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Scientific Research – Types, Purpose and Guide

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Scientific Research

Definition:

Scientific research is the systematic and empirical investigation of phenomena, theories, or hypotheses, using various methods and techniques in order to acquire new knowledge or to validate existing knowledge.

It involves the collection, analysis, interpretation, and presentation of data, as well as the formulation and testing of hypotheses. Scientific research can be conducted in various fields, such as natural sciences, social sciences, and engineering, and may involve experiments, observations, surveys, or other forms of data collection. The goal of scientific research is to advance knowledge, improve understanding, and contribute to the development of solutions to practical problems.

Types of Scientific Research

There are different types of scientific research, which can be classified based on their purpose, method, and application. In this response, we will discuss the four main types of scientific research.

Descriptive Research

Descriptive research aims to describe or document a particular phenomenon or situation, without altering it in any way. This type of research is usually done through observation, surveys, or case studies. Descriptive research is useful in generating ideas, understanding complex phenomena, and providing a foundation for future research. However, it does not provide explanations or causal relationships between variables.

Exploratory Research

Exploratory research aims to explore a new area of inquiry or develop initial ideas for future research. This type of research is usually conducted through observation, interviews, or focus groups. Exploratory research is useful in generating hypotheses, identifying research questions, and determining the feasibility of a larger study. However, it does not provide conclusive evidence or establish cause-and-effect relationships.

Experimental Research

Experimental research aims to test cause-and-effect relationships between variables by manipulating one variable and observing the effects on another variable. This type of research involves the use of an experimental group, which receives a treatment, and a control group, which does not receive the treatment. Experimental research is useful in establishing causal relationships, replicating results, and controlling extraneous variables. However, it may not be feasible or ethical to manipulate certain variables in some contexts.

Correlational Research

Correlational research aims to examine the relationship between two or more variables without manipulating them. This type of research involves the use of statistical techniques to determine the strength and direction of the relationship between variables. Correlational research is useful in identifying patterns, predicting outcomes, and testing theories. However, it does not establish causation or control for confounding variables.

Scientific Research Methods

Scientific research methods are used in scientific research to investigate phenomena, acquire knowledge, and answer questions using empirical evidence. Here are some commonly used scientific research methods:

Observational Studies

This method involves observing and recording phenomena as they occur in their natural setting. It can be done through direct observation or by using tools such as cameras, microscopes, or sensors.

Experimental Studies

This method involves manipulating one or more variables to determine the effect on the outcome. This type of study is often used to establish cause-and-effect relationships.

Survey Research

This method involves collecting data from a large number of people by asking them a set of standardized questions. Surveys can be conducted in person, over the phone, or online.

Case Studies

This method involves in-depth analysis of a single individual, group, or organization. Case studies are often used to gain insights into complex or unusual phenomena.

Meta-analysis

This method involves combining data from multiple studies to arrive at a more reliable conclusion. This technique can be used to identify patterns and trends across a large number of studies.

Qualitative Research

This method involves collecting and analyzing non-numerical data, such as interviews, focus groups, or observations. This type of research is often used to explore complex phenomena and to gain an understanding of people’s experiences and perspectives.

Quantitative Research

This method involves collecting and analyzing numerical data using statistical techniques. This type of research is often used to test hypotheses and to establish cause-and-effect relationships.

Longitudinal Studies

This method involves following a group of individuals over a period of time to observe changes and to identify patterns and trends. This type of study can be used to investigate the long-term effects of a particular intervention or exposure.

Data Analysis Methods

There are many different data analysis methods used in scientific research, and the choice of method depends on the type of data being collected and the research question. Here are some commonly used data analysis methods:

• Descriptive statistics: This involves using summary statistics such as mean, median, mode, standard deviation, and range to describe the basic features of the data.
• Inferential statistics: This involves using statistical tests to make inferences about a population based on a sample of data. Examples of inferential statistics include t-tests, ANOVA, and regression analysis.
• Qualitative analysis: This involves analyzing non-numerical data such as interviews, focus groups, and observations. Qualitative analysis may involve identifying themes, patterns, or categories in the data.
• Content analysis: This involves analyzing the content of written or visual materials such as articles, speeches, or images. Content analysis may involve identifying themes, patterns, or categories in the content.
• Data mining: This involves using automated methods to analyze large datasets to identify patterns, trends, or relationships in the data.
• Machine learning: This involves using algorithms to analyze data and make predictions or classifications based on the patterns identified in the data.

Application of Scientific Research

Scientific research has numerous applications in many fields, including:

• Medicine and healthcare: Scientific research is used to develop new drugs, medical treatments, and vaccines. It is also used to understand the causes and risk factors of diseases, as well as to develop new diagnostic tools and medical devices.
• Agriculture : Scientific research is used to develop new crop varieties, to improve crop yields, and to develop more sustainable farming practices.
• Technology and engineering : Scientific research is used to develop new technologies and engineering solutions, such as renewable energy systems, new materials, and advanced manufacturing techniques.
• Environmental science : Scientific research is used to understand the impacts of human activity on the environment and to develop solutions for mitigating those impacts. It is also used to monitor and manage natural resources, such as water and air quality.
• Education : Scientific research is used to develop new teaching methods and educational materials, as well as to understand how people learn and develop.
• Business and economics: Scientific research is used to understand consumer behavior, to develop new products and services, and to analyze economic trends and policies.
• Social sciences : Scientific research is used to understand human behavior, attitudes, and social dynamics. It is also used to develop interventions to improve social welfare and to inform public policy.

How to Conduct Scientific Research

Conducting scientific research involves several steps, including:

• Identify a research question: Start by identifying a question or problem that you want to investigate. This question should be clear, specific, and relevant to your field of study.
• Conduct a literature review: Before starting your research, conduct a thorough review of existing research in your field. This will help you identify gaps in knowledge and develop hypotheses or research questions.
• Develop a research plan: Once you have a research question, develop a plan for how you will collect and analyze data to answer that question. This plan should include a detailed methodology, a timeline, and a budget.
• Collect data: Depending on your research question and methodology, you may collect data through surveys, experiments, observations, or other methods.
• Analyze data: Once you have collected your data, analyze it using appropriate statistical or qualitative methods. This will help you draw conclusions about your research question.
• Interpret results: Based on your analysis, interpret your results and draw conclusions about your research question. Discuss any limitations or implications of your findings.
• Communicate results: Finally, communicate your findings to others in your field through presentations, publications, or other means.

Purpose of Scientific Research

The purpose of scientific research is to systematically investigate phenomena, acquire new knowledge, and advance our understanding of the world around us. Scientific research has several key goals, including:

• Exploring the unknown: Scientific research is often driven by curiosity and the desire to explore uncharted territory. Scientists investigate phenomena that are not well understood, in order to discover new insights and develop new theories.
• Testing hypotheses: Scientific research involves developing hypotheses or research questions, and then testing them through observation and experimentation. This allows scientists to evaluate the validity of their ideas and refine their understanding of the phenomena they are studying.
• Solving problems: Scientific research is often motivated by the desire to solve practical problems or address real-world challenges. For example, researchers may investigate the causes of a disease in order to develop new treatments, or explore ways to make renewable energy more affordable and accessible.
• Advancing knowledge: Scientific research is a collective effort to advance our understanding of the world around us. By building on existing knowledge and developing new insights, scientists contribute to a growing body of knowledge that can be used to inform decision-making, solve problems, and improve our lives.

Examples of Scientific Research

Here are some examples of scientific research that are currently ongoing or have recently been completed:

• Clinical trials for new treatments: Scientific research in the medical field often involves clinical trials to test new treatments for diseases and conditions. For example, clinical trials may be conducted to evaluate the safety and efficacy of new drugs or medical devices.
• Genomics research: Scientists are conducting research to better understand the human genome and its role in health and disease. This includes research on genetic mutations that can cause diseases such as cancer, as well as the development of personalized medicine based on an individual’s genetic makeup.
• Climate change: Scientific research is being conducted to understand the causes and impacts of climate change, as well as to develop solutions for mitigating its effects. This includes research on renewable energy technologies, carbon capture and storage, and sustainable land use practices.
• Neuroscience : Scientists are conducting research to understand the workings of the brain and the nervous system, with the goal of developing new treatments for neurological disorders such as Alzheimer’s disease and Parkinson’s disease.
• Artificial intelligence: Researchers are working to develop new algorithms and technologies to improve the capabilities of artificial intelligence systems. This includes research on machine learning, computer vision, and natural language processing.
• Space exploration: Scientific research is being conducted to explore the cosmos and learn more about the origins of the universe. This includes research on exoplanets, black holes, and the search for extraterrestrial life.

When to use Scientific Research

Some specific situations where scientific research may be particularly useful include:

• Solving problems: Scientific research can be used to investigate practical problems or address real-world challenges. For example, scientists may investigate the causes of a disease in order to develop new treatments, or explore ways to make renewable energy more affordable and accessible.
• Decision-making: Scientific research can provide evidence-based information to inform decision-making. For example, policymakers may use scientific research to evaluate the effectiveness of different policy options or to make decisions about public health and safety.
• Innovation : Scientific research can be used to develop new technologies, products, and processes. For example, research on materials science can lead to the development of new materials with unique properties that can be used in a range of applications.
• Knowledge creation : Scientific research is an important way of generating new knowledge and advancing our understanding of the world around us. This can lead to new theories, insights, and discoveries that can benefit society.

Advantages of Scientific Research

There are many advantages of scientific research, including:

• Improved understanding : Scientific research allows us to gain a deeper understanding of the world around us, from the smallest subatomic particles to the largest celestial bodies.
• Evidence-based decision making: Scientific research provides evidence-based information that can inform decision-making in many fields, from public policy to medicine.
• Technological advancements: Scientific research drives technological advancements in fields such as medicine, engineering, and materials science. These advancements can improve quality of life, increase efficiency, and reduce costs.
• New discoveries: Scientific research can lead to new discoveries and breakthroughs that can advance our knowledge in many fields. These discoveries can lead to new theories, technologies, and products.
• Economic benefits : Scientific research can stimulate economic growth by creating new industries and jobs, and by generating new technologies and products.
• Improved health outcomes: Scientific research can lead to the development of new medical treatments and technologies that can improve health outcomes and quality of life for people around the world.
• Increased innovation: Scientific research encourages innovation by promoting collaboration, creativity, and curiosity. This can lead to new and unexpected discoveries that can benefit society.

