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The Practice of Experimental Psychology: An Inevitably Postmodern Endeavor

The aim of psychology is to understand the human mind and behavior. In contemporary psychology, the method of choice to accomplish this incredibly complex endeavor is the experiment. This dominance has shaped the whole discipline from the self-concept as an empirical science and its very epistemological and theoretical foundations, via research practice and the scientific discourse to teaching. Experimental psychology is grounded in the scientific method and positivism, and these principles, which are characteristic for modern thinking, are still upheld. Despite this apparently stalwart adherence to modern principles, experimental psychology exhibits a number of aspects which can best be described as facets of postmodern thinking although they are hardly acknowledged as such. Many psychologists take pride in being “real natural scientists” because they conduct experiments, but it is particularly difficult for psychologists to evade certain elements of postmodern thinking in view of the specific nature of their subject matter. Postmodernism as a philosophy emerged in the 20th century as a response to the perceived inadequacy of the modern approach and as a means to understand the complexities, ambiguities, and contradictions of the times. Therefore, postmodernism offers both valuable insights into the very nature of experimental psychology and fruitful ideas on improving experimental practice to better reflect the complexities and ambiguities of human mind and behavior. Analyzing experimental psychology along postmodern lines begins by discussing the implications of transferring the scientific method from fields with rather narrowly defined phenomena—the natural sciences—to a much broader and more heterogeneous class of complex phenomena, namely the human mind and behavior. This ostensibly modern experimental approach is, however, per se riddled with postmodern elements: (re-)creating phenomena in an experimental setting, including the hermeneutic processes of generating hypotheses and interpreting results, is no carbon copy of “reality” but rather an active construction which reflects irrevocably the pre-existing ideas of the investigator. These aspects, analyzed by using postmodern concepts like hyperreality and simulacra, did not seep in gradually but have been present since the very inception of experimental psychology, and they are necessarily inherent in its philosophy of science. We illustrate this theoretical analysis with the help of two examples, namely experiments on free will and visual working memory. The postmodern perspective reveals some pitfalls in the practice of experimental psychology. Furthermore, we suggest that accepting the inherently fuzzy nature of theoretical constructs in psychology and thinking more along postmodern lines would actually clarify many theoretical problems in experimental psychology.

Introduction

Postmodernism is, in essence, an attempt to achieve greater clarity in our perception, thinking, and behavior by scrutinizing their larger contexts and preconditions, based on the inextricably intertwined levels of both the individual and the society. Psychology also studies the human mind and behavior, which indicates that psychology should dovetail with postmodern approaches. In the 1990s and early 2000s, several attempts were made to introduce postmodern thought as potentially very fruitful ideas into general academic psychology ( Jager, 1991 ; Kvale, 1992 ; Holzman and Morss, 2000 ; Holzman, 2006 ). However, overall they were met with little response.

Postmodern thoughts have been taken up by several fringe areas of academic psychology, e.g., psychoanalysis ( Leffert, 2007 ; Jiménez, 2015 ; but see Holt, 2005 ), some forms of therapy and counseling ( Ramey and Grubb, 2009 ; Hansen, 2015 ), humanistic ( Krippner, 2001 ), feminist and gender ( Hare-Mustin and Marecek, 1988 ; Sinacore and Enns, 2005 ), or cultural psychology ( Gemignani and Peña, 2007 ).

However, there is resistance against suggestions to incorporate postmodern ideas into the methodology and the self-perception of psychology as academic—and scientific!—discipline. In fact, postmodern approaches are often rejected vehemently, sometimes even very vocally. For instance, Gergen (2001) argued that the “core tenets” of postmodernism are not at odds with those of scientific psychology but rather that they can enrich the discipline by opening up new possibilities. His suggestions were met with reservation and were even outright rejected on the following grounds: postmodernism, “like anthrax of the intellect, if allowed [our italics] into mainstream psychology, […] will poison the field” ( Locke, 2002 , 458), that it “wishes to return psychology to a prescientific subset of philosophy” ( Kruger, 2002 , 456), and that psychology “needs fewer theoretical and philosophical orientations, not more” ( Hofmann, 2002 , 462; see also Gergen ’s, 2001 , replies to the less biased and more informed commentaries on his article).

In the following years, and continuing the so-called science wars of the 1990s ( Segerstråle, 2000 ), several other attacks were launched against a perceived rise or even dominance of postmodern thought in psychology. Held(2007 ; see also the rebuttal by Martin and Sugarman, 2009 ) argued that anything postmodern would undermine rationality and destroy academic psychology. Similarly, postmodernism was identified—together with “radical environmentalism” and “pseudoscience” among other things—as a “key threat to scientific psychology” ( Lilienfeld, 2010 , 282), or as “inimical to progress in the psychology of science” ( Capaldi and Proctor, 2013 , 331). The following advice was given to psychologists: “We [psychologists] should also push back against the pernicious creep of these untested concepts into our field” ( Tarescavage, 2020 , 4). Furthermore, the term “postmodern” is even employed as an all-purpose invective in a popular scientific book by psychologist Steven Pinker (2018) .

Therefore, it seems that science and experimental psychology on the one hand and postmodern thinking on the other are irreconcilable opposites. However, following Gergen (2001) and Holtz (2020) , we argue that this dichotomy is only superficial because postmodernism is often misunderstood. A closer look reveals that experimental psychology contains many postmodern elements. Even more, there is reason to assume that a postmodern perspective may be beneficial for academic psychology: First, the practice of experimental psychology would be improved by integrating postmodern thinking because it reveals a side of the human psyche for which experimental psychology is mostly blind. Second, the postmodern perspective can tell us much about the epistemological and social background of experimental psychology and how this affects our understanding of the human psyche.

A Postmodern Perspective on Experimental Psychology

Experimental psychology and the modern scientific worldview.

It lies within the nature of humans to try to find out more about themselves and their world, but the so-called Scientific Revolution of the early modern period marks the beginning of a new era in this search for knowledge. The Scientific Revolution, which has led to impressive achievements in the natural sciences and the explanation of the physical world (e.g., Olby et al., 1991 ; Henry, 1997 ; Cohen, 2015 ; Osterlind, 2019 ), is based on the following principle: to “measure what can be measured and make measurable what cannot be measured.” This famous appeal—falsely attributed to Galileo Galilei but actually from the 19th century ( Kleinert, 2009 )—illustrates the two fundamental principles of modern science: First, the concept of “measurement” encompasses the idea that phenomena can be quantified, i.e., expressed numerically. Second, the concept of “causal connections” pertains to the idea that consistent, non-random relationships can be established between measurable phenomena. Quantification allows that relationships between phenomena can be expressed, calculated, and predicted in precise mathematical and numerical terms.

However, there are two important issues to be aware of. First, while it is not difficult to measure “evident” aspects, such as mass and distance, more complex phenomena cannot be measured easily. In such cases, it is therefore necessary to find ways of making these “elusive” phenomena measurable. This can often only be achieved by reducing complex phenomena to their simpler—and measurable!—elements. For instance, in order to measure memory ability precisely, possible effects of individual preexisting knowledge which introduce random variance and thus impreciseness have to be eliminated. Indeed, due to this reason, in many memory experiments, meaningless syllables are used as study material.

Second, it is not difficult to scientifically prove a causal relationship between a factor and an outcome if the relationship is simple, that is, if there is only one single factor directly influencing the outcome. In such a case, showing that a manipulation of the factor causes a change in the outcome is clear evidence for a causal relationship because there are no other factors which may influence the outcome as well. However, in situations where many factors influence an outcome in a complex, interactive way, proving a causal relationship is much more difficult. To prove the causal effect of one factor in such a situation the effects of all other factors—called confounding factors from the perspective of the factor of interest—have to be eliminated so that a change in the outcome can be truly attributed to a causal effect of the factor of interest. However, this has an important implication: The investigator has to divide the factors present in a given situation into interesting versus non-interesting factors with respect to the current context of the experiment. Consequently, while experiments reveal something about local causal relationships, they do not necessarily provide hints about the net effect of all causal factors present in the given situation.

The adoption of the principles of modern science has also changed psychology. Although the beginnings of psychology—as the study of the psyche —date back to antiquity, psychology as an academic discipline was established in the mid to late 19th century. This enterprise was also inspired by the success of the natural sciences, and psychology was explicitly modeled after this example by Wilhelm Wundt—the “father of experimental psychology”—although he emphasized the close ties to the humanities as well. The experiment quickly became the method of choice. There were other, more hermeneutic approaches during this formative phase of modern psychology, such as psychoanalysis or introspection according to the Würzburg School, but their impact on academic psychology was limited. Behaviorism emerged as a direct reaction against these perceived unscientific approaches, and its proponents emphasized the scientific character of their “new philosophy of psychology.” It is crucial to note that in doing so they also emphasized the importance of the experiment and the necessity of quantifying directly observable behavior in psychological research. Behaviorism quickly became a very influential paradigm which shaped academic psychology. Gestalt psychologists, whose worldview is radically different from behaviorism, also relied on experiments in their research. Cognitive psychology, which followed, complemented, and partly superseded behaviorism, relies heavily on the experiment as a means to gain insight into mental processes, although other methods such as modeling are employed as well. Interestingly, there is a fundamental difference between psychoanalysis and humanistic psychology, which do not rely on the experiment, and the other above-mentioned approaches as the former focus on the psychic functioning of individuals, whereas the latter focus more on global laws of psychic functioning across individuals. This is reflected in the fact that psychological laws in experimental psychology are established on the arithmetic means across examined participants—a difference we will elaborate on later in more detail. Today, psychology is the scientific —in the sense of empirical-quantitative—study of the human mind and behavior, and the experiment is often considered the gold standard in psychological research (e.g., Mandler, 2007 ; Goodwin, 2015 ; Leahey, 2017 ).

The experiment is closely associated with the so-called scientific method ( Haig, 2014 ; Nola and Sankey, 2014 ) and the epistemological tenets philosophy of positivism—in the sense as Martin (2003) ; Michell (2003) , and Teo (2018) explain—which sometimes exhibit characteristics of naïve empiricism. Roughly speaking, the former consists of observing, formulating hypotheses, and testing these hypotheses in experiments. The latter postulates that knowledge is based on sensory experience, that it is testable, independent of the investigator and therefore objective as it accurately depicts the world as it is. This means that in principle all of reality can not only be measured but eventually be entirely explained by science. This worldview is attacked by postmodern thinkers who contend that the world is far more complex and that the modern scientific approach cannot explain all of reality and its phenomena.

The Postmodern Worldview

Postmodern thinking (e.g., Bertens, 1995 ; Sim, 2011 ; Aylesworth, 2015 ) has gained momentum since the 1980s, and although neither the term “postmodernism” nor associated approaches can be defined in a unanimous or precise way, they are characterized by several intertwined concepts, attitudes, and aims. The most basic trait is a general skepticism and the willingness to question literally everything from the ground up—even going so far as to question not only the foundation of any idea, but also the question itself. This includes the own context, the chosen premises, thinking, and the use of language. Postmodernism therefore has a lot in common with science’s curiosity to understand the world: the skeptical attitude paired with the desire to discover how things really are.

Postmodern investigations often start by looking at the language and the broader context of certain phenomena due to the fact that language is the medium in which many of our mental activities—which subsequently influence our behavior—take place. Thus, the way we talk reveals something about how and why we think and act. Additionally, we communicate about phenomena using language, which in turn means that this discourse influences the way we think about or see those phenomena. Moreover, this discourse is embedded in a larger social and historical context, which also reflects back on the use of language and therefore on our perception and interpretation of certain phenomena.

Generally speaking, postmodern investigations aim at detecting and explaining how the individual is affected by societal influences and their underlying, often hidden ideas, structures, or mechanisms. As these influences are often fuzzy, contradictory, and dependent on their context, the individual is subject to a multitude of different causalities, and this already complex interplay is further complicated by the personal history, motivations, aims, or ways of thinking of the individual. Postmodernism attempts to understand all of this complexity as it is in its entirety.

The postmodern approaches have revealed three major general tendencies which characterize the contemporary world: First, societies and the human experience since the 20th century have displayed less coherence and conversely a greater diversity than the centuries before in virtually all areas, e.g., worldviews, modes of thinking, societal structures, or individual behavior. Second, this observation leads postmodern thinkers to the conclusion that the grand narratives which dominated the preceding centuries and shaped whole societies by providing frames of references have lost—at least partially—their supremacy and validity. Examples are religious dogmas, nationalism, industrialization, the notion of linear progress—and modern science because it works according to certain fundamental principles. Third, the fact that different but equally valid perspectives, especially on social phenomena or even whole worldviews, are possible and can coexist obviously affects the concepts of “truth,” “reality,” and “reason” in such a way that these concepts lose their immutable, absolute, and universal or global character, simply because they are expressions and reflections of a certain era, society, or worldview.

At this point, however, it is necessary to clarify a common misconception: Interpreting truth, reality, or reason as relative, subjective, and context-dependent—as opposed to absolute, objective, and context-independent—does naturally neither mean that anything can be arbitrarily labeled as true, real, or reasonable, nor, vice versa, that something cannot be true, real, or reasonable. For example, the often-quoted assumption that postmodernism apparently even denies the existence of gravity or its effects as everything can be interpreted arbitrarily or states that we cannot elucidate these phenomena with adequate accuracy because everything is open to any interpretation ( Sokal, 1996 ), completely misses the point.

First, postmodernism is usually not concerned with the laws of physics and the inanimate world as such but rather focuses on the world of human experience. However, the phenomenon itself, e.g., gravity, is not the same as our scientific knowledge of phenomena—our chosen areas of research, methodological paradigms, data, theories, and explanations—or our perception of phenomena, which are both the results of human activities. Therefore, the social context influences our scientific knowledge, and in that sense scientific knowledge is a social construction ( Hodge, 1999 ).

Second, phenomena from human experience, although probably more dependent on the social context than physical phenomena, cannot be interpreted arbitrarily either. The individual context—such as the personal history, motivations, aims, or worldviews—determines whether a certain behavior makes sense for a certain individual in a certain situation. As there are almost unlimited possible backgrounds, this might seem completely random or arbitrary from an overall perspective. But from the perspective of an individual the phenomenon in question may be explained entirely by a theory for a specific—and not universal—context.

As described above, the postmodern meta-perspective directly deals with human experience and is therefore especially relevant for psychology. Moreover, any discipline—including the knowledge it generates—will certainly benefit from understanding its own (social) mechanisms and implications. We will show below that postmodern thinking not only elucidates the broader context of psychology as an academic discipline but rather that experimental psychology exhibits a number of aspects which can best be described as facets of postmodern thinking although they are not acknowledged as such.

The Postmodern Context of Experimental Psychology

Paradoxically, postmodern elements have been present since the very beginning of experimental psychology although postmodernism gained momentum only decades later. One of the characteristics of postmodernism is the transplantation of certain elements from their original context to new contexts, e.g., the popularity of “Eastern” philosophies and practices in contemporary “Western” societies. These different elements are often juxtaposed and combined to create something new, e.g., new “westernized” forms of yoga ( Shearer, 2020 ).

Similarly, the founders of modern academic psychology took up the scientific method, which was originally developed in the context of the natural sciences, and transplanted it to the study of the human psyche in the hope to repeat the success of the natural sciences. By contrast, methods developed specifically in the context of psychology such as psychoanalysis ( Wax, 1995 ) or introspection according to the Würzburg School ( Hackert and Weger, 2018 ) have gained much less ground in academic psychology. The way we understand both the psyche and psychology has been shaped to a great extent by the transfer of the principles of modern science, namely quantitative measurement and experimental methods, although it is not evident per se that this is the best approach to elucidate mental and behavioral phenomena. Applying the methods of the natural sciences to a new and different context, namely to phenomena pertaining to the human psyche , is a truly postmodern endeavor because it juxtaposes two quite distinct areas and merges them into something new—experimental psychology.

The postmodern character of experimental psychology becomes evident on two levels: First, the subject matter—the human psyche —exhibits a postmodern character since mental and behavioral phenomena are highly dependent on the idiosyncratic contexts of the involved individuals, which makes it impossible to establish unambiguous general laws to describe them. Second, experimental psychology itself displays substantial postmodern traits because both its method and the knowledge it produces—although seemingly objective and rooted in the modern scientific worldview—inevitably contain postmodern elements, as will be shown below.

The Experiment as Simulacrum

The term “simulacrum” basically means “copy,” often in the sense of “inferior copy” or “phantasm/illusion.” However, in postmodern usage “simulacrum” has acquired a more nuanced and concrete meaning. “Simulacrum” is a key term in the work of postmodern philosopher Jean Baudrillard, who arguably presented the most elaborate theory on simulacra (1981/1994). According to Baudrillard, a simulacrum “is the reflection of a profound [‘real’] reality” (16/6). Simulacra, however, are more than identical carbon copies because they gain a life of their own and become “real” in the sense of becoming an own entity. For example, the personality a pop star shows on stage is not “real” in the sense that it is their “normal,” off-stage personality, but it is certainly “real” in the sense that it is perceived by the audience even if they are aware that it might be an “artificial” personality. Two identical cars can also be “different” for one might be used as a means of transportation while the other might be a status symbol. Even an honest video documentation of a certain event is not simply a copy of the events that took place because it lies within the medium video that only certain sections can be recorded from a certain perspective. Additionally, the playback happens in other contexts as the original event, which may also alter the perception of the viewer.

The post-structuralist—an approach closely associated with postmodernism—philosopher Roland Barthes pointed out another important aspect of simulacra. He contended that in order to understand something—an “object” in Barthes’ terminology—we necessarily create simulacra because we “ reconstruct [our italics] an ‘object’ in such a way as to manifest thereby the rules of functioning [⋯] of this object” ( Barthes, 1963 , 213/214). In other words, when we investigate an object—any phenomenon, either material, mental, or social—we have to perceive it first. This means that we must have some kind of mental representation of the phenomenon/object—and it is crucial to note that this representation is not the same thing as the “real” object itself. All our mental operations are therefore not performed on the “real” object but on mental representations of the object. We decompose a phenomenon in order to understand it, that is, we try to identify its components. In doing so, we effect a change in the object because our phenomenon is no longer the original phenomenon “as it is” for we are performing a mental operation on it, thereby transforming the original phenomenon. Identifying components may be simple, e.g., dividing a tree into roots, trunk, branches, and leaves may seem obvious or even “natural” but it is nevertheless us as investigators who create this structure—the tree itself is probably not aware of it. Now that we have established this structure, we are able to say that the tree consists of several components and name these components. Thus, we have introduced “new” elements into our understanding of the tree. This is the important point, even though the elements, i.e., the branches and leaves themselves “as they are,” have naturally always been “present.” Our understanding of “tree” has therefore changed completely because a tree is now something which is composed of several elements. In that sense, we have changed the original phenomenon by adding something—and this has all happened in our thinking and not in the tree itself. It is also possible to find different structures and different components for the tree, e.g., the brown and the green, which shows that we construct this knowledge.

Next, we can investigate the components to see how they interact with and relate to each other and to the whole system. Also, we can work out their functions and determine the conditions under which a certain event will occur. We can even expand the scope of our investigation and examine the tree in the context of its ecosystem. But no matter what we do or how sophisticated our investigation becomes, everything said above remains true here, too, because neither all these actions listed above nor the knowledge we gain from them are the object itself. Rather, we have added something to the object and the more we know about our object, the more knowledge we have constructed. This addition is what science—gaining knowledge—is all about. Or in the words of Roland Barthes: “the simulacrum is intellect added to object, and this addition has an anthropological value, in that it is man himself, his history, his situation, his freedom and the very resistance which nature offers to his mind” (1963/1972, 214/215).

In principle, this holds truth regarding all scientific investigations. But the more complex phenomena are, the more effort and personal contribution is required on behalf of the investigator to come up with structures, theories, or explanations. Paraphrasing Barthes: When dealing with complex phenomena, more intellect must be added to the object, which means in turn that there are more possibilities for different approaches and perspectives, that is, the constructive element becomes larger. As discussed previously, this does not mean that investigative and interpretative processes are arbitrary. But it is clear from this train of thought that “objectivity” or “truth” in a “positivist,” naïve empiricist “realist,” or absolute sense are not attainable. Nevertheless, we argue here that this is not a drawback, as many critics of postmodernism contend (see above), but rather an advantage because it allows more accurate scientific investigations of true-to-life phenomena, which are typically complex in the case of psychology.

The concepts of simulacra by Baudrillard and Barthes can be combined to provide a description of the experiment in psychology. Accordingly, our understanding of the concept of the “simulacrum” entails that scientific processes—indeed all investigative processes—necessarily need to duplicate the object of their investigation in order to understand it. In doing so, constructive elements are necessarily introduced. These elements are of a varying nature, which means that investigations of one and the same phenomenon may differ from each other and different investigations may find out different things about the phenomenon in question. These investigations then become entities on their own—in the Baudrillardian sense—and therefore simulacra.

In a groundbreaking article on “the meaning and limits of exact science” physicist Max Planck stated that “[a]n experiment is a question which science poses to nature, and a measurement is the recording of nature’s answer” ( Planck, 1949 , 325). The act of “asking a question” implies that the person asking the question has at least a general idea of what the answer might look like ( Heidegger, 1953 , §2). For example: When asking someone for their name, we obviously do not know what they are called, but we assume that they have a name and we also have an idea of how the concept “name” works. Otherwise we could not even conceive, let alone formulate, and pose our question. This highlights how a certain degree of knowledge and understanding of a concept is necessary so that we are able to ask questions about it. Likewise, we need to have a principal idea or assumption of possible mechanisms if we want to find out how more complex phenomena function. It is—at least at the beginning—irrelevant whether these ideas are factually correct or entirely wrong, for without them we would be unable to approach our subject matter in the first place.

The context of the investigator—their general worldview, their previous knowledge and understanding, and their social situation—obviously plays an important part in the process of forming a question which can be asked in the current research context. Although this context may be analyzed along postmodern lines in order to find out how it affects research, production of knowledge, and—when the knowledge is applied—possible (social) consequences, there is a much more profound implication pertaining to the very nature of the experiment as a means to gain knowledge.

Irrespective of whether it is a simple experiment in physics such as Galileo Galilei’s or an experiment on a complex phenomenon from social or cognitive psychology, the experiment is a situation which is specifically designed to answer a certain type of questions, usually causal relationships, such as: “Does A causally affect B?” Excluding the extremely complex discussion on the nature of causality and causation (e.g., Armstrong, 1997 ; Pearl, 2009 ; Paul and Hall, 2013 ), it is crucial to note that we need the experiment as a tool to answer this question. Although we may theorize about a phenomenon and infer causal relationships simply by observing, we cannot—at least according to the prevailing understanding of causality in the sciences—prove causal relationships without the experiment.

