research role of science

• Skip to primary navigation
• Skip to main content
• Skip to primary sidebar
• Skip to footer

Understanding Science

How science REALLY works...

• Understanding Science 101

An overview

To understand what ​​ science is, just look around you. What do you see? Perhaps your hand on the mouse, a computer screen, papers, ballpoint pens, the family cat, the sun shining through the window …. Science is, in one sense, our knowledge of all that — all the stuff that is in the universe, including the tiniest subatomic particles in a single atom of the metal in your computer’s circuits, the nuclear reactions that formed the immense ball of gas that is our sun, and the complex chemical interactions and electrical fluctuations within your own body that allow you to read and understand these words. But science is not just a collection of knowledge. Just as importantly, science is also a reliable process by which we learn about all that stuff in the universe. And science is different from many other ways of learning because of the way it is done. Science relies on ​​ testing ideas with ​​ evidence gathered from the ​​ natural world . This website will help you learn more about science as a process of learning about the natural world and access the parts of science that affect your life.

Science helps to satisfy the natural curiosity with which we are all born: Why is the sky blue? How did the leopard get its spots? What is a solar eclipse? With science, we can answer such questions without resorting to magical explanations. And science can lead to technological advances, as well as helping us learn about enormously important and useful topics, such as our health, the environment, and natural hazards. Without science, the modern world would not be modern at all. Still, we have so much to learn. Millions of scientists all over the world are working to solve different parts of the puzzle of how the universe works, peering into its nooks and crannies and deploying their microscopes, telescopes, and other tools to unravel its secrets.

Science is complex and multi-faceted, but the most important characteristics of science are straightforward:

• Science is a way of learning about what is in the natural world, how the natural world works, and how the natural world got to be the way it is. It is not simply a collection of facts ; rather it is a path to understanding.
• Science focuses exclusively on the natural world and does not deal with supernatural explanations.
• Although scientists work in many different ways, all science relies on testing ideas by figuring out what expectations are generated by an idea and making observations to find out whether those expectations hold true.
• Accepted scientific ideas are reliable because they have been subjected to rigorous testing. But, as new evidence is acquired and new perspectives emerge, these ideas can be revised.
• Science is a community endeavor. It relies on a system of checks and balances, which helps ensure that science moves in the direction of greater accuracy and understanding. This system is facilitated by diversity within the scientific community, which offers a broad range of perspectives on scientific ideas.

To many, science may seem like an arcane, ivory-towered institution — but that impression is based on a misunderstanding of science. In fact:

• Science affects your life everyday in all sorts of different ways.
• Science can be fun and is accessible to everyone.
• You are probably already using scientific thinking in your everyday life – maybe without even knowing it.
• Anyone can “do” science by investigating questions scientifically.

Where to begin?

Here are some places you may want to start your investigation:

• What is science? Find out what makes science science .
• How does it work? Probe the nuts and bolts of the process of science .
• Why is it important? Learn how science affects your life everyday and how you can apply an understanding of the nature of science in your everyday life.

What is science?

Subscribe to our newsletter

• The science flowchart
• Science stories
• Grade-level teaching guides
• Teaching resource database
• Journaling tool
• Misconceptions

What Is Research and Why We Do It

• First Online: 23 June 2020

Cite this chapter

• Carlo Ghezzi 2  

2976 Accesses

2 Altmetric

The notions of science and scientific research are discussed and the motivations for doing research are analyzed. Research can span a broad range of approaches, from purely theoretical to practice-oriented; different approaches often coexist and fertilize each other. Research ignites human progress and societal change. In turn, society drives and supports research. The specific role of research in Informatics is discussed. Informatics is driving the current transition towards the new digital society in which we will live in the future.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

• Available as EPUB and PDF
• Read on any device
• Instant download
• Own it forever
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

In [ 34 ], P.E. Medawar discusses what he calls the “snobismus” of pure versus applied science. In his words, this is one of the most damaging forms of snobbism, which draws a class distinction between pure and applied science.

Originality, rigor, and significance have been defined and used as the key criteria to evaluate research outputs by the UK Research Excellence Framework (REF) [ 46 ]. A research evaluation exercise has been performed periodically since 1986 on UK higher education institutions and their research outputs have been rated according to their originality, rigor, and significance.

The importance of realizing that “we don’t know” was apparently first stated by Socrates, according to Plato’s account of his thought. This is condensed in the famous paradox “I know that I don’t know.”

This view applies mainly to natural and physical sciences.

Roy Amara was President of the Institute for Future, a USA-based think tank, from 1971 until 1990.

The Turing Award is generally recognized as the Nobel prize of Informatics.

See http://uis.unesco.org/apps/visualisations/research-and-development-spending/ .

Israel is a very good example. Investments in research resulted in a proliferation of new, cutting-edge enterprises. The term start-up nation has been coined by Dan Senor and Saul Singer in their successful book [ 51 ] to characterize this phenomenon.

https://ec.europa.eu/programmes/horizon2020/en/h2020-section/societal-challenges .

https://ec.europa.eu/programmes/horizon2020/en/h2020-section/cross-cutting-activities-focus-areas .

This figure has been adapted from a presentation by A. Fuggetta, which describes the mission of Cefriel, an Italian institution with a similar role of Fraunhofer, on a smaller scale.

The ERC takes an ecumenical approach and calls the research sector “Computer Science and Informatics.”

I discuss here the effect of “big data” on research, although most sectors of society—industry, finance, health, …—are also deeply affected.

Carayannis, E., Campbell, D.: Mode 3 knowledge production in quadruple helix innovation systems. In: E. Carayannis, D. Campbell (eds.) Mode 3 Knowledge Production in Quadruple Helix Innovation Systems: 21st-Century Democracy, Innovation, and Entrepreneurship for Development. SpringerBriefs in Business, New York, NY (2012)

Google Scholar  

Etzkowitz, H., Leydesdorff, L.: The triple helix – university-industry-government relations: A laboratory for knowledge based economic development. EASST Review 14 (1), 14–19 (1995)

Harari, Y.: Sapiens: A Brief History of Humankind. Random House (2014). URL https://books.google.it/books?id=1EiJAwAAQBAJ

Harari, Y.: Homo Deus: A Brief History of Tomorrow. Random House (2016). URL https://books.google.it/books?id=dWYyCwAAQBAJ

Hopcroft, J.E., Motwani, R., Ullman, J.D.: Introduction to Automata Theory, Languages, and Computation (3rd Edition). Addison-Wesley Longman Publishing Co., Inc., USA (2006)

MATH   Google Scholar  

Medawar, P.: Advice To A Young Scientist. Alfred P. Sloan Foundation series. Basic Books (2008)

OECD: Frascati Manual. OECD Publishing (2015). https://doi.org/10.1787/9789264239012-en . URL https://www.oecd-ilibrary.org/content/publication/9789264239012-en

REF2019/2: Panel criteria and working methods (2019). URL https://www.ref.ac.uk/media/1084/ref-2019_02-panel-criteria-and-working-methods.pdf

Senor, D., Singer, S.: Start-Up Nation: The Story of Israel’s Economic Miracle. McClelland & Stewart, Toronto, Canada (2011)

Stokes, D.E.: Pasteur’s Quadrant: Basic Science and Technological Innovation. Brookings Institution Press, Washington, D.C. (1997)

Thurston, R.H.: The growth of the steam engine. Popular Science Monthly 12 (1877)

Vardi, M.Y.: The long game of research. Commun. ACM 62 (9), 7–7 (2019). https://doi.org/10.1145/3352489 . URL http://doi.acm.org/10.1145/3352489

Download references

Author information

Authors and affiliations.

Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy

Carlo Ghezzi

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Carlo Ghezzi .

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Ghezzi, C. (2020). What Is Research and Why We Do It. In: Being a Researcher. Springer, Cham. https://doi.org/10.1007/978-3-030-45157-8_1

Download citation

DOI : https://doi.org/10.1007/978-3-030-45157-8_1

Published : 23 June 2020

Publisher Name : Springer, Cham

Print ISBN : 978-3-030-45156-1

Online ISBN : 978-3-030-45157-8

eBook Packages : Computer Science Computer Science (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

4 Theories in scientific research

As we know from previous chapters, science is knowledge represented as a collection of ‘theories’ derived using the scientific method. In this chapter, we will examine what a theory is, why we need theories in research, the building blocks of a theory, how to evaluate theories, how can we apply theories in research, and also present illustrative examples of five theories frequently used in social science research.

Theories are explanations of a natural or social behaviour, event, or phenomenon. More formally, a scientific theory is a system of constructs (concepts) and propositions (relationships between those constructs) that collectively presents a logical, systematic, and coherent explanation of a phenomenon of interest within some assumptions and boundary conditions (Bacharach 1989). [1]

Theories should explain why things happen, rather than just describe or predict. Note that it is possible to predict events or behaviours using a set of predictors, without necessarily explaining why such events are taking place. For instance, market analysts predict fluctuations in the stock market based on market announcements, earnings reports of major companies, and new data from the Federal Reserve and other agencies, based on previously observed correlations . Prediction requires only correlations. In contrast, explanations require causations , or understanding of cause-effect relationships. Establishing causation requires three conditions: one, correlations between two constructs, two, temporal precedence (the cause must precede the effect in time), and three, rejection of alternative hypotheses (through testing). Scientific theories are different from theological, philosophical, or other explanations in that scientific theories can be empirically tested using scientific methods.

Explanations can be idiographic or nomothetic. Idiographic explanations are those that explain a single situation or event in idiosyncratic detail. For example, you did poorly on an exam because: you forgot that you had an exam on that day, you arrived late to the exam due to a traffic jam, you panicked midway through the exam, you had to work late the previous evening and could not study for the exam, or even your dog ate your textbook. The explanations may be detailed, accurate, and valid, but they may not apply to other similar situations, even involving the same person, and are hence not generalisable. In contrast, nomothetic explanations seek to explain a class of situations or events rather than a specific situation or event. For example, students who do poorly in exams do so because they did not spend adequate time preparing for exams or because they suffer from nervousness, attention-deficit, or some other medical disorder. Because nomothetic explanations are designed to be generalisable across situations, events, or people, they tend to be less precise, less complete, and less detailed. However, they explain economically, using only a few explanatory variables. Because theories are also intended to serve as generalised explanations for patterns of events, behaviours, or phenomena, theoretical explanations are generally nomothetic in nature.

While understanding theories, it is also important to understand what theories are not. A theory is not data, facts, typologies, taxonomies, or empirical findings. A collection of facts is not a theory, just as a pile of stones is not a house. Likewise, a collection of constructs (e.g., a typology of constructs) is not a theory, because theories must go well beyond constructs to include propositions, explanations, and boundary conditions. Data, facts, and findings operate at the empirical or observational level, while theories operate at a conceptual level and are based on logic rather than observations.