Limitations of Scientific Research

Scientific research has some limitations that researchers should be aware of. These limitations can include:

• Research design limitations : The design of a research study can impact the reliability and validity of the results. Poorly designed studies can lead to inaccurate or inconclusive results. Researchers must carefully consider the study design to ensure that it is appropriate for the research question and the population being studied.
• Sample size limitations: The size of the sample being studied can impact the generalizability of the results. Small sample sizes may not be representative of the larger population, and may lead to incorrect conclusions.
• Time and resource limitations: Scientific research can be costly and time-consuming. Researchers may not have the resources necessary to conduct a large-scale study, or may not have sufficient time to complete a study with appropriate controls and analysis.
• Ethical limitations : Certain types of research may raise ethical concerns, such as studies involving human or animal subjects. Ethical concerns may limit the scope of the research that can be conducted, or require additional protocols and procedures to ensure the safety and well-being of participants.
• Limitations of technology: Technology may limit the types of research that can be conducted, or the accuracy of the data collected. For example, certain types of research may require advanced technology that is not yet available, or may be limited by the accuracy of current measurement tools.
• Limitations of existing knowledge: Existing knowledge may limit the types of research that can be conducted. For example, if there is limited knowledge in a particular field, it may be difficult to design a study that can provide meaningful results.

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What is Research? – Purpose of Research

• By DiscoverPhDs
• September 10, 2020

The purpose of research is to enhance society by advancing knowledge through the development of scientific theories, concepts and ideas. A research purpose is met through forming hypotheses, collecting data, analysing results, forming conclusions, implementing findings into real-life applications and forming new research questions.

What is Research

Simply put, research is the process of discovering new knowledge. This knowledge can be either the development of new concepts or the advancement of existing knowledge and theories, leading to a new understanding that was not previously known.

As a more formal definition of research, the following has been extracted from the Code of Federal Regulations :

While research can be carried out by anyone and in any field, most research is usually done to broaden knowledge in the physical, biological, and social worlds. This can range from learning why certain materials behave the way they do, to asking why certain people are more resilient than others when faced with the same challenges.

The use of ‘systematic investigation’ in the formal definition represents how research is normally conducted – a hypothesis is formed, appropriate research methods are designed, data is collected and analysed, and research results are summarised into one or more ‘research conclusions’. These research conclusions are then shared with the rest of the scientific community to add to the existing knowledge and serve as evidence to form additional questions that can be investigated. It is this cyclical process that enables scientific research to make continuous progress over the years; the true purpose of research.

What is the Purpose of Research

From weather forecasts to the discovery of antibiotics, researchers are constantly trying to find new ways to understand the world and how things work – with the ultimate goal of improving our lives.

The purpose of research is therefore to find out what is known, what is not and what we can develop further. In this way, scientists can develop new theories, ideas and products that shape our society and our everyday lives.

Although research can take many forms, there are three main purposes of research:

• Exploratory: Exploratory research is the first research to be conducted around a problem that has not yet been clearly defined. Exploration research therefore aims to gain a better understanding of the exact nature of the problem and not to provide a conclusive answer to the problem itself. This enables us to conduct more in-depth research later on.
• Descriptive: Descriptive research expands knowledge of a research problem or phenomenon by describing it according to its characteristics and population. Descriptive research focuses on the ‘how’ and ‘what’, but not on the ‘why’.
• Explanatory: Explanatory research, also referred to as casual research, is conducted to determine how variables interact, i.e. to identify cause-and-effect relationships. Explanatory research deals with the ‘why’ of research questions and is therefore often based on experiments.

Characteristics of Research

There are 8 core characteristics that all research projects should have. These are:

• Empirical  – based on proven scientific methods derived from real-life observations and experiments.
• Logical  – follows sequential procedures based on valid principles.
• Cyclic  – research begins with a question and ends with a question, i.e. research should lead to a new line of questioning.
• Controlled  – vigorous measures put into place to keep all variables constant, except those under investigation.
• Hypothesis-based  – the research design generates data that sufficiently meets the research objectives and can prove or disprove the hypothesis. It makes the research study repeatable and gives credibility to the results.
• Analytical  – data is generated, recorded and analysed using proven techniques to ensure high accuracy and repeatability while minimising potential errors and anomalies.
• Objective  – sound judgement is used by the researcher to ensure that the research findings are valid.
• Statistical treatment  – statistical treatment is used to transform the available data into something more meaningful from which knowledge can be gained.

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Types of Research

Research can be divided into two main types: basic research (also known as pure research) and applied research.

Basic Research

Basic research, also known as pure research, is an original investigation into the reasons behind a process, phenomenon or particular event. It focuses on generating knowledge around existing basic principles.

Basic research is generally considered ‘non-commercial research’ because it does not focus on solving practical problems, and has no immediate benefit or ways it can be applied.

While basic research may not have direct applications, it usually provides new insights that can later be used in applied research.

Applied Research

Applied research investigates well-known theories and principles in order to enhance knowledge around a practical aim. Because of this, applied research focuses on solving real-life problems by deriving knowledge which has an immediate application.

Methods of Research

Research methods for data collection fall into one of two categories: inductive methods or deductive methods.

Inductive research methods focus on the analysis of an observation and are usually associated with qualitative research. Deductive research methods focus on the verification of an observation and are typically associated with quantitative research.

Qualitative Research

Qualitative research is a method that enables non-numerical data collection through open-ended methods such as interviews, case studies and focus groups .

It enables researchers to collect data on personal experiences, feelings or behaviours, as well as the reasons behind them. Because of this, qualitative research is often used in fields such as social science, psychology and philosophy and other areas where it is useful to know the connection between what has occurred and why it has occurred.

Quantitative Research

Quantitative research is a method that collects and analyses numerical data through statistical analysis.

It allows us to quantify variables, uncover relationships, and make generalisations across a larger population. As a result, quantitative research is often used in the natural and physical sciences such as engineering, biology, chemistry, physics, computer science, finance, and medical research, etc.

What does Research Involve?

Research often follows a systematic approach known as a Scientific Method, which is carried out using an hourglass model.

A research project first starts with a problem statement, or rather, the research purpose for engaging in the study. This can take the form of the ‘ scope of the study ’ or ‘ aims and objectives ’ of your research topic.

Subsequently, a literature review is carried out and a hypothesis is formed. The researcher then creates a research methodology and collects the data.

The data is then analysed using various statistical methods and the null hypothesis is either accepted or rejected.

In both cases, the study and its conclusion are officially written up as a report or research paper, and the researcher may also recommend lines of further questioning. The report or research paper is then shared with the wider research community, and the cycle begins all over again.

Although these steps outline the overall research process, keep in mind that research projects are highly dynamic and are therefore considered an iterative process with continued refinements and not a series of fixed stages.

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Research: Meaning and Purpose

• First Online: 27 October 2022

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• Kazi Abusaleh 4 &
• Akib Bin Anwar 5  

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The objective of the chapter is to provide the conceptual framework of the research and research process and draw the importance of research in social sciences. Various books and research papers were reviewed to write the chapter. The chapter defines ‘research’ as a deliberate and systematic scientific investigation into a phenomenon to explore, analyse, and predict about the issues or circumstances, and characterizes ‘research’ as a systematic and scientific mode of inquiry, a way to testify the existing knowledge and theories, and a well-designed process to answer questions in a reliable and unbiased way. This chapter, however, categorizes research into eight types under four headings, explains six steps to carry out a research work scientifically, and finally sketches the importance of research in social sciences.

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Abusaleh, K., Anwar, A.B. (2022). Research: Meaning and Purpose. In: Islam, M.R., Khan, N.A., Baikady, R. (eds) Principles of Social Research Methodology. Springer, Singapore. https://doi.org/10.1007/978-981-19-5441-2_2

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Purpose of Research

The purpose of research can be a complicated issue and varies across different scientific fields and disciplines. At the most basic level, science can be split, loosely, into two types, 'pure research' and 'applied research'.

This article is a part of the guide:

• Definition of Research
• Research Basics
• What is Research?
• Steps of the Scientific Method
• What is the Scientific Method?

Browse Full Outline

• 1 Research Basics
• 2.1 What is Research?
• 2.2 What is the Scientific Method?
• 2.3 Empirical Research
• 3.1 Definition of Research
• 3.2 Definition of the Scientific Method
• 3.3 Definition of Science
• 4 Steps of the Scientific Method
• 5 Scientific Elements
• 6 Aims of Research
• 7 Purpose of Research
• 8 Science Misconceptions

Both of these types follow the same structures and protocols for propagating and testing hypotheses and predictions, but vary slightly in their ultimate purpose.

An excellent example for illustrating the difference is by using pure and applied mathematics. Pure maths is concerned with understanding underlying abstract principles and describing them with elegant theories. Applied maths, by contrast, uses these equations to explain real life phenomena, such as mechanics, ecology and gravity.

Pure Scientific Research

Some science, often referred to as 'pure science', is about explaining the world around us and trying to understand how the universe operates. It is about finding out what is already there without any greater purpose of research than the explanation itself. It is a direct descendent of philosophy, where philosophers and scientists try to understand the underlying principles of existence.

Whilst offering no direct benefits, pure research often has indirect benefits, which can contribute greatly to the advancement of humanity.

For example, pure research into the structure of the atom has led to x-rays, nuclear power and silicon chips.

Applied Scientific Research

Applied scientists might look for answers to specific questions that help humanity, for example medical research or environmental studies. Such research generally takes a specific question and tries to find a definitive and comprehensive answer.

The purpose of research is about testing theories, often generated by pure science, and applying them to real situations, addressing more than just abstract principles.

Applied scientific research can be about finding out the answer to a specific problem, such as 'Is global warming avoidable?' or 'Does a new type of medicine really help the patients?'

Generating Testable Data

However, they all involve generating a theory to explain why something is happening and using the full battery of scientific tools and methods to test it rigorously.

This process opens up new areas for further study and a continued refinement of the hypotheses.

Observation is not accurate enough, with statistically testable and analyzable data the only results accepted across all scientific disciplines. The exact nature of the experimental process may vary, but they all adhere to the same basic principles.

Scientists can be opinionated, like anybody else, and often will adhere to their own theories, even if the evidence shows otherwise. Research is a tool by which they can test their own, and each others' theories, by using this antagonism to find an answer and advance knowledge.