The basic idea of the experiment is to create conditions which differ in only one single factor which is suspected as a causal factor for an effect. The influence of all other potential causal relationships is kept identical because they are considered as confounding factors which are irrelevant from the perspective of the research question of the current experiment. Then, if a difference is found in the outcome between the experimental conditions, this is considered as proof that the aspect in question exerts indeed a causal effect. This procedure and the logic behind it are not difficult to understand. However, a closer look reveals that this is actually far from simple or obvious.

To begin with, an experiment is nothing which occurs “naturally” but a situation created for a specific purpose, i.e., an “artificial” situation, because other causal factors exerting influence in “real” life outside the laboratory are deliberately excluded and considered as “confounding” factors. This in itself shows that the experiment contains a substantial postmodern element because instead of creating something it rather re- creates it. This re-creation is of course based on phenomena from the “profound” reality—in the Baudrillardian sense—since the explicit aim is to find out something about this profound reality and not to create something new or something else. However, as stated above, this re-creation must contain constructive elements reflecting the presuppositions, conceptual-theoretical assumptions, and aims of the investigator. By focusing on one factor and by reducing the complexity of the profound reality, the practical operationalization and realization thus reflect both the underlying conceptual structure and the anticipated outcome as they are specifically designed to test for the suspected but hidden or obscured causal relationships.

At this point, another element becomes relevant, namely the all-important role of language, which is emphasized in postmodern thinking (e.g., Harris, 2005 ). Without going into the intricacies of semiotics, there is an explanatory gap ( Chalmers, 2005 )—to borrow a phrase from philosophy of mind—between the phenomenon on the one hand and the linguistic and/or mental representation of it on the other. This relationship is far from clear and it is therefore problematic to assume that our linguistic or mental representations—our words and the concepts they designate—are identical with the phenomena themselves. Although we cannot, at least according to our present knowledge and understanding, fully bridge this gap, it is essential to be aware of it in order to avoid some pitfalls, as will be shown in the examples below.

Even a seemingly simple word like “tree”—to take up once more our previous example—refers to a tangible phenomenon because there are trees “out there.” However, they come in all shapes and sizes, there are different kinds of trees, and every single one of them may be labeled as “tree.” Furthermore, trees are composed of different parts, and the leaf—although part of the tree—has its own word, i.e., linguistic and mental representation. Although the leaf is part of the tree—at least according to our concepts—it is unclear whether “tree” also somehow encompasses “leaf.” The same holds true for the molecular, atomic, or even subatomic levels, where there “is” no tree. Excluding the extremely complex ontological implications of this problem, it has become clear that we are referring to a certain level of granularity when using the word “tree.” The level of granularity reflects the context, aims, and concepts of the investigator, e.g., an investigation of the rain forest as an ecosystem will ignore the subatomic level.

How does this concern experimental psychology? Psychology studies intangible phenomena, namely mental and behavioral processes, such as cognition, memory, learning, motivation, emotion, perception, consciousness, etc. It is important to note that these terms designate theoretical constructs as, for example, memory cannot be observed directly. We may provide the subjects of an experiment a set of words to learn and observe later how many words they reproduce correctly. A theoretical construct therefore describes such relationships between stimulus and behavior, and we may draw conclusions from this observable data about memory. But neither the observable behavior of the subject, the resulting data, nor our conclusions are identical with memory itself.

This train of thought demonstrates the postmodern character of experimental psychology because we construct our knowledge. But there is more to it than that: Even by trying to define a theoretical construct as exactly as possible—e.g., memory as “the process of maintaining information over time” ( Matlin, 2012 , 505) or “the means by which we retain and draw on our past experiences to use this information in the present” ( Sternberg and Sternberg, 2011 , 187)—the explanatory gap between representation and phenomenon cannot be bridged. Rather, it becomes even more complicated because theoretical constructs are composed of other theoretical constructs, which results in some kind of self-referential circularity where constructs are defined by other constructs which refer to further constructs. In the definitions above, for instance, hardly any key term is self-evident and unambiguous for there are different interpretations of the constructs “process,” “maintaining,” “information,” “means,” “retain,” “draw on,” “experiences,” and “use” according to their respective contexts. Only the temporal expressions “over time,” “past,” and “present” are probably less ambiguous here because they are employed as non-technical, everyday terms. However, the definitions above are certainly not entirely incomprehensible—in fact, they are rather easy to understand in everyday language—and it is quite clear what the authors intend to express . The italics indicate constructive elements, which demonstrates that attempts to give a precise definition in the language of science result in fuzziness and self-reference.

Based on a story by Jorge Luis Borges, Baudrillard (1981) found an illustrative allegory: a map so precise that it portrays everything in perfect detail—but therefore inevitably so large that it shrouds the entire territory it depicts. Similarly, Taleb (2007) coined the term “ludic fallacy” for mistaking the model/map—in our context: experiments in psychology—for the reality/territory, that is, a mental or behavioral phenomenon. Similar to the functionality of a seemingly “imprecise” map which contains only the relevant landmarks so the user may find their way, the fuzziness of language poses no problems in everyday communication. So why is it a problem in experimental psychology? Since the nature of theoretical constructs in psychology lies precisely in their very fuzziness, the aim of reaching a high degree of granularity and precision in experimental psychology seems to be unattainable (see the various failed attempts to create “perfect” languages which might depict literally everything “perfectly,” e.g., Carapezza and D’Agostino, 2010 ).

Without speculating about ontic or epistemic implications, it is necessary to be aware of the explanatory gap and to refrain from identifying the experiment and the underlying operationalization with the theoretical construct. Otherwise, this gap is “filled” unintentionally and uncontrollably if the results of an experiment are taken as valid proof for a certain theoretical construct, which is actually fuzzy and potentially operationalizable in a variety of ways. If this is not acknowledged, words, such as “memory,” become merely symbols devoid of concrete meaning, much like a glass bead game—or in postmodern terminology: a hyperreality.

Experiments and Hyperreality

“Hyperreality” is another key term in the work of Jean Baudrillard (1981) and it denotes a concept closely related to the simulacrum. Accordingly, in modern society the simulacra are ubiquitous and they form a system of interconnected simulacra which refer to each other rather than to the real, thereby possibly hiding or replacing the real. Consequently, the simulacra become real in their own right and form a “more real” reality, namely the hyperreality. One may or may not accept Baudrillard’s conception, especially the all-embracing social and societal implications, but the core concept of “hyperreality” is nevertheless a fruitful tool to analyze experimental psychology. We have already seen that the experiment displays many characteristics of a simulacrum, so it is not surprising that the concept of hyperreality is applicable here as well, although in a slightly different interpretation than Baudrillard’s.

The hyperreal character of the experiment can be discussed on two levels: the experiment itself and the discourse wherein it is embedded.

On the level of the experiment itself, two curious observations must be taken into account. First, and in contrast to the natural sciences where the investigator is human and the subject matter (mostly) non-human and usually inanimate, in psychology both the investigator and the subject matter are human. This means that the subjects of the experiment, being autonomous persons, are not malleable or completely controllable by the investigator because they bring their own background, history, worldview, expectations, and motivations. They interpret the situation—the experiment—and act accordingly, but not necessarily in the way the investigator had planned or anticipated ( Smedslund, 2016 ). Therefore, the subjects create their own versions of the experiment, or, in postmodern terminology, a variety of simulacra, which may be more or less compatible with the framework of the investigator. This holds true for all subjects of an experiment, which means that the experiment as a whole may also be interpreted as an aggregation of interconnected simulacra—a hyperreality.

The hyperreal character becomes even more evident because what contributes in the end to the interpretation of the results of the experiment are not the actual performances and results of the individual subjects as they were intended by them but rather how their performances and results are handled, seen, and interpreted by the investigator. Even if the investigator tries to be as faithful as possible and aims at an exact and unbiased measurement—i.e., an exact copy—there are inevitably constructive elements which introduce uncertainty into the experiment. Investigators can never be certain what the subjects were actually doing and thinking so they must necessarily work with interpretations. Or in postmodern terms: Because the actual performances and results of the subjects are not directly available the investigators must deal with simulacra. These simulacra become the investigators’ reality and thus any further treatment—statistical analyses, interpretations, or discussions—becomes a hyperreality, that is, a set of interconnected simulacra which have become “real.”

On the level of the discourse wherein the experiment is embedded, another curious aspect also demonstrates the hyperreal character of experimental psychology. Psychology is, according to the standard definition, the scientific study of mental and behavioral processes of the individual (e.g., Gerrig, 2012 ). This definition contains two actually contradictory elements. On the one hand, the focus is on processes of the individual. On the other hand, the—scientific—method to elucidate these processes does not look at individuals per se but aggregates their individual experiences and transforms them into a “standard” experience. The results from experiments, our knowledge of the human psyche, reflect psychological functioning at the level of the mean across individuals. And even if we assume that the mean is only an estimator and not an exact description or prediction, the question remains open how de-individualized observations are related to the experience of an individual. A general mechanism, a law—which was discovered by abstracting from a multitude of individual experiences—is then ( re -)imposed in the opposite direction back onto the individual. In other words, a simulacrum—namely, the result of an experiment—is viewed and treated as reality, thus becoming hyperreal. Additionally, and simply because it is considered universally true, this postulated law acquires thereby a certain validity and “truth”—often irrespective of its actual, factual, or “profound” truth—on its own. Therefore, it can become impossible to distinguish between “profound” and “simulacral” truth, which is the hallmark of hyperreality.

Measuring the Capacity of the Visual Working Memory

Vision is an important sensory modality and there is extensive research on this area ( Hutmacher, 2019 ). Much of our daily experience is shaped by seeing a rich and complex world around us, and it is therefore an interesting question how much visual information we can store and process. Based on the development of a seminal experimental paradigm, Luck and Vogel (1997) have shown that visual working memory has a storage capacity of about four items. This finding is reported in many textbooks (e.g., Baddeley, 2007 ; Parkin, 2013 ; Goldstein, 2015 ) and has almost become a truism in cognitive psychology.

The experimental paradigm developed by Luck and Vogel (1997) is a prime example of an experiment which closely adheres to the scientific principles outlined above. In order to make a very broad and fuzzy phenomenon measurable, simple abstract forms are employed as visual stimuli—such as colored squares, triangles, or lines, usually on a “neutral,” e.g., gray, background—which can be counted in order to measure the capacity of visual working memory. Reducing the exuberant diversity of the “outside visual world” to a few abstract geometric forms is an extremely artificial situation. The obvious contrast between simple geometrical forms and the rich panorama of the “real” visual world illustrates the pitfalls of controlling supposed confounding variables, namely the incontrollable variety of the “real” world and how we see it. Precisely by abstracting and by excluding potential confounding variables it is possible to count the items and to make the capacity of the visual working memory measurable. But in doing so the original phenomenon—seeing the whole world—is lost. In other words: A simulacrum has been created.

The establishment of the experimental paradigm by Luck and Vogel has led to much research and sparked an extensive discussion how the limitation to only four items might be explained (see the summaries by Brady et al., 2011 ; Luck and Vogel, 2013 ; Ma et al., 2014 ; Schurgin, 2018 ). However, critically, several studies have shown that the situation is different when real-world objects are used as visual stimuli rather than simple abstract forms, revealing that the capacity of the visual working memory is higher for real-world objects ( Endress and Potter, 2014 ; Brady et al., 2016 ; Schurgin et al., 2018 ; Robinson et al., 2020 ; also Schurgin and Brady, 2019 ). Such findings show that the discourse about the mechanisms behind the limitations of the visual working memory is mostly about an artificial phenomenon which has no counterpart in “reality”—the perfect example of a hyperreality.

This hyperreal character does not mean that the findings of Luck and Vogel (1997) or similar experiments employing artificial stimuli are irrelevant or not “true.” The results are true—but it is a local truth, only valid for the specific context of specific experiments, and not a global truth which applies to the visual working memory in general . That is, speaking about “visual working memory” based on the paradigm of Luck and Vogel is a mistake because it is actually about “visual working memory for simple abstract geometrical forms in front of a gray background.”

Free Will and Experimental Psychology

The term “free will” expresses the idea of having “a significant kind of control [italics in the original] over one’s actions” ( O’Connor and Franklin, 2018 , n.p.). This concept has occupied a central position in Western philosophy since antiquity because it has far-reaching consequences for our self-conception as humans and our position in the world, including questions of morality, responsibility, and the nature of legal systems (e.g., Beebee, 2013 ; McKenna and Pereboom, 2016 ; O’Connor and Franklin, 2018 ). Being a topic of general interest, it is not surprising that experimental psychologists have tried to investigate free will as well.

The most famous study was conducted by Libet et al. (1983) , and this experiment has quickly become a focal point in the extensive discourse on free will because it provides empirical data and a scientific investigation. Libet et al.’s experiment seems to show that the subjective impression when persons consciously decide to act is in fact preceded by objectively measurable but unconscious physical processes. This purportedly proves that our seemingly voluntary actions are actually predetermined by physical processes because the brain has unconsciously reached a decision already before the person becomes aware of it and that our conscious intentions are simply grafted onto it. Therefore, we do not have a free will, and consequently much of our social fabric is based on an illusion. Or so the story goes.

This description, although phrased somewhat pointedly, represents a typical line of thought in the discourse on free will (e.g., the prominent psychologists Gazzaniga, 2011 ; Wegner, 2017 ; see Kihlstrom, 2017 , for further examples).

Libet’s experiment sparked an extensive and highly controversial discussion: For some authors, it is a refutation or at least threat to various concepts of free will, or, conversely, an indicator or even proof for some kind of material determinism. By contrast, other authors deny that the experiment refutes or counts against free will. Furthermore, a third group—whose position we adopt for our further argumentation—denies that Libet’s findings are even relevant for this question at all (for summaries of this complex and extensive discussion and various positions including further references see Nahmias, 2010 ; Radder and Meynen, 2013 ; Schlosser, 2014 ; Fischborn, 2016 ; Lavazza, 2016 ; Schurger, 2017 ). Libet’s own position, although not entirely consistent, opposes most notions of free will ( Roskies, 2011 ; Seifert, 2011 ). Given this background, it is not surprising that there are also numerous further experimental studies on various aspects of this subject area (see the summaries by Saigle et al., 2018 ; Shepard, 2018 ; Brass et al., 2019 ).

However, we argue that this entire discourse is best understood along postmodern lines as hyperreality and that Libet’s experiment itself is a perfect example of a simulacrum. A closer look at the concrete procedure of the experiment shows that Libet actually asked his participants to move their hand or finger “at will” while their brain activity was monitored with an EEG. They were instructed to keep watch in an introspective manner for the moment when they felt the “urge” to move their hand and to record this moment by indicating the clock-position of a pointer. This is obviously a highly artificial situation where the broad and fuzzy concept of “free will” is abstracted and reduced to the movement of the finger, the only degree of freedom being the moment of the movement. The question whether this is an adequate operationalization of free will is of paramount importance, and there are many objections that Libet’s setup fails to measure free will at all (e.g., Mele, 2007 ; Roskies, 2011 ; Kihlstrom, 2017 ; Brass et al., 2019 ).

Before Libet, there was no indication that the decision when to move a finger might be relevant for the concept of free will and the associated discourse. The question whether we have control over our actions referred to completely different levels of granularity. Free will was discussed with respect to questions such as whether we are free to live our lives according to our wishes or whether we are responsible for our actions in social contexts (e.g., Beebee, 2013 ; McKenna and Pereboom, 2016 ; O’Connor and Franklin, 2018 ), and not whether we lift a finger now or two seconds later. Libet’s and others’ jumping from very specific situations to far-reaching conclusions about a very broad and fuzzy theoretical construct illustrates that an extremely wide chasm between two phenomena, namely moving the finger and free will, is bridged in one fell swoop.

In other words, Libet’s experiment is a simulacrum as it duplicates a phenomenon from our day-to-day experience—namely free will—but in doing so the operationalization alters and reduces the theoretical construct. The outcome is a questionable procedure whose relationship to the phenomenon is highly controversial. Furthermore, the fact that, despite its tenuous connection to free will, Libet’s experiment sparked an extensive discussion on this subject reveals the hyperreal nature of the entire discourse because what is being discussed is not the actual question—namely free will—but rather a simulacrum. Everything else—the arguments, counter-arguments, follow-up experiments, and their interpretations—built upon Libet’s experiment are basically commentaries to a simulacrum and not on the real phenomena. Therefore, a hyperreality is created where the discourse revolves around entirely artificial phenomena, but where the arguments in this discussion refer back to and affect the real as suggestions are made to alter the legal system and our ideas of responsibility—which, incidentally, is not a question of empirical science but of law, ethics, and philosophy.

All of the above is not meant to say that this whole discourse is meaningless or even gratuitous—on the contrary, our understanding of the subject matter has greatly increased. Although our knowledge of free will has hardly increased, we have gained much insight into the hermeneutics and methodology—and pitfalls!—of investigations of free will, possible consequences on the individual and societal level, and the workings of scientific discourses. And this is exactly what postmodernism is about.

As shown above, there are a number of postmodern elements in the practice of experimental psychology: The prominent role of language, the gap between the linguistic or mental representation and the phenomenon, the “addition of intellect to the object,” the simulacral character of the experiment itself in its attempt to re-create phenomena, which necessarily transforms the “real” phenomenon due to the requirements of the experiment, and finally the creation of a hyperreality if experiments are taken as the “real” phenomenon and the scientific discourse becomes an exchange of symbolic expressions referring to the simulacra created in experiments, replacing the real. All these aspects did not seep gradually into experimental psychology in the wake of postmodernism but have been present since the very inception of experimental psychology as they are necessarily inherent in its philosophy of science.

Given these inherent postmodern traits in experimental psychology, it is puzzling that there is so much resistance against a perceived “threat” of psychology’s scientificness. Although a detailed investigation of the reasons lies outside the scope of this analysis, we suspect there are two main causes: First, an insufficient knowledge of the history of science and understanding of philosophy of science may result in idealized concepts of a “pure” natural science. Second, lacking familiarity with basic tenets of postmodern approaches may lead to the assumption that postmodernism is just an idle game of arbitrary words. However, “science” and “postmodernism” and their respective epistemological concepts are not opposites ( Gergen, 2001 ; Holtz, 2020 ). This is especially true for psychology, which necessarily contains a social dimension because not only the investigators are humans but also the very subject matter itself.

The (over-)reliance on quantitative-experimental methods in psychology, often paired with a superficial understanding of the philosophy of science behind it, has been criticized, either from the theoretical point of view (e.g., Bergmann and Spence, 1941 ; Hearnshaw, 1941 ; Petrie, 1971 ; Law, 2004 ; Smedslund, 2016 ) or because the experimental approach has failed to produce reliable, valid, and relevant applicable knowledge in educational psychology ( Slavin, 2002 ). It is perhaps symptomatic that a textbook teaching the principles of science for psychologists does not contain even one example from experimental psychology but employs only examples from physics, plus Darwin’s theory of evolution ( Wilton and Harley, 2017 ).

On the other hand, the postmodern perspective on experimental psychology provides insight into some pitfalls, as illustrated by the examples above. On the level of the experiment, the methodological requirements imply the creation of an artificial situation, which opens up a gap between the phenomenon as it is in reality and as it is concretely operationalized in the experimental situation. This is not a problem per se as long as is it clear—and clearly communicated!—that the results of the experiment are only valid in a certain context. The problems begin if the movement of a finger is mistaken for free will. Similarly, being aware that local causalities do not explain complex phenomena such as mental and behavioral processes in their entirety also prevents (over-) generalization, especially if communicated appropriately. These limitations make it clear that the experiment should not be made into an absolute or seen as the only valid way of understanding the psyche and the world.

On the level of psychology as an academic discipline, any investigation must select the appropriate level of granularity and strike a balance between the methodological requirements and the general meaning of the theoretical concept in question to find out something about the “real” world. If the level of granularity is so fine that results cannot be tied back to broader theoretical constructs rather than providing a helpful understanding of our psychological functioning, academic psychology is in danger of becoming a self-referential hyperreality.

The postmodern character of experimental psychology also allows for a different view on the so-called replication crisis in psychology. Authors contending that there is no replication crisis often employ arguments which exhibit postmodern elements, such as the emphasis on specific local conditions in experiments which may explain different outcomes of replication studies ( Stroebe and Strack, 2014 ; Baumeister, 2019 ). In other words, they invoke the simulacral character of experiments. This explanation may be valid or not, but the replication crisis has shown the limits of a predominantly experimental approach in psychology.

Acknowledging the postmodern nature of experimental psychology and incorporating postmodern thinking explicitly into our research may offer a way out of this situation. Our subject matter—the psyche —is extremely complex, ambiguous, and often contradictory. And postmodern thinking has proven capable of successfully explaining such phenomena (e.g., Bertens, 1995 ; Sim, 2011 ; Aylesworth, 2015 ). Thus, paradoxically, by accepting and considering the inherently fuzzy nature of theoretical constructs, they often become much clearer ( Ronzitti, 2011 ). Therefore, thinking more along postmodern lines in psychology would actually sharpen the theoretical and conceptual basis of experimental psychology—all the more as experimental psychology has inevitably been a postmodern endeavor since its very beginning.

Author Contributions

RM, CK, and CL developed the idea for this article. RM drafted the manuscript. CK and CL provided feedback and suggestions. All authors approved the manuscript for submission.

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.

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Experimental Methods In Psychology

March 7, 2021 - paper 2 psychology in context | research methods.

There are three experimental methods in the field of psychology; Laboratory, Field and Natural Experiments. Each of the experimental methods holds different characteristics in relation to; the manipulation of the IV, the control of the EVs and the ability to accurately replicate the study in exactly the same way.

When conducting research, it is important to create an aim and a hypothesis,  click here  to learn more about the formation of aims and hypotheses.

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Experimental Method ( AQA A Level Psychology )

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Experimental Method

Laboratory experiments .

