäger (Landau: Verlag Empirische Pädagogik), 288–294.

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.

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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]

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• Published: 15 March 2024

Realizing the full potential of behavioural science for climate change mitigation

• Kristian S. Nielsen   ORCID: orcid.org/0000-0002-8395-4007 1 ,
• Viktoria Cologna   ORCID: orcid.org/0000-0003-3706-8669 2 ,
• Jan M. Bauer   ORCID: orcid.org/0000-0002-6331-4061 1 ,
• Sebastian Berger   ORCID: orcid.org/0000-0003-2392-1024 3 ,
• Cameron Brick   ORCID: orcid.org/0000-0002-7174-8193 4 , 5 ,
• Thomas Dietz 6 , 7 ,
• Ulf J. J. Hahnel   ORCID: orcid.org/0000-0002-0595-5899 8 , 9 ,
• Laura Henn   ORCID: orcid.org/0000-0003-4036-1487 10 ,
• Florian Lange   ORCID: orcid.org/0000-0002-8336-5608 11 ,
• Paul C. Stern   ORCID: orcid.org/0000-0002-9481-3904 12 &
• Kimberly S. Wolske   ORCID: orcid.org/0000-0003-2295-115X 13  

Nature Climate Change ( 2024 ) Cite this article

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• Climate-change mitigation
• Energy and behaviour

Behavioural science has yielded insights about the actions of individuals, particularly as consumers, that affect climate change. Behaviours in other spheres of life remain understudied. In this Perspective, we propose a collaborative research agenda that integrates behavioural science insights across multiple disciplines. To this end, we offer six recommendations for optimizing the quality and impact of research on individual climate behaviour. The recommendations are united by a shift towards more solutions-focused research that is directly useful to citizens, policymakers and other change agents. Achieving this vision will require overcoming challenges such as the limited funding for behavioural and social sciences and structural barriers within and beyond the academic system that impede collaborations across disciplines.

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Ivanova, D. et al. Quantifying the potential for climate change mitigation of consumption options. Environ. Res. Lett. 15 , 093001 (2020).

Article   ADS   CAS   Google Scholar  

Clayton, S. et al. Psychological research and global climate change. Nat. Clim. Change 5 , 640–646 (2015).

Article   ADS   Google Scholar  

Composto, J. W. & Weber, E. U. Effectiveness of behavioural interventions to reduce household energy demand: a scoping review. Environ. Res. Lett. 17 , 063005 (2022).

Creutzig, F. et al. in Climate Change 2022: Mitigation of Climate Change (eds Shukla. P. R. et al.) 752–943 (IPCC, Cambridge Univ. Press, 2022).

Newell, P., Twena, M. & Daley, F. Scaling behaviour change for a 1.5-degree world: challenges and opportunities. Glob. Sustain. 4 , e22 (2021).

Google Scholar  

Nielsen, K. S. et al. How psychology can help limit climate change. Am. Psychol. 76 , 130–144 (2021).

Article   PubMed   Google Scholar  

Wolske, K. S. & Stern, P. C. in Psychology and Climate Change (eds Clayton, S. & Manning, C.) 127–160 (Academic Press, 2018).

Nielsen, K. S. et al. Biodiversity conservation as a promising frontier for behavioural science. Nat. Hum. Behav. 5 , 550–556 (2021).

Nisa, C. F., Bélanger, J. J., Schumpe, B. M. & Faller, D. G. Meta-analysis of randomised controlled trials testing behavioural interventions to promote household action on climate change. Nat. Commun. 10 , 4545 (2019).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Wynes, S., Nicholas, K. A., Zhao, J. & Donner, S. D. Measuring what works: quantifying greenhouse gas emission reductions of behavioural interventions to reduce driving, meat consumption, and household energy use. Environ. Res. Lett. 13 , 113002 (2018).

Kastner, I. & Stern, P. C. Examining the decision-making processes behind household energy investments: a review. Energy Res. Soc. Sci. 10 , 72–89 (2015).

Article   Google Scholar  

IPCC: Summary for Policymakers. In Climate Change 2022: Mitigation of Climate Change (eds Shukla, P. R. et al.) (Cambridge Univ. Press, 2022).

Nielsen, K. S., Nicholas, K. A., Creutzig, F., Dietz, T. & Stern, P. C. The role of high-socioeconomic-status people in locking in or rapidly reducing energy-driven greenhouse gas emissions. Nat. Energy 6 , 1011–1016 (2021).

Composto, J. W., Constantino, S. M. & Weber, E. U. Predictors and consequences of pro-environmental behavior at work. Curr. Res. Ecol. Soc. Psychol. 10 , 100107 (2023).

Wolske, K. S., Todd-Blick, A. & Tome, E. Increasing the reach of low-income energy programmes through behaviourally informed peer referral. Nat. Energy https://doi.org/10.1038/s41560-023-01298-5 (2023).

Kraft-Todd, G. T., Bollinger, B., Gillingham, K., Lamp, S. & Rand, D. G. Credibility-enhancing displays promote the provision of non-normative public goods. Nature 563 , 245–248 (2018).

Article   ADS   CAS   PubMed   Google Scholar  

Dietz, T. & Whitley, C. T. Inequality, decisions, and altruism. Sociol. Dev. 4 , 282–303 (2018).

Kölbel, J. F., Heeb, F., Paetzold, F. & Busch, T. Can sustainable investing save the world? Reviewing the mechanisms of investor impact. Organ. Environ. 33 , 554–574 (2020).

Wynes, S., Motta, M. & Donner, S. D. Understanding the climate responsibility associated with elections. One Earth 4 , 363–371 (2021).

Fisher, D. R., Berglund, O. & Davis, C. J. How effective are climate protests at swaying policy—and what could make a difference? Nature 623 , 910–913 (2023).

Brudermann, T., Reinsberger, K., Orthofer, A., Kislinger, M. & Posch, A. Photovoltaics in agriculture: a case study on decision making of farmers. Energy Policy 61 , 96–103 (2013).

Cerullo, G. & Nielsen, K. S. Decade on restoration needs behavioural science. Preprint at PsyArXiv https://doi.org/10.31234/osf.io/g85j9 (2022).

Geiger, N., Swim, J. K. & Fraser, J. Creating a climate for change: interventions, efficacy and public discussion about climate change. J. Environ. Psychol. 51 , 104–116 (2017).

Rand, J. & Hoen, B. Thirty years of North American wind energy acceptance research: what have we learned? Energy Res. Soc. Sci. 29 , 135–148 (2017).