The purpose of research is really an ongoing process of correcting and refining hypotheses , which should lead to the acceptance of certain scientific truths .

Whilst no scientific proof can be accepted as ultimate fact, rigorous testing ensures that proofs can become presumptions. Certain basic presumptions are made before embarking on any research project, and build upon this gradual accumulation of knowledge.

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Martyn Shuttleworth (Aug 2, 2008). Purpose of Research. Retrieved May 13, 2024 from Explorable.com: https://explorable.com/purpose-of-research

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1 Science and scientific research

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

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

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

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

Scientific knowledge

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

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

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

Scientific research

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

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

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

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

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

Scientific method

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

Replicability : Others should be able to independently replicate or repeat a scientific study and obtain similar, if not identical, results. Precision : Theoretical concepts, which are often hard to measure, must be defined with such precision that others can use those definitions to measure those concepts and test that theory. Falsifiability : A theory must be stated in such a way that it can be disproven. Theories that cannot be tested or falsified are not scientific theories and any such knowledge is not scientific knowledge. A theory that is specified in imprecise terms or whose concepts are not accurately measureable cannot be tested, and is therefore not scientific. Sigmund Freud’s ideas on psychoanalysis fall into this category and are therefore not considered a ‘theory’, even though psychoanalysis may have practical utility in treating certain types of ailments. Parsimony: When there are multiple different explanations of a phenomenon, scientists must always accept the simplest or logically most economical explanation. This concept is called parsimony or ‘Occam’s razor’. Parsimony prevents scientists from pursuing overly complex or outlandish theories with an endless number of concepts and relationships that may explain a little bit of everything but nothing in particular. Any branch of inquiry that does not allow the scientific method to test its basic laws or theories cannot be called ‘science’. For instance, theology (the study of religion) is not science because theological ideas—such as the presence of God—cannot be tested by independent observers using a logical, confirmable, repeatable, and scrutinisable. Similarly, arts, music, literature, humanities, and law are also not considered science, even though they are creative and worthwhile endeavours in their own right.

The scientific method, as applied to social sciences, includes a variety of research approaches, tools, and techniques for collecting and analysing qualitative or quantitative data. These methods include laboratory experiments, field surveys, case research, ethnographic research, action research, and so forth. Much of this book is devoted to learning about these different methods. However, recognise that the scientific method operates primarily at the empirical level of research, i.e., how to make observations and analyse these observations. Very little of this method is directly pertinent to the theoretical level, which is really the more challenging part of scientific research.

Types of scientific research

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

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

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

History of scientific thought

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

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

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

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

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

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

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

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

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

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

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Scientific Method

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

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

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

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

1. Overview and organizing themes

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

3. Logic of method and critical responses

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

5. Method in Practice

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

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

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

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

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

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

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

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

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

5.2 Computer methods and ‘new ways’ of doing science

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

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

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

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

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

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

6. Discourse on scientific method

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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2.1 Why Is Research Important?

Learning objectives.

By the end of this section, you will be able to:

• Explain how scientific research addresses questions about behavior
• Discuss how scientific research guides public policy
• Appreciate how scientific research can be important in making personal decisions

Scientific research is a critical tool for successfully navigating our complex world. Without it, we would be forced to rely solely on intuition, other people’s authority, and blind luck. While many of us feel confident in our abilities to decipher and interact with the world around us, history is filled with examples of how very wrong we can be when we fail to recognize the need for evidence in supporting claims. At various times in history, we would have been certain that the sun revolved around a flat earth, that the earth’s continents did not move, and that mental illness was caused by possession ( Figure 2.2 ). It is through systematic scientific research that we divest ourselves of our preconceived notions and superstitions and gain an objective understanding of ourselves and our world.

The goal of all scientists is to better understand the world around them. Psychologists focus their attention on understanding behavior, as well as the cognitive (mental) and physiological (body) processes that underlie behavior. In contrast to other methods that people use to understand the behavior of others, such as intuition and personal experience, the hallmark of scientific research is that there is evidence to support a claim. Scientific knowledge is empirical : It is grounded in objective, tangible evidence that can be observed time and time again, regardless of who is observing.

While behavior is observable, the mind is not. If someone is crying, we can see behavior. However, the reason for the behavior is more difficult to determine. Is the person crying due to being sad, in pain, or happy? Sometimes we can learn the reason for someone’s behavior by simply asking a question, like “Why are you crying?” However, there are situations in which an individual is either uncomfortable or unwilling to answer the question honestly, or is incapable of answering. For example, infants would not be able to explain why they are crying. In such circumstances, the psychologist must be creative in finding ways to better understand behavior. This chapter explores how scientific knowledge is generated, and how important that knowledge is in forming decisions in our personal lives and in the public domain.

Use of Research Information

Trying to determine which theories are and are not accepted by the scientific community can be difficult, especially in an area of research as broad as psychology. More than ever before, we have an incredible amount of information at our fingertips, and a simple internet search on any given research topic might result in a number of contradictory studies. In these cases, we are witnessing the scientific community going through the process of reaching a consensus, and it could be quite some time before a consensus emerges. For example, the explosion in our use of technology has led researchers to question whether this ultimately helps or hinders us. The use and implementation of technology in educational settings has become widespread over the last few decades. Researchers are coming to different conclusions regarding the use of technology. To illustrate this point, a study investigating a smartphone app targeting surgery residents (graduate students in surgery training) found that the use of this app can increase student engagement and raise test scores (Shaw & Tan, 2015). Conversely, another study found that the use of technology in undergraduate student populations had negative impacts on sleep, communication, and time management skills (Massimini & Peterson, 2009). Until sufficient amounts of research have been conducted, there will be no clear consensus on the effects that technology has on a student's acquisition of knowledge, study skills, and mental health.

In the meantime, we should strive to think critically about the information we encounter by exercising a degree of healthy skepticism. When someone makes a claim, we should examine the claim from a number of different perspectives: what is the expertise of the person making the claim, what might they gain if the claim is valid, does the claim seem justified given the evidence, and what do other researchers think of the claim? This is especially important when we consider how much information in advertising campaigns and on the internet claims to be based on “scientific evidence” when in actuality it is a belief or perspective of just a few individuals trying to sell a product or draw attention to their perspectives.

We should be informed consumers of the information made available to us because decisions based on this information have significant consequences. One such consequence can be seen in politics and public policy. Imagine that you have been elected as the governor of your state. One of your responsibilities is to manage the state budget and determine how to best spend your constituents’ tax dollars. As the new governor, you need to decide whether to continue funding early intervention programs. These programs are designed to help children who come from low-income backgrounds, have special needs, or face other disadvantages. These programs may involve providing a wide variety of services to maximize the children's development and position them for optimal levels of success in school and later in life (Blann, 2005). While such programs sound appealing, you would want to be sure that they also proved effective before investing additional money in these programs. Fortunately, psychologists and other scientists have conducted vast amounts of research on such programs and, in general, the programs are found to be effective (Neil & Christensen, 2009; Peters-Scheffer, Didden, Korzilius, & Sturmey, 2011). While not all programs are equally effective, and the short-term effects of many such programs are more pronounced, there is reason to believe that many of these programs produce long-term benefits for participants (Barnett, 2011). If you are committed to being a good steward of taxpayer money, you would want to look at research. Which programs are most effective? What characteristics of these programs make them effective? Which programs promote the best outcomes? After examining the research, you would be best equipped to make decisions about which programs to fund.

Link to Learning

Watch this video about early childhood program effectiveness to learn how scientists evaluate effectiveness and how best to invest money into programs that are most effective.

Ultimately, it is not just politicians who can benefit from using research in guiding their decisions. We all might look to research from time to time when making decisions in our lives. Imagine that your sister, Maria, expresses concern about her two-year-old child, Umberto. Umberto does not speak as much or as clearly as the other children in his daycare or others in the family. Umberto's pediatrician undertakes some screening and recommends an evaluation by a speech pathologist, but does not refer Maria to any other specialists. Maria is concerned that Umberto's speech delays are signs of a developmental disorder, but Umberto's pediatrician does not; she sees indications of differences in Umberto's jaw and facial muscles. Hearing this, you do some internet searches, but you are overwhelmed by the breadth of information and the wide array of sources. You see blog posts, top-ten lists, advertisements from healthcare providers, and recommendations from several advocacy organizations. Why are there so many sites? Which are based in research, and which are not?

In the end, research is what makes the difference between facts and opinions. Facts are observable realities, and opinions are personal judgments, conclusions, or attitudes that may or may not be accurate. In the scientific community, facts can be established only using evidence collected through empirical research.

NOTABLE RESEARCHERS

Psychological research has a long history involving important figures from diverse backgrounds. While the introductory chapter discussed several researchers who made significant contributions to the discipline, there are many more individuals who deserve attention in considering how psychology has advanced as a science through their work ( Figure 2.3 ). For instance, Margaret Floy Washburn (1871–1939) was the first woman to earn a PhD in psychology. Her research focused on animal behavior and cognition (Margaret Floy Washburn, PhD, n.d.). Mary Whiton Calkins (1863–1930) was a preeminent first-generation American psychologist who opposed the behaviorist movement, conducted significant research into memory, and established one of the earliest experimental psychology labs in the United States (Mary Whiton Calkins, n.d.).

Francis Sumner (1895–1954) was the first African American to receive a PhD in psychology in 1920. His dissertation focused on issues related to psychoanalysis. Sumner also had research interests in racial bias and educational justice. Sumner was one of the founders of Howard University’s department of psychology, and because of his accomplishments, he is sometimes referred to as the “Father of Black Psychology.” Thirteen years later, Inez Beverly Prosser (1895–1934) became the first African American woman to receive a PhD in psychology. Prosser’s research highlighted issues related to education in segregated versus integrated schools, and ultimately, her work was very influential in the hallmark Brown v. Board of Education Supreme Court ruling that segregation of public schools was unconstitutional (Ethnicity and Health in America Series: Featured Psychologists, n.d.).