There are many benefits to using theories in research. First, theories provide the underlying logic for the occurrence of natural or social phenomena by explaining the key drivers and outcomes of the target phenomenon, and the underlying processes responsible for driving that phenomenon. Second, they aid in sense-making by helping us synthesise prior empirical findings within a theoretical framework and reconcile contradictory findings by discovering contingent factors influencing the relationship between two constructs in different studies. Third, theories provide guidance for future research by helping identify constructs and relationships that are worthy of further research. Fourth, theories can contribute to cumulative knowledge building by bridging gaps between other theories and by causing existing theories to be re-evaluated in a new light.

However, theories can also have their own share of limitations. As simplified explanations of reality, theories may not always provide adequate explanations of the phenomenon of interest based on a limited set of constructs and relationships. Theories are designed to be simple and parsimonious explanations, while reality may be significantly more complex. Furthermore, theories may impose blinders or limit researchers’ ‘range of vision’, causing them to miss out on important concepts that are not defined by the theory.

Building blocks of a theory

David Whetten (1989) [2] suggests that there are four building blocks of a theory: constructs, propositions, logic, and boundary conditions/assumptions. Constructs capture the ‘what’ of theories (i.e., what concepts are important for explaining a phenomenon?), propositions capture the ‘how’ (i.e., how are these concepts related to each other?), logic represents the ‘why’ (i.e., why are these concepts related?), and boundary conditions/assumptions examines the ‘who, when, and where’ (i.e., under what circumstances will these concepts and relationships work?). Though constructs and propositions were previously discussed in Chapter 2, we describe them again here for the sake of completeness.

Constructs are abstract concepts specified at a high level of abstraction that are chosen specifically to explain the phenomenon of interest. Recall from Chapter 2 that constructs may be unidimensional (i.e., embody a single concept), such as weight or age, or multi-dimensional (i.e., embody multiple underlying concepts), such as personality or culture. While some constructs, such as age, education, and firm size, are easy to understand, others, such as creativity, prejudice, and organisational agility, may be more complex and abstruse, and still others such as trust, attitude, and learning may represent temporal tendencies rather than steady states. Nevertheless, all constructs must have clear and unambiguous operational definitions that should specify exactly how the construct will be measured and at what level of analysis (individual, group, organisational, etc.). Measurable representations of abstract constructs are called variables . For instance, IQ score is a variable that is purported to measure an abstract construct called ‘intelligence’. As noted earlier, scientific research proceeds along two planes: a theoretical plane and an empirical plane. Constructs are conceptualised at the theoretical plane, while variables are operationalised and measured at the empirical (observational) plane. Furthermore, variables may be independent, dependent, mediating, or moderating, as discussed in Chapter 2. The distinction between constructs (conceptualised at the theoretical level) and variables (measured at the empirical level) is shown in Figure 4.1.

Propositions are associations postulated between constructs based on deductive logic. Propositions are stated in declarative form and should ideally indicate a cause-effect relationship (e.g., if X occurs, then Y will follow). Note that propositions may be conjectural but must be testable, and should be rejected if they are not supported by empirical observations. However, like constructs, propositions are stated at the theoretical level, and they can only be tested by examining the corresponding relationship between measurable variables of those constructs. The empirical formulation of propositions, stated as relationships between variables, are called hypotheses . The distinction between propositions (formulated at the theoretical level) and hypotheses (tested at the empirical level) is depicted in Figure 4.1.

The third building block of a theory is the logic that provides the basis for justifying the propositions as postulated. Logic acts like a ‘glue’ that connects the theoretical constructs and provides meaning and relevance to the relationships between these constructs. Logic also represents the ‘explanation’ that lies at the core of a theory. Without logic, propositions will be ad hoc, arbitrary, and meaningless, and cannot be tied into the cohesive ‘system of propositions’ that is the heart of any theory.

Finally, all theories are constrained by assumptions about values, time, and space, and boundary conditions that govern where the theory can be applied and where it cannot be applied. For example, many economic theories assume that human beings are rational (or boundedly rational) and employ utility maximisation based on cost and benefit expectations as a way of understand human behaviour. In contrast, political science theories assume that people are more political than rational, and try to position themselves in their professional or personal environment in a way that maximises their power and control over others. Given the nature of their underlying assumptions, economic and political theories are not directly comparable, and researchers should not use economic theories if their objective is to understand the power structure or its evolution in an organisation. Likewise, theories may have implicit cultural assumptions (e.g., whether they apply to individualistic or collective cultures), temporal assumptions (e.g., whether they apply to early stages or later stages of human behaviour), and spatial assumptions (e.g., whether they apply to certain localities but not to others). If a theory is to be properly used or tested, all of the implicit assumptions that form the boundaries of that theory must be properly understood. Unfortunately, theorists rarely state their implicit assumptions clearly, which leads to frequent misapplications of theories to problem situations in research.

Attributes of a good theory

Theories are simplified and often partial explanations of complex social reality. As such, there can be good explanations or poor explanations, and consequently, there can be good theories or poor theories. How can we evaluate the ‘goodness’ of a given theory? Different criteria have been proposed by different researchers, the more important of which are listed below:

Logical consistency: Are the theoretical constructs, propositions, boundary conditions, and assumptions logically consistent with each other? If some of these ‘building blocks’ of a theory are inconsistent with each other (e.g., a theory assumes rationality, but some constructs represent non-rational concepts), then the theory is a poor theory.

Explanatory power: How much does a given theory explain (or predict) reality? Good theories obviously explain the target phenomenon better than rival theories, as often measured by variance explained (R-squared) value in regression equations.

Falsifiability: British philosopher Karl Popper stated in the 1940s that for theories to be valid, they must be falsifiable. Falsifiability ensures that the theory is potentially disprovable, if empirical data does not match with theoretical propositions, which allows for their empirical testing by researchers. In other words, theories cannot be theories unless they can be empirically testable. Tautological statements, such as ‘a day with high temperatures is a hot day’ are not empirically testable because a hot day is defined (and measured) as a day with high temperatures, and hence, such statements cannot be viewed as a theoretical proposition. Falsifiability requires the presence of rival explanations, it ensures that the constructs are adequately measurable, and so forth. However, note that saying that a theory is falsifiable is not the same as saying that a theory should be falsified. If a theory is indeed falsified based on empirical evidence, then it was probably a poor theory to begin with.

Parsimony: Parsimony examines how much of a phenomenon is explained with how few variables. The concept is attributed to fourteenth century English logician Father William of Ockham (and hence called ‘Ockham’s razor’ or ‘Occam’s razor’), which states that among competing explanations that sufficiently explain the observed evidence, the simplest theory (i.e., one that uses the smallest number of variables or makes the fewest assumptions) is the best. Explanation of a complex social phenomenon can always be increased by adding more and more constructs. However, such an approach defeats the purpose of having a theory, which is intended to be a ‘simplified’ and generalisable explanation of reality. Parsimony relates to the degrees of freedom in a given theory. Parsimonious theories have higher degrees of freedom, which allow them to be more easily generalised to other contexts, settings, and populations.

Approaches to theorising

How do researchers build theories? Steinfeld and Fulk (1990) [3] recommend four such approaches. The first approach is to build theories inductively based on observed patterns of events or behaviours. Such an approach is often called ‘grounded theory building’, because the theory is grounded in empirical observations. This technique is heavily dependent on the observational and interpretive abilities of the researcher, and the resulting theory may be subjective and non-confirmable. Furthermore, observing certain patterns of events will not necessarily make a theory, unless the researcher is able to provide consistent explanations for the observed patterns. We will discuss the grounded theory approach in a later chapter on qualitative research.

The second approach to theory building is to conduct a bottom-up conceptual analysis to identify different sets of predictors relevant to the phenomenon of interest using a predefined framework. One such framework may be a simple input-process-output framework, where the researcher may look for different categories of inputs, such as individual, organisational, and/or technological factors potentially related to the phenomenon of interest (the output), and describe the underlying processes that link these factors to the target phenomenon. This is also an inductive approach that relies heavily on the inductive abilities of the researcher, and interpretation may be biased by researcher’s prior knowledge of the phenomenon being studied.

The third approach to theorising is to extend or modify existing theories to explain a new context, such as by extending theories of individual learning to explain organisational learning. While making such an extension, certain concepts, propositions, and/or boundary conditions of the old theory may be retained and others modified to fit the new context. This deductive approach leverages the rich inventory of social science theories developed by prior theoreticians, and is an efficient way of building new theories by expanding on existing ones.

The fourth approach is to apply existing theories in entirely new contexts by drawing upon the structural similarities between the two contexts. This approach relies on reasoning by analogy, and is probably the most creative way of theorising using a deductive approach. For instance, Markus (1987) [4] used analogic similarities between a nuclear explosion and uncontrolled growth of networks or network-based businesses to propose a critical mass theory of network growth. Just as a nuclear explosion requires a critical mass of radioactive material to sustain a nuclear explosion, Markus suggested that a network requires a critical mass of users to sustain its growth, and without such critical mass, users may leave the network, causing an eventual demise of the network.

Examples of social science theories

In this section, we present brief overviews of a few illustrative theories from different social science disciplines. These theories explain different types of social behaviors, using a set of constructs, propositions, boundary conditions, assumptions, and underlying logic. Note that the following represents just a simplistic introduction to these theories. Readers are advised to consult the original sources of these theories for more details and insights on each theory.

Agency theory. Agency theory (also called principal-agent theory), a classic theory in the organisational economics literature, was originally proposed by Ross (1973) [5] to explain two-party relationships—such as those between an employer and its employees, between organisational executives and shareholders, and between buyers and sellers—whose goals are not congruent with each other. The goal of agency theory is to specify optimal contracts and the conditions under which such contracts may help minimise the effect of goal incongruence. The core assumptions of this theory are that human beings are self-interested individuals, boundedly rational, and risk-averse, and the theory can be applied at the individual or organisational level.

The two parties in this theory are the principal and the agent—the principal employs the agent to perform certain tasks on its behalf. While the principal’s goal is quick and effective completion of the assigned task, the agent’s goal may be working at its own pace, avoiding risks, and seeking self-interest—such as personal pay—over corporate interests, hence, the goal incongruence. Compounding the nature of the problem may be information asymmetry problems caused by the principal’s inability to adequately observe the agent’s behaviour or accurately evaluate the agent’s skill sets. Such asymmetry may lead to agency problems where the agent may not put forth the effort needed to get the task done (the moral hazard problem) or may misrepresent its expertise or skills to get the job but not perform as expected (the adverse selection problem). Typical contracts that are behaviour-based, such as a monthly salary, cannot overcome these problems. Hence, agency theory recommends using outcome-based contracts, such as commissions or a fee payable upon task completion, or mixed contracts that combine behaviour-based and outcome-based incentives. An employee stock option plan is an example of an outcome-based contract, while employee pay is a behaviour-based contract. Agency theory also recommends tools that principals may employ to improve the efficacy of behaviour-based contracts, such as investing in monitoring mechanisms—e.g. hiring supervisors—to counter the information asymmetry caused by moral hazard, designing renewable contracts contingent on the agent’s performance (performance assessment makes the contract partially outcome-based), or by improving the structure of the assigned task to make it more programmable and therefore more observable.

Theory of planned behaviour. Postulated by Azjen (1991), [6] the theory of planned behaviour (TPB) is a generalised theory of human behaviour in social psychology literature that can be used to study a wide range of individual behaviours. It presumes that individual behaviour represents conscious reasoned choice, and is shaped by cognitive thinking and social pressures. The theory postulates that behaviours are based on one’s intention regarding that behaviour, which in turn is a function of the person’s attitude toward the behaviour, subjective norm regarding that behaviour, and perception of control over that behaviour (see Figure 4.2). Attitude is defined as the individual’s overall positive or negative feelings about performing the behaviour in question, which may be assessed as a summation of one’s beliefs regarding the different consequences of that behaviour, weighted by the desirability of those consequences. Subjective norm refers to one’s perception of whether people important to that person expect the person to perform the intended behaviour, and is represented as a weighted combination of the expected norms of different referent groups such as friends, colleagues, or supervisors at work. Behavioural control is one’s perception of internal or external controls constraining the behaviour in question. Internal controls may include the person’s ability to perform the intended behaviour (self-efficacy), while external control refers to the availability of external resources needed to perform that behaviour (facilitating conditions). TPB also suggests that sometimes people may intend to perform a given behaviour but lack the resources needed to do so, and therefore posits that behavioural control can have a direct effect on behaviour, in addition to the indirect effect mediated by intention.

TPB is an extension of an earlier theory called the theory of reasoned action, which included attitude and subjective norm as key drivers of intention, but not behavioural control. The latter construct was added by Ajzen in TPB to account for circumstances when people may have incomplete control over their own behaviours (such as not having high-speed Internet access for web surfing).

Innovation diffusion theory. Innovation diffusion theory (IDT) is a seminal theory in the communications literature that explains how innovations are adopted within a population of potential adopters. The concept was first studied by French sociologist Gabriel Tarde, but the theory was developed by Everett Rogers in 1962 based on observations of 508 diffusion studies. The four key elements in this theory are: innovation, communication channels, time, and social system. Innovations may include new technologies, new practices, or new ideas, and adopters may be individuals or organisations. At the macro (population) level, IDT views innovation diffusion as a process of communication where people in a social system learn about a new innovation and its potential benefits through communication channels—such as mass media or prior adopters— and are persuaded to adopt it. Diffusion is a temporal process—the diffusion process starts off slow among a few early adopters, then picks up speed as the innovation is adopted by the mainstream population, and finally slows down as the adopter population reaches saturation. The cumulative adoption pattern is therefore an s-shaped curve, as shown in Figure 4.3, and the adopter distribution represents a normal distribution. All adopters are not identical, and adopters can be classified into innovators, early adopters, early majority, late majority, and laggards based on the time of their adoption. The rate of diffusion also depends on characteristics of the social system such as the presence of opinion leaders (experts whose opinions are valued by others) and change agents (people who influence others’ behaviours).

At the micro (adopter) level, Rogers (1995) [7] suggests that innovation adoption is a process consisting of five stages: one, knowledge : when adopters first learn about an innovation from mass-media or interpersonal channels, two, persuasion : when they are persuaded by prior adopters to try the innovation, three, decision : their decision to accept or reject the innovation, four,: their initial utilisation of the innovation, and five, confirmation : their decision to continue using it to its fullest potential (see Figure 4.4). Five innovation characteristics are presumed to shape adopters’ innovation adoption decisions: one, relative advantage : the expected benefits of an innovation relative to prior innovations, two, compatibility : the extent to which the innovation fits with the adopter’s work habits, beliefs, and values, three, complexity : the extent to which the innovation is difficult to learn and use, four, trialability : the extent to which the innovation can be tested on a trial basis, and five, observability : the extent to which the results of using the innovation can be clearly observed. The last two characteristics have since been dropped from many innovation studies. Complexity is negatively correlated to innovation adoption, while the other four factors are positively correlated. Innovation adoption also depends on personal factors such as the adopter’s risk-taking propensity, education level, cosmopolitanism, and communication influence. Early adopters are venturesome, well educated, and rely more on mass media for information about the innovation, while later adopters rely more on interpersonal sources—such as friends and family—as their primary source of information. IDT has been criticised for having a ‘pro-innovation bias’—that is for presuming that all innovations are beneficial and will be eventually diffused across the entire population, and because it does not allow for inefficient innovations such as fads or fashions to die off quickly without being adopted by the entire population or being replaced by better innovations.

Elaboration likelihood model . Developed by Petty and Cacioppo (1986), [8] the elaboration likelihood model (ELM) is a dual-process theory of attitude formation or change in psychology literature. It explains how individuals can be influenced to change their attitude toward a certain object, event, or behaviour and the relative efficacy of such change strategies. The ELM posits that one’s attitude may be shaped by two ‘routes’ of influence: the central route and the peripheral route, which differ in the amount of thoughtful information processing or ‘elaboration required of people (see Figure 4.5). The central route requires a person to think about issue-related arguments in an informational message and carefully scrutinise the merits and relevance of those arguments, before forming an informed judgment about the target object. In the peripheral route, subjects rely on external ‘cues’ such as number of prior users, endorsements from experts, or likeability of the endorser, rather than on the quality of arguments, in framing their attitude towards the target object. The latter route is less cognitively demanding, and the routes of attitude change are typically operationalised in the ELM using the argument quality and peripheral cues constructs respectively.

Whether people will be influenced by the central or peripheral routes depends upon their ability and motivation to elaborate the central merits of an argument. This ability and motivation to elaborate is called elaboration likelihood . People in a state of high elaboration likelihood (high ability and high motivation) are more likely to thoughtfully process the information presented and are therefore more influenced by argument quality, while those in the low elaboration likelihood state are more motivated by peripheral cues. Elaboration likelihood is a situational characteristic and not a personal trait. For instance, a doctor may employ the central route for diagnosing and treating a medical ailment (by virtue of his or her expertise of the subject), but may rely on peripheral cues from auto mechanics to understand the problems with his car. As such, the theory has widespread implications about how to enact attitude change toward new products or ideas and even social change.

General deterrence theory. Two utilitarian philosophers of the eighteenth century, Cesare Beccaria and Jeremy Bentham, formulated general deterrence theory (GDT) as both an explanation of crime and a method for reducing it. GDT examines why certain individuals engage in deviant, anti-social, or criminal behaviours. This theory holds that people are fundamentally rational (for both conforming and deviant behaviours), and that they freely choose deviant behaviours based on a rational cost-benefit calculation. Because people naturally choose utility-maximising behaviours, deviant choices that engender personal gain or pleasure can be controlled by increasing the costs of such behaviours in the form of punishments (countermeasures) as well as increasing the probability of apprehension. Swiftness, severity, and certainty of punishments are the key constructs in GDT.

While classical positivist research in criminology seeks generalised causes of criminal behaviours, such as poverty, lack of education, psychological conditions, and recommends strategies to rehabilitate criminals, such as by providing them job training and medical treatment, GDT focuses on the criminal decision-making process and situational factors that influence that process. Hence, a criminal’s personal situation—such as his personal values, his affluence, and his need for money—and the environmental context—such as how protected the target is, how efficient the local police are, how likely criminals are to be apprehended—play key roles in this decision-making process. The focus of GDT is not how to rehabilitate criminals and avert future criminal behaviours, but how to make criminal activities less attractive and therefore prevent crimes. To that end, ‘target hardening’ such as installing deadbolts and building self-defence skills, legal deterrents such as eliminating parole for certain crimes, ‘three strikes law’ (mandatory incarceration for three offences, even if the offences are minor and not worth imprisonment), and the death penalty, increasing the chances of apprehension using means such as neighbourhood watch programs, special task forces on drugs or gang-related crimes, and increased police patrols, and educational programs such as highly visible notices such as ‘Trespassers will be prosecuted’ are effective in preventing crimes. This theory has interesting implications not only for traditional crimes, but also for contemporary white-collar crimes such as insider trading, software piracy, and illegal sharing of music.

• Bacharach, S.B. (1989). Organizational theories: some criteria for evaluation. Academy of Management Review , 14(4), 496-515. ↵
• Whetten, D. (1989). What constitutes a theoretical contribution? Academy of Management Review , 14(4), 490-495. ↵
• Steinfield, C.W. and Fulk, J. (1990). The theory imperative. In J. Fulk & C.W. (Eds.), Organizations and communications technology (pp. 13–26). Newsburt Park, CA: Sage Publications. ↵
• Markus, M.L. (1987). Toward a ‘critical mass’ theory of interactive media: universal access, interdependence and diffusion. Communication Research , 14(5), 491-511. ↵
• Ross, S.A. (1973). The economic theory of agency: The principal’s problem. American Economic , 63(2), 134-139 ↵
• Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes , (50), 179–211. ↵
• Rogers, E. (1995). Diffusion of innovations (4th ed.). New York: Free Press. ↵
• Petty, R.E. and Cacioppo, J.T. (1986). C ommunication and persuasion: Central and peripheral routes to attitude change . New York: Springer-Verlag. ↵

Social Science Research: Principles, Methods and Practices (Revised edition) Copyright © 2019 by Anol Bhattacherjee is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Share This Book

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• 01 May 2024

Why it’s essential to study sex and gender, even as tensions rise

You have full access to this article via your institution.

In 2023, students protested against a new policy in Texas, where parents would be notified if their child asks to be identified as transgender. Credit: Brett Coomer/Houston Chronicle/Getty

This week, Nature is launching a collection of opinion articles on sex and gender in research. Further articles will be published in the coming months. The series will highlight the necessity and challenges of studying a topic that is both hugely under-researched and, increasingly, the focus of arguments worldwide — many of which are neither healthy nor constructive.

Some scientists have been warned off studying sex differences by colleagues. Others, who are already working on sex or gender-related topics, are hesitant to publish their views. Such a climate of fear and reticence serves no one. To find a way forward we need more knowledge, not less.

Collection: Sex and gender in science

Nearly 20 researchers from diverse fields, including neuroscience, psychology, immunology and cancer, have contributed to the series, which provides a snapshot of where scholars studying sex and gender are aligned — and where they are not. In time, we hope this collection will help to shape research, and provide a reference point for moderating often-intemperate debates.

In practice, people use sex and gender to mean different things. But researchers studying animals typically use sex to refer to male and female individuals , as defined by various anatomical and other biological features. In studies involving humans, participants are generally asked to identify their own sex and/or gender category. Here, gender usually encompasses social and environmental factors , including gender roles, expectations and identity.

For as long as scientific inquiry has existed, people have mainly studied men or male animals. Even as recently as 2009, only 26% of studies using animals included both female and male individuals, according to a review of 10 fields in the biological sciences 1 . This bias has had serious consequences. Between 1997 and 2000, for instance, eight prescription drugs were removed from the US market, because clinical testing had not revealed women’s greater risk of developing health problems after taking the drugs.

Male–female comparisons are powerful in biomedical research — don’t abandon them

The tide, however, is turning. Many journals, including those in the Nature Portfolio , and funders, such as the US National Institutes of Health, have developed guidelines and mandates to encourage scientists to consider sex and, where appropriate, gender in their work.

These efforts are reaping benefits 2 . Studies, for example, are showing that a person’s sex and/or gender can influence their risk of disease and chances of survival when it comes to many common causes of death — including cardiovascular conditions and cancer.

Despite this, many researchers remain unconvinced that the inclusion of sex and gender information is important in their field. Others, who are already doing so, have told Nature that they’re afraid of how their work is perceived and of how it could be misunderstood, or misused.

Podcast: Sex and gender discussions don't need to be toxic

Because researchers who are exploring the effects of sex and gender come from many disciplines, there will be disagreements. An often-raised and valid concern, for example, is that when researchers compare responses between female and male animals, or between men and women, they exclude those whose sex and/or gender doesn’t fall into a binary categorization scheme. Another is that variability between individuals of the same sex could be more important than that between sexes.

Sometimes sense does seem to get lost in the debates. That the term sex refers to a lot of interacting factors, which are not fully understood, does not invalidate its usefulness as a concept 3 . That some people misinterpret and misuse findings concerning differences between sexes, particularly in relation to the human brain , should not mean denying that any differences exist.

Tempering the debate

Many of the questions being raised, however, are important to ask, especially given concerns about how best to investigate biological differences between groups of humans , and the continued — and, in some regions, worsening — marginalization of people whose sex and/or gender identity doesn’t fall into narrowly defined norms. Often, such questions and concerns can be addressed through research. For example, studies might find that variability between individuals of the same sex in diet, or body weight, say, are more important predictors of how likely they are to develop anaemia than whether they are male or female.

We need more-nuanced approaches to exploring sex and gender in research

The problem, then is not the discussions alone: science exists to examine and interrogate disagreements. Rather, the problem is that debates — and work on sex and gender, in general — are being used to polarize opinions about gender identity. As Arthur Arnold, a biologist at the University of California, Los Angeles, and his colleagues describe in their Comment article , last September, legislation banning gender-affirming medical care for people under 18 years old was introduced in Texas on the basis of claims that everyone belongs to one of two gender groups, and that this reality is settled by science. It isn’t. Scientists are reluctant to study sex and gender, not just because of concerns about the complexity and costs of the research, but also because of current tensions.

But it is crucial that scholars do not refrain from considering the effects of sex and gender if such analyses are relevant to their field. Improved knowledge will help to resolve concerns and allow a scholarly consensus to be reached, where possible. Where disagreements persist, our hope is that Nature ’s collection of opinion articles will equip researchers with the tools needed to help them persuade others that going back to assuming that male individuals represent everyone is no longer an option.

Nature 629 , 7-8 (2024)

doi: https://doi.org/10.1038/d41586-024-01207-0

Beery, A. K. & Zucker, I. Neurosci. Biobehav. Rev. 35 , 565–572 (2011).

Article   PubMed   Google Scholar  

Tannenbaum, C., Ellis, R. P., Eyssel, F., Zou, J. & Schiebinger, L. Nature 575 , 137–146 (2019).

Velocci, B. Cell 187 , 1343–1346 (2024).

Download references

Reprints and permissions

Related Articles

• Research data
• Research management

Japan can embrace open science — but flexible approaches are key

Correspondence 07 MAY 24

Researchers want a ‘nutrition label’ for academic-paper facts

Nature Index 17 APR 24

Adopt universal standards for study adaptation to boost health, education and social-science research

Correspondence 02 APR 24

US funders to tighten oversight of controversial ‘gain-of-function’ research

News 07 MAY 24

France’s research mega-campus faces leadership crisis

News 03 MAY 24

Hunger on campus: why US PhD students are fighting over food

Career Feature 03 MAY 24

US National Academies report outlines barriers and solutions for scientist carers

Career News 02 MAY 24

Comment 01 MAY 24

Postdoctoral Associate- Electrophysiology

Houston, Texas (US)

Baylor College of Medicine (BCM)

Postdoctoral Scholar - Clinical Pharmacy & Translational Science

Memphis, Tennessee

The University of Tennessee Health Science Center (UTHSC)

Postdoctoral Scholar - Pathology

Faculty Positions in School of Engineering, Westlake University

The School of Engineering (SOE) at Westlake University is seeking to fill multiple tenured or tenure-track faculty positions in all ranks.

Hangzhou, Zhejiang, China

Westlake University

High-Level Talents at the First Affiliated Hospital of Nanchang University

For clinical medicine and basic medicine; basic research of emerging inter-disciplines and medical big data.

Nanchang, Jiangxi, China

The First Affiliated Hospital of Nanchang University

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

All About Science

The Role of Science in the 21st Century

The challenging environment for science in the 21st century

Successful universities the world over are deeply connected with the social, economic and political environment in which they serve. However, universities should also operate independently as they are not factories or political tools, and they do not need charismatic leaders the way armies and churches might.

Universities are collectives, and open, critical discourse based on democratic principles is essential for their success. As part of their time-honoured compact with society, universities should not blandly surrender to outside pressures, but actively and critically engage with them.

Social and political context

There are an increasing number of national imperatives that universities need to consider in deciding how to position themselves. For instance, since the dawn of South Africa’s democracy, there has been a push to broaden access to higher education in order to accommodate more students with different prior experiences, different goals and ambitions, and different levels of preparedness. Importantly, transformation of our universities has included calls to diversify the professoriate and to change the culture of universities.

The weak South African economy suggests that the country’s universities should prepare their students more directly for the job market. There are cries for a stronger focus on practical skills development, almost akin to vocational training. There is an expectation that academics and researchers should make more direct contributions to marketable innovations, and be more inventive with developing practical applications and solutions to everyday problems.

Funding agencies call for research programmes with more direct relevance to South Africa. There are, thus, enormous pressures for curriculum reform, and invariably arguments for decolonisation, with all its political ramifications.

Coupled to the above, a burgeoning agenda for African development is being set by the African Union’s Agenda 2063, and looking further afield, the global socio-economic and political environment for change is being defined fairly comprehensively by the United Nations’ Sustainable Development Goals. These are further considerations that universities are being asked to take on board to frame their research and teaching programmes if they are going to be relevant in the future.

Politicisation of research

The challenges for science in the 21st century do not end there. A real problem today is that truth is increasingly being undervalued, and scientific research is becoming politicised, for example, in the context of climate change. This is a scourge that is spreading world-wide.

The efforts required to advance knowledge for societal benefit are not always understood and appreciated by society, including by decision-makers. The need for an independent, critical academy is not always appreciated and, on the contrary, is often seen to be a threat by many autocratic regimes.

It is becoming difficult to discriminate between real and bogus information ‘out there’ because much of the information on the internet has not been sufficiently tested for veracity and truth. Lies can be propagated at a phenomenal rate. For universities, which should pride themselves on uncovering the truth, this is debilitating. In this environment, it is also becoming more difficult to counter plagiarism and protect intellectual property – matters that are of profound importance for our universities.

Most, but not all, citizens of the world have free and easy access to information, which begs the question: “Are our universities becoming less relevant?” They will be if educational and research systems are not adjusted. We certainly need more discussion on how universities should change, and this will remain a hotly contested area in planning for the future of universities for many years to come.

Global challenges

There is a growing number of substantive challenges in academia. Across a majority of disciplines, we are moving into the era of extremely large data sets, calling for smarter and more secure means of storing and transporting data, as well as accessing and mining data intelligently for research and decision-making.

This means that an increasing number of researchers across many different disciplines, including the humanities and social sciences, need to become more computationally competent. In addition, these researchers need to be preparing to work in larger, multidisciplinary teams to resolve quantitative problems more effectively. While this need is set to grow, this transition is arguably not happening fast enough.

The world-wide science system has become enormous, and it is proving to be extremely difficult to keep up with research outputs in one’s own narrow research area of interest, let alone more broadly. The flood of information is overwhelming and we need smarter ways to keep up, or else we run the risk of duplicating efforts and falling behind.

On the topic of peer-reviewed publications, it has finally dawned on academics and universities that they should not be paying exorbitant costs to access publicly-funded research and in so doing enrich large corporations. The entire world of publications in this age of the internet is in a process of radical change. Academics need to seriously contemplate the pros and cons of open access, and actively participate in the global discussions currently taking place, for example, around the proposed European Plan S.

Developing science responsibly

There are enormous disparities in science around the world, which demand that we think more deeply about how we develop science more extensively on a global scale for the good of all of humanity.

The big science questions need big – meaning expensive – research infrastructures. This calls for large, multidisciplinary teams and multinational collaborations. We must ask how we can participate more effectively, especially from the southern tip of Africa. The rest of Africa is falling behind because there has been relatively little commitment from many African countries to invest in scientific research infrastructure and in people development. This will continue to hold Africa back.

South Africa is globally connected though the internet, which means that the country is also susceptible to international terror through breaches in cybersecurity. The ways in which some international agencies and governments are protecting themselves against cyber-attacks are top secret for obvious reasons, which means that many countries in the developing world are left in the dark and will need to figure out their own solutions. African countries and their universities need to invest in their own programmes to interrogate cybersecurity for their own well-being and national security.

Open-ended, unfettered science in its purest form has, over the centuries, been pursued in the interests of understanding nature in a fundamental way, and long may that continue. Scientific ideas and discoveries have often been very successfully exploited for commercial gain and societal improvements, and much of the science system today the world over is designed to push scientists in the direction of more relevance. The applications of science coupled with critical thought have been essential in solving many problems facing society.

Usually, that impact has been positive, but not always. There has been collateral damage and unintended consequences along the way; for example, plastics in our oceans, and other harmful environmental effects. The military has been a strong supporter of science in many countries, including during apartheid South Africa. Science has been driven in particular ways to gain superior might. Many authoritarian states, such as North Korea, have invested significantly in a very narrow set of scientific endeavours and technologies with a singular purpose in mind.

Through the millennia, there has always been the potential for scientific outputs to be misused, from the time the simple domestic knife was invented. Science in the wrong hands can be catastrophic – and a climate for the misuse of science is growing.

Limited global resources

Some of the more difficult questions that academics need to think about relate to the consequences of the rapidly increasing global population and the stress this places on our resources and environment. This is already resulting in a power struggle for limited resources. The future of the human race depends on scientists finding more intelligent answers to difficult questions, and here researchers have a central role to play.

With the rapidly increasing world population one can conclude that, purely from a statistical viewpoint, each life is becoming less significant. It should boggle the mind, then, to think about what this could imply in terms of the potential for increased unethical behaviour towards our fellow human beings, for example, in terms of mass exterminations, human experimentation and cruelty.

We should think deeply about this and how academics can try to counter these tendencies in their work – by identifying the problem early on, and proposing solutions before the problem gets beyond our control.

History will show that so much has been accomplished by so few with so little over the past 100 years. This period has been unprecedented in the history of the human race. It is difficult to believe that the electron was discovered just over 100 years ago, and through science and the applications of science, technology, industrialisation and commercialisation, and sheer ingenuity, humans have been able to harness the fullest potential of the electron to fundamentally change the way in which we live our lives, not only in a technical sense, but also in a social sense. This tiny particle has come to define our age, namely the electronic age.

This stunning growth over a short period does raise unrealistic expectations that new scientific ideas and technologies needed to solve challenges in the 21st century will emerge just as easily, just as rapidly and just as cheaply, with the snap of a finger, so to speak.

But that is not correct.

Support for science

Our universities are working under extremely tight fiscal constraints. Academics are being asked to do much more with much less at a time when our universities are under enormous pressures to be ‘world class’. Science needs much more support for the public good.

In striving to be nationally responsive and world class, South African science must be connected with the global environment that frames science. We should be consolidating and setting the foundations to be world class. We need to be excellent in all aspects of the academic enterprise including our management, operations, teaching and learning, research and external engagements.

Universities in South Africa have been in a state of stress for a while, and one wonders whether an era of stability is possible in which to focus on core functions.

A successful and prosperous South Africa depends on a modern, scientifically literate and technically competent workforce, and here, universities have a central role to play. They are a precious resource.

Stakeholders need to engage more intelligently and constructively with each other within and without the university, or the idea of the university will be under threat. We are but temporary custodians of the institutions we inherit. The hope and expectation are that we will build on the foundations that have been laid by others over the years, and to leave it in a better state than we found it.

Contact us for more information.

Nithaya Chetty is professor of physics at the University of Pretoria and dean-elect of the faculty of science at the University of the Witwatersrand in South Africa. He is vice president of the International Union of Pure and Applied Physics. He writes in his personal capacity.

• latest-news

Recent Posts

• Why Technology Is Important In Our Daily Life?
• The Evolution and Importance of Science Education in the 21st Century
• Role of Science and Technology in the 21st Century
• The role of science in the 21st century
• The Greatest Scientists of the 21st Century
• February 2024
• December 2023
• November 2023
• October 2023
• September 2023
• January 2023
• August 2022
• October 2021
• September 2021
• August 2021
• November 2018

About Us !!!

Times are changing. In the earlier days, we used to go to the library, today we search and archive our papers online. We have collaborations per email, hold telephone seminars, organize virtual networks, write blogs, and make our seminars available on the internet.

Popular Posts

© 2024 Science 21st Century

Theme by Anders Noren — Up ↑

• Publications
• Conferences & Events
• Professional Learning
• Science Standards
• Awards & Competitions
• Instructional Materials
• Free Resources
• American Rescue Plan
• For Preservice Teachers
• NCCSTS Case Collection
• Partner Jobs in Education
• Interactive eBooks+
• Digital Catalog
• Regional Product Representatives
• e-Newsletters
• Bestselling Books
• Latest Books
• Popular Book Series
• Prospective Authors
• Web Seminars
• Exhibits & Sponsorship
• Conference Reviewers
• National Conference • Denver 24
• Leaders Institute 2024
• National Conference • New Orleans 24
• Submit a Proposal
• Latest Resources
• Professional Learning Units & Courses
• For Districts
• Online Course Providers
• Schools & Districts
• College Professors & Students
• The Standards
• Teachers and Admin
• eCYBERMISSION
• Toshiba/NSTA ExploraVision
• Junior Science & Humanities Symposium
• Teaching Awards
• Climate Change
• Earth & Space Science
• New Science Teachers
• Early Childhood
• Middle School
• High School
• Postsecondary
• Informal Education
• Journal Articles
• Lesson Plans
• e-newsletters
• Science & Children
• Science Scope
• The Science Teacher
• Journal of College Sci. Teaching
• Connected Science Learning
• NSTA Reports
• Next-Gen Navigator
• Science Update
• Teacher Tip Tuesday
• Trans. Sci. Learning

MyNSTA Community

• My Collections

Position Statement

The Role of Research on Science Teaching and Learning

Share Start a Discussion

Download PDF

Introduction

Research on science teaching and learning plays an important role in helping all students become proficient in science and making science education more equitable and inclusive, two goals called for in the   Framework for K–12 Science Education   (NRC 2012). NSTA promotes a research agenda that is focused on the goal of enhancing student learning through effective and equitable teaching practices that are based on current research. NSTA encourages ALL stakeholders in science education, including K–16 teachers of science and administrators, informal science educators, and school board members to recognize the importance of educational research, promote more research in schools, and participate in research when possible.

NSTA considers a broad range of activities to be within the scope of research, including research conducted by teachers that can lead to immediate classroom changes as well as research that contributes to a larger body of knowledge such as long-term or large-scale studies. Research on science teaching and learning involves identifying and asking appropriate questions, designing and conducting investigations, collecting evidence, drawing conclusions, and communicating and defending findings (NSTA 2004).

To produce research that has meaningful outcomes and the ability to improve the teaching and learning of science, NSTA advocates that research and practice be linked and support compatible goals. This synergistic relationship between research and practice includes teachers and researchers communicating goals, activities, and findings with the greater science education community in ways that make research accessible, understandable, meaningful, and relevant to teachers, administrators, and policy makers.

The process of research is the essence of the scientific enterprise and of scientific inquiry. Science education builds on the best of research in both worlds—science and education. By engaging in continual inquiry into teaching and learning, we can promote scientific literacy for students in the 21st century.

NSTA makes the following recommendations to promote effective research on science teaching and learning.

Declarations

Regarding the focus of research on science teaching and learning, NSTA recommends those conducting research

• examine questions that are relevant to enhancing science teaching and learning for all learners;
• focus on ways to make science education more equitable and inclusive;
• address areas that have either been insufficiently investigated or not investigated at all and have the potential to improve what is known about science teaching and learning; and
• extend theories of science teaching and learning in order to contribute to a coherent body of knowledge.

Regarding the practice of research on science teaching and learning, NSTA recommends those conducting research

• draw and build upon previous research that may exist in the area of study;
• focus on studies that build on promising areas of research and link to a larger body of work;
• form collaborations and partnerships among those involved in science education (e.g., teachers, administrators, college faculty, informal science educators) as they examine science teaching and learning;
• demonstrate, when possible, the degree to which student learning is affected;
• engage in rigorous peer review that challenges the status quo and values varying perspectives on research pertaining to science teaching and learning;
• view everyday experiences as opportunities to conduct research that yields findings to improve teaching practices and student learning;
• support the participants in research with ample professional development to enhance their ability to design, conduct, interpret, and apply science education research; and
• share research results with the wider science education community inside and outside the classroom.

Regarding the use of research on science teaching and learning, NSTA recommends

• researchers communicate about research in ways that can be understood and embraced by science educators, administrators, policy makers, and others in the science education community;
• researchers make research readily accessible by disseminating it to teachers and other decision makers using many forms of communication, including practitioner journals, professional conferences, websites, and social media;
• researchers recognize and state the limitations of their research;
• researchers and consumers of research discuss, critique, and apply findings;
• school researchers have ample administrative support, time, and resources to conduct research in the classroom, share their findings with colleagues, and implement results to improve student learning; and
• science educators embrace a culture of inquiry grounded in research that focuses on examining practice and improving student outcomes.

— Adopted by the NSTA Board of Directors, September 2010 Revised, October 2017

National Research Council (NRC). 2012.   A framework for K–12 science education: Practices, crosscutting concepts, and core ideas . Washington, DC: National Academies Press.

Exploring MRI's role in neuroscience research on model organisms

Magnetic Resonance Imaging (MRI) is an essential tool in neuroscience. It allows researchers to delve deep into the brain, analyze neural anatomy, and map complex brain functions with precision. In addition to its more familiar use in human research, MRI is also invaluable for basic and translational research in rodents and other model organisms. Despite the technique’s central importance, its reliance on the principles of electromagnetism and large, expensive equipment can be daunting to those new to the field. 

Recognizing the potential for wider application in small-animal neuroscience research, the  Neurosciences Preclinical Imaging Lab (NPIL) at the Wu Tsai Neurosciences Institute hosted its third annual symposium. The event showcased the capabilities of MRI in supporting research on model organisms and highlighted the latest advancements and applications in MRI technology. 

As one of the  seven Neurosciences Community Laboratories within Wu Tsai Neuro, NPIL equips scientists with cutting-edge MRI technology and expertise. Led by  Jieun Kim , NPIL director, the lab supports a wide range of preclinical imaging applications in diverse animal studies. Kim encourages neuroscientists of all experience levels to consider incorporating MRI into their research projects to enhance insight and discovery.

After NPIL Faculty Director  Jin Hyung Lee provided a brief overview of the lab’s origins, Saaussan Madi, a senior applications scientist from MRI manufacturer Bruker, a co-sponsor of the event, explained the new features of the NPIL’s unique 7-Tesla large-bore preclinical MRI magnet.

Madi’s presentation outlined recent software enhancements that have significantly improved its capabilities. These updates streamline imaging processes by reducing image distortion and scan times, and introduce new capabilities for detailed neurotransmitter analysis and sophisticated data visualization.

Highlighting the collaborative potential of MRI in small-animal neuroscience research, a panel discussion on "MRI for Basic Neuroscientists" featured leading preclinical imaging scientists from Bruker, neuroimaging experts from Stanford, and the National Institutes of Health. 

The panelists discussed the versatile applications of NPIL's advanced MRI technology, the comprehensive services provided by NPIL, and effective data management practices. They also offered insights on how neuroscience researchers at all experience levels, including those new to MRI, can leverage NPIL resources to enhance their studies.

Keynote speaker Peter Basser, Senior Investigator at the National Institute of Child Health and Human Development (NICHD) in the Section on Quantitative Imaging and Tissue Sciences, showcased the capabilities of mesoscale imaging in MRI. This technique reveals intricate brain structures such as cortical layers and nuclei, often invisible in conventional scans. 

Basser highlighted its broad applicability, providing examples of how it benefits various neuroscience and clinical disciplines. He discussed the current challenges and potential advancements in mesoscale MRI, including the need for improved voxel (3-D pixel) resolution and more effective experimental designs. Basser noted that innovations in high-field, high-gradient MRI could revolutionize both pre-clinical and clinical applications, bridging significant gaps in neuroscience research.

NPIL awards annual Pilot Grants to support new researchers in the field or enable novel or experimental studies by more experienced researchers. Four of the 2023 NPIL Pilot Grant Awardees presented the progress they had made in their research projects at the 2024 Symposium.

Sedona Ewbank , a neuroscience PhD student and NSF Graduate Research Fellow working in the  laboratory of Raag Airan in the Department of Neurosciences, used a technique called multi-shell diffusion-weighted imaging to study structural changes induced by ketamine in the brains of mice. Her research aims to quantify these changes, explore variables that might affect ketamine-induced structural plasticity, and investigate its potential to reverse tissue microstructure deficits in genetic disease models. 

Vanessa Doulames , a research scientist in  Sarah Heilshorn's Biomaterials Group in the Department of Advanced Materials, presented her research on therapies to repair spinal cord injuries by guiding pluripotent stem cells to regenerate damaged tissue in rat models. Her project aims to refine cell implantation techniques, increase the sample size, and conduct comprehensive analyses of various experimental groups.

Birgitt Schuele , an associate professor of pathology at Stanford, shared her work on the role of alpha-synuclein — a key protein in Parkinson’s disease — using a rodent model to examine how gene dosage affects behavior, brain activity, structural connectivity, and metabolites. Her NPIL-funded research aims to provide deeper insights into the disease’s mechanisms.

Richard Coca, now a PhD student at Boston University who started his research as a Stanford undergraduate in the  Wheeler Lab , described his NPIL research exploring the impacts of the ALG13 gene mutation using a novel mouse model. His project utilized neuroimaging to understand how this mutation contributes to epilepsy and other symptoms, with the long-term goal of translating these findings into clinical practice.

After the 2023 Pilot Grant recipients shared their findings, Tim Doyle , Director of the Neurosciences Community Labs at Wu Tsai Neuro, and Jin Hyung Lee, associate professor of neurology, neurosurgery, and bioengineering and the NPIL’s faculty director, announced the 2024 NPIL Pilot Grant Awardees.

The 2024 NPIL Pilot Grant projects are led by scientists from various departments across the School of Medicine and Stanford Health Care, including microbiology, immunology, neurosurgery, neurology, and pediatrics (see below). 

The symposium concluded with a poster session where attendees networked and discussed how to integrate the day’s insights into their research. Refreshments were served as scientists exchanged ideas and planned collaborations. To close, Kim encouraged attendees to visit NPIL for more discussions and a tour of the facilities.

Funded projects

Brain response to influenza virus infection in the lung.

• Yueh-hsiu Chien , Microbiology and Immunology, SOM

This project aims to find how the brain regulates the immune system during influenza virus infections, focusing specifically on the role of glucocorticoids (GC), which are essential for managing immune and physiological responses. The study will employ MRI technology to investigate how influenza-induced hypoxia—low oxygen levels—affects the brain in mice. Ultimately, this research seeks to uncover the mechanisms behind glucocorticoid dysregulation during influenza, potentially enhancing our understanding of immune responses in viral infections.

Decoding stem cell-induced recovery in time and space: a longitudinal DTI and MRS study to assess microstructural and metabolic changes following stem cell transplantation in stroke-injured rat brains

• Pardes Habib , Neurosurgery, SOM
• PI: Gary K. Steinberg , Neurosurgery and Neurosciences, SOM

This project will explore the use of human neural stem cells (hNSCs) to treat chronic ischemic stroke in rats. Focusing on a specific MRI signal in the premotor cortex that correlates with clinical improvement, researchers aim to understand how hNSCs facilitate brain recovery. Advanced imaging techniques will be used to map microstructural and metabolic changes, potentially identifying new biomarkers and treatments for future clinical trials.

MRI evaluation of a novel murine model of RhoBTB2-related epileptic encephalopathy facilitating development of an antisense oligonucleotide therapeutic

Thomas LaRocca , Pediatrics, SOM

This project will develop a new mouse model to study a rare form of epilepsy linked to the RhoBTB2 gene, which currently has no effective treatments. Utilizing MRI, researchers will investigate how this genetic variant influences brain development and function. This team aims to use these insights to uncover structural relationships and neurodevelopmental impacts of the gene variant, potentially leading to novel treatment options for patients with this severe condition.

Assessment of MAP4K4 in regulating tumor cell invasion in a preclinical model of pediatric high-grade glioma

Laura Prolo , Neurosurgery, SHC, SOM

This project will study the role of MAP4K4, a critical kinase in signaling pathways, in the progression of pediatric high-grade gliomas (pHGGs)—aggressive brain tumors with few effective treatments. By employing advanced genetic editing tools like CRISPR and using MRI technology, researchers aim to understand how this protein impacts tumor growth and resistance in mouse models. The research will test whether inhibiting MAP4K4 reduces tumor invasion and improves survival, potentially identifying new targets for therapeutic intervention.

Investigating longitudinal white matter changes after juvenile stroke

• Elizabeth Mayne , Neurology, SOM

This project will investigate how strokes affect brain development in children differently than in adults, focusing on white matter, which facilitates brain communication. Preliminary data suggests that the response to stroke in young brains involves unique inflammatory processes that disrupt myelin formation, leading to cognitive challenges. By comparing juvenile and adult mice using advanced neuroimaging, this project aims to uncover age-specific responses and inform targeted therapies for young stroke survivors.

Mapping myelin plasticity in mouse models of generalized epilepsy

• Juliet Knowles , Neurology and Pediatrics, SOM

This project will explore myelin plasticity in mouse models to better understand epilepsy, a condition where brain networks change unfavorably, often leading to medication-resistant seizures and cognitive decline. By using MRI, the study aims to map how myelin—the protective coating on nerve cells that facilitates brain communication—is altered by seizures. The project will test whether these myelin changes can be reversed and will seek to understand the underlying mechanisms, with the ultimate goal of developing treatments that prevent worsening seizures and cognitive loss in children with epilepsy.

Watch a recording of the symposium here

Learn more about the Neuroscience Preclinical Imaging Laboratory (NPIL)

Related News

Neuroimaging symposium empowers neuroscientists to utilize MRI

New brain imaging facility offers high-end preclinical MRI services to research ...

After another statistical speed bump, is the science of fmri learning from its m....

Princeton University

Science has an ai problem. this group says they can fix it..

By Scott Lyon

May 1, 2024

Researchers recommend 32 best practices to stamp out a smoldering crisis that threatens to engulf all of science: thousands of AI-driven claims across dozens of fields that cannot be reproduced. Illustration courtesy Adobe Stock

AI holds the potential to help doctors find early markers of disease and policymakers to avoid decisions that lead to war. But a growing body of evidence has revealed deep flaws in how machine learning is used in science, a problem that has swept through dozens of fields and implicated thousands of erroneous papers.

Now an interdisciplinary team of 19 researchers, led by Princeton University computer scientists Arvind Narayanan and Sayash Kapoor, has published guidelines for the responsible use of machine learning in science.

“When we graduate from traditional statistical methods to machine learning methods, there are a vastly greater number of ways to shoot oneself in the foot,” said Narayanan , director of Princeton’s Center for Information Technology Policy and a professor of computer science . “If we don’t have an intervention to improve our scientific standards and reporting standards when it comes to machine learning-based science, we risk not just one discipline but many different scientific disciplines rediscovering these crises one after another.”

The authors say their work is an effort to stamp out this smoldering crisis of credibility that threatens to engulf nearly every corner of the research enterprise. A paper detailing their guidelines appeared May 1 in the journal Science Advances .

Because machine learning has been adopted across virtually every scientific discipline, with no universal standards safeguarding the integrity of those methods, Narayanan said the current crisis, which he calls the reproducibility crisis , could become far more serious than the replication crisis that emerged in social psychology more than a decade ago.

The good news is that a simple set of best practices can help resolve this newer crisis before it gets out of hand, according to the authors, who come from computer science, mathematics, social science and health research.

“This is a systematic problem with systematic solutions,” said Kapoor , a graduate student who works with Narayanan and who organized the effort to produce the new consensus-based checklist.

The checklist focuses on ensuring the integrity of research that uses machine learning. Science depends on the ability to independently reproduce results and validate claims. Otherwise, new work cannot be reliably built atop old work, and the entire enterprise collapses. While other researchers have developed checklists that apply to discipline-specific problems, notably in medicine, the new guidelines start with the underlying methods and apply them to any quantitative discipline.

One of the main takeaways is transparency. The checklist calls on researchers to provide detailed descriptions of each machine learning model, including the code, the data used to train and test the model, the hardware specifications used to produce the results, the experimental design, the project’s goals and any limitations of the study’s findings. The standards are flexible enough to accommodate a wide range of nuance, including private datasets and complex hardware configurations, according to the authors.

While the increased rigor of these new standards might slow the publication of any given study, the authors believe wide adoption of these standards would increase the overall rate of discovery and innovation, potentially by a lot.

“What we ultimately care about is the pace of scientific progress,” said sociologist Emily Cantrell , one of the lead authors, who is pursuing her Ph.D. at Princeton. “By making sure the papers that get published are of high quality and that they’re a solid base for future papers to build on, that potentially then speeds up the pace of scientific progress. Focusing on scientific progress itself and not just getting papers out the door is really where our emphasis should be.”

Kapoor concurred. The errors hurt. “At the collective level, it’s just a major time sink,” he said. That time costs money. And that money, once wasted, could have catastrophic downstream effects, limiting the kinds of science that attract funding and investment, tanking ventures that are inadvertently built on faulty science, and discouraging countless numbers of young researchers.

In working toward a consensus about what should be included in the guidelines, the authors said they aimed to strike a balance: simple enough to be widely adopted, comprehensive enough to catch as many common mistakes as possible.

They say researchers could adopt the standards to improve their own work; peer reviewers could use the checklist to assess papers; and journals could adopt the standards as a requirement for publication.

“The scientific literature, especially in applied machine learning research, is full of avoidable errors,” Narayanan said. “And we want to help people. We want to keep honest people honest.”

The paper, “ Consensus-based recommendations for machine-learning-based science ,” published on May 1 in Science Advances, included the following authors:

Sayash Kapoor, Princeton University; Emily Cantrell, Princeton University; Kenny Peng, Cornell University; Thanh Hien (Hien) Pham, Princeton University; Christopher A. Bail, Duke University; Odd Erik Gundersen, Norwegian University of Science and Technology; Jake M. Hofman, Microsoft Research; Jessica Hullman, Northwestern University; Michael A. Lones, Heriot-Watt University; Momin M. Malik, Center for Digital Health, Mayo Clinic; Priyanka Nanayakkara, Northwestern; Russell A. Poldrack, Stanford University; Inioluwa Deborah Raji, University of California-Berkeley; Michael Roberts, University of Cambridge; Matthew J. Salganik, Princeton University; Marta Serra-Garcia, University of California-San Diego; Brandon M. Stewart, Princeton University; Gilles Vandewiele, Ghent University; and Arvind Narayanan, Princeton University.

Related News

Grad alum Avi Wigderson wins Turing Award for groundbreaking insights in computer science

Holographic displays offer a glimpse into an immersive future

Retro-reflectors could help future cities keep their cool

The science of static shock jolted into the 21st century

Justice Department designates Mayer to serve as first chief science and technology adviser and chief AI officer

Built for AI, this chip moves beyond transistors for huge computational gains

Arvind Narayanan

Data Science

Related departments and centers.

Computer Science

Center for Information Technology Policy

share this!

May 7, 2024

This article has been reviewed according to Science X's editorial process and policies . Editors have highlighted the following attributes while ensuring the content's credibility:

fact-checked

peer-reviewed publication

trusted source

Decoding development: mRNA's role in embryo formation

by Hebrew University of Jerusalem

A new study at Hebrew University reveals insights into mRNA regulation during embryonic development. The study sheds light on the intricate process of mRNA regulation during embryonic development, providing novel insights into how pluripotent cells adopt specialized identities through gene expression.

The work is published in the journal Nature Communications .The research was led by Ph.D. Student Lior Fishman and team under the guidance of researcher Dr. Michal Rabani from The Alexander Silberman Institute of Life Science at Hebrew University, and in collaboration with researchers from the National Institutes of Health in the U.S

Embryonic development involves pluripotent cells assuming specialized identities by adopting particular gene expression profiles. However, understanding the relative contributions of mRNA transcription and degradation to shaping these profiles has been challenging, particularly within embryos with diverse cellular identities.