• A lab experiment is a type of research method in which the researcher is able to exert high levels of control over what happens as part of the experimental process
• The researcher controls the environmental factors, such as noise and temperature (possible extraneous variables ) so that the effects of the independent variable (IV) upon the dependent variable (DV) can be clearly observed and measured
• The same number of participants take part in each condition of the IV
• Each participant is given the same instructions (apart from instructions regarding the task as this will differ per condition as per the IV)
• The same task/materials are used as far as is possible given the IV
• Participants are given the same amount of time to complete the task per condition and across conditions if the IV allows it
• All variables are kept the same/constant : only the independent variable changes between conditions
• Keeping all variables constant means the DV can be measured exactly using quantitative data

Evaluation Points 

Field Experiments 

• A field experiment is a research method which takes place in a natural setting, away from the lab
• The researcher has less control over what happens as part of the experimental process
• The researcher controls the environment to some extent but they have to allow the fact that many extraneous variables are included in field experiments
• A confederate of the researcher pretends to collapse on a subway train: the IV is whether the victim appears to be drunk or disabled, the DV is the number of people who go to the victim’s aid
• A researcher implements a ‘Kindness’ programme with half of the Year 5 students in a primary school: the IV is whether the students have followed the ‘Kindness’ programme or not, the DV is the score they achieve on a questionnaire about prosocial behaviour after one month
• Interviews with passengers who witnessed the ‘victim’ collapsing on the train
• Teachers’ observations of behavioural differences in the ‘Kindness’ programme children across the month of the study
• Any qualitative data collected could be used to comment on the quantitative findings and shed light on the actions of the participants

Natural Experiments 

• A natural experiment is a research method which does not manipulate the IV, it uses naturally-occurring phenomena , for example:
• Age e.g. an experiment in which digit-span recall is tested between a group of young people compared to a group of older people
• Gender e.g. the performance of girls is compared to the performance of boys in an experiment testing emotional intelligence
• Circumstances e.g. a group of teachers from one school who have received training in empathy are compared to a group of teachers from another school who have not had this training on a task involving correctly identifying emotional states
• The researcher has less control over what happens as part of the experimental process as they cannot randomly allocate participants to condition (the participants are the conditions e.g. either young/old, trained/untrained)
• Natural experiments collect quantitative data 

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• Research Methods
• Scientific Processes
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• Statistics: Inferential & Descriptive
• Gender & Culture in Psychology
• Free Will & Determinism
• The Nature-Nurture Debate
• Holism & Reductionism
• Idiographic & Nomothetic Approaches
• Ethical Implications of Research

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• Natural Experiment

Whilst oftentimes people tend to think of experiments occurring in laboratories and controlled settings, psychologists also consider real-world environments as opportunities to investigate phenomena. Behaviour changes depending on the setting, and investigating research areas in their natural settings can amplify the validity of the findings. Natural experiments offer researchers the opportunity to investigate human behaviour in everyday life. 

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• Cell Biology

Which of the following experiments does not involve the researcher manipulating the independent variable? 

True or false: Similar to lab experiments, natural experiments are conducted in controlled settings.

True or false: Confounding/ extraneous variables can be an issue in natural experiments. 

After Hurricane Katrina, researchers wanted to investigate how the natural disaster affected mental health. What type of experiment is likely to be conducted? 

Sampling bias can be an issue in natural experiments; this can influence the research's...

True or false: Ethical issues can be a potential concern for natural experiments. 

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• Approaches in Psychology
• Basic Psychology
• Biological Bases of Behavior
• Biopsychology
• Careers in Psychology
• Clinical Psychology
• Cognition and Development
• Cognitive Psychology
• Data Handling and Analysis
• Developmental Psychology
• Eating Behaviour
• Emotion and Motivation
• Famous Psychologists
• Forensic Psychology
• Health Psychology
• Individual Differences Psychology
• Issues and Debates in Psychology
• Personality in Psychology
• Psychological Treatment
• Relationships
• Research Methods in Psychology
• Aims and Hypotheses
• Causation in Psychology
• Coding Frame Psychology
• Correlational Studies
• Cross Cultural Research
• Cross Sectional Research
• Ethical Issues and Ways of Dealing with Them
• Experimental Designs
• Features of Science
• Field Experiment
• Independent Group Design
• Lab Experiment
• Longitudinal Research
• Matched Pairs Design
• Meta Analysis
• Observational Design
• Online Research
• Paradigms and Falsifiability
• Peer Review and Economic Applications of Research
• Pilot Studies and the Aims of Piloting
• Quality Criteria
• Questionnaire Construction
• Repeated Measures Design
• Research Methods
• Sampling Frames
• Sampling Psychology
• Scientific Processes
• Scientific Report
• Scientific Research
• Self-Report Design
• Self-Report Techniques
• Semantic Differential Rating Scale
• Snowball Sampling
• Schizophrenia
• Scientific Foundations of Psychology
• Scientific Investigation
• Sensation and Perception
• Social Context of Behaviour
• Social Psychology
• We are going to explore natural experiments used in psychological research.
• We will start by highlighting the natural experiment definition.
• We will then explore how natural experiments are used in psychology and cover natural experiment examples of research to demonstrate to help illustrate our points.
• Moving on, we will cover natural and field experiments to highlight the differences between the two types of investigations.
• And to finish, we will explore the natural experiment's advantages and disadvantages.

Natural Experiment Defintion

Natural experiments are essentially experiments that investigate naturally occurring phenomena. The natural experiment definition is a research procedure that occurs in the participant's natural setting that requires no manipulation by the researcher.

In experiments, changes in the independent variable (IV) are observed to identify if these changes affect the dependent variable (DV). However, in natural experiments, the researcher does not manipulate the IV. Instead, they observe the natural changes that occur.

Some examples of naturally occurring IVs are sex at birth, whether people have experienced a natural disaster, experienced a traumatic experience, or been diagnosed with a specific illness.

These examples show that it's next to impossible for the researcher to manipulate these.

Natural Experiment: Psychology

Why may researchers choose to use a natural experiment? As we have just discussed, sometimes researchers can't manipulate the IV. But, they may still wish to see how changes in the IV affect the DV, so use a natural experiment.

Sometimes a researcher can manipulate the IV, but it may be unethical or impractical to do so, so they conduct a natural experiment.

In natural experiments, the researcher can see how changes in the IV affect a DV, but unlike in lab experiments, the researcher has to identify how the IV is changing. In contrast, lab experiments pre-determine how the IV will be manipulated.

Natural Experiment: Examples

Natural experiments often take place in real-world settings. An example can be seen in examining the effect of female and male performance in an office environment and if gender plays a role in the retention of customers. Other examples include examining behaviours in schools, and the effect age has on behaviour.

Let's look at a hypothetical study that uses a natural experiment research method.

A research team was interested in investigating attitudes towards the community after experiencing a natural disaster.

The study collected data using interviews. The IV was naturally occurring as the researcher did not manipulate the IV; instead, they recruited participants who had recently experienced a natural disaster.

Natural Experiment vs Field Experiment

The table below summarises the key similarities and differences between natural experiments vs field experiments.

Natural Experiment: Advantages and Disadvantages

In the following section will present the natural experiment's advantages and disadvantages. We will discuss the new research possibilities, causal conclusions, rare opportunities, pre-existing sampling bias and ethical issues.

New Research Opportunities

Natural experiments provide opportunities for research that can't be done for ethical and practical reasons.

For example, it is impossible to manipulate a natural disaster or maternal deprivation on participants.

So, natural experiments are the only ethical way for researchers to investigate the causal relationship of the above topics. Thus, natural experiments open up practical research opportunities to study conditions that cannot be manipulated.

High Ecological Validity

Natural experiments have high ecological validity because natural experiments study real-world problems that occur naturally in real-life settings.

When research is found to use and apply real-life settings and techniques, it is considered to have high mundane realism.

And the advantage of this is that the results are more likely applicable and generalisable to real-life situations.

Rare Opportunities

There are scarce opportunities for researchers to conduct a natural experiment. Most natural events are ‘one-off’ situations. Because natural events are unique, the results have limited generalisability to similar situations.

In addition, it is next to impossible for researchers to replicate natural experiments; therefore, it is difficult to establish the reliability of findings.

Pre-Existing Sampling Bias

In natural experiments, pre-existing sampling bias can be a problem. In natural experiments, researchers cannot randomly assign participants to different conditions because naturally occurring events create them. Therefore, in natural experiments, participant differences may act as confounding variables .

As a result, sample bias in natural experiments can lead to low internal validity and generalisability of the research.

Ethical Issues

Although natural experiments are considered the only ethically acceptable method for studying conditions that can't be manipulated, ethical issues may still arise. Because natural experiments are often conducted after traumatic events, interviewing or observing people after the event could cause psychological harm to participants.

Researchers should prepare for potential ethical issues, such as psychological harm, usually dealt with by offering therapy. However, this can be pretty costly. And the ethical issue may lead participants to drop out of the research, which can also affect the quality of the research.

Natural Experiment - Key takeaways

The natural experiment definition is a research procedure that occurs in the participant's natural setting that requires no manipulation of the researcher.

The advantages of natural experiments are that they provide opportunities for research that researchers cannot do for ethical or practical reasons and have high ecological validity.

The disadvantages of natural experiments are reliability issues, pre-existing sample bias, and ethical issues, such as conducting a study after traumatic events may cause psychological distress.

Flashcards in Natural Experiment 6

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Frequently Asked Questions about Natural Experiment

What is a natural experiment?

The natural experiment definition is a research procedure that occurs in the participant's natural setting that requires no manipulation of the researcher. 

What is an example of natural experiment?

Beckett (2006) investigated the effects of deprivation on children’s IQ at age 11. They compared 128 Romanian children who UK families had adopted at various ages and 50 UK children who had been adopted before six months. They found that Romanian children who had been adopted before six months of age had similar IQs to the UK children; however, Romanian children adopted after six months of age had much worse scores. 

What are the characteristics of a natural experiment?

The characteristics of natural experiments are that they are carried out in a natural setting and the IV is not manipulated in this type of experiment. 

What are the advantages and disadvantages of natural experiments?

And the disadvantages of natural experiments are reliability issues, pre-existing sample bias, and ethical issues, such as conducting a study after traumatic events may cause psychological distress.

What are natural experiments in research?

Natural experiments in psychology research are often used when manipulating a variable is unethical or impractical.

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Experimental Method

The experimental method.

Experiments are one of the most popular and useful research methods in psychology. The key types are laboratory and field experiments.

Role in psychology

• Experiments play a major role throughout psychology.
• As a method, experiments allow one variable to be manipulated while keeping everything the same.
• This allows researchers to show cause and effect.

Laboratory experiments

• Some experiments take place under controlled condition, such as in a university room supervised by the researchers.
• These are called laboratory (or ‘lab’) experiments.
• The advantage of laboratory experiments is that they increase the level of control that a researcher can have.
• But they reduce the level of ecological validity of the research.

Field experiments

• Other experiments take place in a participant’s natural surroundings, such as their school or workplace.
• These are called field experiments.
• The advantage of field experiments is that they increase the ecological validity of the study by making the surroundings more realistic.
• But they reduce the level of control.

True experiments

• Both field experiments and lab experiments control the variables under investigation, and randomly allocate participants to groups.
• These characteristics mean that they are true experiments.

Quasi-Experiments

Quasi-experiments are not true experiments because they lack control over the experimental groups used.

Lack of random allocation

• For example, if one of the variables under investigation is gender, people can’t be randomly allocated to ‘male’ and ‘female’ conditions.
• A study is termed a quasi-experiment if it lacks random allocation to groups but is like a true experiment in most or all other ways.

Examples of quasi-experiments

• Other examples of quasi-experiments include studies which compare different types of personality (e.g. introverts versus extroverts) or compare people who have a psychological disorder with a control group who do not.
• Such studies cannot randomly allocate people to groups.

Quasi vs lab

• Quasi-experiments could take place in a lab, and all other aspects of the research and data gathering can be controlled.
• This means they are easy to mix up with laboratory experiments.

Natural Experiments

Natural experiments are logically similar to true experiments, but the situation happens by itself and so is completely uncontrolled by the researcher.

• For example, it wouldn’t be ethically correct to expose people to a lot of stress to investigate its effects.
• In such situations, a researcher may use a natural experiment.

Similarity to true experiments

• For example, they could compare the educational outcomes of school pupils who experience a lot of stress versus those who do not.

Differences to true experiments

• In contrast to a true experiment or a quasi-experiment, the variable under investigation happens by itself and so is completely uncontrolled by the researcher.
• The researcher also has no control at all over who is in each ‘experimental’ group.

Location of natural experiments

• Because natural experiments are not set up by the researcher, they always take place in participants’ everyday surroundings such as their home or school.
• This means they are easy to mix up with field experiments.