Although the establishment of psychology’s scientific roots occurred first in Europe and the United States, it did not take much time until researchers from around the world began to establish their own laboratories and research programs. For example, some of the first experimental psychology laboratories in South America were founded by Horatio Piñero (1869–1919) at two institutions in Buenos Aires, Argentina (Godoy & Brussino, 2010). In India, Gunamudian David Boaz (1908–1965) and Narendra Nath Sen Gupta (1889–1944) established the first independent departments of psychology at the University of Madras and the University of Calcutta, respectively. These developments provided an opportunity for Indian researchers to make important contributions to the field (Gunamudian David Boaz, n.d.; Narendra Nath Sen Gupta, n.d.).

When the American Psychological Association (APA) was first founded in 1892, all of the members were White males (Women and Minorities in Psychology, n.d.). However, by 1905, Mary Whiton Calkins was elected as the first female president of the APA, and by 1946, nearly one-quarter of American psychologists were female. Psychology became a popular degree option for students enrolled in the nation’s historically Black higher education institutions, increasing the number of Black Americans who went on to become psychologists. Given demographic shifts occurring in the United States and increased access to higher educational opportunities among historically underrepresented populations, there is reason to hope that the diversity of the field will increasingly match the larger population, and that the research contributions made by the psychologists of the future will better serve people of all backgrounds (Women and Minorities in Psychology, n.d.).

The Process of Scientific Research

Scientific knowledge is advanced through a process known as the scientific method . Basically, ideas (in the form of theories and hypotheses) are tested against the real world (in the form of empirical observations), and those empirical observations lead to more ideas that are tested against the real world, and so on. In this sense, the scientific process is circular. The types of reasoning within the circle are called deductive and inductive. In deductive reasoning , ideas are tested in the real world; in inductive reasoning , real-world observations lead to new ideas ( Figure 2.4 ). These processes are inseparable, like inhaling and exhaling, but different research approaches place different emphasis on the deductive and inductive aspects.

In the scientific context, deductive reasoning begins with a generalization—one hypothesis—that is then used to reach logical conclusions about the real world. If the hypothesis is correct, then the logical conclusions reached through deductive reasoning should also be correct. A deductive reasoning argument might go something like this: All living things require energy to survive (this would be your hypothesis). Ducks are living things. Therefore, ducks require energy to survive (logical conclusion). In this example, the hypothesis is correct; therefore, the conclusion is correct as well. Sometimes, however, an incorrect hypothesis may lead to a logical but incorrect conclusion. Consider this argument: all ducks are born with the ability to see. Quackers is a duck. Therefore, Quackers was born with the ability to see. Scientists use deductive reasoning to empirically test their hypotheses. Returning to the example of the ducks, researchers might design a study to test the hypothesis that if all living things require energy to survive, then ducks will be found to require energy to survive.

Deductive reasoning starts with a generalization that is tested against real-world observations; however, inductive reasoning moves in the opposite direction. Inductive reasoning uses empirical observations to construct broad generalizations. Unlike deductive reasoning, conclusions drawn from inductive reasoning may or may not be correct, regardless of the observations on which they are based. For instance, you may notice that your favorite fruits—apples, bananas, and oranges—all grow on trees; therefore, you assume that all fruit must grow on trees. This would be an example of inductive reasoning, and, clearly, the existence of strawberries, blueberries, and kiwi demonstrate that this generalization is not correct despite it being based on a number of direct observations. Scientists use inductive reasoning to formulate theories, which in turn generate hypotheses that are tested with deductive reasoning. In the end, science involves both deductive and inductive processes.

For example, case studies, which you will read about in the next section, are heavily weighted on the side of empirical observations. Thus, case studies are closely associated with inductive processes as researchers gather massive amounts of observations and seek interesting patterns (new ideas) in the data. Experimental research, on the other hand, puts great emphasis on deductive reasoning.

We’ve stated that theories and hypotheses are ideas, but what sort of ideas are they, exactly? A theory is a well-developed set of ideas that propose an explanation for observed phenomena. Theories are repeatedly checked against the world, but they tend to be too complex to be tested all at once; instead, researchers create hypotheses to test specific aspects of a theory.

A hypothesis is a testable prediction about how the world will behave if our idea is correct, and it is often worded as an if-then statement (e.g., if I study all night, I will get a passing grade on the test). The hypothesis is extremely important because it bridges the gap between the realm of ideas and the real world. As specific hypotheses are tested, theories are modified and refined to reflect and incorporate the result of these tests Figure 2.5 .

To see how this process works, let’s consider a specific theory and a hypothesis that might be generated from that theory. As you’ll learn in a later chapter, the James-Lange theory of emotion asserts that emotional experience relies on the physiological arousal associated with the emotional state. If you walked out of your home and discovered a very aggressive snake waiting on your doorstep, your heart would begin to race and your stomach churn. According to the James-Lange theory, these physiological changes would result in your feeling of fear. A hypothesis that could be derived from this theory might be that a person who is unaware of the physiological arousal that the sight of the snake elicits will not feel fear.

A scientific hypothesis is also falsifiable , or capable of being shown to be incorrect. Recall from the introductory chapter that Sigmund Freud had lots of interesting ideas to explain various human behaviors ( Figure 2.6 ). However, a major criticism of Freud’s theories is that many of his ideas are not falsifiable; for example, it is impossible to imagine empirical observations that would disprove the existence of the id, the ego, and the superego—the three elements of personality described in Freud’s theories. Despite this, Freud’s theories are widely taught in introductory psychology texts because of their historical significance for personality psychology and psychotherapy, and these remain the root of all modern forms of therapy.

In contrast, the James-Lange theory does generate falsifiable hypotheses, such as the one described above. Some individuals who suffer significant injuries to their spinal columns are unable to feel the bodily changes that often accompany emotional experiences. Therefore, we could test the hypothesis by determining how emotional experiences differ between individuals who have the ability to detect these changes in their physiological arousal and those who do not. In fact, this research has been conducted and while the emotional experiences of people deprived of an awareness of their physiological arousal may be less intense, they still experience emotion (Chwalisz, Diener, & Gallagher, 1988).

Scientific research’s dependence on falsifiability allows for great confidence in the information that it produces. Typically, by the time information is accepted by the scientific community, it has been tested repeatedly.

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• 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).

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

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Coming off Ozempic slowly could prevent weight gain, study shows

by Ashleigh Furlong, Bloomberg News

Patients could wean themselves off blockbuster obesity drugs such as Ozempic or Wegovy without piling the pounds back on, according to a scientific study.

Data presented at the European Congress on Obesity in Venice, Italy on Sunday provides some of the first evidence that it could be possible to stop taking Novo Nordisk's Ozempic or Wegovy and not regain any weight that has been lost—as long as a healthy lifestyle is maintained.

When drugs like Ozempic and Eli Lilly's Zepbound were first launched, they were pitched as long-term medicines, with research indicating that patients regain large amounts of the weight they have lost when they stop taking the drugs.

The Danish study of patients using semaglutide, which is the active ingredient in Ozempic and Wegovy, alongside a weight management program run through the Embla app, suggests that tapering the drug —instead of a hard stop—could potentially prevent weight regain.

However, just 353 patients were in the sample of patients who stopped semaglutide, which is a small study size. These patients had reached their target weight and reduced their semaglutide dose over nine weeks. Patients continued to lose weight as they tapered, losing an average of 2.1% over the nine weeks.

The study also suggests that patients might be able to maintain their weight for several months after stopping. The researchers had data for 85 patients at 26 weeks after stopping semaglutide, and found that they maintained a stable weight.

"The combination of support in making lifestyle changes and tapering seems to allow patients to avoid regaining weight after coming off semaglutide," said Henrik Gudbergsen, lead researcher and Embla's chief medical officer, in a statement.

Obesity drugs have led to a surge in investment and a race to capture a market that analysts at Goldman Sachs forecast could hit $100 billion by 2030.

The study also looked at changing how semaglutide doses are ramped up when patients begin taking the drug. The 2,246 patients in the wider study started on semaglutide alongside advice from a nutritionist. They also had access to doctors, nurses and psychologists through Embla.

The patients' doses were closely monitored and increased more slowly than standard treatment. The average weight loss was 14.8% at 64 weeks, similar levels to other studies of semaglutide.

2024 Bloomberg L.P. Distributed by Tribune Content Agency, LLC.

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Sleep does not help brain wash out toxins, study suggests

Finding that clearance of fluid in mice brains is lower in sleep and anaesthesia runs counter to dominant view in neuroscience

The restorative effect of a good night’s rest is widely recognised and the popular scientific explanation has been that the brain washes out toxins during sleep.

However, new findings suggest this theory, which has become a dominant view in neuroscience, could be wrong. The study found that the clearance and movement of fluid in the brains of mice was, in fact, markedly reduced during sleep and anaesthesia.

“It sounded like a Nobel prize-winning idea,” said Prof Nick Franks, a professor of biophysics and anaesthetics at Imperial College London, and co-lead of the study.

“If you are sleep-deprived, countless things go wrong – you don’t remember things clearly, hand-eye coordination is poor,” he added. “The idea that your brain is doing this basic housekeeping during sleep just seems to make sense.”

However, there was only indirect evidence that the brain’s waste-removal system ramps up activity during sleep, Franks said.

In the latest study, published in the journal Nature Neuroscience , researchers used a fluorescent dye to study the brains of mice. This allowed them to see how quickly the dye moved from fluid-filled cavities, called the ventricles, to other brain regions and enabled them to measure the rate of clearance of the dye from the brain directly.

The study showed that the clearance of the dye was reduced by about 30% in sleeping mice, and 50% in mice that were under anaesthetic, compared with mice that were kept awake.

“The field has been so focused on the clearance idea as one of the key reasons why we sleep, and we were of course very surprised to observe the opposite in our results,” said Franks. “We found that the rate of clearance of dye from the brain was significantly reduced in animals that were asleep, or under anaesthetic.”

The researchers predict that the findings will extend to humans as sleep is a core need shared by all mammals.

Prof Bill Wisden, the interim director of the UK Dementia Research Institute at Imperial College London and co-lead author, said: “There are many theories as to why we sleep, and although we have shown that clearing toxins may not be a key reason, it cannot be disputed that sleep is important.”

The findings have relevance for dementia research due to the increasing evidence of a link between poor sleep and Alzheimer’s risk. It has not been clear whether lack of sleep might cause Alzheimer’s, or whether it is simply an early symptom. Some had hypothesised that without enough sleep, the brain may not be able to clear toxins effectively, but the latest research raises doubts about the plausibility of this explanation.

“Because that idea has held such sway, it’s probably increased people’s anxiety that if they don’t sleep they’ll be more likely to develop dementia,” said Franks.

Wisden said: “Disrupted sleep is a common symptom experienced by people living with dementia. However, we still do not know if this is a consequence or a driving factor in the disease progression. It may well be that having good sleep does help to reduce dementia risk for reasons other than clearing toxins.”

He added: “The other side to our study is that we have shown that brain clearance is highly efficient during the waking state. In general, being awake, active and exercising may more efficiently clean the brain of toxins.”

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National Research Council (US) Committee on Assessing Behavioral and Social Science Research on Aging; Feller I, Stern PC, editors. A Strategy for Assessing Science: Behavioral and Social Research on Aging. Washington (DC): National Academies Press (US); 2007.

A Strategy for Assessing Science: Behavioral and Social Research on Aging.

• Hardcopy Version at National Academies Press

1 The Purpose of the Study

The U.S. federal government supports scientific and technological research to address a broad range of national needs and objectives and to gain fundamental understanding of the processes that shape the world in which people live. Each federal science agency promotes scientific progress toward these objectives in the areas of its mission responsibilities. This is done most obviously by providing funds for research and its infrastructure, including the education and training of succeeding cohorts of researchers; by organizing and setting rules for the external groups that advise on worthy research investments; and by setting research priorities and making choices among specific research programs and projects. Each agency also does so by recruiting, training, and evaluating research managers for their scientific expertise and managerial skills. Agencies redirect support among lines of research when opportunities arise to open new and exciting paths to knowledge and societal benefit, when changes occur in the relative importance of the results from past investments in research, and when specific lines of inquiry or modes of research support are deemed no longer to be productive.

Historically, federal government support has been instrumental in the development of important new fields of science and technology, such as materials science and computer science. Less well understood is how closely the rise or demise of a research field may be tied to federal support. At any point in time, numerous research fields are in an embryonic state and are potential candidates for further maturation. Not all flourish, however, beyond the involvement of small cadres of researchers subsumed within larger sub-specialties and disciplines. Support from federal government agencies can make the difference between development or stagnation for embryonic or fledgling research fields. Priority-setting decisions by federal science agencies thus affect the vitality of existing fields of research, although the strength of this effect is not well known.

Because society has limited resources to support scientific activities, assessing scientific progress and setting priorities are perennial practical components of national science policy decision making. Perennial questions arise, too, about matching agency funding practices with the conditions perceived to be most likely to lead to program or project success in terms of contributions to scientific knowledge and societal objectives. Which objectives most deserve support for science—defense, economic competitiveness, energy, health, the environment, or some other? How should funds be distributed across agencies? Which fields of science most require or merit public-sector support? And which modes of research support are most productive? These questions raise issues of outcomes—assessing the likely benefits 1 of the scientific work being supported—and processes—ensuring that decisions are made in ways that satisfy the criteria of equity, transparency, expertise, and accountability normally demanded of public decisions in a democratic society.

Concerns about the processes and outcomes of priority setting in U.S. government research agencies have intensified in recent years because of a confluence of several clusters of influences. One of these relates to increased demands for accountability and documented performance throughout government, extending across all functional activities, including support of research. For federal government agencies, these demands are most visible in the requirements of the Government Performance and Results Act (GPRA) of 1993 and the Office of Management and Budget’s Performance Assessment Rating Tool (PART) process. Demands for accountability, amounting in some accounts to an audit explosion ( Power, 1997 ), have also become more acute because of a recent deceleration or reversal of research budget growth across many functional areas of federal government activities, except for defense and homeland security. In the specific case of the National Institutes of Health (NIH), it is evident that the recent era, which produced a doubling of funds, has ended. The success rates of research grant applications at NIH have declined rapidly over the past several years: for new applications, from around 20 percent in 1999–2002 to 9 percent in 2005; for renewal applications, from around 50 percent to 32.4 percent in the same period ( Mandel and Vesell, 2006 ).

An important source of the renewed interest in reexamination of agency priority-setting criteria and processes has been the endeavors of federal agencies and their division directors and other managers for continuous improvement of the quality of their programs. In their roles as professional science managers, agency officials seek to direct agency resources to support missions most effectively and efficiently. In doing this, they must simultaneously work to satisfy the priorities expressed by the administration and Congress via the budget process, adhere to the related legislative and administrative requirements on the allocation and expenditure of federal research support, respond to the communities of researchers currently active in relevant fields of inquiry, and in periods of scientific transformation attract and nurture researchers whose work connects to their agency’s mission—all this while being responsive to often unpredictable changes in the potential for progress along different paths of scientific inquiry. Science managers do this differently depending on the responsibilities, powers, and activities associated with their positions in their agencies ( Seidman, 1998 ). 2

Related to both these influences are increased demands for evidence-based decision making and particularly for decision making based on quantitative evidence. These demands are contained in the provisions of GPRA and PART. They also appear implicitly in the call by the president’s science adviser and the director of the Office of Science and Technology Policy, John Marburger (2005) , for a “social science of science policy” that would, among other things, use econometrics and other social science methods to help examine the effectiveness of federal investments in science ( American Sociological Association, 2006 ).

Yet another stimulus for renewed attention to priority setting has been the beliefs, latent in recent assessments of the state of U.S. science, that the decision processes of science agencies are unduly conservative in program and project selection and that they fall short in converting research findings into usable and useful applications. When coupled, these two propositions imply that the national investment in scientific research is not yielding its expected returns in improvements in the quality of life and suggest that the United States may lose its preeminience in world science.

THE COMMITTEE’S CHARGE

This national context sets the framework for this report, which responds to the specific needs of the Behavioral and Social Research Program (BSR) of the National Institute on Aging (NIA) to assess the progress and prospects of behavioral and social science research on the processes of aging at both the individual and societal levels. Specifically, BSR asked the National Academies to organize this study with two major goals: “to explore methodologies for assessing the progress and vitality of areas of behavioral and social science research on aging … [and] to identify the factors that contribute to the likelihood of discoveries in areas of aging research” (National Academies’ proposal to NIA, 2003).

Contained within these two major goals are several specific questions and subthemes, including the following: Given increasing pressures for accountability and for research to have broader impacts, widening choices of research areas to support, and the resulting increased competition for funds, what information can research managers rely upon to guide their allocations of research resources? How can they more effectively advance scientific disciplines and other research areas and make important discoveries more likely? Can we measure or at least compare the progress in different disciplines and research areas? Can the vitality of research areas be defined and assessed? What indicators for fields, as well as for individuals, would be useful? Can progress be effectively tracked through discoveries? How would discoveries be determined and selected for this purpose? BSR has requested advice on methods for the retrospective assessment of scientific progress and for addressing the prospective problem of priority setting to promote the future progress of areas of research. 3

We have addressed the two major issues for this study by reviewing and assessing relevant literatures and techniques. We also commissioned a special pilot project to assess the validity and feasibility of newly developed bibliometric methods for assessing research progress.

This study is being conducted at a time of considerable ferment and disagreement about the optimal portfolio of research funding mechanisms. In a stylized (and at times unduly polarized) manner, the choice is presented as between single investigator-initiated, discipline-based proposals and multidisciplinary, team-based proposals prepared in response to an agency request, referred to occasionally as Mode I and Mode II forms of research ( Gibbons et al., 1994 ). As detailed in the following chapters, endorsements of the merits of each approach (and implicit or explicit criticism of other funding approaches), as well as advocacy of numerous intermediate arrangements, are easy to find in current statements on science policy. Systematic empirical work that would permit more evidence-based assessments of policy options, however, is not easy to find, either generally across areas of federally funded research or specifically with respect to aging research.

Both conceptually and empirically, the two components of our charge overlap. For example, to determine the factors that contribute to scientific discoveries, one must rely on the same set of methods (e.g., peer review, bibliometrics) that are used for assessing the progress and potential of these fields and for informing research policy decisions about them. We have therefore collapsed much of the discussion of both elements of the charge into the treatment of assessment methodologies, while separately discussing bodies of knowledge specifically addressed to factors deemed to contribute to scientific advances, especially as they may pertain to research on aging. As those discussions make clear, there are significant gaps and uncertainties in knowledge about the factors that contribute to scientific advances, so that considerable interpretation and judgment are necessary in evaluating the past progress of science or projecting future prospects.

• TECHNIQUES AND PROCESSES FOR SCIENCE ASSESSMENT

Interest in methods for setting science priorities on the part of Congress, executive branch units, and agency science program managers has a long history (early contributions include Scherer, 1965 ; Nelson, 1959 ; National Academy of Sciences 1965 ; Rettig et al., 1974 ; U.S. Congress, Office of Technology Assessment, 1986 ; a more recent effort is National Science and Technology Council, 1996 ). Interest continues because of the continued salience of the underlying questions and a widespread belief that, for all their sophistication, the existing studies do not fully satisfy the needs of science program managers for reliable and defensible methods for making priority-setting decisions.

Interest in identifying the conditions that lead to advances in the social sciences also has a long, distinguished pedigree. Antecedents may be found in the 1933 President’s Research Committee on Social Trends (see Gerstein, 1986 ; Smelser, 1986 ). A more recent inquiry along these lines was the work of Deutsch et al. (1971) , who identified and analyzed the conditions that underlay 62 “major advances in the social sciences.” This work stimulated a continuing line of inquiry that has sought to distill the relative influence on the conduct of research of such factors as whether the research was conducted by individuals or teams; whether it focused on theory, method, or empirical study; the age of the researcher; and the use of capital equipment. This line of research has confronted but not resolved important methodological and conceptual issues, such as how to select “advances” for study and distinctions between “discovery” and “application” (see Smelser, 2005 ). Research on the conditions for scientific advances thus encompasses a large and diverse range of inquiry into the organization and performance of scientific endeavors.

The questions raised by our charge arise across the sciences. For example, many of the questions posed by BSR also arise in the conduct of industrially funded research, have been posed by industrial research and development managers ( Industrial Research Institute, 1999 ), and are the subject of an extensive literature on research and development portfolio selection (e.g., Bretschneider, 1993 ). These questions are also commonplace in the science priority-setting processes of other countries, as evidenced prominently in the Foresight and related activities (described below) that enter into the formulation of the European Union’s Framework programs ( Pichler, 2006 ). To review all the literature on these questions across the sciences and internationally would widen the scope of our inquiry to unmanageable dimensions.