In the study, researchers used a technique called single-cell RNA sequencing along with metabolic labeling to track how genes are turned on and off over time in zebrafish embryos . They could tell apart the mRNA that was made new (from the embryo itself) and the mRNA that was already there (from the mother). Using mathematical models, they measured how fast genes were turned on and off in different types of cells as they developed.

The results of the study reveal highly varied regulatory rates across thousands of genes. The researchers observed coordinated transcription and destruction rates for many transcripts and linked differences in degradation to specific sequence elements. Importantly, they identified cell-type-specific differences in degradation, including selective retention of maternal transcripts within primordial germ cells and enveloping layer cells, two of the earliest specified cell types.

Dr. Rabani, senior author of the study, commented, "Our study provides a quantitative approach to studying mRNA regulation during a dynamic spatio-temporal response. This work opens up new avenues for understanding the molecular mechanisms underlying cell fate determination during embryonic development ."

The findings of this study contribute to a deeper understanding of mRNA regulation and its role in shaping cellular identities during embryonic development. The research team hopes that their work will pave the way for future studies aimed at unraveling the complexities of gene expression regulation in various biological contexts.

Journal information: Nature Communications

Provided by Hebrew University of Jerusalem

Explore further

Feedback to editors

Every drop counts: New algorithm tracks Texas's daily reservoir evaporation rates

4 hours ago

Genetic study finds early summer fishing can have an evolutionary impact, resulting in smaller salmon

6 hours ago

Researchers discovery family of natural compounds that selectively kill parasites

Study suggests heavy snowfall and rain may contribute to some earthquakes

7 hours ago

The spread of misinformation varies by topic and by country in Europe, study finds

Webb presents best evidence to date for rocky exoplanet atmosphere

Human activity is making it harder for scientists to interpret oceans' past

Quantum simulators solve physics puzzles with colored dots

Chemists produce new-to-nature enzyme containing boron

Improving timing precision of millisecond pulsars using polarization

Relevant physicsforums posts, is it usual for vaccine injection site to hurt again during infection.

8 hours ago

Who chooses official designations for individual dolphins, such as FB15, F153, F286?

9 hours ago

The Cass Report (UK)

May 1, 2024

Is 5 milliamps at 240 volts dangerous?

Apr 29, 2024

Major Evolution in Action

Apr 22, 2024

If theres a 15% probability each month of getting a woman pregnant...

Apr 19, 2024

More from Biology and Medical

Related Stories

Research quantifies how millions of cells in zebrafish embryos are affected by key gene alterations

Nov 16, 2023

New tool reveals gene behavior in bacteria

Jan 24, 2024

Researchers develop novel data representation for transcription factor-binding sequences

Oct 20, 2023

Scientists develop new method to distinguish newly made gene transcripts from old ones

Mar 6, 2020

'Roadmap' tracks trajectories of embryo development

Mar 14, 2022

Cell lesson: better coordinated than isolated

Apr 5, 2019

Recommended for you

Researchers map out anatomy of wooden breast syndrome in broiler chickens

An adjuvant made in yeast could lower vaccine cost and boost availability

10 hours ago

Marine bacteria team up to produce a vital vitamin

Study pinpoints cellular response to pressure in sea star embryos

New technology changes how proteins in individual cells are studied

Let us know if there is a problem with our content.

Use this form if you have come across a typo, inaccuracy or would like to send an edit request for the content on this page. For general inquiries, please use our contact form . For general feedback, use the public comments section below (please adhere to guidelines ).

Please select the most appropriate category to facilitate processing of your request

Thank you for taking time to provide your feedback to the editors.

Your feedback is important to us. However, we do not guarantee individual replies due to the high volume of messages.

E-mail the story

Your email address is used only to let the recipient know who sent the email. Neither your address nor the recipient's address will be used for any other purpose. The information you enter will appear in your e-mail message and is not retained by Phys.org in any form.

Newsletter sign up

Get weekly and/or daily updates delivered to your inbox. You can unsubscribe at any time and we'll never share your details to third parties.

More information Privacy policy

Donate and enjoy an ad-free experience

We keep our content available to everyone. Consider supporting Science X's mission by getting a premium account.

E-mail newsletter

Numbers, Facts and Trends Shaping Your World

Read our research on:

Full Topic List

Regions & Countries

• Publications
• Our Methods
• Short Reads
• Tools & Resources

Read Our Research On:

How Americans View the Coronavirus, COVID-19 Vaccines Amid Declining Levels of Concern

• Acknowledgments

Table of Contents

• COVID-19 vaccination among adults ages 65 and older, by party
• Views of long COVID
• Views of the threat posed by the coronavirus
• Personal concern about getting or spreading COVID-19
• Uptake of the flu shot
• The American Trends Panel survey methodology
• Appendix: Detailed chart and tables

This report is made possible by The Pew Charitable Trusts. It is a collaborative effort based on the input and analysis of the following individuals. Find related reports online at: pewresearch.org/pewresearch-org/science .

Primary research team       

Alec Tyson, Associate Director, Science and Society Research Brian Kennedy, Senior Researcher Giancarlo Pasquini, Research Associate Emma Kikuchi, Research Assistant    

Editorial and graphic design          

Rebecca Leppert, Copy Editor Kaitlyn Radde, Associate Information Graphics Designer

Communications and web publishing      

Haley Nolan, Communications Manager Talia Price, Communications Associate Reem Nadeem, Digital Producer

In addition, the project benefited greatly from the guidance of the Pew Research Center methodology team: Courtney Kennedy, Andrew Mercer, Ashley Amaya, Dorene Asare-Marfo, Dana Popky, Anna Brown and Arnold Lau.

Sign up for our weekly newsletter

Fresh data delivery Saturday mornings

Sign up for The Briefing

Weekly updates on the world of news & information

• Coronavirus (COVID-19)
• COVID-19 & Politics
• COVID-19 & Science
• Medicine & Health

Americans’ Trust in Scientists, Positive Views of Science Continue to Decline

Americans’ largely positive views of childhood vaccines hold steady, lack of preparedness among top reactions americans have to public health officials’ covid-19 response, about half of recent online daters in u.s. say it’s important to see covid-19 vaccination status on profiles, partisan differences are common in the lessons americans take away from covid-19, most popular, report materials.

1615 L St. NW, Suite 800 Washington, DC 20036 USA (+1) 202-419-4300 | Main (+1) 202-857-8562 | Fax (+1) 202-419-4372 |  Media Inquiries

Research Topics

• Age & Generations
• Economy & Work
• Family & Relationships
• Gender & LGBTQ
• Immigration & Migration
• International Affairs
• Internet & Technology
• Methodological Research
• News Habits & Media
• Non-U.S. Governments
• Other Topics
• Politics & Policy
• Race & Ethnicity
• Email Newsletters

ABOUT PEW RESEARCH CENTER  Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of  The Pew Charitable Trusts .

Copyright 2024 Pew Research Center

Terms & Conditions

Privacy Policy

Cookie Settings

Reprints, Permissions & Use Policy

• Alzheimer's disease & dementia
• Arthritis & Rheumatism
• Attention deficit disorders
• Autism spectrum disorders
• Biomedical technology
• Diseases, Conditions, Syndromes
• Endocrinology & Metabolism
• Gastroenterology
• Gerontology & Geriatrics
• Health informatics
• Inflammatory disorders
• Medical economics
• Medical research
• Medications
• Neuroscience
• Obstetrics & gynaecology
• Oncology & Cancer
• Ophthalmology
• Overweight & Obesity
• Parkinson's & Movement disorders
• Psychology & Psychiatry
• Radiology & Imaging
• Sleep disorders
• Sports medicine & Kinesiology
• Vaccination
• Breast cancer
• Cardiovascular disease
• Chronic obstructive pulmonary disease
• Colon cancer
• Coronary artery disease
• Heart attack
• Heart disease
• High blood pressure
• Kidney disease
• Lung cancer
• Multiple sclerosis
• Myocardial infarction
• Ovarian cancer
• Post traumatic stress disorder
• Rheumatoid arthritis
• Schizophrenia
• Skin cancer
• Type 2 diabetes
• Full List »

share this!

May 7, 2024

This article has been reviewed according to Science X's editorial process and policies . Editors have highlighted the following attributes while ensuring the content's credibility:

fact-checked

trusted source

Who rules the roost? Research reveals tots play key role in shaping the home environment

by University of York

In a study carried out by experts in child development at the University of York, researchers have found that pre-school children actively select, shape and create their own experiences to match their genetic tendencies.

The researchers looked at how genes and the environment work together to shape the brain development of children between the ages of two and four.

They found that rather than being passive recipients of the environment around them—such as the behavior and likes and dislikes of their parents—they also had a strong say in creating their own experiences based on their inherited preferences and character traits.

The researchers looked at early cognitive stimulation at home including talking, singing, nursery rhymes, books and engaging with toys and puzzles. They found that children drove decisions on which activities to focus on and how often to do them at least as much as their caregivers.

Professor Sophie von Stumm from the Department of Education at the University of York, said, "As many parents will know, small children are already very clear about what they do and don't like and this study cements the theory that, even at a young age, children are actively shaping their experiences at home.

"A child's preferences and differences in character will evoke distinct reactions from their caregivers. For example, if a child enjoys reading they will sit and focus on a book, which is likely to motivate caregivers to read with them more and provide more books.

"The debate over whether a child's character and abilities are down to nature or nurture is long-running, but our findings show how genes and the environment act as a combined force. Our study can also explain why even siblings growing up in the same house can differ so widely in their behavioral tendencies, emotional development , and learning abilities."

The research is the first systematic study of genetics and the environment and how they interplay in very young children. The report is titled "Gene-environment interplay in early life cognitive development."

The study used data from the Twins Early Development Study, which followed over 15,000 families with twins born between 1994 and 1996 in England and Wales. Over the course of this longitudinal study, twins' verbal and nonverbal cognitive development was assessed at different ages, including at two, three and four, via standardized testing and extensive parent and self-reports.

Lead author of the report, Dr. Alexandra Starr, a researcher in the Department of Education at the University of York, said, "We wanted to look at the early years because we know that children are already very different before they start school, and these differences in cognitive and socio-emotional development have important long-term consequences. Early differences grow and become greater as children get older, leading to a 'snowball effect."

"The early home environment is particularly important to brain development, we know so many outcomes in later life are related to this—from educational achievement, career success and income level to well-being and the ability to have stable relationships with others."

To tease apart the factors that aid children's development, the researchers used a powerful method called polygenic scores . Polygenic scores capture DNA variants that are passed on from parents to children and that can indicate how likely a person is to, for example, do well at school. The researchers tested interactions between polygenic scores for cognitive development and environmental factors.

"If we understand how children's differences come about in early life it could help to identify children in need of intervention as soon as possible," added Dr. Starr.

"For example, we could use DNA to identify children at genetic risk of developing reading problems, and offer them early intervention before maladaptive behaviors, like avoiding books, manifest. Preventive measures have a greater chance of being successful when implemented early in life.