Winter, K., Hornsey, M. J., Pummerer, L. & Sassenberg, K. Anticipating and defusing the role of conspiracy beliefs in shaping opposition to wind farms. Nat. Energy 7 , 1200–1207 (2022).

Franta, B. Weaponizing economics: Big Oil, economic consultants, and climate policy delay. Environ. Polit. 31 , 555–575 (2022).

Oreskes, N. & Conway, E. M. Merchants of Doubt: How a Handful of Scientists Obscured the Truth on Issues from Tobacco Smoke to Global Warming (Bloomsbury, 2011).

Reichl, J., Cohen, J. J., Klöckner, C. A., Kollmann, A. & Azarova, V. The drivers of individual climate actions in Europe. Glob. Environ. Change 71 , 102390 (2021).

Javaid, A., Creutzig, F. & Bamberg, S. Determinants of low-carbon transport mode adoption: systematic review of reviews. Environ. Res. Lett. 15 , 103002 (2020).

Kaiser, F. G. Climate change mitigation within the Campbell paradigm: doing the right thing for a reason and against all odds. Curr. Opin. Behav. Sci. 42 , 70–75 (2021).

Heeb, F., Kölbel, J. F., Paetzold, F. & Zeisberger, S. Do investors care about impact?. Rev. Financ. Stud. 36 , 1737–1787 (2023).

Stern, P. C., Dietz, T., Abel, T., Guagnano, G. A. & Kalof, L. A value–belief–norm theory of support for social movements: the case of environmentalism. Hum. Ecol. Rev. 6 , 81–97 (1999).

Michie, S., van Stralen, M. M., & West, R. The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implement. Sci. 6 , 42 (2011).

Article   PubMed   PubMed Central   Google Scholar  

Norris, E., Finnerty, A. N., Hastings, J., Stokes, G. & Michie, S. A scoping review of ontologies related to human behaviour change. Nat. Hum. Behav. 3 , 164–172 (2019).

Constantino, S. M. et al. Scaling up change: a critical review and practical guide to harnessing social norms for climate action. Psychol. Sci. Public Interest 23 , 50–97 (2022).

Nyborg, K. et al. Social norms as solutions. Science 354 , 42–43 (2016).

Hargreaves, T. & Middlemiss, L. The importance of social relations in shaping energy demand. Nat. Energy 5 , 195–201 (2020).

Bryant, C. J., Prosser, A. M. B. & Barnett, J. Going veggie: identifying and overcoming the social and psychological barriers to veganism. Appetite 169 , 105812 (2022).

Wolske, K. S., Gillingham, K. T. & Schultz, P. W. Peer influence on household energy behaviours. Nat. Energy 5 , 202–212 (2020).

Bollinger, B. & Gillingham, K. Peer effects in the diffusion of solar photovoltaic panels. Mark. Sci. 31 , 900–912 (2012).

Walker, R. E., Keane, C. R. & Burke, J. G. Disparities and access to healthy food in the United States: a review of food deserts literature. Health Place 16 , 876–884 (2010).

Furszyfer Del Rio, D. D., Sovacool, B. K., Griffiths, S., Foley, A. M. & Furszyfer Del Rio, J. A cross-country analysis of sustainability, transport and energy poverty. npj Urban Sustain. 3 , 41 (2023).

Sovacool, B. K. et al. Policy prescriptions to address energy and transport poverty in the United Kingdom. Nat. Energy 8 , 273–283 (2023).

Balmford, A. et al. Making more effective use of human behavioural science in conservation interventions. Biol. Conserv. 261 , 109256 (2021).

Seto, K. C. et al. Carbon lock-in: types, causes, and policy implications. Annu. Rev. Environ. Resour. 41 , 425–452 (2016).

Ivanova, D. et al. Carbon mitigation in domains of high consumer lock-in. Glob. Environ. Change 52 , 117–130 (2018).

Tam, K.-P. Understanding the psychology X politics interaction behind environmental activism: the roles of governmental trust, density of environmental NGOs, and democracy. J. Environ. Psychol. 71 , 101330 (2020).

Lunetto, M., Hale, J. & Michie, S. Achieving effective climate action in cities by understanding behavioral systems. One Earth 5 , 745–748 (2022).

Oliver, T. H. et al. A safe and just operating space for human identity: a systems perspective. Lancet Planet. Health 6 , e919–e927 (2022).

Schill, C. et al. A more dynamic understanding of human behaviour for the Anthropocene. Nat. Sustain. 2 , 1075–1082 (2019).

Rauthmann, J. F., Sherman, R. A. & Funder, D. C. Principles of situation research: towards a better understanding of psychological situations. Eur. J. Pers. 29 , 363–381 (2015).

Pike, S. & Lubell, M. Geography and social networks in transportation mode choice. J. Transp. Geogr. 57 , 184–193 (2016).

Niamir, L., Ivanova, O., Filatova, T., Voinov, A. & Bressers, H. Demand-side solutions for climate mitigation: bottom-up drivers of household energy behavior change in the Netherlands and Spain. Energy Res. Soc. Sci. 62 , 101356 (2020).

Minor, K., Bjerre-Nielsen, A., Jonasdottir, S. S., Lehmann, S. & Obradovich, N. Rising temperatures erode human sleep globally. One Earth 5 , 534–549 (2022).

Jain, R. K., Qin, J. & Rajagopal, R. Data-driven planning of distributed energy resources amidst socio-technical complexities. Nat. Energy 2 , 1–11 (2017).

Kaaronen, R. O. & Strelkovskii, N. Cultural evolution of sustainable behaviors: pro-environmental tipping points in an agent-based model. One Earth 2 , 85–97 (2020).

Liu, J. et al. Coupled human and natural systems: the evolution and applications of an integrated framework. Ambio 50 , 1778–1783 (2021).

Article   ADS   PubMed   PubMed Central   Google Scholar  

Nielsen, K. S. et al. Improving climate change mitigation analysis: a framework for examining feasibility. One Earth 3 , 325–336 (2020).

Steg, L. et al. A method to identify barriers to and enablers of implementing climate change mitigation options. One Earth 5 , 1216–1227 (2022).

Stern, P. C., Gardner, G. T., Vandenbergh, M. P., Dietz, T. & Gilligan, J. M. Design principles for carbon emissions reduction programs. Environ. Sci. Technol. 44 , 4847–4848 (2010).

National Research Council Public Participation in Environmental Assessment and Decision Making (National Academies Press, 2008).