Restricting our search only to literature dealing explicitly with behavioral and social science research on aging, however, would unacceptably narrow the scope of this study. If we used the narrower approach, we would fail to take advantage of research in other areas of science that has lessons to offer. Thus, we have used our judgment in selecting sources of knowledge that seem to provide useful insight for the tasks facing BSR, providing passing coverage of some and omitting others we deemed less germane. We have concentrated on some widely applicable techniques for assessing the past performance and progress of scientific fields and the prospects for scientific progress, and on frequently discussed variants or alternatives to these techniques. We have also conducted a pilot study using some new and promising bibliometric techniques, customized to correspond to substantive areas of research supported by BSR.

This work leads to the conclusion that all available techniques for assessing the progress and prospects of scientific fields embody significant uncertainties and will continue to do so for the foreseeable future. By itself, this is neither a novel nor a surprising conclusion. It reaffirms similar conclusions offered by both older and more recent undertakings (e.g., National Research Council, 2005c ). Similarly, our review of studies of the factors likely to contribute to scientific discoveries reveals broad consensus about major influences—“adequate funding,” for example—but uncertainty and indeed disagreement about their programmatic implications. For example, many sources point to the importance of interdisciplinary teams working in an open “collegial” environment. However, more systematic research is needed before it can be concluded that these lessons apply to the conduct of behavioral and social science research on aging.

Given the inconclusive and open-ended nature of current knowledge, the key practical problem for BSR is how to make wise choices when even the best techniques of analysis give uncertain information. BSR, and most likely other parts of NIH and other federal science agencies, need to establish processes for considering, interpreting, and using assessments offered by different parties—congressional and executive branch decision makers, researchers, user communities, program managers—of the potential contributions of different fields of science toward mission objectives. Good processes can integrate improved analytic techniques as they appear, while ensuring that imperfect measures do not trump good judgment.

Working from a focus on process, we propose a strategy that BSR can use for assessing and comparing the value of research across areas of inquiry. The strategy uses quantitative measures, indicators, and the like to inform judgment rather than to replace it. 4 It treats analytic techniques, including the application of indicators, as useful for disciplining the judgments of expert groups and focusing their deliberations, but it emphasizes the essential contribution of expert deliberation for interpreting quantitative information and informing strategic decisions about research policy. While recognizing the limitations of existing and emerging methodologies, it sees value in experimenting with promising techniques.

We propose this strategy from a recognition that an assessment strategy should address both the internal needs for decision making—the agency’s specific mission and the methods it finds acceptable for formulating priorities and assessing progress—and the decision context. Context refers to the structures and procedures of the NIH, NIA, and BSR within which decisions are made and to the distribution of decision-making authority and influence among various actors, including program managers, other decision-making entities in the agency, and extramural scientific bodies and policy actors. 5

Among the key contextual factors for BSR is the paradigmatic use of peer review and expert judgment mechanisms in NIH, as in other federal science agencies, such as the National Science Foundation (NSF) and the Department of Energy. The context also includes the accepted structures for priority setting and proposal selection in NIH (see Chapter 2 ). Over time, what is learned from this study and others with similar objectives (e.g., National Research Council, 2005c ) may lead to broader understanding of the generic issues that may be useful for assessing science and setting research priorities in other domains and organizational contexts as well.

The tasks for this study involve both prospective and retrospective assessment. Priority setting has a prospective focus. Working in a decision-making context shaped by legislative and executive branch mandates, budget allocations, political imperatives, stakeholder interests, and inputs from the affected scientific communities, agency program managers consider how best to distribute their programs’ available resources among many possible lines of science to maximize attainment of the program’s goals, such as the advancement of scientific knowledge and human well-being.

Assessments are usually retrospective. They may be conducted for summative purposes—that is, to determine how well past funds have been spent—and for formative purposes—to generate lessons learned for future decisions. Thus, retrospective assessments can affect the allocation of future funds across research fields, types of funding mechanisms (such as between individual investigator awards and multidisciplinary centers), and types of recipients (individuals or organizations).

The connection of the past with the future is not always linear or predictive. This is especially the case during periods such as the present when there is a widespread consensus in NIH, as indicated by its Roadmap initiatives ( http://nihroadmap.nih.gov/initiatives.asp ), and other federal science agencies, as illustrated by the National Science Board’s 2020 Vision for the National Science Foundation ( http://www.nsf.gov/pubs/2006/nsb05142/nsb05142.pdf ), that transformational changes are occurring in relationships among scientific and technological fields and increased attention is being given to the need to translate research findings into techniques, methods, and policies that enhance human health and well-being.

In this report, we propose a strategy for assessing the progress and prospects of science that embeds analytic techniques in a structured deliberative process. We think this strategy will make sense both to science managers and to working scientists involved in BSR’s domain of responsibility and that it will allow for discrepancies in judgment between different individuals or groups to be deliberated in a more informed way than in the past. Over time, with examination and reflection on how the strategy is used in BSR’s advisory processes, it will be possible to continue to improve practice.

• GOALS OF THE STUDY

The BSR Program of the NIA is the lead federal agency assigned the mission of supporting behavioral and social science research related to aging. As described on the NIA web site ( http://www.nia.nih.gov/ResearchInformation/ExtramuralPrograms/BehavioralAndSocialResearch/ ), BSR focuses its research support on the following topics:

• How people change during the adult lifespan
• Interrelationships between older people and social institutions
• The societal impact of the changing age composition of the population

BSR support has emphasized “(1) the dynamic interplay between individuals’ aging; (2) their changing biomedical, social, and physical environments; and (3) multilevel interactions among psychological, physiological, social, and cultural levels.” In pursuit of its objectives, “BSR supports research, training, and the development of research resources and methodologies to produce a scientific knowledge base for maximizing active life and health expectancy. This knowledge base is required for informed and effective public policy, professional practice, and everyday life. BSR also encourages the translation of behavioral and social research into practical applications.” NIA expends the bulk of its funds on grants and contracts.

BSR is seeking to address the challenges of research assessment and priority setting explicitly and systematically. It seeks to develop valid and defensible procedures for making judgments about the progress and prospects of the scientific activities it supports at the level of lines or areas of research. It seeks to identify the factors that contribute to discovery so as to have a firmer basis for allocating and reallocating funding across types of funding instruments and types of recipients (e.g., grants for research projects versus programs; grants to individuals versus research groups; disciplinary versus interdisciplinary research teams).

It seeks improved procedures for assessing scientific progress and prospects and firmer rationales for allocating incremental research funds across areas on other than a percentage-based formula and, as appropriate, for reallocating research funds from one area to another. By requesting this study, BSR has offered itself as a test bed for addressing important generic priority-setting questions that arise in many areas of federal government science policy. One of these is how best to assess the performance of investments in science when some of the objectives of those investments are hard to quantify (e.g., improving knowledge, the quality of policy decisions, or human well-being). Another is how to compare the performance of different kinds of investments when the sponsoring agency has multiple goals and different lines of research contribute to different goals.

A third is how to assess the progress and prospects of scientific fields that differ systematically in their basic objectives, methods, and philosophical underpinnings. The social and behavioral sciences exemplify this issue well. Despite much-discussed trends toward consilience across fields of science and convergence and cross-fertilization among the behavioral and social sciences (e.g., behavioral economics), significant differences in philosophical underpinnings and methodologies remain among and even within these disciplines (see, e.g., Furner, 1975 ; Ross, 2003 ; Ash, 2003 ; Stigler, 1999 ). 6 These differences underlie the historical division of the behavioral and social sciences into disciplines and subdisciplines, are unlikely to be easily resolved, and serve as the basis for competitive claims on the support provided by research sponsors, such as BSR. 7

A fourth issue is the effects of priority-setting decisions by major research funding organizations on the competition among disciplines and departments in the contemporary American research university. Assessments of scientific fields at times become enmeshed in disciplinary rivalries. Indeed, our assessment highlights the challenge to BSR of disengaging its problem or mission focus on aging from the claims of different academic disciplines to “own” a particular facet of research on aging. The progress of disciplines, however measured, does not automatically translate into progress in the kinds of areas of inquiry of greatest interest to BSR or similarly mission-oriented science programs. In this report, we use such terms as research “areas” or “fields” flexibly to refer to topics or lines of inquiry that may be as appropriately defined by a problem as by a discipline or subdiscipline.

The questions that BSR is asking, especially about the comparisons among the several areas of behavioral and social science research it supports, have received surprisingly little systematic attention. Research agencies often engage in serious efforts at priority setting, but comparative assessments of lines of research within or across scientific fields are usually approached indirectly or implicitly. For example, the National Research Council has often been asked to advise federal agencies on criteria for making such assessments (e.g., Institute of Medicine, 1998 , 2004 ; Committee on Science, Engineering, and Public Policy, 2004 ; National Research Council, 2005c ) or to identify priority areas for research from among a broad range of possibilities in many disciplines (e.g., Institute of Medicine, 1991 ; National Research Council, 2001b ). The typical method for providing an answer involves creating an expert group and asking it, often after considering input solicited from the relevant research communities, to deliberate on the question at hand and arrive at a consensus judgment that is advisory to the relevant decision makers. Only occasionally have such groups been self-conscious about developing and applying explicit methods for comparing fields so as to set priorities among them (e.g., National Research Council, 2005a , 2005c ).

Scholarly work on the assessment of science and the operation of scientific advisory panels has focused on somewhat different questions. For example, there has been considerable empirical research on the process of review for individual research proposals (e.g., Cole, Rubin, and Cole, 1978 ; Cole and Cole, 1981 ; Cole, Cole, and Simon, 1981 ; Abrams, 1991 ; Blank, 1991 ; Wessely, 1996 ; Lamont and Mallard, 2005 ), and some studies aimed at comparing larger scale activities of a single type, such as graduate departments in the same field (e.g., National Research Council, 2003 ) or research enterprises in a single field but in different countries (e.g., Committee on Science, Engineering, and Public Policy, 2000 ). Scientists and science policy analysts do sometimes make comparisons among research fields, but seldom in ways that would provide validated decision techniques to a research program manager. Members of scientific communities sometimes disagree about federal agency research priorities, as evidenced by disagreements concerning the budgetary priorities that should be accorded to the superconducting supercollider, the relative emphasis in energy research between discovering new fuel sources or improving energy-saving technologies, and the relative priority of manned and unmanned space exploration. However, research communities typically do not try to resolve such disagreements by applying formal assessment methodologies, such as those of benefit-cost or decision analysis. When challenges are posed to the intellectual substance or vitality of lines of research, they typically are directed at newly emerging ones, particularly those whose conceptual or methodological underpinnings deviate markedly from mainstream fields—and they are focused on attributes of the field in question rather than on techniques for comparison.