1 Social Influence

1.1 Social Influence

1.1.1 Conformity

1.1.2 Asch (1951)

1.1.3 Sherif (1935)

1.1.4 Conformity to Social Roles

1.1.5 BBC Prison Study

1.1.6 End of Topic Test - Conformity

1.1.7 Obedience

1.1.8 Analysing Milgram's Experiment

1.1.9 Agentic State & Legitimate Authority

1.1.10 Variables of Obedience

1.1.11 Resistance to Social Influence

1.1.12 Minority Influence & Social Change

1.1.13 Minority Influence & Social Impact Theory

1.1.14 End of Topic Test - Social Influences

1.1.15 Exam-Style Question - Conformity

1.1.16 Top Grade AO2/AO3 - Social Influence

2.1.1 Multi-Store Model of Memory

2.1.2 Short-Term vs Long-Term Memory

2.1.3 Long-Term Memory

2.1.4 Support for the Multi-Store Model of Memory

2.1.5 Duration Studies

2.1.6 Capacity Studies

2.1.7 Coding Studies

2.1.8 The Working Memory Model

2.1.9 The Working Memory Model 2

2.1.10 Support for the Working Memory Model

2.1.11 Explanations for Forgetting

2.1.12 Studies on Interference

2.1.13 Cue-Dependent Forgetting

2.1.14 Eye Witness Testimony - Loftus & Palmer

2.1.15 Eye Witness Testimony Loftus

2.1.16 Eyewitness Testimony - Post-Event Discussion

2.1.17 Eyewitness Testimony - Age & Misleading Questions

2.1.18 Cognitive Interview

2.1.19 Cognitive Interview - Geiselman & Fisher

2.1.20 End of Topic Test - Memory

2.1.21 Exam-Style Question - Memory

2.1.22 A-A* (AO3/4) - Memory

3 Attachment

3.1 Attachment

3.1.1 Caregiver-Infant Interaction

3.1.2 Condon & Sander (1974)

3.1.3 Schaffer & Emerson (1964)

3.1.4 Multiple Attachments

3.1.5 Studies on the Role of the Father

3.1.6 Animal Studies of Attachment

3.1.7 Explanations of Attachment

3.1.8 Attachment Types - Strange Situation

3.1.9 Cultural Differences in Attachment

3.1.10 Disruption of Attachment

3.1.11 Disruption of Attachment - Privation

3.1.12 Overcoming the Effects of Disruption

3.1.13 The Effects of Institutionalisation

3.1.14 Early Attachment

3.1.15 Critical Period of Attachment

3.1.16 End of Topic Test - Attachment

3.1.17 Exam-Style Question - Attachment

3.1.18 Top Grade AO2/AO3 - Attachment

4 Psychopathology

4.1 Psychopathology

4.1.1 Definitions of Abnormality

4.1.2 Definitions of Abnormality 2

4.1.3 Phobias, Depression & OCD

4.1.4 Phobias: Behavioural Approach

4.1.5 Evaluation of Behavioural Explanations of Phobias

4.1.6 Depression: Cognitive Approach

4.1.7 OCD: Biological Approach

4.1.8 Evidence for the Biological Approach

4.1.9 End of Topic Test - Psychopathy

4.1.10 Exam-Style Question - Phobias

4.1.11 Top Grade AO2/AO3 - Psychopathology

5 Approaches in Psychology

5.1 Approaches in Psychology

5.1.1 Psychology as a Science

5.1.2 Origins of Psychology

5.1.3 Reductionism & Problems with Introspection

5.1.4 The Behaviourist Approach - Classical Conditioning

5.1.5 Pavlov's Experiment

5.1.6 Little Albert Study

5.1.7 The Behaviourist Approach - Operant Conditioning

5.1.8 Social Learning Theory

5.1.9 The Cognitive Approach 1

5.1.10 The Cognitive Approach 2

5.1.11 The Biological Approach

5.1.12 Gottesman (1991) - Twin Studies

5.1.13 Brain Scanning

5.1.14 Structure of Personality & Little Hans

5.1.15 The Psychodynamic Approach (A2 only)

5.1.16 Humanistic Psychology (A2 only)

5.1.17 Aronoff (1957) (A2 Only)

5.1.18 Rogers' Client-Centred Therapy (A2 only)

5.1.19 End of Topic Test - Approaches in Psychology

5.1.20 Exam-Style Question - Approaches in Psychology

5.2 Comparison of Approaches (A2 only)

5.2.1 Psychodynamic Approach

5.2.2 Cognitive Approach

5.2.3 Biological Approach

5.2.4 Behavioural Approach

5.2.5 End of Topic Test - Comparison of Approaches

6 Biopsychology

6.1 Biopsychology

6.1.1 Nervous System Divisions

6.1.2 Neuron Structure & Function

6.1.3 Neurotransmitters

6.1.4 Endocrine System Function

6.1.5 Fight or Flight Response

6.1.6 The Brain (A2 only)

6.1.7 Localisation of Brain Function (A2 only)

6.1.8 Studying the Brain (A2 only)

6.1.9 CIMT (A2 Only) & Postmortem Examinations

6.1.10 Biological Rhythms (A2 only)

6.1.11 Studies on Biological Rhythms (A2 Only)

6.1.12 End of Topic Test - Biopsychology

6.1.13 Top Grade AO2/AO3 - Biopsychology

7 Research Methods

7.1 Research Methods

7.1.1 Experimental Method

7.1.2 Observational Techniques

7.1.3 Covert, Overt & Controlled Observation

7.1.4 Self-Report Techniques

7.1.5 Correlations

7.1.6 Exam-Style Question - Research Methods

7.1.7 End of Topic Test - Research Methods

7.2 Scientific Processes

7.2.1 Aims, Hypotheses & Sampling

7.2.2 Pilot Studies & Design

7.2.3 Questionnaires

7.2.4 Variables & Control

7.2.5 Demand Characteristics & Investigator Effects

7.2.6 Ethics

7.2.7 Limitations of Ethical Guidelines

7.2.8 Consent & Protection from Harm Studies

7.2.9 Peer Review & The Economy

7.2.10 Validity (A2 only)

7.2.11 Reliability (A2 only)

7.2.12 Features of Science (A2 only)

7.2.13 Paradigms & Falsifiability (A2 only)

7.2.14 Scientific Report (A2 only)

7.2.15 Scientific Report 2 (A2 only)

7.2.16 End of Topic Test - Scientific Processes

7.3 Data Handling & Analysis

7.3.1 Types of Data

7.3.2 Descriptive Statistics

7.3.3 Correlation

7.3.4 Evaluation of Descriptive Statistics

7.3.5 Presentation & Display of Data

7.3.6 Levels of Measurement (A2 only)

7.3.7 Content Analysis (A2 only)

7.3.8 Case Studies (A2 only)

7.3.9 Thematic Analysis (A2 only)

7.3.10 End of Topic Test - Data Handling & Analysis

7.4 Inferential Testing

7.4.1 Introduction to Inferential Testing

7.4.2 Sign Test

7.4.3 Piaget Conservation Experiment

7.4.4 Non-Parametric Tests

8 Issues & Debates in Psychology (A2 only)

8.1 Issues & Debates in Psychology (A2 only)

8.1.1 Culture Bias

8.1.2 Sub-Culture Bias

8.1.3 Gender Bias

8.1.4 Ethnocentrism

8.1.5 Cross Cultural Research

8.1.6 Free Will & Determinism

8.1.7 Comparison of Free Will & Determinism

8.1.8 Reductionism & Holism

8.1.9 Reductionist & Holistic Approaches

8.1.10 Nature-Nurture Debate

8.1.11 Interactionist Approach

8.1.12 Nature-Nurture Methods

8.1.13 Nature-Nurture Approaches

8.1.14 Idiographic & Nomothetic Approaches

8.1.15 Socially Sensitive Research

8.1.16 End of Topic Test - Issues and Debates

9 Option 1: Relationships (A2 only)

9.1 Relationships: Sexual Relationships (A2 only)

9.1.1 Sexual Selection & Human Reproductive Behaviour

9.1.2 Intersexual & Intrasexual Selection

9.1.3 Evaluation of Sexual Selection Behaviour

9.1.4 Factors Affecting Attraction: Self-Disclosure

9.1.5 Evaluation of Self-Disclosure Theory

9.1.6 Self Disclosure in Computer Communication

9.1.7 Factors Affecting Attraction: Physical Attributes

9.1.8 Matching Hypothesis Studies

9.1.9 Factors Affecting Physical Attraction

9.1.10 Factors Affecting Attraction: Filter Theory 1

9.1.11 Factors Affecting Attraction: Filter Theory 2

9.1.12 Evaluation of Filter Theory

9.1.13 End of Topic Test - Sexual Relationships

9.2 Relationships: Romantic Relationships (A2 only)

9.2.1 Social Exchange Theory

9.2.2 Evaluation of Social Exchange Theory

9.2.3 Equity Theory

9.2.4 Evaluation of Equity Theory

9.2.5 Rusbult’s Investment Model

9.2.6 Evaluation of Rusbult's Investment Model

9.2.7 Relationship Breakdown

9.2.8 Studies on Relationship Breakdown

9.2.9 Evaluation of Relationship Breakdown

9.2.10 End of Topic Test - Romantic relationships

9.3 Relationships: Virtual & Parasocial (A2 only)

9.3.1 Virtual Relationships in Social Media

9.3.2 Evaluation of Reduced Cues & Hyperpersonal

9.3.3 Parasocial Relationships

9.3.4 Attachment Theory & Parasocial Relationships

9.3.5 Evaluation of Parasocial Relationship Theories

9.3.6 End of Topic Test - Virtual & Parasocial Realtions

10 Option 1: Gender (A2 only)

10.1 Gender (A2 only)

10.1.1 Sex, Gender & Androgyny

10.1.2 Gender Identity Disorder

10.1.3 Biological & Social Explanations of GID

10.1.4 Biological Influences on Gender

10.1.5 Effects of Hormones on Gender

10.1.6 End of Topic Test - Gender 1

10.1.7 Kohlberg’s Theory of Gender Constancy

10.1.8 Evaluation of Kohlberg's Theory

10.1.9 Gender Schema Theory

10.1.10 Psychodynamic Approach to Gender Development 1

10.1.11 Psychodynamic Approach to Gender Development 2

10.1.12 Social Approach to Gender Development

10.1.13 Criticisms of Social Theory

10.1.14 End of Topic Test - Gender 2

10.1.15 Media Influence on Gender Development

10.1.16 Cross Cultural Research

10.1.17 Childcare & Gender Roles

10.1.18 End of Topic Test - Gender 3

11 Option 1: Cognition & Development (A2 only)

11.1 Cognition & Development (A2 only)

11.1.1 Piaget’s Theory of Cognitive Development 1

11.1.2 Piaget's Theory of Cognitive Development 2

11.1.3 Schema Accommodation Assimilation & Equilibration

11.1.4 Piaget & Inhelder’s Three Mountains Task (1956)

11.1.5 Conservation & Class Inclusion

11.1.6 Evaluation of Piaget

11.1.7 End of Topic Test - Cognition & Development 1

11.1.8 Vygotsky

11.1.9 Evaluation of Vygotsky

11.1.10 Baillargeon

11.1.11 Baillargeon's studies

11.1.12 Evaluation of Baillargeon

11.1.13 End of Topic Test - Cognition & Development 2

11.1.14 Sense of Self & Theory of Mind

11.1.15 Baron-Cohen Studies

11.1.16 Selman’s Five Levels of Perspective Taking

11.1.17 Biological Basis of Social Cognition

11.1.18 Evaluation of Biological Basis of Social Cognition

11.1.19 Important Issues in Social Neuroscience

11.1.20 End of Topic Test - Cognition & Development 3

11.1.21 Top Grade AO2/AO3 - Cognition & Development

12 Option 2: Schizophrenia (A2 only)

12.1 Schizophrenia: Diagnosis (A2 only)

12.1.1 Classification & Diagnosis

12.1.2 Reliability & Validity of Diagnosis

12.1.3 Gender & Cultural Bias

12.1.4 Pinto (2017) & Copeland (1971)

12.1.5 End of Topic Test - Scizophrenia Diagnosis

12.2 Schizophrenia: Treatment (A2 only)

12.2.1 Family-Based Psychological Explanations

12.2.2 Evaluation of Family-Based Explanations

12.2.3 Cognitive Explanations

12.2.4 Drug Therapies

12.2.5 Evaluation of Drug Therapies

12.2.6 Biological Explanations for Schizophrenia

12.2.7 Dopamine Hypothesis

12.2.8 End of Topic Test - Schizoprenia Treatment 1

12.2.9 Psychological Therapies 1

12.2.10 Psychological Therapies 2

12.2.11 Evaluation of Psychological Therapies

12.2.12 Interactionist Approach - Diathesis-Stress Model

12.2.13 Interactionist Approach - Triggers & Treatment

12.2.14 Evaluation of the Interactionist Approach

12.2.15 End of Topic Test - Scizophrenia Treatments 2

13 Option 2: Eating Behaviour (A2 only)

13.1 Eating Behaviour (A2 only)

13.1.1 Explanations for Food Preferences

13.1.2 Birch et al (1987) & Lowe et al (2004)

13.1.3 Control of Eating Behaviours

13.1.4 Control of Eating Behaviour: Leptin

13.1.5 Biological Explanations for Anorexia Nervosa

13.1.6 Psychological Explanations: Family Systems Theory

13.1.7 Psychological Explanations: Social Learning Theory

13.1.8 Psychological Explanations: Cognitive Theory

13.1.9 Biological Explanations for Obesity

13.1.10 Biological Explanations: Studies

13.1.11 Psychological Explanations for Obesity

13.1.12 Psychological Explanations: Studies

13.1.13 End of Topic Test - Eating Behaviour

14 Option 2: Stress (A2 only)

14.1 Stress (A2 only)

14.1.1 Physiology of Stress

14.1.2 Role of Stress in Illness

14.1.3 Role of Stress in Illness: Studies

14.1.4 Social Readjustment Rating Scales

14.1.5 Hassles & Uplifts Scales

14.1.6 Stress, Workload & Control

14.1.7 Stress Level Studies

14.1.8 End of Topic Test - Stress 1

14.1.9 Physiological Measures of Stress

14.1.10 Individual Differences

14.1.11 Stress & Gender

14.1.12 Drug Therapy & Biofeedback for Stress

14.1.13 Stress Inoculation Therapy

14.1.14 Social Support & Stress

14.1.15 End of Topic Test - Stress 2

15 Option 3: Aggression (A2 only)

15.1 Aggression: Physiological (A2 only)

15.1.1 Neural Mechanisms

15.1.2 Serotonin

15.1.3 Hormonal Mechanisms

15.1.4 Genetic Factors

15.1.5 Genetic Factors 2

15.1.6 End of Topic Test - Aggression: Physiological 1

15.1.7 Ethological Explanation

15.1.8 Innate Releasing Mechanisms & Fixed Action Pattern

15.1.9 Evolutionary Explanations

15.1.10 Buss et al (1992) - Sex Differences in Jealousy

15.1.11 Evaluation of Evolutionary Explanations

15.1.12 End of Topic Test - Aggression: Physiological 2

15.2 Aggression: Social Psychological (A2 only)

15.2.1 Social Psychological Explanation

15.2.2 Buss (1963) - Frustration/Aggression

15.2.3 Social Psychological Explanation 2

15.2.4 Social Learning Theory (SLT) 1

15.2.5 Social Learning Theory (SLT) 2

15.2.6 Limitations of Social Learning Theory (SLT)

15.2.7 Deindividuation

15.2.8 Deindividuation 2

15.2.9 Deindividuation - Diener et al (1976)

15.2.10 End of Topic Test - Aggression: Social Psychology

15.2.11 Institutional Aggression: Prisons

15.2.12 Evaluation of Dispositional & Situational

15.2.13 Influence of Computer Games

15.2.14 Influence of Television

15.2.15 Evaluation of Studies on Media

15.2.16 Desensitisation & Disinhibition

15.2.17 Cognitive Priming

15.2.18 End of Topic Test - Aggression: Social Psychology

16 Option 3: Forensic Psychology (A2 only)

16.1 Forensic Psychology (A2 only)

16.1.1 Defining Crime

16.1.2 Measuring Crime

16.1.3 Offender Profiling

16.1.4 Evaluation of Offender Profiling

16.1.5 John Duffy Case Study

16.1.6 Biological Explanations 1

16.1.7 Biological Explanations 2

16.1.8 Evaluation of the Biological Explanation

16.1.9 Cognitive Explanations

16.1.10 Moral Reasoning

16.1.11 Psychodynamic Explanation 1

16.1.12 Psychodynamic Explanation 2

16.1.13 End of Topic Test - Forensic Psychology 1

16.1.14 Differential Association Theory

16.1.15 Custodial Sentencing

16.1.16 Effects of Prison

16.1.17 Evaluation of the Effects of Prison

16.1.18 Recidivism

16.1.19 Behavioural Treatments & Therapies

16.1.20 Effectiveness of Behavioural Treatments

16.1.21 Restorative Justice

16.1.22 End of Topic Test - Forensic Psychology 2

17 Option 3: Addiction (A2 only)

17.1 Addiction (A2 only)

17.1.1 Definition

17.1.2 Brain Neurochemistry Explanation

17.1.3 Learning Theory Explanation

17.1.4 Evaluation of a Learning Theory Explanation

17.1.5 Cognitive Bias

17.1.6 Griffiths on Cognitive Bias

17.1.7 Evaluation of Cognitive Theory (A2 only)

17.1.8 End of Topic Test - Addiction 1

17.1.9 Gambling Addiction & Learning Theory

17.1.10 Social Influences on Addiction 1

17.1.11 Social Influences on Addiction 2

17.1.12 Personal Influences on Addiction

17.1.13 Genetic Explanations of Addiction

17.1.14 End of Topic Test - Addiction 2

17.2 Treating Addiction (A2 only)

17.2.1 Drug Therapy

17.2.2 Behavioural Interventions

17.2.3 Cognitive Behavioural Therapy

17.2.4 Theory of Reasoned Action

17.2.5 Theory of Planned Behaviour

17.2.6 Six Stage Model of Behaviour Change

17.2.7 End of Topic Test - Treating Addiction

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• Introduction

Comparison with controlled study design

Natural experiments as quasi experiments, instrumental variables.

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• Table Of Contents

natural experiment , observational study in which an event or a situation that allows for the random or seemingly random assignment of study subjects to different groups is exploited to answer a particular question. Natural experiments are often used to study situations in which controlled experimentation is not possible, such as when an exposure of interest cannot be practically or ethically assigned to research subjects. Situations that may create appropriate circumstances for a natural experiment include policy changes, weather events, and natural disasters. Natural experiments are used most commonly in the fields of epidemiology , political science , psychology , and social science .

Key features of experimental study design include manipulation and control. Manipulation, in this context , means that the experimenter can control which research subjects receive which exposures. For instance, subjects randomized to the treatment arm of an experiment typically receive treatment with the drug or therapy that is the focus of the experiment, while those in the control group receive no treatment or a different treatment. Control is most readily accomplished through random assignment, which means that the procedures by which participants are assigned to a treatment and control condition ensure that each has equal probability of assignment to either group. Random assignment ensures that individual characteristics or experiences that might confound the treatment results are, on average, evenly distributed between the two groups. In this way, at least one variable can be manipulated, and units are randomly assigned to the different levels or categories of the manipulated variables.

In epidemiology, the gold standard in research design generally is considered to be the randomized control trial (RCT). RCTs, however, can answer only certain types of epidemiologic questions, and they are not useful in the investigation of questions for which random assignment is either impracticable or unethical. The bulk of epidemiologic research relies on observational data, which raises issues in drawing causal inferences from the results. A core assumption for drawing causal inference is that the average outcome of the group exposed to one treatment regimen represents the average outcome the other group would have had if they had been exposed to the same treatment regimen. If treatment is not randomly assigned, as in the case of observational studies, the assumption that the two groups are exchangeable (on both known and unknown confounders) cannot be assumed to be true.

As an example, suppose that an investigator is interested in the effect of poor housing on health. Because it is neither practical nor ethical to randomize people to variable housing conditions, this subject is difficult to study using an experimental approach. However, if a housing policy change, such as a lottery for subsidized mortgages, was enacted that enabled some people to move to more desirable housing while leaving other similar people in their previous substandard housing, it might be possible to use that policy change to study the effect of housing change on health outcomes. In another example, a well-known natural experiment in Helena , Montana, smoking was banned from all public places for a six-month period. Investigators later reported a 60-percent drop in heart attacks for the study area during the time the ban was in effect.

Because natural experiments do not randomize participants into exposure groups, the assumptions and analytical techniques customarily applied to experimental designs are not valid for them. Rather, natural experiments are quasi experiments and must be thought about and analyzed as such. The lack of random assignment means multiple threats to causal inference , including attrition , history, testing, regression , instrumentation, and maturation, may influence observed study outcomes. For this reason, natural experiments will never unequivocally determine causation in a given situation. Nevertheless, they are a useful method for researchers, and if used with care they can provide additional data that may help with a research question and that may not be obtainable in any other way.

The major limitation in inferring causation from natural experiments is the presence of unmeasured confounding. One class of methods designed to control confounding and measurement error is based on instrumental variables (IV). While useful in a variety of applications, the validity and interpretation of IV estimates depend on strong assumptions, the plausibility of which must be considered with regard to the causal relation in question.

In particular, IV analyses depend on the assumption that subjects were effectively randomized, even if the randomization was accidental (in the case of an administrative policy change or exposure to a natural disaster) and adherence to random assignment was low. IV methods can be used to control for confounding in observational studies, to control for confounding due to noncompliance, and to correct for misclassification.

IV analysis, however, can produce serious biases in effect estimates. It can also be difficult to identify the particular subpopulation to which the causal effect IV estimate applies. Moreover, IV analysis can add considerable imprecision to causal effect estimates. Small sample size poses an additional challenge in applying IV methods.

• A-Z Publications

Annual Review of Public Health

Volume 38, 2017, review article, open access, natural experiments: an overview of methods, approaches, and contributions to public health intervention research.

• Peter Craig 1 , Srinivasa Vittal Katikireddi 1 , Alastair Leyland 1 , and Frank Popham 1
• View Affiliations Hide Affiliations Affiliations: MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, Glasgow G2 3QB, United Kingdom; email: [email protected] , [email protected] , [email protected] , [email protected]
• Vol. 38:39-56 (Volume publication date March 2017) https://doi.org/10.1146/annurev-publhealth-031816-044327
• First published as a Review in Advance on January 11, 2017
• Copyright © 2017 Annual Reviews. This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 (CC-BY-SA) International License, which permits unrestricted use, distribution, and reproduction in any medium and any derivative work is made available under the same, similar, or a compatible license. See credit lines of images or other third-party material in this article for license information.

Population health interventions are essential to reduce health inequalities and tackle other public health priorities, but they are not always amenable to experimental manipulation. Natural experiment (NE) approaches are attracting growing interest as a way of providing evidence in such circumstances. One key challenge in evaluating NEs is selective exposure to the intervention. Studies should be based on a clear theoretical understanding of the processes that determine exposure. Even if the observed effects are large and rapidly follow implementation, confidence in attributing these effects to the intervention can be improved by carefully considering alternative explanations. Causal inference can be strengthened by including additional design features alongside the principal method of effect estimation. NE studies often rely on existing (including routinely collected) data. Investment in such data sources and the infrastructure for linking exposure and outcome data is essential if the potential for such studies to inform decision making is to be realized.

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How to Conduct a Psychology Experiment

Conducting your first psychology experiment can be a long, complicated, and sometimes intimidating process. It can be especially confusing if you are not quite sure where to begin or which steps to take.

Like other sciences, psychology utilizes the  scientific method  and bases conclusions upon empirical evidence. When conducting an experiment, it is important to follow the seven basic steps of the scientific method:

• Ask a testable question
• Define your variables
• Conduct background research
• Design your experiment
• Perform the experiment
• Collect and analyze the data
• Draw conclusions
• Share the results with the scientific community

At a Glance

It's important to know the steps of the scientific method if you are conducting an experiment in psychology or other fields. The processes encompasses finding a problem you want to explore, learning what has already been discovered about the topic, determining your variables, and finally designing and performing your experiment. But the process doesn't end there! Once you've collected your data, it's time to analyze the numbers, determine what they mean, and share what you've found.

Find a Research Problem or Question

Picking a research problem can be one of the most challenging steps when you are conducting an experiment. After all, there are so many different topics you might choose to investigate.

Are you stuck for an idea? Consider some of the following:

Investigate a Commonly Held Belief

Folk knowledge is a good source of questions that can serve as the basis for psychological research. For example, many people believe that staying up all night to cram for a big exam can actually hurt test performance.

You could conduct a study to compare the test scores of students who stayed up all night with the scores of students who got a full night's sleep before the exam.

Review Psychology Literature

Published studies are a great source of unanswered research questions. In many cases, the authors will even note the need for further research. Find a published study that you find intriguing, and then come up with some questions that require further exploration.

Think About Everyday Problems

There are many practical applications for psychology research. Explore various problems that you or others face each day, and then consider how you could research potential solutions. For example, you might investigate different memorization strategies to determine which methods are most effective.

Define Your Variables

Variables are anything that might impact the outcome of your study. An operational definition describes exactly what the variables are and how they are measured within the context of your study.

For example, if you were doing a study on the impact of sleep deprivation on driving performance, you would need to operationally define sleep deprivation and driving performance .

An operational definition refers to a precise way that an abstract concept will be measured. For example, you cannot directly observe and measure something like test anxiety . You can, however, use an anxiety scale and assign values based on how many anxiety symptoms a person is experiencing. 

In this example, you might define sleep deprivation as getting less than seven hours of sleep at night. You might define driving performance as how well a participant does on a driving test.

What is the purpose of operationally defining variables? The main purpose is control. By understanding what you are measuring, you can control for it by holding the variable constant between all groups or manipulating it as an independent variable .

Develop a Hypothesis

The next step is to develop a testable hypothesis that predicts how the operationally defined variables are related. In the recent example, the hypothesis might be: "Students who are sleep-deprived will perform worse than students who are not sleep-deprived on a test of driving performance."

Null Hypothesis

In order to determine if the results of the study are significant, it is essential to also have a null hypothesis. The null hypothesis is the prediction that one variable will have no association to the other variable.

In other words, the null hypothesis assumes that there will be no difference in the effects of the two treatments in our experimental and control groups .

The null hypothesis is assumed to be valid unless contradicted by the results. The experimenters can either reject the null hypothesis in favor of the alternative hypothesis or not reject the null hypothesis.

It is important to remember that not rejecting the null hypothesis does not mean that you are accepting the null hypothesis. To say that you are accepting the null hypothesis is to suggest that something is true simply because you did not find any evidence against it. This represents a logical fallacy that should be avoided in scientific research.  

Conduct Background Research

Once you have developed a testable hypothesis, it is important to spend some time doing some background research. What do researchers already know about your topic? What questions remain unanswered?

You can learn about previous research on your topic by exploring books, journal articles, online databases, newspapers, and websites devoted to your subject.

Reading previous research helps you gain a better understanding of what you will encounter when conducting an experiment. Understanding the background of your topic provides a better basis for your own hypothesis.

After conducting a thorough review of the literature, you might choose to alter your own hypothesis. Background research also allows you to explain why you chose to investigate your particular hypothesis and articulate why the topic merits further exploration.

As you research the history of your topic, take careful notes and create a working bibliography of your sources. This information will be valuable when you begin to write up your experiment results.

Select an Experimental Design

After conducting background research and finalizing your hypothesis, your next step is to develop an experimental design. There are three basic types of designs that you might utilize. Each has its own strengths and weaknesses:

Pre-Experimental Design

A single group of participants is studied, and there is no comparison between a treatment group and a control group. Examples of pre-experimental designs include case studies (one group is given a treatment and the results are measured) and pre-test/post-test studies (one group is tested, given a treatment, and then retested).

Quasi-Experimental Design

This type of experimental design does include a control group but does not include randomization. This type of design is often used if it is not feasible or ethical to perform a randomized controlled trial.

True Experimental Design

A true experimental design, also known as a randomized controlled trial, includes both of the elements that pre-experimental designs and quasi-experimental designs lack—control groups and random assignment to groups.

Standardize Your Procedures

In order to arrive at legitimate conclusions, it is essential to compare apples to apples.

Each participant in each group must receive the same treatment under the same conditions.

For example, in our hypothetical study on the effects of sleep deprivation on driving performance, the driving test must be administered to each participant in the same way. The driving course must be the same, the obstacles faced must be the same, and the time given must be the same.

Choose Your Participants

In addition to making sure that the testing conditions are standardized, it is also essential to ensure that your pool of participants is the same.

If the individuals in your control group (those who are not sleep deprived) all happen to be amateur race car drivers while your experimental group (those that are sleep deprived) are all people who just recently earned their driver's licenses, your experiment will lack standardization.

When choosing subjects, there are some different techniques you can use.

Simple Random Sample

In a simple random sample, the participants are randomly selected from a group. A simple random sample can be used to represent the entire population from which the representative sample is drawn.

Drawing a simple random sample can be helpful when you don't know a lot about the characteristics of the population.

Stratified Random Sample

Participants must be randomly selected from different subsets of the population. These subsets might include characteristics such as geographic location, age, sex, race, or socioeconomic status.

Stratified random samples are more complex to carry out. However, you might opt for this method if there are key characteristics about the population that you want to explore in your research.

Conduct Tests and Collect Data

After you have selected participants, the next steps are to conduct your tests and collect the data. Before doing any testing, however, there are a few important concerns that need to be addressed.

Address Ethical Concerns

First, you need to be sure that your testing procedures are ethical . Generally, you will need to gain permission to conduct any type of testing with human participants by submitting the details of your experiment to your school's Institutional Review Board (IRB), sometimes referred to as the Human Subjects Committee.

Obtain Informed Consent

After you have gained approval from your institution's IRB, you will need to present informed consent forms to each participant. This form offers information on the study, the data that will be gathered, and how the results will be used. The form also gives participants the option to withdraw from the study at any point in time.

Once this step has been completed, you can begin administering your testing procedures and collecting the data.

Analyze the Results

After collecting your data, it is time to analyze the results of your experiment. Researchers use statistics to determine if the results of the study support the original hypothesis and if the results are statistically significant.

Statistical significance means that the study's results are unlikely to have occurred simply by chance.

The types of statistical methods you use to analyze your data depend largely on the type of data that you collected. If you are using a random sample of a larger population, you will need to utilize inferential statistics.

These statistical methods make inferences about how the results relate to the population at large.

Because you are making inferences based on a sample, it has to be assumed that there will be a certain margin of error. This refers to the amount of error in your results. A large margin of error means that there will be less confidence in your results, while a small margin of error means that you are more confident that your results are an accurate reflection of what exists in that population.

Share Your Results After Conducting an Experiment

Your final task in conducting an experiment is to communicate your results. By sharing your experiment with the scientific community, you are contributing to the knowledge base on that particular topic.

One of the most common ways to share research results is to publish the study in a peer-reviewed professional journal. Other methods include sharing results at conferences, in book chapters, or academic presentations.

In your case, it is likely that your class instructor will expect a formal write-up of your experiment in the same format required in a professional journal article or lab report :

• Introduction
• Tables and figures

What This Means For You

Designing and conducting a psychology experiment can be quite intimidating, but breaking the process down step-by-step can help. No matter what type of experiment you decide to perform, always check with your instructor and your school's institutional review board for permission before you begin.

NOAA SciJinks. What is the scientific method? .

Nestor, PG, Schutt, RK. Research Methods in Psychology . SAGE; 2015.

Andrade C. A student's guide to the classification and operationalization of variables in the conceptualization and eesign of a clinical study: Part 2 .  Indian J Psychol Med . 2021;43(3):265-268. doi:10.1177/0253717621996151

Purna Singh A, Vadakedath S, Kandi V. Clinical research: A review of study designs, hypotheses, errors, sampling types, ethics, and informed consent .  Cureus . 2023;15(1):e33374. doi:10.7759/cureus.33374

Colby College. The Experimental Method .