Kennedy, C. Boycott products from states with dirty energy. Nature 551 , 294–295 (2017).

Poore, J. & Nemecek, T. Reducing food’s environmental impacts through producers and consumers. Science 992 , 987–992 (2018).

Dietz, T., Frank, K. A., Whitley, C. T., Kelly, J. & Kelly, R. Political influences on greenhouse gas emissions from US states. Proc. Natl Acad. Sci. USA 112 , 8254–8259 (2015).

Peng, W. et al. Climate policy models need to get real about people—here’s how. Nature 594 , 174–176 (2021).

Dietz, T., Gardner, G. T., Gilligan, J., Stern, P. C. & Vandenbergh, M. P. Household actions can provide a behavioral wedge to rapidly reduce US carbon emissions. Proc. Natl Acad. Sci. USA 106 , 18452–18456 (2009).

Khanna, T. M. et al. A multi-country meta-analysis on the role of behavioural change in reducing energy consumption and CO 2 emissions in residential buildings. Nat. Energy 6 , 925–932 (2021).

Bergquist, M., Thiel, M., Goldberg, M. H. & van der Linden, S. Field interventions for climate change mitigation behaviors: a second-order meta-analysis. Proc. Natl Acad. Sci. USA 120 , e2214851120 (2023).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Chancel, L. Global carbon inequality over 1990–2019. Nat. Sustain. 5 , 931–938 (2022).

Green, F. & Healy, N. How inequality fuels climate change: the climate case for a Green New Deal. One Earth 5 , 635–649 (2022).

Nilsen, P. & Birken, S. A. Handbook on Implementation Science (Edward Elgar, 2020).

Pülzl, H. & Treib, O. in Handbook of Public Policy Analysis (eds Fischer, F. et al.) 89–107 (Routledge, 2007).

Fransen, T. et al. Taking stock of the implementation gap in climate policy. Nat. Clim. Change https://doi.org/10.1038/s41558-023-01755-9 (2023).

Fesenfeld, L., Rudolph, L. & Bernauer, T. Policy framing, design and feedback can increase public support for costly food waste regulation. Nat. Food 3 , 227–235 (2022).

Bergquist, M., Nilsson, A., Harring, N. & Jagers, S. C. Meta-analyses of fifteen determinants of public opinion about climate change taxes and laws. Nat. Clim. Change 12 , 235–240 (2022).

Carattini, S., Kallbekken, S. & Orlov, A. How to win public support for a global carbon tax. Nature 565 , 289–291 (2019).

Mildenberger, M., Lachapelle, E., Harrison, K. & Stadelmann-Steffen, I. Limited impacts of carbon tax rebate programmes on public support for carbon pricing. Nat. Clim. Change 12 , 141–147 (2022).

Stern, P. C. Design principles for global commons natural resources and emerging technologies. Int. J. Commons 5 , 213–232 (2011).

Stern, P. C., Dietz, T., Nielsen, K. S., Peng, W. & Vandenbergh, M. P. Feasible climate mitigation. Nat. Clim. Change 13 , 6–8 (2023).

Lange, F., Steinke, A. & Dewitte, S. The Pro-Environmental Behavior Task: A laboratory measure of actual pro-environmental behavior. J. Environ. Psychol. 56 , 46–54 (2018).

Kormos, C. & Gifford, R. The validity of self-report measures of proenvironmental behavior: a meta-analytic review. J. Environ. Psychol. 40 , 359–371 (2014).

Lange, F. et al. Beyond self-reports: a call for more behavior in environmental psychology. J. Environ. Psychol. 86 , 101965 (2023).

Nielsen, K. S. et al. The motivation–impact gap in pro-environmental clothing consumption. Nat. Sustain. 5 , 665–668 (2022).

Lange, F. & Dewitte, S. Measuring pro-environmental behavior: review and recommendations. J. Environ. Psychol. 63 , 92–100 (2019).

Klein, S. A. & Hilbig, B. E. On the lack of real consequences in consumer choice research. Exp. Psychol. 66 , 68–76 (2019).

Wille, F. & Lange, F. Potential contributions of behavior analysis to research on pro-environmental behavior. Front. Psychol. 13 , 685621 (2022).

Nielsen, K. S., Cologna, V., Lange, F., Brick, C. & Stern, P. C. The case for impact-focused environmental psychology. J. Environ. Psychol. 74 , 101559 (2021).

Bolderdijk, J. W., Knockaert, J., Steg, E. M. & Verhoef, E. T. Effects of pay-as-youdrive vehicle insurance on young drivers’ speed choice: results of a Dutch field experiment. Accid. Anal. Prev. 43 , 1181–1186 (2011).

Article   CAS   PubMed   Google Scholar  

Tiefenbeck, V. et al. Overcoming salience bias: how real-time feedback fosters resource conservation. Manage. Sci. 64 , 1458–1476 (2018).

Cropley, M., Sprajcer, M. & Dawson, D. Wastogram: validation of a new tool to measure household food waste. J. Environ. Psychol . https://doi.org/10.1016/j.jenvp.2022.101896 (2022).

Castiglione, A., Brick, C., Miles-Urdan, E. & Aron, A. R. Discovering the psychological building blocks underlying climate action—a longitudinal study of real-world activism. R. Soc. Open Sci. 9 , 210006 (2022).

Allen, S., Dietz, T. & Mccright, A. M. Measuring household energy efficiency behaviors with attention to behavioral plasticity in the United States. Energy Res . Soc. Sci. 10 , 133–140 (2015).

Berger, S. & Wyss, A. M. Measuring pro-environmental behavior using the carbon emission task. J. Environ. Psychol. 75 , 101613 (2021).

Lange, F. Behavioral paradigms for studying pro‑environmental behavior: a systematic review. Behav. Res. Methods 55 , 600–622 (2022).

Article   ADS   PubMed   Google Scholar  

Caggiano, H. & Weber, E. U. Advances in qualitative methods in environmental research. Annu. Rev. Environ. Resour. 48 , 793–811 (2023).

Rozin, P. Social psychology and science: some lessons from Solomon Asch. Pers. Soc. Psychol. Rev. 5 , 2–14 (2001).

Gerring, J. Mere description. Br. J. Polit. Sci. 42 , 721–746 (2012).

Diener, E., Northcott, R., Zyphur, M. J. & West, S. G. Beyond experiments. Perspect. Psychol. Sci. 17 , 1101–1119 (2022).

