theoretical and empirical research differences

Difference between Theoretical and Empirical Research

The difference between theoretical and empirical research is fundamental to scientific, scholarly research, as it separates the development of ideas and models from their testing and validation.

These two approaches are used in many different fields of inquiry, including the natural sciences, social sciences, and humanities, and they serve different purposes and employ different methods.

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What is theoretical research.

Theoretical research involves the development of models, frameworks, and theories based on existing knowledge, logic, and intuition.

It aims to explain and predict phenomena, generate new ideas and insights, and provide a foundation for further research.

Theoretical research often takes place at the conceptual level and is typically based on existing knowledge, data, and assumptions.

What is Empirical Research?

In contrast, empirical research involves collecting and analysing data to test theories and models.

Empirical research is often conducted at the observational or experimental level and is based on direct or indirect observation of the world.

Empirical research involves testing theories and models, establishing cause-and-effect relationships, and refining or rejecting existing knowledge.

Theoretical vs Empirical Research

Theoretical research is often seen as the starting point for empirical research, providing the ideas and models that must be tested and validated.

Theoretical research can be qualitative or quantitative and involve mathematical models, simulations, and other computational methods.

Theoretical research is often conducted in isolation, without reference to primary data or observations.

On the other hand, empirical research is often seen as the final stage in the scientific process, as it provides evidence that supports or refutes theoretical models.

Empirical research can be qualitative or quantitative, involving surveys, experiments, observational studies, and other data collection methods.

Empirical research is often conducted in collaboration with others and is based on systematic data collection, analysis, and interpretation.

It is important to note that theoretical and empirical research are not mutually exclusive and can often complement each other.

For example, empirical data can inform the development of theories and models, and theoretical models can guide the design of empirical studies.

The most valuable research combines theoretical and empirical approaches in many fields, allowing for a comprehensive understanding of the studied phenomena.

It is important to note that this table is not meant to be exhaustive or prescriptive but rather to provide a general overview of the main difference between theoretical and empirical research.

The boundaries between these two approaches are not always clear, and in many cases, research may involve a combination of theoretical and empirical methods.

What are the Limitations of Theoretical Research?

Assumptions and simplifications may be made that do not accurately reflect the complexity of real-world phenomena, which is one of its limitations. Theoretical research relies heavily on logic and deductive reasoning, which can sometimes be biased or limited by the researcher’s assumptions and perspectives.

Furthermore, theoretical research may not be directly applicable to real-world situations without empirical validation. Applying theoretical ideas to practical situations is difficult if no empirical evidence supports or refutes them.

Furthermore, theoretical research may be limited by the availability of data and the researcher’s ability to access and interpret it, which can further limit the validity and applicability of theories.

What are the Limitations of Empirical Research?

There are many limitations to empirical research, including the limitations of the data available and the quality of the data that can be collected. Data collection can be limited by the resources available to collect the data, accessibility to populations or individuals of interest, or ethical constraints.

The researchers or participants may also introduce biases into empirical research, resulting in inaccurate or unreliable findings.

Lastly, due to confounding variables or other methodological limitations, empirical research may be limited by the inability to establish causal relationships between variables, even when statistical associations are identified.

What Methods Are Used In Theoretical Research?

In theoretical research, deductive reasoning, logical analysis, and conceptual frameworks generate new ideas and hypotheses. To identify gaps and inconsistencies in the present understanding of a phenomenon, theoretical research may involve analyzing existing literature and theories.

To test hypotheses and generate predictions, mathematical or computational models may also be developed.

Researchers may also use thought experiments or simulations to explore the implications of their ideas and hypotheses without collecting empirical data as part of theoretical research.

Theoretical research seeks to develop a conceptual framework for empirically testing and validating phenomena.

What Methods Are Used In Empirical Research?

Methods used in empirical research depend on the research questions, type of data collected, and study design. Surveys, experiments, observations, case studies, and interviews are common methods used in empirical research.

An empirical study tests hypotheses and generates new knowledge about phenomena by systematically collecting and analyzing data.

These methods may utilize standardized instruments or protocols for data collection consistency and reliability. Statistical analysis, content analysis, or qualitative analysis may be used for the data collection type.

As a result of empirical research, the findings can inform theories, models, and practical applications.

Conclusion: Theoretical vs Empirical Research

In conclusion, theoretical and empirical research are two distinct but interrelated approaches to scientific inquiry, and they serve different purposes and employ different methods.

Theoretical research involves the development of ideas and models, while empirical research involves testing and validating these ideas.

Both approaches are essential to research and can be combined to provide a more complete understanding of the world.

• Dictionary.com. “ Empirical vs Theoretical “.
• PennState University Libraries. “ Empirical Research in the Social Sciences and Education “.
• William M. Landes and Richard A. Posner. “ Legal Precedent: A Theoretical and Empirical Analysis “, The Journal of Law and Economics, 1976.

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1.2: Theory and Empirical Research

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• Jenkins-Smith et al.
• University of Oklahoma via University of Oklahoma Libraries

This book is concerned with the connection between theoretical claims and empirical data. It is about using statistical modeling; in particular, the tool of regression analysis, which is used to develop and refine theories. We define theory broadly as a set of interrelated propositions that seek to explain and, in some cases, predict an observed phenomenon.

Theory: A set of interrelated propositions that seek to explain and predict an observed phenomenon.

Theories contain three important characteristics that we discuss in detail below.

Characteristics of Good Theories Coherent and internally consistent Causal in nature Generate testable hypotheses

1.2.1 Coherent and Internally Consistent

The set of interrelated propositions that constitute a well-structured theory are based on concepts . In well-developed theories, the expected relationships among these concepts are both coherent and internally consistent. Coherence means the identification of concepts and the specified relationships among them are logical, ordered, and integrated. An internally consistent theory will explain relationships with respect to a set of common underlying causes and conditions, providing for consistency in expected relationships (and avoidance of contradictions). For systematic quantitative research, the relevant theoretical concepts are defined such that they can be measured and quantified. Some concepts are relatively easy to quantify, such as the number of votes cast for the winning Presidential candidate in a specified year or the frequency of arrests for gang-related crimes in a particular region and time period. Others are more difficult, such as the concepts of democratization, political ideology or presidential approval. Concepts that are more difficult to measure must be carefully operationalized , which is a process of relating a concept to an observation that can be measured using a defined procedure. For example, political ideology is often operationalized through public opinion surveys that ask respondents to place themselves on a Likert-type scale of ideological categories.

Concepts and Variables

A concept is a commonality across observed individual events or cases. It is a regularity that we find in a complex world. Concepts are our building blocks to understanding the world and to developing theory that explains the world. Once we have identified concepts we seek to explain them by developing theories based on them. Once we have explained a concept we need to define it. We do so in two steps. First, we give it a dictionary-like definition, called a nominal definition. Then, we develop an operational definition that identifies how we can measure and quantify it.

Once a concept has been quantified, it is employed in modeling as a variable . In statistical modeling, variables are thought of as either dependent or independent variables. A dependent variable , Y, is the outcome variable; this is the concept we are trying to explain and/or predict. The independent variable(s) , X, is the variable(s) that is used to predict or explain the dependent variable. The expected relationships between (and among) the variables are specified by the theory.

Measurement

When measuring concepts, the indicators that are used in building and testing theories should be both valid and reliable . Validity refers to how well the measurement captures the concept. Face validity, for example, refers to the plausibility and general acceptance of the measure, while the domain validity of the measure concerns the degree to which it captures all relevant aspects of the concept. Reliability, by contrast, refers to how consistent the measure is with repeated applications. A measure is reliable if, when applied to the repeated observations in similar settings, the outcomes are consistent.

Assessing the Quality of a Measure

Measurement is the process of assigning numbers to the phenomenon or concept that you are interested in. Measurement is straight-forward when we can directly observe the phenomenon. One agrees on a metric, such as inches or pounds, and then figures out how many of those units are present for the case in question. Measurement becomes more challenging when you cannot directly observe the concept of interest. In political science and public policy, some of the things we want to measure are directly observable: how many dollars were spent on a project or how many votes the incumbent receives, but many of our concepts are not observable: is issue X on the public’s agenda, how successful is a program, or how much do citizens trust the president. When the concept is not directly observable the operational definition is especially important. The operational definition explains exactly what the researcher will do to assign a number for each subject/case.

In reality, there is always some possibility that the number assigned does not reflect the true value for that case, i.e., there may be some error involved. Error can come about for any number of reasons, including mistakes in coding, the need for subjective judgments, or a measuring instrument that lacks precision. These kinds of error will generally produce inconsistent results; that is, they reduce reliability. We can assess the reliability of an indicator using one of two general approaches. One approach is a test-retest method where the same subjects are measured at two different points in time. If the measure is reliable the correlation between the two observations should be high. We can also assess reliability by using multiple indicators of the same concept and determining if there is a strong inter-correlation among them using statistical formulas such as Cronbach’s alpha or Kuder-Richardson Formula 20 (KR-20).

We can also have error when our measure is not valid. Valid indicators measure the concept we think they are measuring. The indicator should both converge with the concept and discriminate between the concept and similar yet different concepts. Unfortunately, there is no failsafe way to determine whether an indicator is valid. There are, however, a few things you can do to gain confidence in the validity of the indicator. First, you can simply look at it from a logical perspective and ask if it seems like it is valid. Does it have face validity? Second, you can see if it correlates well with other indicators that are considered valid, and in ways that are consistent with theory. This is called construct validity. Third, you can determine if it works in the way expected, which is referred to as predictive validity. Finally, we have more confidence if other researchers using the same concept agree that the indicator is considered valid. This consensual validity at least ensures that different researchers are talking about the same thing.

Measurement of Different Kinds of Concepts

Measurement can be applied to different kinds of concepts, which causes measures of different concepts to vary. There are three primary levels of measurement ; ordinal, interval, and nominal. Ordinal level measures indicate relative differences, such as more or less, but do not provide equal distances between intervals on the measurement scale. Therefore, ordinal measures cannot tell us how much more or less one observation is than another. Imagine a survey question asking respondents to identify their annual income. Respondents are given a choice of five different income levels: $0-20,000, $20,000-50,000, $50,000-$100,000, and $100,000+. This measure gives us an idea of the rank order of respondents’ income, but it is impossible for us to identify consistent differences between these responses. With an interval level measure, the variable is ordered and the differences between values are consistent. Sticking with the example of income, survey respondents are now asked to provide their annual income to the nearest ten thousand dollar mark (e.g., $10,000, $20,000, $30,000, etc.). This measurement technique produces an interval level variable because we have both a rank ordering and equal spacing between values. Ratio scales are interval measures with the special characteristic that the value of zero (0) indicates the absence of some property. A value of zero (0) income in our example may indicate a person does not have a job. Another example of a ratio scale is the Kelvin temperature scale because zero (0) degrees Kelvin indicates the complete absence of heat. Finally, a nominal level measure identifies categorical differences among observations. Numerical values assigned to nominal variables have no inherent meaning, but only differentiate one type" (e.g., gender, race, religion) from another.

1.2.2 Theories and Causality

Theories should be causal in nature, meaning that an independent variable is thought to have a causal influence on the dependent variable. In other words, a change in the independent variable causes a change in the dependent variable. Causality can be thought of as the motor" that drives the model and provides the basis for explanation and (possibly) prediction.

The Basis of Causality in Theories

• Time Ordering: The cause precedes the effect, X→Y
• Co-Variation: Changes in X are associated with changes in Y
• Non-Spuriousness: There is not a variable Z that causes both X and Y

To establish causality we want to demonstrate that a change in the independent variable is a necessary and sufficient condition for a change in the dependent variable (though more complex, interdependent relationships can also be quantitatively modeled). We can think of the independent variable as a treatment, τ, and we speculate that τ causes a change in our dependent variable, Y. The gold standard’’ for causal inference is an experiment where a) the level of ττ is controlled by the researcher and b) subjects are randomly assigned to a treatment or control group. The group that receives the treatment has outcome Y 1 and the control group has outcome Y 0 ; the treatment effect can be defined as τ=Y 1 -Y 0 . Causality is inferred because the treatment was only given to one group, and since these groups were randomly assigned other influences should wash out. Thus the difference τ=Y 1 -Y0 can be attributed to the treatment.

Given the nature of social science and public policy theorizing, we often can’t control the treatment of interest. For example, our case study in this text concerns the effect of political ideology on views about the environment. For this type of relationship, we cannot randomly assign ideology in an experimental sense. Instead, we employ statistical controls to account for the possible influences of confounding factors, such as age and gender. Using multiple regression we control for other factors that might influence the dependent variable. 1

1.2.3 Generation of Testable Hypothesis

Theory building is accomplished through the testing of hypotheses derived from theory. In simple form, a theory implies (sets of) relationships among concepts. These concepts are then operationalized. Finally, models are developed to examine how the measures are related. Properly specified hypotheses can be tested with empirical data, which are derived from the application of valid and reliable measures to relevant observations. The testing and re-testing of hypotheses develops levels of confidence that we can have for the core propositions that constitute the theory. In short, empirically grounded theories must be able to posit clear hypotheses that are testable. In this text, we discuss hypotheses and test them using relevant models and data.

As noted above, this text uses the concepts of political ideology and views about the environment as a case study in order to generate and test hypotheses about the relationships between these variables. For example, based on popular media accounts, it is plausible to expect that political conservatives are less likely to be concerned about the environment than political moderates or liberals. Therefore, we can pose the working hypothesis that measures of political ideology will be systematically related to measures of concern for the environment – with conservatives showing less concern for the environment. In classical hypothesis testing, the working hypothesis is tested against a null hypothesis . A null hypothesis is an implicit hypothesis that posits the independent variable has no effect (i.e., null effect) on the dependent variable. In our example, the null hypothesis states ideology has no effect on environmental concern.

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Theory and Observation in Science

Scientists obtain a great deal of the evidence they use by collecting and producing empirical results. Much of the standard philosophical literature on this subject comes from 20 th century logical empiricists, their followers, and critics who embraced their issues while objecting to some of their aims and assumptions. Discussions about empirical evidence have tended to focus on epistemological questions regarding its role in theory testing. This entry follows that precedent, even though empirical evidence also plays important and philosophically interesting roles in other areas including scientific discovery, the development of experimental tools and techniques, and the application of scientific theories to practical problems.

The logical empiricists and their followers devoted much of their attention to the distinction between observables and unobservables, the form and content of observation reports, and the epistemic bearing of observational evidence on theories it is used to evaluate. Philosophical work in this tradition was characterized by the aim of conceptually separating theory and observation, so that observation could serve as the pure basis of theory appraisal. More recently, the focus of the philosophical literature has shifted away from these issues, and their close association to the languages and logics of science, to investigations of how empirical data are generated, analyzed, and used in practice. With this shift, we also see philosophers largely setting aside the aspiration of a pure observational basis for scientific knowledge and instead embracing a view of science in which the theoretical and empirical are usefully intertwined. This entry discusses these topics under the following headings:

1. Introduction

2.1 traditional empiricism, 2.2 the irrelevance of observation per se, 2.3 data and phenomena, 3.1 perception, 3.2 assuming the theory to be tested, 3.3 semantics, 4.1 confirmation, 4.2 saving the phenomena, 4.3 empirical adequacy, 5. conclusion, other internet resources, related entries.

Philosophers of science have traditionally recognized a special role for observations in the epistemology of science. Observations are the conduit through which the ‘tribunal of experience’ delivers its verdicts on scientific hypotheses and theories. The evidential value of an observation has been assumed to depend on how sensitive it is to whatever it is used to study. But this in turn depends on the adequacy of any theoretical claims its sensitivity may depend on. For example, we can challenge the use of a particular thermometer reading to support a prediction of a patient’s temperature by challenging theoretical claims having to do with whether a reading from a thermometer like this one, applied in the same way under similar conditions, should indicate the patient’s temperature well enough to count in favor of or against the prediction. At least some of those theoretical claims will be such that regardless of whether an investigator explicitly endorses, or is even aware of them, her use of the thermometer reading would be undermined by their falsity. All observations and uses of observational evidence are theory laden in this sense (cf. Chang 2005, Azzouni 2004). As the example of the thermometer illustrates, analogues of Norwood Hanson’s claim that seeing is a theory laden undertaking apply just as well to equipment generated observations (Hanson 1958, 19). But if all observations and empirical data are theory laden, how can they provide reality-based, objective epistemic constraints on scientific reasoning?

Recent scholarship has turned this question on its head. Why think that theory ladenness of empirical results would be problematic in the first place? If the theoretical assumptions with which the results are imbued are correct, what is the harm of it? After all, it is in virtue of those assumptions that the fruits of empirical investigation can be ‘put in touch’ with theorizing at all. A number scribbled in a lab notebook can do a scientist little epistemic good unless she can recruit the relevant background assumptions to even recognize it as a reading of the patient’s temperature. But philosophers have embraced an entangled picture of the theoretical and empirical that goes much deeper than this. Lloyd (2012) advocates for what she calls “complex empiricism” in which there is “no pristine separation of model and data” (397). Bogen (2016) points out that “impure empirical evidence” (i.e. evidence that incorporates the judgements of scientists) “often tells us more about the world that it could have if it were pure” (784). Indeed, Longino (2020) has urged that “[t]he naïve fantasy that data have an immediate relation to phenomena of the world, that they are ‘objective’ in some strong, ontological sense of that term, that they are the facts of the world directly speaking to us, should be finally laid to rest” and that “even the primary, original, state of data is not free from researchers’ value- and theory-laden selection and organization” (391).

There is not widespread agreement among philosophers of science about how to characterize the nature of scientific theories. What is a theory? According to the traditional syntactic view, theories are considered to be collections of sentences couched in logical language, which must then be supplemented with correspondence rules in order to be interpreted. Construed in this way, theories include maximally general explanatory and predictive laws (Coulomb’s law of electrical attraction and repulsion, and Maxwellian electromagnetism equations for example), along with lesser generalizations that describe more limited natural and experimental phenomena (e.g., the ideal gas equations describing relations between temperatures and pressures of enclosed gasses, and general descriptions of positional astronomical regularities). In contrast, the semantic view casts theories as the space of states possible according to the theory, or the set of mathematical models permissible according to the theory (see Suppe 1977). However, there are also significantly more ecumenical interpretations of what it means to be a scientific theory, which include elements of diverse kinds. To take just one illustrative example, Borrelli (2012) characterizes the Standard Model of particle physics as a theoretical framework involving what she calls “theoretical cores” that are composed of mathematical structures, verbal stories, and analogies with empirical references mixed together (196). This entry aims to accommodate all of these views about the nature of scientific theories.

In this entry, we trace the contours of traditional philosophical engagement with questions surrounding theory and observation in science that attempted to segregate the theoretical from the observational, and to cleanly delineate between the observable and the unobservable. We also discuss the more recent scholarship that supplants the primacy of observation by human sensory perception with an instrument-inclusive conception of data production and that embraces the intertwining of theoretical and empirical in the production of useful scientific results. Although theory testing dominates much of the standard philosophical literature on observation, much of what this entry says about the role of observation in theory testing applies also to its role in inventing, and modifying theories, and applying them to tasks in engineering, medicine, and other practical enterprises.

2. Observation and data

Reasoning from observations has been important to scientific practice at least since the time of Aristotle, who mentions a number of sources of observational evidence including animal dissection (Aristotle(a), 763a/30–b/15; Aristotle(b), 511b/20–25). Francis Bacon argued long ago that the best way to discover things about nature is to use experiences (his term for observations as well as experimental results) to develop and improve scientific theories (Bacon 1620, 49ff). The role of observational evidence in scientific discovery was an important topic for Whewell (1858) and Mill (1872) among others in the 19th century. But philosophers didn’t talk about observation as extensively, in as much detail, or in the way we have become accustomed to, until the 20 th century when logical empiricists transformed philosophical thinking about it.

One important transformation, characteristic of the linguistic turn in philosophy, was to concentrate on the logic of observation reports rather than on objects or phenomena observed. This focus made sense on the assumption that a scientific theory is a system of sentences or sentence-like structures (propositions, statements, claims, and so on) to be tested by comparison to observational evidence. It was assumed that the comparisons must be understood in terms of inferential relations. If inferential relations hold only between sentence-like structures, it follows that theories must be tested, not against observations or things observed, but against sentences, propositions, etc. used to report observations (Hempel 1935, 50–51; Schlick 1935). Theory testing was treated as a matter of comparing observation sentences describing observations made in natural or laboratory settings to observation sentences that should be true according to the theory to be tested. This was to be accomplished by using laws or lawlike generalizations along with descriptions of initial conditions, correspondence rules, and auxiliary hypotheses to derive observation sentences describing the sensory deliverances of interest. This makes it imperative to ask what observation sentences report.

According to what Hempel called the phenomenalist account , observation reports describe the observer’s subjective perceptual experiences.

… Such experiential data might be conceived of as being sensations, perceptions, and similar phenomena of immediate experience. (Hempel 1952, 674)

This view is motivated by the assumption that the epistemic value of an observation report depends upon its truth or accuracy, and that with regard to perception, the only thing observers can know with certainty to be true or accurate is how things appear to them. This means that we cannot be confident that observation reports are true or accurate if they describe anything beyond the observer’s own perceptual experience. Presumably one’s confidence in a conclusion should not exceed one’s confidence in one’s best reasons to believe it. For the phenomenalist, it follows that reports of subjective experience can provide better reasons to believe claims they support than reports of other kinds of evidence.

However, given the expressive limitations of the language available for reporting subjective experiences, we cannot expect phenomenalistic reports to be precise and unambiguous enough to test theoretical claims whose evaluation requires accurate, fine-grained perceptual discriminations. Worse yet, if experiences are directly available only to those who have them, there is room to doubt whether different people can understand the same observation sentence in the same way. Suppose you had to evaluate a claim on the basis of someone else’s subjective report of how a litmus solution looked to her when she dripped a liquid of unknown acidity into it. How could you decide whether her visual experience was the same as the one you would use her words to report?

Such considerations led Hempel to propose, contrary to the phenomenalists, that observation sentences report ‘directly observable’, ‘intersubjectively ascertainable’ facts about physical objects

… such as the coincidence of the pointer of an instrument with a numbered mark on a dial; a change of color in a test substance or in the skin of a patient; the clicking of an amplifier connected with a Geiger counter; etc. (ibid.)

That the facts expressed in observation reports be intersubjectively ascertainable was critical for the aims of the logical empiricists. They hoped to articulate and explain the authoritativeness widely conceded to the best natural, social, and behavioral scientific theories in contrast to propaganda and pseudoscience. Some pronouncements from astrologers and medical quacks gain wide acceptance, as do those of religious leaders who rest their cases on faith or personal revelation, and leaders who use their political power to secure assent. But such claims do not enjoy the kind of credibility that scientific theories can attain. The logical empiricists tried to account for the genuine credibility of scientific theories by appeal to the objectivity and accessibility of observation reports, and the logic of theory testing. Part of what they meant by calling observational evidence objective was that cultural and ethnic factors have no bearing on what can validly be inferred about the merits of a theory from observation reports. So conceived, objectivity was important to the logical empiricists’ criticism of the Nazi idea that Jews and Aryans have fundamentally different thought processes such that physical theories suitable for Einstein and his kind should not be inflicted on German students. In response to this rationale for ethnic and cultural purging of the German educational system, the logical empiricists argued that because of its objectivity, observational evidence (rather than ethnic and cultural factors) should be used to evaluate scientific theories (Galison 1990). In this way of thinking, observational evidence and its subsequent bearing on scientific theories are objective also in virtue of being free of non-epistemic values.

Ensuing generations of philosophers of science have found the logical empiricist focus on expressing the content of observations in a rarefied and basic observation language too narrow. Search for a suitably universal language as required by the logical empiricist program has come up empty-handed and most philosophers of science have given up its pursuit. Moreover, as we will discuss in the following section, the centrality of observation itself (and pointer readings) to the aims of empiricism in philosophy of science has also come under scrutiny. However, leaving the search for a universal pure observation language behind does not automatically undercut the norm of objectivity as it relates to the social, political, and cultural contexts of scientific research. Pristine logical foundations aside, the objectivity of ‘neutral’ observations in the face of noxious political propaganda was appealing because it could serve as shared ground available for intersubjective appraisal. This appeal remains alive and well today, particularly as pernicious misinformation campaigns are again formidable in public discourse (see O’Connor and Weatherall 2019). If individuals can genuinely appraise the significance of empirical evidence and come to well-justified agreement about how the evidence bears on theorizing, then they can protect their epistemic deliberations from the undue influence of fascists and other nefarious manipulators. However, this aspiration must face subtleties arising from the social epistemology of science and from the nature of empirical results themselves. In practice, the appraisal of scientific results can often require expertise that is not readily accessible to members of the public without the relevant specialized training. Additionally, precisely because empirical results are not pure observation reports, their appraisal across communities of inquirers operating with different background assumptions can require significant epistemic work.

The logical empiricists paid little attention to the distinction between observing and experimenting and its epistemic implications. For some philosophers, to experiment is to isolate, prepare, and manipulate things in hopes of producing epistemically useful evidence. It had been customary to think of observing as noticing and attending to interesting details of things perceived under more or less natural conditions, or by extension, things perceived during the course of an experiment. To look at a berry on a vine and attend to its color and shape would be to observe it. To extract its juice and apply reagents to test for the presence of copper compounds would be to perform an experiment. By now, many philosophers have argued that contrivance and manipulation influence epistemically significant features of observable experimental results to such an extent that epistemologists ignore them at their peril. Robert Boyle (1661), John Herschell (1830), Bruno Latour and Steve Woolgar (1979), Ian Hacking (1983), Harry Collins (1985) Allan Franklin (1986), Peter Galison (1987), Jim Bogen and Jim Woodward (1988), and Hans-Jörg Rheinberger (1997), are some of the philosophers and philosophically-minded scientists, historians, and sociologists of science who gave serious consideration to the distinction between observing and experimenting. The logical empiricists tended to ignore it. Interestingly, the contemporary vantage point that attends to modeling, data processing, and empirical results may suggest a re-unification of observation and intervention under the same epistemological framework. When one no longer thinks of scientific observation as pure or direct, and recognizes the power of good modeling to account for confounds without physically intervening on the target system, the purported epistemic distinction between observation and intervention loses its bite.

Observers use magnifying glasses, microscopes, or telescopes to see things that are too small or far away to be seen, or seen clearly enough, without them. Similarly, amplification devices are used to hear faint sounds. But if to observe something is to perceive it, not every use of instruments to augment the senses qualifies as observational.

Philosophers generally agree that you can observe the moons of Jupiter with a telescope, or a heartbeat with a stethoscope. The van Fraassen of The Scientific Image is a notable exception, for whom to be ‘observable’ meant to be something that, were it present to a creature like us, would be observed. Thus, for van Fraassen, the moons of Jupiter are observable “since astronauts will no doubt be able to see them as well from close up” (1980, 16). In contrast, microscopic entities are not observable on van Fraassen’s account because creatures like us cannot strategically maneuver ourselves to see them, present before us, with our unaided senses.

Many philosophers have criticized van Fraassen’s view as overly restrictive. Nevertheless, philosophers differ in their willingness to draw the line between what counts as observable and what does not along the spectrum of increasingly complicated instrumentation. Many philosophers who don’t mind telescopes and microscopes still find it unnatural to say that high energy physicists ‘observe’ particles or particle interactions when they look at bubble chamber photographs—let alone digital visualizations of energy depositions left in calorimeters that are not themselves inspected. Their intuitions come from the plausible assumption that one can observe only what one can see by looking, hear by listening, feel by touching, and so on. Investigators can neither look at (direct their gazes toward and attend to) nor visually experience charged particles moving through a detector. Instead they can look at and see tracks in the chamber, in bubble chamber photographs, calorimeter data visualizations, etc.

In more contentious examples, some philosophers have moved to speaking of instrument-augmented empirical research as more like tool use than sensing. Hacking (1981) argues that we do not see through a microscope, but rather with it. Daston and Galison (2007) highlight the inherent interactivity of a scanning tunneling microscope, in which scientists image and manipulate atoms by exchanging electrons between the sharp tip of the microscope and the surface to be imaged (397). Others have opted to stretch the meaning of observation to accommodate what we might otherwise be tempted to call instrument-aided detections. For instance, Shapere (1982) argues that while it may initially strike philosophers as counter-intuitive, it makes perfect sense to call the detection of neutrinos from the interior of the sun “direct observation.”

The variety of views on the observable/unobservable distinction hint that empiricists may have been barking up the wrong philosophical tree. Many of the things scientists investigate do not interact with human perceptual systems as required to produce perceptual experiences of them. The methods investigators use to study such things argue against the idea—however plausible it may once have seemed—that scientists do or should rely exclusively on their perceptual systems to obtain the evidence they need. Thus Feyerabend proposed as a thought experiment that if measuring equipment was rigged up to register the magnitude of a quantity of interest, a theory could be tested just as well against its outputs as against records of human perceptions (Feyerabend 1969, 132–137). Feyerabend could have made his point with historical examples instead of thought experiments. A century earlier Helmholtz estimated the speed of excitatory impulses traveling through a motor nerve. To initiate impulses whose speed could be estimated, he implanted an electrode into one end of a nerve fiber and ran a current into it from a coil. The other end was attached to a bit of muscle whose contraction signaled the arrival of the impulse. To find out how long it took the impulse to reach the muscle he had to know when the stimulating current reached the nerve. But

[o]ur senses are not capable of directly perceiving an individual moment of time with such small duration …

and so Helmholtz had to resort to what he called ‘artificial methods of observation’ (Olesko and Holmes 1994, 84). This meant arranging things so that current from the coil could deflect a galvanometer needle. Assuming that the magnitude of the deflection is proportional to the duration of current passing from the coil, Helmholtz could use the deflection to estimate the duration he could not see (ibid). This sense of ‘artificial observation’ is not to be confused e.g., with using magnifying glasses or telescopes to see tiny or distant objects. Such devices enable the observer to scrutinize visible objects. The minuscule duration of the current flow is not a visible object. Helmholtz studied it by cleverly concocting circumstances so that the deflection of the needle would meaningfully convey the information he needed. Hooke (1705, 16–17) argued for and designed instruments to execute the same kind of strategy in the 17 th century.

It is of interest that records of perceptual observation are not always epistemically superior to data collected via experimental equipment. Indeed, it is not unusual for investigators to use non-perceptual evidence to evaluate perceptual data and correct for its errors. For example, Rutherford and Pettersson conducted similar experiments to find out if certain elements disintegrated to emit charged particles under radioactive bombardment. To detect emissions, observers watched a scintillation screen for faint flashes produced by particle strikes. Pettersson’s assistants reported seeing flashes from silicon and certain other elements. Rutherford’s did not. Rutherford’s colleague, James Chadwick, visited Pettersson’s laboratory to evaluate his data. Instead of watching the screen and checking Pettersson’s data against what he saw, Chadwick arranged to have Pettersson’s assistants watch the screen while unbeknownst to them he manipulated the equipment, alternating normal operating conditions with a condition in which particles, if any, could not hit the screen. Pettersson’s data were discredited by the fact that his assistants reported flashes at close to the same rate in both conditions (Stuewer 1985, 284–288).

When the process of producing data is relatively convoluted, it is even easier to see that human sense perception is not the ultimate epistemic engine. Consider functional magnetic resonance images (fMRI) of the brain decorated with colors to indicate magnitudes of electrical activity in different regions during the performance of a cognitive task. To produce these images, brief magnetic pulses are applied to the subject’s brain. The magnetic force coordinates the precessions of protons in hemoglobin and other bodily stuffs to make them emit radio signals strong enough for the equipment to respond to. When the magnetic force is relaxed, the signals from protons in highly oxygenated hemoglobin deteriorate at a detectably different rate than signals from blood that carries less oxygen. Elaborate algorithms are applied to radio signal records to estimate blood oxygen levels at the places from which the signals are calculated to have originated. There is good reason to believe that blood flowing just downstream from spiking neurons carries appreciably more oxygen than blood in the vicinity of resting neurons. Assumptions about the relevant spatial and temporal relations are used to estimate levels of electrical activity in small regions of the brain corresponding to pixels in the finished image. The results of all of these computations are used to assign the appropriate colors to pixels in a computer generated image of the brain. In view of all of this, functional brain imaging differs, e.g., from looking and seeing, photographing, and measuring with a thermometer or a galvanometer in ways that make it uninformative to call it observation. And similarly for many other methods scientists use to produce non-perceptual evidence.

The role of the senses in fMRI data production is limited to such things as monitoring the equipment and keeping an eye on the subject. Their epistemic role is limited to discriminating the colors in the finished image, reading tables of numbers the computer used to assign them, and so on. While it is true that researchers typically use their sense of sight to take in visualizations of processed fMRI data—or numbers on a page or screen for that matter—this is not the primary locus of epistemic action. Researchers learn about brain processes through fMRI data, to the extent that they do, primarily in virtue of the suitability of the causal connection between the target processes and the data records, and of the transformations those data undergo when they are processed into the maps or other results that scientists want to use. The interesting questions are not about observability, i.e. whether neuronal activity, blood oxygen levels, proton precessions, radio signals, and so on, are properly understood as observable by creatures like us. The epistemic significance of the fMRI data depends on their delivering us the right sort of access to the target, but observation is neither necessary nor sufficient for that access.

Following Shapere (1982), one could respond by adopting an extremely permissive view of what counts as an ‘observation’ so as to allow even highly processed data to count as observations. However, it is hard to reconcile the idea that highly processed data like fMRI images record observations with the traditional empiricist notion that calculations involving theoretical assumptions and background beliefs must not be allowed (on pain of loss of objectivity) to intrude into the process of data production. Observation garnered its special epistemic status in the first place because it seemed more direct, more immediate, and therefore less distorted and muddled than (say) detection or inference. The production of fMRI images requires extensive statistical manipulation based on theories about the radio signals, and a variety of factors having to do with their detection along with beliefs about relations between blood oxygen levels and neuronal activity, sources of systematic error, and more. Insofar as the use of the term ‘observation’ connotes this extra baggage of traditional empiricism, it may be better to replace observation-talk with terminology that is more obviously permissive, such as that of ‘empirical data’ and ‘empirical results.’

Deposing observation from its traditional perch in empiricist epistemologies of science need not estrange philosophers from scientific practice. Terms like ‘observation’ and ‘observation reports’ do not occur nearly as much in scientific as in philosophical writings. In their place, working scientists tend to talk about data . Philosophers who adopt this usage are free to think about standard examples of observation as members of a large, diverse, and growing family of data production methods. Instead of trying to decide which methods to classify as observational and which things qualify as observables, philosophers can then concentrate on the epistemic influence of the factors that differentiate members of the family. In particular, they can focus their attention on what questions data produced by a given method can be used to answer, what must be done to use that data fruitfully, and the credibility of the answers they afford (Bogen 2016).

Satisfactorily answering such questions warrants further philosophical work. As Bogen and Woodward (1988) have argued, there is often a long road between obtaining a particular dataset replete with idiosyncrasies born of unspecified causal nuances to any claim about the phenomenon ultimately of interest to the researchers. Empirical data are typically produced in ways that make it impossible to predict them from the generalizations they are used to test, or to derive instances of those generalizations from data and non ad hoc auxiliary hypotheses. Indeed, it is unusual for many members of a set of reasonably precise quantitative data to agree with one another, let alone with a quantitative prediction. That is because precise, publicly accessible data typically cannot be produced except through processes whose results reflect the influence of causal factors that are too numerous, too different in kind, and too irregular in behavior for any single theory to account for them. When Bernard Katz recorded electrical activity in nerve fiber preparations, the numerical values of his data were influenced by factors peculiar to the operation of his galvanometers and other pieces of equipment, variations among the positions of the stimulating and recording electrodes that had to be inserted into the nerve, the physiological effects of their insertion, and changes in the condition of the nerve as it deteriorated during the course of the experiment. There were variations in the investigators’ handling of the equipment. Vibrations shook the equipment in response to a variety of irregularly occurring causes ranging from random error sources to the heavy tread of Katz’s teacher, A.V. Hill, walking up and down the stairs outside of the laboratory. That’s a short list. To make matters worse, many of these factors influenced the data as parts of irregularly occurring, transient, and shifting assemblies of causal influences.

The effects of systematic and random sources of error are typically such that considerable analysis and interpretation are required to take investigators from data sets to conclusions that can be used to evaluate theoretical claims. Interestingly, this applies as much to clear cases of perceptual data as to machine produced records. When 19 th and early 20 th century astronomers looked through telescopes and pushed buttons to record the time at which they saw a star pass a crosshair, the values of their data points depended, not only upon light from that star, but also upon features of perceptual processes, reaction times, and other psychological factors that varied from observer to observer. No astronomical theory has the resources to take such things into account.

Instead of testing theoretical claims by direct comparison to the data initially collected, investigators use data to infer facts about phenomena, i.e., events, regularities, processes, etc. whose instances are uniform and uncomplicated enough to make them susceptible to systematic prediction and explanation (Bogen and Woodward 1988, 317). The fact that lead melts at temperatures at or close to 327.5 C is an example of a phenomenon, as are widespread regularities among electrical quantities involved in the action potential, the motions of astronomical bodies, etc. Theories that cannot be expected to predict or explain such things as individual temperature readings can nevertheless be evaluated on the basis of how useful they are in predicting or explaining phenomena. The same holds for the action potential as opposed to the electrical data from which its features are calculated, and the motions of astronomical bodies in contrast to the data of observational astronomy. It is reasonable to ask a genetic theory how probable it is (given similar upbringings in similar environments) that the offspring of a parent or parents diagnosed with alcohol use disorder will develop one or more symptoms the DSM classifies as indicative of alcohol use disorder. But it would be quite unreasonable to ask the genetic theory to predict or explain one patient’s numerical score on one trial of a particular diagnostic test, or why a diagnostician wrote a particular entry in her report of an interview with an offspring of one of such parents (see Bogen and Woodward, 1988, 319–326).

Leonelli has challenged Bogen and Woodward’s (1988) claim that data are, as she puts it, “unavoidably embedded in one experimental context” (2009, 738). She argues that when data are suitably packaged, they can travel to new epistemic contexts and retain epistemic utility—it is not just claims about the phenomena that can travel, data travel too. Preparing data for safe travel involves work, and by tracing data ‘journeys,’ philosophers can learn about how the careful labor of researchers, data archivists, and database curators can facilitate useful data mobility. While Leonelli’s own work has often focused on data in biology, Leonelli and Tempini (2020) contains many diverse case studies of data journeys from a variety of scientific disciplines that will be of value to philosophers interested in the methodology and epistemology of science in practice.

The fact that theories typically predict and explain features of phenomena rather than idiosyncratic data should not be interpreted as a failing. For many purposes, this is the more useful and illuminating capacity. Suppose you could choose between a theory that predicted or explained the way in which neurotransmitter release relates to neuronal spiking (e.g., the fact that on average, transmitters are released roughly once for every 10 spikes) and a theory which explained or predicted the numbers displayed on the relevant experimental equipment in one, or a few single cases. For most purposes, the former theory would be preferable to the latter at the very least because it applies to so many more cases. And similarly for theories that predict or explain something about the probability of alcohol use disorder conditional on some genetic factor or a theory that predicted or explained the probability of faulty diagnoses of alcohol use disorder conditional on facts about the training that psychiatrists receive. For most purposes, these would be preferable to a theory that predicted specific descriptions in a single particular case history.

However, there are circumstances in which scientists do want to explain data. In empirical research it is often crucial to getting a useful signal that scientists deal with sources of background noise and confounding signals. This is part of the long road from newly collected data to useful empirical results. An important step on the way to eliminating unwanted noise or confounds is to determine their sources. Different sources of noise can have different characteristics that can be derived from and explained by theory. Consider the difference between ‘shot noise’ and ‘thermal noise,’ two ubiquitous sources of noise in precision electronics (Schottky 1918; Nyquist 1928; Horowitz and Hill 2015). ‘Shot noise’ arises in virtue of the discrete nature of a signal. For instance, light collected by a detector does not arrive all at once or in perfectly continuous fashion. Photons rain onto a detector shot by shot on account of being quanta. Imagine building up an image one photon at a time—at first the structure of the image is barely recognizable, but after the arrival of many photons, the image eventually fills in. In fact, the contribution of noise of this type goes as the square root of the signal. By contrast, thermal noise is due to non-zero temperature—thermal fluctuations cause a small current to flow in any circuit. If you cool your instrument (which very many precision experiments in physics do) then you can decrease thermal noise. Cooling the detector is not going to change the quantum nature of photons though. Simply collecting more photons will improve the signal to noise ratio with respect to shot noise. Thus, determining what kind of noise is affecting one’s data, i.e. explaining features of the data themselves that are idiosyncratic to the particular instruments and conditions prevailing during a specific instance of data collection, can be critical to eventually generating a dataset that can be used to answer questions about phenomena of interest. In using data that require statistical analysis, it is particularly clear that “empirical assumptions about the factors influencing the measurement results may be used to motivate the assumption of a particular error distribution”, which can be crucial for justifying the application of methods of analysis (Woodward 2011, 173).

There are also circumstances in which scientists want to provide a substantive, detailed explanation for a particular idiosyncratic datum, and even circumstances in which procuring such explanations is epistemically imperative. Ignoring outliers without good epistemic reasons is just cherry-picking data, one of the canonical ‘questionable research practices.’ Allan Franklin has described Robert Millikan’s convenient exclusion of data he collected from observing the second oil drop in his experiments of April 16, 1912 (1986, 231). When Millikan initially recorded the data for this drop, his notebooks indicate that he was satisfied his apparatus was working properly and that the experiment was running well—he wrote “Publish” next to the data in his lab notebook. However, after he had later calculated the value for the fundamental electric charge that these data yielded, and found it aberrant with respect to the values he calculated using data collected from other good observing sessions, he changed his mind, writing “Won’t work” next to the calculation (ibid., see also Woodward 2010, 794). Millikan not only never published this result, he never published why he failed to publish it. When data are excluded from analysis, there ought to be some explanation justifying their omission over and above lack of agreement with the experimenters’ expectations. Precisely because they are outliers, some data require specific, detailed, idiosyncratic causal explanations. Indeed, it is often in virtue of those very explanations that outliers can be responsibly rejected. Some explanation of data rejected as ‘spurious’ is required. Otherwise, scientists risk biasing their own work.

Thus, while in transforming data as collected into something useful for learning about phenomena, scientists often account for features of the data such as different types of noise contributions, and sometimes even explain the odd outlying data point or artifact, they simply do not explain every individual teensy tiny causal contribution to the exact character of a data set or datum in full detail. This is because scientists can neither discover such causal minutia nor would their invocation be necessary for typical research questions. The fact that it may sometimes be important for scientists to provide detailed explanations of data, and not just claims about phenomena inferred from data, should not be confused with the dubious claim that scientists could ‘in principle’ detail every causal quirk that contributed to some data (Woodward 2010; 2011).

In view of all of this, together with the fact that a great many theoretical claims can only be tested directly against facts about phenomena, it behooves epistemologists to think about how data are used to answer questions about phenomena. Lacking space for a detailed discussion, the most this entry can do is to mention two main kinds of things investigators do in order to draw conclusions from data. The first is causal analysis carried out with or without the use of statistical techniques. The second is non-causal statistical analysis.

First, investigators must distinguish features of the data that are indicative of facts about the phenomenon of interest from those which can safely be ignored, and those which must be corrected for. Sometimes background knowledge makes this easy. Under normal circumstances investigators know that their thermometers are sensitive to temperature, and their pressure gauges, to pressure. An astronomer or a chemist who knows what spectrographic equipment does, and what she has applied it to will know what her data indicate. Sometimes it is less obvious. When Santiago Ramón y Cajal looked through his microscope at a thin slice of stained nerve tissue, he had to figure out which, if any, of the fibers he could see at one focal length connected to or extended from things he could see only at another focal length, or in another slice. Analogous considerations apply to quantitative data. It was easy for Katz to tell when his equipment was responding more to Hill’s footfalls on the stairs than to the electrical quantities it was set up to measure. It can be harder to tell whether an abrupt jump in the amplitude of a high frequency EEG oscillation was due to a feature of the subjects brain activity or an artifact of extraneous electrical activity in the laboratory or operating room where the measurements were made. The answers to questions about which features of numerical and non-numerical data are indicative of a phenomenon of interest typically depend at least in part on what is known about the causes that conspire to produce the data.

Statistical arguments are often used to deal with questions about the influence of epistemically relevant causal factors. For example, when it is known that similar data can be produced by factors that have nothing to do with the phenomenon of interest, Monte Carlo simulations, regression analyses of sample data, and a variety of other statistical techniques sometimes provide investigators with their best chance of deciding how seriously to take a putatively illuminating feature of their data.

But statistical techniques are also required for purposes other than causal analysis. To calculate the magnitude of a quantity like the melting point of lead from a scatter of numerical data, investigators throw out outliers, calculate the mean and the standard deviation, etc., and establish confidence and significance levels. Regression and other techniques are applied to the results to estimate how far from the mean the magnitude of interest can be expected to fall in the population of interest (e.g., the range of temperatures at which pure samples of lead can be expected to melt).

The fact that little can be learned from data without causal, statistical, and related argumentation has interesting consequences for received ideas about how the use of observational evidence distinguishes science from pseudoscience, religion, and other non-scientific cognitive endeavors. First, scientists are not the only ones who use observational evidence to support their claims; astrologers and medical quacks use them too. To find epistemically significant differences, one must carefully consider what sorts of data they use, where it comes from, and how it is employed. The virtues of scientific as opposed to non-scientific theory evaluations depend not only on its reliance on empirical data, but also on how the data are produced, analyzed and interpreted to draw conclusions against which theories can be evaluated. Secondly, it does not take many examples to refute the notion that adherence to a single, universally applicable ‘scientific method’ differentiates the sciences from the non-sciences. Data are produced, and used in far too many different ways to treat informatively as instances of any single method. Thirdly, it is usually, if not always, impossible for investigators to draw conclusions to test theories against observational data without explicit or implicit reliance on theoretical resources.

Bokulich (2020) has helpfully outlined a taxonomy of various ways in which data can be model-laden to increase their epistemic utility. She focuses on seven categories: data conversion, data correction, data interpolation, data scaling, data fusion, data assimilation, and synthetic data. Of these categories, conversion and correction are perhaps the most familiar. Bokulich reminds us that even in the case of reading a temperature from an ordinary mercury thermometer, we are ‘converting’ the data as measured, which in this case is the height of the column of mercury, to a temperature (ibid., 795). In more complicated cases, such as processing the arrival times of acoustic signals in seismic reflection measurements to yield values for subsurface depth, data conversion may involve models (ibid.). In this example, models of the composition and geometry of the subsurface are needed in order to account for differences in the speed of sound in different materials. Data ‘correction’ involves common practices we have already discussed like modeling and mathematically subtracting background noise contributions from one’s dataset (ibid., 796). Bokulich rightly points out that involving models in these ways routinely improves the epistemic uses to which data can be put. Data interpolation, scaling, and ‘fusion’ are also relatively widespread practices that deserve further philosophical analysis. Interpolation involves filling in missing data in a patchy data set, under the guidance of models. Data are scaled when they have been generated in a particular scale (temporal, spatial, energy) and modeling assumptions are recruited to transform them to apply at another scale. Data are ‘fused,’ in Bokulich’s terminology, when data collected in diverse contexts, using diverse methods are combined, or integrated together. For instance, when data from ice cores, tree rings, and the historical logbooks of sea captains are merged into a joint climate dataset. Scientists must take care in combining data of diverse provenance, and model new uncertainties arising from the very amalgamation of datasets (ibid., 800).

Bokulich contrasts ‘synthetic data’ with what she calls ‘real data’ (ibid., 801–802). Synthetic data are virtual, or simulated data, and are not produced by physical interaction with worldly research targets. Bokulich emphasizes the role that simulated data can usefully play in testing and troubleshooting aspects of data processing that are to eventually be deployed on empirical data (ibid., 802). It can be incredibly useful for developing and stress-testing a data processing pipeline to have fake datasets whose characteristics are already known in virtue of having been produced by the researchers, and being available for their inspection at will. When the characteristics of a dataset are known, or indeed can be tailored according to need, the effects of new processing methods can be more readily traced than without. In this way, researchers can familiarize themselves with the effects of a data processing pipeline, and make adjustments to that pipeline in light of what they learn by feeding fake data through it, before attempting to use that pipeline on actual science data. Such investigations can be critical to eventually arguing for the credibility of the final empirical results and their appropriate interpretation and use.

Data assimilation is perhaps a less widely appreciated aspect of model-based data processing among philosophers of science, excepting Parker (2016; 2017). Bokulich characterizes this method as “the optimal integration of data with dynamical model estimates to provide a more accurate ‘assimilation estimate’ of the quantity” (2020, 800). Thus, data assimilation involves balancing the contributions of empirical data and the output of models in an integrated estimate, according to the uncertainties associated with these contributions.

Bokulich argues that the involvement of models in these various aspects of data processing does not necessarily lead to better epistemic outcomes. Done wrong, integrating models and data can introduce artifacts and make the processed data unreliable for the purpose at hand (ibid., 804). Indeed, she notes that “[t]here is much work for methodologically reflective scientists and philosophers of science to do in string out cases in which model-data symbiosis may be problematic or circular” (ibid.)

3. Theory and value ladenness

Empirical results are laden with values and theoretical commitments. Philosophers have raised and appraised several possible kinds of epistemic problems that could be associated with theory and/or value-laden empirical results. They have worried about the extent to which human perception itself is distorted by our commitments. They have worried that drawing upon theoretical resources from the very theory to be appraised (or its competitors) in the generation of empirical results yields vicious circularity (or inconsistency). They have also worried that contingent conceptual and/or linguistic frameworks trap bits of evidence like bees in amber so that they cannot carry on their epistemic lives outside of the contexts of their origination, and that normative values necessarily corrupt the integrity of science. Do the theory and value-ladenness of empirical results render them hopelessly parochial? That is, when scientists leave theoretical commitments behind and adopt new ones, must they also relinquish the fruits of the empirical research imbued with their prior commitments too? In this section, we discuss these worries and responses that philosophers have offered to assuage them.

If you believe that observation by human sense perception is the objective basis of all scientific knowledge, then you ought to be particularly worried about the potential for human perception to be corrupted by theoretical assumptions, wishful thinking, framing effects, and so on. Daston and Galison recount the striking example of Arthur Worthington’s symmetrical milk drops (2007, 11–16). Working in 1875, Worthington investigated the hydrodynamics of falling fluid droplets and their evolution upon impacting a hard surface. At first, he had tried to carefully track the drop dynamics with a strobe light to burn a sequence of images into his own retinas. The images he drew to record what he saw were radially symmetric, with rays of the drop splashes emanating evenly from the center of the impact. However, when Worthington transitioned from using his eyes and capacity to draw from memory to using photography in 1894, he was shocked to find that the kind of splashes he had been observing were irregular splats (ibid., 13). Even curiouser, when Worthington returned to his drawings, he found that he had indeed recorded some unsymmetrical splashes. He had evidently dismissed them as uninformative accidents instead of regarding them as revelatory of the phenomenon he was intent on studying (ibid.) In attempting to document the ideal form of the splashes, a general and regular form, he had subconsciously down-played the irregularity of individual splashes. If theoretical commitments, like Worthington’s initial commitment to the perfect symmetry of the physics he was studying, pervasively and incorrigibly dictated the results of empirical inquiry, then the epistemic aims of science would be seriously undermined.

Perceptual psychologists, Bruner and Postman, found that subjects who were briefly shown anomalous playing cards, e.g., a black four of hearts, reported having seen their normal counterparts e.g., a red four of hearts. It took repeated exposures to get subjects to say the anomalous cards didn’t look right, and eventually, to describe them correctly (Kuhn 1962, 63). Kuhn took such studies to indicate that things don’t look the same to observers with different conceptual resources. (For a more up-to-date discussion of theory and conceptual perceptual loading see Lupyan 2015.) If so, black hearts didn’t look like black hearts until repeated exposures somehow allowed subjects to acquire the concept of a black heart. By analogy, Kuhn supposed, when observers working in conflicting paradigms look at the same thing, their conceptual limitations should keep them from having the same visual experiences (Kuhn 1962, 111, 113–114, 115, 120–1). This would mean, for example, that when Priestley and Lavoisier watched the same experiment, Lavoisier should have seen what accorded with his theory that combustion and respiration are oxidation processes, while Priestley’s visual experiences should have agreed with his theory that burning and respiration are processes of phlogiston release.

The example of Pettersson’s and Rutherford’s scintillation screen evidence (above) attests to the fact that observers working in different laboratories sometimes report seeing different things under similar conditions. It is plausible that their expectations influence their reports. It is plausible that their expectations are shaped by their training and by their supervisors’ and associates’ theory driven behavior. But as happens in other cases as well, all parties to the dispute agreed to reject Pettersson’s data by appealing to results that both laboratories could obtain and interpret in the same way without compromising their theoretical commitments. Indeed, it is possible for scientists to share empirical results, not just across diverse laboratory cultures, but even across serious differences in worldview. Much as they disagreed about the nature of respiration and combustion, Priestley and Lavoisier gave quantitatively similar reports of how long their mice stayed alive and their candles kept burning in closed bell jars. Priestley taught Lavoisier how to obtain what he took to be measurements of the phlogiston content of an unknown gas. A sample of the gas to be tested is run into a graduated tube filled with water and inverted over a water bath. After noting the height of the water remaining in the tube, the observer adds “nitrous air” (we call it nitric oxide) and checks the water level again. Priestley, who thought there was no such thing as oxygen, believed the change in water level indicated how much phlogiston the gas contained. Lavoisier reported observing the same water levels as Priestley even after he abandoned phlogiston theory and became convinced that changes in water level indicated free oxygen content (Conant 1957, 74–109).

A related issue is that of salience. Kuhn claimed that if Galileo and an Aristotelian physicist had watched the same pendulum experiment, they would not have looked at or attended to the same things. The Aristotelian’s paradigm would have required the experimenter to measure

… the weight of the stone, the vertical height to which it had been raised, and the time required for it to achieve rest (Kuhn 1962, 123)

and ignore radius, angular displacement, and time per swing (ibid., 124). These last were salient to Galileo because he treated pendulum swings as constrained circular motions. The Galilean quantities would be of no interest to an Aristotelian who treats the stone as falling under constraint toward the center of the earth (ibid., 123). Thus Galileo and the Aristotelian would not have collected the same data. (Absent records of Aristotelian pendulum experiments we can think of this as a thought experiment.)

Interests change, however. Scientists may eventually come to appreciate the significance of data that had not originally been salient to them in light of new presuppositions. The moral of these examples is that although paradigms or theoretical commitments sometimes have an epistemically significant influence on what observers perceive or what they attend to, it can be relatively easy to nullify or correct for their effects. When presuppositions cause epistemic damage, investigators are often able to eventually make corrections. Thus, paradigms and theoretical commitments actually do influence saliency, but their influence is neither inevitable nor irremediable.

Thomas Kuhn (1962), Norwood Hanson (1958), Paul Feyerabend (1959) and others cast suspicion on the objectivity of observational evidence in another way by arguing that one cannot use empirical evidence to test a theory without committing oneself to that very theory. This would be a problem if it leads to dogmatism but assuming the theory to be tested is often benign and even necessary.

For instance, Laymon (1988) demonstrates the manner in which the very theory that the Michelson-Morley experiments are considered to test is assumed in the experimental design, but that this does not engender deleterious epistemic effects (250). The Michelson-Morley apparatus consists of two interferometer arms at right angles to one another, which are rotated in the course of the experiment so that, on the original construal, the path length traversed by light in the apparatus would vary according to alignment with or against the Earth’s velocity (carrying the apparatus) with respect to the stationary aether. This difference in path length would show up as displacement in the interference fringes of light in the interferometer. Although Michelson’s intention had been to measure the velocity of the Earth with respect to the all-pervading aether, the experiments eventually came to be regarded as furnishing tests of the Fresnel aether theory itself. In particular, the null results of these experiments were taken as evidence against the existence of the aether. Naively, one might suppose that whatever assumptions were made in the calculation of the results of these experiments, it should not be the case that the theory under the gun was assumed nor that its negation was.

Before Michelson’s experiments, the Fresnel aether theory did not predict any sort of length contraction. Although Michelson assumed no contraction in the arms of the interferometer, Laymon argues that he could have assumed contraction, with no practical impact on the results of the experiments. The predicted fringe shift is calculated from the anticipated difference in the distance traveled by light in the two arms is the same, when higher order terms are neglected. Thus, in practice, the experimenters could assume either that the contraction thesis was true or that it was false when determining the length of the arms. Either way, the results of the experiment would be the same. After Michelson’s experiments returned no evidence of the anticipated aether effects, Lorentz-Fitzgerald contraction was postulated precisely to cancel out the expected (but not found) effects and save the aether theory. Morley and Miller then set out specifically to test the contraction thesis, and still assumed no contraction in determining the length of the arms of their interferometer (ibid., 253). Thus Laymon argues that the Michelson-Morley experiments speak against the tempting assumption that “appraisal of a theory is based on phenomena which can be detected and measured without using assumptions drawn from the theory under examination or from competitors to that theory ” (ibid., 246).

Epistemological hand-wringing about the use of the very theory to be tested in the generation of the evidence to be used for testing, seems to spring primarily from a concern about vicious circularity. How can we have a genuine trial, if the theory in question has been presumed innocent from the outset? While it is true that there would be a serious epistemic problem in a case where the use of the theory to be tested conspired to guarantee that the evidence would turn out to be confirmatory, this is not always the case when theories are invoked in their own testing. Woodward (2011) summarizes a tidy case:

For example, in Millikan’s oil drop experiment, the mere fact that theoretical assumptions (e.g., that the charge of the electron is quantized and that all electrons have the same charge) play a role in motivating his measurements or a vocabulary for describing his results does not by itself show that his design and data analysis were of such a character as to guarantee that he would obtain results supporting his theoretical assumptions. His experiment was such that he might well have obtained results showing that the charge of the electron was not quantized or that there was no single stable value for this quantity. (178)

For any given case, determining whether the theoretical assumptions being made are benign or straight-jacketing the results that it will be possible to obtain will require investigating the particular relationships between the assumptions and results in that case. When data production and analysis processes are complicated, this task can get difficult. But the point is that merely noting the involvement of the theory to be tested in the generation of empirical results does not by itself imply that those results cannot be objectively useful for deciding whether the theory to be tested should be accepted or rejected.

Kuhn argued that theoretical commitments exert a strong influence on observation descriptions, and what they are understood to mean (Kuhn 1962, 127ff; Longino 1979, 38–42). If so, proponents of a caloric account of heat won’t describe or understand descriptions of observed results of heat experiments in the same way as investigators who think of heat in terms of mean kinetic energy or radiation. They might all use the same words (e.g., ‘temperature’) to report an observation without understanding them in the same way. This poses a potential problem for communicating effectively across paradigms, and similarly, for attributing the appropriate significance to empirical results generated outside of one’s own linguistic framework.

It is important to bear in mind that observers do not always use declarative sentences to report observational and experimental results. Instead, they often draw, photograph, make audio recordings, etc. or set up their experimental devices to generate graphs, pictorial images, tables of numbers, and other non-sentential records. Obviously investigators’ conceptual resources and theoretical biases can exert epistemically significant influences on what they record (or set their equipment to record), which details they include or emphasize, and which forms of representation they choose (Daston and Galison 2007, 115–190, 309–361). But disagreements about the epistemic import of a graph, picture or other non-sentential bit of data often turn on causal rather than semantical considerations. Anatomists may have to decide whether a dark spot in a micrograph was caused by a staining artifact or by light reflected from an anatomically significant structure. Physicists may wonder whether a blip in a Geiger counter record reflects the causal influence of the radiation they wanted to monitor, or a surge in ambient radiation. Chemists may worry about the purity of samples used to obtain data. Such questions are not, and are not well represented as, semantic questions to which semantic theory loading is relevant. Late 20 th century philosophers may have ignored such cases and exaggerated the influence of semantic theory loading because they thought of theory testing in terms of inferential relations between observation and theoretical sentences.

Nevertheless, some empirical results are reported as declarative sentences. Looking at a patient with red spots and a fever, an investigator might report having seen the spots, or measles symptoms, or a patient with measles. Watching an unknown liquid dripping into a litmus solution an observer might report seeing a change in color, a liquid with a PH of less than 7, or an acid. The appropriateness of a description of a test outcome depends on how the relevant concepts are operationalized. What justifies an observer to report having observed a case of measles according to one operationalization might require her to say no more than that she had observed measles symptoms, or just red spots according to another.

In keeping with Percy Bridgman’s view that

… in general, we mean by a concept nothing more than a set of operations; the concept is synonymous with the corresponding sets of operations (Bridgman 1927, 5)

one might suppose that operationalizations are definitions or meaning rules such that it is analytically true, e.g., that every liquid that turns litmus red in a properly conducted test is acidic. But it is more faithful to actual scientific practice to think of operationalizations as defeasible rules for the application of a concept such that both the rules and their applications are subject to revision on the basis of new empirical or theoretical developments. So understood, to operationalize is to adopt verbal and related practices for the purpose of enabling scientists to do their work. Operationalizations are thus sensitive and subject to change on the basis of findings that influence their usefulness (Feest 2005).

Definitional or not, investigators in different research traditions may be trained to report their observations in conformity with conflicting operationalizations. Thus instead of training observers to describe what they see in a bubble chamber as a whitish streak or a trail, one might train them to say they see a particle track or even a particle. This may reflect what Kuhn meant by suggesting that some observers might be justified or even required to describe themselves as having seen oxygen, transparent and colorless though it is, or atoms, invisible though they are (Kuhn 1962, 127ff). To the contrary, one might object that what one sees should not be confused with what one is trained to say when one sees it, and therefore that talking about seeing a colorless gas or an invisible particle may be nothing more than a picturesque way of talking about what certain operationalizations entitle observers to say. Strictly speaking, the objection concludes, the term ‘observation report’ should be reserved for descriptions that are neutral with respect to conflicting operationalizations.

If observational data are just those utterances that meet Feyerabend’s decidability and agreeability conditions, the import of semantic theory loading depends upon how quickly, and for which sentences reasonably sophisticated language users who stand in different paradigms can non-inferentially reach the same decisions about what to assert or deny. Some would expect enough agreement to secure the objectivity of observational data. Others would not. Still others would try to supply different standards for objectivity.

With regard to sentential observation reports, the significance of semantic theory loading is less ubiquitous than one might expect. The interpretation of verbal reports often depends on ideas about causal structure rather than the meanings of signs. Rather than worrying about the meaning of words used to describe their observations, scientists are more likely to wonder whether the observers made up or withheld information, whether one or more details were artifacts of observation conditions, whether the specimens were atypical, and so on.

Note that the worry about semantic theory loading extends beyond observation reports of the sort that occupied the logical empiricists and their close intellectual descendents. Combining results of diverse methods for making proxy measurements of paleoclimate temperatures in an epistemically responsible way requires careful attention to the variety of operationalizations at play. Even if no ‘observation reports’ are involved, the sticky question about how to usefully merge results obtained in different ways in order to satisfy one’s epistemic aims remains. Happily, the remedy for the worry about semantic loading in this broader sense is likely to be the same—investigating the provenance of those results and comparing the variety of factors that have contributed to their causal production.

Kuhn placed too much emphasis on the discontinuity between evidence generated in different paradigms. Even if we accept a broadly Kuhnian picture, according to which paradigms are heterogeneous collections of experimental practices, theoretical principles, problems selected for investigation, approaches to their solution, etc., connections between components are loose enough to allow investigators who disagree profoundly over one or more theoretical claims to nevertheless agree about how to design, execute, and record the results of their experiments. That is why neuroscientists who disagreed about whether nerve impulses consisted of electrical currents could measure the same electrical quantities, and agree on the linguistic meaning and the accuracy of observation reports including such terms as ‘potential’, ‘resistance’, ‘voltage’ and ‘current’. As we discussed above, the success that scientists have in repurposing results generated by others for different purposes speaks against the confinement of evidence to its native paradigm. Even when scientists working with radically different core theoretical commitments cannot make the same measurements themselves, with enough contextual information about how each conducts research, it can be possible to construct bridges that span the theoretical divides.

One could worry that the intertwining of the theoretical and empirical would open the floodgates to bias in science. Human cognizing, both historical and present day, is replete with disturbing commitments including intolerance and narrow mindedness of many sorts. If such commitments are integral to a theoretical framework, or endemic to the reasoning of a scientist or scientific community, then they threaten to corrupt the epistemic utility of empirical results generated using their resources. The core impetus of the ‘value-free ideal’ is to maintain a safe distance between the appraisal of scientific theories according to the evidence on one hand, and the swarm of moral, political, social, and economic values on the other. While proponents of the value-free ideal might admit that the motivation to pursue a theory or the legal protection of human subjects in permissible experimental methods involve non-epistemic values, they would contend that such values ought not ought not enter into the constitution of empirical results themselves, nor the adjudication or justification of scientific theorizing in light of the evidence (see Intemann 2021, 202).

As a matter of fact, values do enter into science at a variety of stages. Above we saw that ‘theory-ladenness’ could refer to the involvement of theory in perception, in semantics, and in a kind of circularity that some have worried begets unfalsifiability and thereby dogmatism. Like theory-ladenness, values can and sometimes do affect judgments about the salience of certain evidence and the conceptual framing of data. Indeed, on a permissive construal of the nature of theories, values can simply be understood as part of a theoretical framework. Intemann (2021) highlights a striking example from medical research where key conceptual resources include notions like ‘harm,’ ‘risk,’ ‘health benefit,’ and ‘safety.’ She refers to research on the comparative safety of giving birth at home and giving birth at a hospital for low-risk parents in the United States. Studies reporting that home births are less safe typically attend to infant and birthing parent mortality rates—which are low for these subjects whether at home or in hospital—but leave out of consideration rates of c-section and episiotomy, which are both relatively high in hospital settings. Thus, a value-laden decision about whether a possible outcome counts as a harm worth considering can influence the outcome of the study—in this case tipping the balance towards the conclusion that hospital births are more safe (ibid., 206).

Note that the birth safety case differs from the sort of cases at issue in the philosophical debate about risk and thresholds for acceptance and rejection of hypotheses. In accepting an hypothesis, a person makes a judgement that the risk of being mistaken is sufficiently low (Rudner 1953). When the consequences of being wrong are deemed grave, the threshold for acceptance may be correspondingly high. Thus, in evaluating the epistemic status of an hypothesis in light of the evidence, a person may have to make a value-based judgement. However, in the birth safety case, the judgement comes into play at an earlier stage, well before the decision to accept or reject the hypothesis is to be made. The judgement occurs already in deciding what is to count as a ‘harm’ worth considering for the purposes of this research.

The fact that values do sometimes enter into scientific reasoning does not by itself settle the question of whether it would be better if they did not. In order to assess the normative proposal, philosophers of science have attempted to disambiguate the various ways in which values might be thought to enter into science, and the various referents that get crammed under the single heading of ‘values.’ Anderson (2004) articulates eight stages of scientific research where values (‘evaluative presuppositions’) might be employed in epistemically fruitful ways. In paraphrase: 1) orientation in a field, 2) framing a research question, 3) conceptualizing the target, 4) identifying relevant data, 5) data generation, 6) data analysis, 7) deciding when to cease data analysis, and 8) drawing conclusions (Anderson 2004, 11). Similarly, Intemann (2021) lays out five ways “that values play a role in scientific reasoning” with which feminist philosophers of science have engaged in particular:

(1) the framing [of] research problems, (2) observing phenomena and describing data, (3) reasoning about value-laden concepts and assessing risks, (4) adopting particular models, and (5) collecting and interpreting evidence. (208)

Ward (2021) presents a streamlined and general taxonomy of four ways in which values relate to choices: as reasons motivating or justifying choices, as causal effectors of choices, or as goods affected by choices. By investigating the role of values in these particular stages or aspects of research, philosophers of science can offer higher resolution insights than just the observation that values are involved in science at all and untangle crosstalk.

Similarly, fine points can be made about the nature of values involved in these various contexts. Such clarification is likely important for determining whether the contribution of certain values in a given context is deleterious or salutary, and in what sense. Douglas (2013) argues that the ‘value’ of internal consistency of a theory and of the empirical adequacy of a theory with respect to the available evidence are minimal criteria for any viable scientific theory (799–800). She contrasts these with the sort of values that Kuhn called ‘virtues,’ i.e. scope, simplicity, and explanatory power that are properties of theories themselves, and unification, novel prediction and precision, which are properties a theory has in relation to a body of evidence (800–801). These are the sort of values that may be relevant to explaining and justifying choices that scientists make to pursue/abandon or accept/reject particular theories. Moreover, Douglas (2000) argues that what she calls “non-epistemic values” (in particular, ethical value judgements) also enter into decisions at various stages “internal” to scientific reasoning, such as data collection and interpretation (565). Consider a laboratory toxicology study in which animals exposed to dioxins are compared to unexposed controls. Douglas discusses researchers who want to determine the threshold for safe exposure. Admitting false positives can be expected to lead to overregulation of the chemical industry, while false negatives yield underregulation and thus pose greater risk to public health. The decision about where to set the unsafe exposure threshold, that is, set the threshold for a statistically significant difference between experimental and control animal populations, involves balancing the acceptability of these two types of errors. According to Douglas, this balancing act will depend on “whether we are more concerned about protecting public health from dioxin pollution or whether we are more concerned about protecting industries that produce dioxins from increased regulation” (ibid., 568). That scientists do as a matter of fact sometimes make such decisions is clear. They judge, for instance, a specimen slide of a rat liver to be tumorous or not, and whether borderline cases should count as benign or malignant (ibid., 569–572). Moreover, in such cases, it is not clear that the responsibility of making such decisions could be offloaded to non-scientists.

Many philosophers accept that values can contribute to the generation of empirical results without spoiling their epistemic utility. Anderson’s (2004) diagnosis is as follows:

Deep down, what the objectors find worrisome about allowing value judgments to guide scientific inquiry is not that they have evaluative content, but that these judgments might be held dogmatically, so as to preclude the recognition of evidence that might undermine them. We need to ensure that value judgements do not operate to drive inquiry to a predetermined conclusion. This is our fundamental criterion for distinguishing legitimate from illegitimate uses of values in science. (11)

Data production (including experimental design and execution) is heavily influenced by investigators’ background assumptions. Sometimes these include theoretical commitments that lead experimentalists to produce non-illuminating or misleading evidence. In other cases they may lead experimentalists to ignore, or even fail to produce useful evidence. For example, in order to obtain data on orgasms in female stumptail macaques, one researcher wired up females to produce radio records of orgasmic muscle contractions, heart rate increases, etc. But as Elisabeth Lloyd reports, “… the researcher … wired up the heart rate of the male macaques as the signal to start recording the female orgasms. When I pointed out that the vast majority of female stumptail orgasms occurred during sex among the females alone, he replied that yes he knew that, but he was only interested in important orgasms” (Lloyd 1993, 142). Although female stumptail orgasms occurring during sex with males are atypical, the experimental design was driven by the assumption that what makes features of female sexuality worth studying is their contribution to reproduction (ibid., 139). This assumption influenced experimental design in such a way as to preclude learning about the full range of female stumptail orgasms.

Anderson (2004) presents an influential analysis of the role of values in research on divorce. Researchers committed to an interpretive framework rooted in ‘traditional family values’ could conduct research on the assumption that divorce is mostly bad for spouses and any children that they have (ibid., 12). This background assumption, which is rooted in a normative appraisal of a certain model of good family life, could lead social science researchers to restrict the questions with which they survey their research subjects to ones about the negative impacts of divorce on their lives, thereby curtailing the possibility of discovering ways that divorce may have actually made the ex-spouses lives better (ibid., 13). This is an example of the influence that values can have on the nature of the results that research ultimately yields, which is epistemically detrimental. In this case, the values in play biased the research outcomes to preclude recognition of countervailing evidence. Anderson argues that the problematic influence of values comes when research “is rigged in advance” to confirm certain hypotheses—when the influence of values amounts to incorrigible dogmatism (ibid., 19). “Dogmatism” in her sense is unfalsifiability in practice, “their stubbornness in the face of any conceivable evidence”(ibid., 22).

Fortunately, such dogmatism is not ubiquitous and when it occurs it can often be corrected eventually. Above we noted that the mere involvement of the theory to be tested in the generation of an empirical result does not automatically yield vicious circularity—it depends on how the theory is involved. Furthermore, even if the assumptions initially made in the generation of empirical results are incorrect, future scientists will have opportunities to reassess those assumptions in light of new information and techniques. Thus, as long as scientists continue their work there need be no time at which the epistemic value of an empirical result can be established once and for all. This should come as no surprise to anyone who is aware that science is fallible, but it is no grounds for skepticism. It can be perfectly reasonable to trust the evidence available at present even though it is logically possible for epistemic troubles to arise in the future. A similar point can be made regarding values (although cf. Yap 2016).

Moreover, while the inclusion of values in the generation of an empirical result can sometimes be epistemically bad, values properly deployed can also be harmless, or even epistemically helpful. As in the cases of research on female stumptail macaque orgasms and the effects of divorce, certain values can sometimes serve to illuminate the way in which other epistemically problematic assumptions have hindered potential scientific insight. By valuing knowledge about female sexuality beyond its role in reproduction, scientists can recognize the narrowness of an approach that only conceives of female sexuality insofar as it relates to reproduction. By questioning the absolute value of one traditional ideal for flourishing families, researchers can garner evidence that might end up destabilizing the empirical foundation supporting that ideal.

Empirical results are most obviously put to epistemic work in their contexts of origin. Scientists conceive of empirical research, collect and analyze the relevant data, and then bring the results to bear on the theoretical issues that inspired the research in the first place. However, philosophers have also discussed ways in which empirical results are transferred out of their native contexts and applied in diverse and sometimes unexpected ways (see Leonelli and Tempini 2020). Cases of reuse, or repurposing of empirical results in different epistemic contexts raise several interesting issues for philosophers of science. For one, such cases challenge the assumption that theory (and value) ladenness confines the epistemic utility of empirical results to a particular conceptual framework. Ancient Babylonian eclipse records inscribed on cuneiform tablets have been used to generate constraints on contemporary geophysical theorizing about the causes of the lengthening of the day on Earth (Stephenson, Morrison, and Hohenkerk 2016). This is surprising since the ancient observations were originally recorded for the purpose of making astrological prognostications. Nevertheless, with enough background information, the records as inscribed can be translated, the layers of assumptions baked into their presentation peeled back, and the results repurposed using resources of the contemporary epistemic context, the likes of which the Babylonians could have hardly dreamed.

Furthermore, the potential for reuse and repurposing feeds back on the methodological norms of data production and handling. In light of the difficulty of reusing or repurposing data without sufficient background information about the original context, Goodman et al. (2014) note that “data reuse is most possible when: 1) data; 2) metadata (information describing the data); and 3) information about the process of generating those data, such as code, all all provided” (3). Indeed, they advocate for sharing data and code in addition to results customarily published in science. As we have seen, the loading of data with theory is usually necessary to putting that data to any serious epistemic use—theory-loading makes theory appraisal possible. Philosophers have begun to appreciate that this epistemic boon does not necessarily come at the cost of rendering data “tragically local” (Wylie 2020, 285, quoting Latour 1999). But it is important to note the useful travel of data between contexts is significantly aided by foresight, curation, and management for that aim.

In light of the mediated nature of empirical results, Boyd (2018) argues for an “enriched view of evidence,” in which the evidence that serves as the ‘tribunal of experience’ is understood to be “lines of evidence” composed of the products of data collection and all of the products of their transformation on the way to the generation of empirical results that are ultimately compared to theoretical predictions, considered together with metadata associated with their provenance. Such metadata includes information about theoretical assumptions that are made in data collection, processing, and the presentation of empirical results. Boyd argues that by appealing to metadata to ‘rewind’ the processing of assumption-imbued empirical results and then by re-processing them using new resources, the epistemic utility of empirical evidence can survive transitions to new contexts. Thus, the enriched view of evidence supports the idea that it is not despite the intertwining of the theoretical and empirical that scientists accomplish key epistemic aims, but often in virtue of it (ibid., 420). In addition, it makes the epistemic value of metadata encoding the various assumptions that have been made throughout the course of data collection and processing explicit.

The desirability of explicitly furnishing empirical data and results with auxiliary information that allow them to travel can be appreciated in light of the ‘objectivity’ norm, construed as accessibility to interpersonal scrutiny. When data are repurposed in novel contexts, they are not only shared between subjects, but can in some cases be shared across radically different paradigms with incompatible theoretical commitments.

4. The epistemic value of empirical evidence

One of the important applications of empirical evidence is its use in assessing the epistemic status of scientific theories. In this section we briefly discuss philosophical work on the role of empirical evidence in confirmation/falsification of scientific theories, ‘saving the phenomena,’ and in appraising the empirical adequacy of theories. However, further philosophical work ought to explore the variety of ways that empirical results bear on the epistemic status of theories and theorizing in scientific practice beyond these.

It is natural to think that computability, range of application, and other things being equal, true theories are better than false ones, good approximations are better than bad ones, and highly probable theoretical claims are better than less probable ones. One way to decide whether a theory or a theoretical claim is true, close to the truth, or acceptably probable is to derive predictions from it and use empirical data to evaluate them. Hypothetico-Deductive (HD) confirmation theorists proposed that empirical evidence argues for the truth of theories whose deductive consequences it verifies, and against those whose consequences it falsifies (Popper 1959, 32–34). But laws and theoretical generalization seldom if ever entail observational predictions unless they are conjoined with one or more auxiliary hypotheses taken from the theory they belong to. When the prediction turns out to be false, HD has trouble explaining which of the conjuncts is to blame. If a theory entails a true prediction, it will continue to do so in conjunction with arbitrarily selected irrelevant claims. HD has trouble explaining why the prediction does not confirm the irrelevancies along with the theory of interest.

Another approach to confirmation by empirical evidence is Inference to the Best Explanation (IBE). The idea is roughly that an explanation of the evidence that exhibits certain desirable characteristics with respect to a family of candidate explanations is likely to be the true on (Lipton 1991). On this approach, it is in virtue of their successful explanation of the empirical evidence that theoretical claims are supported. Naturally, IBE advocates face the challenges of defending a suitable characterization of what counts as the ‘best’ and of justifying the limited pool of candidate explanations considered (Stanford 2006).

Bayesian approaches to scientific confirmation have garnered significant attention and are now widespread in philosophy of science. Bayesians hold that the evidential bearing of empirical evidence on a theoretical claim is to be understood in terms of likelihood or conditional probability. For example, whether empirical evidence argues for a theoretical claim might be thought to depend upon whether it is more probable (and if so how much more probable) than its denial conditional on a description of the evidence together with background beliefs, including theoretical commitments. But by Bayes’ Theorem, the posterior probability of the claim of interest (that is, its probability given the evidence) is proportional to that claim’s prior probability. How to justify the choice of these prior probability assignments is one of the most notorious points of contention arising for Bayesians. If one makes the assignment of priors a subjective matter decided by epistemic agents, then it is not clear that they can be justified. Once again, one’s use of evidence to evaluate a theory depends in part upon one’s theoretical commitments (Earman 1992, 33–86; Roush 2005, 149–186). If one instead appeals to chains of successive updating using Bayes’ Theorem based on past evidence, one has to invoke assumptions that generally do not obtain in actual scientific reasoning. For instance, to ‘wash out’ the influence of priors a limit theorem is invoked wherein we consider very many updating iterations, but much scientific reasoning of interest does not happen in the limit, and so in practice priors hold unjustified sway (Norton 2021, 33).

Rather than attempting to cast all instances of confirmation based on empirical evidence as belonging to a universal schema, a better approach may be to ‘go local’. Norton’s material theory of induction argues that inductive support arises from background knowledge, that is, from material facts that are domain specific. Norton argues that, for instance, the induction from “Some samples of the element bismuth melt at 271°C” to “all samples of the element bismuth melt at 271°C” is admissible not in virtue of some universal schema that carries us from ‘some’ to ‘all’ but matters of fact (Norton 2003). In this particular case, the fact that licenses the induction is a fact about elements: “their samples are generally uniform in their physical properties” (ibid., 650). This is a fact pertinent to chemical elements, but not to samples of material like wax (ibid.). Thus Norton repeatedly emphasizes that “all induction is local”.

Still, there are those who may be skeptical about the very possibility of confirmation or of successful induction. Insofar as the bearing of evidence on theory is never totally decisive, insofar there is no single trusty universal schema that captures empirical support, perhaps the relationship between empirical evidence and scientific theory is not really about support after all. Giving up on empirical support would not automatically mean abandoning any epistemic value for empirical evidence. Rather than confirm theory, the epistemic role of evidence could be to constrain, for example by furnishing phenomena for theory to systematize or to adequately model.

Theories are said to ‘save’ observable phenomena if they satisfactorily predict, describe, or systematize them. How well a theory performs any of these tasks need not depend upon the truth or accuracy of its basic principles. Thus according to Osiander’s preface to Copernicus’ On the Revolutions , a locus classicus, astronomers “… cannot in any way attain to true causes” of the regularities among observable astronomical events, and must content themselves with saving the phenomena in the sense of using

… whatever suppositions enable … [them] to be computed correctly from the principles of geometry for the future as well as the past … (Osiander 1543, XX)

Theorists are to use those assumptions as calculating tools without committing themselves to their truth. In particular, the assumption that the planets revolve around the sun must be evaluated solely in terms of how useful it is in calculating their observable relative positions to a satisfactory approximation. Pierre Duhem’s Aim and Structure of Physical Theory articulates a related conception. For Duhem a physical theory

… is a system of mathematical propositions, deduced from a small number of principles, which aim to represent as simply and completely, and exactly as possible, a set of experimental laws. (Duhem 1906, 19)

‘Experimental laws’ are general, mathematical descriptions of observable experimental results. Investigators produce them by performing measuring and other experimental operations and assigning symbols to perceptible results according to pre-established operational definitions (Duhem 1906, 19). For Duhem, the main function of a physical theory is to help us store and retrieve information about observables we would not otherwise be able to keep track of. If that is what a theory is supposed to accomplish, its main virtue should be intellectual economy. Theorists are to replace reports of individual observations with experimental laws and devise higher level laws (the fewer, the better) from which experimental laws (the more, the better) can be mathematically derived (Duhem 1906, 21ff).

A theory’s experimental laws can be tested for accuracy and comprehensiveness by comparing them to observational data. Let EL be one or more experimental laws that perform acceptably well on such tests. Higher level laws can then be evaluated on the basis of how well they integrate EL into the rest of the theory. Some data that don’t fit integrated experimental laws won’t be interesting enough to worry about. Other data may need to be accommodated by replacing or modifying one or more experimental laws or adding new ones. If the required additions, modifications or replacements deliver experimental laws that are harder to integrate, the data count against the theory. If the required changes are conducive to improved systematization the data count in favor of it. If the required changes make no difference, the data don’t argue for or against the theory.

On van Fraassen’s (1980) semantic account, a theory is empirically adequate when the empirical structure of at least one model of that theory is isomorphic to what he calls the “appearances” (45). In other words, when the theory “has at least one model that all the actual phenomena fit inside” (12). Thus, for van Fraassen, we continually check the empirical adequacy of our theories by seeing if they have the structural resources to accommodate new observations. We’ll never know that a given theory is totally empirically adequate, since for van Fraassen, empirical adequacy obtains with respect to all that is observable in principle to creatures like us, not all that has already been observed (69).

The primary appeal of dealing in empirical adequacy rather than confirmation is its appropriate epistemic humility. Instead of claiming that confirming evidence justifies belief (or boosted confidence) that a theory is true, one is restricted to saying that the theory continues to be consistent with the evidence as far as we can tell so far. However, if the epistemic utility of empirical results in appraising the status of theories is just to judge their empirical adequacy, then it may be difficult to account for the difference between adequate but unrealistic theories, and those equally adequate theories that ought to be taken seriously as representations. Appealing to extra-empirical virtues like parsimony may be a way out, but one that will not appeal to philosophers skeptical of the connection thereby supposed between such virtues and representational fidelity.

On an earlier way of thinking, observation was to serve as the unmediated foundation of science—direct access to the facts upon which the edifice of scientific knowledge could be built. When conflict arose between factions with different ideological commitments, observations could furnish the material for neutral arbitration and settle the matter objectively, in virtue of being independent of non-empirical commitments. According to this view, scientists working in different paradigms could at least appeal to the same observations, and propagandists could be held accountable to the publicly accessible content of theory and value-free observations. Despite their different theories, Priestley and Lavoisier could find shared ground in the observations. Anti-Semites would be compelled to admit the success of a theory authored by a Jewish physicist, in virtue of the unassailable facts revealed by observation.

This version of empiricism with respect to science does not accord well with the fact that observation per se plays a relatively small role in many actual scientific methodologies, and the fact that even the most ‘raw’ data is often already theoretically imbued. The strict contrast between theory and observation in science is more fruitfully supplanted by inquiry into the relationship between theorizing and empirical results.

Contemporary philosophers of science tend to embrace the theory ladenness of empirical results. Instead of seeing the integration of the theoretical and the empirical as an impediment to furthering scientific knowledge, they see it as necessary. A ‘view from nowhere’ would not bear on our particular theories. That is, it is impossible to put empirical results to use without recruiting some theoretical resources. In order to use an empirical result to constrain or test a theory it has to be processed into a form that can be compared to that theory. To get stellar spectrograms to bear on Newtonian or relativistic cosmology, they need to be processed—into galactic rotation curves, say. The spectrograms by themselves are just artifacts, pieces of paper. Scientists need theoretical resources in order to even identify that such artifacts bear information relevant for their purposes, and certainly to put them to any epistemic use in assessing theories.

This outlook does not render contemporary philosophers of science all constructivists, however. Theory mediates the connection between the target of inquiry and the scientific worldview, it does not sever it. Moreover, vigilance is still required to ensure that the particular ways in which theory is ‘involved’ in the production of empirical results are not epistemically detrimental. Theory can be deployed in experiment design, data processing, and presentation of results in unproductive ways, for instance, in determining whether the results will speak for or against a particular theory regardless of what the world is like. Critical appraisal of the roles of theory is thus important for genuine learning about nature through science. Indeed, it seems that extra-empirical values can sometimes assist such critical appraisal. Instead of viewing observation as the theory-free and for that reason furnishing the content with which to appraise theories, we might attend to the choices and mistakes that can be made in collecting and generating empirical results with the help of theoretical resources, and endeavor to make choices conducive to learning and correct mistakes as we discover them.

Recognizing the involvement of theory and values in the constitution and generation of empirical results does not undermine the special epistemic value of empirical science in contrast to propaganda and pseudoscience. In cases where the influence of cultural, political, and religious values hinder scientific inquiry, it is often the case that they do so by limiting or determining the nature of the empirical results. Yet, by working to make the assumptions that shape results explicit we can examine their suitability for our purposes and attempt to restructure inquiry as necessary. When disagreements arise, scientists can attempt to settle them by appealing to the causal connections between the research target and the empirical data. The tribunal of experience speaks through empirical results, but it only does so through via careful fashioning with theoretical resources.

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How to cite this entry . Preview the PDF version of this entry at the Friends of the SEP Society . Look up topics and thinkers related to this entry at the Internet Philosophy Ontology Project (InPhO). Enhanced bibliography for this entry at PhilPapers , with links to its database.

• Confirmation , by Franz Huber, in the Internet Encyclopedia of Philosophy .
• Transcript of Katzmiller v. Dover Area School District (on the teaching of intelligent design).

Bacon, Francis | Bayes’ Theorem | constructive empiricism | Duhem, Pierre | empiricism: logical | epistemology: Bayesian | feminist philosophy, topics: perspectives on science | incommensurability: of scientific theories | Locke, John | measurement: in science | models in science | physics: experiment in | science: and pseudo-science | scientific objectivity | scientific research and big data | statistics, philosophy of

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Empirical or Theoretical?

Empirical: Based on data gathered by original experiments or observations.

Theoretical: Analyzes and makes connections between empirical studies to define or advance a theoretical position.

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Module 2 Chapter 2: The Link Between Theory, Research, and Social Justice

Theory has been mentioned several times in Chapter 1 discussions. In this chapter, we explore the relationship between theory and research, paying particular attention to how theory and research relate to promoting social justice.

In this chapter you will read about:

• Why theory matters to social work
• How theory and research relate to social justice

The Significance of Theory

It is helpful to begin with thinking about what theory  is. Theory is defined as a belief, idea, or set of principles that explain something—the set of principles may be organized in a complex manner or may be quite simple in nature. Theories are not facts; they are conjectures and predictions that need to be tested for their goodness-of-fit with reality. A scientific theory is an explanation supported by empirical evidence. Therefore, scientific theory is based on careful, reasonable examination of facts, and theories are tested and confirmed for their ability to explain and predict the phenomena of interest.

Theory is central to the development of social work interventions, as it determines the nature of our solutions to identified problems. Consider an example whereby programmatic and social policy responses might be influenced by the way that a problem like teen pregnancy is defined and the theories about the problem. In Table 2-1 you can see different definitions or theories of the problem on the left, and the logical responses on the right. In many cases, the boxes on the right can also be supplemented with content from other boxes addressing other problem definitions or theories.

Table 2-1. Analysis of teen pregnancy: How defining a problem determines responses

Hopefully, through this example, you can see how the way we define a problem and our theories about its causes determines the types of solutions we develop. Solutions are also dependent on whether they are feasible, practical, reasonable, ethical, culturally appropriate, and politically acceptable.

Theory, Research, and Social Justice

Theory is integral to research and research is integral to theory. Theory guides the development of many research questions and research helps generate new theories, as well as determining whether support for theories exists. What is important to remember is that theory is not fact: it is a belief about how things work; theory is a belief or “best guess” that awaits the support of empirical evidence.

“The best theories are those that have been substantiated or validated in the real world by research studies” (Dudley, 2011, p. 6).

Countless theories exist, and some are more well-developed and well-defined than others. More mature theories have had more time and effort devoted to supporting research, newer theories may be more tentative as supporting evidence is being developed. Theories are sometimes disproven and need to be scrapped completely or heavily overhauled as new research evidence emerges. And, exploratory research leads to the birth of new theories as new phenomena and questions arise, or as practitioners discover ways that existing theory does not fit reality.

Examples of theories and theoretical models with which you may have become familiar in other coursework are developed, tested, and applied in research from multiple disciplines, including social work. You may be familiar with the concepts of multidisciplinary  and interdisciplinary  practice, research and theory, but you also might be interested to learn the concept of transdisciplinary  research and theory. The social work profession engages in all three, as described in Figure 2-1.

Figure 2-1. Comparison of multidisciplinary, interdisciplinary, and transdisciplinary as concepts

An example of transdisciplinarity is demonstrated in motivational interviewing. The principles and practice of motivational interviewing skills transcends disciplines—it is relevant and effective regardless of a practitioner’s discipline and regardless of which discipline is conducting research that provides supporting evidence. An example of interdisciplinarity is when social workers, nurses, pediatricians, occupational therapists, respiratory therapists, and physical therapists work together to create a system for reducing infants’ and young children’s environmental exposure to heavy metal contamination in their home, childcare, recreational, food and water (e.g., lead and mercury). An example of multidisciplinarity is when a pediatrician, nurse, occupational therapist, social worker, and physical therapist each deliver discipline-specific services to children with intellectual disabilities.

Here are examples of theories that you may have encountered or eventually will encounter in your career:

• behavioral theory
• cognitive theory
• conflict theory
• contact theory (groups)
• critical race theory
• developmental theory (families)
• developmental theory (individuals)
• feminist theory
• health beliefs model
• information processing theory
• learning theory
• lifecourse or lifespan theory
• neurobiology theories
• organizational theory
• psychoanalytic theory
• role theory
• social capital model
• social ecological theory
• social learning theory
• social network theory
• stress vulnerability model
• systems theory
• theory of reasoned action/planned behavior
• transtheoretical model of behavior change

Social work practitioners generate new theories in the field all the time, but these theories are rarely documented or systematicallyl tested. Applying systematic research methods to test these practice-generated theories can help expand the social work knowledge base. Well-developed theories must be testable through the application of research methods. Furthermore, they both supplement and complement other theories that have the support of strong evidence behind them. In addition to these points, for theories to be relevant to social work, they need to:

• have practice implications at one or more level of intervention—suggest principles of action;
• be responsive to human diversity;
• contribute to the promotion of social justice (Dudley, 2011).

Social Justice and The Grand Challenges for Social Work .  In 2016, the American Academy of Social Work & Social Welfare (AASWSW) rolled out a set of 12 initiatives, challenging the social work profession to develop strategies for having a significant impact on a broad set of problems challenging the nation: “their introduction truly has the potential to be a defining moment in the history of our profession” (Williams, 2016, p. 67). The Grand Challenges directly relate to content presented in the Preamble to the Code of Ethics of the National Association of Social Workers (2017):

The primary mission of the social work profession is to enhance human well-being and help meet the basic human needs of all people, with particular attention to the needs and empowerment of people who are vulnerable, oppressed, and living in poverty. A historic and defining feature of social work is the profession’s dual focus on individual well-being in a social context and the well-being of society. Fundamental to social work is attention to the environmental forces that create, contribute to, and address problems in living (p. 1).

The ambitious Grand Challenges ( http://aaswsw.org/grand-challenges-initiative/12-challenges/ ) call for a synthesis of research and evidence generating endeavors, social work education, and social work practice (at all levels), with promoting social justice and transforming society at the forefront of attention. None of the Grand Challenges can be achieved without collaboration between social work researchers, practitioners, key stakeholders/constituents, policy makers, and members of other professions and disciplines (see Table 2-2). Each of the papers published under the 12 umbrella challenges not only reviews evidence related to the challenge, but also identifies a research and action agenda for promoting change and achieving the stated challenge goals.

Table 2-2. 12 Grand Challenges for Social Work

Theory and its related research is presented in the literature of social work and other disciplines. Being able to locate the relevant literature is an important skill for social work professionals and researchers to master. The next chapter introduces some basic principles about identifying literature that informs us about theory and the research that leads to the development of new theories and the testing of existing theories.

Using Data to Support Social Justice Advocac y

It is one thing to collect data, statistics, and information about the dimensions of a social problem; it is another to apply those data, statistics, and information in action to promote social change around an identified social justice cause. Advocacy is one tool or role important to the social work profession since its earliest days. Data and empirical evidence should routinely support social workers’ social justice advocacy efforts at the micro level—when working with specific client systems (case advocacy). Social justice advocacy also is a macro-level practice (cause advocacy), one that often involves the use of data to raise awareness about a cause, establish change goals, and evaluate the impact of change efforts. Consider, for example, the impact of data on social justice advocacy related to opioid misuse and addiction across the United States. Data regarding the sharp, upward trend in opioid-related deaths have had a powerful impact on public awareness, having captivated the attention of mass media outlets. These are data from the Centers for Disease Control and Prevention (CDC) for the period 1999 to 2016 (see Figure 2-2). The death rate from opioid overdose increased remarkably in the most recent years depicted, including heroin, natural and semisynthetic (morphine, codeine, hydrocodone, oxycodone), and synthetic opioids (fentanyl, fentanyl analogs, tramadol); the rate declined somewhat for deaths related to methadone, a highly controlled prescription medication used to treat opioid addiction (Hedegaard, Warner, & Miniño, 2017). In 2016 the overdose death rate was more than triple the 1999 rate and surpassed all other causes of death (including homicide and automobile crashes) for persons aged 50 or younger (Vestal, 2018). The good news is that the rates dropped significantly across 14 states during 2017 as new policy approaches were implemented—including more widespread use and access to naloxone (an opioid overdose reversal drug). The bad news is that the opioid overdose death rates increased significantly across 5 states and the District of Columbia as fentanyl and related drugs became integrated into the illicit drug supply: “Nationally, the death toll is still rising” (Vestal, 2018, p. 1). The Ohio Department of Health, with Ohio being one of the 5 states with more than a 30% increase in opioid overdose deaths, now recommends that naloxone be used more widely in overdose situations, whether or not opioids are known to be involved.

Figure 2-2. Opioid drug overdose death rate trends 1999-2016, by opioid category

Having data of this sort available to argue the urgency of a cause is of tremendous value. Imagine the potential impact of data on social issues such as child maltreatment, intimate partner violence, human trafficking, and death by suicide. Imagine also the potential impact of data concerning disparities in health, incarceration, mental and traumatic stress disorders, and educational or employment achievement across members of different racial/ethnic, age, economic, and national origin groups. This type of evidence has the potential to be a powerful element in the practice of social justice advocacy.

Follow links below to specific AASWSW Grand Challenges and consider the ways that research evidence is being used to advocate for at least one of the following social justice causes:

• Health Equity: Eradicating Health Inequalities for Future Generations
• From Mass Incarceration to Smart Decarceration
• Safe Children: Reducing Severe and Fatal Maltreatment
• Ending Gender Based Violence: A Grand Challenge for Social Work
• Prevention of Schizophrenia and Severe Mental Illness
• Increasing Productive Engagement in Later Life
• Strengthening the Social Responses to the Human Impacts of Environmental Change

Social Work 3401 Coursebook Copyright © by Dr. Audrey Begun is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License , except where otherwise noted.

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Theoretical vs. Empirical: Know the Difference

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Empirical research in the social sciences and education.

• What is Empirical Research and How to Read It
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Introduction: What is Empirical Research?

Empirical research is based on observed and measured phenomena and derives knowledge from actual experience rather than from theory or belief. 

How do you know if a study is empirical? Read the subheadings within the article, book, or report and look for a description of the research "methodology."  Ask yourself: Could I recreate this study and test these results?

Key characteristics to look for:

• Specific research questions to be answered
• Definition of the population, behavior, or   phenomena being studied
• Description of the process used to study this population or phenomena, including selection criteria, controls, and testing instruments (such as surveys)

Another hint: some scholarly journals use a specific layout, called the "IMRaD" format, to communicate empirical research findings. Such articles typically have 4 components:

• Introduction : sometimes called "literature review" -- what is currently known about the topic -- usually includes a theoretical framework and/or discussion of previous studies
• Methodology: sometimes called "research design" -- how to recreate the study -- usually describes the population, research process, and analytical tools used in the present study
• Results : sometimes called "findings" -- what was learned through the study -- usually appears as statistical data or as substantial quotations from research participants
• Discussion : sometimes called "conclusion" or "implications" -- why the study is important -- usually describes how the research results influence professional practices or future studies

Reading and Evaluating Scholarly Materials

Reading research can be a challenge. However, the tutorials and videos below can help. They explain what scholarly articles look like, how to read them, and how to evaluate them:

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• IF I APPLY A newer model of evaluating sources which encourages you to think about your own biases as a reader, as well as concerns about the item you are reading.
• Credo Video: How to Read Scholarly Materials (4 min.)
• Credo Tutorial: How to Read Scholarly Materials
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• Credo Video: Evaluating Statistics (4 min.)
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Article Contents

Survey design and administration, survey analysis, respondent demographics, reading, collaboration, and coauthoring, views on the interaction of theoretical and empirical work, perceptions of attention, collaboration, and intimidation, good interactions between theoretical and empirical work, bad interactions between theoretical and empirical work, other thoughts, institutional effects, survey problems, interactions with age, gender, career stage, and overall field, implications and discussion, related work, conclusions, supplemental material, references cited.

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Theoretical and Empirical Perspectives in Ecology and Evolution: A Survey

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Benjamin C. Haller, Theoretical and Empirical Perspectives in Ecology and Evolution: A Survey, BioScience , Volume 64, Issue 10, October 2014, Pages 907–916, https://doi.org/10.1093/biosci/biu131

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Scientific research is often conceptually divided into empirical and theoretical approaches, and researchers often specialize as empiricists or theorists. Both approaches are essential to scientific progress, but interactions between the two groups have at times seemed problematic. I present results from a recent survey of 614 scientists (predominantly ecologists and evolutionary biologists) regarding interactions between theoretical and empirical work. Some overall themes emerged from these results. One theme is a widespread and mutual lack of trust, understanding, and interaction between empiricists and theorists. Another is a general desire, among almost all of the respondents, for greater understanding, more collaboration, and closer interactions between empirical and theoretical work. The final theme is that institutions, such as journals, funding agencies, and universities, are often seen as hindering such interactions. These results provide a clear mandate for institutional changes to improve interactions between theorists and empiricists in ecology and evolutionary biology.

Scientific investigation comprises many different sorts of activities, and these activities are often roughly classified as either empirical or theoretical . Empirical work might be defined as the gathering and analysis of data from phenomena observed in the real world; it spans a range from purely observational field studies to hypothesis-driven experimentation in the laboratory. Theoretical work can be inspired by real-world phenomena, but it does not involve the gathering or analysis of data from those phenomena; rather, it might be defined as an exploration of what could be, rather than what is, using tools ranging from analytical models to individual-based simulations. Like most dichotomies, this classification is an oversimplification, and some types of scientific inquiry are not easily categorized, such as biostatistics and meta-analysis. Nevertheless, the distinction between empirical and theoretical work is widely accepted, many types of scientific research do clearly fall into one category or the other, and many researchers consider themselves to work primarily in one area or the other.

One could debate these definitions of empirical and theoretical work, discuss the proper role of each type of work in the scientific process, give further examples of each, and consider in detail the types of interactions that can exist between them (Watt 1962 , Hall 1991 , Odenbaugh 2005 , Bailer-Jones 2009 , Pickett et al. 2010 , Colyvan 2011 , Gorelick 2011 , 2012 , Choate et al. 2012 , Griesemer 2012 , Scheiner 2012 , 2013 , Callebaut 2013 , Pigliucci 2013 ). As worthwhile as it might be, however, such philosophical discussion is not the aim of this article. Instead, I will investigate a more empirical aspect of this dichotomy: how theoretical researchers ( theorists ) and empirical researchers ( empiricists ) interact in their practice of science and how they view that interaction. For this reason, I will assume that the categories of theoretical and empirical research are generally understood, even if they are also somewhat vague and subject to debate. The interaction between theoretical and empirical work in ecology and evolution has been the subject of much discussion (Kareiva 1989 , Grimm 1994 , Noss 1996 , Lawton 1999 , Colyvan and Ginzburg 2003 , Belovsky et al. 2004 , Ellner 2006 , Scheiner and Willig 2008 , Roughgarden 2009 , Gorelick 2011 , Lindenmayer and Likens 2011 , Cadotte et al. 2012 , Fawcett and Higginson 2012 , Lindenmayer et al. 2012 , Scheiner 2013 , Matzek et al. 2014 ). My goal is to both clarify and advance that discussion by providing empirical data on the interaction between theoretical and empirical approaches.

Here, I present results from a recent survey of 614 scientists. These scientists work predominantly in ecology, evolutionary biology, and related fields. They span a wide range of ages, career stages, nationalities, and positions along the theoretical–empirical continuum, and so constitute a broad sampling of perspectives. The survey explored their views on theoretical and empirical work, including questions regarding their reading habits, collaboration, and coauthoring, and their opinions on ideal and actual patterns of interaction between theorists and empiricists. Several free-form, open-ended questions also allowed the respondents an opportunity to express their views. In my discussion of the results, I emphasize three themes: (1) Substantial mistrust and tension exists between theorists and empiricists, but despite this, (2) there is an almost universal desire among ecologists and evolutionary biologists for closer interactions between theoretical and empirical work; however, (3) institutions such as journals, funding agencies, and universities often hinder such increased interactions, which points to a need for institutional reforms.

By “empiricists” we mean researchers that do empirical work: fieldwork, lab work, or any other gathering of data from phenomena observed in the real world. By “theorists” we mean researchers that do theoretical work: mathematical models, numerical simulations, and other such work.

At the time the survey was posted, I was not affiliated with or funded by any institution, so no institutional human subjects protocol applied. To minimize possible concerns in this area, however, no strongly identifying information, such as the respondent's name, e-mail address, institution, or IP (Internet Protocol) address, was collected. The responses were, therefore, anonymous, except where the respondents chose to identify themselves; this anonymity had the side effect of preventing any check for multiple responses from the same individual, but multiple responses are very difficult to prevent in any case, and no duplicate responses were observed. To further protect anonymity, the order of responses to the free-form questions was randomized.

The survey was open from 12 October 2013 to 3 November 2013. I requested that it be publicized by a wide range of forums associated with ecology and evolutionary biology (supplemental table S1). No attempt was made to monitor which forums actually posted the survey.

An analysis of the response data was conducted in R ­(version 3.0.0; R Foundation for Statistical Computing, Vienna, Austria). The responses for country names, career points, and scientific fields were standardized (e.g., “Britain” to “U.K.”). Quotes from free-form responses were lightly edited for clarity. Unedited responses, and the R script used for the analysis, have been made available in the Dryad data repository (doi:10.5061/dryad.jh421); details regarding grouping and standardization of responses are contained in that script.

Two-sample Wilcoxon rank sum tests (also called Mann–Whitney tests; using the function wilcox.test in R) were used to test for differences between groups in the location of the distribution of quantitative responses (Everitt and Hothorn 2010 ); these tests were two sided in all cases. Randomization tests to calculate an approximate Pearson's chi-squared value were used to test for significance in the differences between groups in their responses to multiple-choice questions (using the function chisq_test in the R package coin, 10 6 replicates). These randomization tests were conditional on row and column marginal totals; there is debate as to the correct method, but this choice was conservative since unconditional tests would provide smaller p -values (Agresti 2002 ).

A total of 614 responses were obtained, spanning a broad demographic spectrum (figure 1 ). The respondents’ ages ranged from 20 to 82 years; six of the respondents did not answer that question (mean = 37.6, median = 35.0, standard deviation = 10.7). The respondents designated their gender as “male” (350 respondents; 57%), “female” (260 respondents; 42%), or “other” (1 respondent); 3 respondents did not answer that question.

Respondent demographics. (a) Age distribution of the respondents. Note the decreasing representation of women with increasing age. (b) The country of citizenship and of research. The United States and Canada are grouped separately from the rest of the Americas because of the United States's size and Canada's obvious differences from Central and South America. Note the proportionately larger theoretical community in Europe, and the almost total absence of theorists in Canada, Oceania, Africa, and Asia. (c) Career stage. Note the greater focus on evolutionary ecology among postdocs and professors, and the greater focus on ecology among students and professional researchers. Abbreviations: Eco, researchers in ecology; EcoEvo, researchers in both ecology and evolutionary biology; Evo, researchers in evolutionary biology; NA, not applicable.

They were citizens of 45 countries (with 2 dual citizens and 10 blank responses): predominantly, the United States (247), Germany (49), Canada (43), the United Kingdom (28), and France (27); other countries were represented by fewer than 20 respondents. They reported performing research in 42 countries (with 5 dual-country researchers and 15 blank responses): predominantly, the United States (247), Germany (41), the United Kingdom (37), Canada (31), France (27), and Sweden (25); other countries were represented by fewer than 18 respondents. Of the respondents who specified countries of both citizenship and research, a majority (69.4%) conducted their research in their country of citizenship.

They were primarily students (5 undergraduate, 28 master's, 152 PhD), paid researchers (144 postdocs, 48 other types), and professors (87 untenured, 142 tenured, 2 emeritus); 6 did not fit into these categories. Overall, 206 (34%) worked in ecology, 161 (26%) in evolutionary biology, and 232 (38%) in both, with 11 (2%) in other areas of biology and 4 (1%) in nonbiology fields. The respondents also reported their specific subfields (not reported here).

Empirical work predominated, with 299 (49%) of the respondents doing “mostly empirical work” and 103 (17%) doing “exclusively empirical work”; these respondents are hereafter called the empirical group . By contrast, 50 (8%) “mostly” and 39 (6%) “exclusively” did theoretical work (the theoretical group ). A total of 123 (20%) reported doing both roughly equally (the middle group ).

Figure 1 illustrates these demographics and shows interactions between age and gender (figure 1a ), between citizenship or research country and empirical or theoretical position (figure 1b ), and between career position and overall field (figure 1c ). Notably, the percentage of female respondents fell steadily with age, from 54% among those under 30, to 49% among those in their 30s, 27% of those in their 40s, and only 25% of those 50 and over—a pattern that has been attributed to a “leaky pipeline” in academia (Alper 1993 , Blickenstaff 2005 ). Among the respondents, as a result, women accounted for 50% of the students and 53% of the postdoctoral researchers, but 40% of untenured professors and only 22% of tenured professors. Women were 43% of the respondents in both ecology and evolutionary biology but were not equally represented across research countries: 52% of the respondents in Africa and Asia, 50% in Oceania, 47% in Canada, 43% in the United States, 40% in Europe, and only 28% in Central and South America were female (and the results were similar for the citizenship country). Women were also unequally represented across the ­theoretical–empirical continuum: 29% of the theoretical group and 30% of the middle group were female, as compared with 49% of the empirical group.

Figure 2a shows the paper-reading habits of the respondents, for papers classified by the respondent as “primarily theoretical,” “primarily empirical,” and “roughly equal parts theoretical and empirical.” Both the theoretical group and the empirical group reported reading more papers per week from their own group than from the other group, but this tendency was significantly more pronounced in the empirical group (Wilcoxon rank sum test on the differences between within-group and opposite-group papers read; empirical group, median = 2.9, n = 402; theoretical group, median = 0.5, 
 n = 89; W = 28,650, p < .001). This does not necessarily represent greater bias on the part of empiricists, because there are probably fewer theoretical papers than empirical papers published each year (Scheiner 2013 ).

Reading, collaboration, and coauthorship. (a) The number of papers read per week of each of three types: primarily empirical (E), roughly equal parts theoretical and empirical (M), and primarily theoretical (T). (b) The percentage of the respondents reporting having collaborated with empiricists and with theorists. (c) The percentage reporting having coauthored at least one paper with an empiricist and with a theorist. The responses of the empirical and theoretical groups are significantly different in all panels (see the text).

Figure 2b shows patterns of collaboration. The theoretical and empirical groups both reported having collaborated within their group more often than outside their group, but this tendency was significantly more pronounced in the empirical group, with 93% of theorists having collaborated with empiricists but only 60% of empiricists having collaborated with theorists (Wilcoxon rank sum test on the differences between within-group collaboration and opposite-group collaboration; empirical group, median = 0, n = 402; theoretical group, median = 0, n = 89; W = 23,797, p < .001). Again, this does not necessarily represent greater bias among empiricists, because there are apparently fewer theorists than empiricists in ecology and evolutionary biology (figure 1b ).

Figure 2c shows patterns of coauthorship. The theoretical and empirical groups both reported having coauthored papers with others within their group more often than with those outside their group, but again, this tendency was significantly more pronounced in the empirical group, with 78% of the theorists having coauthored with empiricists but only 42% of the empiricists having coauthored with theorists (Wilcoxon rank sum test on differences between within-group coauthorship and opposite-group coauthorship; empirical group, median = 1, n = 402; theoretical group, median = 0, n = 89; W = 24,493, p < .001). As before, this does not necessarily indicate greater bias among empiricists, given the relative scarcity of theorists.

A pair of questions explored two related concepts: how respondents believe that theoretical work and empirical work should interact, and how they actually do interact (figure 3 ). There was remarkable consensus regarding how they should interact, with a large majority of the empirical, middle, and theoretical groups all believing that, ideally, “theoretical work and empirical work would coexist tightly, driving each other in a continuing feedback loop” (figure 3a ). The theoretical group was closer to unanimity, however, with 91% supporting this idea; no other choice received more than 4% of their vote. The empirical group showed 80% support for this idea; much of the rest (13%) believed that, ideally, “work would primarily be data-driven; theory would be developed in response to questions raised by empirical findings.” The two groups were significantly different ­(randomization test, χ 2 = 11.16, p = .027).

Views on the ideal and actual patterns of interaction between theoretical (T) and empirical (E) work, for all respondents and for the empirical, middle, and theoretical groups. (a) The percentage of respondents who agreed with each of seven statements regarding the ideal interaction pattern. Abbreviations: E → T, theoretical work should be driven by empirical findings; T = E, the distinction between the two should not exist; T → E, empirical work should be driven by theoretical predictions; T → E, the two should coexist without much interaction; T ↔ E, the two should coexist in a tight feedback loop; X( E ), empirical work should not exist; X( T ), theoretical work should not exist. (b) The percentage of respondents who agreed with each of seven statements regarding the actual interaction pattern. Abbreviations: E → T, theoretical work is driven by empirical findings; T = E, the distinction between the two is meaningless; T → E, empirical work is driven by theoretical predictions; T → E, the two coexist without much interaction; T ↔ E, the two coexist in a tight feedback loop; X( E ), empirical work is worthless/pointless; X( T ), theoretical work is worthless/pointless. The exact wording of all statements is given in the full survey text (see supplement S1, questions 18 and 19). The responses of the empirical and theoretical groups were significantly different in both panels (see the text).

Much less consensus existed regarding how theoretical and empirical work actually interact in today's world, but the responses of the empirical group and the theoretical group were nevertheless remarkably similar (figure 3b ). Across all respondents, a large plurality (47%) believed that theoretical and empirical work “coexist, but do not interact much,” a somewhat smaller subset (26%) believed that empirical findings drive theoretical work, a smaller subset (18%) believed that the two “coexist tightly” in a “feedback loop,” and still fewer (7%) believed that theoretical findings drive empirical work; no other choice received more than 1%. One striking difference did exist: A minority of the empirical group (8%) claimed a belief in the idea that theoretical findings presently drive empirical work, whereas not a single theorist shared that belief. The two groups were significantly different ­(randomization test, χ 2 = 14.11, p = .034).

In summary, then, many believed that, in today's world, empirical and theoretical work coexist in separate, noninteracting realms or that empirical work largely drives the scientific process, but most respondents believed that in an ideal world theoretical and empirical work would be tightly bound in a continuing feedback loop. Small but significant minorities of the empiricists believed that empirical work ought to drive the process and that theorists presently drive the process; almost no theorists shared those beliefs.

A set of questions asked respondents for their opinions regarding whether theorists and empiricists pay attention to each other's findings, whether they would like to collaborate more but have difficulty doing so, whether each finds the other intimidating, and whether each skips over the technical sections of papers written by the other. All of these questions asked the respondents to rate given statements from “strongly agree” to “strongly disagree” (or “don't know”). These responses are shown in figure 4 , including indications of statistical significance; some highlights are summarized here.

Degrees of agreement or disagreement with statements about attention, collaboration, and intimidation. For each statement, three stacked bars are shown, representing, from top to bottom, responses from the empirical (E), middle (M), and theoretical (T) groups. Each stacked bar shows the proportion of respondents in the depicted group that expressed a given reaction to the statement: “strongly agree” (SA), “agree” (A), “neutral” (N), “disagree” (D), “strongly disagree” (SD), or “don't know” (?); all segments in each stacked bar therefore add up to 1.0. The center line is drawn at 0.5, as an easy way to see whether a majority of the respondents agreed with each statement. The statistical significance of the difference between the empirical and theoretical groups (excluding the middle group from the tests) was determined using randomization tests (see the “Survey analysis” section).*p < .001. •p = .0091. All others, p > 0.05. The Bonferroni-corrected significance threshold across these tests was 0.00294.

The divergence of worldviews between the empirical and theoretical groups was often striking. For example, a majority of the theorists agreed or strongly agreed that theorists generally “pay a lot of attention to empirical findings,” but a substantial plurality of the empiricists disagreed or strongly disagreed that theorists do so. Conversely, the empiricists were split as to whether empiricists generally “pay a lot of attention to theoretical findings,” but a majority of the theorists disagreed or strongly disagreed that empiricists do so. However, when they were asked about their own habits, a substantial majority of both groups agreed or strongly agreed that they themselves pay a lot of attention to both empirical and theoretical findings.

Close to or more than a majority of the respondents in each group agreed or strongly agreed that many empiricists would like to collaborate with theorists but have trouble making that connection, and a plurality of empiricists agreed that this is the case for them personally. A majority of the theorists also agreed or strongly agreed that many theorists would like to collaborate with empiricists but are unable to connect (although few felt this way about their own collaborations); few of the empiricists agreed or strongly agreed with this, however, and a plurality stated that they did not know.

Few respondents, among both the theorists and the empiricists, agreed or strongly agreed that theorists find empirical methods “intimidating and/or obscure,” but a large majority of both groups agreed or strongly agreed that empiricists find theoretical methods to be so. The idea that empiricists often find theoretical methods difficult was supported by the respondents’ own feelings about empirical and theoretical methods.

When the empiricists read empirical papers and when the theorists read theoretical papers, a large majority of both groups disagreed or strongly disagreed that they skip over technical sections (materials and methods in empirical papers, formulas and model descriptions in theoretical papers). However, close to half of both groups agreed or strongly agreed that they skip technical sections in papers from the other group. The respondents from both groups knew that empiricists often skip theoretical technical sections, but many of the empiricists seemed not to know that theorists often skip empirical technical sections.

In my opinion good interactions are more likely if the project integrates theory and empirical work from an early stage. No theoretician should be satisfied with an untested model, and no empiricist should be satisfied with a pattern without an underlying model to explain the pattern. The thing that connects the best examples is that the researchers are thoughtful, and prioritize really ­ wanting to know over politics or preconceptions.

I don't like when empiricists wrap up their work in an existing theory/big theoretical question to make it look sexier, even though their work is actually poorly related to this theory, or the links between theory and experiment are not convincingly drawn. I have often read theoretical papers that are so arcane as to leave them entirely useless for empiricists. By arcane, I do not mean that the models are too complicated to be understood, but rather that they are so far removed from reality as to leave them largely untestable. Theorists are not just consultants to help empiricists with the fiddly details of models to mathematically show what the empiricist already knows. Theoreticians thinking they have the answer to all biological problems and that empiricists are just servants that produce data for them.

Theoreticians sometimes struggle if they have limited biology in their background. I feel like a big part of the divide comes from empirical ecologists who don't take time out to consider models or theoretical work… Training courses or workshops where theorists could explain their models to empiricists and talk about crossover would help I think. Many interactions between theorists and empiricists are hampered because they do not understand the purpose of each other. It would be great if theorists had on-call empiricists, and vice-versa.

I think [papers at the interface of theory and empirical work] are more difficult to get through peer review because of the higher frequency of mostly empirical researchers and because such papers have to please a wider audience of peer reviewers. It would be nice if editors were more awed by work that actually connects models and empirical work. Papers published in journals totally oriented towards theory in reality are not followed by people conducting empirical research; it could be important to change the philosophy of these journals.

Especially in ecology & evolution, where everything varies all the time, theory needs to be tested in multiple systems, in multiple contexts. However, the U.S. funding system and the global publication system does not reward this. I think that most funding is awarded to strictly empirical projects which emphasize study system and organism. Theoretical funding is really hard to get unless you are at the very top of your field. Requests for funding for work at the interface of theory and empirical data collection, like papers, has to please a very broad audience and is hard to get. Funding agencies should give extra points for explicit collaborations and explicit inclusion of both approaches in a grant.

More professors from the ‘two sides’. .. should join together to co-teach graduate classes. It would be extremely nice to have somebody in the department who knew theory and could combine it with empirical work. Both need training to appreciate each others work. Ecology/evolution programs need better quantitative training. Theorists need experiences with empiricists to understand difficulties/limitations of data collection. This has been paid lip service for a long time, but it really needs to happen.

The respondents mentioned a few institutions (the National Center for Ecological Analysis and Synthesis, the National Evolutionary Synthesis Center, and the Bering Sea Integrated Ecosystem Research Program) and a few journals ( Ecology , Trends in Ecology and Evolution , and The American Naturalist ) that promote good interactions between theorists and empiricists.

The final free-form question, regarding difficulties with the survey itself, received 155 responses, many of which (approximately 66) expressed a positive view of the survey and its accuracy in capturing the respondent's opinions, but many of which did not. The responses often questioned the definitions of “theoretical” and “empirical” used (or the assumption that such a dichotomy exists), objected to being asked what a particular group thinks or feels, objected to the stark alternatives provided in multiple-choice questions, or stated that the survey was biased. In particular, many respondents felt that question 21, regarding difficulties in establishing collaborations, was worded ambiguously; it might therefore be best to discount those results.

The interactions between the results presented and the demographic variables of age, gender, career stage, and overall field (ecology, evolutionary biology, or both) were generally small. In particular, gender, age, and career stage had little effect; the conclusions inferred here are, therefore, robust across demographic subsets. The conclusions inferred here are also robust for ecologists and for evolutionary biologists, analyzed individually; however, small differences did exist between these two groups (see the “Related work” section below).

Three main themes emerged from the results of this survey. First, the worldviews of the theorists and the empiricists had areas of strong divergence. Both groups tended toward insularity in their reading habits and their collaborations, and the empiricists seemed particularly isolated from theoretical work. A plurality of each group believed that the other group ignored their work. Some of the theorists felt that empiricists perceived them as “consultants” to “help empiricists with the fiddly details,” whereas some of the empiricists felt that theorists perceived them as “servants to produce data for them.” These results do not indicate a healthy relationship.

Second, despite these areas of divergence, there are areas of overlap and agreement that provide cause for optimism. One thing I found particularly heartening, in conducting this survey, was that many respondents named colleagues who exemplified a good balance between theoretical and empirical approaches, even though the survey did not explicitly ask them to do so. Another encouraging fact is that 82% of the respondents felt that theoretical and empirical work should “coexist tightly”, even though only 18% felt that they “coexist tightly” in today's world.

Third, many of the respondents stated that institutions in ecology and evolutionary biology—journals, funding agencies, and universities—do not help empirical and theoretical researchers to interact productively; indeed, these institutions often hinder such interactions. Journals are often focused exclusively on either theoretical or empirical work, which encourages isolationism, and research that bridges the gap is harder to get through peer review because it has to satisfy both audiences. This criticism also seems to apply to conferences, where separate theoretical and empirical sessions encourage theorists and empiricists to avoid interactions even when working on the same scientific questions. Funding agencies also discourage work at the interface; programs specifically aimed at promoting such research do not exist, and such research has to please reviewers in both groups. Universities do not encourage coteaching by empiricists and theorists and often neglect training in theory for empiricists entirely, whereas theorists often come from physics or mathematics and receive little or no empirical training. University faculties often do not have a balanced mix of empiricists and theorists, which deprives students (and themselves) of opportunities for interaction.

A number of previous papers have raised related issues regarding the interaction of theoretical and empirical approaches. A comparison of the results presented here with that previous work is fruitful; I will briefly discuss three particularly relevant examples here.

Kareiva ( 1989 ) provides the earliest paper I have found in which these issues are discussed at length. Twenty-five years have passed since its publication, and so it is striking that Kareiva's ( 1989 ) statement that “experimental ecology and theoretical ecology are developing as almost separate fields and both pursuits suffer as a result” (p. 68) seems to ring true for so many—even today (figure 3 ). Many of the specific issues raised by Kareiva ( 1989 )—a lack of constructive dialogue, PhD training that neglects the theoretical–empirical interface, theoretical models that are too removed from reality to be useful, empirical studies that ignore relevant theory—were also raised by many of the respondents to this survey. For a more in-depth discussion of the problems raised here, Kareiva's paper is an excellent resource, because (unfortunately) little seems to have changed during the intervening years.

Fawcett and Higginson ( 2012 ) explored a specific aspect of the theoretical–empirical interaction: the effect of mathematical equations in a paper on the paper's tendency to be cited. They found that papers with many equations tended to be cited somewhat more frequently in theoretical papers but much less frequently in empirical papers; there was thus an overall decrease of 28% in the citation count for each additional equation per page in the main text of a paper. This provides quantitative evidence in support of the present survey's results: Empiricists read relatively few theoretical papers (figure 2a ), find theoretical methods intimidating and/or obscure (figure 4 ), and often skip over formulas and model details when they read theoretical papers (figure 4 ). As Fawcett and Higginson ( 2012 ) discussed, there are ways in which theorists might make their work more accessible—but because theory is fundamentally mathematical in nature, there is also a need for better training in quantitative methods for empiricists, as many of the survey respondents observed.

Finally, Scheiner ( 2013 ) conducted a quantitative investigation of the theoretical underpinnings of articles published in three prominent journals, and found that theoretical and empirical approaches are less integrated in ecology than in evolutionary biology. Survey results analyzed separately for ecologists and for evolutionary biologists (not presented here) indicated that empiricists in ecology collaborate and coauthor with theorists less frequently than do empiricists in evolutionary biology. Ecologists also more frequently believe that empirical work should drive research or that theoretical and empirical work ought to be separate, whereas evolutionary biologists more frequently believe that theoretical and empirical work should interact tightly or that the two should be considered the same. Similar differences were observed in the responses to many other questions. These differences were small; nevertheless, these results both support the findings of Scheiner ( 2013 ) and suggest some underlying causal mechanisms driving the difference that he observed.

Overall, these results convey a very clear message: Theorists and empiricists often interact poorly (if at all), and yet, there is overwhelming agreement that theorists and empiricists should interact closely. Notably, many of the respondents felt that this problem was exacerbated by institutions such as journals, funding agencies, and universities, which often specifically discourage interaction. In addition to suggesting that individuals might work toward improved interactions, then, these results also provide a clear mandate for institutional changes to promote increased interaction between theorists and empiricists.

I have seen little discussion of this institutional aspect of the problem, and yet, such institutional changes seem relatively tractable. Journal editorial policies, conference schedules, funding decisions, university hires, and other institutional decisions are often controlled by just a handful of individuals—individuals who probably, like almost all of the respondents to this survey, would like to see a closer interaction between theoretical and empirical approaches. Changes to institutional practices seem more likely to bear fruit than expecting individual scientists to altruistically change their behavior in the face of institutional incentives that often specifically discourage stronger interactions. Once institutional changes have been made that provide better incentives for interaction, a shift in individual behavior should follow.

By raising such issues and ideas, I hope that this article initiates a constructive discussion about achieving better interactions and, ultimately, better science.

Thanks to Daniel I. Bolnick, Kiyoko M. Gotanda, Lisa Jacquin, and Felipe Pérez-Jvostov for comments on the survey design. Thanks to Andrew P. Hendry, Maria R. Servedio, Tadeas Priklopil, Philip N. Cohen, and three anonymous reviewers for comments on a previous version of this manuscript. Thanks also to all of the institutions and groups that posted the survey, and to the many individuals who took the time to respond to it.

The supplemental material is available online at Supplementary Data .

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empirical vs. theoretical

Empirical vs. theoretical: what's the difference.

Empirical means based on observations or experience. Theoretical means based on theories and hypotheses. The two terms are often used in scientific practice to refer to data, methods, or probabilities.

• derived from or guided by direct experience or by experiment, rather than abstract principles or theory: Empirical evidence of changes in kelp consumption was gathered by measuring the bite marks in seaweed fronds.
• depending upon experience or observation alone, without using scientific method or theory, and hence sometimes insufficiently authoritative, especially as in medicine: That is nothing but an empirical conclusion with no regard for the laws of thermodynamics.
• provable or verifiable by experience or experiment, as scientific laws: Theoretical physics is criticized for producing complex concepts that are mathematical, not empirical.

theoretical

• of, relating to, or consisting in theory ; not practical (distinguished from applied ).
• existing only in theory; hypothetical .
• given to, forming, or dealing with theories; speculative .

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Towards an Individual Differences Perspective in Mindfulness Training Research: Theoretical and Empirical Considerations

A growing body of research indicates that mindfulness training can have beneficial effects on critical aspects of psychological well-being, cognitive function, and brain health. Although these benefits have been generalized to the population level, individual variability in observed effects of mindfulness training has not been systematically investigated. Research on other similar forms of psychological intervention demonstrates that individual differences are prominent in terms of intervention responsiveness and outcomes. Furthermore, individual characteristics such as personality traits have been shown to play a crucial role in influencing the effects of intervention. In light of these lines of evidence, we review representative work on individual differences in mindfulness training and advocate for an individual difference perspective in mindfulness training research. We discuss relevant empirical evidence of individual differences potentially influencing behavioral outcomes of mindfulness training, focusing on both cognitive function and psychological well-being. Finally, theoretical considerations and potentially fruitful research strategies and directions for studying individual differences in mindfulness training are discussed, including those involving cognitive neuroscience methods.

Introduction

Mindfulness training (or mindfulness-based intervention) has garnered popularity both within the scientific community, and among the lay public, as an effective intervention that promotes health and well-being. Adapting practices and techniques from Eastern Buddhist traditions, mindfulness training was first introduced to Western culture in the 1970s as a set of secular and contemplative practices that alleviate stress and psychological distress ( Kabat-Zinn, 1990 ). Subsequent development of these practices has given rise to many different yet similar mindfulness training programs, which all have the same goal of promoting attention to and awareness of present moment experiences with a non-judgmental and accepting attitude ( Hölzel et al., 2011 ). Over the past few decades, a growing body of research has indicated that mindfulness training can have beneficial effects on critical aspects of psychological well-being, cognitive function, physiology, and brain health ( Grossman et al., 2004 ; Chiesa et al., 2011 ; Gu et al., 2015 ; Tang et al., 2015 ). Currently, mindfulness training not only serves an active role in clinical treatment for patients with emotion-related disorders ( Khoury et al., 2013 ), but has also become part of a healthy lifestyle/wellness approach adopted by individuals without a clinical diagnosis, who seek to enhance their psychological well-being and quality of life.

Nonetheless, this widespread application of mindfulness training deserves careful scrutiny. Most of the extant literature on mindfulness training has generalized its benefits to the population level, assuming homogeneous effects of training for every individual based on group-averaged results. However, this one-size-fits-all model is in tension with existing theoretical frameworks and empirical evidence that have been amassed with regard to individual variability in response to psychological interventions ( Caspi and Bell, 2004 ; Gully and Chen, 2010 ). Furthermore, overlooking individual differences when evaluating the outcomes of intervention may inadvertently incur negative consequences, in that individual differences may potentially be contributing to several non-replications or mixed findings of previously observed mindfulness training benefits in cognition and psychological well-being ( MacCoon et al., 2012 , 2014 ; Rosenkranz et al., 2013 ). This is because group-averaged effects will be subject to fluctuation among samples with different distribution and variability (i.e., some samples may contain more individuals who would respond to the intervention than others). From a practical standpoint, considering individual variability in training responsiveness is critical for the application of mindfulness training both in clinical settings and in public domain, as this would enable a better understanding of the specific subgroups of individuals for whom the training would be most or least effective in achieving the desirable outcomes.

Indeed, the Precision Medicine Initiative announced by the U.S. government directly highlights the importance of tailoring prevention and intervention programs based on individual characteristics, in order to achieve better outcomes ( Collins and Varmus, 2015 ). Consequently, it calls for more systematic investigation to identify and understand characteristics responsible for differential intervention effects across individuals. For mindfulness training, the motivation to adopt an individual difference perspective both in research and in application becomes increasingly apparent. As a relevant and related domain, psychotherapy research has made rapid progress in identifying potential moderators of intervention effects ( Kraemer et al., 2002 ; Kraemer et al., 2006 ; Kazdin, 2007 ; Wallace et al., 2013 ). Although the effect sizes of these moderators are relatively modest, most studies have found individual characteristics to play a key role. For instance, personality traits have been shown to affect the degree to which depressive patients benefit from cognitive behavioral therapy (CBT) ( Bagby et al., 2008 ). However, systematic investigations focusing on individual differences are relatively sparse in the field of mindfulness training. Perhaps since mindfulness training research is still in its infancy, much attention has been devoted to understanding its basic effects and mechanisms in changing brain and behavior, but less so on the potential individual variability present in these training-related effects.

Nonetheless, encouraging and emerging efforts have been made to advance our understanding of individual differences in domains such as personality traits ( de Vibe et al., 2015 ; Nyklíček and Irrmischer, 2017 ), psychological well-being ( Greeson et al., 2015 ; Gawrysiak et al., 2016 ), cognitive functions ( Mrazek et al., 2013 ), and their respective roles in influencing mindfulness training effectiveness and outcomes. Rather than providing an exhaustive or comprehensive review of the literature, the goal of this paper is instead to provide case-study illustrations that highlight relevant domains of individual difference that can impact mindfulness training. Moreover, we propose useful research strategies and directions to point the field to this promising and fruitful research area, advocating for an individual differences perspective in scientific investigations of mindfulness training. We hope that this discussion will encourage and facilitate further rigorous and systematic investigations of individual differences in mindfulness training. The first section of this paper focuses on personality and dispositional traits, while the second and third sections describe how pre-existing individual differences in emotion regulation and cognitive abilities could influence training effects and outcomes. Finally, the fourth section discusses theoretical considerations and challenges, as well as relevant promising research strategies and future directions, including those involving cutting-edge cognitive neuroscience methods. We provide a table that summarizes key details of the studies discussed in these sections (see Table 1 ).

Studies of individual differences in intervention and outcomes.

Could Personality and Dispositional Traits Influence Mindfulness Training Effects?

Personality and dispositional traits are the most frequently assessed dimension of individual difference, and are also popular moderators of intervention effects in psychotherapy research. Accumulating evidence demonstrates that personality traits can predict differential targeted behavioral changes in psychotherapy ( Chapman et al., 2014 ). In the following section, traits that could play an important role in influencing mindfulness training effectiveness will each be identified and discussed based on supporting theories and empirical evidence from prior literature. