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One of the hardest parts of writing a research paper can be just finding a good topic to write about. Fortunately we've done the hard work for you and have compiled a list of 113 interesting research paper topics. They've been organized into ten categories and cover a wide range of subjects so you can easily find the best topic for you.

In addition to the list of good research topics, we've included advice on what makes a good research paper topic and how you can use your topic to start writing a great paper.

What Makes a Good Research Paper Topic?

Not all research paper topics are created equal, and you want to make sure you choose a great topic before you start writing. Below are the three most important factors to consider to make sure you choose the best research paper topics.

#1: It's Something You're Interested In

A paper is always easier to write if you're interested in the topic, and you'll be more motivated to do in-depth research and write a paper that really covers the entire subject. Even if a certain research paper topic is getting a lot of buzz right now or other people seem interested in writing about it, don't feel tempted to make it your topic unless you genuinely have some sort of interest in it as well.

#2: There's Enough Information to Write a Paper

Even if you come up with the absolute best research paper topic and you're so excited to write about it, you won't be able to produce a good paper if there isn't enough research about the topic. This can happen for very specific or specialized topics, as well as topics that are too new to have enough research done on them at the moment. Easy research paper topics will always be topics with enough information to write a full-length paper.

Trying to write a research paper on a topic that doesn't have much research on it is incredibly hard, so before you decide on a topic, do a bit of preliminary searching and make sure you'll have all the information you need to write your paper.

#3: It Fits Your Teacher's Guidelines

Don't get so carried away looking at lists of research paper topics that you forget any requirements or restrictions your teacher may have put on research topic ideas. If you're writing a research paper on a health-related topic, deciding to write about the impact of rap on the music scene probably won't be allowed, but there may be some sort of leeway. For example, if you're really interested in current events but your teacher wants you to write a research paper on a history topic, you may be able to choose a topic that fits both categories, like exploring the relationship between the US and North Korea. No matter what, always get your research paper topic approved by your teacher first before you begin writing.

113 Good Research Paper Topics

Below are 113 good research topics to help you get you started on your paper. We've organized them into ten categories to make it easier to find the type of research paper topics you're looking for.

Arts/Culture

  • Discuss the main differences in art from the Italian Renaissance and the Northern Renaissance .
  • Analyze the impact a famous artist had on the world.
  • How is sexism portrayed in different types of media (music, film, video games, etc.)? Has the amount/type of sexism changed over the years?
  • How has the music of slaves brought over from Africa shaped modern American music?
  • How has rap music evolved in the past decade?
  • How has the portrayal of minorities in the media changed?

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Current Events

  • What have been the impacts of China's one child policy?
  • How have the goals of feminists changed over the decades?
  • How has the Trump presidency changed international relations?
  • Analyze the history of the relationship between the United States and North Korea.
  • What factors contributed to the current decline in the rate of unemployment?
  • What have been the impacts of states which have increased their minimum wage?
  • How do US immigration laws compare to immigration laws of other countries?
  • How have the US's immigration laws changed in the past few years/decades?
  • How has the Black Lives Matter movement affected discussions and view about racism in the US?
  • What impact has the Affordable Care Act had on healthcare in the US?
  • What factors contributed to the UK deciding to leave the EU (Brexit)?
  • What factors contributed to China becoming an economic power?
  • Discuss the history of Bitcoin or other cryptocurrencies  (some of which tokenize the S&P 500 Index on the blockchain) .
  • Do students in schools that eliminate grades do better in college and their careers?
  • Do students from wealthier backgrounds score higher on standardized tests?
  • Do students who receive free meals at school get higher grades compared to when they weren't receiving a free meal?
  • Do students who attend charter schools score higher on standardized tests than students in public schools?
  • Do students learn better in same-sex classrooms?
  • How does giving each student access to an iPad or laptop affect their studies?
  • What are the benefits and drawbacks of the Montessori Method ?
  • Do children who attend preschool do better in school later on?
  • What was the impact of the No Child Left Behind act?
  • How does the US education system compare to education systems in other countries?
  • What impact does mandatory physical education classes have on students' health?
  • Which methods are most effective at reducing bullying in schools?
  • Do homeschoolers who attend college do as well as students who attended traditional schools?
  • Does offering tenure increase or decrease quality of teaching?
  • How does college debt affect future life choices of students?
  • Should graduate students be able to form unions?

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  • What are different ways to lower gun-related deaths in the US?
  • How and why have divorce rates changed over time?
  • Is affirmative action still necessary in education and/or the workplace?
  • Should physician-assisted suicide be legal?
  • How has stem cell research impacted the medical field?
  • How can human trafficking be reduced in the United States/world?
  • Should people be able to donate organs in exchange for money?
  • Which types of juvenile punishment have proven most effective at preventing future crimes?
  • Has the increase in US airport security made passengers safer?
  • Analyze the immigration policies of certain countries and how they are similar and different from one another.
  • Several states have legalized recreational marijuana. What positive and negative impacts have they experienced as a result?
  • Do tariffs increase the number of domestic jobs?
  • Which prison reforms have proven most effective?
  • Should governments be able to censor certain information on the internet?
  • Which methods/programs have been most effective at reducing teen pregnancy?
  • What are the benefits and drawbacks of the Keto diet?
  • How effective are different exercise regimes for losing weight and maintaining weight loss?
  • How do the healthcare plans of various countries differ from each other?
  • What are the most effective ways to treat depression ?
  • What are the pros and cons of genetically modified foods?
  • Which methods are most effective for improving memory?
  • What can be done to lower healthcare costs in the US?
  • What factors contributed to the current opioid crisis?
  • Analyze the history and impact of the HIV/AIDS epidemic .
  • Are low-carbohydrate or low-fat diets more effective for weight loss?
  • How much exercise should the average adult be getting each week?
  • Which methods are most effective to get parents to vaccinate their children?
  • What are the pros and cons of clean needle programs?
  • How does stress affect the body?
  • Discuss the history of the conflict between Israel and the Palestinians.
  • What were the causes and effects of the Salem Witch Trials?
  • Who was responsible for the Iran-Contra situation?
  • How has New Orleans and the government's response to natural disasters changed since Hurricane Katrina?
  • What events led to the fall of the Roman Empire?
  • What were the impacts of British rule in India ?
  • Was the atomic bombing of Hiroshima and Nagasaki necessary?
  • What were the successes and failures of the women's suffrage movement in the United States?
  • What were the causes of the Civil War?
  • How did Abraham Lincoln's assassination impact the country and reconstruction after the Civil War?
  • Which factors contributed to the colonies winning the American Revolution?
  • What caused Hitler's rise to power?
  • Discuss how a specific invention impacted history.
  • What led to Cleopatra's fall as ruler of Egypt?
  • How has Japan changed and evolved over the centuries?
  • What were the causes of the Rwandan genocide ?

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  • Why did Martin Luther decide to split with the Catholic Church?
  • Analyze the history and impact of a well-known cult (Jonestown, Manson family, etc.)
  • How did the sexual abuse scandal impact how people view the Catholic Church?
  • How has the Catholic church's power changed over the past decades/centuries?
  • What are the causes behind the rise in atheism/ agnosticism in the United States?
  • What were the influences in Siddhartha's life resulted in him becoming the Buddha?
  • How has media portrayal of Islam/Muslims changed since September 11th?

Science/Environment

  • How has the earth's climate changed in the past few decades?
  • How has the use and elimination of DDT affected bird populations in the US?
  • Analyze how the number and severity of natural disasters have increased in the past few decades.
  • Analyze deforestation rates in a certain area or globally over a period of time.
  • How have past oil spills changed regulations and cleanup methods?
  • How has the Flint water crisis changed water regulation safety?
  • What are the pros and cons of fracking?
  • What impact has the Paris Climate Agreement had so far?
  • What have NASA's biggest successes and failures been?
  • How can we improve access to clean water around the world?
  • Does ecotourism actually have a positive impact on the environment?
  • Should the US rely on nuclear energy more?
  • What can be done to save amphibian species currently at risk of extinction?
  • What impact has climate change had on coral reefs?
  • How are black holes created?
  • Are teens who spend more time on social media more likely to suffer anxiety and/or depression?
  • How will the loss of net neutrality affect internet users?
  • Analyze the history and progress of self-driving vehicles.
  • How has the use of drones changed surveillance and warfare methods?
  • Has social media made people more or less connected?
  • What progress has currently been made with artificial intelligence ?
  • Do smartphones increase or decrease workplace productivity?
  • What are the most effective ways to use technology in the classroom?
  • How is Google search affecting our intelligence?
  • When is the best age for a child to begin owning a smartphone?
  • Has frequent texting reduced teen literacy rates?

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How to Write a Great Research Paper

Even great research paper topics won't give you a great research paper if you don't hone your topic before and during the writing process. Follow these three tips to turn good research paper topics into great papers.

#1: Figure Out Your Thesis Early

Before you start writing a single word of your paper, you first need to know what your thesis will be. Your thesis is a statement that explains what you intend to prove/show in your paper. Every sentence in your research paper will relate back to your thesis, so you don't want to start writing without it!

As some examples, if you're writing a research paper on if students learn better in same-sex classrooms, your thesis might be "Research has shown that elementary-age students in same-sex classrooms score higher on standardized tests and report feeling more comfortable in the classroom."

If you're writing a paper on the causes of the Civil War, your thesis might be "While the dispute between the North and South over slavery is the most well-known cause of the Civil War, other key causes include differences in the economies of the North and South, states' rights, and territorial expansion."

#2: Back Every Statement Up With Research

Remember, this is a research paper you're writing, so you'll need to use lots of research to make your points. Every statement you give must be backed up with research, properly cited the way your teacher requested. You're allowed to include opinions of your own, but they must also be supported by the research you give.

#3: Do Your Research Before You Begin Writing

You don't want to start writing your research paper and then learn that there isn't enough research to back up the points you're making, or, even worse, that the research contradicts the points you're trying to make!

Get most of your research on your good research topics done before you begin writing. Then use the research you've collected to create a rough outline of what your paper will cover and the key points you're going to make. This will help keep your paper clear and organized, and it'll ensure you have enough research to produce a strong paper.

What's Next?

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Want to know the fastest and easiest ways to convert between Fahrenheit and Celsius? We've got you covered! Check out our guide to the best ways to convert Celsius to Fahrenheit (or vice versa).

These recommendations are based solely on our knowledge and experience. If you purchase an item through one of our links, PrepScholar may receive a commission.

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Christine graduated from Michigan State University with degrees in Environmental Biology and Geography and received her Master's from Duke University. In high school she scored in the 99th percentile on the SAT and was named a National Merit Finalist. She has taught English and biology in several countries.

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Chapter 4 Theories in Scientific Research

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

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

Theories should explain why things happen, rather than just describe or predict. Note that it is possible to predict events or behaviors using a set of predictors, without necessarily explaining why such events are taking place. For instance, market analysts predict fluctuations in the stock market based on market announcements, earnings reports of major companies, and new data from the Federal Reserve and other agencies, based on previously observed correlations . Prediction requires only correlations. In contrast, explanations require causations , or understanding of cause-effect relationships. Establishing causation requires three conditions: (1) correlations between two constructs, (2) temporal precedence (the cause must precede the effect in time), and (3) rejection of alternative hypotheses (through testing). Scientific theories are different from theological, philosophical, or other explanations in that scientific theories can be empirically tested using scientific methods.

Explanations can be idiographic or nomothetic. Idiographic explanations are those that explain a single situation or event in idiosyncratic detail. For example, you did poorly on an exam because: (1) you forgot that you had an exam on that day, (2) you arrived late to the exam due to a traffic jam, (3) you panicked midway through the exam, (4) you had to work late the previous evening and could not study for the exam, or even (5) your dog ate your text book. The explanations may be detailed, accurate, and valid, but they may not apply to other similar situations, even involving the same person, and are hence not generalizable. In contrast, nomothetic explanations seek to explain a class of situations or events rather than a specific situation or event. For example, students who do poorly in exams do so because they did not spend adequate time preparing for exams or that they suffer from nervousness, attention-deficit, or some other medical disorder. Because nomothetic explanations are designed to be generalizable across situations, events, or people, they tend to be less precise, less complete, and less detailed. However, they explain economically, using only a few explanatory variables. Because theories are also intended to serve as generalized explanations for patterns of events, behaviors, or phenomena, theoretical explanations are generally nomothetic in nature.

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

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

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

Building Blocks of a Theory

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

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

Flowchart showing the theoretical plane with construct A leading to a proposition of construct B, then the emprical plane below with the independent variable leading to a hypothesis about the dependent variable.

Figure 4.1. Distinction between theoretical and empirical concepts

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

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

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

Attributes of a Good Theory

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

  • Logical consistency : Are the theoretical constructs, propositions, boundary conditions, and assumptions logically consistent with each other? If some of these “building blocks” of a theory are inconsistent with each other (e.g., a theory assumes rationality, but some constructs represent non-rational concepts), then the theory is a poor theory.
  • Explanatory power : How much does a given theory explain (or predict) reality? Good theories obviously explain the target phenomenon better than rival theories, as often measured by variance explained (R-square) value in regression equations.
  • Falsifiability : British philosopher Karl Popper stated in the 1940’s that for theories to be valid, they must be falsifiable. Falsifiability ensures that the theory is potentially disprovable, if empirical data does not match with theoretical propositions, which allows for their empirical testing by researchers. In other words, theories cannot be theories unless they can be empirically testable. Tautological statements, such as “a day with high temperatures is a hot day” are not empirically testable because a hot day is defined (and measured) as a day with high temperatures, and hence, such statements cannot be viewed as a theoretical proposition. Falsifiability requires presence of rival explanations it ensures that the constructs are adequately measurable, and so forth. However, note that saying that a theory is falsifiable is not the same as saying that a theory should be falsified. If a theory is indeed falsified based on empirical evidence, then it was probably a poor theory to begin with!
  • Parsimony : Parsimony examines how much of a phenomenon is explained with how few variables. The concept is attributed to 14 th century English logician Father William of Ockham (and hence called “Ockham’s razor” or “Occam’s razor), which states that among competing explanations that sufficiently explain the observed evidence, the simplest theory (i.e., one that uses the smallest number of variables or makes the fewest assumptions) is the best. Explanation of a complex social phenomenon can always be increased by adding more and more constructs. However, such approach defeats the purpose of having a theory, which are intended to be “simplified” and generalizable explanations of reality. Parsimony relates to the degrees of freedom in a given theory. Parsimonious theories have higher degrees of freedom, which allow them to be more easily generalized to other contexts, settings, and populations.

Approaches to Theorizing

How do researchers build theories? Steinfeld and Fulk (1990) [2] recommend four such approaches. The first approach is to build theories inductively based on observed patterns of events or behaviors. Such approach is often called “grounded theory building”, because the theory is grounded in empirical observations. This technique is heavily dependent on the observational and interpretive abilities of the researcher, and the resulting theory may be subjective and non -confirmable. Furthermore, observing certain patterns of events will not necessarily make a theory, unless the researcher is able to provide consistent explanations for the observed patterns. We will discuss the grounded theory approach in a later chapter on qualitative research.

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

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

The fourth approach is to apply existing theories in entirely new contexts by drawing upon the structural similarities between the two contexts. This approach relies on reasoning by analogy, and is probably the most creative way of theorizing using a deductive approach. For instance, Markus (1987) [3] used analogic similarities between a nuclear explosion and uncontrolled growth of networks or network-based businesses to propose a critical mass theory of network growth. Just as a nuclear explosion requires a critical mass of radioactive material to sustain a nuclear explosion, Markus suggested that a network requires a critical mass of users to sustain its growth, and without such critical mass, users may leave the network, causing an eventual demise of the network.

Examples of Social Science Theories

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

Agency Theory. Agency theory (also called principal-agent theory), a classic theory in the organizational economics literature, was originally proposed by Ross (1973) [4] to explain two-party relationships (such as those between an employer and its employees, between organizational executives and shareholders, and between buyers and sellers) whose goals are not congruent with each other. The goal of agency theory is to specify optimal contracts and the conditions under which such contracts may help minimize the effect of goal incongruence. The core assumptions of this theory are that human beings are self-interested individuals, boundedly rational, and risk-averse, and the theory can be applied at the individual or organizational level.

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

Theory of Planned Behavior. Postulated by Azjen (1991) [5] , the theory of planned behavior (TPB) is a generalized theory of human behavior in the social psychology literature that can be used to study a wide range of individual behaviors. It presumes that individual behavior represents conscious reasoned choice, and is shaped by cognitive thinking and social pressures. The theory postulates that behaviors are based on one’s intention regarding that behavior, which in turn is a function of the person’s attitude toward the behavior, subjective norm regarding that behavior, and perception of control over that behavior (see Figure 4.2). Attitude is defined as the individual’s overall positive or negative feelings about performing the behavior in question, which may be assessed as a summation of one’s beliefs regarding the different consequences of that behavior, weighted by the desirability of those consequences.

Subjective norm refers to one’s perception of whether people important to that person expect the person to perform the intended behavior, and represented as a weighted combination of the expected norms of different referent groups such as friends, colleagues, or supervisors at work. Behavioral control is one’s perception of internal or external controls constraining the behavior in question. Internal controls may include the person’s ability to perform the intended behavior (self-efficacy), while external control refers to the availability of external resources needed to perform that behavior (facilitating conditions). TPB also suggests that sometimes people may intend to perform a given behavior but lack the resources needed to do so, and therefore suggests that posits that behavioral control can have a direct effect on behavior, in addition to the indirect effect mediated by intention.

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

Flowchart theory of planned behavior showing a consequence leading to attitude, a norm leading to subjective norms, control leading to behavioral control, and all of these things leading to the intention and then the behavior.

Figure 4.2. Theory of planned behavior

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

At the micro (adopter) level, Rogers (1995) [6] suggests that innovation adoption is a process consisting of five stages: (1) knowledge: when adopters first learn about an innovation from mass-media or interpersonal channels, (2) persuasion: when they are persuaded by prior adopters to try the innovation, (3) decision: their decision to accept or reject the innovation, (4) implementation: their initial utilization of the innovation, and (5) confirmation: their decision to continue using it to its fullest potential (see Figure 4.4). Five innovation characteristics are presumed to shape adopters’ innovation adoption decisions: (1) relative advantage: the expected benefits of an innovation relative to prior innovations, (2) compatibility: the extent to which the innovation fits with the adopter’s work habits, beliefs, and values, (3) complexity: the extent to which the innovation is difficult to learn and use, (4) trialability: the extent to which the innovation can be tested on a trial basis, and (5) observability: the extent to which the results of using the innovation can be clearly observed. The last two characteristics have since been dropped from many innovation studies. Complexity is negatively correlated to innovation adoption, while the other four factors are positively correlated. Innovation adoption also depends on personal factors such as the adopter’s risk- taking propensity, education level, cosmopolitanism, and communication influence. Early adopters are venturesome, well educated, and rely more on mass media for information about the innovation, while later adopters rely more on interpersonal sources (such as friends and family) as their primary source of information. IDT has been criticized for having a “pro-innovation bias,” that is for presuming that all innovations are beneficial and will be eventually diffused across the entire population, and because it does not allow for inefficient innovations such as fads or fashions to die off quickly without being adopted by the entire population or being replaced by better innovations.

S-shaped diffusion curve showing the comparison with the traditional bell-shaped curve with 2.5% as innovators, 13.5% as early adopters, 34% as early majority, 34% as the late majority, and 16% as laggards.

Figure 4.3. S-shaped diffusion curve

Innovation adoption process showing knowledge then persuasion then decision then implementation and then confirmation.

Figure 4.4. Innovation adoption process.

Elaboration Likelihood Model . Developed by Petty and Cacioppo (1986) [7] , the elaboration likelihood model (ELM) is a dual-process theory of attitude formation or change in the psychology literature. It explains how individuals can be influenced to change their attitude toward a certain object, events, or behavior and the relative efficacy of such change strategies. The ELM posits that one’s attitude may be shaped by two “routes” of influence, the central route and the peripheral route, which differ in the amount of thoughtful information processing or “elaboration” required of people (see Figure 4.5). The central route requires a person to think about issue-related arguments in an informational message and carefully scrutinize the merits and relevance of those arguments, before forming an informed judgment about the target object. In the peripheral route, subjects rely on external “cues” such as number of prior users, endorsements from experts, or likeability of the endorser, rather than on the quality of arguments, in framing their attitude towards the target object. The latter route is less cognitively demanding, and the routes of attitude change are typically operationalized in the ELM using the argument quality and peripheral cues constructs respectively.

Argument quality (central route), motivation and ability (elaboration likelihood) and source credibility (peripheral route) all lead to attitude change

Figure 4.5. Elaboration likelihood model

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

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

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

[1] Bacharach, S. B. (1989). “Organizational Theories: Some Criteria for Evaluation,” Academy of Management Review (14:4), 496-515.

[2] Steinfield, C.W. and Fulk, J. (1990). “The Theory Imperative,” in Organizations and Communications Technology , J. Fulk and C. W. Steinfield (eds.), Newbury Park, CA: Sage Publications.

[3] Markus, M. L. (1987). “Toward a ‘Critical Mass’ Theory of Interactive Media: Universal Access, Interdependence, and Diffusion,” Communication Research (14:5), 491-511.

[4] Ross, S. A. (1973). “The Economic Theory of Agency: The Principal’s Problem,” American Economic Review (63:2), 134-139.

[5] Ajzen, I. (1991). “The Theory of Planned Behavior,” Organizational Behavior and Human Decision Processes (50), 179-211.

[6] Rogers, E. (1962). Diffusion of Innovations . New York: The Free Press. Other editions 1983, 1996, 2005.

[7] Petty, R. E., and Cacioppo, J. T. (1986). Communication and Persuasion: Central and Peripheral Routes to Attitude Change . New York: Springer-Verlag.

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

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What Is a Theoretical Framework? | Guide to Organizing

Published on October 14, 2022 by Sarah Vinz . Revised on November 20, 2023 by Tegan George.

A theoretical framework is a foundational review of existing theories that serves as a roadmap for developing the arguments you will use in your own work.

Theories are developed by researchers to explain phenomena, draw connections, and make predictions. In a theoretical framework, you explain the existing theories that support your research, showing that your paper or dissertation topic is relevant and grounded in established ideas.

In other words, your theoretical framework justifies and contextualizes your later research, and it’s a crucial first step for your research paper , thesis , or dissertation . A well-rounded theoretical framework sets you up for success later on in your research and writing process.

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Why do you need a theoretical framework, how to write a theoretical framework, structuring your theoretical framework, example of a theoretical framework, other interesting articles, frequently asked questions about theoretical frameworks.

Before you start your own research, it’s crucial to familiarize yourself with the theories and models that other researchers have already developed. Your theoretical framework is your opportunity to present and explain what you’ve learned, situated within your future research topic.

There’s a good chance that many different theories about your topic already exist, especially if the topic is broad. In your theoretical framework, you will evaluate, compare, and select the most relevant ones.

By “framing” your research within a clearly defined field, you make the reader aware of the assumptions that inform your approach, showing the rationale behind your choices for later sections, like methodology and discussion . This part of your dissertation lays the foundations that will support your analysis, helping you interpret your results and make broader generalizations .

  • In literature , a scholar using postmodernist literary theory would analyze The Great Gatsby differently than a scholar using Marxist literary theory.
  • In psychology , a behaviorist approach to depression would involve different research methods and assumptions than a psychoanalytic approach.
  • In economics , wealth inequality would be explained and interpreted differently based on a classical economics approach than based on a Keynesian economics one.

To create your own theoretical framework, you can follow these three steps:

  • Identifying your key concepts
  • Evaluating and explaining relevant theories
  • Showing how your research fits into existing research

1. Identify your key concepts

The first step is to pick out the key terms from your problem statement and research questions . Concepts often have multiple definitions, so your theoretical framework should also clearly define what you mean by each term.

To investigate this problem, you have identified and plan to focus on the following problem statement, objective, and research questions:

Problem : Many online customers do not return to make subsequent purchases.

Objective : To increase the quantity of return customers.

Research question : How can the satisfaction of company X’s online customers be improved in order to increase the quantity of return customers?

2. Evaluate and explain relevant theories

By conducting a thorough literature review , you can determine how other researchers have defined these key concepts and drawn connections between them. As you write your theoretical framework, your aim is to compare and critically evaluate the approaches that different authors have taken.

After discussing different models and theories, you can establish the definitions that best fit your research and justify why. You can even combine theories from different fields to build your own unique framework if this better suits your topic.

Make sure to at least briefly mention each of the most important theories related to your key concepts. If there is a well-established theory that you don’t want to apply to your own research, explain why it isn’t suitable for your purposes.

3. Show how your research fits into existing research

Apart from summarizing and discussing existing theories, your theoretical framework should show how your project will make use of these ideas and take them a step further.

You might aim to do one or more of the following:

  • Test whether a theory holds in a specific, previously unexamined context
  • Use an existing theory as a basis for interpreting your results
  • Critique or challenge a theory
  • Combine different theories in a new or unique way

A theoretical framework can sometimes be integrated into a literature review chapter , but it can also be included as its own chapter or section in your dissertation. As a rule of thumb, if your research involves dealing with a lot of complex theories, it’s a good idea to include a separate theoretical framework chapter.

There are no fixed rules for structuring your theoretical framework, but it’s best to double-check with your department or institution to make sure they don’t have any formatting guidelines. The most important thing is to create a clear, logical structure. There are a few ways to do this:

  • Draw on your research questions, structuring each section around a question or key concept
  • Organize by theory cluster
  • Organize by date

It’s important that the information in your theoretical framework is clear for your reader. Make sure to ask a friend to read this section for you, or use a professional proofreading service .

As in all other parts of your research paper , thesis , or dissertation , make sure to properly cite your sources to avoid plagiarism .

To get a sense of what this part of your thesis or dissertation might look like, take a look at our full example .

If you want to know more about AI for academic writing, AI tools, or research bias, make sure to check out some of our other articles with explanations and examples or go directly to our tools!

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While a theoretical framework describes the theoretical underpinnings of your work based on existing research, a conceptual framework allows you to draw your own conclusions, mapping out the variables you may use in your study and the interplay between them.

A literature review and a theoretical framework are not the same thing and cannot be used interchangeably. While a theoretical framework describes the theoretical underpinnings of your work, a literature review critically evaluates existing research relating to your topic. You’ll likely need both in your dissertation .

A theoretical framework can sometimes be integrated into a  literature review chapter , but it can also be included as its own chapter or section in your dissertation . As a rule of thumb, if your research involves dealing with a lot of complex theories, it’s a good idea to include a separate theoretical framework chapter.

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Science has been a fascination since the dawn of time. It allows us to explore new dimensions and learn about the world. As such, science research paper topics are becoming increasingly popular among students. However, choosing the right one for your assignment can be daunting. There is a wide range of interesting topics in science, from studying our universe to the microscopic world. This article helps you understand science research topics in-depth, guiding you to choose a title that fits your needs and your professor's expectations. We will also provide over 250 topics to help you explore areas you are most interested in. Get ready to pick a topic to your liking or ask a paper writer for expert help.

What Are Science Research Paper Topics?

Science research paper topics require a scientific inquiry to answer. They focus on the natural world, such as the environment, climate change, energy sources, and the medical field. People may explore topics that range from basic principles to cutting-edge discoveries. Students should consider current issues and debates when choosing a scientific research topic. They need to ensure that the chosen title is within the scope of their scientific discipline and can be researched using accepted tools. Furthermore, an element of novelty to spur further discovery is a must. Ultimately, topics in science should be chosen to expand knowledge in a particular field.

Characteristics of Good Science Topics for Research Papers

Good science research topics should have the following characteristics:

  • Relevance to real-world issues: Focus on current societal issues with implications for policy or decision-making.
  • Novelty and originality: This brings new insights to the field while providing an opportunity to improve knowledge or discoveries.
  • Accessibility: Should be easily researched using available resources, including primary research materials such as data sets, surveys, interviews, or experiments.
  • Clarity of purpose and scope: Focus on a clearly defined problem or question and cover the time frame, geographical area, or other relevant parameters.
  • Feasibility: Can be completed within the time frame available to researchers.
  • Ethics: Adheres to ethical research standards, ensuring informed consent.

These characteristics should be considered while selecting good research topics for science.

How to Choose a Science Research Paper Topic?

Choosing a science research paper topic requires careful consideration, as the subjects must meet academic and personal interests. Here are some steps to consider when selecting a science related topic to research :

  • Identify a research area Examine the field of science and an area of interest.
  • Research available resources It is essential to evaluate which resources are available to you.
  • Brainstorm relevant topics Take time to brainstorm potential research paper titles and themes related to the area of science.
  • Narrow your focus Narrow your focus by considering what information is available and how feasible it would be to write an in-depth paper on the subject.
  • Analyze current research Explore existing studies as well as theories related to your topic so that the research paper can provide new insights.
  • Select a topic Finally, select one of your proposed titles that meets all the above criteria.

By following these steps and considering the characteristics discussed earlier, it is possible to select science topics for a research paper that are both interesting and achievable. However, it is also important to remember that your paper's success depends mainly on how well a chosen topic suits your skills and knowledge.

Best Scientific Research Topics List

When selecting scientific topics to research, it is crucial to ensure that your subject is interesting, relevant, and achievable. This list provides some of the best science topics students can use to develop their knowledge:

  • Artificial intelligence's role in the future of medicine.
  • Age-related diseases.
  • Genetics and gene therapy.
  • The potential of personalized medicine to reduce health care disparities in low-income populations.
  • Impact of nanotechnology on health care.
  • Developing renewable energy sources.
  • Deforestation effect on global warming.
  • Robotics in manufacturing and industrial production.
  • Social media impact on youth mental health .
  • How climate change affects biodiversity.

Interesting Science Topics

Interesting science research topics must have both academic and personal appeal. Your chosen title should excite your curiosity and encourage further exploration. Use some of the most interesting science topics or create your own ideas with our Topic Generator :

  • Gene therapy application.
  • Development of self-driving cars.
  • Dark matter and its role in the universe.
  • Extraterrestrial life forms and their environment.
  • Global warming mitigation strategies.
  • Developing technologies in renewable energy sources.
  • Possibility of life beyond Earth.
  • Impact of climate change on human health.
  • Genetic engineering advancements in plants and animals.
  • The use of robotics in healthcare.
  • Virtual reality's use for medical applications.
  • Investigation of the role of microbial biofilms in antibiotic resistance development.
  • The effects of molecular structure on drug interaction.
  • Nanotechnology's importance in medical research and treatments.
  • Technology's role in the education sector.

By researching one or more subjects discussed above, students can gain valuable insights into interesting science topics to research. In addition, doing so will also help to expand their scientific knowledge, thus aiding in academic pursuits.

Intriguing Science Research Paper Topics

When looking for intriguing science research paper topic ideas, you need to consider topics that your reader can spend time reading without reconsidering. Here are some intriguing areas:

  • Biological impacts of climate change on humans and other species.
  • Space exploration's effect on Earth's environment.
  • Researching the potential implications of artificial intelligence on our society.
  • Nanotechnology's effects on human health and the environment.
  • Latest advancements in gene therapy and genetic engineering.
  • The potential of virtual reality for medical applications.
  • Implications of robotics in manufacturing, production, and healthcare.
  • Bioinformatics' role in transforming biology.
  • Potential of renewable energy sources.
  • How self-driving cars affect people's lives.
  • Dark matter application on our universe.
  • Developing strategies to mitigate global warming effects .
  • Technology's impact on education, work, and daily life.
  • Effects of deforestation on climate change.
  • Potential benefits and implications of self-driving cars.

By researching any one of these intriguing science topics for a research paper, students can gain valuable insights into possible ideas to cover in their paper.

Cool Science Topics to Research

There are plenty of fun options for you regarding cool science research topics. Here are some interesting yet achievable areas that you can consider exploring:

  • Effects of global warming on sea levels.
  • The use of renewable energy sources such as solar and wind.
  • Potential implications of artificial intelligence on our lives.
  • How genetic engineering and gene therapy can be used in cancer treatments.
  • Latest advancements in robotics technology and its impact on the workforce.
  • Sleep's importance in brain development.
  • Exploring the possibilities of life on other planets.
  • Technology's role in transforming healthcare.
  • Cancer survivors' vulnerability to Covid-19.
  • Possibilities for sustainable urban development strategies.
  • The downside of genetic modification.
  • Strategies to reduce human-caused global warming effects.
  • The future of NASA and its possible impact on humanity.
  • Gradual change in forensic science over the years.
  • Implications of self-driving cars on transportation systems.

By researching any cool science topics, you can gain valuable knowledge and develop interesting content.

Popular Scientific Topics

Popular science topics are preferred by students due to their relative ease of research as well as interesting concepts. Here are some scientific topics for research papers:

  • Implantation of false memory and its implications.
  • Nanotechnology's use in medical and industrial applications.
  • Recent advancements in quantum computing and its potential.
  • Blockchain technology's impact on the global economy.
  • Implications of virtual reality on society.
  • Injury-related falls in the elderly population.
  • Impact of human activities on water pollution and ocean acidification.
  • Artificial intelligence implications on employment and the workforce.
  • Equipment introduced to improve AIDS treatment.
  • Space exploration's impact on our knowledge of the universe.
  • Understanding the nuances of dark matter and its implications.
  • Potential applications of robotics in production and healthcare.
  • Psychological effects of social media usage.
  • Effectiveness of asylums in treating mental problems.
  • Potential of renewable energy sources for industrial and commercial applications.

Your work will be more exciting and comprehensive by exploring any popular scientific areas listed above.

Outstanding Science Topics to Write About

When looking for science topics to research, choosing one that is interesting and researchable is essential. So here are some interesting science topic ideas:

  • Global positioning systems use for navigation and communication.
  • Impact of nanotechnology on drug delivery and medical diagnostics.
  • Implications of artificial intelligence for criminal justice systems.
  • Effects of climate change on marine ecosystems.
  • Understanding the possibilities of life on other planets and moons.
  • Applying virtual reality in education.
  • How blockchain technology can be used to prevent cyber-attacks.
  • Potential implications of 3D printing technologies on the manufacturing industry.
  • Temperature's impact on matter's chemical reaction.
  • Exploring strategies for sustainable urban development designs.
  • Understanding the implications of dark matter in space exploration efforts.
  • Technology's impact on education and work-related activities.
  • Negative impact of mining on the environment.
  • Developments geared towards converting arid areas to fertile land.

By researching these areas, you can write a persuasive work that will provide valuable information while contributing to the scientific community.

Excellent Science Topics for Research Papers

An excellent science topic for research paper provides an opportunity to explore innovative and exciting ideas. Here are some great topics for scientific research papers:

  • Understanding the implications of gene therapy and genetic engineering on human health.
  • Using big data analytics to predict natural disasters and their effects.
  • Impact of renewable energy sources on environmental conservation and sustainability.
  • Potential applications of robotics for healthcare, manufacturing, and transportation.
  • Future use of robotics in eliminating invasive species.
  • Climate change effect on global food security and nutrition outcomes.
  • Future use of artificial intelligence in research.
  • Social media usage impact interpersonal relationships and communication skills.
  • The science behind extraterrestrial life and space exploration efforts.
  • Strategies for sustainable urban planning designs to reduce environmental impacts.
  • Efforts towards eliminating weeds through technology.
  • The use of food distribution systems in preventing food waste.
  • Modern-day use of drone technology in agriculture.
  • Efforts by agricultural scientists in preparation for natural disasters.
  • The nuances of dark matter and its implications in space science research.

Science Research Papers Topics for Students

Your goal when choosing science topics for research paper should be to select a subject that is interesting, relevant, and has enough information available in the literature so you can develop an argument. An assignment for science class requires more effort than simply writing about your favorite idea. Here, we offer you multiple science topics to research, ranging from physical science to life science and beyond. Let us begin by looking into possible research topics for middle-school students.

Science Research Topics for Middle School

Students in middle school are often required to write science research papers. These middle school science research topics for papers are full of interesting ideas. They can be used in courses such as Physical Science, Biology, Chemistry, Earth Science, and Environmental Science. See a list of research areas for middle school students below.

  • How stream pollution generated from mining affects aquatic life.
  • Sun's role in weather patterns.
  • Benefits and risks of genetically modified organisms.
  • The impact of acid rain on waterways.
  • Benefits of implementing an electronic health system in healthcare facilities.
  • The use of chemical reactions to create everyday products.
  • Plate tectonics' impact on the Earth's surface.
  • Formation of different types of rocks.
  • What causes earthquakes and volcanic eruptions?
  • Impact of human activity on oceans.
  • Photosynthesis' s role in plant growth.
  • How solar winds and radiation affect atmospheric conditions.
  • Impact of human activities on ecosystems.
  • Benefits and risks associated with nuclear energy.
  • How sound travels through different mediums.

These science topics to research for middle school offer a huge starting point for your assignment on a wide range of scientific prompts. We hope that this list has provided you with some exciting ideas to write about.

Science Topics for High School

The science research topics for high school students are designed to stimulate thought and encourage you to explore the scope further. Here are 15 scientific research projects for high school students to review:

  • How nanotechnology helps fight cancer.
  • Benefits and risks of using pesticides.
  • Vaccines' use in protecting against diseases.
  • The role of humans in animal and plant evolution.
  • The different types of renewable energy.
  • Impact of fossil fuels on the environment.
  • Genetics' role in human diseases.
  • Methods for conserving natural resources.
  • Space exploration's benefit to humanity.
  • DNA's use in identifying individuals.
  • Role of microorganisms in bioremediation .
  • How artificial intelligence and machine learning are changing our lives.
  • The role of physics in robotics.
  • Use of big data and analytics in solving problems.
  • Air pollution and its impact on human health.

With careful research and writing, you can craft a paper that is both interesting as well as informative. We hope these high school science research topics will spackle your curiosity while motivating you to start writing.

Science Research Topics for College Students

When you go to college, research requires more effort and a deeper understanding of scientific ideologies. Here are 15 science topics for college students to explore:

  • The role of genetics in obesity.
  • Utilizing nanotechnology in improving drug delivery.
  • Correlation between marine geology and natural hazards.
  • Benefits of genetically engineered crops.
  • Exploring the limits of quantum computing.
  • Artificial intelligence' impact on human society.
  • Big data role in smart cities.
  • Efforts driven towards reducing food waste.
  • Smart textile technology benefits.
  • Limits to human life expectancy.
  • The Internet of Things (IoT) impacts human lives.
  • Renewable energy's role in reducing carbon emissions.
  • Blockchain technology and its use in the healthcare industry.
  • Role played by gravity in speeding rolling objects.
  • Robotics' role in manufacturing.

These topics for college challenge students to delve deeper into scientific concepts as well as explore potential applications. They present an opportunity to gain an understanding of scientific research paper topics at an advanced level while putting knowledge into practice.

Science Topics for Research Papers by Subject

Science has a wide range of subjects, and students must understand their interests before choosing a topic. Selecting science paper topics also requires engaging your professor on the ideas to be presented. Here, we delve into possible science research paper topics by subject to help you get a glimpse of the titles available. These subjects include Natural Science, Biology, Geology, Physics, Chemistry, Medical Science, Environment, and Psychology.

Natural Science Topics for a Research Paper

Natural science is a fascinating and rewarding subject to study. Students interested in exploring the natural world may be particularly excited by the chance to write assignments. Natural science topics for research papers allow you to discover areas in Physical, Chemical, And Biological Sciences. Here are 15 natural science research topics for you to consider:

  • Photosynthesis's role in plant growth.
  • Significance of soil health for plant growth.
  • Climate change's impact on marine life.
  • Microbes' role in the arctic ecosystem.
  • The impact of light pollution on wildlife.
  • Genes' role in human evolution.
  • Change in neuron structure during sleep.
  • Exploring the emergence of new species.
  • Volcanoes' role in the Earth's ecosystem.
  • Ocean acidification's impact on marine life.
  • Mass extinction events' role in shaping the natural world.
  • Effects of deforestation on animal habitats.
  • Human impact role on wildfire frequency.
  • Ozone layer depletion role in global warming.
  • Impact of pollution on marine life.

Biology Research Paper Topics for Science

Biology students have a range of biology project topics in science to choose from for their papers. Research is done to gain knowledge and understand the world around us. These Biology research paper topics can provide an opportunity for students to explore life science and its related fields. See interesting science research paper topic ideas you can choose for your biology project below:

  • Space flight companies and their role in future exploration.
  • GMO foods' impact on human health.
  • Cells and cell division: a look at the cell cycle.
  • Assessing the benefits of organic farming practices.
  • Plant growth hormones' role in agriculture.
  • The use of stem cell treatment now and in the future.
  • The future of genetically modified plans in the world economy.
  • Microorganisms' role in wastewater treatment.
  • Body systems and homeostasis: how the body maintains balance.
  • Understanding the role of soil conservation in crop yields.
  • Parasites' role in human diseases.
  • Insects' contribution to food production and preservation.
  • Causes of high survival rates of tumor cells.
  • Understanding the genetic basis for hereditary diseases.
  • Cell division's role in developmental biology.

Geology Science Research Topics

Geology is a scientific exploration of the Earth's geologic features and history, including its rocks, minerals, soils, oceans, and landforms. Researching geology scientific research ideas can be an excellent way for students to gain knowledge about geology and explore the geologic processes that shape the Earth. Scientific article topics for your consideration are:

  • Volcanic eruptions' role on the atmosphere.
  • Exploring the role of oceans in climate change.
  • Earthquakes' impact on human settlements.
  • Examining glaciers and their global impact.
  • Plate tectonics's role in shaping landscapes.
  • Investigating soil characteristics and their impact on agriculture.
  • Exploring the significance of meteorites in geology.
  • Examining the role of fossils in geological dating.
  • Investigating oceanic currents and their impact on climate change.
  • Examining human impacts on natural landscapes and ecosystems.
  • Exploring the role of groundwater in geology.
  • Examining the causes and effects of coastal erosion.
  • Investigating geological landforms and their impact on human activity.
  • Understanding the role of rocks in climate change.
  • Understanding earth's tectonic plates and their movement.

Science Research Paper Topics on Physics

Physics deals with the fundamental laws of nature. Researching physics science paper topics can be an engaging way to gain knowledge and explore the universe around us. Topics providing ideas for science research projects which may help students better know how the forces interact are provided below:

  • Quantum mechanics' role in particle physics.
  • Understanding the role of forces in motion in space-time.
  • Investigating the physics behind dark energy and dark matter.
  • Relationship between vacuum and intensity of sound.
  • The role of electromagnetism in our lives.
  • Investigating the impact of friction on the motion.
  • Exploring the physics behind sound and light.
  • Understanding nuclear energy and its impact on society.
  • Examining the role of black holes in space-time.
  • Investigating wave-particle duality in quantum mechanics.
  • Exploring the physics of light refraction and reflection.
  • Role of thermodynamics in physics.
  • Nuclear fusion's role in energy production.
  • Exploring the physics behind fluid dynamics.
  • Superconductivity's role in materials science.

Science Research Topics on Chemistry

Students interested in chemistry can explore science reports ideas through either lab-based or theoretical studies. The scientific paper topics on chemistry cover various aspects of chemistry while providing students an opportunity to develop creative ideas in the field. See these ideas below for guidance:

  • The impact of particle size on reaction rate.
  • Effects of PH on enzyme activity.
  • The products formed in different types of chemical reactions.
  • Effectiveness of catalysts in organic chemistry reactions.
  • Methods used to synthesize nanoparticles for use in drug delivery.
  • Alternative energy sources and their impact on the environment.
  • The use of polymer chemistry in developing lightweight structures.
  • Nanoparticles in pharmaceutical manufacturing processes.
  • Effectiveness of green chemistry technologies in industrial production.
  • The use of renewable resources to produce cleaner fuels.
  • The chemical composition of oceans and rivers to understand pollution.
  • Reactions of metals with different chemicals in variable environments.
  • Organic molecules break down during waste treatment.
  • The use of reagents in liquid chromatography and gas chromatography.
  • Interaction of different organic compounds with various ions.

Couldn’t spot a fitting idea? Look through more research topics for Chemistry to find the best title. 

Medical Science Research Paper Topics

Medical science research is an essential component of medical schools and universities. The topics provided below cover different areas in medical sciences and facilitate the development of innovative ideas. Here are 15 interesting scientific research paper topic ideas:

  • 3D bioprinting for tissue engineering.
  • Stem cell therapy for heart disease treatment.
  • Investigating the long-term impact of drug use on human health.
  • Role of genetics in cancer prediction and treatment.
  • Impacts of vaccines on immunization and infectious diseases .
  • Assessing the effects of mental illness on cognitive functioning across lifespan development.
  • The role of ultrasound in diagnostic imaging and treatment.
  • Prevention of bacterial diseases and hope for future development.
  • Investigating the impact of nutrition on mental well-being.
  • Ethics in using animals for medical research.
  • Use of robotics for physiotherapy in rehabilitation patients.
  • Artificial intelligence's role in clinical care delivery.
  • Impact of technology on mental health diagnosis and treatment.
  • Using natural products for antibiotic resistance against pathogens.
  • Effects of exercise on cognitive functioning and mental health.

You can choose from the above scientific topics for a research paper. Additionally, consult with your lecturer or professor to ensure your chosen idea falls within the scope of your course.

>> Read more: Medical Research Topics

Environmental Science Topics for a Research Paper

Environmental science research is a scientific inquiry that aims to understand and address environmental challenges. Environmental science report topics range from law and policy, climate change, air pollution, marine conservation, and energy production. Here are some environmental science research paper ideas:

  • Environmental impact of industrial waste on human health.
  • Possible change in environmental sustainability.
  • The impact of population growth on the environment.
  • Role of renewable energy sources in mitigating climate change impacts.
  • Air pollution affects human health in urban areas.
  • Contribution of urban green spaces to climate change mitigation.
  • Geospatial technology's role in disaster risk management.
  • Effects of genetically modified organisms on human health and ecosystems.
  • Soil sustainability practices' use for food security.
  • Water scarcity's impact on human health and the environment.
  • Interaction between the atmosphere, soil, and water for sustainable land management.
  • The role of drones in wildlife conservation and population monitoring.
  • Most recent effective methods used in controlling invasive species.
  • The use of ocean acidification for marine conservation.
  • Deforestation's role in wildlife habitats and ecosystems.

Check our list of Environmental research paper topics in case you need more ideas.

Science Research Paper Topics on Psychology

Psychology involves the study of behavior, cognition, and emotion. Research areas in psychology cover emotions, consciousness, personality, as well as social behavior. Here are some scientific topics for a research paper on psychology:

  • Attachment theory role in mental health.
  • The role of language in revealing stress responses.
  • Effects of stress on cognitive functioning.
  • Emotion regulation role in mental well-being.
  • The impact of childhood trauma on adolescent development.
  • Sleep deprivation's impact on memory and learning.
  • Impact of mindfulness and meditation on mental health.
  • Self-esteem role in social interactions.
  • Gender roles' impact on mental health outcomes.
  • The association between cognitive flexibility and problem-solving skills.
  • Group therapy's role in mental health treatment.
  • Music's impact on mood regulation and emotions.
  • Link between belief bias and cognitive performance.
  • Interpersonal relationships' role in mental well-being.
  • Factors influencing self-control and self-regulation.

Psychology research paper topics in science can provide opportunities for students to explore various mental health topics and build research skills.

Science Research Questions

Scientific research questions are designed to explore a topic's phenomenon, understand concepts and develop knowledge. Therefore, they should be specific, focused, and answerable. We have come up with some questions for science research ideas for you to check out:

  • What is the relationship between air pollution and climate change?
  • How can renewable energy sources reduce global warming?
  • How do urban green spaces help mitigate climate change?
  • What are the effects of genetically modified organisms on human health and ecosystems?
  • What is the impact of air pollution on human health in cities?
  • How can geospatial technology be used for disaster risk management?
  • What are the effects of soil sustainability practices on food security?
  • How does water scarcity affect human health and the environment?
  • What is the interaction between atmosphere, soil, and water for sustainable land management?
  • What is the relationship between lung capacity and age?
  • What impact does diet have on the flow of sex hormones in women?
  • What role do telescopes play in studying protoplanetary disks?
  • What are the effects of stress on cognitive functioning?
  • How does attachment theory influence mental health outcomes?
  • What role can industries play in conserving energy consumption?
  • How does emotion regulation affect mental well-being?
  • How does childhood trauma influence adolescent development?
  • What impact does sleep deprivation have on memory and learning?
  • What relationship exists between a hen's diet and its egg size?
  • How do gender roles influence mental health outcomes?

You can choose from the above scientific research questions or use them to generate your idea and understand scientific research projects. The possibilities are endless!

Bottom Line on Science Research Paper Topics

Science related research topics can provide great ideas for students to explore. The lists in this article give you a wide range of science topics for research papers to choose from and adapt to your specific requirements. From climate change and air pollution to mental health and gender roles, questions can be used to develop knowledge and understand scientific phenomena. With the proper research and writing skills, you can create fantastic science research topic ideas to impress your peers. So get creative and start exploring!

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FAQ About Science Research Topics

1. what are the most interesting topics in science.

The most interesting scientific topics are current and relevant. Some areas include:

  • Artificial Intelligence: AI is becoming increasingly important in our world, and its implications are vast.
  • Climate Change: Scientists study how it affects our environment and health as the planet gets warmer.
  • Space Exploration: From robotic probes to human-led missions, space exploration remains an exciting field of research.
  • Genetics: With the emergence of gene editing technology, genetic research has taken center stage in many fields of science and medicine.

Your selection of interesting topics about science will depend on your preferences.

2. Are there any funny science research topics?

Some students might prefer funny science topics for research papers, which can be interesting to explore. Some examples are:

  • How does music affect plant growth?
  • Does the color of food affect how we perceive taste?
  • Does talking to animals affect their behavior?

3. How to find easy science research topics?

Easy scientific research topics can be found by exploring scientific fields such as health and medicine, technology, biology, ecology, psychology, and sociology. Students need to choose an area that answers the 5Ws (who, what, when, where (place), why), thus allowing them to conduct comprehensive scientific research. Additionally, you must select a topic that interests you to make your research enjoyable.

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The Structure of Scientific Theories

Scientific inquiry has led to immense explanatory and technological successes, partly as a result of the pervasiveness of scientific theories. Relativity theory, evolutionary theory, and plate tectonics were, and continue to be, wildly successful families of theories within physics, biology, and geology. Other powerful theory clusters inhabit comparatively recent disciplines such as cognitive science, climate science, molecular biology, microeconomics, and Geographic Information Science (GIS). Effective scientific theories magnify understanding, help supply legitimate explanations, and assist in formulating predictions. Moving from their knowledge-producing representational functions to their interventional roles (Hacking 1983), theories are integral to building technologies used within consumer, industrial, and scientific milieus.

This entry explores the structure of scientific theories from the perspective of the Syntactic, Semantic, and Pragmatic Views. Each of these answers questions such as the following in unique ways. What is the best characterization of the composition and function of scientific theory? How is theory linked with world? Which philosophical tools can and should be employed in describing and reconstructing scientific theory? Is an understanding of practice and application necessary for a comprehension of the core structure of a scientific theory? Finally, and most generally, how are these three views ultimately related?

1.1 Syntactic, Semantic, and Pragmatic Views: The Basics

1.2 two examples: newtonian mechanics and population genetics, 2.1 theory structure per the syntactic view, 2.2 a running example: newtonian mechanics, 2.3 interpreting theory structure per the syntactic view, 2.4 taking stock: syntactic view, 3.1 theory structure per the semantic view, 3.2 a running example: newtonian mechanics, 3.3 interpreting theory structure per the semantic view, 3.4 taking stock: semantic view, 4.1 theory structure per the pragmatic view, 4.2 a running example: newtonian mechanics, 4.3 interpreting theory structure per the pragmatic view, 4.4 taking stock: pragmatic view, 5. population genetics, 6. conclusion, other internet resources, related entries, 1. introduction.

In philosophy, three families of perspectives on scientific theory are operative: the Syntactic View , the Semantic View , and the Pragmatic View. Savage distills these philosophical perspectives thus:

The syntactic view that a theory is an axiomatized collection of sentences has been challenged by the semantic view that a theory is a collection of nonlinguistic models, and both are challenged by the view that a theory is an amorphous entity consisting perhaps of sentences and models, but just as importantly of exemplars, problems, standards, skills, practices and tendencies. (Savage 1990, vii–viii)

Mormann (2007) characterizes the Syntactic and Semantic Views in similar terms, and is among the first to use the term “Pragmatic View” to capture the third view (137). The three views are baptized via a trichotomy from linguistics deriving from the work of Charles Morris, following Charles S. Peirce. In a classic exposition, the logical positivist Carnap writes:

If in an investigation explicit reference is made to the speaker, or, to put it in more general terms, to the user of a language, then we assign it to the field of pragmatics . (Whether in this case reference to designata is made or not makes no difference for this classification.) If we abstract from the user of the language and analyze only the expressions and their designata, we are in the field of semantics . And if, finally, we abstract from the designata also and analyze only the relations between the expressions, we are in (logical) syntax . The whole science of language, consisting of the three parts mentioned, is called semiotic . (1942, 9; see also Carnap 1939, 3–5, 16)

To summarize, syntax concerns grammar and abstract structures; semantics investigates meaning and representation; and pragmatics explores use. Importantly, while no view is oblivious to the syntax, semantics, or pragmatics of theory, the baptism of each is a product of how one of the three aspects of language is perceived to be dominant: theory as syntactic logical reconstruction (Syntactic View); theory as semantically meaningful mathematical modeling (Semantic View); or theory structure as complex and as closely tied to theory pragmatics, i.e., function and context (Pragmatic View). Each of these philosophical perspectives on scientific theory will be reviewed in this entry. Their relations will be briefly considered in the Conclusion.

It will be helpful to pare each perspective down to its essence. Each endorses a substantive thesis about the structure of scientific theories.

For the Syntactic View, the structure of a scientific theory is its reconstruction in terms of sentences cast in a metamathematical language. Metamathematics is the axiomatic machinery for building clear foundations of mathematics, and includes predicate logic, set theory, and model theory (e.g., Zach 2009; Hacking 2014). A central question of the Syntactic View is: in which logical language should we recast scientific theory?

Some defenders of the Semantic View keep important aspects of this reconstructive agenda, moving the metamathematical apparatus from predicate logic to set theory. Other advocates of the Semantic View insist that the structure of scientific theory is solely mathematical. They argue that we should remain at the mathematical level, rather than move up (or down) a level, into foundations of mathematics. A central question for the Semantic View is: which mathematical models are actually used in science?

Finally, for the Pragmatic View, scientific theory is internally and externally complex. Mathematical components, while often present, are neither necessary nor sufficient for characterizing the core structure of scientific theories. Theory also consists of a rich variety of nonformal components (e.g., analogies and natural kinds). Thus, the Pragmatic View argues, a proper analysis of the grammar (syntax) and meaning (semantics) of theory must pay heed to scientific theory complexity, as well as to the multifarious assumptions, purposes, values, and practices informing theory. A central question the Pragmatic View poses is: which theory components and which modes of theorizing are present in scientific theories found across a variety of disciplines?

In adopting a descriptive perspective on the structure of scientific theories, each view also deploys, at least implicitly, a prescriptive characterization of our central topic. In other words, postulating that scientific theory is \(X\) (e.g., \(X\) = a set-theoretic structure, as per Suppes 1960, 1962, 1967, 1968, 2002) also implies that what is not \(X\) (or could not be recast as \(X\)) is not (or could not possibly be) a scientific theory, and would not help us in providing scientific understanding, explanation, prediction, and intervention. For the Syntactic View, what is not (or cannot be) reconstructed axiomatically is not theoretical, while for the Semantic View, what is not (or cannot be) modeled mathematically is not theoretical. In contrast, in part due to its pluralism about what a scientific theory actually (and possibly) is, and because it interprets theory structure as distributed in practices, the Pragmatic View resists the definitional and normative terms set by the other two views. As a result, the Pragmatic View ultimately reforms the very concepts of “theory” and “theory structure.”

This encyclopedia entry will be organized as follows. After presenting this piece’s two sustained examples, immediately below, the three views are reviewed in as many substantive sections. Each section starts with a brief overview before characterizing that perspective’s account of theory structure. Newtonian mechanics is used as a running example within each section. The interpretation of theory structure—viz., how theory “hooks up” with phenomena, experiment, and the world—is also reviewed in each section. In the final section of this entry, we turn to population genetics and an analysis of the Hardy-Weinberg Principle (HWP) to compare and contrast each view. The Conclusion suggests, and remains non-committal about, three kinds of relations among the views: identity , combat , and complementarity . Theory is not a single, static entity that we are seeing from three different perspectives, as we might represent the Earth using three distinct mathematical map projections. Rather, theory itself changes as a consequence of perspective adopted.

Two examples will be used to illustrate differences between the three views: Newtonian mechanics and population genetics. While relativity theory is the preferred theory of the Syntactic View, Newtonian mechanics is more straightforward. Somewhat permissively construed, the theory of Newtonian mechanics employs the basic conceptual machinery of inertial reference frames, centers of mass, Newton’s laws of motion, etc., to describe the dynamics and kinematics of, among other phenomena, point masses acting vis-à-vis gravitational forces (e.g. the solar system) or with respect to forces involved in collisions (e.g., pool balls on a pool table; a closed container filled with gas). Newtonian mechanics is explored in each section.

Population genetics investigates the genetic composition of populations of natural and domesticated species, including the dynamics and causes of changes in gene frequencies in such populations (for overviews, see Lloyd 1994 [1988]; Gould 2002; Pigliucci and Müller 2010; Okasha 2012). Population genetics emerged as a discipline with the early 20 th century work of R.A. Fisher, Sewall Wright, and J.B.S. Haldane, who synthesized Darwinian evolutionary theory and Mendelian genetics. One important part of population genetic theory is the Hardy-Weinberg Principle. HWP is a null model mathematically stating that gene frequencies remain unchanged across generations when there is no selection, migration, random genetic drift, or other evolutionary forces acting in a given population. HWP peppers early chapters of many introductory textbooks (e.g., Crow and Kimura 1970; Hartl and Clark 1989; Bergstrom and Dugatkin 2012). We return to HWP in Section 5 and here merely state questions each view might ask about population genetics.

The Syntactic View focuses on questions regarding the highest axiomatic level of population genetics (e.g., Williams 1970, 1973; Van Valen 1976; Lewis 1980; Tuomi 1981, 1992). Examples of such queries are:

  • What would be the most convenient metamathematical axiomatization of evolutionary processes (e.g., natural selection, drift, migration, speciation, competition)? In which formal language(s) would and could such axiomatizations be articulated (e.g., first-order predicate logic, set theory, and category theory)?
  • Which single grammars could contain a variety of deep evolutionary principles and concepts, such as HWP, “heritability,” and “competitive exclusion”?
  • Which formal and methodological tools would permit a smooth flow from the metamathematical axiomatization to the mathematical theory of population genetics?

Investigations of the axiomatized rational reconstruction of theory shed light on the power and promises, and weaknesses and incompleteness, of the highest-level theoretical edifice of population genetics.

Secondly, the Semantic View primarily examines questions regarding the mathematical structure of population genetics (Lewontin 1974, Beatty 1981; López Beltrán 1987; Thompson 1989, 2007; Lloyd 1994 [1988]). Very generally, this exploration involves the following questions:

  • What is the form and content of the directly presented class of mathematical models of evolutionary theory (e.g., HWP)? How could and should we organize the cluster of mathematical models (sensu Levins 1966) of population genetics?
  • Which additional models (e.g., diagrammatic, narrative, scale) might be used to enrich our understanding of evolutionary theory?
  • What are the relations among theoretical mathematical models, data models, and experimental models? How does theory explain and shape data? How do the data constrain and confirm theory?

The main subject of investigation is mathematical structure, rather than metamathematics or even alternative model types or modeling methods.

Finally, the Pragmatic View asks about the internal complexity of population genetic theory, as well as about the development and context of population genetics. In so doing, it inquires into how purposes and values have influenced the theoretical structure of evolutionary theory, selecting and shaping current population genetics from a wide variety of possible alternative theoretical articulations. The following questions about the structure of population genetic theory might be here addressed:

  • What role did R.A. Fisher’s interest in animal husbandry, and his tenure at Rothamsted Experimental Station, play in shaping his influential methodologies of Analysis of Variance (ANOVA) and experimental design involving randomization, blocking, and factorial designs?
  • How did the development of computers and computational practices, statistical techniques, and the molecularization of genetics, shape theory and theorizing in population genetics, especially from the 1980s to today?
  • How might normative context surrounding the concept of “race” impact the way concepts such as “heritability” and “lineage,” or principles such as HWP, are deployed in population genetics?

As when studying an organism, the structure of theory cannot be understood independently of its history and function.

2. The Syntactic View

According to the Syntactic View, which emerged mainly out of work of the Vienna Circle and Logical Empiricism (see Coffa 1991; Friedman 1999; Creath 2014; Uebel 2014), philosophy most generally practiced is, and should be, the study of the logic of natural science, or Wissenschaftslogik (Carnap 1937, 1966; Hempel 1966). Robust and clear logical languages allow us to axiomatically reconstruct theories, which—by the Syntacticists’ definition—are sets of sentences in a given logical domain language (e.g., Campbell 1920, 122; Hempel 1958, 46; cf. Carnap 1967 [1928], §156, “Theses about the Constructional System”). Domain languages include “the language of physics, the language of anthropology” (Carnap 1966, 58).

This view has been variously baptized as the Received View (Putnam 1962; Hempel 1970), the Syntactic Approach (van Fraassen 1970, 1989), the Syntactic View (Wessels 1976), the Standard Conception (Hempel 1970), the Orthodox View (Feigl 1970), the Statement View (Moulines 1976, 2002; Stegmüller 1976), the Axiomatic Approach (van Fraassen 1989), and the Once Received View (Craver 2002). For historical reasons, and because of the linguistic trichotomy discussed above, the “Syntactic View” shall be the name of choice in this entry.

Some conceptual taxonomy is required in order to understand the logical framework of the structure of scientific theories for the Syntactic View. We shall distinguish terms , sentences , and languages (see Table 1).

2.1.1 Terms

Building upwards from the bottom, let us start with the three kinds of terms or vocabularies contained in a scientific language: theoretical, logical, and observational. Examples of theoretical terms are “molecule,” “atom,” “proton,” and “protein,” and perhaps even macro-level objects and properties such as “proletariat” and “aggregate demand.” Theoretical terms or concepts can be classificatory (e.g., “cat” or “proton”), comparative (e.g., “warmer”), or quantitative (e.g., “temperature”) (Hempel 1952; Carnap 1966, Chapter 5). Moreover, theoretical terms are “theoretical constructs” introduced “jointly” as a “theoretical system” (Hempel 1952, 32). Logical terms include quantifiers (e.g., \(\forall, \exists\)) and connectives (e.g., \(\wedge, \rightarrow\)). Predicates such as “hard,” “blue,” and “hot,” and relations such as “to the left of” and “smoother than,” are observational terms.

2.1.2 Sentences

Terms can be strung together into three kinds of sentences: theoretical, correspondence, and observational. \(T_S\) is the set of theoretical sentences that are the axioms, theorems, and laws of the theory. Theoretical sentences include the laws of Newtonian mechanics and of the Kinetic Theory of Gases, all suitably axiomatized (e.g., Carnap 1966; Hempel 1966). Primitive theoretical sentences (e.g., axioms) can be distinguished from derivative theoretical sentences (e.g., theorems; see Reichenbach 1969 [1924]; Hempel 1958; Feigl 1970). \(C_S\) is the set of correspondence sentences tying theoretical sentences to observable phenomena or “to a ‘piece of reality’” (Reichenbach 1969 [1924], 8; cf. Einstein 1934, 1936 [1936], 351). To simplify, they provide the theoretical syntax with an interpretation and an application, i.e., a semantics. Suitably axiomatized version of the following sentences provide semantics to Boyle’s law, \(PV = nRT\): “\(V\) in Boyle’s law is equivalent to the measurable volume \(xyz\) of a physical container such as a glass cube that is \(x\), \(y\), and \(z\) centimeters in length, width, and height, and in which the gas measured is contained” and “\(T\) in Boyle’s law is equivalent to the temperature indicated on a reliable thermometer or other relevant measuring device properly calibrated, attached to the physical system, and read.” Carnap (1987 [1932], 466) presents two examples of observational sentences, \(O_S\): “Here (in a laboratory on the surface of the earth) is a pendulum of such and such a kind,” and “the length of the pendulum is 245.3 cm.” Importantly, theoretical sentences can only contain theoretical and logical terms; correspondence sentences involve all three kinds of terms; and observational sentences comprise only logical and observational terms.

2.1.3 Languages

The total domain language of science consists of two languages: the theoretical language, \(L_T\), and the observational language, \(L_O\) (e.g., Hempel 1966, Chapter 6; Carnap 1966, Chapter 23; the index entry for “Language,” of Feigl, Scriven, and Maxwell 1958, 548 has three subheadings: “observation,” “theoretical,” and “ordinary”). The theoretical language includes theoretical vocabulary, while the observational language involves observational terms. Both languages contain logical terms. Finally, the theoretical language includes, and is constrained by, the logical calculus, Calc , of the axiomatic system adopted (e.g., Hempel 1958, 46; Suppe 1977, 50-53). This calculus specifies sentence grammaticality as well as appropriate deductive and non-ampliative inference rules (e.g., modus ponens) pertinent to, especially, theoretical sentences. Calc can itself be written in theoretical sentences.

2.1.4 Theory Structure, in General

Table 1 summarizes the Syntactic View’s account of theory structure:

The salient divide is between theory and observation. Building on Table 1, there are three different levels of scientific knowledge, according to the Syntactic View:

\(\{T_S\} =\) The uninterpreted syntactic system of the scientific theory. \(\{T_S, C_S\} =\) The scientific theory structure of a particular domain (e.g., physics, anthropology). \(\{T_S,C_S,O_S\} =\) All of the science of a particular domain.

Scientific theory is thus taken to be a syntactically formulated set of theoretical sentences (axioms, theorems, and laws) together with their interpretation via correspondence sentences. As we have seen, theoretical sentences and correspondence sentences are cleanly distinct, even if both are included in the structure of a scientific theory.

Open questions remain. Is the observation language a sub-language of the theoretical language, or are they both parts of a fuller language including all the vocabulary? Can the theoretical vocabulary or language be eliminated in favor of a purely observational vocabulary or language? Are there other ways of carving up kinds of languages? First, a “dialectical opposition” between “logic and experience,” “form and content,” “constitutive principles and empirical laws,” and “‘from above’… [and] ‘from below’” pervades the work of the syntacticists (Friedman 1999, 34, 63). Whether syntacticists believe that a synthesis or unification of this general opposition between the theoretical (i.e., logic, form) and the observational (i.e., experience, content) is desirable remains a topic of ongoing discussion. Regarding the second question, Hempel 1958 deflates what he calls “the theoretician’s dilemma”—i.e., the putative reduction without remainder of theoretical concepts and sentences to observational concepts and sentences. Finally, other language divisions are possible, as Carnap 1937 argues (see Friedman 1999, Chapter 7). Returning to the main thread of this section, the distinction toolkit of theoretical and observational terms, sentences, and languages (Table 1) permit the syntacticists to render theoretical structure sharply, thereby aiming at the reconstructive “logic of science” ( Wissenschafstlogik ) that they so desire.