Cialdini, R. B. We have to break up. Perspect. Psychol. Sci. 4 , 5–6 (2009).

Scheel, A. M., Tiokhin, L., Isager, P. M. & Lakens, D. Why hypothesis testers should spend less time testing hypotheses. Perspect. Psychol. Sci. 16 , 744–755 (2021).

Fell, M. J. The history of heat-as-a-service for promoting domestic demand-side flexibility: lessons from the case of Budget Warmth. J. Energy Hist . https://energyhistory.eu/en/node/239 (2021).

Jenny, M. A. & Betsch, C. Large-scale behavioural data are key to climate policy. Nat. Hum. Behav. 6 , 1444–1447 (2022).

Masuda, Y. J. et al. Innovation diffusion within large environmental NGOs through informal network agents. Nat. Sustain. 1 , 190–197 (2018).

Debnath, R., van der Linden, S., Alvarez, R. M. & Sovacool, B. K. Facilitating system-level behavioural climate action using computational social science. Nat. Hum. Behav. 7 , 155–156 (2023).

Rai, V. & Henry, A. D. Agent-based modelling of consumer energy choices. Nat. Clim. Change 6 , 556–562 (2016).

Walzberg, J., Carpenter, A. & Heath, G. A. Role of the social factors in success of solar photovoltaic reuse and recycle programmes. Nat. Energy 6 , 913–924 (2021).

Henrich, J., Heine, S. J. & Norenzayan, A. The weirdest people in the world? Behav. Brain Sci. 33 , 61–83 (2010).

Ghai, S., de-Wit, L. & Mak, Y. How we investigated the diversity of our undergraduate curriculum. Nature https://doi.org/10.1038/d41586-023-00614-z (2023).

Adetula, A., Forscher, P. S., Basnight-Brown, D., Azouaghe, S. & IJzerman, H. Psychology should generalize from—not just to—Africa. Nat. Rev. Psychol. 1 , 370–371 (2022).

Ghai, S. It’s time to reimagine sample diversity and retire the WEIRD dichotomy. Nat. Hum. Behav. 5 , 971–972 (2021).

Gardner, G. T. & Stern, P. C. Environmental Problems and Human Behavior (Allyn & Bacon, 1996).

Bryan, C. J., Tipton, E. & Yeager, D. S. Behavioural science is unlikely to change the world without a heterogeneity revolution. Nat. Hum. Behav. 5 , 980–989 (2021).

Owen, A. & Barrett, J. Reducing inequality resulting from UK low-carbon policy. Clim. Policy 20 , 1193–1208 (2020).

Zhao, S., Fujimori, S., Hasegawa, T., Oshiro, K. & Sasaki, K. Poverty and inequality implications of carbon pricing under the long-term climate target. Sustain. Sci. 17 , 2513–2528 (2022).

Moshontz, H. et al. The Psychological Science Accelerator: advancing psychology through a distributed collaborative network. Adv. Methods Pract. Psychol. Sci. 1 , 501–515 (2018).

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

Munafò, M. R. et al. A manifesto for reproducible science. Nat. Hum. Behav. 1 , 0021 (2017).

Frank, K. A., Lin, Q., Xu, R., Maroulis, S. & Mueller, A. Quantifying the robustness of causal inferences: sensitivity analysis for pragmatic social science. Soc. Sci. Res. 110 , 102815 (2022).

Prosser, A. M. B. et al. When open data closes the door: a critical examination of the past, present and the potential future for open data guidelines in journals. Br. J. Soc. Psychol . https://doi.org/10.1111/bjso.12576 (2022).

Michie, S. et al. Behaviour change techniques: the development and evaluation of a taxonomic method for reporting and describing behaviour change interventions (a suite of five studies involving consensus methods, randomised controlled trials and analysis of qualitative data). Health Technol. Assess. 19 , 1–187 (2015).

Chester, D. & Lasko, E. Construct validation of experimental manipulations in social psychology: current practices and recommendations for the future. Perspect. Psychol. Sci. 16 , 377–395 (2021).

Hoffmann, T. C. et al. Better reporting of interventions: template for intervention description and replication (TIDieR) checklist and guide. Br. Med. J. 348 , g1687 (2014).

Duncan, E. et al. Guidance for reporting intervention development studies in health research (GUIDED): an evidence-based consensus study. BMJ Open 10 , e033516 (2020).

Skivington, K. et al. A new framework for developing and evaluating complex interventions: update of Medical Research Council guidance. Br. Med. J. 374 , n2061 (2021).

Lange, F., Nielsen, K. S., Cologna, V., Brick, C. & Stern, P. C. Making theory useful for understanding high-impact behavior. A response to van Valkengoed et al. (2021). J. Environ. Psychol. 75 , 101611 (2021).

van Valkengoed, A. M. et al. Theory enhances impact. Reply to: ‘The case for impact-focused environmental psychology’. J. Environ. Psychol. 75 , 101597 (2021).

Eronen, M. I. & Bringmann, L. F. The theory crisis in psychology: how to move forward. Perspect. Psychol. Sci. 16 , 779–788 (2021).

Michie, S. et al. The Human Behaviour-Change Project: harnessing the power of artificial intelligence and machine learning for evidence synthesis and interpretation. Implement. Sci. 12 , 121 (2017).

Elliott, J. H. et al. Decision makers need ‘living’ evidence synthesis. Nature 600 , 383–385 (2021).

Watts, D. J. Should social science be more solution-oriented? Nat. Hum. Behav. 1 , 0015 (2017).

Brownson, R. C., Royer, C., Ewing, R. & McBride, T. D. Researchers and policymakers: travelers in parallel universes. Am. J. Prev. Med. 30 , 164–172 (2006).

Cairney, P. & Kwiatkowski, R. How to communicate effectively with policymakers: combine insights from psychology and policy studies. Palgrave Commun. 3 , 37 (2017).

Vandenbergh, M. P. & Gilligan, J. M. Beyond Politics (Cambridge Univ. Press, 2017).

Nielsen, K. S., van der Linden, S. & Stern, P. C. How behavioral interventions can reduce the climate impact of energy use. Joule 4 , 1613–1616 (2020).

Come together. Nat. Energy 6 , 765 (2021).

De Bruin, W. B. & Granger, M. Reflections on an interdisciplinary collaboration to inform public understanding of climate change, mitigation, and impacts. Proc. Natl Acad. Sci. USA 116 , 7676–7683 (2019).