Leite DFB, Padilha MAS, Cecatti JG. Approaching literature review for academic purposes: The Literature Review Checklist .  Clinics (Sao Paulo) . 2019;74:e1403. doi:10.6061/clinics/2019/e1403

Salkind NJ. Encyclopedia of Research Design . SAGE Publications, Inc.; 2010. doi:10.4135/9781412961288

Miller CJ, Smith SN, Pugatch M. Experimental and quasi-experimental designs in implementation research .  Psychiatry Res . 2020;283:112452. doi:10.1016/j.psychres.2019.06.027

Nijhawan LP, Manthan D, Muddukrishna BS, et. al. Informed consent: Issues and challenges . J Adv Pharm Technol Rese . 2013;4(3):134-140. doi:10.4103/2231-4040.116779

Serdar CC, Cihan M, Yücel D, Serdar MA. Sample size, power and effect size revisited: simplified and practical approaches in pre-clinical, clinical and laboratory studies .  Biochem Med (Zagreb) . 2021;31(1):010502. doi:10.11613/BM.2021.010502

American Psychological Association.  Publication Manual of the American Psychological Association  (7th ed.). Washington DC: The American Psychological Association; 2019.

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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29 Experimental Psychology

Howard Thorsheim is a Professor of Psychology and Neuroscience at St. Olaf College.

• Published: 01 May 2014
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This chapter includes concrete and practical ideas and activities that might fit and work in your course to engage students in learning experimental psychology research skills across topic areas in the psychology curriculum, whether you are a beginning or veteran teacher. My own history of adapting and implementing best practices to teach experimental psychology over four decades has influenced my thinking, and pedagogy, and informs the recommendations in this chapter. The chapter begins with historical roots of experimentation in psychological science, then grows to include contemporary ideas about student learning, teaching goals, and other resources to make use of investigative activities, including suggestions about Why? When? What? How? Who? And, Where? Future directions include ideas from NSF-sponsored research to teach experimental research-oriented skills by incorporating developments in psychophysiology neuroscience to explore mind-body interactions, with links to successful examples across America.

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Types of Experiment: Overview

Last updated 6 Sept 2022

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Different types of methods are used in research, which loosely fall into 1 of 2 categories.

Experimental (Laboratory, Field & Natural) & N on experimental ( correlations, observations, interviews, questionnaires and case studies).

All the three types of experiments have characteristics in common. They all have:

• an independent variable (I.V.) which is manipulated or a naturally occurring variable
• a dependent variable (D.V.) which is measured
• there will be at least two conditions in which participants produce data.

Note – natural and quasi experiments are often used synonymously but are not strictly the same, as with quasi experiments participants cannot be randomly assigned, so rather than there being a condition there is a condition.

Laboratory Experiments

These are conducted under controlled conditions, in which the researcher deliberately changes something (I.V.) to see the effect of this on something else (D.V.).

Control – lab experiments have a high degree of control over the environment & other extraneous variables which means that the researcher can accurately assess the effects of the I.V, so it has higher internal validity.

Replicable – due to the researcher’s high levels of control, research procedures can be repeated so that the reliability of results can be checked.

Limitations

Lacks ecological validity – due to the involvement of the researcher in manipulating and controlling variables, findings cannot be easily generalised to other (real life) settings, resulting in poor external validity.

Field Experiments

These are carried out in a natural setting, in which the researcher manipulates something (I.V.) to see the effect of this on something else (D.V.).

Validity – field experiments have some degree of control but also are conducted in a natural environment, so can be seen to have reasonable internal and external validity.

Less control than lab experiments and therefore extraneous variables are more likely to distort findings and so internal validity is likely to be lower.

Natural / Quasi Experiments

These are typically carried out in a natural setting, in which the researcher measures the effect of something which is to see the effect of this on something else (D.V.). Note that in this case there is no deliberate manipulation of a variable; this already naturally changing, which means the research is merely measuring the effect of something that is already happening.

High ecological validity – due to the lack of involvement of the researcher; variables are naturally occurring so findings can be easily generalised to other (real life) settings, resulting in high external validity.

Lack of control – natural experiments have no control over the environment & other extraneous variables which means that the researcher cannot always accurately assess the effects of the I.V, so it has low internal validity.

Not replicable – due to the researcher’s lack of control, research procedures cannot be repeated so that the reliability of results cannot be checked.

• Laboratory Experiment
• Field experiment
• Quasi Experiment
• Natural Experiment
• Field experiments

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• Published: 02 September 2024

Research on hydrogen distribution characteristics in town hydrogen-doped methane pipeline

• Jiuqing Ban 1 , 2 ,
• Liyang Zhu 3 ,
• Ruofei Shen 4 ,
• Wei Yang 1 , 2 ,
• Mengqi Hao 5 ,
• Gang Liu 5 &
• Xiaodong Wang 3  

Scientific Reports volume  14 , Article number:  20347 ( 2024 ) Cite this article

Metrics details

• Energy science and technology
• Natural gas

The study of hydrogen concentration distribution law of hydrogen-doped methane pipeline is directly related to the safety and stability of hydrogen-doped methane pipeline network. Based on the theory of fluid dynamics, this paper established a model of hydrogen-doped methane pipeline and simulated the operation and shutdown status of hydrogen-doped methane pipeline by adopting the computational fluid dynamics method and selecting the mixture multiphase model and standard k - ε turbulence model. This paper investigates the hydrogen concentration distribution law in hydrogen-doped methane pipelines as well as the influence law of different hydrogen-doping ratios, operating flow velocities, operating pressures, shutdown time and gas usage on the hydrogen concentration distribution in gas pipeline. The results show that: under the operation condition, there is a weak uneven distribution of hydrogen in the pipeline, the hydrogen-doping ratio, flow velocity, pressure on the hydrogen volume fraction of the change in the 0.9% or less, the effect can be ignored; in the shutdown status, there is a clear stratification phenomenon, the hydrogen-doping ratio increased from 10 to 25%, the change in the volume fraction of hydrogen in the 11.2% or less, a positive correlation; with the extension of the shutdown time to 900s, the pipeline firstly appeared obvious stratification phenomenon in the branch pipe, the thickness of the gas with hydrogen volume fraction above 40% on the upper wall surface of the branch pipe increased to 0.7 mm, and after the shutdown time was extended to 10 h, obvious stratification phenomenon appeared in the main pipeline, and the volume fraction of hydrogen near the top of the main pipe of about 16.5 mm was above 30%, which was positively correlated; In the shutdown status, the shutdown time has the greatest effect on the stratification phenomenon in the pipe, followed by the hydrogen-doping ratio, and the gas usage has the least effect.

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Introduction.

In the face of the deteriorating global climate environment and rising demand for energy, accelerating energy transition and reducing carbon emissions have become the first choice for solving environmental and energy problems 1 , 2 , 3 . Hydrogen energy as a clean and efficient secondary energy is being studied and utilized one after another 4 . The hydrogen energy industry chain is long and involves a wide range of technical fields and numerous categories, among which hydrogen storage and transportation accounts for about one-third of the total industry chain operation cost, which is one of the main bottlenecks restricting the large-scale development of hydrogen energy industry 5 , 6 , 7 . Among the storage and transportation methods of hydrogen energy, blending hydrogen into natural gas pipeline for transportation with lower economic cost can effectively solve the problem of large-scale transportation of hydrogen, which is of great significance for enriching the gas source, reducing carbon emission, protecting the environment, and promoting the development of energy industry 8 .

Hydrogen blending in urban gas pipeline network is the main transportation method for downstream residential and industrial users. Compared with natural gas long-distance pipeline, urban gas pipeline network is more complex in terms of user types and pressure rating system, more diverse pipe types, and more complicated in terms of external environment and external loads 9 . At the same time, the separation of lower content hydrogen- blended natural gas faces greater economic constraints, and lower content hydrogen- blended natural gas can be directly applied in conventional natural gas-based household gas appliances, so at the present stage, direct transmission of hydrogen- blended natural gas at the end of the gas pipeline network can be better to reduce the cost 10 , 11 . The sensitivity of various types of users and different pressure levels to hydrogen is different, and it is of great significance to realize the safe and efficient transportation of hydrogen-doped methane without changing the existing pipeline network and user facilities. However, hydrogen blending into natural gas changes the composition of the pipeline gas, resulting in changes in physical parameters such as compression factor, density, viscosity, and heat generation 12 . Meanwhile, due to the density difference between hydrogen and natural gas, there is a volume fraction inhomogeneity of hydrogen-doped methane, resulting in stratification in the hydrogen-doped methane transmission pipeline 13 , 14 . Uneven mixing of hydrogen and natural gas is a major safety hazard that affects the safe operation of pipelines, especially for urban gas pipelines, the local hydrogen concentration is too high, which may lead to hydrogen brittleness of the pipeline, hydrogen bubbling and hydrogen corrosion and other risks 15 , 16 , which have an impact on the mechanical properties of the pipe, coupled with the danger of gas pipeline transmission itself, the use of the existing gas pipeline transmission of hydrogen-doped methane has safety problems 17 , 18 . Due to the small molecular volume of hydrogen, the permeation velocity of hydrogen-blended gas is relatively large. In PE pipes and sealing materials of urban gas pipeline network, hydrogen permeability coefficient is 4–5 times larger than that of methane, and hydrogen permeability coefficient in most sealing materials is larger than that of PE pipes, especially the permeability coefficients in natural rubber and nitrile rubber are 26 and 21 times larger than that in PE pipes, respectively, and permeability coefficients increase with the increase of pipeline pressure. Therefore, there is a risk of permeation leakage in hydrogen-enriched areas 19 . Meanwhile, the explosion limit of hydrogen is 4.0–75.9%, and the explosion limit of methane is 5–15%, and the explosion limit of hydrogen is wide, which is more dangerous than the explosion of methane, and once a leak occurs after blending hydrogen into natural gas there may be a high-speed jet flame 20 , 21 . Therefore, to study the uniformity of hydrogen-blended gas pipeline delivery in urban areas, to predict the hydrogen-enriched areas, and to obtain the distribution law of hydrogen concentration under different influencing factors are the prerequisites and basic conditions for the safety of hydrogen-blended delivery in gas pipelines. At the same time, compared to long-distance pipelines and underground pipelines, hydrogen-doped gas pipelines to households are characterized by unstable operation status. When residential gas demand is high, the pipeline is in flow status during peak gas consumption; when residential gas demand is low, the pipeline is in shutdown status during non-peak gas consumption. And it is common for pipelines to be in shutdown status. When the pipeline is in the shutdown status, the phenomenon of stratification is more likely to occur inside the pipeline. Therefore, it is a prerequisite and basic condition for the safety of hydrogen doping in gas pipelines to study the homogeneity of the gas mixture in the town, to predict the hydrogen-enriched area, and to obtain the distribution law of hydrogen concentration in the shutdown status pipeline and the flow status pipeline under different influencing factors.

At present, the research on hydrogen-doped methane pipeline transport mainly includes experimental research and numerical simulation research, and most of the scholars at home and abroad mainly carry out the research through numerical simulation method, while relatively less research is carried out through experimental way. Uilhoorn et al. 22 found that the temperature drop and pressure drop of natural gas pipeline decreases after hydrogen blending. Hafsi et al. 23 focused on the flow pattern of hydrogen -doped natural gas in a circular pipeline network and found that increasing the hydrogen-doping ratio decreases the pressure fluctuation and the power of the gas delivered in the pipeline. Abd et al. 24 investigated the effect of hydrogen concentration in natural gas on the flow performance and the characteristics of the gas, the results showed that the presence of hydrogen in the mixture increases the critical pressure and decreases the critical temperature. Elaoud 25 numerically analyzed the flow of a high-pressure hydrogen-doped methane mixture in a pipeline network under steady state and transient conditions and concluded that hydrogen blending has a significant effect on transient pressure and flow characteristics. The “Hy Deploy” natural gas hydrogen blending project was carried out by Energy Supply UK in 2017 26 , and in the first phase of the project, safety experiments were conducted at the University of Keele, which concluded that the addition of hydrogen at a molar mass fraction of 20% can be safely operated using an existing natural gas pipeline 27 . The turbulence models commonly used by most scholars in multiphase flow problems include the standard \(\:k\) - \(\:\epsilon\:\) model, the RNG \(\:k\) - \(\:\epsilon\:\) model, and the realizable \(\:k\) - \(\:\epsilon\:\) model. For example, in Ghazanfari V’s study on the influence of nanofluids on the efficiency of heat exchangers, the turbulence models used then include the standard \(\:k\) - \(\:\epsilon\:\) model, RNG \(\:k\) - \(\:\epsilon\:\) model, and realizable \(\:k\) - \(\:\epsilon\:\) model 28 . Domestic and foreign scholars have also conducted some research on the phenomenon of hydrogen aggregation in hydrogen-doped methane pipeline. Marangon et al. 29 experimentally tested the diffusion process of hydrogen-methane mixture from the bottom of a closed box filled with oxygen, and after stopping the injection for 0.78 h, there was an 8% difference in the concentration of hydrogen-methane mixture between the top and the bottom of the box, which indicated that there was a tendency of stratification of the gas mixing after static. Wu 30 conducted a simulation test study of hydrogen-doped methane static in a riser, and the results of the study showed that the hydrogen-doped methane riser does not undergo stratification in the static state. Ren et al. 31 , 32 studied the effect of inhomogeneous methane-air distribution on gas mixture combustion in closed containers by combining numerical simulation and experiment, and the results showed that the concentration distribution of gas mixture was not uniform under the effect of gravity, and the bigger the container with the larger length-to-diameter ratio, the bigger the gradient of the concentration distribution of gas mixture. Liu et al. 33 used the theoretical method of computational fluid dynamics to construct a mixing model of hydrogen-doped methane, and simulated the change of hydrogen volume fraction under the conditions of storage cylinder, pipeline shutdown, and pipeline flow to obtain the distribution law of the volume fraction of hydrogen components of hydrogen-doped methane under certain parameters. An et al. 34 used CFD software to numerically simulate T-shaped doped pipelines and variable diameter doped pipelines under hydrogen and natural gas doping conditions. The results show that for the T-shaped doped pipeline, there is still significant stratification within the pipe length 35 times the pipe diameter, and the width occupies 1/3 of the pipe diameter. For variable-diameter blending lines, it was found that the closer the variable diameter to the blending center, the narrower the diameter, and the lower the height, the more likely hydrogen enrichment would occur. Zhu et al. 35 investigated the process of static gas stratification after stopping the transmission of undulating natural gas pipelines uniformly blended with different volume ratios of hydrogen, focusing on the effect of height difference on the development of static stratification and the highest volume ratio of hydrogen after stabilization. Yan et al. 36 numerically simulated the natural gas blending process with different gas compositions and found that the concentration of the natural gas components varied along the course downstream of the confluence point. Some of the components reached a uniform distribution of cross-section concentration at 150 m from the sink, and the uniform mixing of all components was realized at 280 m. The choice of turbulence model is also a matter of concern in pipeline flow simulation. For example, in Ghazanfari V’s study, the influence of twisted tubes and Al 2 O 3 nanofluid on the thermal performance of the shell and tube heat exchanger is numerically studied. It employed computational fluid dynamics (CFD) with different turbulence models (realizable \(\:k\) - \(\:\epsilon\:\) and realizable \(\:k\) - \(\:\epsilon\:\) 2nd order). The results were compared with the published study in which the first-order realizable \(\:k\) - \(\:\epsilon\:\) model was nominated as the acceptable method 37 . In the research related to hydrogen-doped methane in pipeline transmission, there are relatively few studies on the stratification state, especially for the hydrogen aggregation phenomenon in low-pressure urban gas pipelines, and there is a lack of research on the impact of complex gas usage situations.

This paper focuses on the hydrogen concentration distribution law of hydrogen-doped methane in flow status pipeline and shutdown status pipeline, and further researches the influence law of different hydrogen doping ratios, operating flow velocities, operating pressures, shutdown time and gas usage on the distribution of hydrogen concentration in gas pipeline.

Materials and methods

Model assumption.

In this paper, the distribution law of hydrogen concentration in hydrogen-doped methane pipelines and the influencing factors are investigated by numerical simulation. The main mathematical models involved are mixture multiphase model and gas turbulence model. The gas turbulence model is standard \(\:k\) - \(\:\epsilon\:\) turbulence model.

Mixture multiphase model

The Mixture model is a simplified Eulerian multiphase flow model that can be employed to simulate multiphase flows comprising phases moving at disparate velocities, or multiphase flows comprising n phases (fluids or particles). This is achieved by solving the momentum, continuity, and energy equations of the mixture, the volume fraction equations of the secondary phases, and the algebraic expressions for the relative velocities. Solve the governing equation for the hydrogen-doped methane stratification process as:

where \(\:{\varvec{\rho\:}}_{\varvec{m}}\) is the mixture density, kg/m 3 ; \(\:{\varvec{\rho\:}}_{\varvec{k}}\) is the kth phase density, kg/m 3 ; \(\:\varvec{t}\) is the time, s; \(\:{\varvec{v}}_{\mathbf{m}}\) is the average velocity of the mixture, m/s; \(\:{\varvec{v}}_{\varvec{k}}\) is the velocity of the kth phase, m/s; \(\:{\varvec{\alpha\:}}_{\varvec{k}}\) is the volume fraction of the kth phase (hydrogen and natural gas); n is the number of phases; \(\:{\varvec{\mu\:}}_{\text{m}}\) is the mixture kinetic viscosity, Pa· s; \(\:{\varvec{\mu\:}}_{\varvec{k}}\:\) is the kth phase kinetic viscosity, Pa· s; \(\:{\varvec{v}}_{\mathbf{d}\mathbf{r},\mathbf{k}}\:\) is the drift velocity, m/s; \(\:\varvec{p}\:\) is the static pressure, Pa; \(\:\varvec{g}\) is the gravitational acceleration, m/s 2 .

Standard k - ε turbulence model

The standard \(\:k\) - \(\:\epsilon\:\) model has advantages of high simulation accuracy, moderate computational volume and large accumulation of data, and a wide range of applications. Considering that the flow from the main pipeline to the branch pipeline is in a turbulent state in this model. Therefore, in this paper, the standard \(\:k\) - \(\:\epsilon\:\) turbulence model is chosen to keep the set of equations closed:

where  \(\:{\varvec{C}}_{\varvec{\varepsilon\:}1}\) , \(\:{\varvec{\sigma\:}}_{\varvec{\varepsilon\:}}\) and \(\:{\varvec{C}}_{{\varepsilon\:}2}\) are empirical constants taken as 1.45, 1.30 and 1.90 respectively.

Basic parameters setting

Geometrical model and basic parameters.

The research calculations in this paper are scenarios for the study of hydrogen concentration distribution in hydrogen-doped methane pipelines.

The geometric model of the hydrogen-doped methane pipeline consists of a main pipeline and three horizontal branch pipelines. The diameter of the main pipeline is 50 mm and the height is 9,735.5 mm. The diameter of the branch pipeline is 25 mm and the length is 1,500 mm. The Y-axis negative direction is the direction of gravity and the acceleration of gravity is 9.81 m/s 2 . Taking the center point of the circular surface at the bottom of the main pipeline as the origin, the main pipeline rises vertically in the direction of gravity and branches out into three horizontal branch pipelines at y = 3,000 mm, y = 6,000 mm, and y = 9,000 mm respectively. The horizontal branch pipeline is extended in the positive direction along the x-axis. The horizontal branch pipelines are parallel to each other, and the distance between the branch pipelines is 3,000 mm. Figure  1 shows the schematic diagram of the three-dimensional geometric model established in the CFD fluid dynamics software.

Hydrogen-doped methane pipeline geometrical model schematics.

In this simulation, a mixture of methane and hydrogen is chosen instead of a mixture of natural gas and hydrogen to achieve a simplified study of stratification phenomena after hydrogen blending in gas pipelines. Methane is the main component of natural gas and is relatively less dense than the other components. If stratification exists in hydrogen-methane mixtures, it is more pronounced in hydrogen-natural gas. The physical parameters of methane and hydrogen are shown in Table  1 .

The geometrical model shown is divided using the structured mesh and local encryption of the mesh is done near the inlet of the pipe, the outlet of the pipe, the wall of the pipe, and the reducer. Three monitoring points (S1.1-S3.3) were set at 1 mm from the upper wall surface, at the center of the cross-section, and at 1 mm from the lower wall surface of each branch pipe, and one monitoring point was set at the center of the top of the main pipe, S4. As shown in Fig.  2 , a total of 10 monitoring points were set up to monitor the aggregation of hydrogen as well as the changes in hydrogen concentration. The number of mesh cells is changed by changing the mesh cell size, and the total number of meshes is 450,269, 604,262, 802,778, 936,874, 1123,650 in order. This simulation adopts the mixture multiphase model, the pipe inlet is set as the pressure inlet, and the pipe outlet is set as the velocity outlet. In the process of calculating, the heat transfer with the outside world is not considered, and the pipe wall is an adiabatic wall surface. A pipe pressure of 5 kPa, a flow velocity of 2.26 m/s, and a hydrogen-doping ratio of 15% were set to perform grid-independence tests on five grid models. Table  2 shows the results of the comparison between the maximum hydrogen volume fraction and the hydrogen volume fraction at the three monitoring points S1.1, S2.1, and S3.1 at 1 mm above the upper wall at the end of the three branch pipes at different numbers of meshes.

From Table  2 , it can be seen that the choice of Mesh-3 (total number of meshes 802,778) has achieved the expected computational accuracy. Meanwhile, considering the computational power problem, if the mesh is too dense, it occupies larger computational resources. Therefore, Mesh-3 with a total number of 802,778 meshes is selected for numerical calculations in this study, and Fig.  2 shows a localized schematic of the meshes used.

Localized diagram of the mesh.

Ultimately, the models and computational methods selected for this study include the following:

About the selection of the mesh: the total number of meshes selected is 802,778, and the meshes near the pipeline inlet, pipeline outlet, pipeline wall, and reducer are encrypted. Taking the pipeline wall as the boundary, the number of boundary layer mesh layers is set to 5, and the height growth rate of layer height is 1.2, which is used to capture the simulation data of the boundary layer.

Regarding the choice of solver: a three-dimensional double-precision, pressure-based solver is chosen. The heat transfer with the outside is not considered, the pipeline wall is an adiabatic wall boundary. The steady state calculation is used for the pipeline flow status and the transient calculation is used for the pipeline shutdown status, and the convergence criterion of the residuals in the calculation monitoring is 10 −6 . The direction of gravity along the Y-axis is set to be − 9.81 m/s 2 .

For model selection: The Mixture model was chosen, with phase1 set to methane and phase2 set to hydrogen. The turbulence model chosen is the standard \(\:k\) - \(\:\epsilon\:\) model.

Setting of the solution algorithm: SIMPLE algorithm and first-order windward format of the pressure-velocity coupling method are used for the calculation.

Setting of boundary conditions: In flow status, the pipeline inlet is set as the boundary of pressure inlet, and the pipeline outlet is set as the boundary of velocity outlet (by selecting the velocity inlet, and at the same time, the velocity takes a negative value to realize the setting of velocity outlet). In the calculation process, the heat transfer with the outside is not considered, and the pipeline wall is the adiabatic wall boundary. Table  3 shows the boundary conditions of the pipeline in flow status.