Reichenbach 1969 [1924] stands as a canonical attempt by a central developer of the Syntactic View of axiomatizing a physical theory, viz., relativity theory (cf. Friedman 1983, 1999; see also Reichenbach 1965 [1920]). For the purposes of this encyclopedia entry, it is preferable to turn to another syntactic axiomatization effort. In axiomatizing Newtonian mechanics, the mid-20 th century mathematical logician Hans Hermes spent significant energy defining the concept of mass (Hermes 1938, 1959; Jammer 1961). More precisely, he defines the theoretical concept of “mass ratio” of two particles colliding inelastically in an inertial reference frame \(S\). Here is his full definition of mass ratio (1959, 287):

One paraphrase of this definition is, “‘the mass of \(x\) is α times that of \(x_0\)’ is equivalent to ‘there exists a system \(S\), an instant \(t\), momentary mass points \(y\) and \(y_0\), and initial velocities \(v\) and \(v_0\), such that \(y\) and \(y_0\) are genidentical, respectively, with \(x\) and \(x_0\); the joined mass points move with a velocity of 0 with respect to frame \(S\) immediately upon colliding at time \(t\); and \(y\) and \(y_0\) have determinate velocities \(v\) and \(v_0\) before the collision in the ratio α, which could also be 1 if \(x\) and \(x_0\) are themselves genidentical.’” Hermes employs the notion of “genidentical” to describe the relation between two temporal sections of a given particle’s world line (Jammer 1961, 113). Set aside the worry that two distinct particles cannot be genidentical per Hermes’ definition, though they can have identical properties. In short, this definition is syntactically complete and is written in first-order predicate logic, as are the other axioms and definitions in Hermes (1938, 1959). Correspondence rules connecting a postulated mass \(x\) with an actual mass were not articulated by Hermes.

The link between theory structure and the world, under the Syntactic View, is contained in the theory itself: \(C_S\), the set of correspondence rules. The term “correspondence rules” (Margenau 1950; Nagel 1961, 97–105; Carnap 1966, Chapter 24) has a variety of near-synonyms:

  • Dictionary (Campbell 1920)
  • Operational rules (Bridgman 1927)
  • Coordinative definitions (Reichenbach 1969 [1924], 1938)
  • Reduction sentences (Carnap 1936/1937; Hempel 1952)
  • Correspondence postulates (Carnap 1963)
  • Bridge principles (Hempel 1966; Kitcher 1984)
  • Reduction functions (Schaffner 1969, 1976)
  • Bridge laws (Sarkar 1998)

Important differences among these terms cannot be mapped out here. However, in order to better understand correspondence rules, two of their functions will be considered: (i) theory interpretation (Carnap, Hempel) and (ii) theory reduction (Nagel, Schaffner). The dominant perspective on correspondence rules is that they interpret theoretical terms. Unlike “mathematical theories,” the axiomatic system of physics “cannot have… a splendid isolation from the world” (Carnap 1966, 237). Instead, scientific theories require observational interpretation through correspondence rules. Even so, surplus meaning always remains in the theoretical structure (Hempel 1958, 87; Carnap 1966). Second, correspondence rules are seen as necessary for inter-theoretic reduction (van Riel and Van Gulick 2014). For instance, they connect observation terms such as “temperature” in phenomenological thermodynamics (the reduced theory) to theoretical concepts such as “mean kinetic energy” in statistical mechanics (the reducing theory). Correspondence rules unleash the reducing theory’s epistemic power. Notably, Nagel (1961, Chapter 11; 1979) and Schaffner (1969, 1976, 1993) allow for multiple kinds of correspondence rules, between terms of either vocabulary, in the reducing and the reduced theory (cf. Callender 1999; Winther 2009; Dizadji-Bahmani, Frigg, and Hartmann 2010). Correspondence rules are a core part of the structure of scientific theories and serve as glue between theory and observation.

Finally, while they are not part of the theory structure, and although we saw some examples above, observation sentences are worth briefly reviewing. Correspondence rules attach to the content of observational sentences. Observational sentences were analyzed as (i) protocol sentences or Protokollsätze (e.g., Schlick 1934; Carnap 1987 [1932], 1937, cf. 1963; Neurath 1983 [1932]), and as (ii) experimental laws (e.g., Campbell 1920; Nagel 1961; Carnap 1966; cf. Duhem 1954 [1906]). Although constrained by Calc , the grammar of these sentences is determined primarily by the order of nature, as it were. In general, syntacticists do not consider methods of data acquisition, experiment, and measurement to be philosophically interesting. In contrast, the confirmation relation between (collected) data and theory, especially as developed in inductive logic (e.g., Reichenbach 1938, 1978; Carnap 1962 [1950], 1952), as well as questions about the conventionality, grammaticality, foundationalism, atomism, and content of sense-data and synthetic statements, are considered philosophically important (e.g., Carnap 1987 [1932], 1937, 1966; Neurath 1983 [1932]; Reichenbach 1951; Schlick 1925 [1918], 1934; for contemporary commentary, see, e.g., Creath 1987, 2014; Rutte 1991; Friedman 1999).

To summarize, the Syntactic View holds that there are three kinds of terms or vocabularies: logical, theoretical, and observational; three kinds of sentences: \(T_S\), \(C_S\), and \(O_S\); and two languages: \(L_T\) and \(L_O\). Moreover, the structure of scientific theories could be analyzed using the logical tools of metamathematics. The goal is to reconstruct the logic of science, viz. to articulate an axiomatic system.

Interestingly, this perspective has able and active defenders today, who discuss constitutive and axiomatized principles of the historical “relativized a priori” (Friedman 2001, cf. 2013), argue that “the semantic view, if plausible, is syntactic” (Halvorson 2013), and explore “logicism” for, and in, the philosophy of science (Demopulous 2003, 2013; van Benthem 2012). Furthermore, for purposes of the syntactic reconstruction of scientific theories, some continue espousing—or perhaps plea for the resurrection of—predicate logic (e.g., Lutz 2012, 2014), while other contemporary syntacticists (e.g., Halvorson 2012, 2013, 2019) endorse more recently developed metamathematical and mathematical equipment, such as category theory, which “turns out to be a kind of universal mathematical language like set theory” (Awodey 2006, 2; see Eilenberg and MacLane 1945). Importantly, Halvorson (2019) urges that interlocutors adopt “structured” rather than “flat” views of theories. For the case of the syntactic view this would mean that rather than accept the usual formulation that a theory is a set of sentences, “… [we] might say that a theory consists of both sentences and inferential relations between those sentences” (Halvorson 2019, 277–8). Classical syntacticists such as Rudolf Carnap (Friedman 1999, 2011; Carus 2007; Blatti and Lapointe 2016; Koellner ms. in Other Internet Resources) and Joseph Henry Woodger (Nicholson and Gawne 2014) have recently received increasing attention.

3. The Semantic View

An overarching theme of the Semantic View is that analyzing theory structure requires employing mathematical tools rather than predicate logic. After all, defining scientific concepts within a specific formal language makes any axiomatizing effort dependent on the choice, nature, and idiosyncrasies of that narrowly-defined language. For instance, Suppes understands first-order predicate logic, with its “linguistic” rather than “set-theoretical” entities, as “utterly impractical” for the formalization of “theories with more complicated structures like probability theory” (Suppes 1957, 232, 248–9; cf. Suppes 2002). Van Fraassen, another influential defender of the Semantic View, believes that the logical apparatus of the Syntactic View “had moved us mille milles de toute habitation scientifique , isolated in our own abstract dreams” (van Fraassen 1989, 225). Indeed, what would the appropriate logical language for specific mathematical structures be, especially when such structures could be reconstructed in a variety of formal languages? Why should we imprison mathematics and mathematical scientific theory in syntactically defined language(s) when we could, instead, directly investigate the mathematical objects, relations, and functions of scientific theory?

Consistent with the combat strategy (discussed in the Conclusion), here is a list of grievances against the Syntactic View discussed at length in the work of some semanticists.

  • First-Order Predicate Logic Objection . Theoretical structure is intrinsically and invariably tied to the specific choice of a language, \(L_T\), expressed in first-order predicate logic. This places heavy explanatory and representational responsibility on relatively inflexible and limited languages.
  • Theory Individuation Objection . Since theories are individuated by their linguistic formulations, every change in high-level syntactic formulations will bring forth a distinct theory. This produces a reductio: if \(T_1 = p \rightarrow q\) and \(T_2 = \neg p \vee q\) then \(T_1\) and \(T_2\), though logically equivalent, have different syntactic formulations and would be distinct theories.
  • Theoretical/Observational Languages Objection . Drawing the theoretical/observational distinction in terms of language is inappropriate, as observability pertains to entities rather than to concepts.
  • Unintended Models Objection . There is no clear way of distinguishing between intended and unintended models for syntactically characterized theories (e.g., the Löwenheim-Skolem theorem, Bays 2014).
  • Confused Correspondence Rules Objection . Correspondence rules are a confused medley of direct meaning relationships between terms and world, means of inter-theoretic reduction, causal relationship claims, and manners of theoretical concept testing.
  • Trivially True yet Non-Useful Objection . Presenting scientific theory in a limited axiomatic system, while clearly syntactically correct, is neither useful nor honest, since scientific theories are mathematical structures.
  • Practice and History Ignored Objection . Syntactic approaches do not pay sufficient attention to the actual practice and history of scientific theorizing and experimenting.

What, then, does the Semantic View propose to put in the Syntactic View’s place?

Even a minimal description of the Semantic View must acknowledge two distinct strategies of characterizing and comprehending theory structure: the state-space and the set-/model-theoretic approaches.

3.1.1 The State-Space Approach

The state-space approach emphasizes the mathematical models of actual science, and draws a clear line between mathematics and metamathematics. The structure of a scientific theory is identified with the “class,” “family” or “cluster” of mathematical models constituting it, rather than with any metamathematical axioms “yoked to a particular syntax” (van Fraassen 1989, 366). Under this analysis, “the correct tool for philosophy of science is mathematics, not metamathematics”—this is Suppes’ slogan, per van Fraassen (1989, 221; 1980, 65). In particular, a state space or phase space is an \(N\)-dimensional space, where each of the relevant variables of a theory correspond to a single dimension and each point in that space represents a possible state of a real system. An actual, real system can take on, and change, states according to different kinds of laws, viz., laws of succession determining possible trajectories through that space (e.g., Newtonian kinematic laws); laws of co-existence specifying the permitted regions of the total space (e.g., Boyle’s law); and laws of interaction combining multiple laws of succession or co-existence, or both (e.g., population genetic models combining laws of succession for selection and genetic drift, Wright 1969; Lloyd 1994 [1988]; Rice 2004; Clatterbuck, Sober, and Lewontin 2013). Different models of a given theory will share some dimensions of their state space while differing in others. Such models will also partially overlap in laws (for further discussion of state spaces, laws, and models pertinent to the Semantic View, see Suppe 1977, 224–8; Lloyd 1994, Chapter 2; Nolte 2010; Weisberg 2013, 26–9).

Historically, the state-space approach emerged from work by Evert Beth, John von Neumann, and Hermann Weyl, and has important parallels with Przełęcki (1969) and Dalla Chiara Scabia and Toraldo di Francia (1973) (on the history of the approach see: Suppe 1977; van Fraassen 1980, 65–67; Lorenzano 2013; advocates of the approach include: Beatty 1981; Giere 1988, 2004; Giere, Bickle, and Mauldin 2006; Lloyd 1983, 1994 [1988], 2013 In Press; Suppe 1977, 1989; Thompson, 1989, 2007; van Fraassen 1980, 1989, 2008; for alternative early analyses of models see, e.g., Braithwaite 1962; Hesse 1966, 1967). Interestingly, van Fraassen (1967, 1970) provides a potential reconstruction of state spaces via an analysis of “semi-interpreted languages.” Weisberg (2013), building on many insights from Giere’s work, presents a broad view of modeling that includes mathematical structures that are “trajectories in state spaces” (29), but also permits concrete objects and computational structures such as algorithms to be deemed models. Lorenzano (2013) calls Giere’s (and, by extension, Weisberg’s and even Godfrey-Smith’s 2006) approach “model-based,” separating it out from the state-space approach. A more fine-grained classification of the state-space approach is desirable, particularly if we wish to understand important lessons stemming from the Pragmatic View of Theories, as we shall see below.

As an example of a state-space analysis of modeling, consider a capsule traveling in outer space. An empirically and dynamically adequate mathematical model of the capsule’s behavior would capture the position of the capsule (i.e., three dimensions of the formal state space), as well as the velocity and acceleration vectors for each of the three standard spatial dimensions (i.e., six more dimensions in the formal state space). If the mass were unknown or permitted to vary, we would have to add one more dimension. Possible and actual trajectories of our capsule, with known mass, within this abstract 9-dimensional state space could be inferred via Newtonian dynamical laws of motion (example in Lewontin 1974, 6–8; consult Suppe 1989, 4). Importantly, under the state-space approach, the interesting philosophical work of characterizing theory structure (e.g., as classes of models), theory meaning (e.g., data models mapped to theoretical models), and theory function (e.g., explaining and predicting) happens at the level of mathematical models.

3.1.2 The Set-/Model-Theoretic Approach

Lurking in the background of the state-space conception is the fact that mathematics actually includes set theory and model theory—i.e., mathematical logic. Indeed, according to some interlocutors, “metamathematics is part of mathematics” (Halvorson 2012, 204). Historically, a set-/model-theoretic approach emerged from Tarski’s work and was extensively articulated by Suppes and his associates (van Fraassen 1980, 67). Set theory is a general language for formalizing mathematical structures as collections—i.e., sets—of abstract objects (which can themselves be relations or functions; see Krivine 2013 [1971]). Model theory investigates the relations between, on the one hand, the formal axioms, theorems, and laws of a particular theory and, on the other hand, the mathematical structures—the models—that provide an interpretation of that theory, or put differently, that make the theory’s axioms, theorems, and laws true (Hodges 1997, Chapter 2; Jones 2005). Interestingly, model theory often uses set theory (e.g., Marker 2002); set theory can, in turn, be extended to link axiomatic theories and semantic models via “set-theoretical predicates” (e.g., Suppes 1957, 2002). Finally, there are certain hybrids of these two branches of mathematical logic, including “partial structures” (e.g., da Costa and French 1990, 2003; Bueno 1997; French 2017; French and Ladyman 1999, 2003; Vickers 2009; Bueno, French, and Ladyman 2012). Lorenzano (2013) provides a more complex taxonomy of the intellectual landscape of the Semantic View, including a discussion of Structuralism, a kind of set-/model-theoretic perspective. Structuralism involves theses about “theory-nets,” theory-relative theoretical vs. non-theoretical terms, a diversity of intra- and inter-theoretic laws with different degrees of generality, a typology of inter-theoretic relations, and a rich account of correspondence rules in scientific practice (see Moulines 2002; Pereda 2013; Schmidt 2014; Ladyman 2014). On the whole, the set-/model-theoretic approach of the Semantic View insists on the inseparability of metamathematics and mathematics. In preferring to characterize a theory axiomatically in terms of its intension rather than its extension, it shares the Syntactic View’s aims of reconstructive axiomatization (e.g., Sneed 1979; Stegmüller 1979; Frigg and Votsis 2011; Halvorson 2013, 2019; Lutz 2012, 2014, 2017).

An example will help motivate the relation between theory and model. Two qualifications are required: (i) we return to a more standard set-/model-theoretic illustration below, viz., McKinsey, Sugar, and Suppes’ (1953) axiomatization of particle mechanics, and (ii) this motivational example is not from the heartland of model theory (see Hodges 2013). Following van Fraassen’s intuitive case of “seven-point geometry” (1980, 41–44; 1989, 218–220), also known as “the Fano plane” we see how a particular geometric figure, the model , interprets and makes true a set of axioms and theorems, the theory . In topology and geometry there is rich background theory regarding how to close Euclidean planes and spaces to make finite geometries by, for instance, eliminating parallel lines. Consider the axioms of a projective plane:

  • For any two points, exactly one line lies on both.
  • For any two lines, exactly one point lies on both.
  • There exists a set of four points such that no line has more than two of them.

A figure of a geometric model that makes this theory true is:

Geometric figure including triangle ACE with interior circle BDF and center point G. Point B is on line segment AC, D is on CE, and F is on AE. G is the center of the circle. Point G is on line segments AD, BE, and CF.

This is the smallest geometrical model satisfying the three axioms of the projective plane theory. Indeed, this example fits van Fraassen’s succinct characterization of the theory-model relation:

A model is called a model of a theory exactly if the theory is entirely true if considered with respect to this model alone. (Figuratively: the theory would be true if this model was the whole world.) (1989, 218)

That is, if the entire universe consisted solely of these seven points and seven lines, the projective plane theory would be true. Of course, our universe is bigger. Because Euclidean geometry includes parallel lines, the Fano plane is not a model of Euclidean geometry. Even so, by drawing the plane, we have shown it to be isomorphic to parts of the Euclidean plane. In other words, the Fano plane has been embedded in a Euclidean plane. Below we return to the concepts of embedding and isomorphism, but this example shall suffice for now to indicate how a geometric model can provide a semantics for the axioms of a theory.

In short, for the Semantic View the structure of a scientific theory is its class of mathematical models. According to some advocates of this view, the family of models can itself be axiomatized, with those very models (or other models) serving as axiom truth-makers.

Returning to our running example, consider Suppes’ 1957 model-theoretic articulation of particle mechanics, which builds on his 1953 article with J.C.C. McKinsey and A.C. Sugar. Under this analysis, there is a domain of set-theoretic objects of the form \(\{ P, T, s, m, f, g \}\), where \(P\) and \(T\) are themselves sets, \(s\) and \(g\) are binary functions, \(m\) is a unary and \(f\) a ternary function. \(P\) is the set of particles; \(T\) is a set of real numbers measuring elapsed times; \(s(p, t)\) is the position of particle \(p\) at time \(t\); \(m(p)\) is the mass of particle \(p\); \(f(p, q, t)\) is the force particle \(q\) exerts on \(p\) at time \(t\); and \(g(p, t)\) is the total resultant force (by all other particles) on \(p\) at time \(t\). Suppes and his collaborators defined seven axioms—three kinematical and four dynamical—characterizing Newtonian particle mechanics (see also Simon 1954, 1970). Such axioms include Newton’s third law reconstructed in set-theoretic formulation thus (Suppes 1957, 294):

Importantly, the set-theoretic objects are found in more than one of the axioms of the theory, and Newton’s calculus is reconstructed in a novel, set-theoretic form. Set-theoretic predicates such as “is a binary relation” and “is a function” are also involved in axiomatizing particle mechanics (Suppes 1957, 249). Once these axioms are made explicit, their models can be specified and these can, in turn, be applied to actual systems, thereby providing a semantics for the axioms (e.g., as described in Section 3.3.1 below). A particular system satisfying these seven axioms is a particle mechanics system. (For an example of Newtonian mechanics from the state-space approach, recall the space capsule of Section 3.1.1.)

How is the theory structure, described in Section 3.1, applied to empirical phenomena? How do we connect theory and data via observation and experimental and measuring techniques? The Semantic View distinguishes theory individuation from both theory-phenomena and theory-world relations. Three types of analysis of theory interpretation are worth investigating: (i) a hierarchy of models (e.g., Suppes; Suppe), (ii) similarity (e.g., Giere; Weisberg), and (iii) isomorphism (e.g., van Fraassen; French and Ladyman).

3.3.1 A Hierarchy of Models

One way of analyzing theory structure interpretation is through a series of models falling under the highest-level axiomatizations. This series has been called “a hierarchy of models,” though it need not be considered a nested hierarchy. These models include models of theory, models of experiment, and models of data (Suppes 1962, 2002). Here is a summary of important parts of the hierarchy (Suppes 1962, Table 1, 259; cf. Giere 2010, Figure 1, 270):

  • Axioms of Theory . Axioms define set-theoretic predicates, and constitute the core structure of scientific theories, as reviewed in Section 3.1.2.
  • Models of Theory. “Representation Theorems,” permit us “to discover if an interesting subset of models for the theory may be found such that any model for the theory is isomorphic to some member of this subset” (Suppes 1957, 263). Representation theorem methodology can be extended (i) down the hierarchy, both to models of experiment and models of data, and (ii) from isomorphism to homomorphism (Suppes 2002, p. 57 ff.; Suppe 2000; Cartwright 2008).
  • Models of Experiment . Criteria of experimental design motivate choices for how to set up and analyze experiments. There are complex mappings between models of experiment thus specified, and (i) models of theory, (ii) theories of measurement, and (iii) models of data.
  • Models of Data . In building models of data, phenomena are organized with respect to statistical goodness-of-fit tests and parameter estimation, in the context of models of theory. Choices about which parameters to represent must be made.

The temptation to place phenomena at the bottom of the hierarchy must be resisted because phenomena permeate all levels. Indeed, the “class of phenomena” pertinent to a scientific theory is its “intended scope” (Suppe 1977, 223; Weisberg 2013, 40). Furthermore, this temptation raises fundamental questions about scientific representation: “there is the more profound issue of the relationship between the lower most representation in the hierarchy—the data model perhaps—and reality itself, but of course this is hardly something that the semantic approach alone can be expected to address” (French and Ladyman 1999, 113; cf. van Fraassen 2008, 257–258, “The ‘link’ to reality”). Borrowing from David Chalmers, the “hard problem” of philosophy of science remains connecting abstract structures to concrete phenomena, data, and world.

3.3.2 Similarity

The similarity analysis of theory interpretation combines semantic and pragmatic dimensions (Giere 1988, 2004, 2010; Giere, Bickle, and Mauldin 2006; Weisberg 2013). According to Giere, interpretation is mediated by theoretical hypotheses positing representational relations between a model and relevant parts of the world. Such relations may be stated as follows:

Here \(S\) is a scientist, research group or community, \(W\) is a part of the world, and \(X\) is, broadly speaking, any one of a variety of models (Giere 2004, 743, 747, 2010). Model-world similarity judgments are conventional and intentional:

Note that I am not saying that the model itself represents an aspect of the world because it is similar to that aspect. …Anything is similar to anything else in countless respects, but not anything represents anything else. It is not the model that is doing the representing; it is the scientist using the model who is doing the representing. (2004, 747)

Relatedly, Weisberg (2013) draws upon Tversky (1977) to develop a similarity metric for model interpretation (equation 8.10, 148). This metric combines (i) model-target semantics (90–97), and (ii) the pragmatics of “context, conceptualization of the target, and the theoretical goals of the scientist” (149). Giere and Weisberg thus endorse an abundance of adequate mapping relations between a given model and the world. From this diversity, scientists and scientific communities must select particularly useful similarity relationships for contextual modeling purposes. Because of semantic pluralism and irreducible intentionality, this similarity analysis of theory interpretation cannot be accommodated within a hierarchy of models approach, interpreted as a neat model nesting based on pre-given semantic relations among models at different levels.

3.3.3 Isomorphism

The term “isomorphism” is a composite of the Greek words for “equal” and “shape” or “form.” Indeed, in mathematics, isomorphism is a perfect one-to-one, bijective mapping between two structures or sets. Figure (2) literally and figuratively captures the term:

Script writing of isomorphism with mirror image underneath

Especially in set theory, category theory, algebra, and topology, there are various kinds of “-morphisms,” viz., of mapping relations between two structures or models. Figure (3) indicates five different kinds of homomorphism, arranged in a Venn diagram.

Venn diagram with outer circle Hom and 3 intersecting interior circles: Mon, Epi, and End. The intersection of all 3 is Aut and the intersection of Mon and Epi is Iso.

Although philosophers have focused on isomorphism, other morphisms such as monomorphism (i.e., an injective homomorphism where some elements in the co-domain remain unmapped from the domain) might also be interesting to investigate, especially for embedding data (i.e., the domain) into rich theoretical structures (i.e., the co-domain). To complete the visualization above, an epimorphism is a surjective homomorphism, and an endomorphism is a mapping from a structure to itself, although it need not be a symmetrical—i.e., invertible—mapping, which would be an automorph.

Perhaps the most avid supporter of isomorphism and embedding as the way to understand theory interpretation is van Fraassen. In a nutshell, if we distinguish (i) theoretical models, (ii) “empirical substructures” (van Fraassen 1980, 64, 1989, 227; alternatively: “surface models” 2008, 168), and (iii) “observable phenomena” (1989, 227, 2008, 168), then, van Fraassen argues, theory interpretation is a relation of isomorphism between observable phenomena and empirical substructures, which are themselves isomorphic with one or more theoretical models. Moreover, if a relation of isomorphism holds between \(X\) and a richer \(Y\), we say that we have embedded \(X\) in \(Y\). For instance, with respect to the seven-point geometry above (Figure 1), van Fraassen contends that isomorphism gives embeddability, and that the relation of isomorphism “is important because it is also the exact relation a phenomenon bears to some model or theory, if that theory is empirically adequate” (1989, 219–20; this kind of statement seems to be simultaneously descriptive and prescriptive about scientific representation, see Section 1.1 above). In The Scientific Image he is even clearer about fleshing out the empirical adequacy of a theory (with its theoretical models) in terms of isomorphism between “appearances” (i.e., “the structures which can be described in experimental and measurement reports,” 1980, 64, italics removed) and empirical substructures. Speaking metaphorically,

the phenomena are, from a theoretical point of view, small, arbitrary, and chaotic—even nasty, brutish, and short…—but can be understood as embeddable in beautifully simple but much larger mathematical models. (2008, 247; see also van Fraassen 1981, 666 and 1989, 230)

Interestingly, and as a defender of an identity strategy (see Conclusion), Friedman also appeals to embedding and subsumption relations between theory and phenomena in his analyses of theory interpretation (Friedman 1981, 1983). Bueno, da Costa, French, and Ladyman also employ embedding and (partial) isomorphism in the empirical interpretation of partial structures (Bueno 1997; Bueno, French, and Ladyman 2012; da Costa and French 1990, 2003; French 2017; French and Ladyman 1997, 1999, 2003; Ladyman 2004). Suárez discusses complexities in van Fraassen’s analyses of scientific representation and theory interpretation (Suárez 1999, 2011). On the one hand, representation is structural identity between the theoretical and the empirical. On the other hand, “There is no representation except in the sense that some things are used, made, or taken, to represent some things as thus or so” (van Fraassen 2008, 23, italics removed). The reader interested in learning how van Fraassen simultaneously endorses acontextually structural and contextually pragmatic aspects of representation and interpretation should refer to van Fraassen’s (2008) investigations of maps and “the essential indexical.” [To complement the structure vs. function distinction, see van Fraassen 2008, 309–311 for a structure (“structural relations”) vs. history (“the intellectual processes that lead to those models”) distinction; cf. Ladyman et al. 2011] In all of this, embedding via isomorphism is a clear contender for theory interpretation under the Semantic View.

In short, committing to either a state-space or a set-/model-theoretic view on theory structure does not imply any particular perspective on theory interpretation (e.g., hierarchy of models, similarity, embedding). Instead, commitments to the former are logically and actually separable from positions on the latter (e.g., Suppes and Suppe endorse different accounts of theory structure, but share an understanding of theory interpretation in terms of a hierarchy of models). The Semantic View is alive and well as a family of analyses of theory structure, and continues to be developed in interesting ways both in its state-space and set-/model-theoretic approaches.

4. The Pragmatic View

The Pragmatic View recognizes that a number of assumptions about scientific theory seem to be shared by the Syntactic and Semantic Views. Both perspectives agree, very roughly, that theory is (1) explicit, (2) mathematical, (3) abstract, (4) systematic, (5) readily individualizable, (6) distinct from data and experiment, and (7) highly explanatory and predictive (see Flyvbjerg 2001, 38–39; cf. Dreyfus 1986). The Pragmatic View imagines the structure of scientific theories rather differently, arguing for a variety of theses:

  • Limitations . Idealized theory structure might be too weak to ground the predictive and explanatory work syntacticists and semanticists expect of it (e.g., Cartwright 1983, 1999a, b, 2019; Morgan and Morrison 1999; Suárez and Cartwright 2008).
  • Pluralism . Theory structure is plural and complex both in the sense of internal variegation and of existing in many types. In other words, there is an internal pluralism of theory (and model) components (e.g., mathematical concepts, metaphors, analogies, ontological assumptions, values, natural kinds and classifications, distinctions, and policy views, e.g., Kuhn 1970; Boumans 1999), as well as a broad external pluralism of different types of theory (and models) operative in science (e.g., mechanistic, historical, and mathematical models, e.g., Hacking 2009, Longino 2013). Indeed, it may be better to speak of the structures of scientific theories, in the double-plural.
  • Nonformal aspects. The internal pluralism of theory structure (thesis #2) includes many nonformal aspects deserving attention. That is, many components of theory structure, such as metaphors, analogies, values, and policy views have a non-mathematical and “informal” nature, and they lie implicit or hidden (e.g., Bailer-Jones 2002; Craver 2002; Contessa 2006; Morgan 2012). Interestingly, the common understanding of “formal,” which identifies formalization with mathematization, may itself be a conceptual straightjacket; the term could be broadened to include “diagram abstraction” and “principle extraction” (e.g., Griesemer 2013, who explicitly endorses what he also calls a “Pragmatic View of Theories”).
  • Function. Characterizations of the nature and dynamics of theory structure should pay attention to the user as well as to purposes and values (e.g., Apostel 1960; Minsky 1965; Morrison 2007; Winther 2012a).
  • Practice . Theory structure is continuous with practice and “the experimental life,” making it difficult to neatly dichotomize theory and practice (e.g., Hacking 1983, 2009; Shapin and Schaffer 1985; Galison 1987, 1988, 1997; Suárez and Cartwright 2008, Cartwright 2019).

These are core commitments of the Pragmatic View.

It is important to note at the outset that the Pragmatic View takes its name from the linguistic trichotomy discussed above, in the Introduction. This perspective need not imply commitment to, or association with, American Pragmatism (e.g. the work of Charles S. Peirce, William James, or John Dewey; cf. Hookway 2013; Richardson 2002). For instance, Hacking (2007a) distinguishes his pragmatic attitudes from the school of Pragmatism. He maps out alternative historical routes of influence, in general and on him, vis-à-vis fallibilism (via Imre Lakatos, Karl Popper; Hacking 2007a, §1), historically conditioned truthfulness (via Bernard Williams; Hacking 2007a, §3), and realism as intervening (via Francis Everitt, Melissa Franklin; Hacking 2007a, §4). To borrow a term from phylogenetics, the Pragmatic View is “polyphyletic.” The components of its analytical framework have multiple, independent origins, some of which circumnavigate American Pragmatism.

With this qualification and the five theses above in mind, let us now turn to the Pragmatic View’s analysis of theory structure and theory interpretation.

We should distinguish two strands of the Pragmatic View: the Pragmatic View of Models and a proper Pragmatic View of Theories .

4.1.1 The Pragmatic View of Models

Nancy Cartwright’s How the Laws of Physics Lie crystallized the Pragmatic View of Models. Under Cartwright’s analysis, models are the appropriate level of investigation for philosophers trying to understand science. She argues for significant limitations of theory (thesis #1), claiming that laws of nature are rarely true, and are epistemically weak. Theory as a collection of laws cannot, therefore, support the many kinds of inferences and explanations that we have come to expect it to license. Cartwright urges us to turn to models and modeling, which are central to scientific practice. Moreover, models “lie”—figuratively and literally—between theory and the world (cf. Derman 2011). That is, “to explain a phenomenon is to find a model that fits it into the basic framework of the theory and that thus allows us to derive analogues for the messy and complicated phenomenological laws which are true of it.” A plurality of models exist, and models “serve a variety of purposes” (Cartwright 1983, 152; cf. Suppes 1978). Cartwright is interested in the practices and purposes of scientific models, and asks us to focus on models rather than theories.

Cartwright’s insights into model pluralism and model practices stand as a significant contribution of “The Stanford School” (cf. Cat 2014), and were further developed by the “models as mediators” group, with participants at LSE, University of Amsterdam, and University of Toronto (Morgan and Morrison 1999; Chang 2011; cf. Martínez 2003). This group insisted on the internal pluralism of model components (thesis #2). According to Morgan and Morrison, building a model involves “fitting together… bits which come from disparate sources,” including “stories” (Morgan and Morrison 1999, 15). Boumans (1999) writes:

model building is like baking a cake without a recipe. The ingredients are theoretical ideas, policy views, mathematisations of the cycle, metaphors and empirical facts. (67) Mathematical moulding is shaping the ingredients in such a mathematical form that integration is possible… (90)

In an instructive diagram, Boumans suggests that a variety of factors besides theory and data feed into a model: metaphors, analogies, policy views, stylised facts, mathematical techniques, and mathematical concepts (93). The full range of components involved in a model will likely vary according to discipline, and with respect to explanations and interventions sought (e.g., analogies but not policy views will be important in theoretical physics). In short, model building involves a complex variety of internal nonformal aspects, some of which are implicit (theses #2 and #3).

As one example of a nonformal component of model construction and model structure, consider metaphors and analogies (e.g., Bailer-Jones 2002). Geary (2011) states the “simplest equation” of metaphor thus: “\(X = Y\)” (8, following Aristotle: “Metaphor consists in giving the thing a name that belongs to something else… ,” Poetics , 1457b). The line between metaphor and analogy in science is blurry. Some interlocutors synonymize them (e.g., Hoffman 1980; Brown 2003), others reduce one to the other (analogy is a form of metaphor, Geary 2011; metaphor is a kind of analogy, Gentner 1982, 2003), and yet others bracket one to focus on the other (e.g., Oppenheimer 1956 sets aside metaphor). One way to distinguish them is to reserve “analogy” for concrete comparisons, with clearly identifiable and demarcated source and target domains, and with specific histories, and use “metaphor” for much broader and indeterminate comparisons, with diffuse trajectories across discourses. Analogies include the “lines of force” of electricity and magnetism (Maxwell and Faraday), the atom as a planetary system (Rutherford and Bohr), the benzene ring as a snake biting its own tail (Kekulé), Darwin’s “natural selection” and “entangled bank,” and behavioral “drives” (Tinbergen) (e.g., Hesse 1966, 1967; Bartha 2010). Examples of metaphor are genetic information, superorganism, and networks (e.g., Keller 1995). More could be said about other informal model components, but this discussion of metaphors and analogies shall suffice to hint at how models do not merely lie between theory and world. Models express a rich internal pluralism (see also de Chadarevian and Hopwood 2004; Morgan 2012).