Overland, I. & Sovacool, B. K. The misallocation of climate research funding. Energy Res. Soc. Sci. 62 , 101349 (2020).

Otto, I. M. et al. Social tipping dynamics for stabilizing Earth’s climate by 2050. Proc. Natl Acad. Sci. USA 117 , 2354–2365 (2020).

Polizzi di Sorrentino, E., Woelbert, E. & Sala, S. Consumers and their behavior: state of the art in behavioral science supporting use phase modeling in LCA and ecodesign. Int. J. Life Cycle Assess. 21 , 237–251 (2016).

Sohn, J., Nielsen, K. S., Birkved, M., Joanes, T. & Gwozdz, W. The environmental impacts of clothing: evidence from United States and three European countries. Sustain. Prod. Consum. 27 , 2153–2164 (2021).

Moore, F. C. et al. Determinants of emissions pathways in the coupled climate–social system. Nature 603 , 103–111 (2022).

Beckage, B. et al. Linking models of human behaviour and climate alters projected climate change. Nat. Clim. Change 8 , 79–84 (2018).

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Acknowledgements

K.S.N. gratefully acknowledges financial support from the Carlsberg Foundation, grant number CF22-1056. V.C. acknowledges support from the Swiss National Science Foundation Postdoc Mobility Fellowship (P500PS_202935). S.B. acknowledges support from the Swiss Federal Office of Energy (SI/502093-01). T.D. was supported in part by Michigan AgBio Research. F.L. was supported by an FWO postdoctoral fellowship (12U1221N).

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Nielsen, K.S., Cologna, V., Bauer, J.M. et al. Realizing the full potential of behavioural science for climate change mitigation. Nat. Clim. Chang. (2024). https://doi.org/10.1038/s41558-024-01951-1

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Scientific Consensus

It’s important to remember that scientists always focus on the evidence, not on opinions. Scientific evidence continues to show that human activities ( primarily the human burning of fossil fuels ) have warmed Earth’s surface and its ocean basins, which in turn have continued to impact Earth’s climate . This is based on over a century of scientific evidence forming the structural backbone of today's civilization.

NASA Global Climate Change presents the state of scientific knowledge about climate change while highlighting the role NASA plays in better understanding our home planet. This effort includes citing multiple peer-reviewed studies from research groups across the world, 1 illustrating the accuracy and consensus of research results (in this case, the scientific consensus on climate change) consistent with NASA’s scientific research portfolio.

With that said, multiple studies published in peer-reviewed scientific journals 1 show that climate-warming trends over the past century are extremely likely due to human activities. In addition, most of the leading scientific organizations worldwide have issued public statements endorsing this position. The following is a partial list of these organizations, along with links to their published statements and a selection of related resources.

American Scientific Societies

Statement on climate change from 18 scientific associations.

"Observations throughout the world make it clear that climate change is occurring, and rigorous scientific research demonstrates that the greenhouse gases emitted by human activities are the primary driver." (2009) 2

American Association for the Advancement of Science

"Based on well-established evidence, about 97% of climate scientists have concluded that human-caused climate change is happening." (2014) 3

American Chemical Society

"The Earth’s climate is changing in response to increasing concentrations of greenhouse gases (GHGs) and particulate matter in the atmosphere, largely as the result of human activities." (2016-2019) 4

American Geophysical Union

"Based on extensive scientific evidence, it is extremely likely that human activities, especially emissions of greenhouse gases, are the dominant cause of the observed warming since the mid-20th century. There is no alterative explanation supported by convincing evidence." (2019) 5

American Medical Association

"Our AMA ... supports the findings of the Intergovernmental Panel on Climate Change’s fourth assessment report and concurs with the scientific consensus that the Earth is undergoing adverse global climate change and that anthropogenic contributions are significant." (2019) 6

American Meteorological Society

"Research has found a human influence on the climate of the past several decades ... The IPCC (2013), USGCRP (2017), and USGCRP (2018) indicate that it is extremely likely that human influence has been the dominant cause of the observed warming since the mid-twentieth century." (2019) 7

American Physical Society

"Earth's changing climate is a critical issue and poses the risk of significant environmental, social and economic disruptions around the globe. While natural sources of climate variability are significant, multiple lines of evidence indicate that human influences have had an increasingly dominant effect on global climate warming observed since the mid-twentieth century." (2015) 8

The Geological Society of America

"The Geological Society of America (GSA) concurs with assessments by the National Academies of Science (2005), the National Research Council (2011), the Intergovernmental Panel on Climate Change (IPCC, 2013) and the U.S. Global Change Research Program (Melillo et al., 2014) that global climate has warmed in response to increasing concentrations of carbon dioxide (CO2) and other greenhouse gases ... Human activities (mainly greenhouse-gas emissions) are the dominant cause of the rapid warming since the middle 1900s (IPCC, 2013)." (2015) 9

Science Academies

International academies: joint statement.

"Climate change is real. There will always be uncertainty in understanding a system as complex as the world’s climate. However there is now strong evidence that significant global warming is occurring. The evidence comes from direct measurements of rising surface air temperatures and subsurface ocean temperatures and from phenomena such as increases in average global sea levels, retreating glaciers, and changes to many physical and biological systems. It is likely that most of the warming in recent decades can be attributed to human activities (IPCC 2001)." (2005, 11 international science academies) 1 0

U.S. National Academy of Sciences

"Scientists have known for some time, from multiple lines of evidence, that humans are changing Earth’s climate, primarily through greenhouse gas emissions." 1 1

U.S. Government Agencies

U.s. global change research program.

"Earth’s climate is now changing faster than at any point in the history of modern civilization, primarily as a result of human activities." (2018, 13 U.S. government departments and agencies) 12

Intergovernmental Bodies

Intergovernmental panel on climate change.

“It is unequivocal that the increase of CO 2 , methane, and nitrous oxide in the atmosphere over the industrial era is the result of human activities and that human influence is the principal driver of many changes observed across the atmosphere, ocean, cryosphere, and biosphere. “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 3-17

Other Resources

List of worldwide scientific organizations.

The following page lists the nearly 200 worldwide scientific organizations that hold the position that climate change has been caused by human action. http://www.opr.ca.gov/facts/list-of-scientific-organizations.html

U.S. Agencies

The following page contains information on what federal agencies are doing to adapt to climate change. https://www.c2es.org/site/assets/uploads/2012/02/climate-change-adaptation-what-federal-agencies-are-doing.pdf

Technically, a “consensus” is a general agreement of opinion, but the scientific method steers us away from this to an objective framework. In science, facts or observations are explained by a hypothesis (a statement of a possible explanation for some natural phenomenon), which can then be tested and retested until it is refuted (or disproved).