The simulation study in the shutdown status is based on the steady state results when entering the shutdown status, and the results of the flow status are used as the initial conditions for the transient simulation in the shutdown status, with all the pipeline inlets and pipeline outlets closed, and the boundary type of all the entrances and exits is selected as wall. the pipeline is set up as all-wall to realize the simulation in the shutdown status. The simulation of the pipe in shutdown status adopts transient calculation, in which the time step of the 10 h shutdown simulation is 1 s, and the time step of the rest of the simulation is 0.1 s. In the calculation process, the heat transfer with the outside is not considered, and the pipeline wall is the adiabatic wall boundary. Table  4 shows the boundary conditions of the pipeline in shutdown status.

Control variable settings

This paper focuses on the distribution of hydrogen concentration in a hydrogen-doped methane pipeline under flow status and shutdown status, and the main control variables are hydrogen-doping ratio, flow velocity, pressure, shutdown time and gas usage. A total of 25 cases are set up in this simulation, as shown in Table  5 .

The hydrogen-doping ratios are set at 10%, 15%, 20% and 25% by synthesizing the hydrogen blending demonstration projects around the world and the studies of other scholars. The setting of flow velocity according to the relevant standards and the data of domestic gas, the consumption of natural gas for single-eye stove is 0.1–0.4 m 3 /h, and the water heater is 2 m 3 /h. If both double-eye stove and water heater are used at the same time, the flow velocity can be up to 2.8 m 3 /h. Meanwhile, taking into account the corrections and the safety coefficients, the basic flow velocity of this study is set to be 4 m 3 /h, and the basic flow velocity of the branch pipe is 2.26 m/s. The simulated flow velocity values are set at 0.75–1.5 times the base flow velocity, namely 1.695 m/s, 2.260 m/s, 2.825 m/s, and 3.390 m/s. The indoor gas pressure is based on the Liaoning Chaoyang Hydrogen Blending Demonstration Project, and this simulation sets the base pressure at 5 kPa, and sets the pressure control groups at 3 and 7 kPa. Six different gas usage scenarios are set up, taking into account the fact that some of the branches may be in operation, and some of them are in a shutdown status at different times of the day when the gas is being used by the users.

Numerical method verification

There is no research report on the shutdown stratification experiments of hydrogen and methane gas mixtures. Therefore, in order to prove the reliability of the model established in this paper, the experimental results of methane diffusion by injection into an air-filled closed tank were chosen for simulation verification 38 . A model identical to the experimental setup in the literature was developed as shown in Fig.  3 . The height of the tank is 0.33 m, the diameter of the tank is 0.14 m, the wall thickness is 10 mm, the initial pressure is 1 MPa, and the initial temperature is 294 K. The filling inlet is located at 4/5 of the height of the vessel, and the diameter of the circular filling inlet is 10 mm, and the filling pressure is 0.11 MPa. The simulation is performed using the multiphase flow Mixture model, the SIMPLE algorithm, transient calculations and parameter settings are exactly the same as the experiments. The mesh adopts tetrahedral grids with the number of 253,233. Time step set to 0.01s and the convergence criterion of the residuals in the calculation monitoring is 10 −6 . Heat transfer to the outside is not considered in the calculation process.

The model of numerical method verification.

In the cited literature, the average methane concentrations measured at 1/5 and 4/5 vessel height were 14.2 and 15.9%, respectively. In the simulation results, the methane concentrations at 1/5 and 4/5 vessel height were 14.52132 and 15.4962%, respectively. Figure  4 shows the comparison between the simulation and experimental results.

Numerical method verification results graph.

As shown in Fig.  4 , the simulated values can be in good agreement with the experimental values, and the maximum error is only 2.54%. Therefore, it can be considered that it is feasible to use this numerical simulation method to simulate the stratification phenomenon of hydrogen-doped methane.

Results and discussion

Pipeline flow status.

Cases 1-9 are numerical simulations of hydrogen concentration distribution in hydrogen-doped methane pipeline under flow status. This part of the simulation is based on case 1, to explore the stratification phenomenon in the pipeline under the flow status of hydrogen-doped methane pipeline, and to carry out the research on the influence of hydrogen-doping ratio, flow velocity and pressure on the hydrogen concentration distribution law in the hydrogen-doped methane pipeline under flow status. In analyzing the factors influencing the phenomenon of stratification in the pipeline under flow status, the method of changing a single parameter and controlling the other parameters to be fixed is adopted. The effects of changes in the hydrogen doping ratio, flow velocity, and pressure on the distribution law of hydrogen concentration in the hydrogen-doped methane pipeline were studied separately and individually.

Stratification phenomenon in pipeline flow status

Based on case 1, the pipeline is initially filled with a well-mixed methane-hydrogen gas with a methane volume fraction of 85% and a hydrogen volume fraction of 15%. The flow inside the hydrogen-blended gas pipeline is simulated at a hydrogen-doping ratio of 15%, a pressure of 5 kPa and a flow velocity of 2.26 m/s. As shown in Fig.  5 , the hydrogen-doped methane showed a non-uniform distribution of hydrogen concentration during the flow in the gas pipeline. As the horizontal flow distance increases, in the region near the upper wall surface of the three branches of the model, a red region representing a higher hydrogen volume fraction appears, which represents a localized increase in hydrogen concentration in the horizontal branch in the region near the upper wall surface. In the region near the lower wall of the three branches of the model, a blue region representing a lower hydrogen volume fraction appears, which represents a localized decrease in hydrogen concentration in the horizontal branch near the lower wall.

Cloud figure of hydrogen volume fraction distribution for hydrogen-doped methane pipeline model (flow status).

As shown in Fig.  6 , the cross sections were taken at 0.4 m, 0.9 m and 1.2 m of the second branch pipe along the flow direction and the contour cloud plots of hydrogen volume fraction distribution were generated. In Fig.  6 , the pipeline cross-section cloud map at 0.4 m along the flow direction of the branch pipe shows uniform coloring, at which point the hydrogen and methane are more evenly mixed. With the increase of the horizontal flow distance, the upper wall area of the pipeline cross-section cloud map at 0.9 m shows a localized yellow color, and the upper wall area of the pipeline cross-section cloud map at 1.2 m shows a red color representing a higher hydrogen volume fraction. The gas mixture in the branch gradually becomes inhomogeneous from a uniformly mixed state, with increasing contour lines and gradients.

Cloud figure of hydrogen volume fraction distribution in the second branch pipe (flow status).

The hydrogen volume fraction data of the three horizontal branch pipes were taken at equal distance from 0.4 m to 1.2 m along the flow direction, 1 mm from the upper wall and 1 mm from the lower wall and read for graphing. The hydrogen volume fraction change curve of each horizontal branch along the flow direction was obtained, as shown in Fig.  7 . With the increase of flow distance, the three curves at 1 mm from the lower wall surface of the three branches showed a gradual decreasing trend, and the hydrogen volume fraction decreased from about 14.87% to about 14.66%, with an average decrease of 0.21%. The three curves at 1 mm from the upper wall surface of the three branches showed a gradual increasing trend, and the hydrogen volume fraction increased from about 15.06% to about 15.27%, with an average increase of 0.21%. The decreasing value of hydrogen volume fraction is basically equal in magnitude to the corresponding increasing value of hydrogen volume fraction. This is consistent with the changes in the cloud diagram in Fig.  6 .

Hydrogen volume fraction change curve in horizontal branch flow direction.

Figure  7 Hydrogen volume fraction change curve in horizontal branch flow direction.

The density of hydrogen is much less than that of methane, and under the effect of gravity, the less dense hydrogen will drift upward in the direction of gravity. In the flow state, a low-flow zone occurs near the wall of the pipeline, and the closer to the wall, the lower the flow velocity of the fluid. This causes hydrogen to accumulate more easily near the upper wall surface. The phenomenon of non-uniform distribution of hydrogen in the pipeline is slowly presented. At the same time, when the hydrogen-doped natural gas enters the branch pipe along the main pipe, the flow direction is changed, at this time, the turbulence intensity of the fluid in the pipeline is larger, and the hydrogen and natural gas are mixed more evenly. With the increase of the flow distance, the flow in the pipe tends to stabilize, and the turbulence intensity is weakened. The upward drift of hydrogen along the direction of gravity is enhanced at this time, and a weak non-uniform distribution of hydrogen volume fraction occurs in the pipeline.

Therefore, it can be concluded that in the flow status, hydrogen-blended natural gas pipeline in the horizontal pipeline of gas pipeline, with the increase of the flow distance, the hydrogen gas gradually drifts upward inside the pipeline, and there will be a slight non-uniform distribution phenomenon of hydrogen gas. The hydrogen volume fraction distribution is higher up and lower down, and a slight stratification phenomenon occurs.

Analysis of the effect of hydrogen-doping ratio on hydrogen concentration distribution law in the flow status of pipelines

In order to study the hydrogen concentration distribution law in the hydrogen-doped methane pipeline, and to further investigate the effect of hydrogen-doping ratio on the hydrogen concentration distribution in the hydrogen-doped methane pipeline, this section carries out the study based on Case 1. The fixed flow velocity is 2.26 m/s, the pressure is 5 kPa, and the hydrogen-doping ratios are changed to 10%, 15%, 20%, and 25% in order to carry out the numerical simulation of hydrogen distribution in hydrogen-doped methane pipeline with different hydrogen-doping ratios under the flow status of the pipeline. Table  6 shows the maximum and minimum values of hydrogen volume fraction and the gradient values of hydrogen volume fraction in hydrogen-doped methane pipelines with different hydrogen-doping ratios under the flow status of the pipelines.

In Table  6 , the gradient of hydrogen volume fraction in the hydrogen-doped methane pipeline in the gravity direction increased from 0.66758 to 1.21841% as the hydrogen-doping ratio increased from 10 to 25%. The hydrogen volume fraction at each monitoring point also increased with the increase in hydrogen-doping ratio. The upward drift of hydrogen along the direction of gravity was more pronounced. However, overall, there is no significant aggregation and the stratification in the pipeline is weak.

Figure  8 shows the hydrogen volume fraction distribution curves of the second branch pipe at 1 mm from the upper wall along the flow direction and at the end of the branch pipe along the radial direction with different hydrogen-doping ratios, respectively. In Fig.  8 , the hydrogen-doped methane with different hydrogen-doping ratios showed a slow increase in the hydrogen volume fraction along the flow direction and a first increase, then stabilization and then increase along the radial direction.

Hydrogen volume fraction curves in branch flow direction and radial direction for different hydrogen-doping ratios.

Figure  9 shows the cloud figure of hydrogen volume fraction distribution in radial section at the end of the second branch pipe for different hydrogen-doping ratios. In Fig.  9 , the arc-shaped region of increasing hydrogen concentration occurs in all the upper wall regions and the arc-shaped region of decreasing hydrogen concentration occurs in all the lower wall regions of the branch pipe, and the higher the hydrogen-doping ratio is, the larger the gradient of hydrogen volume fraction is. The density of hydrogen is small, the higher the hydrogen volume fraction in the pipeline under flow status, the greater the percentage of hydrogen drifting upward along the direction of gravity. The aggregation of hydrogen continues to accumulate, and the aggregation near the upper wall surface of the pipeline is relatively more obvious, and the hydrogen volume fraction gradient rises. Therefore, it can be concluded that the hydrogen-doping ratio has a slight effect on the hydrogen aggregation phenomenon under the flow status of the pipeline, which is positively correlated.

Cloud figure of hydrogen volume fraction distribution for different hydrogen-doping ratios (flow status).

Analysis of the effect of flow velocity on hydrogen concentration distribution law in the flow status of pipelines

In order to study the hydrogen concentration distribution law in the hydrogen-doped methane pipeline, and further study the effect of flow velocity on the hydrogen concentration distribution in the hydrogen-blended gas pipeline, this section is based on the case 1, and the numerical simulation of hydrogen distribution in the hydrogen-blended gas pipeline with different flow velocities under the pipeline flow status is carried out. The fixed hydrogen-doping ratio is 15%, the pressure is 5 kPa, and the flow velocity is changed to 1.695 m/s, 2.260 m/s, 2.825 m/s, and 3.390 m/s in order. Table  7 shows the maximum and minimum values of hydrogen volume fraction and the gradient values of hydrogen volume fraction in hydrogen-doped methane pipelines with different flow velocities under the flow status of the pipelines.

In Table  7 , as the flow velocity increases from 1.695 m/s to 3.390 m/s, the minimum hydrogen volume fraction in the hydrogen-doped methane pipeline gradually increases and the maximum hydrogen volume fraction gradually decreases, and the gradient of hydrogen volume fraction in the hydrogen-doped methane pipeline in the gravity direction decreases from 1.38865 to 0.50158%. The hydrogen volume fraction at each monitoring point also decreased with increasing flow velocity.

Figure  10 a shows the distribution curves of hydrogen volume fraction in the second branch pipe in the flow direction at a distance of 1 mm from the upper wall at different flow velocities. On the basis of other working conditions remaining unchanged, the hydrogen volume fraction increases with the distance of flow in the horizontal branch pipe to varying degrees. The smaller the flow velocity of hydrogen-doped methane, the faster the increase of hydrogen volume fraction in the flow direction. Figure  10 b shows the distribution curve of hydrogen volume fraction in the gravity direction of the pipe diameter at the end section of the branch pipe. In the radial direction of the end section of the branch pipe, the hydrogen volume fraction shows a gradual decrease near the lower wall, a gradual increase near the upper wall, and a more stable trend in the middle of the pipe. The smaller the flow velocity, the larger the gradient of hydrogen volume fraction in the pipe, and the more obvious the change in the gravity direction.

Hydrogen volume fraction curves in branch flow direction and radial direction for different flow velocities.

Figure  11 shows the contour cloud figure of hydrogen volume fraction distribution at the end section of the second branch pipe for different flow velocities. In the flow branches with different flow velocities, there are arc-shaped regions of gradually increasing hydrogen concentration in the upper wall region of the branch, and arc-shaped regions of gradually decreasing hydrogen concentration in the lower wall of the branch. And the lower the flow velocity, the more dark-colored regions representing higher hydrogen volume fractions and the larger the hydrogen volume fraction gradient.

Under the hydrogen-doped methane pipeline operating status, the higher the flow velocity of the fluid in the pipeline, the more unstable its flow status is, and the intensity of turbulence is greater. It will lead to the movement between hydrogen and methane is relatively more intense, and the hydrogen accumulation phenomenon is relatively weakened. From the above analysis of the simulation results, it can be concluded that under the flow status of the pipeline, the smaller the flow velocity is, the more obvious the phenomenon of hydrogen drifting upward and gathering in the hydrogen-doped methane pipeline.

Contour cloud figure of hydrogen volume fraction distribution for different flow velocities.

Analysis of the effect of pressure on hydrogen concentration distribution law in the flow status of pipelines

In order to study the hydrogen concentration distribution law in the hydrogen-doped methane pipeline, and further study the effect of pressure on the hydrogen concentration distribution in the hydrogen-doped methane pipeline, this section is based on case 1, and numerical simulation of hydrogen distribution in the hydrogen-blended gas pipeline with different pressures in the flow status of the pipeline is carried out. The fixed hydrogen-doped methane pipeline blending ratio is 15%, the flow velocity is 2.260 m/s, and the flow velocity pressure is changed to 3 kPa, 5 kPa, and 7 kPa. Table  8 shows the maximum and minimum values of the hydrogen volume fraction and the gradient values of the hydrogen volume fraction in the hydrogen-doped methane pipeline with different pressures under the flow status of the pipeline.

The pressure at the end of gas pipelines in town is low, especially in the pipelines entering households. Therefore, a pressure range of 3–5 kPa was used in this study. When the pressure range varied from 3 kPa to 7 kPa, the hydrogen volume fraction at each monitoring point did not change by more than 0.035%, the change in the lowest value of hydrogen concentration in the pipeline did not exceed 0.01%, the change in the highest value did not exceed 0.003%, and the change in the hydrogen volume fraction gradient did not exceed 0.01%. According to the simulation results, the change in pressure affects the hydrogen volume fraction inside the hydrogen-doped gas pipeline within 0.035%, and has little effect on the distribution of hydrogen volume fraction inside the hydrogen-doped methane pipeline.

The pressure change range receives the constraints of the operating range of the town gas pipeline, which is relatively small, and the effect of this change on the phenomenon of upward drift of hydrogen by gravity and the molecular movement in the pipeline is relatively small. Therefore, in the gas pipeline operating pressure range, the effect of pressure on the stratification phenomenon of the pipeline is negligible.

Sensitivity analysis of pipeline in flow status

The ratio of the change in the gradient value of the hydrogen volume fraction within a hydrogen-doped methane pipeline to the change in the corresponding parameter value is used as the sensitivity coefficient. By comparing the sensitivity coefficients of different parameters, the degree of influence of a specific pipeline parameter on the phenomenon of non-uniform distribution of hydrogen in the pipeline was derived. A hydrogen doping ratio of 15%, a pressure of 5 kPa, and a flow rate of 2.26 m/s were selected as the base parameters. The sensitivity analysis was carried out by taking ± 75% of the base parameter value as the analysis interval, and the sensitivity analysis figure of the influence of each pipeline parameter on the gradient value of hydrogen volume fraction in the pipeline was obtained, as shown in Fig.  12 .

Sensitivity analysis curves of pipeline parameters (flow status).

The sensitivity analysis revealed that the influence of different parameters on the gradient value of hydrogen volume fraction in the hydrogen-doped methane pipeline varied greatly. The influence of flow velocity was the largest and had the highest sensitivity. The hydrogen doping ratio had the second highest effect. The sensitivity coefficient of pressure is the smallest, and its hydrogen volume fraction gradient value is almost unchanged in the selected analysis interval. Therefore, it can be concluded that under the flow status in the pipeline, the flow velocity in the pipeline has relatively the greatest influence on the phenomenon of upward drift and aggregation of hydrogen, followed by the hydrogen doping ratio, and the pressure has the smallest influence.

Pipeline shutdown status

Cases 10-25 show the numerical simulation study of hydrogen concentration distribution law of hydrogen-doped methane pipeline under shutdown status. In order to investigate the stratification phenomenon in the hydrogen-doped methane pipeline under the shutdown status, the study on the influence of hydrogen-doping ratio, shutdown time and gas usage on the hydrogen concentration distribution law in the hydrogen-doped methane pipeline under the shutdown status is carried out. In analyzing the factors influencing the phenomenon of stratification in the pipeline under shutdown status, the method of changing a single parameter and controlling the other parameters to be fixed is adopted. The effects of changes in the hydrogen doping ratio, shutdown time, and gas usage on the distribution law of hydrogen concentration in the hydrogen-doped methane pipeline were studied separately and individually.

Stratification phenomenon in pipeline shutdown status

In case 11, the pipe is initially filled with a well-mixed methane-hydrogen gas with a methane volume fraction of 85% and a hydrogen volume fraction of 15%. And a methane-hydrogen gas mixture with a hydrogen-doping ratio of 15% is injected into the pipe at a flow velocity of 2.26 m/s. After the operation was stabilized, the boundary conditions were set to full-wall, and transient calculations were performed to simulate the shutdown condition inside the hydrogen-doped methane pipeline at a hydrogen-doping ratio of 15%, a pressure of 5 kPa, and a shutdown time of 300s. Figure  13 shows a cloud figure of the hydrogen volume fraction distribution after the calculation under the above simulation conditions. Due to the large gradient of the hydrogen volume fraction, the zoomed-in three horizontal branches were turned around the X-axis by 90° to show a top-down view for convenient observation. As shown in Fig.  13 , during the 300s shutdown of the hydrogen-doped methane pipeline, a non-uniform distribution of hydrogen concentration was observed in all three branch pipes. As the horizontal flow distance increases, in the region near the upper wall surface of the three branches of the model, a red region representing a higher hydrogen volume fraction appears, which represents a localized increase in hydrogen concentration in the horizontal branch in the region near the upper wall surface. In the region near the lower wall of the three branches of the model, a blue region representing a lower hydrogen volume fraction appears, which represents a localized decrease in hydrogen concentration in the horizontal branch near the lower wall.

Cloud figure of hydrogen volume fraction distribution for hydrogen-doped methane pipeline model (shutdown status).

Figure  14 shows the contour cloud figures of hydrogen volume fraction distribution along the flow direction at 0.4 m, 0.9 m, and 1.2 m of the second branch pipe. As shown in Fig.  14 , the hydrogen-doped methane shows obvious stratification after 300s of shutdown, red areas representing higher hydrogen volume fractions appear at the top of the cross section, blue areas representing lower hydrogen volume fractions appear at the bottom, and in the middle of the cross-section are large areas of more uniformly colored green. The stratification is obvious and relatively large gradient in the cloud figure.

Cloud figure of hydrogen volume fraction distribution in the second branch pipe (shutdown status).

By taking equally spaced points along the radial direction of the three horizontal branches at x = 0.9 m and reading their hydrogen volume fraction data for graphing, the hydrogen volume fraction variation curves along the radial direction of each horizontal branch under the shutdown status were obtained, as shown in Fig.  15 . Along the negative direction of gravity, the hydrogen volume fraction in the cross section at x = 0.9 m of the three branch pipelines shows a rapid increase from about 1.20–15% and then stabilized, and then increased abruptly to 51.46%. The gradient value of hydrogen volume fraction in the pipeline reaches more than 50% after the pipeline shutdown for 300s.

Hydrogen volume fraction change curve for horizontal branch radial direction.

Compared with the flow status of the pipeline, also due to the fact that the density of hydrogen is much smaller than that of methane, under the gravity, the less dense hydrogen will drift upward along the direction of gravity, presenting a stratification phenomenon in which the hydrogen concentration in the pipeline rises in the region near the upper wall and decreases in the region near the lower wall. The difference is that, since the pipeline in the shutdown status, at this time under the action of gravity the upward drift phenomenon of hydrogen and the downward deposition phenomenon of methane are in the dominant position. As the shutdown time progresses, a clear stratification phenomenon occurs in the hydrogen-doped methane pipe. Inside the pipeline near the upper wall area, there is a clear phenomenon of hydrogen aggregation, and hydrogen continues to accumulate. Near the lower wall, hydrogen moves upward, methane accumulates, and hydrogen concentration decreases.