Model complexity can also be seen in the external plurality of models (thesis #2). Not all models are mathematical, or even ideally recast as mathematical. Non-formalized (i.e., non–state-space, non-set-/model-theoretic) models such as physical, diagrammatic, material, historical, “remnant,” and fictional models are ubiquitous across the sciences (e.g., Frigg and Hartmann 2012; for the biological sciences, see Hull 1975; Beatty 1980; Griesemer 1990, 1991 a, b, 2013; Downes 1992; Richards 1992; Winther 2006a; Leonelli 2008; Weisberg 2013). Moreover, computer simulations differ in important respects from more standard analytical mathematical models (e.g., Smith 1996; Winsberg 2010; Weisberg 2013). According to some (e.g., Griesemer 2013; Downes 1992; Godfrey-Smith 2006; Thomson-Jones 2012), this diversity belies claims by semanticists that models can always be cast “into set theoretic terms” (Lloyd 2013 In Press), are “always a mathematical structure” (van Fraassen 1970, 327), or that “formalisation of a theory is an abstract representation of the theory expressed in a formal deductive framework… in first-order predicate logic with identity, in set theory, in matrix algebra and indeed, any branch of mathematics...” (Thompson 2007, 485–6). Even so, internal pluralism has been interpreted as supporting a “deflationary semantic view,” which is minimally committed to the perspective that “model construction is an important part of scientific theorizing” (Downes 1992, 151). Given the formal and mathematical framework of the Semantic View (see above), however, the broad plurality of kinds of models seems to properly belong under a Pragmatic View of Models.

4.1.2 The Pragmatic View of Theories

Interestingly, while critiquing the Syntactic and Semantic Views on most matters, the Pragmatic View of Models construed theory, the process of theorizing, and the structure of scientific theories, according to terms set by the two earlier views. For instance, Cartwright tends to conceive of theory as explicit, mathematical, abstract, and so forth (see the first paragraph of Section 4). She always resisted “the traditional syntactic/semantic view of theory” for its “vending machine” view, in which a theory is a deductive and automated machine that upon receiving empirical input “gurgitates” and then “drops out the sought-for representation” (1999a, 184–5). Rather than reform Syntactic and Semantic accounts of theory and theory structure, however, she invites us, as we just saw, to think of science as modeling, “with theory as one small component” (Cartwright, Shomar, and Suárez 1995, 138; Suárez and Cartwright 2008). Many have followed her. Kitcher’s predilection is also to accept the terms of the Syntactic and Semantic Views. For instance, he defines theories as “axiomatic deductive systems” (1993, 93). In a strategy complementary to Cartwright’s modeling turn, Kitcher encourages us to focus on practice, including practices of modeling and even practices of theorizing. In The Advancement of Science , practice is analyzed as a 7-tuple, with the following highly abbreviated components: (i) a language; (ii) questions; (iii) statements (pictures, diagrams); (iv) explanatory patterns; (v) standard examples; (vi) paradigms of experimentation and observation, plus instruments and tools; and (vii) methodology (Kitcher 1993, 74). Scientific practice is also center stage for those singing the praises of “the experimental life” (e.g., Hacking 1983; Shapin and Schaffer 1985; Galison 1987), and those highlighting the cognitive grounds of science (e.g., Giere 1988; Martínez 2014) and science’s social and normative context (e.g., Kitcher 1993, 2001; Longino 1995, 2002; Ziman 2000; cf. Simon 1957). Indeed, the modeling and practice turns in the philosophy of science were reasonable reactions to the power of axiomatic reconstructive and mathematical modeling analyses of the structure of scientific theories.

Yet, a Pragmatic View of Theories is also afoot, one resisting orthodox characterizations of theory often embraced, at least early on, by Pragmatic View philosophers such as Cartwright, Hacking, Kitcher, and Longino. For instance, Craver (2002) accepts both the Syntactic and Semantic Views, which he humorously and not inaccurately calls “the Once Received View” and the “Model Model View.” But he also observes:

While these analyses have advanced our understanding of some formal aspects of theories and their uses, they have neglected or obscured those aspects dependent upon nonformal patterns in theories. Progress can be made in understanding scientific theories by attending to their diverse nonformal patterns and by identifying the axes along which such patterns might differ from one another. (55)

Craver then turns to mechanistic theory as a third theory type (and a third philosophical analysis of theory structure) that highlights nonformal patterns:

Different types of mechanisms can be distinguished on the basis of recurrent patterns in their organization. Mechanisms may be organized in series, in parallel, or in cycles. They may contain branches and joins, and they often include feedback and feedforward subcomponents. (71)

Consistent with theses #2 and #3 of the Pragmatic View, we must recognize the internal pluralism of theories as including nonformal components. Some of these are used to represent organizational and compositional relations of complex systems (Craver 2007; Wimsatt 2007; Winther 2011; Walsh 2015). While mechanistic analyses such as Craver’s may not wish to follow every aspect of the Pragmatic View of Theories, there are important and deep resonances between the two.

In a review of da Costa and French (2003), Contessa (2006) writes:

Philosophers of science are increasingly realizing that the differences between the syntactic and the semantic view are less significant than semanticists would have it and that, ultimately, neither is a suitable framework within which to think about scientific theories and models. The crucial divide in philosophy of science, I think, is not the one between advocates of the syntactic view and advocates of the semantic view, but the one between those who think that philosophy of science needs a formal framework or other and those who think otherwise. (376)

Again, we are invited to develop a non-formal framework of science and presumably also of scientific theory. (Halvorson 2012, 203 takes Contessa 2006 to task for advocating “informal philosophy of science.”) Moreover, in asking “what should the content of a given theory be taken to be on a given occasion?”, Vickers (2009) answers:

It seems clear that, in addition to theories being vague objects in the way that ‘heaps’ of sand are, there will be fundamentally different ways to put together theoretical assumptions depending on the particular investigation one is undertaking. For example, sometimes it will be more appropriate to focus on the assumptions which were used by scientists, rather than the ones that were believed to be true. (247, footnote suppressed)

A Pragmatic View of Theories helps make explicit nonformal internal components of theory structure.

Key early defenders of the modeling and practice turns have also recently begun to envision theory in a way distinct from the terms set by the Syntactic and Semantic Views. Suárez and Cartwright (2008) extend and distribute theory by arguing that “What we know ‘theoretically’ is recorded in a vast number of places in a vast number of different ways—not just in words and formulae but in machines, techniques, experiments and applications as well” (79). And while her influence lies primarily in the modeling turn, even in characterizing the “vending machine” view, Cartwright calls for a “reasonable philosophical account of theories” that is “much more textured, and… much more laborious” than that adopted by the Syntactic and Semantic Views (1999a, 185). The theory-data and theory-world axes need to be rethought. In her 2019 book on “artful modeling”, Cartwright emphasizes the importance of know-how and creativity in scientific practice, and “praise[s] engineers and cooks and inventors, as well as experimental physicists like Millikan and Melissa Franklin” (Cartwright 2019, 76). Kitcher wishes to transform talk of theories into discussion of “significance graphs” (2001, 78 ff.). These are network diagrams illustrating which (and how) questions are considered significant in the context of particular scientific communities and norms (cf. Brown 2010). Consistently with a Pragmatic View of Theories, Morrison (2007) reconsiders and reforms canonical conceptualizations of “theory.” Finally, Longino (2013) proposes an archaeology of assumptions behind and under different research programs and theories of human behavior such as neurobiological, molecular behavioral genetic, and social-environmental approaches (e.g., Oyama 2000). For instance, two shared or recurring assumptions across programs and theories are:

(1) that the approach in question has methods of measuring both the behavioral outcome that is the object of investigation and the factors whose association with it are the topic of investigation and (2) that the resulting measurements are exportable beyond the confines of the approach within which they are made. (Longino 2013, 117)

A Pragmatic View of Theories expands the notion of theory to include nonformal aspects, which surely must include elements from Boumans’ list above (e.g., metaphors, analogies, policy views), as well as more standard components such as ontological assumptions (e.g., Kuhn 1970; Levins and Lewontin 1985; Winther 2006b), natural kinds (e.g., Hacking 2007b), and conditions of application or scope (e.g., Longino 2013).

In addition to exploring internal theory diversity and in parallel with plurality of modeling, a Pragmatic View of Theories could also explore pluralism of modes of theorizing, and of philosophically analyzing theoretical structure (thesis #2). Craver (2002) provides a start in this direction in that he accepts three kinds of scientific theory and of philosophical analysis of scientific theory. A more synoptic view of the broader pragmatic context in which theories are embedded can be found in the literature on different “styles” of scientific reasoning and theorizing (e.g., Crombie 1994, 1996; Vicedo 1995; Pickstone 2000; Davidson 2001; Hacking 2002, 2009; Winther 2012b; Elwick 2007; Mancosu 2010). While there is no univocal or dominant classification of styles, two lessons are important. First, a rough consensus exists that theoretical investigations of especially historical, mechanistic, and mathematical structures and relations will involve different styles. Second, each style integrates theoretical products and theorizing processes in unique ways, thus inviting an irreducible pragmatic methodological pluralism in our philosophical analysis of the structure of scientific theories. For instance, the structure of theories of mechanisms in molecular biology or neuroscience involves flow charts, and is distinct from the structure of theories of historical processes and patterns as found in systematics and phylogenetics, which involves phylogenetic trees. As Crombie suggests, we need a “comparative historical anthropology of thinking.” (1996, 71; see Hacking 2009) Mathematical theory hardly remains regnant. It gives way to a pluralism of theory forms and theory processes. Indeed, even mathematical theorizing is a pluralistic motley, as Hacking (2014) argues. Although a “deflationary” Semantic View could account for pluralism of theory forms, the Pragmatic View of Theories, drawing on styles, is required to do justice to the immense variety of theorizing processes, and of philosophical accounts of theory and theory structure.

Finally, outstanding work remains in sorting out the philosophical utility of a variety of proposed units in addition to styles, such as Kuhn’s (1970) paradigms, Lakatos’ (1980) research programmes, Laudan’s (1977) research traditions, and Holton’s (1988) themata. A rational comparative historical anthropology of both theorizing and philosophical analyses of theorizing remains mostly unmapped (cf. Matheson and Dallmann 2014). Such a comparative meta-philosophical analysis should also address Davidson’s (1974) worries about “conceptual schemes” and Popper’s (1996 [1976]) critique of “the myth of the framework” (see Hacking 2002; Godfrey-Smith 2003).

Cartwright has done much to develop a Pragmatic View. Start by considering Newton’s second law:

Here \(F\) is the resultant force on a mass \(m\), and \(a\) is the net acceleration of \(m\); both \(F\) and \(a\) are vectors. This law is considered a “general” (Cartwright 1999a, 187) law expressed with “abstract quantities” (Cartwright 1999b, 249). Newton’s second law can be complemented with other laws, such as (i) Hooke’s law for an ideal spring:

Here \(k\) is the force constant of the spring, and \(x\) the distance along the x-axis from the equilibrium position, and (ii) Coulomb’s law modeling the force between two charged particles:

Here \(K\) is Coulomb’s electrical constant, \(q\) and \(q'\) are the charges of the two objects, and \(r\) the distance between the two objects. The picture Cartwright draws for us is that Newton’s, Hooke’s, and Coulomb’s laws are abstract, leaving out many details. They can be used to derive mathematical models of concrete systems. For instance, by combining (1) and (2), the law of gravitation (a “fundamental” law, Cartwright 1983, 58–59), other source laws, and various simplifying assumptions, we might create a model for the orbit of Mars, treating the Sun and Mars as a 2-body system, ignoring the other planets, asteroids, and Mars’ moons. Indeed, the Solar System is a powerful “nomological machine” (Cartwright 1999a, 50–53), which “is a fixed (enough) arrangement of components, or factors, with stable (enough) capacities that in the right sort of stable (enough) environment will, with repeated operation, give rise to the kind of regular behaviour that we represent in our scientific laws” (Cartwright 1999a, 50). Importantly, most natural systems are complex and irregular, and cannot be neatly characterized as nomological machines. For these cases, abstract laws “run out” (Cartwright 1983) and are rarely smoothly “deidealised” (Suárez 1999). In general, abstract laws predict and explain only within a given domain of application, and only under ideal conditions. More concrete laws or models are not directly deduced from them (e.g., Suárez 1999, Suárez and Cartwright 2008), and they can rarely be combined to form effective “super-laws” (Cartwright 1983, 70–73). In short, the move from (1) and (2) or from (1) and (3) to appropriate phenomenological models, is not fully specified by either abstract law pairing. Indeed, Cartwright developed her notion of “capacities” to discuss how “the principles of physics” “are far better rendered as claims about capacities, capacities that can be assembled and reassembled in different nomological machines, unending in their variety, to give rise to different laws” (1999a, 52). Articulating concrete models requires integrating a mix of mathematical and nonformal components. Laws (1), (2), and (3) remain only one component, among many, of the models useful for, e.g., exploring the behavior of the Solar System, balls on a pool table, or the behavior of charges in electrical fields.

Shifting examples but not philosophical research program, Suárez and Cartwright (2008) explains how analogies such as superconductors as diamagnets (as opposed to ferromagnets) were an integral part of the mathematical model of superconductivity developed by Fritz and Heinz London in the 1930s (63; cf. London and London 1935). Suárez and Cartwright gladly accept that this model “is uncontroversially grounded in classic electromagnetic theory” (64). However, contra Semantic View Structuralists such as Bueno, da Costa, French, and Ladyman, they view nonformal aspects as essential to practices of scientific modeling and theorizing: “The analogy [of diamagnets] helps us to understand how the Londons work with their model… which assumptions they add and which not… a formal reconstruction of the model on its own cannot help us to understand that” (69). In short, the running example of Newtonian mechanics, in conjunction with a glimpse into the use of analogies in mathematical modeling, illustrates the Pragmatic View’s account of theory syntax: theory is constituted by a plurality of formal and informal components.

As we have explored throughout this section, models and theories have informal internal components, and there are distinct modes of modeling and theorizing. Because of the Pragmatic View’s attention to practice, function, and application, distinguishing structure from interpretation is more difficult here than under the Syntactic and Semantic Views. Any synchronic analysis of the structure of models and theories must respect intentional diachronic processes of interpreting and using, as we shall now see.

Regarding the import of function in models and theories (thesis #4), already the Belgian philosopher of science Apostel defined modeling thus: “Let then \(R(S,P,M,T)\) indicate the main variables of the modelling relationship. The subject \(S\) takes, in view of the purpose \(P\), the entity \(M\) as a model for the prototype \(T\)” (1960, 128, see also Apostel 1970). Purposes took center-stage in his article title: “Towards the Formal Study of Models in the Non-Formal Sciences.” MIT Artificial Intelligence trailblazer Minsky also provided a pragmatic analysis:

We use the term “model” in the following sense: To an observer \(B\), an object \(A^*\) is a model of an object \(A\) to the extent that \(B\) can use \(A^*\) to answer questions that interest him about \(A\). The model relation is inherently ternary. Any attempt to suppress the role of the intentions of the investigator \(B\) leads to circular definitions or to ambiguities about “essential features” and the like. (1965, 45)

This account is thoroughly intentionalist and anti-essentialist. That is, mapping relations between model and world are left open and overdetermined. Specifying the relevant relations depends on contextual factors such as questions asked, and the kinds of similarities and isomorphisms deemed to be of interest. The appropriate relations are selected from an infinite (or, at least, near-infinite) variety of possible relations (e.g., Rosenblueth and Wiener 1945; Lowry 1965).

Regarding practice (thesis #5), in addition to ample work on the experimental life mentioned above, consider a small example. A full understanding of the content and structure of the London brothers’ model of superconductivity requires attention to informal aspects such as analogies. Even London and London (1935) state in the summary of their paper that “the current [”in a supraconductor“] is characterized as a kind of diamagnetic volume current” (88). They too saw the diamagnetic analogy as central to their theoretical practices. Criteria and practices of theory confirmation also differ from the ones typical of the Syntactic and Semantic Views. While predictive and explanatory power as well as empirical adequacy remain important, the Pragmatic View also insists on a variety of other justificatory criteria, including pragmatic virtues (sensu Kuhn 1977; Longino 1995) such as fruitfulness and utility. In a nutshell, the Pragmatic View argues that scientific theory structure is deeply shaped and constrained by functions and practices, and that theory can be interpreted and applied validly according to many different criteria.

The analytical framework of the Pragmatic View remains under construction. The emphasis is on internal diversity, and on the external pluralism of models and theories, of modeling and theorizing, and of philosophical analyses of scientific theories. The Pragmatic View acknowledges that scientists use and need different kinds of theories for a variety of purposes. There is no one-size-fits-all structure of scientific theories. Notably, although the Pragmatic View does not necessarily endorse the views of the tradition of American Pragmatism, it has important resonances with the latter school’s emphasis on truth and knowledge as processual, purposive, pluralist, and context-dependent, and on the social and cognitive structure of scientific inquiry.

A further qualification in addition to the one above regarding American Pragmatism is in order. The Pragmatic View has important precursors in the historicist or “world view” perspectives of Feyerabend, Hanson, Kuhn, and Toulmin, which were an influential set of critiques of the Syntactic View utterly distinct from the Semantic View. This philosophical tradition focused on themes such as meaning change and incommensurability of terms across world views (e.g., paradigms), scientific change (e.g., revolutionary: Kuhn 1970; evolutionary: Toulmin 1972), the interweaving of context of discovery and context of justification, and scientific rationality (Preston 2012; Bird 2013; Swoyer 2014). The historicists also opposed the idea that theories can secure meaning and empirical support from a theory-neutral and purely observational source, as the Syntactic View had insisted on with its strong distinction between theoretical and observational vocabularies (cf. Galison 1988). Kuhn’s paradigms or, more precisely, “disciplinary matrices” even had an internal anatomy with four components: (i) laws or symbolic generalizations, (ii) ontological assumptions, (iii) values, and (iv) exemplars (Kuhn 1970, postscript; Godfrey-Smith 2003; Hacking 2012). This work was concerned more with theory change than with theory structure and had fewer conceptual resources from sociology of science and history of science than contemporary Pragmatic View work. Moreover, paradigms never quite caught on the way analyses of models and modeling have. Even so, this work did much to convince later scholars, including many of the Pragmatic View, of certain weaknesses in understanding theories as deductive axiomatic structures.

As a final way to contrast the three views, we return to population genetics and, especially, to the Hardy-Weinberg Principle (HWP). Both Woodger (1937, 1959) and Williams (1970, 1973) provide detailed axiomatizations of certain parts of biology, especially genetics, developmental biology, and phylogenetics. For instance, Woodger (1937) constructs an axiomatic system based on ten logical predicates or relations, including \(\bP\) ( part of ), \(\bT\) ( before in time ), \(\bU\) ( reproduced by cell division or cell fusion ), \(\bm\) ( male gamete ), \(\bff\) ( female gamete ), and \(\bgenet\) ( genetic property ) (cf. Nicholson and Gawne 2014). Woodger (1959) elaborates these logical predicates or relations to produce a careful reconstruction of Mendelian genetics. Here are two axioms in his system (which are rewritten in contemporary notation, since Woodger used Russell and Whitehead’s Principia Mathematica notation):

The first axiom should be read thus: “no gamete is both male and female” (1959, 416). In the second axiom, given that \(DLZxyz\) is a primitive relation defined as “\(x\) is a zygote which develops in the environment \(y\) into the life \(z\)” (1959, 415), the translation is “every life develops in one and only one environment from one and only one zygote” (416). Woodger claims that “the whole of Mendel’s work can be expressed…” via this axiomatic system. Woodger briefly mentions that if one assumes that the entire system or population is random with respect to gamete fusions, “then the Pearson-Hardy law is derivable” (1959, 427). This was a reference to HWP. In her explorations of various axiomatizations of Darwinian lineages and “subclans,” and the process of the “expansion of the fitter,” Williams (1970, 1973) also carefully defines concepts, and axiomatizes basic biological principles of reproduction, natural selection, fitness, and so forth. However, she does not address HWP. Of interest is the lack of axiomatization of HWP or other mathematical principles of population genetics in Woodger’s and Williams’ work. Were such principles considered secondary or uninteresting by Woodger and Williams? Might Woodger’s and Williams’ respective axiomatic systems simply lack the power and conceptual resources to axiomatically reconstruct a mathematical edifice actually cast in terms of probability theory? Finally, other friends of the Syntactic View, such as the early Michael Ruse, do not provide an axiomatization of HWP (Ruse 1975, 241).

Proponents of the Semantic View claim that their perspective on scientific theory accurately portrays the theoretical structure of population genetics. Thompson (2007) provides both set-theoretical and state-space renditions of Mendelian genetics. The first involves defining a set-theoretic predicate for the system, viz., \(\{P, A, f, g\}\), where \(P\) and \(A\) are sets representing, respectively, the total collection of alleles and loci in the population, while \(f\) and \(g\) are functions assigning an allele to a specific location in, respectively, the diploid cells of an individual or the haploid gametic cells. Axioms in this set-theoretic formalization include “The sets \(P\) and \(A\) are finite and non empty” (2007, 498). In contrast, the state-space approach of the Semantic View articulates a phase space with each dimension representing allelic (or genotypic) frequencies (e.g., cover and Chapter 3 of Lloyd 1994 [1988]). As an example, “for population genetic theory, a central law of succession is the Hardy-Weinberg law” (Thompson 2007, 499). Mathematically, the diploid version of HWP is written thus:

Here \(p\) and \(q\) are the frequencies of two distinct alleles at a biallelic locus. The left-hand side represents the allele frequencies in the parental generation and a random mating pattern, while the right-hand side captures genotype frequencies in the offspring generation, as predicted from the parental generation. This is a null theoretical model—actual genotypic and allelic frequencies of the offspring generation often deviate from predicted frequencies (e.g., a lethal homozygote recessive would make the \(q^2_{\text{off}}\) term = 0). Indeed, HWP holds strictly only in abstracted and idealized populations with very specific properties (e.g., infinitely large, individuals reproduce randomly) and only when there are no evolutionary forces operating in the population (e.g., no selection, mutation, migration, or drift) (e.g., Hartl and Clark 1989; Winther et al. 2015). HWP is useful also in the way it interacts with laws of succession for selection, mutation, and so forth (e.g., Okasha 2012). This powerful population genetic principle is central to Semantic View analyses of the mathematical articulation of the theoretical structure of population genetics (see also Lorenzano 2014, Ginnobili 2016).

Recall that the Pragmatic View highlights the internal and external pluralism—as well as the purposiveness—of model and theory structure. Consider recent uses of population genetic theory to specify the kinds and amounts of population structure existing in Homo sapiens . In particular, different measures and mathematical modeling methodologies are employed in investigating human genomic diversity (e.g., Jobling et al. 2004; Barbujani et al. 2013; Kaplan and Winther 2013). It is possible to distinguish at least two different research projects, each of which has a unique pragmatic content (e.g., aims, values, and methods). Diversity partitioning assesses genetic variation within and among pre-determined groups using Analysis of Variance (also crucial to estimating heritability, Downes 2014). Clustering analysis uses Bayesian modeling techniques to simultaneously produce clusters and assign individuals to these “unsupervised” cluster classifications. The robust result of the first modeling project is that (approximately) 85% of all genetic variance is found within human subpopulations (e.g., Han Chinese or Sami), 10% across subpopulations within a continental region, and only 5% is found across continents (i.e., “African,” “Asian,” and “European” – Lewontin 1972, 1974). (Recall also that we are all already identical at, on average, 999 out of 1000 nucleotides.) To calculate diversity partitions at these three nested levels, Lewontin (1972) used a Shannon information-theoretic measure closely related to Sewall Wright’s \(F\)-statistic:

Here \(H_T\) is the total heterozygosity of the population assessed, and \(\bar{H}_S\) is the heterozygosity of each subpopulation (group) of the relevant population, averaged across all the subpopulations. \(F_{ST}\) is bounded by 0 and 1, and is a measure of population structure, with higher \(F_{ST}\) values suggesting more structure, viz., more group differentiation. HWP appears implicitly in both \(H_T\) and \(\bar{H}_S\), which take heterozygosity (\(2pq\)) to be equal to the expected proportion of heterozygotes under HWP rather than the actual frequency of heterozygotes. \(H_T\) is computed by using the grand population average of \(p\) and \(q\), whereas calculating \(\bar{H}_S\) involves averaging across the expected heterozygosities of each subpopulation. If random mating occurs—and thus HWP applies—across the entire population without respecting subpopulation borders, then \(H_T\) and \(\bar{H}_S\) will be equal (i.e., \(p\) of the total population and of each individual subpopulation will be the same; likewise for \(q\)). If, instead, HWP applies only within subpopulations but not across the population as a whole, then \(\bar{H}_S\) will be smaller than \(H_T\), and \(F_{ST}\) will be positive (i.e., there will be “excess homozygosity” across subpopulations, which is known as the “Wahlund Principle” in population genetics). This is one way among many to deploy the population-genetic principle of HWP. Thus, the Lewontin-style diversity partitioning result that only roughly 5% of the total genetic variance is among races is equivalent to saying that \(F_{ST}\) across the big three continental populations in Lewontin’s three-level model is 0.05 (e.g., Barbujani et al. 1997). The basic philosophical tendency is to associate the diversity partitioning research project’s (approximately) 85%-10%-5% result with an anti-realist interpretation of biological race.

In contrast, clustering analysis (e.g., Pritchard et al. 2000; Rosenberg et al. 2002; cf. Edwards 2003) can be readily performed even with the small amount of among-continent genetic variance in Homo sapiens . For instance, when the Bayesian modeling computer program STRUCTURE is asked to produce 5 clusters, continental “races” appear—African, Amerindian, Asian, European, and Pacific Islanders. Interestingly, this modeling technique is also intimately linked to HWP: “Our main modeling assumptions are Hardy-Weinberg equilibrium within populations and complete linkage equilibrium between loci within populations” (Pritchard et al. 2000, 946). That is, for a cluster to eventually be robust in the modeling runs, it should meet HWP expectations. Clustering analysis has sometimes been interpreted as a justification for a realist stance towards biological race (see discussions in Hochman 2013; Winther and Kaplan 2013; Edge and Rosenberg 2015; Spencer 2015).

This example of the mathematical modeling of human genomic diversity teaches that basic and simple formal components can be used in different ways to develop and apply theory, both inside and outside of science. In contrast to the Syntactic and Semantic Views, the Pragmatic View foregrounds tensions vis-à-vis ontological assumptions and political consequences regarding the existence (or not) of biological race between diversity partitioning (Lewontin 1972) and clustering analysis (Pritchard et al. 2000) research packages. These ontological ruptures can be identified despite the fact that both research projects assess population structure by examining departures from HWP (i.e., they measure excess homozygosity), and are completely consistent (e.g., Winther 2014; Ludwig 2015; Edge and Rosenberg 2015).

This exploration of how the three views on the structure of scientific theory address population genetics, and in particular HWP, invites a certain meta-pluralism. That is, the Syntactic View carefully breaks down fundamental concepts and principles in genetics and population genetics, articulating definitions and relations among terms. The Semantic View insightfully decomposes and interweaves the complex mathematical edifice of population genetics. The Pragmatic View sheds light on modeling choices and on distinct interpretations and applications of the same theory or model, both within and without science. The three perspectives are hardly mutually exclusive. (N.B., the two running examples concern theory structure in Newtonian mechanics and population genetics, independently considered. While interesting, debates about “evolutionary forces” are beyond the scope of the current entry; see, e.g., Hitchcock and Velasco 2014.)

The structure of scientific theories is a rich topic. Theorizing and modeling are core activities across the sciences, whether old (e.g., relativity theory, evolutionary theory) or new (e.g., climate modeling, cognitive science, and systems biology). Furthermore, theory remains essential to developing multipurpose tools such as statistical models and procedures (e.g., Bayesian models for data analysis, agent-based models for simulation, network theory for systems analysis). Given the strength and relevance of theory and theorizing to the natural sciences, and even to the social sciences (e.g., microeconomics, physical, if not cultural, anthropology), philosophical attention to the structure of scientific theories could and should increase. This piece has focused on a comparison of three major perspectives: Syntactic View, Semantic View, and Pragmatic View. In order to handle these complex debates effectively, we have sidestepped certain key philosophical questions, including questions about scientific realism; scientific explanation and prediction; theoretical and ontological reductionism; knowledge-production and epistemic inference; the distinction between science and technology; and the relationship between science and society. Each of these topics bears further philosophical investigation in light of the three perspectives here explored.

A table helps summarize general aspects of the three views’ analyses of the structure of scientific theories:

Table 2. General aspects of each view’s analysis of the structure of scientific theories.

The Syntactic, Semantic, and Pragmatic views are often taken to be mutually exclusive and, thus, to be in competition with one another. They indeed make distinct claims about the anatomy of scientific theories. But one can also imagine them to be complementary, focusing on different aspects and questions of the structure of scientific theories and the process of scientific theorizing. For instance, in exploring nonformal and implicit components of theory, the Pragmatic View accepts that scientific theories often include mathematical parts, but tends to be less interested in these components. Moreover, there is overlap in questions—e.g., Syntactic and Semantic Views share an interest in formalizing theory; the Semantic and Pragmatic Views both exhibit concern for scientific practice.

How are these three views ultimately related? A standard philosophical move is to generalize and abstract, understanding a situation from a higher level. One “meta” hypothesis is that a given philosophical analysis of theory structure tends to be associated with a perceived relationship among the three views here discussed. The Syntactic View is inclined to interpret the Semantic View’s formal machinery as continuous with its own generalizing axiomatic strategy, and hence diagnoses many standard Semantic View critiques (Section 3) as missing their mark (the strategy of identity ; e.g., Friedman 1982; Worrall 1984; Halvorson 2012, 2013, 2019; Lutz 2012, 2017; cf. Chakravartty 2001). The Semantic View explicitly contrasts its characterization of theory structure with the “linguistic” or “metamathematical” apparatus of the Syntactic View (the strategy of combat ; e.g., Suppe 1977; van Fraassen 1980, 1989; Lloyd 1994 [1988]). Finally, the Pragmatic View, which did not exist as a perspective until relatively recently, imagines theory as pluralistic and can thus ground a holistic philosophical investigation. It envisions a meta-pluralism in which reconstructive axiomatization and mathematical modeling remain important, though not necessary for all theories. This third view endorses a panoply of theoretical structures and theorizing styles, negotiating continuity both between theorizing and “the experimental life,” and among philosophical analyses of the structure of scientific theories (the strategy of complementarity ; e.g., Hacking 1983, 2009; Galison 1988, 1997; Craver 2002; Suárez and Cartwright 2008; Griesemer 2013). Interestingly, Suárez and Pero (2019) explicitly concur with the Pragmatic View as described in this article, but believe that “the semantic conception in its bare minimal expression” is compatible with, if not sufficient for, capturing “pragmatic elements and themes involved in a more flexible and open-ended approach to scientific theory” (Suárez and Pero 2019, 348). By design, the ecumenical meta-pluralism sanctioned by the Pragmatic View does not completely offset identity and combat strategies. Moreover, only “partial acceptance” of the respective views may ultimately be possible. Even so, the complementarity strategy might be worth developing further. Compared to identity and combat meta-perspectives, it provides broader—or at least different—insights into the structure of scientific theories. More generally, exploring the relations among these views is itself a rich topic for future philosophical work, as is investigating their role in, and interpretation of, active scientific fields ripe for further philosophical analysis such as climate change (e.g., Winsberg 2018), model organisms (e.g., Ankeny and Leonelli 2020), and cartography and GIS (e.g., Winther 2020).

  • Ankeny, R. and S. Leonelli, 2020, Model Organisms , Cambridge: Cambridge University Press.
  • Apostel, L., 1960, “Towards the Formal Study of Models in the Non-Formal Sciences,” Synthese , 12 (23): 125–161.
  • –––, 1970, “The Justification of Formalisation,” Quality and Quantity , 4 (1): 3–38.
  • Awodey, S., 2006, Category Theory , Oxford: Oxford University Press.
  • Bailer-Jones, D.M., 2002, “Models, Metaphors and Analogies,” in Blackwell Guide to the Philosophy of Science , P.K. Machamer and M. Silberstein (eds.), Oxford: Blackwell, pp. 108–127.
  • Barbujani, G., S. Ghirotto, and F. Tassi, 2013, “Nine Things to Remember about Human Genome Diversity,” Tissue Antigens , 82 (3): 155–164.
  • Barbujani, G.A., Magagni, E. Minch, and L.L. Cavalli-Sforza, 1997, “An Apportionment of Human DNA Diversity,” Proceedings of the National Academy of Sciences , 94 (9): 4516–4519.
  • Bartha, P.F.A., 2010, By Parallel Reasoning: The Construction and Evaluation of Analogical Arguments , New York: Oxford University Press
  • Bays, T., 2014, “Skolem’s Paradox”, The Stanford Encyclopedia of Philosophy (Spring 2014 Edition), E. N. Zalta (ed.), URL = < https://plato.stanford.edu/archives/spr2014/entries/paradox-skolem/ >.
  • Beatty, J., 1981, “What’s Wrong with the Received View of Evolutionary Theory?” PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1980 , (2): 397–426.
  • Bergstrom, C. and L. Dugatkin, 2012, Evolution , New York: Norton.
  • Bird, A., 2013, “Thomas Kuhn”, The Stanford Encyclopedia of Philosophy (Fall 2013 Edition), E. N. Zalta (ed.), URL = < https://plato.stanford.edu/archives/fall2013/entries/thomas-kuhn/ >.
  • Blatti, S. and S. Lapointe (eds.), 2016, Ontology After Carnap , Oxford: Oxford University Press.
  • Boumans, M., 1999, “Built-In Justification,” in Models as Mediators: Perspectives on Natural and Social Science , M.S. Morgan and M. Morrison (eds.), Cambridge: Cambridge University Press, pp. 66–96.
  • Braithwaite, R., 1962, “Models in the Empirical Sciences,” in Logic, Methodology and Philosophy of Science: Proceedings of the 1960 International Congress , E. Nagel, P. Suppes, and A. Tarski (eds.), Stanford, CA: Stanford University Press, pp. 224–231.
  • Bridgman, P.W., 1927, The Logic of Modern Physics , New York: Macmillan.
  • Bueno, O., 1997, “Empirical Adequacy: A Partial Structures Approach,” Studies in History and Philosophy of Science (Part A) , 28 (4): 585–610.
  • Bueno, O., S. French, and J. Ladyman, 2012, “Models and Structures: Phenomenological and Partial,” Studies in History and Philosophy of Science (Part B) , 43 (1): 43–46.
  • Brown, T., 2003, Making Truth: Metaphor in Science , Urbana: University of Illinois Press.
  • Brown, M.J., 2010, “Genuine Problems and the Significance of Science,” Contemporary Pragmatism , 7 (2): 131–153.
  • Callender, C., 1999, “Reducing Thermodynamics to Statistical Mechanics: The Case of Entropy,” The Journal of Philosophy , 96 (7): 348–373.
  • Campbell, N.R., 1920, Physics: The Elements , Cambridge: Cambridge University Press.
  • Carnap, R., 1967 [1928], The Logical Structure of the World , translated by R.A. George, Berkeley, CA: University of California Press. Original: Der logische Aufbau der Welt , Leipzig: Felix Meiner.
  • –––, 1932, “Über Protokollsätze”, Erkenntnis , 3: 215–228; transl. by R. Creath and R. Nollan, “On Protocol Sentences,” Noûs , 21 (4) (1987): 457–470.
  • –––, 1936/1937, “Testability and Meaning,” Philosophy of Science , 1936, 3 (4): 419–471; 1937, 4 (1): 1–40.
  • –––, 1937, The Logical Syntax of Language , London: Kegan Paul, Trench, & Trübner.
  • –––, 1939, Foundations of Logic and Mathematics (International Encyclopedia of Unified Science, Volume 1, Number 3), Chicago: University of Chicago Press.
  • –––, 1942, Introduction to Semantics , Cambridge, MA: Harvard University Press.
  • –––, 1952, The Continuum of Inductive Methods , Chicago: University of Chicago Press.
  • –––, 1962 [1950], Logical Foundations of Probability , Chicago: University of Chicago Press, 2 nd edition.
  • –––, 1963, “Philosopher Replies,” in The Philosophy of Rudolf Carnap (Library of Living Philosophers, Volume 11), P. Schilpp (ed.), La Salle: Open Court, pp. 889–999.
  • –––, 1966, Philosophical Foundations of Science , New York: Basic Books; repr. as An Introduction to the Philosophy of Science , 1972; repr. New York: Dover, 1996.
  • Cartwright, N., 1983, How the Laws of Physics Lie , New York: Oxford University Press.
  • –––, 1989, Nature’s Capacities and Their Measurement , New York: Oxford University Press.
  • –––, 1999a, The Dappled World: A Study of the Boundaries of Science , Cambridge: Cambridge University Press.
  • –––, 1999b, “Models and the Limits of Theories: Quantum Hamiltonians and the BCS Model of Superconductivity,” in Models as Mediators: Perspectives on Natural and Social Science , M. Morgan and M. Morrison (eds.), (Perspectives on Natural and Social Sciences), Cambridge: Cambridge University Press, pp. 241–281.
  • –––, 2008, “In Praise of the Representation Theorem,” in Representation, Evidence, and Justification: Themes from Suppes , W.K. Essler and M. Frauchiger (eds.), Ontos Verlag, pp. 83–90.
  • –––, 2019, Nature, the Artful Modeler: Lectures on Laws, Science, How Nature Arranges the World and How We Can Arrange It Better , Chicago, IL: Open Court.
  • Cartwright, N., T. Shomar, and M. Suárez, 1995, “The Tool Box of Science: Tools for the Building of Models with a Superconductivity Example,” in Theories and Models in Scientific Processes (Poznan Studies in the Philosophy of the Sciences and the Humanities, Volume 44), W. Herfel, W. Krajewski, I. Niiniluoto, and R. Wojcicki (eds.), Amsterdam: Rodopi, pp. 137–149.
  • Carus, A.W., 2007, Carnap and Twentieth-Century Thought: Explication as Enlightenment , Cambridge: Cambridge University Press.
  • Cat, J., 2014, “The Unity of Science”, The Stanford Encyclopedia of Philosophy (Winter 2014 Edition), E. N. Zalta (ed.), URL = < https://plato.stanford.edu/archives/win2014/entries/scientific-unity/ >.
  • Chakravartty, A., 2001, “The Semantic or Model-Theoretic View of Theories and Scientific Realism,” Synthese , 127 (3): 325–345.
  • Chang, H., 2011, “The Philosophical Grammar of Scientific Practice” in International Studies in the Philosophy of Science , 25 (3): 205–221.
  • Clatterbuck, H., E. Sober, and R. Lewontin, 2013, “Selection Never Dominates Drift (Nor Vice Versa),” Biology & Philosophy , 28 (4): 577–592.
  • Coffa, A. J., 1991, The Semantic Tradition From Kant to Carnap: To the Vienna Station , Cambridge: Cambridge University Press.
  • Contessa, G., 2006, “Scientific Models, Partial Structures and the New Received View of Theories,” Studies in History and Philosophy of Science (Part A) , 37 (2): 370–377.
  • Craver, C.F., 2002, “Structures of Scientific Theories,” in Blackwell Guide to the Philosophy of Science , P.K. Machamer and M. Silberstein (eds.), Oxford: Blackwell, pp. 55–79.
  • –––, 2007, Explaining the Brain: Mechanisms and the Mosaic Unity of Neuroscience , New York: Oxford University Press.
  • Creath, R., 1987, “The Initial Reception of Carnap’s Doctrine of Analyticity,” Noûs , 21 (4): 477–499.
  • –––, 2014, “Logical Empiricism”, The Stanford Encyclopedia of Philosophy (Spring 2014 Edition), E. N. Zalta (ed.), URL = < https://plato.stanford.edu/archives/spr2014/entries/logical-empiricism/ >.
  • Crombie, A.C., 1994, Styles of Scientific Thinking in the European Tradition (Volumes 1–3), London: Duckworth.
  • –––, 1996, “Commitments and Styles of European Scientific Thinking,” Theoria , 11 (25): 65–76.
  • Crow J. and M. Kimura, 1970, An Introduction to Population Genetics Theory , Edina, MN: Burgess International Group Incorporated.
  • da Costa, N.C.A. and S. French, 1990, “The Model-Theoretic Approach in the Philosophy of Science,” Philosophy of Science , 57 (2): 248–65.
  • –––, 2003. Science and Partial Truth: A Unitary Approach to Models and Scientific Reasoning , Oxford: Oxford University Press.
  • Dalla Chiara Scabia, M.L. and G. Toraldo di Francia, 1973, “A Logical Analysis of Physical Theories,” La Rivista del Nuovo Cimento , 3 (1): 1–20.
  • Davidson, A., 2001, The emergence of sexuality: Historical epistemology and the formation of concepts , Cambridge, MA: Harvard University Press.
  • Davidson, D., 1974, “On the Very Idea of a Conceptual Scheme,” Proceedings and Addresses of the American Philosophical Association , 47: 5–20.
  • de Chadarevian, S. and N. Hopwood, 2004, Models: The Third Dimension of Science , Stanford, CA: Stanford University Press.
  • Demopoulos, W., 2003, “On the Rational Reconstruction of our Theoretical Knowledge,” The British Journal for the Philosophy of Science , 54 (3): 371–403.
  • –––, 2013, Logicism and Its Philosophical Legacy , Cambridge: Cambridge University Press.
  • Derman, E., 2011, Models Behaving Badly: Why Confusing Illusion with Reality Can Lead to Disaster, on Wall Street and in Life , New York: Free Press.
  • Dizadji-Bahmani, F., R. Frigg, and S. Hartmann, 2010, “Who’s Afraid of Nagelian Reduction?,” Erkenntnis , 73 (3): 393–412.
  • Döring, A. and R.G. Winther, forthcoming, “The Human Condition is an Ocean: Philosophy and the Mediterranean Sea,” in Words and Worlds: Use and Abuse of Analogies and Metaphors within Sciences and Humanities , S. Wuppuluri and A.C. Grayling (eds.), Synthese Library Series.
  • Downes, S., 1992, “The Importance of Models in Theorizing: A Deflationary Semantic View,” PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1992 , (1): 142–153.
  • –––, “Heritability,” The Stanford Encyclopedia of Philosophy (Spring 2014 Edition), E. N. Zalta (ed.), URL = < https://plato.stanford.edu/archives/spr2014/entries/heredity/ >.
  • Dreyfus, H., 1986, “Why Studies of Human Capacities Modeled on Ideal Natural Science Can Never Achieve their Goal,” in Rationality, Relativism, and the Human Sciences , J. Margolis, M. Krausz, and R. Burian (eds.), Dordrecht: Martinus Nijhoff, pp. 3–22.
  • Duhem, P., 1906, La théorie physique: Son objet et sa structure , Paris: Chevalier et Rivière; transl. by P.W. Wiener, The Aim and Structure of Physical Theory , Princeton, NJ: Princeton University Press (1954).
  • Edge, M.D. and N. Rosenberg, 2015, “Implications of the Apportionment of Human Genetic Diversity for the Apportionment of Human Phenotypic Diversity,” Studies in History and Philosophy of Biological and Biomedical Sciences , 52: 32–45.
  • Edwards, A.W.F., 2003, “Human Genetic Diversity: Lewontin‘s Fallacy” BioEssays , 25 (8): 798–801.
  • Eilenberg, S. and S. MacLane, 1945, “General Theory of Natural Equivalences,” Transactions of the American Mathematical Society , 58 (2): 231–294.
  • Einstein, A., 1934, “On the Method of Theoretical Physics,” Philosophy of Science , 1 (2): 163–169.
  • –––, 1936, “Physik und Realität,” Journal of The Franklin Institute , 221 (3): 313–347; transl. by J. Piccard, “Physics and Reality,” Journal of the Franklin Institute , 221 (3) (1936): 349–382.
  • Elwick, J., 2007, Styles of Reasoning in British Life Sciences: Shared Assumptions, 1820–1858 , London: Pickering & Chatto.
  • Feigl, H., 1970, “The ‘Orthodox’ View of Theories: Remarks in Defense as Well as Critique,” in Analyses of Theories and Methods of Physics and Psychology (Minnesota Studies in the Philosophy of Science, Volume 4), M. Radner and S. Winokur (eds.), Minneapolis: University of Minnesota Press, pp. 3–16.
  • Feigl, H., M. Scriven, and G. Maxwell (eds.), 1958, Minnesota Studies in the Philosophy of Science (Volume 2), Minneapolis: University of Minnesota Press.
  • Flyvbjerg, B., 2001, Making Social Science Matter: Why Social Inquiry Fails and How it Can Succeed Again , Cambridge: Cambridge University Press.
  • French, S., 2017, “Identity Conditions, Idealisations and Isomorphisms: a Defence of the Semantic Approach,” first online 19 September 2017, Synthese . doi:10.1007/s11229-017-1564-z
  • French, S. and J. Ladyman, 1997, “Superconductivity and Structures: Revisiting the London Account,” Studies in History and Philosophy of Modern Physics , 28 (3): 363–393.
  • –––, 1999, “Reinflating the Semantic Approach,” International Studies in the Philosophy of Science , 13 (2): 103–121.
  • –––, 2003. “Remodelling Structural Realism: Quantum Physics and the Metaphysics of Structure,” Synthese , 136 (1): 31–56.
  • Friedman, M., 1981, “Theoretical Explanation,” in Reduction, Time, and Reality: Studies in the Philosophy of the Natural Sciences , R. Healey (ed.), New York: Cambridge University Press, pp. 1–16.
  • –––, 1982, “ The Scientific Image , by B. van Fraassen,” The Journal of Philosophy , 79 (5): 274–283.
  • –––, 1983, Foundations of Space-Time Theories: Relativistic Physics and Philosophy of Science , Princeton: Princeton University Press.
  • –––, 1999, Reconsidering Logical Positivism , New York: Cambridge University Press.
  • –––, 2001, Dynamics of Reason , Stanford, CA: CSLI Publications.
  • –––, 2011, “Carnap on Theoretical Terms: Structuralism without Metaphysics,” Synthese , 180 (2): 249–263.
  • –––, 2013, Kant’s Construction of Nature: A Reading of the Metaphysical Foundations of Natural Science , Cambridge: Cambridge University Press.
  • Frigg, R. and S. Hartmann, 2012, “Models in Science”, The Stanford Encyclopedia of Philosophy (Fall 2012 Edition), E. N. Zalta (ed.), URL = < https://plato.stanford.edu/archives/fall2012/entries/models-science/ >.
  • Frigg, R. and I. Votsis, 2011, “Everything You Always Wanted to Know about Structural Realism but Were Afraid to Ask,” European Journal for Philosophy of Science , 1 (2): 227–276.
  • Galison, P., 1987, How Experiments End , Chicago: University of Chicago Press.
  • –––, 1988, “History, Philosophy, and the Central Metaphor,” Science in Context , 2 (1): 197–212.
  • –––, 1997, Image and Logic: A Material Culture of Microphysics , Chicago: University of Chicago Press.
  • Geary, J., 2011, I Is an Other: The Secret Life of Metaphor and How It Shapes the Way We See The World , New York: Harper Perennial.
  • Gentner, D., 1982, “Are Scientific Analogies Metaphors?” in Metaphor: Problems and Perspectives , D. Miall (ed.), Brighton: Harvester Press, pp. 106–132.
  • –––, 2003, “Analogical Reasoning, Psychology of,” in Encyclopedia of Cognitive Science , L. Nadel (ed.), London: Nature Publishing Group, pp. 106–112.
  • Giere, R., 1988, Explaining Science: A Cognitive Approach , Chicago: University of Chicago Press.
  • –––, 2004, “How Models Are Used to Represent Reality,” Philosophy of Science , 71 (5): 742–752.
  • –––, 2010, “An Agent-based Conception of Models and Scientific Representation,” Synthese , 172 (2): 269–281.
  • Giere, R., B. Bickle, and R. Mauldin, 2006, Understanding Scientific Reasoning , Belmont, CA: Thomson/Wadsworth, 5 th edition.
  • Ginnobili, S., 2016, “Missing Concepts in Natural Selection Theory Reconstructions,” History and Philosophy of the Life Sciences , 38 (Article 8). doi:10.1007/s40656-016-0109-y
  • Godfrey-Smith, P., 2003, Theory and Reality: An Introduction to the Philosophy of Science , Chicago: University of Chicago Press.
  • –––, 2006, “The Strategy of Model-Based Science,” Biology and Philosophy , 21 (5): 725–740.
  • Gould, S.J., 2002, The Structure of Evolutionary Theory , Cambridge, MA: Harvard University Press.
  • Griesemer, J., 1990, “Modeling in the Museum: On the Role of Remnant Models in the Work of Joseph Grinnell,” Biology and Philosophy , 5 (1): 3–36.
  • –––, 1991a, “Material Models in Biology,” PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1990 , (2): 79–94.
  • –––, 1991b, “Must Scientific Diagrams Be Eliminable? The Case of Path Analysis,” Biology and Philosophy , 6 (2): 155–180.
  • –––, 2013, “Formalization and the Meaning of Theory in the Inexact Biological Sciences,” Biological Theory , 7 (4): 298–310.
  • Hacking, I., 1983, Representing and Intervening: Introductory Topics in the Philosophy of Natural Science , Cambridge: Cambridge University Press.
  • –––, 2002, Historical Ontology , Cambridge, MA: Harvard University Press.
  • –––, 2007a, “On Not Being a Pragmatist: Eight Reasons and a Cause,” in New Pragmatists , C. Misak (ed.), New York: Oxford University Press, pp. 32–49.
  • –––, 2007b, “Natural Kinds: Rosy Dawn, Scholastic Twilight,” Royal Institute of Philosophy Supplements , 61: 203–240.
  • –––, 2009, Scientific Reason , Taipei: National Taiwan University Press.
  • –––, 2012, “Introduction,” in T.S. Kuhn, The Structure of Scientific Revolutions , 50 th Anniversary ed. (4 th ed.), Chicago: University of Chicago Press, pp. vii–xxxvii.
  • –––, 2014, Why Is There Philosophy of Mathematics At All? , Cambridge: Cambridge University Press.
  • Halvorson, H., 2012, “What Scientific Theories Could Not Be,” Philosophy of Science , 79 (2): 183–206.
  • –––, 2013, “The Semantic View, if Plausible, is Syntactic,” Philosophy of Science , 80 (3): 475–478.
  • –––, 2019, The Logic in Philosophy of Science , Cambridge: Cambridge University Press.
  • Hartl, D. and A. Clark, 1989, Principles of Population Genetics , Sunderland, MA: Sinauer Associates.
  • Hempel, C., 1952, Fundamentals of Concept Formation in Empirical Science , Chicago: University of Chicago Press.
  • –––, 1958, “The Theoretician’s Dilemma,” in Minnesota Studies in the Philosophy of Science (Volume 2), H. Feigl, M. Scriven, and G. Maxwell (eds.), Minneapolis: University of Minnesota Press, pp. 37–98.
  • –––, 1966, Philosophy of Natural Science , Englewood Cliffs, N.J.: Prentice-Hall.
  • –––, 1970, “On the ‘Standard Conception’ of Scientific Theories,” in Minnesota Studies in the Philosophy of Science (Volume 4), M. Radner and S. Winokur (eds.), Minneapolis: University of Minnesota Press, pp. 142–163.
  • Hermes, H. 1938, Eine Axiomatisierung der allgemeinen Mechanik (Forschungen zur Logik und zur Grundlegung der exacten Wissenschaften, Heft 3), Leipzig: S. Hirzel.
  • –––, 1959, “Zur Axiomatisierung der Mechanik,” in The Axiomatic Method with Special Reference to Geometry and Physics: Proceedings of an International Symposium Held at the University of California, Berkeley, December 26, 1957–January 4, 1958 , L. Henkin, P. Suppes, and A. Tarski (eds.), Amsterdam: North Holland, pp. 282–290.
  • Hesse, M., 1966, Models and Analogies in Science , Notre Dame: University of Notre Dame Press.
  • –––, 1967, “Models and Analogy in Science,” in The Encyclopedia of Philosophy (Volume 5), P. Edwards (ed.), New York: Macmillan, pp. 354–359.
  • Hitchcock, C. and J.D. Velasco, 2014, “Newtonian and Evolutionary Forces,” Ergo , 1 (2): 39–77.
  • Hochman, A., 2013, “Against the New Racial Naturalism,” The Journal of Philosophy 110 (6): 331–351.
  • Hodges, W., 1997, A Shorter Model Theory , New York: Cambridge University Press.
  • –––, 2013, “Model Theory”, The Stanford Encyclopedia of Philosophy (Fall 2013 Edition), E. N. Zalta (ed.), URL = < https://plato.stanford.edu/archives/fall2013/entries/model-theory/ >.
  • Hoffman, R., 1980, “Metaphor in Science,” in Cognition and Figurative Language , R. Honeck (ed.), Hillsdale: Lawrence Erlbaum Associates, pp. 393–423.
  • Holton, G., 1988, Thematic Origins of Scientific Thought: Kepler to Einstein , Cambridge, MA: Harvard University Press, 2 nd edition.
  • Hookway, C., 2013, “Pragmatism”, The Stanford Encyclopedia of Philosophy (Winter 2013 Edition), E. N. Zalta (ed.), URL = < https://plato.stanford.edu/archives/win2013/entries/pragmatism/ >.
  • Hull, D., 1975, “Central Subjects and Historical Narratives,” History & Theory , 14 (3): 253–274.
  • Jammer, M., 1961, Concepts of Mass in Classical and Modern Physics , Cambridge, MA: Harvard University Press; reprinted unabridged by Dover in 1997.
  • Jobling, M.A., M. Hurles, C. Tyler-Smith, 2004, Human Evolutionary Genetics. Origins, Peoples and Diseases , New York: Garland Science.
  • Jones, M., 2005, “Idealization and Abstraction: A Framework,” in Idealization XII: Correcting the Model – Idealization and Abstraction in the Sciences (Poznan Studies in the Philosophy of the Sciences and the Humanities, Volume 86), M. Jones and N. Cartwright (eds.), Amsterdam: Rodopi, pp. 173–217. (Same individual as Thomson-Jones 2012.)
  • Kaplan, J.M. and R.G. Winther, 2013, “Prisoners of Abstraction? The Theory and Measure of Genetic Variation, and the Very Concept of ‘Race’,” Biological Theory , 7 (4): 401–412.
  • Keller, E.F., 1995, Reconfiguring Life: Metaphors of Twentieth-Century Biology , New York: Columbia University Press.
  • Kitcher P., 1984, “1953 and All That. A Tale of Two Sciences,” Philosophical Review , 93 (3): 335–373.
  • –––, 1993, The Advancement of Science: Science Without Legend, Objectivity Without Illusion , New York: Oxford University Press.
  • –––, 2001, Science, Truth, and Democracy , New York: Oxford University Press.
  • Krivine, J., 2013 [1971], Introduction to Axiomatic Set Theory (Synthese Library, Volume 34), Dordrecht: D. Reidel.
  • Kuhn, T.S., 1970, The Structure of Scientific Revolutions , Chicago: University of Chicago Press, 2 nd edition.
  • –––, 1977, “Objectivity, Value Judgment, and Theory Choice,” in The Essential Tension: Selected Studies in Scientific Tradition and Change , T.S. Kuhn (ed.), Chicago: University of Chicago Press, pp. 320–339.
  • Ladyman, J., 2014, “Structural Realism”, The Stanford Encyclopedia of Philosophy (Spring 2014 Edition), E. N. Zalta (ed.), URL = < https://plato.stanford.edu/archives/spr2014/entries/structural-realism/ >.
  • Ladyman, J., O. Bueno, M. Suárez, and B. van Fraassen, 2011, “Scientific Representation: A Long Journey from Pragmatics to Pragmatics,” Metascience , 20 (3): 417–442.
  • Lakatos, I., 1980, The Methodology of Scientific Research Programmes (Philosophical Papers: Volume 1), Cambridge: Cambridge University Press.
  • Laudan, L., 1977, Progress and Its Problems: Towards a Theory of Scientific Growth , Berkeley, CA: University of California Press.
  • Leonelli, S., 2008, “Performing Abstraction: Two Ways of Modelling Arabidopsis thaliana ,” Biology and Philosophy , 23 (4): 509–528.
  • Levins, R., 1966, “The Strategy of Model Building in Population Biology,” American Scientist , 54 (4): 421–431.
  • Levins, R. and R. Lewontin, 1985, The Dialectical Biologist , Cambridge, MA: Harvard University Press.
  • Lewis, R.W., 1980, “Evolution: A System of Theories,” Perspectives in Biology and Medicine , 23 (4): 551–572.
  • Lewontin, R.C., 1972, “Apportionment of Human Diversity,” Evolutionary Biology , 6: 381–398.
  • –––, 1974, The Genetic Basis of Evolutionary Change , New York: Columbia University Press.
  • Lloyd, E., 1983, “The Nature of Darwin’s Support for the Theory of Natural Selection,” Philosophy of Science , 50 (1): 112–129.
  • –––, 1994 [1988], The Structure and Confirmation of Evolutionary Theory , Princeton: Princeton University Press.
  • –––, 2013 In Press, “Structure of Evolutionary Theory,” in International Encyclopedia of Social and Behavioral Sciences , W. Durham (ed.), 2 nd edition, Amsterdam: Elsevier.
  • London, F. and H. London, 1935, “The Electromagnetic Equations of the Supraconductor,” Proceedings of the Royal Society of London. Series A, Mathematical and Physical Sciences , 149 (866): 71–88.
  • Longino, H.E., 1995, “Gender, Politics, and the Theoretical Virtues,” Synthese 104 (3): 383–397.
  • –––, 2002, The Fate of Knowledge , Princeton: Princeton University Press.
  • –––, 2013, Studying Human Behavior: How Scientists Investigate Aggression & Sexuality , Chicago: University of Chicago Press.
  • López Beltrán, C., 1987, “La Explicación Evolucionista y el Uso de Modelos,” Masters Thesis, Posgrado en Filosofía de la Ciencia, Universidad Autónoma Metropolitana (Iztapalapa).
  • Lorenzano, P., 2013, “The Semantic Conception and the Structuralist View of Theories: A Critique of Suppe’s Criticisms,” Studies in History and Philosophy of Science (Part A) , 44: 600–607.
  • –––, 2014, “What is the Status of the Hardy-Weinberg Law within Population Genetics?,” in European Philosophy of Science: Philosophy of Science in Europe and the Viennese Heritage (Vienna Circle Institute Yearbook: Volume 17), M.C. Galavotti, E. Nemeth, F.L. Stadler F. (eds.), Cham, Switzerland: Springer, pp. 159–172.
  • Lowry, I., 1965, “A Short Course in Model Design,” Journal of the American Institute of Planners , 31 (2): 158–166.
  • Ludwig, D., 2015. “Against the New Metaphysics of Race,” Philosophy of Science 82: 1–21.
  • Lutz, S., 2012, “On a Straw Man in the Philosophy of Science: A Defense of the Received View,” HOPOS: The Journal of the International Society for the History of Philosophy of Science , 2 (1): 77–120.
  • –––, 2014, “What’s Right with a Syntactic Approach to Theories and Models?” Erkenntnis , 79 (8 supplement): 1475–1492.
  • –––, 2017, What “Was the Syntax-Semantics Debate in the Philosophy of Science About?,” Philosophy and Phenomenological Research , 95 (2): 319–352.
  • Mancosu, P., 2010, “Mathematical Style”, The Stanford Encyclopedia of Philosophy (Spring 2010 Edition), E. N. Zalta (ed.), URL = < https://plato.stanford.edu/archives/spr2010/entries/mathematical-style/ >.
  • Margenau, H., 1950, The Nature of Physical Reality: A Philosophy of Modern Physics , New York: McGraw-Hill.
  • Marker, D., 2002, Model Theory: An Introduction , New York: Springer.
  • Martínez, S., 2003, Geografía de las prácticas científicas: Racionalidad, heurística y normatividad , Mexico City: UNAM Press.
  • –––, 2014, “Technological Scaffolds for Culture and Cognition,” in Developing Scaffolds in Evolution, Culture and Cognition , L. Caporael, J. Griesemer, and W. Wimsatt (eds.), Cambridge, MA: MIT Press, pp. 249–264.
  • Matheson, C. and J. Dallmann, 2014, “Historicist Theories of Scientific Rationality”, The Stanford Encyclopedia of Philosophy (Fall 2014 Edition), E. N. Zalta (ed.), URL = < https://plato.stanford.edu/archives/fall2014/entries/rationality-historicist/ >.
  • McKinsey, J.C.C., A.C. Sugar, and P. Suppes, 1953, “Axiomatic Foundations of Classical Particle Mechanics,” Journal of Rational Mechanics and Analysis , 2 (2): 253–272.
  • Minsky, M., 1965, “Matter, Mind, and Models,” in Proceedings of the International Federation for Information Processing Congress (Volume 1), W. Kalenich (ed.), Washington D.C.: Spartan Books, pp. 45–49.
  • Morgan, M., 2012, The World in the Model: How Economists Work and Think , New York: Cambridge University Press.
  • Morgan, M.S. and M. Morrison (eds.), 1999, Models as Mediators: Perspectives on Natural and Social Science , Cambridge: Cambridge University Press.
  • Mormann, T., 2007, “The Structure of Scientific Theories in Logical Empiricism,” The Cambridge Companion to Logical Empiricism , in A. Richardson and T. Uebel (eds.), Cambridge: Cambridge University Press, pp. 136–162.
  • Morrison, M., 2007, “Where Have All the Theories Gone?,” Philosophy of Science , 74 (2): 195–228.
  • Moulines, C., 1976, “Approximate Application of Empirical Theories: A General Explication,” Erkenntnis , 10 (2): 201–227.
  • –––, 2002, “Introduction: Structuralism as a Program for Modelling Theoretical Science,” Synthese , 130 (1): 1–11.
  • Nagel, E., 1961, The Structure of Science: Problems in the Logic of Scientific Explanation , New York: Harcourt, Brace & World.
  • –––, 1979, “Issues in the Logic of Reductive Explanations,” in Teleology Revisited and Other Essays in the Philosophy and History of Science , New York: Columbia University Press, pp. 95–117.
  • Neurath, O., 1932, “Protokollsätze”, Erkenntnis , 3: 204–214; “Protocol Statements,” in Philosophical Papers 1913-1946 , R.S. Cohen and M. Neurath (eds.), Dordrecht: Reidel (1983), pp. 91–99.
  • Nicholson, D. and R. Gawne, 2014, “Rethinking Woodger’s Legacy in the Philosophy of Biology,” Journal of the History of Biology , 47 (2): 243–292.
  • Nolte, D.D., 2010, “The Tangled Tale of Phase Space,” Physics Today , April: 33–38.
  • Okasha, S., 2012, “Population Genetics”, The Stanford Encyclopedia of Philosophy (Fall 2012 Edition), E. N. Zalta (ed.), URL = < https://plato.stanford.edu/archives/fall2012/entries/population-genetics/ >.
  • Oppenheimer, J.R., 1956, “Analogy in Science,” American Psychologist , 11 (3): 127–135.
  • Oyama, S., 2000, The Ontogeny of Information: Developmental Systems and Evolution , 2 nd ed., Durham: Duke University Press.
  • Pereda, C., 2013, “Ulises Moulines y la concepción estructural de las teorías científicas,” in La filosofía en México en el siglo XX: Apuntes de un participante , C. Pereda, Mexico City: CONACULTA (Consejo Nacional para la Cultura y las Artes), pp. 200–212.
  • Pickstone, J.V., 2000, Ways of Knowing: A New History of Science, Technology and Medicine , Chicago: University of Chicago Press.
  • Pigliucci, M. and G.B. Müller, 2010, Evolution: The Extended Synthesis , Cambridge, MA: MIT Press.
  • Popper, K., 1996 [1976], “The Myth of the Framework,” In The Myth of the Framework: In Defence of Science and Rationality , M. A. Notturno (ed), Abingdon: Routledge, pp. 33–64.
  • Pritchard J.K., M. Stephens, and P. Donnelly, 2000, “Inference of Population Structure Using Multilocus Genotype Data,” Genetics , 155 (2): 945–959.
  • Preston, J., 2012, “Paul Feyerabend”, The Stanford Encyclopedia of Philosophy (Winter 2012 Edition), E. N. Zalta (ed.), URL = < https://plato.stanford.edu/archives/win2012/entries/feyerabend/ >.
  • Przełęcki, M., 1969, The Logic of Empirical Theories , London: Routledge & Kegan Paul.
  • Putnam, H., 1962, “What Theories Are Not,” in Logic, Methodology, and Philosophy of Science: Proceedings of the 1960 International Congress , E. Nagel, P. Suppes, and A. Tarski (eds.), Stanford, CA: Stanford University Press, pp. 240–251.
  • Reichenbach, H., 1938, Experience and Prediction: An Analysis of the Foundations and the Structure of Knowledge , Chicago: University of Chicago Press.
  • –––, 1965 [1920], The Theory of Relativity and A Priori Knowledge , with an introduction by M. Reichenbach, Berkeley: University of California Press. Original: Relativitätstheorie und Erkenntnis apriori , Berlin: Springer.
  • –––, 1969 [1924], The Axiomatization of the Theory of Relativity , with an introduction by W.C. Salmon. Berkeley-Los Angeles: University of California Press. Original: Axiomatik der relativistischen Raum-Zeit-Lehre , Braunschweig: F. Vieweg & Sohn.
  • –––, 1978, Selected Writings, 1909–1953: With a Selection of Biographical and Autobiographical Sketches (Volumes 1–2), Dordrecht: Reidel.
  • Rice, S., 2004, Evolutionary Theory: Mathematical and Conceptual Foundations , Sunderland, MA: Sinauer Associates.
  • Richards, R., 1992, “The Structure of Narrative Explanation in History and Biology,” in History and Evolution , M. Nitecki and D. Nitecki (eds.), Albany: State University of New York Press, pp. 19–53.
  • Richardson, A., 2002, “Engineering Philosophy of Science: American Pragmatism and Logical Empiricism in the 1930s,” Philosophy of Science , 69 (S3): S36–S47.
  • Rosenberg N.A., J.K. Pritchard, J.L. Weber, H.M. Cann, K.K. Kidd, L.A. Zhivotovsky, and M.A. Feldman, 2002, “Genetic Structure of Human Populations,” Science , 298 (5602): 2381–2385.
  • Rosenblueth, A. and N. Wiener, 1945, “The Role of Models in Science,” Philosophy of Science , 12 (4): 316–321.
  • Ruse, M., 1975, “Charles Darwin’s Theory of Evolution: An Analysis,” Journal of the History of Biology , 8 (2): 219–241.
  • Rutte, H., 1991, “Neurath contra Schlick. On the Discussion of Truth in the Vienna Circle,” in Rediscovering the Forgotten Vienna Circle: Austrian studies on Otto Neurath and the Vienna Circle , T. Uebel (ed.), Dordrecht: Kluwer, pp. 169–174.
  • Sarkar, S., 1998, Genetics and Reductionism , Cambridge: Cambridge University Press.
  • Savage, C.W., 1990, “Preface,” in Scientific Theories. Minnesota Studies in the Philosophy of Science. Volume 14, C.W. Savage (ed.), Minneapolis: University of Minnesota Press, pp. vii–ix.
  • Schaffner K., 1969, “Correspondence Rules,” Philosophy of Science , 36 (3): 280–290.
  • –––, 1976, “Reductionism in Biology: Prospects and Problems,” in PSA : Proceedings of the Biennial Meeting of the Philosophy of Science Association 1974 : 613–632.
  • –––, 1993, Discovery and Explanation in Biology and Medicine , Chicago: University of Chicago Press.
  • Schlick, M., 1925 [1918], General Theory of Knowledge , LaSalle, IL: Open Court.
  • –––, 1934, “Über das Fundament der Erkenntnis,” Erkenntnis , 4 (1): 79–99.
  • Schmidt, H.-J., 2014, “Structuralism in Physics”, The Stanford Encyclopedia of Philosophy (Spring 2014 Edition), E. N. Zalta (ed.), URL = < https://plato.stanford.edu/archives/spr2014/entries/physics-structuralism/ >.
  • Shapin, S. and S. Schaffer, 1985, Leviathan and the Air-Pump: Hobbes, Boyle, and the Experimental Life , Princeton: Princeton University Press.
  • Simon, H., 1954, “The Axiomatization of Classical Mechanics,” Philosophy of Science , 21 (4): 340–343.
  • –––, 1957, Models of Man , New York: Wiley.
  • –––, 1970, “The Axiomatization of Physical Theories,” Philosophy of Science , 37 (1): 16–26.
  • Smith, B.C., 1996, On the Origin of Objects , Cambridge, MA: MIT Press.
  • Sneed, J., 1979, The Logical Structure of Mathematical Physics , Dordrecht: D. Reidel, 2 nd edition.
  • Spencer, Q., 2015, “Philosophy of Race Meets Population Genetics,” Studies in History and Philosophy of Biological and Biomedical Sciences 52: 46–55.
  • Stegmüller, W., 1976, The Structure and Dynamics of Theories , New York: Springer.
  • –––, 1979, “The Structuralist View: Survey, Recent Developments and Answers to Some Criticisms”, in The Logic and Epistemology of Scientific Change , I. Niiniluoto and R. Tuomela (eds.), Amsterdam: North Holland.
  • Suárez, M., 1999, “The Role of Models in the Application of Scientific Theories; Epistemological Implications,” in Models as Mediators. Perspectives on Natural and Social Science , M.S. Morgan and M. Morrison (eds.), Cambridge: Cambridge University Press, pp. 168–196.
  • –––, 2011, Comment on van Fraassen Scientific Representation: Paradoxes of Perspective , in Ladyman, J., O. Bueno, M. Suárez, and B. van Fraassen, “Scientific Representation: A Long Journey from Pragmatics to Pragmatics,” Metascience , 20 (3): 428–433.
  • Suárez, M. and N. Cartwright, 2008, “Theories: Tools versus Models,” Studies in History and Philosophy of Modern Physics , 39 (1): 62–81.
  • Suárez, M. and F. Pero, 2019, “The Representational Semantic Conception,” Philosophy of Science , 86 (2): 344–365.
  • Suppe, F., 1977, The Structure of Scientific Theories , Urbana, IL: University of Illinois Press.
  • –––, 1989, The Semantic Conception of Theories and Scientific Realism , Chicago: University of Illinois Press.
  • –––, 2000, “Understanding Scientific Theories: An Assessment of Developments,” PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1998 , (2): S102–S115.
  • Suppes, P., 1957, Introduction to Logic , Princeton: D. Van Nostrand Co.
  • –––, 1960, “A Comparison of the Meaning and Uses of Models in Mathematics and the Empirical Sciences,” Synthese , 12 (2-3): 287–301.
  • –––, 1962, “Models of Data,” in Logic, Methodology, and Philosophy of Science: Proceedings of the 1960 International Congress , E. Nagel, P. Suppes, and A. Tarski (eds.), Stanford, CA: Stanford University Press, pp. 252–261.
  • –––, 1967, “What is a Scientific Theory?,” In Philosophy of Science Today , S. Morgenbesser (ed.), New York: Basic Books, pp. 55–67.
  • –––, 1968, “The Desirability of Formalization in Science,” The Journal of Philosophy , 65 (20): 651–664.
  • –––, 1978, “The Plurality of Science,” PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1978 , (2): 3–16.
  • –––, 2002, Representation and Invariance of Scientific Structures , Stanford, CA: CSLI Publications.
  • Swoyer, C., 2014, “Relativism”, The Stanford Encyclopedia of Philosophy (Winter 2014 Edition), E. N. Zalta (ed.), URL = < https://plato.stanford.edu/archives/win2014/entries/relativism/ >.
  • Thompson, P., 1989, The Structure of Biological Theories , Albany: SUNY Press.
  • –––, 2007, “Formalisations of Evolutionary Biology,” in Philosophy of Biology , M. Matthen and C. Stephens (eds.), Elsevier, Amsterdam, pp. 485–523
  • Thomson-Jones, M., 2012, “Modelling without Mathematics,” Philosophy of Science , 79 (5): 761–772. (Same individual as Jones 2005.)
  • Toulmin, S., 1972, Human Understanding: The Collective Use and Evolution of Concepts , Princeton: Princeton University Press.
  • Tuomi, J., 1981, “Structure and Dynamics of Darwinian Evolutionary Theory,” Systematic Zoology , 30 (1): 22–31.
  • –––, 1992, “Evolutionary Synthesis: A Search for the Strategy,” Philosophy of Science , 59 (3): 429–438.
  • Tversky, A., 1977, “Features of Similarity,” Psychological Review , 84 (4): 327–352.
  • Uebel, T., 2014, “Vienna Circle”, The Stanford Encyclopedia of Philosophy (Spring 2014 Edition), E. N. Zalta (ed.), URL = < https://plato.stanford.edu/archives/spr2014/entries/vienna-circle/ >.
  • van Benthem J., 2012, “The Logic of Empirical Theories Revisited,” Synthese , 186 (3): 775–792.
  • van Fraassen, B., 1967, “Meaning Relations among Predicates,” Noûs , 1 (2): 161–179.
  • –––, 1970, “On the Extension of Beth’s Semantics of Physical Theories,” Philosophy of Science , 37 (3): 325–339.
  • –––, 1980, The Scientific Image , Oxford: Oxford University Press.
  • –––, 1981, “Theory Construction and Experiment: An Empiricist View,” PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1980 , (2): 663–678.
  • –––, 1989, Laws and Symmetry , New York: Oxford University Press.
  • –––, 2008, Scientific Representation: Paradoxes of Perspective , New York: Oxford University Press.
  • van Riel, R. and R. Van Gulick, 2014, “Scientific Reduction”, The Stanford Encyclopedia of Philosophy (Summer 2014 Edition), E. N. Zalta (ed.), URL = < https://plato.stanford.edu/archives/sum2014/entries/scientific-reduction/ >.
  • Van Valen, L., 1976, “Domains, Deduction, the Predictive Method, and Darwin,” Evolutionary Theory , 1: 231–245.
  • Vicedo, M., 1995, “Scientific Styles: Toward Some Common Ground in the History, Philosophy, and Sociology of Science,” Perspectives on Science , 3: 231–254.
  • Vickers, P., 2009, “Can Partial Structures Accommodate Inconsistent Science?” Principia , 13 (2): 233–250.
  • Walsh, D., 2015, Organisms, Agency, and Evolution, Cambridge: Cambridge University Press.
  • Weisberg, M., 2013, Simulation and Similarity: Using Models to Understand the World , New York: Oxford University Press.
  • Wessels, L., 1976, “Laws and Meaning Postulates in van Fraassen’s View of Theories,” in PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1974 : 215–234.
  • Williams, M., 1970, “Deducing the Consequences of Selection: A Mathematical Model,” Journal of Theoretical Biology , 48: 343–385.
  • –––, 1973, “The Logical Status of Natural Selection and other Evolutionary Controversies: Resolution by Axiomatization,” in M. Bunge (ed.), The Methodological Unity of Science , Dordrecht: D. Reidel, pp. 84–102.
  • Wimsatt, W.C., 2007, Re-Engineering Philosophy for Limited Beings: Piecewise Approximations to Reality , Cambridge, MA: Harvard University Press.
  • Winsberg, E., 2010, Science in the Age of Computer Simulation , Chicago: University of Chicago Press.
  • –––, 2018, Philosophy and Climate Science , Cambridge: Cambridge University Press.
  • Winther, R.G., 2006a, “Parts and Theories in Compositional Biology,” Biology and Philosophy , 21 (4): 471–499.
  • –––, 2006b, “Fisherian and Wrightian Perspectives in Evolutionary Genetics and Model-Mediated Imposition of Theoretical Assumptions,” Journal of Theoretical Biology , 240 (2): 218–232.
  • –––, 2009, “Schaffner’s Model of Theory Reduction: Critique and Reconstruction,” Philosophy of Science , 76 (2): 119–142.
  • –––, 2011, “Part-Whole Science,” Synthese , 178 (3): 397–427.
  • –––, 2012a, “Mathematical Modeling in Biology: Philosophy and Pragmatics,” Frontiers in Plant Evolution and Development , 3: 102, doi:10.3389/fpls.2012.00102
  • –––, 2012b, “Interweaving Categories: Styles, Paradigms, and Models,” Studies in History and Philosophy of Science (Part A) , 43 (4): 628–639.
  • –––, 2014, “The Genetic Reification of ‘Race’? A Story of Two Mathematical Methods,” Critical Philosophy of Race , 2 (2): 204–223.
  • –––, 2020, When Maps Become the World , Chicago, IL: University of Chicago Press.
  • Winther, R.G., R. Giordano, M.D. Edge, and R. Nielsen, 2015, “The Mind, the Lab, and the Field: Three Kinds of Populations in Scientific Practice,” Studies in History and Philosophy of Biological and Biomedical Sciences , 52: 12–21.
  • Winther, R.G. and J.M. Kaplan, 2013, “Ontologies and Politics of Biogenomic ‘Race’,” Theoria. A Journal of Social and Political Theory (South Africa) , 60 (3): 54–80.
  • Woodger J.H., 1937, The Axiomatic Method in Biology , Cambridge: Cambridge University Press.
  • –––, 1959, “Studies in the Foundations of Genetics,” in The Axiomatic Method with Special Reference to Geometry and Physics: Proceedings of an International Symposium Held at the University of California, Berkeley, December 26, 1957 – January 4, 1958 , L. Henkin, P. Suppes, and A. Tarski (eds.), Amsterdam: North Holland, pp. 408–428.
  • Worrall, J., 1984, “An Unreal Image,” The British Journal for the Philosophy of Science , 35 (1): 65–80.
  • Wright, S., 1969, Evolution and the Genetics of Populations: A Treatise in Four Volumes (Volume 2: The Theory of Gene Frequencies), Chicago: University of Chicago Press.
  • Zach, R., 2009, “Hilbert’s Program”, The Stanford Encyclopedia of Philosophy (Spring 2009 Edition), E. N. Zalta (ed.), URL = < https://plato.stanford.edu/archives/spr2009/entries/hilbert-program/ >.
  • Ziman, J., 2000, Real Science: What It Is, and What It Means , Cambridge: Cambridge University Press.
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Carnap, Rudolf | cognitive science | confirmation | Darwinism | empiricism: logical | feminist philosophy, interventions: epistemology and philosophy of science | Feyerabend, Paul | genetics: population | incommensurability: of scientific theories | Kuhn, Thomas | models in science | model theory | paradox: Skolem’s | physics: structuralism in | pragmatism | rationality: historicist theories of | reduction, scientific | science: theory and observation in | scientific explanation | scientific realism | scientific representation | simulations in science | statistical physics: philosophy of statistical mechanics | structural realism | style: in mathematics | theoretical terms in science | underdetermination, of scientific theories | Vienna Circle