"Polygenic scores are so powerful because they can predict traits at birth just as well as later in life, acting as an early warning system which could be of particular help for those children who are likely to struggle the most."

The study found that the early home environment contributes more to differences in children than genetic effects, but the researchers believe that in future even better genetic methods will be available. These will show that genetics and the environment contribute equally to the differences between children.

"People tend to be mistrustful of genetic testing because they fear it will result in discrimination," added Professor von Stumm. "Our study makes the case for more research on genetics, so that we can maximize the potential benefits of genetics and minimize their risks in the future, especially in the context of child development .

"Currently we tend to diagnose conditions like dyslexia and reading disorders when children are already struggling and have fallen behind. The latest advancements in genetic testing could mean we may one day be able to help children avoid reading difficulties altogether because we can support them effectively before they experience any of the struggles that are currently associated with dyslexia."

Explore further

Feedback to editors

Individuals of all ages with positive skin or blood test should receive preventive treatment for TB, new study says

2 hours ago

New research reports on financial entanglements between FDA chiefs and the drug industry

A 30-year US study links ultra-processed food to higher risk of early death

A third COVID vaccine dose improves defense for some clinically extremely vulnerable patients

Research team identifies four new genetic risk factors for multiple system atrophy

4 hours ago

About 90% of US adults are on the way to heart disease, study suggests

Researchers identify what drives PARP inhibitor resistance in advanced breast cancer

How infections influence our social empathy

5 hours ago

Health risks of using cannabis are higher in adolescents than in adults, study finds

6 hours ago

Study finds THC lingers in breastmilk with no clear peak point

Related stories.

9 out of 10 kids are not developmentally on track in literacy and numeracy—study of 8 African countries

Apr 10, 2024

Nature via nurture: How parents' cognitive and non-cognitive skills influence their children's education

Aug 24, 2022

A parent's genes can influence a child's educational success, inherited or not

Aug 19, 2021

Q&A: How parents can set children up for reading success

Mar 11, 2024

Genes shown to influence how well children do throughout their time at school

Sep 4, 2018

Good home learning in early years boosts your secondary school achievements

Jul 8, 2019

Recommended for you

New study offers insight into genesis of spina bifida

Researchers may have found an Achilles heel for Hepatitis B

9 hours ago

Gene linked to learning difficulties found to have direct impact on learning and memory

10 hours ago

New genetic mutation identified for congenital thyroid condition

Let us know if there is a problem with our content.

Use this form if you have come across a typo, inaccuracy or would like to send an edit request for the content on this page. For general inquiries, please use our contact form . For general feedback, use the public comments section below (please adhere to guidelines ).

Please select the most appropriate category to facilitate processing of your request

Thank you for taking time to provide your feedback to the editors.

Your feedback is important to us. However, we do not guarantee individual replies due to the high volume of messages.

E-mail the story

Your email address is used only to let the recipient know who sent the email. Neither your address nor the recipient's address will be used for any other purpose. The information you enter will appear in your e-mail message and is not retained by Medical Xpress in any form.

Newsletter sign up

Get weekly and/or daily updates delivered to your inbox. You can unsubscribe at any time and we'll never share your details to third parties.

More information Privacy policy

Donate and enjoy an ad-free experience

We keep our content available to everyone. Consider supporting Science X's mission by getting a premium account.

E-mail newsletter

• Share full article

Yellowstone’s Wolves: A Debate Over Their Role in the Park’s Ecosystem

New research questions the long-held theory that reintroduction of such a predator caused a trophic cascade, spawning renewal of vegetation and spurring biodiversity.

Yellowstone’s ecological transformation through the reintroduction of wolves has become a case study for how to correct out-of-balance ecosystems. But new research challenges that notion. Credit... Elizabeth Boehm/Danita Delimont, via Alamy

Supported by

By Jim Robbins

• April 23, 2024

In 1995, 14 wolves were delivered by truck and sled to the heart of Yellowstone National Park in Wyoming, where the animal had long been absent. Others followed.

Since then, a story has grown up, based on early research, that as the wolves increased in number, they hunted the park’s elk herds, significantly reducing them by about half from 17,000.

The wolves’ return and predatory dominance was believed to have had a widespread effect known as a trophic cascade, by decreasing grazing and restoring and expanding forests, grasses and other wildlife. It supposedly even changed the course of rivers as streamside vegetation returned.

Yellowstone’s dramatic transformation through the reintroduction of wolves has become a global parable for how to correct out-of-balance ecosystems.

In recent years, however, new research has walked that story back. Yes, stands of aspen and willows are thriving again — in some places. But decades of damage from elk herds’ grazing and trampling so thoroughly changed the landscape that large areas remain scarred and may not recover for a long time, if ever.

Wolf packs, in other words, are not magic bullets for restoring ecosystems.

“I would say it’s exaggerated, greatly exaggerated,” said Thomas Hobbs, a professor of natural resource ecology at Colorado State University and the lead author of a long-term study that adds new fuel to the debate over whether Yellowstone experienced a trophic cascade.

“You could argue a trophic trickle maybe,” said Daniel Stahler, the park’s lead wolf biologist who has studied the phenomenon. “Not a trophic cascade.”

Not only is the park’s recovery far less robust than first thought, but the story as it has been told is more complex, Dr. Hobbs said.

But the legend of the wolves’ influence on the park persists.

“How in the world does this lovely story — and it is a beautiful story — come to be seen as fact?” Dr. Hobbs wondered. A chapter of a book tried to answer that, concluding that a video called “ How Wolves Change Rivers ,” which has received tens of millions of views, contributed mightily to the tale.

The ecological record is complicated by the fact that, as elk declined, the number of bison increased substantially, continuing some of the same patterns, like heavy grazing in some places. Moreover, Yellowstone is growing warmer and drier with climate change.

Large numbers of elk in the north of the park had caused significant ecological changes — vegetation disappeared, trampled streams led to extensive erosion, and invasive plant species took hold. Riparian vegetation, or the grasses, the trees and the shrubs along riverbanks and streams, provides a critical habitat for birds, insects and other species to flourish and to maintain biodiversity in the park.

Once elk numbers dwindled, willows and aspens returned along rivers and streams and flourished. The beaver, an engineer of ecosystems, reappeared, using the dense new growth of willows for both food and construction materials. Colonies built new dams, creating ponds that enhanced stream habitats for birds, fish, grizzlies and other bears as well as promoting the growth of more willows and spring vegetation.

But wolves were only one piece of a larger picture, argue Dr. Hobbs and other skeptics of a full-blown trophic cascade at Yellowstone. Grizzly bears and humans played a role, too. For eight years after wolves re-entered the park, hunters killed more elk than the wolves did.

“The other members of the predator guild increased, and human harvest outside of the park has been clearly shown to be responsible for the decline in elk numbers the first 10 years after the wolves were introduced,” Dr. Hobbs said.

The changes attributed to the presence of stalking wolves, some research showed, weren’t only the result of fewer elk, but of a change in elk behavior called “the ecology of fear.” Scientists suggested that the big ungulates could no longer safely hang out along river or stream banks and eat everything in sight. They became extremely cautious, hiding in places where they could be vigilant. That allowed a return of vegetation in those places.

Dr. Hobbs and others contend that subsequent research has not borne that theory out.

Another overlooked factor is that around the same time wolves were returning, 129 beavers were reintroduced by the U.S. Forest Service onto streams north of the park. So it wasn’t just wolf predation on elk and the subsequent return of wolves that enabled an increase in beavers, experts say.

Some researchers say the so-called trophic cascade and rebirth of streamside ecosystems would have been far more robust if it weren’t for the park’s growing bison herd. The bison population is at an all-time high — the most recent count last summer found nearly 5,000 animals. Much larger than elk, bison are less likely to be vulnerable to wolves, which numbered 124 this winter.

The park’s bison, some researchers say, are overgrazing and otherwise seriously damaging the ecosystems — allowing the spread of invasive species and trampling and destroying native plants.

The heavily grazed landscape is why, critics say, some 4,000 bison, also a record, left Yellowstone for Montana in the winter of 2023-24, when an unusually heavy snow buried forage. Because some bison harbor a disease, called brucellosis, that state officials say could infect cattle, they are not welcome outside the park’s borders. (There are no documented cases of transmission between bison and cattle.)

Montana officials say killing animals that may carry disease as they leave the park is the only way to stem the flow. During a hunt that began in the winter of 2023, Native Americans from tribes around the region took part. All told, hunters killed about 1,085 bison; 88 more were shipped to slaughter and 282 were transferred to tribes. This year, just a few animals have left the park.

The Park Service is expected to release a bison management plan in the coming months. It is considering three options: to allow for 3,500 to 5,000 animals, 3,500 to 6,000, or a more natural population that could reach 7,000.

Richard Keigley, who was a research ecologist for the federal Geological Survey in the 1990s, has become an outspoken critic of the park’s bison management.

“They have created this juggernaut where we’ve got thousands of bison and the public believes this is the way things always were,” he said. “The bison that are there now have destroyed and degraded their primary ranges. People have to realize there’s something wrong in Yellowstone.”

Dr. Keigley said the bison population in the park fluctuated in the early years of the park, with about 229 animals in 1967. It has grown steadily since and peaked last year at 5,900.

“There is a hyperabundant bison population in our first national park,” said Robert Beschta, a professor emeritus of forest ecosystems at Oregon State University who has studied Yellowstone riparian areas for 20 years. He pointed to deteriorating conditions along the Lamar River from bison overgrazing.

“They are hammering it,” Mr. Beschta said. “The Lamar ranks right up there with the worst cattle allotments I’ve seen in the American West. Willows can’t grow. Cottonwoods can’t grow.”

A warmer and drier climate, he said, is making matters worse.

Such opinions, however, are not settled science. Some park experts believe that the presence of thousands of bison enhances park habitats because of something called the Green Wave Hypothesis.

Chris Geremia, a park biologist, is an author of a paper that makes the case that a large numbers of bison can stimulate plant growth by grazing grasses to the length of a suburban lawn. “By creating these grazing lawns bison and other herbivores — grasshoppers, elk — these lawns are sustaining more nutritious food for these animals,” he said.

Dr. Geremia contends that a tiny portion — perhaps one-tenth of one percent — of the park may be devoid of some plants. “The other 99.9 percent of those habitats exists in all different levels of willow, aspen and cottonwood,” he said.

The Greater Yellowstone Coalition, a conservation organization, favors a bison population of 4,000 to 6,000 animals. Shana Drimal, who heads the group’s bison conservation program, said that park officials needed to monitor closely changing conditions like climate, drought and bison movement to ensure the ecosystems wouldn’t become further degraded.

Several scientists propose allowing the bison to migrate to the buffer zones beyond the park’s borders, where they are naturally inclined to travel. But it remains controversial because of the threat of disease.

“The only solution is to provide suitable winter range outside the park where they should be tolerated,” said Robert Crabtree, a chief scientist for the Yellowstone Ecological Research Center, a nonprofit. “When they migrate outside the park now it’s to habitat they evolved to prefer — and instead we kill them and ship them away.”

Advertisement