Therefore, it can be concluded that hydrogen-doped methane in the horizontal pipeline of gas pipeline in the status of shutdown will show obvious non-uniform distribution of hydrogen and obvious stratification in the pipeline.

Analysis of the effect of hydrogen-doping ratio on hydrogen concentration distribution law in the shutdown status of pipelines

In order to study the hydrogen concentration distribution law in the hydrogen-doped methane pipeline and further investigate the effect of hydrogen-doping ratio on the hydrogen concentration distribution in the hydrogen-doped methane pipeline, numerical simulation of hydrogen distribution in the hydrogen-doped methane pipeline with different hydrogen-doping ratios in the pipeline shutdown status is carried out in this section. The fixed shutdown time is 600s, the pressure is 5 kPa, the boundary condition is full-wall, and the hydrogen-doping ratios are changed to 10%, 15%, 20%, and 25% sequentially. Table  9 shows the maximum and minimum values of hydrogen volume fractions and gradient values of hydrogen volume fractions in hydrogen-doped methane pipelines with different hydrogen-doping ratios under the pipeline shutdown status.

As can be seen from Table  9 , the minimum hydrogen volume fraction in the hydrogen-doped methane pipeline increases slightly and the maximum hydrogen volume fraction increases gradually as the hydrogen-doping ratio increases from 10 to 25%, and the gradient of the hydrogen volume fraction in the hydrogen-blended gas pipeline in the gravity direction increases from 80.86150 to 92.03260%. The hydrogen volume fraction at each monitoring point also increased with the hydrogen-doping ratio.

Hydrogen volume fraction curves in branch flow direction and radial direction for different hydrogen-doping ratio.

Figure  16 shows the distribution curves of hydrogen volume fraction in the flow direction of the branch pipe 1 mm from the upper wall and the radial direction of the end of the branch pipe after the shutdown of 600 s with different hydrogen-doping ratios. From Fig.  16 a, it can be seen that on the basis of other working conditions remain unchanged, the hydrogen in the region of the branch pipe near the upper wall surface increases rapidly from the initial value of the respective hydrogen-doping ratio to 70% and then rises slowly. And with the increase of hydrogen-doping ratio, the hydrogen volume fraction is higher. From Fig.  16 (b), the hydrogen volume fraction at the end of the branch pipe shows a trend of firstly increasing, then stabilizing and then rapidly increasing along the radial direction under the condition of other working conditions being unchanged. Obvious stratification occurs in the branch pipe. And the larger the hydrogen-doping ratio is, the more obvious the stratification phenomenon is.

Hydrogen volume fraction curve of S2.1.

Figure  17 shows the variation curve of hydrogen volume fraction with shutdown time at monitoring point S2.1. From Fig.  17 , it can be seen that on the basis of other working conditions, the higher the hydrogen-doping ratio, the higher the hydrogen volume fraction at the end of the branch pipe, and the more obvious the hydrogen aggregation phenomenon. However, the change rate of hydrogen volume fraction at the end of the branch pipe with shutdown time for different hydrogen-doping ratios is relatively small.

Figure  18 shows the cloud figure of hydrogen distribution at the end section of the branch pipe with different hydrogen-doping ratios after 600s shutdown. As can be seen from Fig.  18 , after 600s shutdown, hydrogen aggregates in the region near the upper wall of the pipeline. In the middle of the pipeline, hydrogen and natural gas are in a relatively uniform mixing state. Near the lower wall area, there is a decrease of hydrogen volume fraction. Different hydrogen-doped methane pipelines with different hydrogen-doping ratios show the phenomenon of stratification, and the local hydrogen volume fraction reaches more than 95%.

Cloud figure of hydrogen volume fraction distribution for different hydrogen-doping ratios (shutdown status).

In the shutdown status of the pipeline, hydrogen drifts upward also due to gravity. The higher the hydrogen content in the pipeline, the larger the proportion of hydrogen drifting upward along the direction of gravity, and the hydrogen aggregation continues to accumulate. And because the pipeline is in a more stable shutdown status, the aggregation phenomenon near the upper wall of the pipeline is relatively more obvious, and the gradient of the hydrogen volume fraction rises accordingly. From the above simulation results and analysis, it can be concluded that in the shutdown status of the pipeline, the higher the hydrogen doping ratio, the more obvious the stratification phenomenon in the pipeline.

Analysis of the effect of shutdown time on hydrogen concentration distribution law in the shutdown status of pipelines

In order to study the hydrogen concentration distribution law in the hydrogen-doped methane pipeline and further investigate the effect of the shutdown time on the hydrogen concentration distribution, this section is divided into two parts: short shutdown time and long shutdown time.

Shutdown time of 15 min.

The hydrogen-doping ratio was fixed at 15%, the pressure was 5 kPa, and the pipeline shutdown times were changed sequentially to 150s, 300s, 450s, 600s, 750s, and 900s for numerical simulation of hydrogen distribution in the branch of hydrogen-doped methane pipeline under the pipeline shutdown status with different shutdown times. Table  10 shows the maximum and minimum values of hydrogen volume fraction and gradient values of hydrogen volume fraction in the hydrogen-doped methane pipeline for different shutdown times under short pipeline shutdown status.

As shown in Table  10 , the minimum hydrogen volume fraction in the hydrogen-doped methane pipeline gradually decreases to 0 and the maximum hydrogen volume fraction gradually increases to 100% as the shutdown time moves to 900 s. The gradient of hydrogen volume fraction in the hydrogen-doped methane pipeline in the gravity direction increases from 30.07611 to 100%. The hydrogen volume fraction at each monitoring point also increased with the shutdown time.

Figure  19 a, b and c show the hydrogen volume fraction at each monitoring point of the three branch pipes with the shutdown time, respectively. As can be seen from the curve graphs, the hydrogen volume fraction at each monitoring point near the wall in each horizontal branch pipe increases with the shutdown time on the basis of other working conditions remaining unchanged. The volume fraction at the monitoring point at the central axis of the branch basically stabilizes at the set hydrogen-doping ratio of 15%. The hydrogen volume fraction at the monitoring point at the lower wall of the branch decreases with shutdown time. Figure  19 d shows the variation curve of the maximum hydrogen volume fraction with time. The maximum hydrogen volume fraction shows a general trend of increasing with the shutdown time, and the growth rate decreases with time until the maximum hydrogen volume fraction stabilizes.

Hydrogen volume fraction curves for different shutdown time (15 min).

Figure  20 shows the cloud figure of hydrogen volume fraction distribution with time in the radial section at the end of the second branch pipe. In the cloud figure, as the shutdown time progresses, the hydrogen inside the pipe drifts upward along the direction of gravity, and more and more obvious hydrogen aggregation gradually occurs in the region close to the upper wall surface of the pipe.

Cloud figure of hydrogen volume fraction distribution for different shutdown time (15 min).

Figure  21 shows the contour cloud figures of hydrogen volume fraction at the end of the branch pipe after 300s, 600s and 900s of pipe resting, respectively. As can be seen from Fig.  21 , the range of low concentration of hydrogen in the lower half of the pipeline is gradually extended, and the hydrogen volume fraction in the upper half of the pipeline is gradually increased. Obvious stratification phenomenon appears. The thickness of the gas with hydrogen volume fraction above 40% near the upper wall surface gradually increases from 0.3 mm to 0.7 mm, and locally it can reach more than 95%.

Cloud figure of hydrogen volume fraction distribution in the second branch pipe (15 min).

Hydrogen has a low density, and in a mixed fluid, hydrogen will continuously drift upward under the effect of gravity. In a hydrogen-doped methane pipeline in a short time shutdown status, the intermolecular motion in the pipeline is much weaker than in the flow status, mainly hydrogen drifting upward and methane settling downward. Therefore, with the time progress, the hydrogen continuously drifts upward and accumulates on the upper wall of the pipeline, and obvious stratification phenomenon occurs in the horizontal branches. And the longer the shutdown time, the more obvious the stratification phenomenon in the horizontal branch pipe.

Shutdown time of 10 h.

The hydrogen concentration distribution inside the hydrogen-doped methane pipeline with a 10-hour shutdown is explored with Case 19. Table  11 shows the maximum and minimum values of hydrogen volume fraction and the gradient values of hydrogen volume fraction in the hydrogen-doped methane pipeline with different shutdown time for the pipeline in the long shutdown status.

As shown in Table  11 , the highest hydrogen volume fraction in the hydrogen-doped methane pipeline gradually increased to 84.02245% as the shutdown time moved to 10 h. The gradient of hydrogen volume fraction in the hydrogen-doped methane pipeline in the direction of gravity increased from 0.90689 to 84.02245%. Meanwhile the hydrogen volume fraction at each monitoring point increases with the shutdown time.

Figure  22 shows the variation curve of hydrogen volume fraction with shutdown time at each monitoring point. On the basis of other working conditions, the hydrogen volume fraction at each monitoring point on the branch pipe shows a trend of rapid increase to reach the peak and then decrease to a stable state with the shutdown time. The monitoring points at the main pipe showed a slow increase. The hydrogen volume fraction at the monitoring points at the branch pipe basically stabilized at about 50%, and the hydrogen volume fraction at the monitoring points on the top of the main pipe slowly increased to 33% with the shutdown time.

Hydrogen volume fraction curve for different shutdown time (10 h).

Figure  23 shows the variation curve of hydrogen volume fraction with the height of the main pipe from 9 m to the top center axis of the main pipe. From the curve graph, it can be concluded that on the basis of other working conditions remaining unchanged, with the increase in height, there is a significant hydrogen aggregation at the top of the main pipe. The hydrogen volume fraction near the top of the main pipe of about 465.5 mm is more than 20%, and the hydrogen volume fraction of about 16.5 mm is more than 30%.

Hydrogen volume fraction curve at the top of the main pipeline (10 h).

Figure  24 shows the cloud figure of hydrogen volume fraction with time from 9 m to 9.7355 m of the hydrogen-doped methane pipeline. It can be clearly seen with the figure that with the increase of the shutdown time, the hydrogen gradually drifts upward in the main pipe. An increasingly obvious stratification is formed near the top of the main pipe.

Cloud figure of hydrogen volume fraction distribution at the top of the main pipeline.

Hydrogen in the main pipeline likewise drifts upward along the pipeline under the influence of gravity. The drift distance is longer, and the time required for significant stratification to occur is longer than the time required for significant stratification to occur in the branch pipe. In the completely shutdown status, the stratification phenomenon in the pipeline will appear in the upper wall of the branch pipe and the top of the main pipe, and with the progress of the shutdown time, the hydrogen accumulation phenomenon continues to accrue, and the stratification phenomenon becomes more and more obvious.

From the above analysis, it can be concluded that when a hydrogen-doped methane pipeline is in shutdown status for a long period of time, an increasing hydrogen volume fraction will appear at the top of the main pipeline, which will have a negative impact on the safety of the hydrogen-doped gas pipeline, mainly including:

As time increases, the hydrogen volume fraction near the top of the main will increase, which will pose a serious safety concern for the gas piping. When hydrogen accumulates locally in the pipeline, the infiltration of hydrogen into the pipeline will bring hydrogen embrittlement, hydrogen cracking and other risks of pipe failure to the gas pipeline. Once the pipe is damaged, it may cause serious consequences such as fire and explosion. At the same time, because the gas pipeline in this study is a gas pipeline for residential buildings in towns, the population density of residential buildings is large and the environment is complex. Once the pipe failure, it brings great threat to the safety of life and property.

Due to the non-uniform distribution of hydrogen within the main pipe, high-rise residents may use high concentrations of hydrogen-doped natural gas for a short period of time when it is restarted after a long shutdown period, which poses a significant challenge to the residents’ stoves and other appliances that use hydrogen-doped natural gas. Once the acceptable hydrogen concentration range for stoves or other appliances using hydrogen-doped natural gas is exceeded, there is a potential for a safety incident.

At the end of town gas pipelines entering households, pipelines are mostly made of PE pipes and sealing materials, and in PE pipes and sealing materials, the hydrogen permeability coefficient is 4 to 5 times larger than that of methane. Once the high concentration of hydrogen comes into contact with these materials, leakage accidents are very likely to occur.

Analysis of the effect of gas usage cases on hydrogen concentration distribution law in the shutdown status of pipeline

In order to study the hydrogen concentration distribution law in the hydrogen-doped methane pipeline, and further study the influence of different gas usage cases on the hydrogen concentration distribution, this section carries out the numerical simulation of the hydrogen distribution in the hydrogen-doped methane pipeline under different gas usage cases in the shutdown status. The hydrogen-doping ratio is fixed at 15%, the pressure is 5 kPa, the shutdown time is 300 s, and the operation of each branch pipe is changed sequentially. Table  12 shows the maximum and minimum values of hydrogen volume fraction and gradient values of hydrogen volume fraction in the hydrogen-doped methane pipeline for different gas usage cases.

As shown in Table  12 , the hydrogen volume fraction gradient in the shutdown branch is large, up to more than 50%, when part of the branch is shutdown. Significant stratification occurs in the shutdown branch pipe. There is a slight increase in the hydrogen volume fraction at the monitoring point near the upper wall of the flow branch, and a slight uneven distribution of hydrogen. The effect of the number of shutdown branches on the hydrogen volume fraction is small.

Fgures 25 , 26 and 27 show the cloud figure of hydrogen gas volume fraction distribution in the hydrogen-doped methane pipeline model when one branch pipe is in shutdown status and the rest of the branch pipes are in flow status. Due to the large gradient of the hydrogen volume fraction, the zoomed-in three horizontal branches were turned around the X-axis by 90° to show a top-down view for convenient observation. As can be seen from the figure, when a branch pipe is shutdown, the branch pipe in the shutdown status in the distance from the main pipe and branch pipe intersection of about 0.06 m, near the upper wall area of the hydrogen aggregation phenomenon, the local hydrogen volume fraction increased significantly, the pipeline appeared in the stratification phenomenon. The branch pipe in flow status did not show obvious stratification on the cloud figures.

Cloud figure of hydrogen volume fraction distribution for hydrogen-doped methane pipeline model (first branch shutdown).

Cloud figure of hydrogen volume fraction distribution for hydrogen-doped methane pipeline model (second branch shutdown).

Cloud figure of hydrogen volume fraction distribution for hydrogen-doped methane pipeline model (third branch shutdown).

Figures 28 , 29 and 30 show the cloud figures of hydrogen volume fraction distribution of the hydrogen-doped methane pipeline model when one branch pipe is in flow status and the rest of the branch pipes are in shutdown. Due to the large gradient of the hydrogen volume fraction, the zoomed-in three horizontal branches were turned around the X-axis by 90° to show a top-down view for convenient observation. As can be seen from the figures, when two branch pipes are shutdown, the branch pipe in flow status does not see obvious stratification on the cloud figure. The branch pipe in the shutdown status is about 0.06 m away from the intersection of the main pipe and the branch pipe, near the upper wall area, there is obvious hydrogen aggregation phenomenon, and the local hydrogen volume fraction is obviously increased. Obvious stratification was observed in the pipe of the shutdown branch.

Cloud figure of hydrogen volume fraction distribution for hydrogen-doped methane pipeline model (first and second branch shutdown).

Cloud figure of hydrogen volume fraction distribution for hydrogen-doped methane pipeline model (first and third branch shutdown).

Cloud figure of hydrogen volume fraction distribution for hydrogen-doped methane pipeline model (second and third branch shutdown).

Figure  30 Cloud figure of hydrogen volume fraction distribution for hydrogen-doped methane pipeline model (second and third branch shutdown).

The error data in Fig.  31 are derived from the error ranges obtained in the numerical simulation validation. Figure  31 a shows the distribution curve of hydrogen volume fraction along the flow direction at 1 mm from the upper wall of the shutdown branch pipe when one branch pipe is shutdown. Figure  31 b shows the hydrogen volume fraction distribution curve along the flow direction at 1 mm from the upper wall of the running stub when two branch pipes are shutdown. As can be seen from Fig.  31 a, the hydrogen volume fraction in the region near the upper wall of the shutdown branch pipe shows a trend of rapid increase from the initial hydrogen-doping ratio and then stabilizes along the flow direction, while other working conditions remain unchanged. From Fig.  31 b, it can be seen that the hydrogen volume fraction in the region near the upper wall of the operating branch pipe increases steadily along the flow direction, while other working conditions remain unchanged.

Hydrogen volume fraction curves along the flow direction in the branch pipe for different gas usage.

When some of the branches of the hydrogen-doped methane pipeline are in the shutdown status, although the main pipeline and the operating branches are in the flow status with high turbulence intensity, the branch in the shutdown status is less affected by the turbulence effect. Hydrogen drifts upward under the effect of gravity and accumulates on the upper wall of the shutdown branch pipeline, showing the phenomenon of stratification. And the change of the number and height of the shutdown branch pipe has less influence on the stratification phenomenon. Therefore, it can be concluded that significant stratification occurs in the shutdown branch for all different gas usage cases, and upward drift of hydrogen along the direction of gravity occurs in the operating branch.

Sensitivity analysis of pipeline in shutdown status

The ratio of the change in the gradient value of the hydrogen volume fraction within a hydrogen-doped methane pipeline to the change in the corresponding parameter value is used as the sensitivity coefficient. By comparing the sensitivity coefficients of different parameters, the degree of influence of a specific pipeline parameter on the phenomenon of non-uniform distribution of hydrogen in the pipeline was derived. Hydrogen doping ratio of 15%, pressure of 5 kPa, and shutdown time of 600s were selected as the base parameters. The sensitivity analysis was carried out by taking ± 75% of the base parameter value as the analysis interval, and the sensitivity analysis figure of the influence of each pipeline parameter on the gradient value of hydrogen volume fraction in the pipeline was obtained, as shown in Fig.  32 . The sensitivity ratio for the gas usage was not calculated because the magnitude of the change in the different gas usage scenarios is not easy to quantify. However, the data from the simulation results show that the effect of the change in gas usage on the hydrogen volume fraction gradient value is within 3%. The effect of gas usage is minimal compared to the hydrogen doping ratio and shutdown time.

After sensitivity analysis, it was found that the degree of influence of different parameters on the gradient value of hydrogen volume fraction in the hydrogen-doped methane pipeline under the shutdown status varied greatly. Among them, the effect of shutdown time was the largest with the highest sensitivity. The effect of hydrogen doping ratio is the second highest. Therefore, it can be concluded that in the pipeline shutdown status, the effect of the shutdown time on the phenomenon of stratification in the pipeline is relatively the largest, followed by the hydrogen doping ratio, and the effect of the gas usage situation is the smallest.

Sensitivity analysis curves of pipeline parameters (shutdown status).

Conclusions

In order to further study the hydrogen-doped methane pipeline hydrogen concentration distribution law and the influence of hydrogen-doped methane in the gas pipeline, based on the hydrogen-doped methane pipeline model established in this paper, numerical simulation of the pipeline flow status and pipeline shutdown status. The effects of different hydrogen-doping ratios, operating flow velocities, operating pressures, shutdown time, and gas usage on the hydrogen concentration distribution law in the hydrogen-doped methane pipeline are analyzed.

The results show that:

In the hydrogen-doped methane pipeline flow status, the hydrogen-doping ratio increases from 10 to 25%, and the gradient of hydrogen volume fraction in the gravity direction in the hydrogen-doped methane pipeline increases from 0.66758 to 1.21841%, which is positively correlated. The gradient of hydrogen volume fraction in the gravity direction in the hydrogen-doped methane pipeline decreases from 1.38865 to 0.50158% with a negative correlation when the flow velocity is increased from 1.695 m/s to 3.390 m/s. The pressure is increased from 3 kPa to 5 kPa with a negative correlation when the flow velocity is increased from 10 to 25%. The increase of pressure from 3 kPa to 5 kPa has little effect on the distribution of hydrogen volume fraction in the hydrogen-doped methane pipeline, which is negligible.

Under the hydrogen-doped methane pipeline flow status, the change of hydrogen-doping ratio, flow velocity, and pressure on the volume fraction of hydrogen is within 0.9%, and the influence effect is negligible.

In the hydrogen-doped methane pipeline under the shutdown status, obvious stratification phenomenon appears in the pipeline. The hydrogen-doping ratio increased from 10 to 25%, and the gradient of hydrogen volume fraction in the gravity direction in the hydrogen-doped methane pipeline increased from 80.86150 to 92.03260%, with basically the same rate of change with time. There is a clear stratification phenomenon. The shutdown time was extended to 10 h, and obvious stratification phenomenon appeared in the main pipeline, and the volume fraction of hydrogen near the top of the main pipeline about 16.5 mm was above 30%. In different gas usage cases, obvious stratification occurred in the shutdown branch pipe, and hydrogen drifted upward along the direction of gravity in all the operating branch pipes.

The shutdown status of the hydrogen-doped methane pipeline, the shutdown time has the greatest effect on the stratification phenomenon in the pipeline, followed by the hydrogen-doping ratio, and the gas usage has the smallest effect.

Based on the simulation results and conclusions, this study concludes that there is indeed a risk of stratification in the mixing and transportation of hydrogen and natural gas in gas pipelines, which mainly stems from the significant density difference between the two. This stratification phenomenon may not only lead to localized enrichment of hydrogen concentration in the pipeline, but also increase the risk of hydrogen damage, which poses a serious challenge to the safe operation of hydrogen-doped natural gas pipelines. In order to cope with this challenge, the following points are proposed in this paper:

Pipe preferably hydrogen embrittlement-resistant materials, selecting materials with lower sensitivity to hydrogen, such as hydrogen embrittlement-resistant alloy steel, in order to minimize the risk of hydrogen damage, especially in the position of valves and welding points close to the upper wall surface of the pipeline and the top of pipeline position, which need to be more attentive to hydrogen embrittlement-resistant safety protection.

Optimize the pipeline structure at the pipeline design stage. Considering the physical properties of hydrogen and natural gas, optimize the pipeline structure, such as adding mixing devices or changing the shape of the pipeline, in order to promote the uniform mixing of hydrogen and natural gas. Meanwhile, multiple hydrogen concentration monitoring points are set up along the pipeline to monitor the distribution of hydrogen in the pipeline in real time to ensure timely detection of stratification phenomenon and hydrogen-doped natural gas leakage.

Use gas appliances with higher hydrogen resistance. Also, within tolerance, increase the flow rate. The increase in flow velocity will reduce the upward drift of hydrogen in the direction of gravity.

Data availability

Date is provided within the manuscript.

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Acknowledgements

This research was funded by the Postdoctoral Foundation of PetroChina Southwest Oil & Gasfield Company (grant number 20230312-10).