Acknowledgments

The following provided helpful feedback or conversation, or both, Jácome Armas, Nancy Cartwright, Mario Casanueva, Carl Craver, Eugene Earnshaw, Doc Edge, Michael Friedman, Sari Friedman, Fermín Fulda, Ryan Giordano, Ian Hacking, Hervé Kieffel, Elisabeth A. Lloyd, Helen Longino, Carlos López Beltrán, Greg Lusk, Sebastian Lutz, Sergio Martínez, Amir Najmi, Thomas Ryckman, Mette Bannergaard Johansen, Mette Smølz Skau, Bas van Fraassen, Denis Walsh, Ole Wæver, and two anonymous reviewers. Alex Dor, Cory Knudson, and Lucas McGranahan offered expert research assistance.

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100 Science Topics for Research Papers

Science is visible in nearly everything that we do on earth. Every day, new findings are made in science, and old discoveries are updated. Science research topics are one of the core elements in research projects. Many interesting topics in science can serve as awesome ideas for science research projects. For research projects, your scientific research topics have to meet your interest and purpose of research. A good scientific topic for research papers should be precise and informative. In this article, you'll find a scientific research topics list that suits the needs of your research papers.

Interesting Scientific Topics to Research for Your Paper

Astronomy science research papers topics.

  • Are comets the same as stars?
  • How long can a person survive in space?
  • The customized suits for astronauts and why other outfits may be unsuitable for space travel
  • How many planets and galaxies exist in the universe?
  • Space transportation: the features and importance of rockets
  • How calculating speed velocity helps in launching rockets?
  • The distance between the earth and space
  • What does it mean to measure light speed?
  • What scientists hope to achieve with traveling outside earth
  • Why gravity fails in space?
  • Do aliens exist outside earth?
  • Why have there been no astronauts in the sun?
  • What are the important machines used to perform space experiments?
  • Is Mars a better settling option to resolve the problem of overpopulation on earth
  • The potentials that space resources hold
  • Do spaceships exist?

Science Topics to Research on Geology and Mining

  • What are the extremely important machines used in excavation sites?
  • Rare gems: how the value of precious stones is measured?
  • The component of rocks
  • What are the distinguishing factors between rocks and sands?
  • Some important precious metals worth evaluating
  • Why geology remains an important part of science?
  • The difference between earth rocks and rocks from space
  • How do precious metals become buried underground?

Diseases and Experiments Scientific Topics to Research

  • Why experiments are important in scientific findings?
  • The different ways therapeutic techniques have improved over time
  • What are the possible ways of preventing chronic diseases?
  • Factors that trigger allergies in people
  • The makeup of the novelle Coronavirus
  • How artificial insulin helps diabetic patients manage their condition
  • Science's contribution towards helping disabled people live normal
  • The functions of the iron lung compared to oxygen
  • The nature of CT scan for diagnostic
  • How scientific breakthrough on HIV medication has helped make the disease less deadly?
  • Do mermaids exist?
  • Discussing some failed scientific experiments that have resulted in global crises
  • The process of artificial insemination
  • How forensic scientists get their jobs done?
  • How the human body works together?
  • Are human zombies real?

Science Topics for Research Paper on the history and future of science

  • The science behind mass extinction of a species
  • The possibilities of perfect human cloning
  • What is the future of robots inventions?
  • The possibilities of cars that fly
  • Science ability to foretell the future
  • The possibility of creating time travel machines
  • Evaluating past achievements of NASA
  • An insight into how science has made life easier over time
  • The scientific explanation of the big bang theory
  • The history of airplanes invention
  • Holograms: is there a future of instant teleportation with science?

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Interesting Science Topics on Computer and Technology

  • The functional differences between iOS and Android devices
  • What validates the accuracy of archaeological excavations?
  • The invention of electricity
  • What does virus mean in the computer world?
  • The evolution of computers from the time of invention
  • How computers have been made to do smarter things
  • The meaning of coding and programming
  • The technology of retinal security pass
  • Does the iron man suit exist?
  • Can an artificial brain keep a person alive?
  • Why cars can drive themselves
  • The differences between past and present communication patterns
  • How satellite can be used for different purposes?
  • Why games have become more advanced and important over time?
  • Encryption and coding for safeguarding important information
  • Why safes are difficult to crack?
  • How do artificial hearts work?
  • Why do tech jobs pay higher than other jobs?

Engineering and Architecture Cool Science Topics

  • The relationship between science and math
  • Can air turbulence during flights be eradicated?
  • The role of science in erecting structures
  • The improvement in road construction
  • What are the meaning and features of geoengineering?
  • How dams produce electricity?
  • How are bridges successfully constructed over water?
  • 3D printing: the mechanics that power 3D printing
  • The differences between engineering aided by computers and manual engineering
  • Why electric cars are considered better than cars fuelled by gas?

Other Interesting Science Topics

  • The scientific theory for acid rain: its composition and effects on living things
  • Grenades: why you must never pull the pin unless when necessary
  • Why seeds buried in the soil germinate and produce multiple fruits?
  • Natural disasters: the reasons for volcanic erosions and their effect on the environment
  • What is the ozone layer?
  • How do snakes swallow animals that are twice their size?
  • Can global warming be corrected?
  • The possibility of the ozone layer repairing itself
  • An insight into the composition of the galaxy
  • Analyzing the power of the atomic bomb and its effects during the second world war

Cool Science Topics on Biology

  • Cancer: science's ability to curb and prevent the common reoccurrence of cancer
  • Drugs and medication: do traditional herbs and processed tablets perform the same functions?
  • What are the differences between natural oxygen and artificial oxygen?
  • The ability of different animal species to adopt camouflage as a defense mechanism
  • How do the cells make up the human body?
  • Deforestation: What are the importance of preserving trees and flowers?
  • Childbirth: what happens when a woman suffers contractions during labor?
  • What happens after the flatline: is there a life after a person dies?
  • Analyzing the differences between DNA and cells
  • Why is it important for viruses to have a living host to survive?
  • What is the rationale behind stem cell treatment?

Your science research papers topics must be carefully picked because they determine how well your paper turns out. There are many science topics to write about. You can make your choice of science topic for research paper from the scientific topics for research papers listed in this article.

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Theories are formulated to explain, predict, and understand phenomena and, in many cases, to challenge and extend existing knowledge within the limits of critical bounded assumptions or predictions of behavior. The theoretical framework is the structure that can hold or support a theory of a research study. The theoretical framework encompasses not just the theory, but the narrative explanation about how the researcher engages in using the theory and its underlying assumptions to investigate the research problem. It is the structure of your paper that summarizes concepts, ideas, and theories derived from prior research studies and which was synthesized in order to form a conceptual basis for your analysis and interpretation of meaning found within your research.

Abend, Gabriel. "The Meaning of Theory." Sociological Theory 26 (June 2008): 173–199; Kivunja, Charles. "Distinguishing between Theory, Theoretical Framework, and Conceptual Framework: A Systematic Review of Lessons from the Field." International Journal of Higher Education 7 (December 2018): 44-53; Swanson, Richard A. Theory Building in Applied Disciplines . San Francisco, CA: Berrett-Koehler Publishers 2013; Varpio, Lara, Elise Paradis, Sebastian Uijtdehaage, and Meredith Young. "The Distinctions between Theory, Theoretical Framework, and Conceptual Framework." Academic Medicine 95 (July 2020): 989-994.

Importance of Theory and a Theoretical Framework

Theories can be unfamiliar to the beginning researcher because they are rarely applied in high school social studies curriculum and, as a result, can come across as unfamiliar and imprecise when first introduced as part of a writing assignment. However, in their most simplified form, a theory is simply a set of assumptions or predictions about something you think will happen based on existing evidence and that can be tested to see if those outcomes turn out to be true. Of course, it is slightly more deliberate than that, therefore, summarized from Kivunja (2018, p. 46), here are the essential characteristics of a theory.

  • It is logical and coherent
  • It has clear definitions of terms or variables, and has boundary conditions [i.e., it is not an open-ended statement]
  • It has a domain where it applies
  • It has clearly described relationships among variables
  • It describes, explains, and makes specific predictions
  • It comprises of concepts, themes, principles, and constructs
  • It must have been based on empirical data [i.e., it is not a guess]
  • It must have made claims that are subject to testing, been tested and verified
  • It must be clear and concise
  • Its assertions or predictions must be different and better than those in existing theories
  • Its predictions must be general enough to be applicable to and understood within multiple contexts
  • Its assertions or predictions are relevant, and if applied as predicted, will result in the predicted outcome
  • The assertions and predictions are not immutable, but subject to revision and improvement as researchers use the theory to make sense of phenomena
  • Its concepts and principles explain what is going on and why
  • Its concepts and principles are substantive enough to enable us to predict a future

Given these characteristics, a theory can best be understood as the foundation from which you investigate assumptions or predictions derived from previous studies about the research problem, but in a way that leads to new knowledge and understanding as well as, in some cases, discovering how to improve the relevance of the theory itself or to argue that the theory is outdated and a new theory needs to be formulated based on new evidence.

A theoretical framework consists of concepts and, together with their definitions and reference to relevant scholarly literature, existing theory that is used for your particular study. The theoretical framework must demonstrate an understanding of theories and concepts that are relevant to the topic of your research paper and that relate to the broader areas of knowledge being considered.

The theoretical framework is most often not something readily found within the literature . You must review course readings and pertinent research studies for theories and analytic models that are relevant to the research problem you are investigating. The selection of a theory should depend on its appropriateness, ease of application, and explanatory power.

The theoretical framework strengthens the study in the following ways :

  • An explicit statement of  theoretical assumptions permits the reader to evaluate them critically.
  • The theoretical framework connects the researcher to existing knowledge. Guided by a relevant theory, you are given a basis for your hypotheses and choice of research methods.
  • Articulating the theoretical assumptions of a research study forces you to address questions of why and how. It permits you to intellectually transition from simply describing a phenomenon you have observed to generalizing about various aspects of that phenomenon.
  • Having a theory helps you identify the limits to those generalizations. A theoretical framework specifies which key variables influence a phenomenon of interest and highlights the need to examine how those key variables might differ and under what circumstances.
  • The theoretical framework adds context around the theory itself based on how scholars had previously tested the theory in relation their overall research design [i.e., purpose of the study, methods of collecting data or information, methods of analysis, the time frame in which information is collected, study setting, and the methodological strategy used to conduct the research].

By virtue of its applicative nature, good theory in the social sciences is of value precisely because it fulfills one primary purpose: to explain the meaning, nature, and challenges associated with a phenomenon, often experienced but unexplained in the world in which we live, so that we may use that knowledge and understanding to act in more informed and effective ways.

The Conceptual Framework. College of Education. Alabama State University; Corvellec, Hervé, ed. What is Theory?: Answers from the Social and Cultural Sciences . Stockholm: Copenhagen Business School Press, 2013; Asher, Herbert B. Theory-Building and Data Analysis in the Social Sciences . Knoxville, TN: University of Tennessee Press, 1984; Drafting an Argument. Writing@CSU. Colorado State University; Kivunja, Charles. "Distinguishing between Theory, Theoretical Framework, and Conceptual Framework: A Systematic Review of Lessons from the Field." International Journal of Higher Education 7 (2018): 44-53; Omodan, Bunmi Isaiah. "A Model for Selecting Theoretical Framework through Epistemology of Research Paradigms." African Journal of Inter/Multidisciplinary Studies 4 (2022): 275-285; Ravitch, Sharon M. and Matthew Riggan. Reason and Rigor: How Conceptual Frameworks Guide Research . Second edition. Los Angeles, CA: SAGE, 2017; Trochim, William M.K. Philosophy of Research. Research Methods Knowledge Base. 2006; Jarvis, Peter. The Practitioner-Researcher. Developing Theory from Practice . San Francisco, CA: Jossey-Bass, 1999.

Strategies for Developing the Theoretical Framework

I.  Developing the Framework

Here are some strategies to develop of an effective theoretical framework:

  • Examine your thesis title and research problem . The research problem anchors your entire study and forms the basis from which you construct your theoretical framework.
  • Brainstorm about what you consider to be the key variables in your research . Answer the question, "What factors contribute to the presumed effect?"
  • Review related literature to find how scholars have addressed your research problem. Identify the assumptions from which the author(s) addressed the problem.
  • List  the constructs and variables that might be relevant to your study. Group these variables into independent and dependent categories.
  • Review key social science theories that are introduced to you in your course readings and choose the theory that can best explain the relationships between the key variables in your study [note the Writing Tip on this page].
  • Discuss the assumptions or propositions of this theory and point out their relevance to your research.

A theoretical framework is used to limit the scope of the relevant data by focusing on specific variables and defining the specific viewpoint [framework] that the researcher will take in analyzing and interpreting the data to be gathered. It also facilitates the understanding of concepts and variables according to given definitions and builds new knowledge by validating or challenging theoretical assumptions.

II.  Purpose

Think of theories as the conceptual basis for understanding, analyzing, and designing ways to investigate relationships within social systems. To that end, the following roles served by a theory can help guide the development of your framework.

  • Means by which new research data can be interpreted and coded for future use,
  • Response to new problems that have no previously identified solutions strategy,
  • Means for identifying and defining research problems,
  • Means for prescribing or evaluating solutions to research problems,
  • Ways of discerning certain facts among the accumulated knowledge that are important and which facts are not,
  • Means of giving old data new interpretations and new meaning,
  • Means by which to identify important new issues and prescribe the most critical research questions that need to be answered to maximize understanding of the issue,
  • Means of providing members of a professional discipline with a common language and a frame of reference for defining the boundaries of their profession, and
  • Means to guide and inform research so that it can, in turn, guide research efforts and improve professional practice.

Adapted from: Torraco, R. J. “Theory-Building Research Methods.” In Swanson R. A. and E. F. Holton III , editors. Human Resource Development Handbook: Linking Research and Practice . (San Francisco, CA: Berrett-Koehler, 1997): pp. 114-137; Jacard, James and Jacob Jacoby. Theory Construction and Model-Building Skills: A Practical Guide for Social Scientists . New York: Guilford, 2010; Ravitch, Sharon M. and Matthew Riggan. Reason and Rigor: How Conceptual Frameworks Guide Research . Second edition. Los Angeles, CA: SAGE, 2017; Sutton, Robert I. and Barry M. Staw. “What Theory is Not.” Administrative Science Quarterly 40 (September 1995): 371-384.

Structure and Writing Style

The theoretical framework may be rooted in a specific theory , in which case, your work is expected to test the validity of that existing theory in relation to specific events, issues, or phenomena. Many social science research papers fit into this rubric. For example, Peripheral Realism Theory, which categorizes perceived differences among nation-states as those that give orders, those that obey, and those that rebel, could be used as a means for understanding conflicted relationships among countries in Africa. A test of this theory could be the following: Does Peripheral Realism Theory help explain intra-state actions, such as, the disputed split between southern and northern Sudan that led to the creation of two nations?

However, you may not always be asked by your professor to test a specific theory in your paper, but to develop your own framework from which your analysis of the research problem is derived . Based upon the above example, it is perhaps easiest to understand the nature and function of a theoretical framework if it is viewed as an answer to two basic questions:

  • What is the research problem/question? [e.g., "How should the individual and the state relate during periods of conflict?"]
  • Why is your approach a feasible solution? [i.e., justify the application of your choice of a particular theory and explain why alternative constructs were rejected. I could choose instead to test Instrumentalist or Circumstantialists models developed among ethnic conflict theorists that rely upon socio-economic-political factors to explain individual-state relations and to apply this theoretical model to periods of war between nations].

The answers to these questions come from a thorough review of the literature and your course readings [summarized and analyzed in the next section of your paper] and the gaps in the research that emerge from the review process. With this in mind, a complete theoretical framework will likely not emerge until after you have completed a thorough review of the literature .

Just as a research problem in your paper requires contextualization and background information, a theory requires a framework for understanding its application to the topic being investigated. When writing and revising this part of your research paper, keep in mind the following:

  • Clearly describe the framework, concepts, models, or specific theories that underpin your study . This includes noting who the key theorists are in the field who have conducted research on the problem you are investigating and, when necessary, the historical context that supports the formulation of that theory. This latter element is particularly important if the theory is relatively unknown or it is borrowed from another discipline.
  • Position your theoretical framework within a broader context of related frameworks, concepts, models, or theories . As noted in the example above, there will likely be several concepts, theories, or models that can be used to help develop a framework for understanding the research problem. Therefore, note why the theory you've chosen is the appropriate one.
  • The present tense is used when writing about theory. Although the past tense can be used to describe the history of a theory or the role of key theorists, the construction of your theoretical framework is happening now.
  • You should make your theoretical assumptions as explicit as possible . Later, your discussion of methodology should be linked back to this theoretical framework.
  • Don’t just take what the theory says as a given! Reality is never accurately represented in such a simplistic way; if you imply that it can be, you fundamentally distort a reader's ability to understand the findings that emerge. Given this, always note the limitations of the theoretical framework you've chosen [i.e., what parts of the research problem require further investigation because the theory inadequately explains a certain phenomena].

The Conceptual Framework. College of Education. Alabama State University; Conceptual Framework: What Do You Think is Going On? College of Engineering. University of Michigan; Drafting an Argument. Writing@CSU. Colorado State University; Lynham, Susan A. “The General Method of Theory-Building Research in Applied Disciplines.” Advances in Developing Human Resources 4 (August 2002): 221-241; Tavallaei, Mehdi and Mansor Abu Talib. "A General Perspective on the Role of Theory in Qualitative Research." Journal of International Social Research 3 (Spring 2010); Ravitch, Sharon M. and Matthew Riggan. Reason and Rigor: How Conceptual Frameworks Guide Research . Second edition. Los Angeles, CA: SAGE, 2017; Reyes, Victoria. Demystifying the Journal Article. Inside Higher Education; Trochim, William M.K. Philosophy of Research. Research Methods Knowledge Base. 2006; Weick, Karl E. “The Work of Theorizing.” In Theorizing in Social Science: The Context of Discovery . Richard Swedberg, editor. (Stanford, CA: Stanford University Press, 2014), pp. 177-194.

Writing Tip

Borrowing Theoretical Constructs from Other Disciplines

An increasingly important trend in the social and behavioral sciences is to think about and attempt to understand research problems from an interdisciplinary perspective. One way to do this is to not rely exclusively on the theories developed within your particular discipline, but to think about how an issue might be informed by theories developed in other disciplines. For example, if you are a political science student studying the rhetorical strategies used by female incumbents in state legislature campaigns, theories about the use of language could be derived, not only from political science, but linguistics, communication studies, philosophy, psychology, and, in this particular case, feminist studies. Building theoretical frameworks based on the postulates and hypotheses developed in other disciplinary contexts can be both enlightening and an effective way to be more engaged in the research topic.

CohenMiller, A. S. and P. Elizabeth Pate. "A Model for Developing Interdisciplinary Research Theoretical Frameworks." The Qualitative Researcher 24 (2019): 1211-1226; Frodeman, Robert. The Oxford Handbook of Interdisciplinarity . New York: Oxford University Press, 2010.

Another Writing Tip

Don't Undertheorize!

Do not leave the theory hanging out there in the introduction never to be mentioned again. Undertheorizing weakens your paper. The theoretical framework you describe should guide your study throughout the paper. Be sure to always connect theory to the review of pertinent literature and to explain in the discussion part of your paper how the theoretical framework you chose supports analysis of the research problem or, if appropriate, how the theoretical framework was found to be inadequate in explaining the phenomenon you were investigating. In that case, don't be afraid to propose your own theory based on your findings.

Yet Another Writing Tip

What's a Theory? What's a Hypothesis?

The terms theory and hypothesis are often used interchangeably in newspapers and popular magazines and in non-academic settings. However, the difference between theory and hypothesis in scholarly research is important, particularly when using an experimental design. A theory is a well-established principle that has been developed to explain some aspect of the natural world. Theories arise from repeated observation and testing and incorporates facts, laws, predictions, and tested assumptions that are widely accepted [e.g., rational choice theory; grounded theory; critical race theory].

A hypothesis is a specific, testable prediction about what you expect to happen in your study. For example, an experiment designed to look at the relationship between study habits and test anxiety might have a hypothesis that states, "We predict that students with better study habits will suffer less test anxiety." Unless your study is exploratory in nature, your hypothesis should always explain what you expect to happen during the course of your research.

The key distinctions are:

  • A theory predicts events in a broad, general context;  a hypothesis makes a specific prediction about a specified set of circumstances.
  • A theory has been extensively tested and is generally accepted among a set of scholars; a hypothesis is a speculative guess that has yet to be tested.

Cherry, Kendra. Introduction to Research Methods: Theory and Hypothesis. About.com Psychology; Gezae, Michael et al. Welcome Presentation on Hypothesis. Slideshare presentation.

Still Yet Another Writing Tip

Be Prepared to Challenge the Validity of an Existing Theory

Theories are meant to be tested and their underlying assumptions challenged; they are not rigid or intransigent, but are meant to set forth general principles for explaining phenomena or predicting outcomes. Given this, testing theoretical assumptions is an important way that knowledge in any discipline develops and grows. If you're asked to apply an existing theory to a research problem, the analysis will likely include the expectation by your professor that you should offer modifications to the theory based on your research findings.

Indications that theoretical assumptions may need to be modified can include the following:

  • Your findings suggest that the theory does not explain or account for current conditions or circumstances or the passage of time,
  • The study reveals a finding that is incompatible with what the theory attempts to explain or predict, or
  • Your analysis reveals that the theory overly generalizes behaviors or actions without taking into consideration specific factors revealed from your analysis [e.g., factors related to culture, nationality, history, gender, ethnicity, age, geographic location, legal norms or customs , religion, social class, socioeconomic status, etc.].

Philipsen, Kristian. "Theory Building: Using Abductive Search Strategies." In Collaborative Research Design: Working with Business for Meaningful Findings . Per Vagn Freytag and Louise Young, editors. (Singapore: Springer Nature, 2018), pp. 45-71; Shepherd, Dean A. and Roy Suddaby. "Theory Building: A Review and Integration." Journal of Management 43 (2017): 59-86.

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Social Theory Research Paper Topics

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Social theory begins with ordinary questions, like why do some passively accept authority while others respond with political violence? Religions provided answers in a distant past. Social theory emerged as a secular alternative, often joining ethical and positive elements. Three traditions of social theory are important for the social sciences.