As scientists gather more observations, they will build off one explanation and add details to complete the picture. Eventually, a group of hypotheses might be integrated and generalized into a scientific theory, a scientifically acceptable general principle or body of principles offered to explain phenomena.

1. K. Myers, et al, "Consensus revisited: quantifying scientific agreement on climate change and climate expertise among Earth scientists 10 years later", Environmental Research Letters Vol.16 No. 10, 104030 (20 October 2021); DOI:10.1088/1748-9326/ac2774 M. Lynas, et al, "Greater than 99% consensus on human caused climate change in the peer-reviewed scientific literature", Environmental Research Letters Vol.16 No. 11, 114005 (19 October 2021); DOI:10.1088/1748-9326/ac2966 J. Cook et al., "Consensus on consensus: a synthesis of consensus estimates on human-caused global warming", Environmental Research Letters Vol. 11 No. 4, (13 April 2016); DOI:10.1088/1748-9326/11/4/048002 J. Cook et al., "Quantifying the consensus on anthropogenic global warming in the scientific literature", Environmental Research Letters Vol. 8 No. 2, (15 May 2013); DOI:10.1088/1748-9326/8/2/024024 W. R. L. Anderegg, “Expert Credibility in Climate Change”, Proceedings of the National Academy of Sciences Vol. 107 No. 27, 12107-12109 (21 June 2010); DOI: 10.1073/pnas.1003187107 P. T. Doran & M. K. Zimmerman, "Examining the Scientific Consensus on Climate Change", Eos Transactions American Geophysical Union Vol. 90 Issue 3 (2009), 22; DOI: 10.1029/2009EO030002 N. Oreskes, “Beyond the Ivory Tower: The Scientific Consensus on Climate Change”, Science Vol. 306 no. 5702, p. 1686 (3 December 2004); DOI: 10.1126/science.1103618

2. Statement on climate change from 18 scientific associations (2009)

3. AAAS Board Statement on Climate Change (2014)

4. ACS Public Policy Statement: Climate Change (2016-2019)

5. Society Must Address the Growing Climate Crisis Now (2019)

6. Global Climate Change and Human Health (2019)

7. Climate Change: An Information Statement of the American Meteorological Society (2019)

8. American Physical Society (2021)

9. GSA Position Statement on Climate Change (2015)

10. Joint science academies' statement: Global response to climate change (2005)

11. Climate at the National Academies

12. Fourth National Climate Assessment: Volume II (2018)

13. IPCC Fifth Assessment Report, Summary for Policymakers, SPM 1.1 (2014)

14. IPCC Fifth Assessment Report, Summary for Policymakers, SPM 1 (2014)

15. IPCC Sixth Assessment Report, Working Group 1 (2021)

16. IPCC Sixth Assessment Report, Working Group 2 (2022)

17. IPCC Sixth Assessment Report, Working Group 3 (2022)

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Transforming the understanding and treatment of mental illnesses.

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Celebrating 75 Years! Learn More >>

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Decoding the Mind: Basic Science Revolutionizes Treatment of Mental Illnesses

By Linda Brady, Margaret Grabb, Susan Koester, Yael Mandelblat-Cerf, David Panchision, Jonathan Pevsner, Ashlee Van’t-Veer, and Aleksandra Vicentic on behalf of the NIMH Division of Neuroscience and Basic Behavioral Science

March 21, 2024 • 75th Anniversary

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For 75 years, NIMH has transformed the understanding and treatment of mental illnesses through basic and clinical research—bringing hope to millions of people. This Director’s Message, guest written by NIMH’s Division of Neuroscience and Basic Behavioral Science , is part of an anniversary series celebrating this momentous milestone.

The Division of Neuroscience and Basic Behavioral Science (DNBBS) at the National Institute of Mental Health (NIMH) supports research on basic neuroscience, genetics, and basic behavioral science. These are foundational pillars in the quest to decode the human mind and unravel the complexities of mental illnesses.

At NIMH, we are committed to supporting and conducting genomics research as a priority research area . As the institute celebrates its 75th Anniversary , we are spotlighting DNBBS-supported efforts connecting genes to cells to circuits to behavior that have led to a wealth of discoveries and knowledge that can improve the diagnosis, treatment, and prevention of mental illnesses.

Making gene discoveries

Medical conditions often run in families. For instance, if someone in your immediate family has high blood pressure, you are more likely to have it too. It is the same with mental disorders—often they run in families. NIMH is supporting research into human genetics to better understand why this occurs. This research has already led to the discovery of hundreds of gene variants that make us more or less likely to develop a mental disorder.

There are two types of genetic variation: common and rare. Common variation refers to DNA changes often seen in the general population, whereas rare variation is DNA changes found in only a small proportion of the population. Individually, most common gene variants have only a minor impact on the risk for a mental disorder. Instead, most disorders result from many common gene variants that, together, contribute to the risk for and severity of that disorder.

NIMH is committed to uncovering the role of genes in mental disorders with the aim of improving the lives of people who experience them. One of the many ways NIMH contributes to the discovery of common gene variants is by supporting the Psychiatric Genomics Consortium (PGC)   . The consortium of almost 1,000 scientists across the globe, including ones in the NIMH Intramural Research Program and others conducting NIMH-supported research, is one of the largest and most innovative biological investigations in psychiatry.

Global collaborations such as the PGC are critical to amassing the immense sample sizes needed to identify common gene variants. Data from the consortium’s almost one million participants have already led to transformative insights about genetic contributors to mental illnesses and the genetic relationships of these illnesses to each other. To date, studies conducted as part of the consortium have uncovered common variation in over a dozen mental illnesses.

In contrast to common gene variants, rare gene variants are very uncommon in the general population. When they do occur, they often have a major impact on the occurrence of an illness, particularly when they disrupt gene function or regulation. Rare variants involving mutations in a single gene have been linked to several mental disorders, often through NIMH-supported research. For instance, a recent NIMH-funded study found that rare variation in 10 genes substantially increased the risk for schizophrenia. However, it is important to note that genetics is not destiny; even rare variants only raise the risk for mental disorders, but many other factors, including your environment and experiences, play important roles as well.

Because of the strong interest among researchers and the public in understanding how genes translate to changes in the brain and behavior, NIMH has developed a list of human genes associated with mental illnesses. These genes were identified through rare variation studies and are meant to serve as a resource for the research community. The list currently focuses on rare variants, but NIMH plans to continue expanding it as evidence accumulates for additional gene variants (rare or common).

Moreover, mental illnesses are a significant public health burden worldwide . For this reason, NIMH investments in genomics research extend across the globe. NIMH has established the Ancestral Populations Network (APN) to make genomics studies more diverse and shed light on how genetic variation contributes to mental disorders across populations. APN currently includes seven projects with more than 100 researchers across 25 sites worldwide.

Connecting biology to behavior

While hundreds of individual genes have been linked to mental illnesses, the function of most of these genes in the brain remains poorly understood. But high-tech advances and the increased availability of computational tools are enabling researchers to begin unraveling the intricate roles played by genes.

In addition to identifying genetic variation that raises the risk for mental illnesses, NIMH supports research that will help us understand how genes contribute to human behavior. This information is critical to discovering approaches to diagnose, treat, and ultimately prevent or cure mental illnesses.

An NIMH-funded project called the PsychENCODE consortium   focuses on understanding how genes impact brain function. PsychENCODE is furthering knowledge of how gene risk maps onto brain function and dysfunction by cataloging genomic elements in the human brain and studying the actions of different cell types. The PsychENCODE dataset currently includes multidimensional genetic data from the postmortem brains of thousands of people with and without mental disorders.

Findings from the first phase of PsychENCODE were published as a series of 11 papers   examining functional genomics in the developing and adult brains and in mental disorders. A second batch of PsychENCODE papers will be published later this year. These findings help clarify the complex relationships between gene variants and the biological processes they influence.

PsychENCODE and other NIMH-supported projects are committed to sharing biospecimens quickly and openly to help speed research and discovery.

Facilitating these efforts is the NIMH Repository and Genomics Resource (NRGR)   , where samples are stored and shared. NRGR includes hundreds of thousands of samples, such as DNA, RNA, and cell lines, from people with and without mental disorders, along with demographic and diagnostic information.

Another NIMH initiative to connect risk genes to brain function is Scalable and Systematic Neurobiology of Psychiatric and Neurodevelopmental Disorder Risk Genes (SSPsyGene) . This initiative uses cutting-edge techniques to characterize the biological functions of 250 mental health risk genes—within the cells where they are expressed—to better understand how those genes contribute to mental illnesses. By systematically characterizing the biological functions of risk genes in cells, SSPsyGene will empower researchers to learn about biological pathways that may serve as new targets for treatment.

Genes also affect behavior by providing the blueprint for neurons, the basic units of the nervous system. Neurons communicate with each other via circuits in the brain, which enables us to process, integrate, and convey information. NIMH supports many initiatives to study the foundational role of neural networks and brain circuits in shaping diverse mental health-related behaviors like mood, learning, memory, and motivation.

For instance, studies supported through a basic-to-translational science initiative at NIMH focus on modifying neural activity to improve cognitive, emotional, and social processing  . Similarly, another new funding opportunity encourages studies in humans and animals examining how emotional and social cues are represented across brain circuits  to help address a core deficit in many mental disorders. These studies will increase understanding of the biological mechanisms that support behavior throughout life and offer interventions to improve these functions in healthy and clinical populations.

Developing treatments and therapeutics

The gene discovery and biology-to-behavior programs described here will lay the foundation for delivering novel therapeutics. To be prepared to rapidly implement findings from this research, NIMH supports several initiatives to identify behavioral and biological markers for use in clinical studies and increase our ability to translate research into practice.

Through its therapeutics discovery research programs , NIMH advances early stage discovery and development studies in humans and early efficacy trials for mental disorders. Taking these efforts a step further, NIMH supports the National Cooperative Drug Discovery/Development Groups for the Treatment of Mental Disorders , which encourage public–private partnerships to accelerate the discovery and development of novel therapeutics and new biomarkers for use in human trials. Moreover, NIMH is one of several institutes and centers in the NIH Blueprint Neurotherapeutics Network  , launched to enable neuroscientists in academia and biotechnology companies to develop new drugs for nervous system disorders.

For the treatments of tomorrow, NIMH is building a new research program called Pre-Clinical Research on Gene Therapies for Rare Genetic Neurodevelopmental Disorders  , which encourages early stage research to optimize gene therapies to treat disorders with prominent cognitive, social, or affective impairment. In parallel, NIMH’s Planning Grants for Natural History Studies of Rare Genetic Neurodevelopmental Disorders  encourage the analysis of pre-existing data from people with rare disorders to learn about disease progression and enable future clinical trials with these populations.

NIMH's Division of Neuroscience and Basic Behavioral Science supports many different research projects that help us learn about genes and gene functions, how the brain develops and works, and impacts on behavior. By investing in basic neuroscience, genetics, and behavioral research, we're trying to find new targets for treatment and develop better therapies for mental disorders. We're hopeful these efforts will lead to new ways to treat and prevent mental illnesses in the near future and, ultimately, improve the lives of people in this country and across the globe.

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Title: a survey on long video generation: challenges, methods, and prospects.

Abstract: Video generation is a rapidly advancing research area, garnering significant attention due to its broad range of applications. One critical aspect of this field is the generation of long-duration videos, which presents unique challenges and opportunities. This paper presents the first survey of recent advancements in long video generation and summarises them into two key paradigms: divide and conquer temporal autoregressive. We delve into the common models employed in each paradigm, including aspects of network design and conditioning techniques. Furthermore, we offer a comprehensive overview and classification of the datasets and evaluation metrics which are crucial for advancing long video generation research. Concluding with a summary of existing studies, we also discuss the emerging challenges and future directions in this dynamic field. We hope that this survey will serve as an essential reference for researchers and practitioners in the realm of long video generation.

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March 29, 2024

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Scientists close in on a pain-free method for controlling type 1 diabetes

by Vittoria D'Alessio, Horizon: The EU Research & Innovation Magazine

Lee Calladine pricks his skin with a needle up to eight times a day to give himself an injection of insulin—the hormone that keeps his type 1 diabetes in check. Without insulin, his blood-sugar levels would become dangerously high and eventually fatal.

Calladine, aged 54, has been diabetic for 25 years and the insulin top-up routine is so familiar to him that he's long past feeling squeamish about piercing his skin . Still, the practice is burdensome.

"I have to rotate where I do my injections," said Calladine, who lives in the UK city of Portsmouth and works as an event coordinator for the Diabetes Research & Wellness Foundation. "If you inject the same place too often, you damage the tissue and get a lump. And if you then inject into that lump, the insulin won't get absorbed."

While a cure for type 1 diabetes is the holy grail , another game changer for Calladine and millions of other people like him around the world would be the ability to deliver insulin without needles or syringes.

That's where Professor David Fernandez Rivas, a bioengineer at the University of Twente in the Netherlands, believes he can help.

Fernandez Rivas leads a research project to develop a method to deposit liquids into skin and other soft materials using compression rather than needles.

The technique is known as BuBble Gun, which is also the name given to the five-year project that will run until the end of 2024. Fernandez Rivas invented the BuBble Gun with his research team, which is now refining the technology in the hopes of turning it into a reality.

Speedy squeeze

While electronic pumps that release insulin into the body throughout the day do exist, they are imperfect and still require a cannula needle to be inserted at the connection point.

With BuBble Gun, a laser beam is directed at the fluid medicine in a glass cartridge, heating it until it boils and creates a bubble. This bubble grows until it squeezes the liquid at high velocity —30 to 100 meters a second—out of its tube and, in the case of a medicine, into the skin.

Rather than piercing the skin, the way a needle would, the drug is pushed between skin cells. This limits damage both to the skin and to the cells beneath it.

"The liquid effectively becomes the needle," said Fernandez Rivas, who moved to the Netherlands from Cuba in 2007 with a first degree in nuclear engineering and has been working in bioengineering and green technology ever since.

This approach could be a relief not just for 9 million people worldwide including Calladine who have type 1 diabetes but also for millions of others who have unrelated conditions that also require regular injections.

Other potential beneficiaries are the 25% of people who are scared of needles and who can, as a result, avoid certain medical interventions including vaccination.

"Removing pain and fear from the injection process will have a big impact on a lot of people with needle phobia," said Fernandez Rivas.

Tissue challenges

Most injections penetrate the body until they reach muscle. These are regarded as the simplest injections to administer and the dose gradually diffuses out of the muscle into the body's blood-circulation system.

Many drugs, however, can do the job just as well—or perhaps better—when delivered into the more superficial layers of skin.

Fernandez Rivas is confident that many vaccines, for instance, would work just as well when delivered between the layers of the skin . Currently, they tend to be injected into muscle.

This means BuBble Gun has potential uses beyond insulin delivery.

A key technical challenge tackled by the research team relates to skin depth, which varies depending on age, gender, ethnicity and lifestyle. Smoking, for instance, makes the skin thinner.

As a result, the "gun" pressure needs to be adjusted to take these differences into consideration.

"You need the jet of fluid to penetrate the skin to just the right depth without splashback or seeping into nearby tissue or material, which would alter the dose unpredictably," said Fernandez Rivas.

The researchers are still working on precisely controlling the drug jet as it enters soft tissue.

Since 2018 , they've been conducting laboratory experiments on materials that simulate skin as well as on real skin tissue. Tests on human tissue have been in progress since 2022.

If all goes well, trials on healthy human volunteers will start later this year.

Planned prototype

The BuBle Gun team has created a startup company called FlowBeams . Through this, the researchers hope that a prototype of the gun will be ready to showcase to potential industry partners before 2025.

Fernandez Rivas foresees a time eventually when diabetes patients will be able to use a modified version that incorporates the microjet technology into a skin patch.

The patch would include a sensor that both tests blood-sugar levels on a continual basis and pushes insulin into the body as the need arises.

"Imagine how this would transform the life of an anxious parent who wakes up multiple times in the night to check their child with diabetes isn't having a blood-sugar swing in their sleep," Fernandez Rivas said.

• EU diabetes research
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March 25, 2024

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Researchers reveal new method for calculating mechanical properties of solids using machine learning

by Oleg Sherbakov, Skolkovo Institute of Science and Technology

A research team from Skoltech introduced a new method that takes advantage of machine learning for studying the properties of polycrystals, composites, and multiphase systems. It attained high accuracy, nearly as good as that of quantum-mechanical methods, which are only applicable to materials with less than a few hundred atoms.

The new method also benefits from active learning on local atomic environments. The paper is published in the Advanced Theory and Simulations journal.

"Many industrial materials are synthesized as polycrystals or multiphase systems. They contain both a single crystal and amorphous components between single crystal grains. The large number of atoms makes it hard to calculate the properties of these systems using modern quantum-mechanical methods. Density functional theory can only be applied to materials with a few hundred atoms."

"To address the problem, we use a machine-learning approach based on Moment Tensor Potentials (MTP). These potentials have also been developed at Skoltech under the guidance of Professor Alexander Shapeev," commented Faridun Jalolov, the leading author of the study and a Skoltech Ph.D. student in the Materials Science and Engineering program.

As compared to other solutions, the authors see the potential of the new method in active learning on local atomic environments. When calculating a large structure with many hundreds of thousands of atoms, the MTP identifies which atom makes a mistake in the calculations, or is calculated incorrectly. The reason for this could be the limited training dataset, which prevents all possible system configurations from being considered.

A local environment of this atom is then "cut out," and its energy is calculated using quantum mechanics. Afterward, the data is added back to the training set for further learning. As the on-the-fly learning progresses, the calculations continue until they come across another configuration that needs to be included in the training process. Other known machine-learning potentials cannot be learned on small local parts of large structures, which limits their applicability and accuracy.

"As an example, we studied the mechanical properties of diamond polycrystals, which are the hardest naturally occurring materials and often used in industry—for example, when manufacturing drilling equipment for oil wells. The results show that the mechanical properties of these polycrystalline diamonds depend on the grain size —the bigger the grain, the more similar the properties are to those of a single crystal diamond," continued Jalolov.

The authors pointed out that this approach will allow for studying the mechanical properties of nonsingle crystalline materials that are typically synthesized and used in experiments, as well as conducting comprehensive studies of polycrystalline and composite materials and obtaining data as close to experimental results as possible.

"In actual use, materials that are not perfect crystals are frequently employed due to their inability for perfect crystals to meet the requirements of a specific piece of equipment fully."

"A good example of this is tungsten carbide and cobalt. By adding cobalt to tungsten carbide , the material becomes more crack-resistant, making it so valuable in applications. The new method will allow us to investigate the causes and ways of altering the mechanical properties of these multiphase systems on an atomic level," said Alexander Kvashnin, the head of the research and a professor at the Energy Transition Center.

Provided by Skolkovo Institute of Science and Technology

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