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Observation Method in Psychology: Naturalistic, Participant and Controlled

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

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On This Page:

The observation method in psychology involves directly and systematically witnessing and recording measurable behaviors, actions, and responses in natural or contrived settings without attempting to intervene or manipulate what is being observed.

Used to describe phenomena, generate hypotheses, or validate self-reports, psychological observation can be either controlled or naturalistic with varying degrees of structure imposed by the researcher.

There are different types of observational methods, and distinctions need to be made between:

1. Controlled Observations 2. Naturalistic Observations 3. Participant Observations

In addition to the above categories, observations can also be either overt/disclosed (the participants know they are being studied) or covert/undisclosed (the researcher keeps their real identity a secret from the research subjects, acting as a genuine member of the group).

In general, conducting observational research is relatively inexpensive, but it remains highly time-consuming and resource-intensive in data processing and analysis.

The considerable investments needed in terms of coder time commitments for training, maintaining reliability, preventing drift, and coding complex dynamic interactions place practical barriers on observers with limited resources.

Controlled Observation

Controlled observation is a research method for studying behavior in a carefully controlled and structured environment.

The researcher sets specific conditions, variables, and procedures to systematically observe and measure behavior, allowing for greater control and comparison of different conditions or groups.

The researcher decides where the observation will occur, at what time, with which participants, and in what circumstances, and uses a standardized procedure. Participants are randomly allocated to each independent variable group.

Rather than writing a detailed description of all behavior observed, it is often easier to code behavior according to a previously agreed scale using a behavior schedule (i.e., conducting a structured observation).

The researcher systematically classifies the behavior they observe into distinct categories. Coding might involve numbers or letters to describe a characteristic or the use of a scale to measure behavior intensity.

The categories on the schedule are coded so that the data collected can be easily counted and turned into statistics.

For example, Mary Ainsworth used a behavior schedule to study how infants responded to brief periods of separation from their mothers. During the Strange Situation procedure, the infant’s interaction behaviors directed toward the mother were measured, e.g.,

• Proximity and contact-seeking
• Contact maintaining
• Avoidance of proximity and contact
• Resistance to contact and comforting

The observer noted down the behavior displayed during 15-second intervals and scored the behavior for intensity on a scale of 1 to 7.

Sometimes participants’ behavior is observed through a two-way mirror, or they are secretly filmed. Albert Bandura used this method to study aggression in children (the Bobo doll studies ).

A lot of research has been carried out in sleep laboratories as well. Here, electrodes are attached to the scalp of participants. What is observed are the changes in electrical activity in the brain during sleep ( the machine is called an EEG ).

Controlled observations are usually overt as the researcher explains the research aim to the group so the participants know they are being observed.

Controlled observations are also usually non-participant as the researcher avoids direct contact with the group and keeps a distance (e.g., observing behind a two-way mirror).

• Controlled observations can be easily replicated by other researchers by using the same observation schedule. This means it is easy to test for reliability .
• The data obtained from structured observations is easier and quicker to analyze as it is quantitative (i.e., numerical) – making this a less time-consuming method compared to naturalistic observations.
• Controlled observations are fairly quick to conduct which means that many observations can take place within a short amount of time. This means a large sample can be obtained, resulting in the findings being representative and having the ability to be generalized to a large population.

Limitations

• Controlled observations can lack validity due to the Hawthorne effect /demand characteristics. When participants know they are being watched, they may act differently.

Naturalistic Observation

Naturalistic observation is a research method in which the researcher studies behavior in its natural setting without intervention or manipulation.

It involves observing and recording behavior as it naturally occurs, providing insights into real-life behaviors and interactions in their natural context.

Naturalistic observation is a research method commonly used by psychologists and other social scientists.

This technique involves observing and studying the spontaneous behavior of participants in natural surroundings. The researcher simply records what they see in whatever way they can.

In unstructured observations, the researcher records all relevant behavior with a coding system. There may be too much to record, and the behaviors recorded may not necessarily be the most important, so the approach is usually used as a pilot study to see what type of behaviors would be recorded.

Compared with controlled observations, it is like the difference between studying wild animals in a zoo and studying them in their natural habitat.

With regard to human subjects, Margaret Mead used this method to research the way of life of different tribes living on islands in the South Pacific. Kathy Sylva used it to study children at play by observing their behavior in a playgroup in Oxfordshire.

Collecting Naturalistic Behavioral Data

Technological advances are enabling new, unobtrusive ways of collecting naturalistic behavioral data.

The Electronically Activated Recorder (EAR) is a digital recording device participants can wear to periodically sample ambient sounds, allowing representative sampling of daily experiences (Mehl et al., 2012).

Studies program EARs to record 30-50 second sound snippets multiple times per hour. Although coding the recordings requires extensive resources, EARs can capture spontaneous behaviors like arguments or laughter.

EARs minimize participant reactivity since sampling occurs outside of awareness. This reduces the Hawthorne effect, where people change behavior when observed.

The SenseCam is another wearable device that passively captures images documenting daily activities. Though primarily used in memory research currently (Smith et al., 2014), systematic sampling of environments and behaviors via the SenseCam could enable innovative psychological studies in the future.

• By being able to observe the flow of behavior in its own setting, studies have greater ecological validity.
• Like case studies , naturalistic observation is often used to generate new ideas. Because it gives the researcher the opportunity to study the total situation, it often suggests avenues of inquiry not thought of before.
• The ability to capture actual behaviors as they unfold in real-time, analyze sequential patterns of interactions, measure base rates of behaviors, and examine socially undesirable or complex behaviors that people may not self-report accurately.
• These observations are often conducted on a micro (small) scale and may lack a representative sample (biased in relation to age, gender, social class, or ethnicity). This may result in the findings lacking the ability to generalize to wider society.
• Natural observations are less reliable as other variables cannot be controlled. This makes it difficult for another researcher to repeat the study in exactly the same way.
• Highly time-consuming and resource-intensive during the data coding phase (e.g., training coders, maintaining inter-rater reliability, preventing judgment drift).
• With observations, we do not have manipulations of variables (or control over extraneous variables), meaning cause-and-effect relationships cannot be established.

Participant Observation

Participant observation is a variant of the above (natural observations) but here, the researcher joins in and becomes part of the group they are studying to get a deeper insight into their lives.

If it were research on animals , we would now not only be studying them in their natural habitat but be living alongside them as well!

Leon Festinger used this approach in a famous study into a religious cult that believed that the end of the world was about to occur. He joined the cult and studied how they reacted when the prophecy did not come true.

Participant observations can be either covert or overt. Covert is where the study is carried out “undercover.” The researcher’s real identity and purpose are kept concealed from the group being studied.

The researcher takes a false identity and role, usually posing as a genuine member of the group.

On the other hand, overt is where the researcher reveals his or her true identity and purpose to the group and asks permission to observe.

• It can be difficult to get time/privacy for recording. For example, researchers can’t take notes openly with covert observations as this would blow their cover. This means they must wait until they are alone and rely on their memory. This is a problem as they may forget details and are unlikely to remember direct quotations.
• If the researcher becomes too involved, they may lose objectivity and become biased. There is always the danger that we will “see” what we expect (or want) to see. This problem is because they could selectively report information instead of noting everything they observe. Thus reducing the validity of their data.

Recording of Data

With controlled/structured observation studies, an important decision the researcher has to make is how to classify and record the data. Usually, this will involve a method of sampling.

In most coding systems, codes or ratings are made either per behavioral event or per specified time interval (Bakeman & Quera, 2011).

The three main sampling methods are:

Event-based coding involves identifying and segmenting interactions into meaningful events rather than timed units.

For example, parent-child interactions may be segmented into control or teaching events to code. Interval recording involves dividing interactions into fixed time intervals (e.g., 6-15 seconds) and coding behaviors within each interval (Bakeman & Quera, 2011).

Event recording allows counting event frequency and sequencing while also potentially capturing event duration through timed-event recording. This provides information on time spent on behaviors.

• Interval recording is common in microanalytic coding to sample discrete behaviors in brief time samples across an interaction. The time unit can range from seconds to minutes to whole interactions. Interval recording requires segmenting interactions based on timing rather than events (Bakeman & Quera, 2011).
• Instantaneous sampling provides snapshot coding at certain moments rather than summarizing behavior within full intervals. This allows quicker coding but may miss behaviors in between target times.

Coding Systems

The coding system should focus on behaviors, patterns, individual characteristics, or relationship qualities that are relevant to the theory guiding the study (Wampler & Harper, 2014).

Codes vary in how much inference is required, from concrete observable behaviors like frequency of eye contact to more abstract concepts like degree of rapport between a therapist and client (Hill & Lambert, 2004). More inference may reduce reliability.

Coding schemes can vary in their level of detail or granularity. Micro-level schemes capture fine-grained behaviors, such as specific facial movements, while macro-level schemes might code broader behavioral states or interactions. The appropriate level of granularity depends on the research questions and the practical constraints of the study.

Another important consideration is the concreteness of the codes. Some schemes use physically based codes that are directly observable (e.g., “eyes closed”), while others use more socially based codes that require some level of inference (e.g., “showing empathy”). While physically based codes may be easier to apply consistently, socially based codes often capture more meaningful behavioral constructs.

Most coding schemes strive to create sets of codes that are mutually exclusive and exhaustive (ME&E). This means that for any given set of codes, only one code can apply at a time (mutual exclusivity), and there is always an applicable code (exhaustiveness). This property simplifies both the coding process and subsequent data analysis.

For example, a simple ME&E set for coding infant state might include: 1) Quiet alert, 2) Crying, 3) Fussy, 4) REM sleep, and 5) Deep sleep. At any given moment, an infant would be in one and only one of these states.

Macroanalytic coding systems

Macroanalytic coding systems involve rating or summarizing behaviors using larger coding units and broader categories that reflect patterns across longer periods of interaction rather than coding small or discrete behavioral acts. 

Macroanalytic coding systems focus on capturing overarching themes, global qualities, or general patterns of behavior rather than specific, discrete actions.

For example, a macroanalytic coding system may rate the overall degree of therapist warmth or level of client engagement globally for an entire therapy session, requiring the coders to summarize and infer these constructs across the interaction rather than coding smaller behavioral units.

These systems require observers to make more inferences (more time-consuming) but can better capture contextual factors, stability over time, and the interdependent nature of behaviors (Carlson & Grotevant, 1987).

Examples of Macroanalytic Coding Systems:

• Emotional Availability Scales (EAS) : This system assesses the quality of emotional connection between caregivers and children across dimensions like sensitivity, structuring, non-intrusiveness, and non-hostility.
• Classroom Assessment Scoring System (CLASS) : Evaluates the quality of teacher-student interactions in classrooms across domains like emotional support, classroom organization, and instructional support.

Microanalytic coding systems

Microanalytic coding systems involve rating behaviors using smaller, more discrete coding units and categories.

These systems focus on capturing specific, discrete behaviors or events as they occur moment-to-moment. Behaviors are often coded second-by-second or in very short time intervals.

For example, a microanalytic system may code each instance of eye contact or head nodding during a therapy session. These systems code specific, molecular behaviors as they occur moment-to-moment rather than summarizing actions over longer periods.

Microanalytic systems require less inference from coders and allow for analysis of behavioral contingencies and sequential interactions between therapist and client. However, they are more time-consuming and expensive to implement than macroanalytic approaches.

Examples of Microanalytic Coding Systems:

• Facial Action Coding System (FACS) : Codes minute facial muscle movements to analyze emotional expressions.
• Specific Affect Coding System (SPAFF) : Used in marital interaction research to code specific emotional behaviors.
• Noldus Observer XT : A software system that allows for detailed coding of behaviors in real-time or from video recordings.

Mesoanalytic coding systems

Mesoanalytic coding systems attempt to balance macro- and micro-analytic approaches.

In contrast to macroanalytic systems that summarize behaviors in larger chunks, mesoanalytic systems use medium-sized coding units that target more specific behaviors or interaction sequences (Bakeman & Quera, 2017).

For example, a mesoanalytic system may code each instance of a particular type of therapist statement or client emotional expression. However, mesoanalytic systems still use larger units than microanalytic approaches coding every speech onset/offset.

The goal of balancing specificity and feasibility makes mesoanalytic systems well-suited for many research questions (Morris et al., 2014). Mesoanalytic codes can preserve some sequential information while remaining efficient enough for studies with adequate but limited resources.

For instance, a mesoanalytic couple interaction coding system could target key behavior patterns like validation sequences without coding turn-by-turn speech.

In this way, mesoanalytic coding allows reasonable reliability and specificity without requiring extensive training or observation. The mid-level focus offers a pragmatic compromise between depth and breadth in analyzing interactions.

Examples of Mesoanalytic Coding Systems:

• Feeding Scale for Mother-Infant Interaction : Assesses feeding interactions in 5-minute episodes, coding specific behaviors and overall qualities.
• Couples Interaction Rating System (CIRS): Codes specific behaviors and rates overall qualities in segments of couple interactions.
• Teaching Styles Rating Scale : Combines frequency counts of specific teacher behaviors with global ratings of teaching style in classroom segments.

Preventing Coder Drift

Coder drift results in a measurement error caused by gradual shifts in how observations get rated according to operational definitions, especially when behavioral codes are not clearly specified.

This type of error creeps in when coders fail to regularly review what precise observations constitute or do not constitute the behaviors being measured.

Preventing drift refers to taking active steps to maintain consistency and minimize changes or deviations in how coders rate or evaluate behaviors over time. Specifically, some key ways to prevent coder drift include:

• Operationalize codes : It is essential that code definitions unambiguously distinguish what interactions represent instances of each coded behavior. 
• Ongoing training : Returning to those operational definitions through ongoing training serves to recalibrate coder interpretations and reinforce accurate recognition. Having regular “check-in” sessions where coders practice coding the same interactions allows monitoring that they continue applying codes reliably without gradual shifts in interpretation.
• Using reference videos : Coders periodically coding the same “gold standard” reference videos anchors their judgments and calibrate against original training. Without periodic anchoring to original specifications, coder decisions tend to drift from initial measurement reliability.
• Assessing inter-rater reliability : Statistical tracking that coders maintain high levels of agreement over the course of a study, not just at the start, flags any declines indicating drift. Sustaining inter-rater agreement requires mitigating this common tendency for observer judgment change during intensive, long-term coding tasks.
• Recalibrating through discussion : Having meetings for coders to discuss disagreements openly explores reasons judgment shifts may be occurring over time. Consensus on the application of codes is restored.
• Adjusting unclear codes : If reliability issues persist, revisiting and refining ambiguous code definitions or anchors can eliminate inconsistencies arising from coder confusion.

Essentially, the goal of preventing coder drift is maintaining standardization and minimizing unintentional biases that may slowly alter how observational data gets rated over periods of extensive coding.

Through the upkeep of skills, continuing calibration to benchmarks, and monitoring consistency, researchers can notice and correct for any creeping changes in coder decision-making over time.

Reducing Observer Bias

Observational research is prone to observer biases resulting from coders’ subjective perspectives shaping the interpretation of complex interactions (Burghardt et al., 2012). When coding, personal expectations may unconsciously influence judgments. However, rigorous methods exist to reduce such bias.

Coding Manual

A detailed coding manual minimizes subjectivity by clearly defining what behaviors and interaction dynamics observers should code (Bakeman & Quera, 2011).

High-quality manuals have strong theoretical and empirical grounding, laying out explicit coding procedures and providing rich behavioral examples to anchor code definitions (Lindahl, 2001).

Clear delineation of the frequency, intensity, duration, and type of behaviors constituting each code facilitates reliable judgments and reduces ambiguity for coders. Application risks inconsistency across raters without clarity on how codes translate to observable interaction.

Coder Training

Competent coders require both interpersonal perceptiveness and scientific rigor (Wampler & Harper, 2014). Training thoroughly reviews the theoretical basis for coded constructs and teaches the coding system itself.

Multiple “gold standard” criterion videos demonstrate code ranges that trainees independently apply. Coders then meet weekly to establish reliability of 80% or higher agreement both among themselves and with master criterion coding (Hill & Lambert, 2004).

Ongoing training manages coder drift over time. Revisions to unclear codes may also improve reliability. Both careful selection and investment in rigorous training increase quality control.

Blind Methods

To prevent bias, coders should remain unaware of specific study predictions or participant details (Burghardt et al., 2012). Separate data gathering versus coding teams helps maintain blinding.

Coders should be unaware of study details or participant identities that could bias coding (Burghardt et al., 2012).

Separate teams collecting data versus coding data can reduce bias.

In addition, scheduling procedures can prevent coders from rating data collected directly from participants with whom they have had personal contact. Maintaining coder independence and blinding enhances objectivity.

Data Analysis Approaches

Data analysis in behavioral observation aims to transform raw observational data into quantifiable measures that can be statistically analyzed.

It’s important to note that the choice of analysis approach is not arbitrary but should be guided by the research questions, study design, and nature of the data collected.

Interval data (where behavior is recorded at fixed time points), event data (where the occurrence of behaviors is noted as they happen), and timed-event data (where both the occurrence and duration of behaviors are recorded) may require different analytical approaches.

Similarly, the level of measurement (categorical, ordinal, or continuous) will influence the choice of statistical tests.

Researchers typically start with simple descriptive statistics to get a feel for their data before moving on to more complex analyses. This stepwise approach allows for a thorough understanding of the data and can often reveal unexpected patterns or relationships that merit further investigation.

simple descriptive statistics

Descriptive statistics give an overall picture of behavior patterns and are often the first step in analysis.

• Frequency counts tell us how often a particular behavior occurs, while rates express this frequency in relation to time (e.g., occurrences per minute).
• Duration measures how long behaviors last, offering insight into their persistence or intensity.
• Probability calculations indicate the likelihood of a behavior occurring under certain conditions, and relative frequency or duration statistics show the proportional occurrence of different behaviors within a session or across the study.

These simple statistics form the foundation of behavioral analysis, providing researchers with a broad picture of behavioral patterns. 

They can reveal which behaviors are most common, how long they typically last, and how they might vary across different conditions or subjects.

For instance, in a study of classroom behavior, these statistics might show how often students raise their hands, how long they typically stay focused on a task, or what proportion of time is spent on different activities.

contingency analyses

Contingency analyses help identify if certain behaviors tend to occur together or in sequence.

• Contingency tables , also known as cross-tabulations, display the co-occurrence of two or more behaviors, allowing researchers to see if certain behaviors tend to happen together.
• Odds ratios provide a measure of the strength of association between behaviors, indicating how much more likely one behavior is to occur in the presence of another.
• Adjusted residuals in these tables can reveal whether the observed co-occurrences are significantly different from what would be expected by chance.

For example, in a study of parent-child interactions, contingency analyses might reveal whether a parent’s praise is more likely to follow a child’s successful completion of a task, or whether a child’s tantrum is more likely to occur after a parent’s refusal of a request.

These analyses can uncover important patterns in social interactions, learning processes, or behavioral chains.

sequential analyses

Sequential analyses are crucial for understanding processes and temporal relationships between behaviors.

• Lag sequential analysis looks at the likelihood of one behavior following another within a specified number of events or time units.
• Time-window sequential analysis examines whether a target behavior occurs within a defined time frame after a given behavior.

These methods are particularly valuable for understanding processes that unfold over time, such as conversation patterns, problem-solving strategies, or the development of social skills.

observer agreement

Since human observers often code behaviors, it’s important to check reliability . This is typically done through measures of observer agreement.

• Cohen’s kappa is commonly used for categorical data, providing a measure of agreement between observers that accounts for chance agreement.
• Intraclass correlation coefficient (ICC) : Used for continuous data or ratings.

Good observer agreement is crucial for the validity of the study, as it demonstrates that the observed behaviors are consistently identified and coded across different observers or time points.

advanced statistical approaches

As researchers delve deeper into their data, they often employ more advanced statistical techniques.

• For instance, an ANOVA might reveal differences in the frequency of aggressive behaviors between children from different socioeconomic backgrounds or in different school settings.
• This approach allows researchers to account for dependencies in the data and to examine how behaviors might be influenced by factors at different levels (e.g., individual characteristics, group dynamics, and situational factors).
• This method can reveal trends, cycles, or patterns in behavior over time, which might not be apparent from simpler analyses. For instance, in a study of animal behavior, time series analysis might uncover daily or seasonal patterns in feeding, mating, or territorial behaviors.

representation techniques

Representation techniques help organize and visualize data:

• Many researchers use a code-unit grid, which represents the data as a matrix with behaviors as rows and time units as columns.
• This format facilitates many types of analyses and allows for easy visualization of behavioral patterns.
• Standardized formats like the Sequential Data Interchange Standard (SDIS) help ensure consistency in data representation across studies and facilitate the use of specialized analysis software.
• Indeed, the complexity of behavioral observation data often necessitates the use of specialized software tools. Programs like GSEQ, Observer, and INTERACT are designed specifically for the analysis of observational data and can perform many of the analyses described above efficiently and accurately.

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Health impacts of biophilic design from a multisensory interaction perspective: empirical evidence, research designs, and future directions.

1. Introduction

3. research findings, 3.1. bi-sensory, 3.1.1. visual–acoustical, 3.1.2. visual–thermal, 3.1.3. visual–olfactory, 3.1.4. acoustical–thermal, 3.1.5. acoustical–olfactory, 3.1.6. other bi-sensory elements, 3.2. multisensory experience, 4. study design implication, 4.1. study type and subject, 4.2. environmental settings, 4.3. measures, 4.4. study procedure, 5. gaps and future directions, 5.1. beneficial effects of sensory experiences, 5.2. interaction between multisensory exposure to nature, 5.3. subjects heterogeneity and limited generalization, 5.4. study design methodology for multisensory studies, 5.5. the lasting impact of multisensory experiences, 5.6. objective physiological measures, 6. conclusions, author contributions, conflicts of interest.

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Yin, J.; Zhu, H.; Yuan, J. Health Impacts of Biophilic Design from a Multisensory Interaction Perspective: Empirical Evidence, Research Designs, and Future Directions. Land 2024 , 13 , 1448. https://doi.org/10.3390/land13091448

Yin J, Zhu H, Yuan J. Health Impacts of Biophilic Design from a Multisensory Interaction Perspective: Empirical Evidence, Research Designs, and Future Directions. Land . 2024; 13(9):1448. https://doi.org/10.3390/land13091448

Yin, Jie, Haoyue Zhu, and Jing Yuan. 2024. "Health Impacts of Biophilic Design from a Multisensory Interaction Perspective: Empirical Evidence, Research Designs, and Future Directions" Land 13, no. 9: 1448. https://doi.org/10.3390/land13091448

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