One interesting recent exception to these observations is empirical research that is beginning to investigate the characteristics of “successful” interdisciplinary research programs in ways that could help build a knowledge base that could inform systematic comparisons of substantively dissimilar activities or organizations (e.g., Hollingsworth, 2003 ; Mansilla and Gardner, 2004 ; National Research Council, 2005b ; Bruun et al., 2005 ; Boix-Mansilla et al., 2006 ; Feller, 2006 ). Relatedly, as federal science agencies actively promote interdisciplinary research initiatives, as in the NIH Roadmap, they are beginning to experiment with new procedures for making comparative assessments of the quality of proposals from different fields, including more deliberate attention to establishing review panels composed of experts from different disciplines ( Boix-Mansilla et al., 2006 ). Although not directly intended as a means of assessing the scientific vitality of different fields or their projected contribution to important societal objectives, the deliberations and conclusions of such panels may provide insights into how to make comparative assessments across fields.

BSR is seeking more systematic methods for such assessments, in part because of a judgment that its interdisciplinary advisory panels have not responded to the issue of comparative assessment of research fields with assessments that differentiated among fields according to the likelihood of returns from research investments. When such differentiation is needed, BSR wants valid ways to justify its recommendations about program priorities and proposal selections to senior NIH officials, Congress, and affected stakeholder and research communities.

The primary focus of this report is on questions of comparative assessment at the level of areas or fields of scientific research. It is not concerned with the overall assessment of the BSR research portfolio in the larger context of NIA or other NIH institutes. Neither is it concerned with comparisons among individuals, research projects, nor university programs, even though some of the methods we discuss have been applied at these levels of analysis. Also, the report’s focus is primarily on behavioral and social research, though its analysis and conclusions may be applicable to research in other sciences. Finally, the report’s focus is on the needs of an agency whose mission includes both the advancement of basic scientific knowledge and its application to a particular social goal: to improve the health and well-being of older people. An agency with such a twofold mission faces a more complex assessment problem than one whose mission is restricted either to pure science or to specific practical applications of science. In keeping with NIH’s overall mission and traditions, it needs both to adhere and advance standards of the highest scientific merit and assess the contributions of fields of science, existing and embryonic, for their potential contributions to NIH’s overarching missions.

• ORGANIZATION OF THE REPORT

This study has been asked to address several interrelated questions. We have centered our endeavors on what we consider to be the core questions, which concern ways to make defensible assessments of the progress and prospects of areas of scientific research for the purpose of setting priorities among public investments of science within the mission and organizational settings in which BSR functions. In addressing these core questions, we have addressed the remaining questions either explicitly or implicitly. In keeping with our emphasis on context, we begin with BSR’s activities.

Chapter 2 considers the BSR Program at NIA. It describes the strategic goals for research in that program and the parent institute, shows the kinds of research investments that have been made, and describes the ways in which the portfolio of research investments is currently evaluated.

Chapter 3 examines what is at stake in research assessment. It briefly reviews the history of federal science priority setting and the debates over priority setting and science assessment, focusing particularly on how concerns with accountability have supported pressures for quantification and the consequent debate over the strengths and limitations of quantitative and other methods for science assessment, particularly traditional peer review. Finally, it addresses the important question of the balance of power and influence between scientists and managers that underlies debates over quantification.

Chapter 4 presents an overview of theories of scientific progress, with special attention paid to the generic problem of the comparative assessment of research fields. It considers what is known about the nature and processes of scientific progress and about the links from societal progress to societal benefit, the variety of kinds of progress that science makes, the factors that contribute to scientific discovery, and the implications of each of the above for priority setting among scientific fields.

Chapter 5 examines the major methods available for assessing scientific progress in a general framework that distinguishes methods that emphasize the use of quantitative measures (analytic techniques, such as the use of bibliometric indicators and the application of decision analysis), methods that rely heavily on deliberation in groups of experts (e.g., traditional peer review), and those that explicitly combine analysis and deliberation. It considers each method and the three general strategies in the context of NIA’s objectives, the needs for accountability and rational decision making in science policy, and current knowledge about how science progresses.

Chapter 6 presents the committee’s findings and recommendations. It describes our recommended strategy for assessing and comparing the progress and prospects of scientific fields and our specific recommendations for implementing that strategy for assessing the fields of behavioral and social science research supported by NIA.

Benefits are usually considered in terms of two main kinds of values: expanded knowledge and societal gain. These values are made explicit in proposal review criteria. For example, the NSF identifies two review criteria: intellectual merit (e.g., importance to advancing knowledge and understanding, exploration of creative and original concepts) and broader impacts (e.g., promoting teaching, training, and learning; broadening the participation of underrepresented groups; enhancing the infrastructure for research and education; and benefiting society). The NIH lists five criteria for evaluating applications: significance (e.g., importance of the problem, likely effects on scientific knowledge or clinical practice), approach (e.g., adequacy of conceptual framework, research design), innovation (e.g., originality, challenging existing paradigms, testing innovative hypotheses), investigators (their training and suitability), and environment (suitability of the scientific environment for success) (see http://grants ​.nih.gov ​/grants/guide/notice-files ​/NOT-OD-05-002.html ). Among these five, the benefits are listed under significance and innovation. In science policy, benefits are also judged against costs, that is, against alternative uses of the funds, and the efficiency and effectiveness with which funds are used.

We use “science manager” as a generic term to cover a variety of positions and titles found across federal agencies. Generally, these positions include responsibility for developing intra-agency program and budget plans; maintaining contact with relevant scientific communities; overseeing proposal review and selection processes; endorsing, modifying, or rejecting recommendations made by proposal review panels and justifying these choices to higher organizational levels; and identifying research initiatives.

The latter request is a perennial of U.S. science policy. Four decades ago, one of the two questions posed by the U.S. Congress to the National Science and Technology Council ( National Academy of Sciences, 1965 :1) read as follows: “What judgment can be reached on the balance of support now being given by the Federal Government to various fields of scientific endeavor, and on adjustments that should be considered, either within existing levels of overall support or under conditions of increased or decreased overall support?”

We accept van Raan’s (2004 :22) definition of an indicator as “the result of a specific mathematical operation with data” designed to serve the purposes of (a) describing “the recent past in such a way that … can guide us, can inform us about the near future” and (b) contribute to testing “aspects of theories and models of scientific development and its interaction with society.”

The critical role of context in science policy decision making was expressed concisely by Harvey Brooks (1965:99), as follows: “criteria are considerably less important than who applies them …. [T]he fundamental problem of resource allocation within basic research is who makes the important decisions and how they are made.”

Deep philosophical differences exist even within single social science disciplines. Lamont (2004 :8) has observed with reference to sociology that it “produces different types of knowledge … and that this diversity should be acknowledged in our definition of theoretical growth or vitality. To order sociological contributions within a single hierarchy or paradigm, as economists do … would be to weaken it by underestimating the contributions of its various strands. … It also would place our discipline very low on the totem pole of fields, which to my view would grossly misrepresent the many contributions of our paradigmatic discipline.”

For all the interest expressed by behavioral and social scientists in having a secure and stable home in NIH for basic behavioral science research and training, these communities have expressed little interest in changing the structure or functioning of existing basic behavioral and social science research programs across institutes ( Association for Psychological Science, 2005 ).

• Cite this Page National Research Council (US) Committee on Assessing Behavioral and Social Science Research on Aging; Feller I, Stern PC, editors. A Strategy for Assessing Science: Behavioral and Social Research on Aging. Washington (DC): National Academies Press (US); 2007. 1, The Purpose of the Study.
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Just believing that an AI is helping boosts your performance, study finds

by Aalto University

Sometimes it seems like an AI is helping, but the benefit is actually a placebo effect—people performing better simply because they expect to be doing so—according to new research from Aalto University. The study also shows how difficult it is to shake people's trust in the capabilities of AI systems.

In this study, participants were tasked with a simple letter recognition exercise. They performed the task once on their own and once supposedly aided by an AI system. Half of the participants were told the system was reliable and it would enhance their performance, and the other half was told that it was unreliable and would worsen their performance.

The findings are published in Proceedings of the CHI Conference on Human Factors in Computing Systems .

"In fact, neither AI system ever existed. Participants were led to believe an AI system was assisting them, when in reality, what the sham-AI was doing was completely random," explains doctoral researcher Agnes Kloft.

The participants had to pair letters that popped up on screen at varying speeds. Surprisingly, both groups performed the exercise more efficiently—more quickly and attentively—when they believed an AI was involved.

"What we discovered is that people have extremely high expectations of these systems, and we can't make them AI doomers simply by telling them a program doesn't work," says Assistant Professor Robin Welsch.

Following the initial experiments, the researchers conducted an online replication study that produced similar results. They also introduced a qualitative component, inviting participants to describe their expectations of performing with an AI. Most had a positive outlook toward AI and, surprisingly even skeptical people still had positive expectations about its performance.

The findings pose a problem for the methods generally used to evaluate emerging AI systems. "This is the big realization coming from our study—that it's hard to evaluate programs that promise to help you because of this placebo effect ," Welsch says.

While powerful technologies like large language models undoubtedly streamline certain tasks, subtle differences between versions may be amplified or masked by the placebo effect—and this is effectively harnessed through marketing.

The results also pose a significant challenge for research on human-computer interaction , since expectations would influence the outcome unless placebo control studies were used.

"These results suggest that many studies in the field may have been skewed in favor of AI systems," concludes Welsch.

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February 12, 2024

Pseudoscience Has Long Been Used to Oppress Transgender People

Three major waves of opposition to transgender health care in the past century have cited faulty science to justify hostility

By G. Samantha Rosenthal & The Conversation US

An activist holds a poster at a protest supporting the transgender community in Canada.

Artur Widak/NurPhoto via Getty Images

The following essay is reprinted with permission from The Conversation , an online publication covering the latest research.

In the past century, there have been three waves of opposition to transgender health care.