115 Social Theory Research Paper Topics

  • Actor Network Theory
  • Affect Control Theory and Impression Formation
  • Annales School
  • Attribution Theory
  • Behaviorism
  • Biosociological Theories
  • Birmingham School
  • Cognitive Balance Theory (Heider)
  • Cognitive Dissonance Theory (Festinger)
  • Comparative-Historical Sociology
  • Computational Sociology
  • Conflict Theory
  • Constructionism
  • Control Balance Theory
  • Conversation Analysis
  • Critical Realism
  • Critical Theory/Frankfurt School
  • Decision-Making Theory and Research
  • Demographic Transition Theory
  • Dependency Theory
  • Deterrence Theory
  • Dialectical Materialism
  • Diffusion Theories
  • Economic Determinism
  • Elementary Theory
  • Emergent Norm Theory
  • Essentialism and Constructionism
  • Ethnomethodology
  • Exchange Network Theory
  • Existential Sociology
  • Expectation States Theory
  • Field Theory
  • French School of Sociology
  • Functionalism and Structuralism
  • Game Theory
  • Game Theory and Strategic Interaction
  • German Sociology
  • Grounded Theory
  • Hermeneutics
  • Human Sociobiology
  • Identity Control Theory
  • Identity Theory
  • Information and Resource Processing Paradigm
  • Labeling Theory
  • Labor Process
  • Major Personality Theories
  • Management Theory
  • Marxism and Sociology
  • Mate Selection Theories
  • Mathematical Sociology
  • Meta-Analysis
  • Micro–Macro Links
  • Modernization Theory
  • New Institutional Theory
  • Organization Theory
  • Organizations and the Theory of the Firm
  • Personality Theory
  • Phenomenology
  • Poetics in Social Science
  • Political Process Theory
  • Posthumanism
  • Postmodern Social Theory
  • Postmodernism
  • Poststructuralism
  • Power Dependence Theory
  • Practical Knowledge
  • Probability Theory
  • Psychoanalysis
  • Queer Theory
  • Rational Choice Theory
  • Recognition
  • Regulation Theory
  • Relational Cohesion Theory
  • Resource Mobilization Theory
  • Role Theory
  • Routine Activity Theory
  • Scripting Theories
  • Self-Control Theory
  • Situationists
  • Social Comparison Theory
  • Social Darwinism
  • Social Disorganization Theory
  • Social Exchange Theory
  • Social Identity Theory
  • Social Learning Theory
  • Social Network Theory
  • Social Resources Theory
  • Society and Biology
  • Society and Technological Risks
  • Sociocultural Anthropology
  • Sociolinguistics
  • Status Construction Theory
  • Strain Theories
  • Stratification: Functional and Conflict Theories
  • Stress and Stress Theories
  • Structural Functional Theory
  • Structuration Theory
  • Symbolic Interaction Theory
  • System Theories
  • Theoretical Research Programs
  • Theories of Aging and the Life Course
  • Theories of Deviance
  • Theories of Power
  • Theories of Self Esteem
  • Theories of Social Justice
  • Theories of Stratification and Inequality
  • Theory and Methods
  • Theory Construction
  • Value Theory and Research

A first tradition comes from Thomas Hobbes (1588-1679). After years of bloody warfare between Catholics and Protestants, Hobbes’s Leviathan (1651) offered a worldly theory of social order. What was really at issue was power. As an early example of what would be termed ideology critique, Hobbes asks “cui bono?”—whose interest does this idea serve? People obey, he argued, because of fear of violent death. Social order thus turns on who has ultimate power over violence. If there is not one final authority, there would be war of all against all, and life would be “solitary, poor, nasty, brutish, and short.” Better, he argued, is a society founded on fear of a great leviathan, whose power guarantees stability.

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Leviathan relied on no Absolute Good, whether God or Nature. In tracing all “higher” ideas to “lower” things—power, fear, death, the body, violence—Hobbes set the tone for one main strand of social theorizing. This approach continued in writers from Karl Marx (18181883) to Michel Foucault (1926-1984) and Pierre Bourdieu (1930-2002). While each differs, they are Hobbesian in asking “cui bono?”—and answering with a complex power struggle, even if it is denied, for example, in art, religion, and morality. This first type of social theory ferrets out hidden power structures behind everyday interactions and institutions.

Hobbes’s stress on fear led others to ask: Does not social order depend on more? What of obligation or love? How could the passions of a millennium and a half of Christianity be redirected onto earth, without producing the disastrous consequences Hobbes feared?

Such questions led to a second strand of social theory, stemming from Jean-Jacques Rousseau (1712-1778). He emphasized not fear but devotion as the foundation of social order. In our long-forgotten natural condition, Rousseau argued, we were independent, loving ourselves for ourselves; but society creates new needs, amour prope We love ourselves based upon how much others love us. Not power, but the struggle for recognition and status regulates social order.

For Rousseau, justice can transcend nature and inequality. Justice depends in turn on the social contract, wherein each person must totally submit to the general will. Private freedom, he argued, depended on public equality, which required a “lawgiver.” Moreover, the social bond, to last, should be held sacred.

Karl Marx (1818-1883) and V. I. Lenin (18701924) transformed the lawgiver into the revolutionary vanguard; the redefined social contract was the abolition of private property, as the condition of freedom and justice. Emile Durkheim (1858-1917) later pursued Rousseau’s connections between social solidarity and religious sentiment.

Critical theorists—Theodore Adorno (1903-1969), Max Horkheimer (1895-1973), Herbert Marcuse (1898-1979), Axel Honneth—explored how modern societies create vast inequalities, not only in wealth, but respect and self-worth. They expanded Rousseau’s ideas that culture can create unnecessary dependencies, focusing on the “culture industry”—the popular press, music, movies, advertising, and fashions. These sought to promote “needs” like Marx’s false consciousness, where people became blinded to their own interests and dependent upon corporate and political masters. Some, like David Riesman (1909-2002), extended Rousseau’s amour propre to the 1950s conformism of American “other-directed-ness,” while others, like Daniel Bell, analyzed how politicians and corporations could shift the erotic into a political ideology. Thus social theory identified key foundations of power, even if exercised in subtle arenas.

These first two traditions invoke a strong state to right social wrongs, as theoretically defined. The third tradition is more cautious. Alexis de Tocqueville (1805-1859) was equally concerned with the roots of order and governance, but took a different course. Writing after the French Revolution (1789-1799), Tocqueville the aristocrat pondered the implications of equality. Societies emphasizing equality—like postrevolutionary America and France—were hostile toward exceptional talent and excellence; they could level out uniqueness and difference, generating a middling mediocrity. Moreover, equality threatened social identity and meaning: In a hierarchical society, one knew one’s place and did not have to anxiously make one’s place. In equalized societies, all is in doubt: Foreign observers regularly noted that Americans suffered a permanent “identity crisis,” which was spreading globally at the beginning of the twenty-first century.

Traveling across America, Tocqueville commented on the deleterious effects of equality, and potential remedies. Loosed from primordial hierarchies, Americans, he argued, developed a passion for voluntary associations. The town hall and the local church were key examples, sustained by their members’ voluntary efforts more than the weight of tradition or the power of elites (or a leviathan or lawgiver). What mattered was commitment by each participant, and Americans were joiners. The strongest social structures, Tocqueville argued, emerged not just through struggles for power or regard of others, but by citizens voluntarily developing shared commitments in local associations, which trained future leaders.

Tocqueville’s voluntaristic, bottom-up approach informs a third strand of social theorizing. Max Weber (1864-1920) stressed voluntarism in probing the religious roots of capitalism. Capitalists did not just strive to make money. Rather, Weber argued, Puritan sects encouraged their members to seek salvation in voluntary, committed “good works”—against the old nobility that valued leisure over work. Capitalism was the unintended consequence. Though Weber felt we inherited an “iron cage” of capitalist society that we did not choose, his response was volun-taristic: If you are a scholar, do it as a “vocation,” not as a heartless specialist; if you are a politician, lead, do not act as a technocratic bureaucrat. Voluntary commitment was key. In egalitarian America, every social interaction among equal citizens became a source of identity, obligation, and meaning, following G. H. Mead (1863-1931), C. H. Cooley (1864-1929), and Herbert Blumer (1900-1987). Talcott Parsons (1902-1979) extended voluntarism to critique past social theories, but like Weber joined basic values with individual choices. Edward Shils (1911-1995) and Daniel Elazar (1934-1999) continued Tocqueville’s concern for hierarchy, honor, and glory, noting that even within an egalitarian society, they remain social powers. Still others, such as Robert Putnam, suggest that the individualistic strain in voluntarism has gone so far in contemporary American life that the commonwealth Tocqueville saw had weakened, as more Americans “bowl alone.” Some postmodernists are so individualistic and egalitarian that they deny the possibility of meaning beyond the minds of separate individuals.

These three traditions have been revised and combined in efforts to interpret deep social changes. Consider the rise of industry, the division of labor, and bureaucratic organization in the theories of Marx, Durkheim, and Weber.

Marx, working in London, wrote of the English countryside transformed by industrial manufacturing; he saw people from all races and religions living near factories. These proletarians were a nascent class, opposed to capitalist/owners of the forces of production. In his theory, conflicts between such classes drove history.

Durkheim saw similar changes, but focused on the division of labor. Traditional societies, he argued, held together from pressures toward homogeneity. Modern societies are more like organisms. Social cohesion arises from interdependence; individuals perform specialized functions and develop a heightened sense of uniqueness. But without some firm social regulation, normlessness or “anomie” can undermine differentiated societies. Talcott Parsons and Niklas Luhmann (1927-1998) extended Durkheim’s social differentiation into multiple, interconnected subsystems that fill different social functions, while others, such as Robert Merton (1910-2003), developed the idea of anomie and deviance as central to modern life.

Max Weber, writing in Germany, stressed the hierarchical rationality of government bureaucratic officials. Bureaucracies are ancient, but Weber stressed how modern organizations grew ever larger, more rational, and more hierarchical. Not only was the bureaucrat’s personality stunted by his duties, everyone risked bureaucratization— since it was balanced increasingly less by the charisma of religion or respect for tradition. Seeking a “value-neutral” perspective, Weber posited that modern society is increasingly subject to “rational authority,” as opposed to “traditional” or “charismatic authority.” But the theory also had a quasi-moral intent, namely, to provide modern models for styles of action—rooted in the bonds of tradition or the electricity of charisma—which Weber saw threatened by the cold, abstract rationalism of bureaucracy.

Rationality was a political weapon that Enlightenment philosophers used to attack the “irrationality” of the ancient regime before the French Revolution of 1789. The secular theories of Hobbes and Rousseau helped refocus thinking on specific secular arrangements, rather than divinities or kings. But the legacy of this rational approach proved so powerful that Weber feared its excess. Analysis and criticism of rationalism in modern society have been among the most doggedly pursued strands of twentieth-century social thought, especially by Jurgen Habermas and other critical theorists and postmodernists.

Since Marx, Durkheim, and Weber, social theories have continued to stretch the imagination, seeking to capture the times and perhaps guide them. New topics emerge with new social forces: the massive rise of cities and new urban lifestyles; mass media, electronic media, and mass education; increased global interconnection; general increase in leisure time across societies; and a resurgence in the global power of religions are but a few of the subjects whose causes and meanings social theorists continue to pursue.

References:

  • Lemert, Charles, ed. and commentator. 2004. Social Theory: The Multicultural and Classic Readings. 3rd ed. Boulder, CO: Westview Press.
  • Parsons, Talcott, Edward Shils, Kaspar D. Naegele, and Jesse R. Pitts. 1965. Theories of Society. 2 vols. London: Collier-Macmillan.

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4 Theories in scientific research

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

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

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

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

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

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

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

Building blocks of a theory

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

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

Distinction between theoretical and empirical concepts

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

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

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

Attributes of a good theory

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

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

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

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

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

Approaches to theorising

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

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

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

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

Examples of social science theories

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

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

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

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

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

Theory of planned behaviour

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

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

S‑shaped diffusion curve

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

Elaboration likelihood model

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

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

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

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

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

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Organizing Academic Research Papers: Theoretical Framework

  • Purpose of Guide
  • Design Flaws to Avoid
  • Glossary of Research Terms
  • Narrowing a Topic Idea
  • Broadening a Topic Idea
  • Extending the Timeliness of a Topic Idea
  • Academic Writing Style
  • Choosing a Title
  • Making an Outline
  • Paragraph Development
  • Executive Summary
  • Background Information
  • The Research Problem/Question
  • Theoretical Framework
  • Citation Tracking
  • Content Alert Services
  • Evaluating Sources
  • Primary Sources
  • Secondary Sources
  • Tertiary Sources
  • What Is Scholarly vs. Popular?
  • Qualitative Methods
  • Quantitative Methods
  • Using Non-Textual Elements
  • Limitations of the Study
  • Common Grammar Mistakes
  • Avoiding Plagiarism
  • Footnotes or Endnotes?
  • Further Readings
  • Annotated Bibliography
  • Dealing with Nervousness
  • Using Visual Aids
  • Grading Someone Else's Paper
  • How to Manage Group Projects
  • Multiple Book Review Essay
  • Reviewing Collected Essays
  • About Informed Consent
  • Writing Field Notes
  • Writing a Policy Memo
  • Writing a Research Proposal
  • Acknowledgements

Theories are formulated to explain, predict, and understand phenomena and, in many cases, to challenge and extend existing knowledge, within the limits of the critical bounding assumptions. The theoretical framework is the structure that can hold or support a theory of a research study. The theoretical framework introduces and describes the theory which explains why the research problem under study exists.

Importance of Theory

A theoretical framework consists of concepts, together with their definitions, and existing theory/theories that are used for your particular study. The theoretical framework must demonstrate an understanding of theories and concepts that are relevant to the topic of your  research paper and that will relate it to the broader fields of knowledge in the class you are taking.

The theoretical framework is not something that is found readily available in the literature . You must review course readings and pertinent research literature for theories and analytic models that are relevant to the research problem you are investigating. The selection of a theory should depend on its appropriateness, ease of application, and explanatory power.

The theoretical framework strengthens the study in the following ways .

  • An explicit statement of  theoretical assumptions permits the reader to evaluate them critically.
  • The theoretical framework connects the researcher to existing knowledge. Guided by a relevant theory, you are given a basis for your hypotheses and choice of research methods.
  • Articulating the theoretical assumptions of a research study forces you to address questions of why and how. It permits you to move from simply describing a phenomenon observed to generalizing about various aspects of that phenomenon.
  • Having a theory helps you to identify the limits to those generalizations. A theoretical framework specifies which key variables influence a phenomenon of interest. It alerts you to examine how those key variables might differ and under what circumstances.

By virtue of its application nature, good theory in the social sciences is of value precisely because it fulfills one primary purpose: to explain the meaning, nature, and challenges of a phenomenon, often experienced but unexplained in the world in which we live, so that we may use that knowledge and understanding to act in more informed and effective ways.

The Conceptual Framework. College of Education. Alabama State University; Drafting an Argument . Writing@CSU. Colorado State University; Trochim, William M.K. Philosophy of Research. Research Methods Knowledge Base. 2006.

Strategies for Developing the Theoretical Framework

I.  Developing the Framework

Here are some strategies to develop of an effective theoretical framework:

  • Examine your thesis title and research problem . The research problem anchors your entire study and forms the basis from which you construct your theoretical framework.
  • Brainstorm on what you consider to be the key variables in your research . Answer the question, what factors contribute to the presumed effect?
  • Review related literature to find answers to your research question.
  • List  the constructs and variables that might be relevant to your study. Group these variables into independent and dependent categories.
  • Review the key social science theories that are introduced to you in your course readings and choose the theory or theories that can best explain the relationships between the key variables in your study [note the Writing Tip on this page].
  • Discuss the assumptions or propositions of this theory and point out their relevance to your research.

A theoretical framework is used to limit the scope of the relevant data by focusing on specific variables and defining the specific viewpoint (framework) that the researcher will take in analyzing and interpreting the data to be gathered, understanding concepts and variables according to the given definitions, and building knowledge by validating or challenging theoretical assumptions.

II.  Purpose

Think of theories as the conceptual basis for understanding, analyzing, and designing ways to investigate relationships within social systems. To the end, the following roles served by a theory can help guide the development of your framework.*

  • Means by which new research data can be interpreted and coded for future use,
  • Response to new problems that have no previously identified solutions strategy,
  • Means for identifying and defining research problems,
  • Means for prescribing or evaluating solutions to research problems,
  • Way of telling us that certain facts among the accumulated knowledge are important and which facts are not,
  • Means of giving old data new interpretations and new meaning,
  • Means by which to identify important new issues and prescribe the most critical research questions that need to be answered to maximize understanding of the issue,
  • Means of providing members of a professional discipline with a common language and a frame of reference for defining boundaries of their profession, and
  • Means to guide and inform research so that it can, in turn, guide research efforts and improve professional practice.

*Adapted from: Torraco, R. J. “Theory-Building Research Methods.” In Swanson R. A. and E. F. Holton III , editors. Human Resource Development Handbook: Linking Research and Practice . (San Francisco, CA: Berrett-Koehler, 1997): pp. 114-137; Sutton, Robert I. and Barry M. Staw. “What Theory is Not.” Administrative Science Quarterly 40 (September 1995): 371-384.

Structure and Writing Style

The theoretical framework may be rooted in a specific theory , in which case, you are expected to test the validity of an existing theory in relation to specific events, issues, or phenomena. Many social science research papers fit into this rubric. For example, Peripheral Realism theory, which categorizes perceived differences between nation-states as those that give orders, those that obey, and those that rebel, could be used as a means for understanding conflicted relationships among countries in Africa. A test of this theory could be the following: Does Peripheral Realism theory help explain intra-state actions, such as, the growing split between southern and northern Sudan that may likely lead to the creation of two nations?

However, you may not always be asked by your professor to test a specific theory in your paper, but to develop your own framework from which your analysis of the research problem is derived . Given this, it is perhaps easiest to understand the nature and function of a theoretical framework if it is viewed as the answer to two basic questions:

  • What is the research problem/question? [e.g., "How should the individual and the state relate during periods of conflict?"]
  • Why is your approach a feasible solution? [I could choose to test Instrumentalist or Circumstantialists models developed among Ethnic Conflict Theorists that rely upon socio-economic-political factors to explain individual-state relations and to apply this theoretical model to periods of war between nations].

The answers to these questions come from a thorough review of the literature and your course readings [summarized and analyzed in the next section of your paper] and the gaps in the research that emerge from the review process. With this in mind, a complete theoretical framework will likely not emerge until after you have completed a thorough review of the literature .

In writing this part of your research paper, keep in mind the following:

  • Clearly describe the framework, concepts, models, or specific theories that underpin your study . This includes noting who the key theorists are in the field who have conducted research on the problem you are investigating and, when necessary, the historical context that supports the formulation of that theory. This latter element is particularly important if the theory is relatively unknown or it is borrowed from another discipline.
  • Position your theoretical framework within a broader context of related frameworks , concepts, models, or theories . There will likely be several concepts, theories, or models that can be used to help develop a framework for understanding the research problem. Therefore, note why the framework you've chosen is the appropriate one.
  • The present tense is used when writing about theory.
  • You should make your theoretical assumptions as explicit as possible . Later, your discussion of methodology should be linked back to this theoretical framework.
  • Don’t just take what the theory says as a given! Reality is never accurately represented in such a simplistic way; if you imply that it can be, you fundamentally distort a reader's ability to understand the findings that emerge. Given this, always note the limitiations of the theoretical framework you've chosen [i.e., what parts of the research problem require further investigation because the theory does not explain a certain phenomena].

The Conceptual Framework. College of Education. Alabama State University; Conceptual Framework: What Do You Think is Going On? College of Engineering. University of Michigan; Drafting an Argument . Writing@CSU. Colorado State University; Lynham, Susan A. “The General Method of Theory-Building Research in Applied Disciplines.” Advances in Developing Human Resources 4 (August 2002): 221-241; Tavallaei, Mehdi and Mansor Abu Talib. A General Perspective on the Role of Theory in Qualitative Research. Journal of International Social Research 3 (Spring 2010); Trochim, William M.K. Philosophy of Research. Research Methods Knowledge Base. 2006.

Writing Tip

Borrowing Theoretical Constructs from Elsewhere

A growing and increasingly important trend in the social sciences is to think about and attempt to understand specific research problems from an interdisciplinary perspective. One way to do this is to not rely exclusively on the theories you've read about in a particular class, but to think about how an issue might be informed by theories developed in other disciplines. For example, if you are a political science student studying the rhetorical strategies used by female incumbants in state legislature campaigns, theories about the use of language could be derived, not only from political science, but linguistics, communication studies, philosophy, psychology, and, in this particular case, feminist studies. Building theoretical frameworks based on the postulates and hypotheses developed in other disciplinary contexts can be both enlightening and an effective way to be fully engaged in the research topic.

Another Writing Tip

Don't Undertheorize!

Never leave the theory hanging out there in the Introduction never to be mentioned again. Undertheorizing weakens your paper. The theoretical framework you introduce should guide your study throughout the paper. Be sure to always connect theory to the analysis and to explain in the discussion part of your paper how the theoretical framework you chose fit the research problem, or if appropriate, was inadequate in explaining the phenomenon you were investigating. In that case, don't be afraid to propose your own theory based on your findings.

Still Another Writing Tip

What's a Theory? What's a Hypothesis?

The terms theory and hypothesis are often used interchangeably in everyday use. However, the difference between them in scholarly research is important, particularly when using an experimental design. A theory is a well-established principle that has been developed to explain some aspect of the natural world. Theories arise from repeated observation and testing and incorporates facts, laws, predictions, and tested hypotheses that are widely accepted [e.g., rational choice theory; grounded theory].

A hypothesis is a specific, testable prediction about what you expect to happen in your study. For example, an experiment designed to look at the relationship between study habits and test anxiety might have a hypothesis that states, "We predict that students with better study habits will suffer less test anxiety." Unless your study is exploratory in nature, your hypothesis should always explain what you expect to happen during the course of your research.

The key distinctions are:

  • A theory predicts events in a broad, general context;  a hypothesis makes a specific prediction about a specified set of circumstances.
  • A theory has been extensively tested and is generally accepted among scholars; a hypothesis is a speculative guess that has yet to be tested.

Cherry, Kendra. Introduction to Research Methods: Theory and Hypothesis . About.com Psychology; Gezae, Michael et al. Welcome Presentation on Hypothesis . Slideshare presentation.

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300 Cutting-Edge Science Research Topics to impress Your professor

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Science research forms the foundation of human knowledge and drives innovation in every aspect of our lives. Through rigorous investigation, experimentation, and analysis, we gain a deeper understanding of the world around us. That being said, it is always challenging to get started with your science research paper, but beginning with a good topic works as a stepping stone. As professional paper writing solutions  providers, we took it upon ourselves to inform you about a few topics to help you craft an impressive piece. Let’s get to read them all.

Table of Contents

Why is Science Research Important?

Before we begin reading the lists of a few science topics to research on, let’s first try to understand the importance of a scientific paper. 

Advances Our Knowledge

  • Science research expands our understanding of the natural world.
  • It uncovers new insights, theories, and principles.

Drives Innovation

  • Scientific research leads to the development of new technologies, products, and solutions.
  • It fosters innovation across various industries and sectors.

Solves Problems

  • Science research tackles complex problems and challenges.
  • It offers evidence-based approaches to finding solutions.

Improves Our Lives

  • Scientific research contributes to advancements in healthcare, medicine, and treatments.
  • It enhances the quality of life by addressing societal issues and improving living standards.

Addresses Global Challenges

  • Science research is crucial in understanding and mitigating global challenges like climate change, pollution, and resource depletion.
  • It helps inform sustainable practices and policy-making.

Creates a Better Future

  • Scientific research contributes to creating a better future for humanity.
  • It enables progress, fosters critical thinking, and paves the way for a more sustainable and innovative society.

300 Interesting Science Research Topics You Are Looking for

Opting to go with a new or unique topic will always give you an edge in writing an impressive paper. Fortunately, we have huge lists filled with such topics. So, let’s get to reading our first one without further ado. 

Science Research Paper Topics Related To COVID-19

Be prepared to dive into an interesting look at science studies related to COVID-19. Discovering essential information about the virus, its consequences, and the continuous attempts to fight and reduce its effects.

  • Role of scientists in developing SOPs to control the spread of COVID
  • How did science help us create the vaccine for COVID-19?
  • Is it necessary to understand science when protecting residents and staff of long-term care homes from COVID-19?
  • Science of mental health and Addiction in the Country during the Pandemic
  • Is Covid19 more dangerous to addicts?
  • Experiences of Native American communities surrounding COVID-19
  • China’s Coronavirus Epidemic: what are its consequences
  • After the Pandemic, China faces a new challenge: regaining control of its image and discourse
  • Using the Digital Fence system in epidemic prevention is crucial
  • Management of the Covid-19 epidemic by China’s social credit system
  • Research projects in the humanities and social sciences for COVID 19
  • Research projects related to COVID-19 in the basic sciences
  • Evaluating epidemiological research projects
  • in diagnostics, clinical trials, and therapeutics
  • Bats in China are factories for new Coronaviruses
  • Epidemiology-related research projects in the humanities and social sciences
  • Are we on the brink of a novel wave of infectious disease outbreaks?
  • The Covid-19 Pandemic: questions about the ability of the World to Cope with a global health crisis
  • Preventive measures to ensure our collective safety
  • Distribution of Victims: quality of Service and Behavior
  • Mental Health Issues of patients cured of the Coronavirus Covid-19
  • Distribution of respondents according to history before COVID-19 diagnosis
  • COVID-19 before diagnosis
  • Epidemiological comparison between the different viral respiratory infections
  • Elucidating the epidemiological outbreak in the world
  • Evaluation of the health of COVID19 Victims: the possibility of monitoring using technological tools
  • Patients Cured of the “Covid-19” Coronavirus: Care and Evaluation
  • The viral cycle of SARS-CoV-2, the molecular structure of the virus, and host factors
  • Global evolution of the number of confirmed cases of Covid-19
  • A study of the applications on a mobile phone that helped combat the Coronavirus
  • AI Detection Software to Detect and Analyze the Epidemiology of Coronavirus: A case study
  • Scientific and Medical  Achievements Related to Covid-19

Science Research Topics for High School Students

Here’s another list of intriguing scientific research paper topics to help you with writing a good piece. 

  • Recent scientific successes on the front of climate change
  • A research paper on the basics of astronomy
  • Harnessing the seismic potential of white dwarf stars
  • Research Paper on Representations and Fusion
  • Search and analysis of chemically stratified white dwarf stars
  • Search for dark matter using super-heated liquid detectors
  • Is dark matter natural? Have there been any solid proofs, or is it hypothetical?
  • Contribution to the study of the inactivation of microorganisms by plasma
  • Process improvement and the creation of experimental simulators
  • Research Paper on Methods for Detecting and classifying brown dwarfs
  • Research Paper on Numerical Study of self-organized Systems
  • Calculations of the electronic properties of carbon compounds
  • Research Paper on Survey of giant planets around nearby stars
  • Molecular evidence related to human behaviour and human speech development

Unique Science Research Topics

Choosing a topic from this list will take you on a captivating journey through various science research topics encompassing cutting-edge advancements and breakthroughs.

  • Determination of the structure of self-assembled peptide nanofibers
  • Stress correlations in glass-forming liquids
  • Research Papers Topics on the Physics of drying colloidal suspensions
  • Mechanics of a sliding contact on polymer surfaces
  • Nuclear observables for nucleosynthesis processes
  • Synthesis and spectroscopy of boundary superheavy nuclei
  • Intelligent system for neutron radiation protection at accelerators
  • Conducting nanofibers from organic semiconductor polymers
  • Research Paper on Photosynthesis at the Nanoscale
  • How can science help us grow more and help terminate hunger with just a few crops?
  • Famous science research initiatives made related to environmental sciences
  • Study of charge transfer in molecular assemblies by numerical simulation
  • Development of hydrogels and sourced antibacterial films
  • Sustainable Manufacturing Labs with an interdisciplinary approach
  • Near-surface and near-interface materials and fluids
  • Morphological analysis at ranges ranging from nanometers to decimeters
  • Ultrasonic wave characterization of materials at the near surface
  • Create fresh implementation plans and take recycling into account

Good Science Research Topics

Here’s another collection of good scientific research topics to captivate your curiosity.

  • Coefficients of the super-algebra
  • Hepatic tumors applied to stereotactic radiosurgery
  • Interesting research papers topics on stem cells
  • Role of science museums in the Motivation for scientific efforts
  • Ultrasound elastography after endovascular repair of an aneurysm
  • Detection and characterization of new circumstellar disks around low-mass stars
  • Research and characterization of large-separation exoplanets
  • The Effect of elastic stresses on phase separation kinetics in Alloys
  • The search for brown dwarf stars in the solar neighborhood
  • Study of the variability of massive stars
  • Photometric study of white dwarf stars
  • A brief history of science museums
  • Is space exploration a viable commercial idea
  • Organic farming on Mars with genetically modified crops and ideas to finding a food distribution system
  • Commercial space flights: A new step towards evolution

Biology Science Research Topics

Step into the captivating realm of  biology  as we delve into a diverse array of science research titles.

  • The discovery and cure of medical breakthroughs
  • Analyzing the interactions between the mineral and organic worlds
  • A list of human biology research topics in the trending literature
  • Biological and Scientific Debates on Ethics
  • Was there any molecular evidence ever found on Mars to assure the existence of life?
  • The ethical dilemmas associated with biological research
  • What is the importance of studying biology?
  • Geological storage and deposit system that is deep in the Earth
  • Research Paper: What will be the most promising topics in biology shortly?
  • Earth’s primordial state and the emergence of life
  • A process of mineral nucleation and growth
  • The relationship between geochemistry and seismic activity
  • Budget of chemicals in subduction zones
  • Amorphous precursors: a strategy for the future
  • Research Paper: What is space biology, and how does it relate to Mars exploration?
  • Medical, cosmetic, and industrial nanotechnology Its rapid development.
  • Biological constituents of soils and aquatic environments
  • A central volcanic area and a climatic and biological crisis
  • An investigation of the reactivity and kinetics of nucleation, growth, and dissolution of solid phases
  • Famous science research projects of 2022 related to human biology
  • Why are stem cell research papers important?
  • Research papers ideas on stem cells
  • Can artificial intelligence help diagnose human patients of cancer fast?
  • What is the most effective science program for genetic abnormalities in the human body
  • How animal biology made a permanent spot in modern sciences
  • Cool science topics related to cancer research and genetic abnormalities
  • A survey of the scientific research topics on evolutionary biology

Chemistry Science Research Topics

Pick a science best topic from this list and join us on a journey that delves into the realm of chemical reactions, materials, and the intricate workings of the microscopic world

  • Study of the thermal evolution of implantation damage in silicon
  • Radiation effects on pixel silicon detectors
  • Scope of the chemical research in 2023
  • Chemistry of the chemicals found in space resources
  • Plasma spectroscopy for real-time characterization of nanomaterials
  • Implants with bioactive properties for intracranial use
  • What is the role of chemists in alternative energy companies?
  • Catalyst supporting carbon with electroactive properties
  • Evolutionary study of chemistry
  • Physiology and chemistry of substances
  • The Role of Islamic Scientists in the Development of Chemistry
  • The life and contributions of Jaber Ben Heyman, the father of chemistry
  • Protecting heritage cuprous metals
  • The capture of atmospheric carbon dioxide using nanofluids
  • Polymer-ceramic composite electrolyte-based solid-state batteries
  • The use of CO2 gasses to synthesize molecules of high value
  • Triple mesoscopic perovskites: stability and reactivity
  • The age-related chemical reactivity of polymer matrices
  • The relationship between mechanochemistry and biology
  • The structure-property relationship of graphene nanoparticles
  • Chemical engineering, chemistry, and related research tools
  • Analyzing and applying chemical processes to the environment
  • A molecularly imprinted polymer membrane is used to detect toxic molecules
  • An organic semiconductor synthesized by electrosynthesis and chemical modification
  • Characterization of acid-base interactions electrochemically

Zoology Science Research Topics

Embark on a captivating adventure into the world of zoology as we explore an array of scientific research topics dedicated to the study of animals.

  • Veterinary medicine is the study of the biomedical and clinical sciences
  • Detection and analysis of wildlife forensic evidence
  • Scientists are studying toxicogenomics to determine how toxic substances affect the body
  • Wildlife is at risk from a variety of industrial chemicals, drugs, effluents, and pesticides
  • Analyzing biological samples through the development of test methods
  • Using animals in research is fraught with controversy
  • A study of the relationship between agriculture, land use, and ecosystems
  • A study of the evolution of biology and ethology
  • Veterinary science, particularly food pathologies and epidemiology, is studied in zoos.
  • Can zoology research help treat cancer patients?
  • Can commercial space flights help trigger an extraterrestrial migration for humans?
  • Involvement in reproductive physiology research
  • Genetically and taxonomically-based research

Medical Science Research Topics

Delve into a vast array of medical science by choosing a captivating topic from this list of  medical research topics .

  • Promising malaria protocol to reduce transfusion-related transmission
  • Treatment of cancer with cognitive behavioral therapy
  • Developing, rehabilitating, and managing chronic diseases throughout life
  • The reprogramming of skin cells
  • How artificial intelligence can help discover and cure genetic abnormalities in humans
  • Use of space resources in preparation for medicine
  • Resurgent infectious diseases as a significant health threat worldwide
  • How can we treat cancer patients by studying human evolution and genetic engineering?
  • Using ultrasound to permeate the brain for the treatment of cancer
  • The link between neuroscience and mental health
  • Premature death caused by cancer is among the leading causes.

Physics Science Research Topics

Prepare to be captivated by the awe-inspiring realm of physics as we journey into diverse research topics.

  • White dwarf stars studied photometrically in the infrared
  • Detectors based on silicon pixels and radiation effects
  • An approach to molecular dynamics based on tight-binding approximations
  • Quantum Hall effect and non-commutative geometry
  • Physicochemical etching of high-density plasma: a fundamental study
  • At high energies, vector boson scattering occurs
  • How to use space resources effectively and end the energy crisis
  • Electrolytic cells and magnetohydrodynamic stability
  • Molecular crystal charge transport studied from energy bands
  • The study of energy transfer mechanisms from a theoretical perspective
  • Research Paper on Molecular crystals and their electronic properties
  • AFM imaging based on atomic force microscopy
  • Performing a transient absorption experiment at femtoseconds
  • Research Paper on Detector Response to Neutrons of deficient energy
  • Managing phase separation in active systems
  • Active materials: topological defects and many-body physics

The first step of writing a good research paper is to pick a good topic. Ensure the one you choose must have relevant data available that is both credible and supportive with evidence. This interesting article was all about letting you know about scientific topics for research. If you still need help picking up a topic or writing your science research paper, don’t hesitate to count on  our writers .

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Organizing Your Social Sciences Research Paper: Theoretical Framework

  • Purpose of Guide
  • Writing a Research Proposal
  • Design Flaws to Avoid
  • Independent and Dependent Variables
  • Narrowing a Topic Idea
  • Broadening a Topic Idea
  • The Research Problem/Question
  • Academic Writing Style
  • Choosing a Title
  • Making an Outline
  • Paragraph Development
  • The C.A.R.S. Model
  • Background Information
  • Theoretical Framework
  • Citation Tracking
  • Evaluating Sources
  • Reading Research Effectively
  • Primary Sources
  • Secondary Sources
  • What Is Scholarly vs. Popular?
  • Is it Peer-Reviewed?
  • Qualitative Methods
  • Quantitative Methods
  • Common Grammar Mistakes
  • Writing Concisely
  • Avoiding Plagiarism [linked guide]
  • Annotated Bibliography
  • Grading Someone Else's Paper

Theories are formulated to explain, predict, and understand phenomena and, in many cases, to challenge and extend existing knowledge within the limits of critical bounding assumptions. The theoretical framework is the structure that can hold or support a theory of a research study. The theoretical framework introduces and describes the theory that explains why the research problem under study exists.

Abend, Gabriel. "The Meaning of Theory." Sociological Theory 26 (June 2008): 173–199; Swanson, Richard A. Theory Building in Applied Disciplines . San Francisco, CA: Berrett-Koehler Publishers 2013.

Importance of Theory

A theoretical framework consists of concepts and, together with their definitions and reference to relevant scholarly literature, existing theory that is used for your particular study. The theoretical framework must demonstrate an understanding of theories and concepts that are relevant to the topic of your research paper and that relate to the broader areas of knowledge being considered.

The theoretical framework is most often not something readily found within the literature . You must review course readings and pertinent research studies for theories and analytic models that are relevant to the research problem you are investigating. The selection of a theory should depend on its appropriateness, ease of application, and explanatory power.

The theoretical framework strengthens the study in the following ways :

  • An explicit statement of  theoretical assumptions permits the reader to evaluate them critically.
  • The theoretical framework connects the researcher to existing knowledge. Guided by a relevant theory, you are given a basis for your hypotheses and choice of research methods.
  • Articulating the theoretical assumptions of a research study forces you to address questions of why and how. It permits you to intellectually transition from simply describing a phenomenon you have observed to generalizing about various aspects of that phenomenon.
  • Having a theory helps you identify the limits to those generalizations. A theoretical framework specifies which key variables influence a phenomenon of interest and highlights the need to examine how those key variables might differ and under what circumstances.

By virtue of its applicative nature, good theory in the social sciences is of value precisely because it fulfills one primary purpose: to explain the meaning, nature, and challenges associated with a phenomenon, often experienced but unexplained in the world in which we live, so that we may use that knowledge and understanding to act in more informed and effective ways.

The Conceptual Framework . College of Education. Alabama State University; Corvellec, Hervé, ed. What is Theory?: Answers from the Social and Cultural Sciences . Stockholm: Copenhagen Business School Press, 2013; Asher, Herbert B. Theory-Building and Data Analysis in the Social Sciences . Knoxville, TN: University of Tennessee Press, 1984; Drafting an Argument . Writing@CSU. Colorado State University; Ravitch, Sharon M. and Matthew Riggan. Reason and Rigor: How Conceptual Frameworks Guide Research . Second edition. Los Angeles, CA: SAGE, 2017; Trochim, William M.K. Philosophy of Research . Research Methods Knowledge Base. 2006; Jarvis, Peter. The Practitioner-Researcher. Developing Theory from Practice . San Francisco, CA: Jossey-Bass, 1999.

Strategies for Developing the Theoretical Framework

I.  Developing the Framework

Here are some strategies to develop of an effective theoretical framework:

  • Examine your thesis title and research problem . The research problem anchors your entire study and forms the basis from which you construct your theoretical framework.
  • Brainstorm about what you consider to be the key variables in your research . Answer the question, "What factors contribute to the presumed effect?"
  • Review related literature to find how scholars have addressed your research problem. Identify the assumptions from which the author(s) addressed the problem.
  • List  the constructs and variables that might be relevant to your study. Group these variables into independent and dependent categories.
  • Review key social science theories that are introduced to you in your course readings and choose the theory that can best explain the relationships between the key variables in your study [note the Writing Tip on this page].
  • Discuss the assumptions or propositions of this theory and point out their relevance to your research.

A theoretical framework is used to limit the scope of the relevant data by focusing on specific variables and defining the specific viewpoint [framework] that the researcher will take in analyzing and interpreting the data to be gathered. It also facilitates the understanding of concepts and variables according to given definitions and builds new knowledge by validating or challenging theoretical assumptions.

II.  Purpose

Think of theories as the conceptual basis for understanding, analyzing, and designing ways to investigate relationships within social systems. To that end, the following roles served by a theory can help guide the development of your framework.

  • Means by which new research data can be interpreted and coded for future use,
  • Response to new problems that have no previously identified solutions strategy,
  • Means for identifying and defining research problems,
  • Means for prescribing or evaluating solutions to research problems,
  • Ways of discerning certain facts among the accumulated knowledge that are important and which facts are not,
  • Means of giving old data new interpretations and new meaning,
  • Means by which to identify important new issues and prescribe the most critical research questions that need to be answered to maximize understanding of the issue,
  • Means of providing members of a professional discipline with a common language and a frame of reference for defining the boundaries of their profession, and
  • Means to guide and inform research so that it can, in turn, guide research efforts and improve professional practice.

Adapted from: Torraco, R. J. “Theory-Building Research Methods.” In Swanson R. A. and E. F. Holton III , editors. Human Resource Development Handbook: Linking Research and Practice . (San Francisco, CA: Berrett-Koehler, 1997): pp. 114-137; Jacard, James and Jacob Jacoby. Theory Construction and Model-Building Skills: A Practical Guide for Social Scientists . New York: Guilford, 2010; Ravitch, Sharon M. and Matthew Riggan. Reason and Rigor: How Conceptual Frameworks Guide Research . Second edition. Los Angeles, CA: SAGE, 2017; Sutton, Robert I. and Barry M. Staw. “What Theory is Not.” Administrative Science Quarterly 40 (September 1995): 371-384.

Structure and Writing Style

The theoretical framework may be rooted in a specific theory , in which case, your work is expected to test the validity of that existing theory in relation to specific events, issues, or phenomena. Many social science research papers fit into this rubric. For example, Peripheral Realism Theory, which categorizes perceived differences among nation-states as those that give orders, those that obey, and those that rebel, could be used as a means for understanding conflicted relationships among countries in Africa. A test of this theory could be the following: Does Peripheral Realism Theory help explain intra-state actions, such as, the disputed split between southern and northern Sudan that led to the creation of two nations?

However, you may not always be asked by your professor to test a specific theory in your paper, but to develop your own framework from which your analysis of the research problem is derived . Based upon the above example, it is perhaps easiest to understand the nature and function of a theoretical framework if it is viewed as an answer to two basic questions:

  • What is the research problem/question? [e.g., "How should the individual and the state relate during periods of conflict?"]
  • Why is your approach a feasible solution? [i.e., justify the application of your choice of a particular theory and explain why alternative constructs were rejected. I could choose instead to test Instrumentalist or Circumstantialists models developed among ethnic conflict theorists that rely upon socio-economic-political factors to explain individual-state relations and to apply this theoretical model to periods of war between nations].

The answers to these questions come from a thorough review of the literature and your course readings [summarized and analyzed in the next section of your paper] and the gaps in the research that emerge from the review process. With this in mind, a complete theoretical framework will likely not emerge until after you have completed a thorough review of the literature .

Just as a research problem in your paper requires contextualization and background information, a theory requires a framework for understanding its application to the topic being investigated. When writing and revising this part of your research paper, keep in mind the following:

  • Clearly describe the framework, concepts, models, or specific theories that underpin your study . This includes noting who the key theorists are in the field who have conducted research on the problem you are investigating and, when necessary, the historical context that supports the formulation of that theory. This latter element is particularly important if the theory is relatively unknown or it is borrowed from another discipline.
  • Position your theoretical framework within a broader context of related frameworks , concepts, models, or theories . As noted in the example above, there will likely be several concepts, theories, or models that can be used to help develop a framework for understanding the research problem. Therefore, note why the theory you've chosen is the appropriate one.
  • The present tense is used when writing about theory. Although the past tense can be used to describe the history of a theory or the role of key theorists, the construction of your theoretical framework is happening now.
  • You should make your theoretical assumptions as explicit as possible . Later, your discussion of methodology should be linked back to this theoretical framework.
  • Don’t just take what the theory says as a given! Reality is never accurately represented in such a simplistic way; if you imply that it can be, you fundamentally distort a reader's ability to understand the findings that emerge. Given this, always note the limitations of the theoretical framework you've chosen [i.e., what parts of the research problem require further investigation because the theory inadequately explains a certain phenomena].

The Conceptual Framework . College of Education. Alabama State University; Conceptual Framework: What Do You Think is Going On? College of Engineering. University of Michigan; Drafting an Argument . Writing@CSU. Colorado State University; Lynham, Susan A. “The General Method of Theory-Building Research in Applied Disciplines.” Advances in Developing Human Resources 4 (August 2002): 221-241; Tavallaei, Mehdi and Mansor Abu Talib. "A General Perspective on the Role of Theory in Qualitative Research." Journal of International Social Research 3 (Spring 2010); Ravitch, Sharon M. and Matthew Riggan. Reason and Rigor: How Conceptual Frameworks Guide Research . Second edition. Los Angeles, CA: SAGE, 2017; Reyes, Victoria. Demystifying the Journal Article . Inside Higher Education; Trochim, William M.K. Philosophy of Research . Research Methods Knowledge Base. 2006; Weick, Karl E. “The Work of Theorizing.” In Theorizing in Social Science: The Context of Discovery . Richard Swedberg, editor. (Stanford, CA: Stanford University Press, 2014), pp. 177-194.

Writing Tip

Borrowing Theoretical Constructs from Elsewhere

A growing and increasingly important trend in the social and behavioral sciences is to think about and attempt to understand specific research problems from an interdisciplinary perspective. One way to do this is to not rely exclusively on the theories developed within your particular discipline, but to think about how an issue might be informed by theories developed in other disciplines. For example, if you are a political science student studying the rhetorical strategies used by female incumbents in state legislature campaigns, theories about the use of language could be derived, not only from political science, but linguistics, communication studies, philosophy, psychology, and, in this particular case, feminist studies. Building theoretical frameworks based on the postulates and hypotheses developed in other disciplinary contexts can be both enlightening and an effective way to be fully engaged in the research topic.

CohenMiller, A. S. and P. Elizabeth Pate. "A Model for Developing Interdisciplinary Research Theoretical Frameworks." The Qualitative Researcher 24 (2019): 1211-1226; Frodeman, Robert. The Oxford Handbook of Interdisciplinarity . New York: Oxford University Press, 2010.

Another Writing Tip

Don't Undertheorize!

Do not leave the theory hanging out there in the introduction never to be mentioned again. Undertheorizing weakens your paper. The theoretical framework you describe should guide your study throughout the paper. Be sure to always connect theory to the review of pertinent literature and to explain in the discussion part of your paper how the theoretical framework you chose supports analysis of the research problem, or if appropriate, how the theoretical framework was found in some way to be inadequate in explaining the phenomenon you were investigating. In that case, don't be afraid to propose your own theory based on your findings.

Yet Another Writing Tip

What's a Theory? What's a Hypothesis?

The terms theory and hypothesis are often used interchangeably in newspapers and popular magazines and in non-academic settings. However, the difference between theory and hypothesis in scholarly research is important, particularly when using an experimental design. A theory is a well-established principle that has been developed to explain some aspect of the natural world. Theories arise from repeated observation and testing and incorporates facts, laws, predictions, and tested assumptions that are widely accepted [e.g., rational choice theory; grounded theory; critical race theory].

A hypothesis is a specific, testable prediction about what you expect to happen in your study. For example, an experiment designed to look at the relationship between study habits and test anxiety might have a hypothesis that states, "We predict that students with better study habits will suffer less test anxiety." Unless your study is exploratory in nature, your hypothesis should always explain what you expect to happen during the course of your research.

The key distinctions are:

  • A theory predicts events in a broad, general context;  a hypothesis makes a specific prediction about a specified set of circumstances.
  • A theory has been extensively tested and is generally accepted among scholars; a hypothesis is a speculative guess that has yet to be tested.

Cherry, Kendra. Introduction to Research Methods: Theory and Hypothesis . About.com Psychology; Gezae, Michael et al. Welcome Presentation on Hypothesis . Slideshare presentation.

Still Another Writing Tip

Be Prepared to Challenge the Validity of an Existing Theory

Theories are meant to be tested and their underlying assumptions challenged; they are not rigid or intransigent, but are meant to set forth general principles for explaining phenomena or predicting outcomes. Given this, testing theoretical assumptions is an important way that knowledge in any discipline develops and grows. If you're asked to apply an existing theory to a research problem, the analysis may include an expectation by your professor that you should offer modifications to the theory based on your research findings. Indications that theoretical assumptions may need to be modified can include the following:

  • Your findings suggest that the theory does not explain or account for current conditions or circumstances,
  • The study reveals a finding that is significantly incongruent with what the theory attempts to explain or predict, or
  • Your analysis reveals that the theory overly generalizes behaviors or actions without taking into consideration specific factors [e.g., factors related to culture, nationality, history, gender, ethnicity, age, geographic location, legal norms or customs , religion, social class, socioeconomic status, etc.].

Philipsen, Kristian. "Theory Building: Using Abductive Search Strategies." In Collaborative Research Design: Working with Business for Meaningful Findings . Per Vagn Freytag and Louise Young, editors. (Singapore: Springer Nature, 2018), pp. 45-71; Shepherd, Dean A. and Roy Suddaby. "Theory Building: A Review and Integration." Journal of Management 43 (2017): 59-86.

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4: Theories in Scientific Research

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  • Page ID 26231

  • Anol Bhattacherjee
  • University of South Florida via Global Text Project

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

  • 4.1: Theories
  • 4.2: Building Blocks of a Theory
  • 4.3: Attributes of a Good Theory
  • 4.4: Approaches to Theorizing
  • 4.5: Examples of Social Science Theories

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Home » 500+ Physics Research Topics

500+ Physics Research Topics

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Physics Research Topics

Physics is the study of matter, energy, and the fundamental forces that govern the universe. It is a broad and fascinating field that has given us many of the greatest scientific discoveries in history , from the theory of relativity to the discovery of the Higgs boson. As a result, physics research is always at the forefront of scientific advancement, and there are countless exciting topics to explore. In this blog post, we will take a look at some of the most fascinating and cutting-edge physics research topics that are being explored by scientists today. Whether you are a student, researcher, or simply someone with a passion for science, there is sure to be something in this list that will pique your interest.

Physics Research Topics

Physics Research Topics are as follows:

Physics Research Topics for Grade 9

  • Investigating the properties of waves: amplitude, frequency, wavelength, and speed.
  • The effect of temperature on the expansion and contraction of materials.
  • The relationship between mass, velocity, and momentum.
  • The behavior of light in different mediums and the concept of refraction.
  • The effect of gravity on objects and the concept of weight.
  • The principles of electricity and magnetism and their applications.
  • The concept of work, energy, and power and their relationship.
  • The study of simple machines and their efficiency.
  • The behavior of sound waves and the concept of resonance.
  • The properties of gases and the concept of pressure.
  • The principles of heat transfer and thermal energy.
  • The study of motion, including speed, velocity, and acceleration.
  • The behavior of fluids and the concept of viscosity.
  • The concept of density and its applications.
  • The study of electric circuits and their components.
  • The principles of nuclear physics and their applications.
  • The behavior of electromagnetic waves and the concept of radiation.
  • The properties of solids and the concept of elasticity.
  • The study of light and the electromagnetic spectrum.
  • The concept of force and its relationship to motion.
  • The behavior of waves in different mediums and the concept of interference.
  • The principles of thermodynamics and their applications.
  • The study of optics and the concept of lenses.
  • The concept of waves and their characteristics.
  • The study of atomic structure and the behavior of subatomic particles.
  • The principles of quantum mechanics and their applications.
  • The behavior of light and the concept of polarization.
  • The study of the properties of matter and the concept of phase transitions.
  • The concept of work done by a force and its relationship to energy.
  • The study of motion in two dimensions, including projectile motion and circular motion.

Physics Research Topics for Grade 10

  • Investigating the motion of objects on inclined planes
  • Analyzing the effect of different variables on pendulum oscillations
  • Understanding the properties of waves through the study of sound
  • Investigating the behavior of light through refraction and reflection experiments
  • Examining the laws of thermodynamics and their applications in real-life situations
  • Analyzing the relationship between electric fields and electric charges
  • Understanding the principles of magnetism and electromagnetism
  • Investigating the properties of different materials and their conductivity
  • Analyzing the concept of work, power, and energy in relation to mechanical systems
  • Investigating the laws of motion and their application in real-life situations
  • Understanding the principles of nuclear physics and radioactivity
  • Analyzing the properties of gases and the behavior of ideal gases
  • Investigating the concept of elasticity and Hooke’s law
  • Understanding the properties of liquids and the concept of buoyancy
  • Analyzing the behavior of simple harmonic motion and its applications
  • Investigating the properties of electromagnetic waves and their applications
  • Understanding the principles of wave-particle duality and quantum mechanics
  • Analyzing the properties of electric circuits and their applications
  • Investigating the concept of capacitance and its application in circuits
  • Understanding the properties of waves in different media and their applications
  • Analyzing the principles of optics and the behavior of lenses
  • Investigating the properties of forces and their application in real-life situations
  • Understanding the principles of energy conservation and its applications
  • Analyzing the concept of momentum and its conservation in collisions
  • Investigating the properties of sound waves and their applications
  • Understanding the behavior of electric and magnetic fields in charged particles
  • Analyzing the principles of thermodynamics and the behavior of gases
  • Investigating the properties of electric generators and motors
  • Understanding the principles of electromagnetism and electromagnetic induction
  • Analyzing the behavior of waves and their interference patterns.

Physics Research Topics for Grade 11

  • Investigating the effect of temperature on the resistance of a wire
  • Determining the velocity of sound in different mediums
  • Measuring the force required to move a mass on an inclined plane
  • Examining the relationship between wavelength and frequency of electromagnetic waves
  • Analyzing the reflection and refraction of light through various media
  • Investigating the properties of simple harmonic motion
  • Examining the efficiency of different types of motors
  • Measuring the acceleration due to gravity using a pendulum
  • Determining the index of refraction of a material using Snell’s law
  • Investigating the behavior of waves in different mediums
  • Analyzing the effect of temperature on the volume of a gas
  • Examining the relationship between current, voltage, and resistance in a circuit
  • Investigating the principles of Coulomb’s law and electric fields
  • Analyzing the properties of electromagnetic radiation
  • Investigating the properties of magnetic fields
  • Examining the behavior of light in different types of lenses
  • Measuring the speed of light using different methods
  • Investigating the properties of capacitors and inductors in circuits
  • Analyzing the principles of simple harmonic motion in springs
  • Examining the relationship between force, mass, and acceleration
  • Investigating the behavior of waves in different types of materials
  • Determining the energy output of different types of batteries
  • Analyzing the properties of electric circuits
  • Investigating the properties of electric and magnetic fields
  • Examining the principles of radioactivity
  • Measuring the heat capacity of different materials
  • Investigating the properties of thermal conduction
  • Examining the behavior of light in different types of mirrors
  • Analyzing the principles of electromagnetic induction
  • Investigating the properties of waves in different types of strings.

Physics Research Topics for Grade 12

  • Investigating the efficiency of solar panels in converting light energy to electrical energy.
  • Studying the behavior of waves in different mediums.
  • Analyzing the relationship between temperature and pressure in ideal gases.
  • Investigating the properties of electromagnetic waves and their applications.
  • Analyzing the behavior of light and its interaction with matter.
  • Examining the principles of quantum mechanics and their applications.
  • Investigating the properties of superconductors and their potential uses.
  • Studying the properties of semiconductors and their applications in electronics.
  • Analyzing the properties of magnetism and its applications.
  • Investigating the properties of nuclear energy and its applications.
  • Studying the principles of thermodynamics and their applications.
  • Analyzing the properties of fluids and their behavior in different conditions.
  • Investigating the principles of optics and their applications.
  • Studying the properties of sound waves and their behavior in different mediums.
  • Analyzing the properties of electricity and its applications in different devices.
  • Investigating the principles of relativity and their applications.
  • Studying the properties of black holes and their effect on the universe.
  • Analyzing the properties of dark matter and its impact on the universe.
  • Investigating the principles of particle physics and their applications.
  • Studying the properties of antimatter and its potential uses.
  • Analyzing the principles of astrophysics and their applications.
  • Investigating the properties of gravity and its impact on the universe.
  • Studying the properties of dark energy and its effect on the universe.
  • Analyzing the principles of cosmology and their applications.
  • Investigating the properties of time and its effect on the universe.
  • Studying the properties of space and its relationship with time.
  • Analyzing the principles of the Big Bang Theory and its implications.
  • Investigating the properties of the Higgs boson and its impact on particle physics.
  • Studying the properties of string theory and its implications.
  • Analyzing the principles of chaos theory and its applications in physics.

Physics Research Topics for UnderGraduate

  • Investigating the effects of temperature on the conductivity of different materials.
  • Studying the behavior of light in different mediums.
  • Analyzing the properties of superconductors and their potential applications.
  • Examining the principles of thermodynamics and their practical applications.
  • Investigating the behavior of sound waves in different environments.
  • Studying the characteristics of magnetic fields and their applications.
  • Analyzing the principles of optics and their role in modern technology.
  • Examining the principles of quantum mechanics and their implications.
  • Investigating the properties of semiconductors and their use in electronics.
  • Studying the properties of gases and their behavior under different conditions.
  • Analyzing the principles of nuclear physics and their practical applications.
  • Examining the properties of waves and their applications in communication.
  • Investigating the principles of relativity and their implications for the nature of space and time.
  • Studying the behavior of particles in different environments, including accelerators and colliders.
  • Analyzing the principles of chaos theory and their implications for complex systems.
  • Examining the principles of fluid mechanics and their applications in engineering and science.
  • Investigating the principles of solid-state physics and their applications in materials science.
  • Studying the properties of electromagnetic waves and their use in modern technology.
  • Analyzing the principles of gravitation and their role in the structure of the universe.
  • Examining the principles of quantum field theory and their implications for the nature of particles and fields.
  • Investigating the properties of black holes and their role in astrophysics.
  • Studying the principles of string theory and their implications for the nature of matter and energy.
  • Analyzing the properties of dark matter and its role in cosmology.
  • Examining the principles of condensed matter physics and their applications in materials science.
  • Investigating the principles of statistical mechanics and their implications for the behavior of large systems.
  • Studying the properties of plasma and its applications in fusion energy research.
  • Analyzing the principles of general relativity and their implications for the nature of space-time.
  • Examining the principles of quantum computing and its potential applications.
  • Investigating the principles of high energy physics and their role in understanding the fundamental laws of nature.
  • Studying the principles of astrobiology and their implications for the search for life beyond Earth.

Physics Research Topics for Masters

  • Investigating the principles and applications of quantum cryptography.
  • Analyzing the behavior of Bose-Einstein condensates and their potential applications.
  • Studying the principles of photonics and their role in modern technology.
  • Examining the properties of topological materials and their potential applications.
  • Investigating the principles and applications of graphene and other 2D materials.
  • Studying the principles of quantum entanglement and their implications for information processing.
  • Analyzing the principles of quantum field theory and their implications for particle physics.
  • Examining the properties of quantum dots and their use in nanotechnology.
  • Investigating the principles of quantum sensing and their potential applications.
  • Studying the behavior of quantum many-body systems and their potential applications.
  • Analyzing the principles of cosmology and their implications for the early universe.
  • Examining the principles of dark energy and dark matter and their role in cosmology.
  • Investigating the properties of gravitational waves and their detection.
  • Studying the principles of quantum computing and their potential applications in solving complex problems.
  • Analyzing the properties of topological insulators and their potential applications in quantum computing and electronics.
  • Examining the principles of quantum simulations and their potential applications in studying complex systems.
  • Investigating the principles of quantum error correction and their implications for quantum computing.
  • Studying the behavior of quarks and gluons in high energy collisions.
  • Analyzing the principles of quantum phase transitions and their implications for condensed matter physics.
  • Examining the principles of quantum annealing and their potential applications in optimization problems.
  • Investigating the properties of spintronics and their potential applications in electronics.
  • Studying the behavior of non-linear systems and their applications in physics and engineering.
  • Analyzing the principles of quantum metrology and their potential applications in precision measurement.
  • Examining the principles of quantum teleportation and their implications for information processing.
  • Investigating the properties of topological superconductors and their potential applications.
  • Studying the principles of quantum chaos and their implications for complex systems.
  • Analyzing the properties of magnetars and their role in astrophysics.
  • Examining the principles of quantum thermodynamics and their implications for the behavior of small systems.
  • Investigating the principles of quantum gravity and their implications for the structure of the universe.
  • Studying the behavior of strongly correlated systems and their applications in condensed matter physics.

Physics Research Topics for PhD

  • Quantum computing: theory and applications.
  • Topological phases of matter and their applications in quantum information science.
  • Quantum field theory and its applications to high-energy physics.
  • Experimental investigations of the Higgs boson and other particles in the Standard Model.
  • Theoretical and experimental study of dark matter and dark energy.
  • Applications of quantum optics in quantum information science and quantum computing.
  • Nanophotonics and nanomaterials for quantum technologies.
  • Development of advanced laser sources for fundamental physics and engineering applications.
  • Study of exotic states of matter and their properties using high energy physics techniques.
  • Quantum information processing and communication using optical fibers and integrated waveguides.
  • Advanced computational methods for modeling complex systems in physics.
  • Development of novel materials with unique properties for energy applications.
  • Magnetic and spintronic materials and their applications in computing and data storage.
  • Quantum simulations and quantum annealing for solving complex optimization problems.
  • Gravitational waves and their detection using interferometry techniques.
  • Study of quantum coherence and entanglement in complex quantum systems.
  • Development of novel imaging techniques for medical and biological applications.
  • Nanoelectronics and quantum electronics for computing and communication.
  • High-temperature superconductivity and its applications in power generation and storage.
  • Quantum mechanics and its applications in condensed matter physics.
  • Development of new methods for detecting and analyzing subatomic particles.
  • Atomic, molecular, and optical physics for precision measurements and quantum technologies.
  • Neutrino physics and its role in astrophysics and cosmology.
  • Quantum information theory and its applications in cryptography and secure communication.
  • Study of topological defects and their role in phase transitions and cosmology.
  • Experimental study of strong and weak interactions in nuclear physics.
  • Study of the properties of ultra-cold atomic gases and Bose-Einstein condensates.
  • Theoretical and experimental study of non-equilibrium quantum systems and their dynamics.
  • Development of new methods for ultrafast spectroscopy and imaging.
  • Study of the properties of materials under extreme conditions of pressure and temperature.

Random Physics Research Topics

  • Quantum entanglement and its applications
  • Gravitational waves and their detection
  • Dark matter and dark energy
  • High-energy particle collisions and their outcomes
  • Atomic and molecular physics
  • Theoretical and experimental study of superconductivity
  • Plasma physics and its applications
  • Neutrino oscillations and their detection
  • Quantum computing and information
  • The physics of black holes and their properties
  • Study of subatomic particles like quarks and gluons
  • Investigation of the nature of time and space
  • Topological phases in condensed matter systems
  • Magnetic fields and their applications
  • Nanotechnology and its impact on physics research
  • Theory and observation of cosmic microwave background radiation
  • Investigation of the origin and evolution of the universe
  • Study of high-temperature superconductivity
  • Quantum field theory and its applications
  • Study of the properties of superfluids
  • The physics of plasmonics and its applications
  • Experimental and theoretical study of semiconductor materials
  • Investigation of the quantum Hall effect
  • The physics of superstring theory and its applications
  • Theoretical study of the nature of dark matter
  • Study of quantum chaos and its applications
  • Investigation of the Casimir effect
  • The physics of spintronics and its applications
  • Study of the properties of topological insulators
  • Investigation of the nature of the Higgs boson
  • The physics of quantum dots and its applications
  • Study of quantum many-body systems
  • Investigation of the nature of the strong force
  • Theoretical and experimental study of photonics
  • Study of topological defects in condensed matter systems
  • Investigation of the nature of the weak force
  • The physics of plasmas in space
  • Study of the properties of graphene
  • Investigation of the nature of antimatter
  • The physics of optical trapping and manipulation
  • Study of the properties of Bose-Einstein condensates
  • Investigation of the nature of the neutrino
  • The physics of quantum thermodynamics
  • Study of the properties of quantum dots
  • Investigation of the nature of dark energy
  • The physics of magnetic confinement fusion
  • Study of the properties of topological quantum field theories
  • Investigation of the nature of gravitational lensing
  • The physics of laser cooling and trapping
  • Study of the properties of quantum Hall states.
  • The effects of dark energy on the expansion of the universe
  • Quantum entanglement and its applications in cryptography
  • The study of black holes and their event horizons
  • The potential existence of parallel universes
  • The relationship between dark matter and the formation of galaxies
  • The impact of solar flares on the Earth’s magnetic field
  • The effects of cosmic rays on human biology
  • The development of quantum computing technology
  • The properties of superconductors at high temperatures
  • The search for a theory of everything
  • The study of gravitational waves and their detection
  • The behavior of particles in extreme environments such as neutron stars
  • The relationship between relativity and quantum mechanics
  • The development of new materials for solar cells
  • The study of the early universe and cosmic microwave background radiation
  • The physics of the human voice and speech production
  • The behavior of matter in extreme conditions such as high pressure and temperature
  • The properties of dark matter and its interactions with ordinary matter
  • The potential for harnessing nuclear fusion as a clean energy source
  • The study of high-energy particle collisions and the discovery of new particles
  • The physics of biological systems such as the brain and DNA
  • The behavior of fluids in microgravity environments
  • The properties of graphene and its potential applications in electronics
  • The physics of natural disasters such as earthquakes and tsunamis
  • The development of new technologies for space exploration and travel
  • The study of atmospheric physics and climate change
  • The physics of sound and musical instruments
  • The behavior of electrons in quantum dots
  • The properties of superfluids and Bose-Einstein condensates
  • The physics of animal locomotion and movement
  • The development of new imaging techniques for medical applications
  • The physics of renewable energy sources such as wind and hydroelectric power
  • The properties of quantum materials and their potential for quantum computing
  • The physics of sports and athletic performance
  • The study of magnetism and magnetic materials
  • The physics of earthquakes and the prediction of seismic activity
  • The behavior of plasma in fusion reactors
  • The properties of exotic states of matter such as quark-gluon plasma
  • The development of new technologies for energy storage
  • The physics of fluids in porous media
  • The properties of quantum dots and their potential for new technologies
  • The study of materials under extreme conditions such as extreme temperatures and pressures
  • The physics of the human body and medical imaging
  • The development of new materials for energy conversion and storage
  • The study of cosmic rays and their effects on the atmosphere and human health
  • The physics of friction and wear in materials
  • The properties of topological materials and their potential for new technologies
  • The physics of ocean waves and tides
  • The behavior of particles in magnetic fields
  • The properties of complex networks and their application in various fields

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Scientific Consensus

scientific theory research paper topics

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

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

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

American Scientific Societies

Statement on climate change from 18 scientific associations.

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

American Association for the Advancement of Science

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

AAAS emblem

American Chemical Society

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

ACS emblem

American Geophysical Union

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

AGU emblem

American Medical Association

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

AMA emblem

American Meteorological Society

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

AMS emblem

American Physical Society

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

APS emblem

The Geological Society of America

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

GSA emblem

Science Academies

International academies: joint statement.

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

U.S. National Academy of Sciences

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

UNSAS emblem

U.S. Government Agencies

U.s. global change research program.

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

USGCRP emblem

Intergovernmental Bodies

Intergovernmental panel on climate change.

“It is unequivocal that the increase of CO 2 , methane, and nitrous oxide in the atmosphere over the industrial era is the result of human activities and that human influence is the principal driver of many changes observed across the atmosphere, ocean, cryosphere, and biosphere. “Since systematic scientific assessments began in the 1970s, the influence of human activity on the warming of the climate system has evolved from theory to established fact.” 1 3-17

IPCC emblem

Other Resources

List of worldwide scientific organizations.

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

U.S. Agencies

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

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

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

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

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

3. AAAS Board Statement on Climate Change (2014)

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

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

6. Global Climate Change and Human Health (2019)

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

8. American Physical Society (2021)

9. GSA Position Statement on Climate Change (2015)

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

11. Climate at the National Academies

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

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

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

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

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

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

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