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In 1933, when the Nazis rose to power, they cracked down on transgender medical research and clinical practice in Europe. In 1979, a research report critical of transgender medicine led to the closure of the most well-respected clinics in the United States. And since 2021, when Arkansas became the first U.S. state among now at least 21 other states banning gender-affirming care for minors, we have been living in a third wave.

In my work as a scholar of transgender history , I study the long history of gender-affirming care in the U.S., which has been practiced since at least the 1940s. Puberty blockers , hormone therapies and anatomical surgeries are neither experimental nor untested and have been safely administered to cisgender, transgender and intersex adults and children for decades.

On the other hand, the archives of transgender medicine demonstrate that backlash against these practices has historically been rooted in pseudoscience. And today, an anti-science movement that aims to discredit science altogether is fueling the fire of the current wave of anti-trans panic.

The 1930s − eugenics and sexology collide

In the 1920s, the new science of hormones was just reaching maturation and entering mainstream consciousness . In the field of sexology – the study of human sexuality, founded in 19th century Europe – scientists were excited about research on animals demonstrating that removing or transplanting gonads could effectively change an organism’s sex.

In 1919, the German sexologist Magnus Hirschfeld founded the Institut für Sexualwissenschaft in Berlin, which became the world’s leading center for queer and transgender research and clinical practice. Hirschfeld worked closely with trans women as co-researchers throughout the 1920s. Several trans women also received care at the institute, including orchiectomies that halted the production of testosterone in their bodies.

Within months of Hitler’s rise to power in early 1933, a mob of far-right students broke into and shuttered the institute for being “ un-German .” Some of the most famous images of Nazi book burning show the institute’s library set ablaze in an outdoor plaza.

Nazi ideology was based on another prominent field of science of that time: eugenics , the belief that certain superior populations should survive while inferior populations must be exterminated. In fact, Hirschfeld’s sexology and Nazi race science had common roots in the Enlightenment-era effort to classify and categorize the world’s life forms.

But in the late 19th century, many scientists went a step further and developed a hierarchy of human types based on race, gender and sexuality. They were inspired by social Darwinism , a set of pseudoscientific beliefs applying the theory of survival of the fittest to human differences. As race scientists imagined a fixed number of human races of varying intelligence, sexologists simultaneously sought to classify sexual behaviors as innate, inherited states of being: the “homosexual” in the 1860s and the “transvestite,” a term coined by Hirschfeld himself, in 1910.

But where Hirschfeld and other sexologists saw the classification of queer and trans people as justifications for legal emancipation, eugenicists of the early 20th century in the U.S. and Europe believed sexually transgressive people should be sterilized and ultimately eradicated.

Based on this premise, the Nazis murdered thousands of LGBTQ people in the Holocaust.

The 1970s − making model citizens

In the 1950s and 1960s, transgender medicine bounced back in the U.S. Scientists and clinicians at several universities began experimenting with new hormonal and surgical interventions . In 1966, Johns Hopkins became the first university hospital in the world to offer trans health care.

By the 1970s, trans medicine went mainstream. Nearly two dozen university hospitals were operating gender identity clinics and treating thousands of transgender Americans. Several trans women and men wrote popular autobiographical accounts of their transitions. Trans people were even on television , talking about their bodies and fighting for their rights.

Yet trouble was brewing behind the scenes. Jon Meyer, a psychiatrist at Johns Hopkins, was skeptical of whether medical interventions really helped transgender people. In 1979, Meyer, along with his secretary Donna Reter, published a short academic paper that ushered in the second wave of historic backlash to trans medicine.

In their study, Meyer and Reter contacted previous patients of the Johns Hopkins Gender Identity Clinic. To understand whether surgery had improved patients’ lives, the authors developed an “adjustment scoring system.” They assigned points to patients who were in heterosexual marriages and had achieved economic security since their operations, while deducting points from those who continued to engage in gender nonconformity, homosexuality, criminality, or sought mental health care.

Meyer and Reter believed that gender-affirming surgeries were successful only if they made model citizens out of transgender people: straight, married and law-abiding.

In their results, the authors found no negative effects from surgery, and no patients expressed regret. They concluded that “sex reassignment surgery confers no objective advantage in terms of social rehabilitation,” but it is “subjectively satisfying” to the patients themselves. This was not a damning conclusion.

Yet, within two months, Johns Hopkins had shuttered its clinic . The New York Times reported that universities would feel pressure to similarly “curtail their operations and discourage others from starting to do them.” Indeed, only a handful of clinics remained by the 1990s. Transgender medicine did not return to Johns Hopkins until 2017 .

In requiring trans patients to enter straight marriages and hold gender-appropriate jobs to be considered successful, Meyer and Reter’s study was homophobic and classist in design . The study exemplified the pseudoscientific beliefs at the heart of transgender medicine in the 1960s through the 1980s, that patients had to conform to societal norms – including heterosexuality, gender conformity, domesticity and marriage – in order to receive care. This was not an ideology rooted in science but in bigotry.

The 2020s − distrust in science

As in the 1930s, opposition to trans medicine today is part of a broad reactionary movement against what some far-right groups consider the “ toxic normalization ” of LGBTQ people.

Legislators have removed books with LGBTQ content from libraries and disparaged them as “filth .” A recent law in Florida threatens trans people with arrest for using public restrooms. Both Florida and Texas have pursued efforts to compile data on their trans citizens . Donald Trump’s campaign platform calls for a nationwide ban on trans health care for minors and severe restrictions for adults.

And similar to the 1970s, opponents of trans medicine today frame gender-affirming care as a “debate,” even though all major U.S. medical associations support these practices as medically necessary and lifesaving.

But widespread distrust in science and medicine in the wake of the COVID-19 pandemic has affected how Americans perceive trans health care. Prohibitions on gender-affirming care have occurred simultaneously with the relaxing of pandemic restrictions, and some scholars argue that the movement against trans health care is part of a broader movement aimed at discrediting scientific consensus.

Yet the adage “ believe in science ” is not an effective rejoinder to these anti-trans policies. Instead, many trans activists today call for diminishing the role of medical authority altogether in gatekeeping access to trans health care . Medical gatekeeping occurs through stringent guidelines that govern access to trans health care, including mandated psychiatric evaluations and extended waiting periods that limit and control patient choice.

Trans activists have fought with the World Professional Association for Transgender Health , the organization that maintains these standards of care, by demanding greater bodily autonomy and depathologizing transsexuality. This includes pivoting to an informed consent model where patients make decisions about their own bodies after discussing the pros and cons with their doctors. Trans activists have been rallying against medical authority since the early 1970s, including calling for access to hormones and surgeries on demand .

It is not clear how the current third wave of backlash to transgender medicine will end. For now, trans health care remains a question dominated by medical experts on one hand and people who question science on the other.

This article was originally published on The Conversation . Read the original article .

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Study reveals patients with brain injuries who died after withdrawal of life support may have recovered

Findings support a more cautious approach to making early decisions on withdrawal of life support following traumatic brain injuries.

Severe traumatic brain injury (TBI) is a major cause of hospitalizations and deaths around the world, affecting more than five million people each year. Predicting outcomes following a brain injury can be challenging, yet families are asked to make decisions about continuing or withdrawing life-sustaining treatment within days of injury.

In a new study, Mass General Brigham investigators analyzed potential clinical outcomes for TBI patients enrolled in the Transforming Research and Clinical Knowledge in TBI (TRACK-TBI) study for whom life support was withdrawn. The investigators found that some patients for whom life support was withdrawn may have survived and recovered some level of independence a few months after injury. These findings suggest that delaying decisions on withdrawing life support might be beneficial for some patients.

Families are often asked to make decisions to withdraw life support measures, such as mechanical breathing, within 72 hours of a brain injury. Information relayed by physicians suggesting a poor neurologic prognosis is the most common reason families opt for withdrawing life support measures. However, there are currently no medical guidelines or precise algorithms that determine which patients with severe TBI are likely to recover.

Using data collected over a 7.5-year period on 1,392 TBI patients in intensive care units at 18 United States trauma centers, the researchers created a mathematical model to calculate the likelihood of withdrawal of life-sustaining treatment, based on properties like demographics, socioeconomic factors and injury characteristics. Then, they paired individuals for whom life-sustaining treatment was not withdrawn (WLST-) to individuals with similar model scores, but for whom life-sustaining treatment was withdrawn (WLST+).

Based on follow-up of their WLST- paired counterparts, the estimated six-month outcomes for a substantial proportion of the WLST+ group was either death or recovery of at least some independence in daily activities. Of survivors, more than 40 percent of the WLST- group recovered at least some independence. In addition, the research team found that remaining in a vegetative state was an unlikely outcome by six-months after injury. Importantly, none of the patients who died in this study were pronounced brain dead, and thus the results are not applicable to brain death.

According to the authors, the findings suggest there is a cyclical, self-fulfilling prophecy taking place: Clinicians assume patients will do poorly based on outcomes data. This assumption results in withdrawal of life support, which in turn increases poor outcomes rates and leads to even more decisions to withdraw life support.

The authors suggest that further studies involving larger sample sizes that allow for more precise matching of WLST+ and WLST- cohorts are needed to understand variable recovery trajectories for patients who sustain traumatic brain injuries.

"Our findings support a more cautious approach to making early decisions on withdrawal of life support," said corresponding author Yelena Bodien, PhD, of the Department of Neurology's Center for Neurotechnology and Neurorecovery at Massachusetts General Hospital and of the Spaulding-Harvard Traumatic Brain Injury Model Systems. "Traumatic brain injury is a chronic condition that requires long term follow-ups to understand patient outcomes. Delaying decisions regarding life support may be warranted to better identify patients whose condition may improve."

Read more in the study, published May 13, in the Journal of Neurotrauma .

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Story Source:

Materials provided by Mass General Brigham . Note: Content may be edited for style and length.

Journal Reference :

• William R. Sanders, Jason K. Barber, Nancy R. Temkin, Brandon Foreman, Joseph T. Giacino, Theresa Williamson, Brian L. Edlow, Geoffrey T. Manley, Yelena G. Bodien. Recovery Potential in Patients Who Died After Withdrawal of Life-Sustaining Treatment: A TRACK-TBI Propensity Score Analysis . Journal of Neurotrauma , 2024; DOI: 10.1089/neu.2024.0014

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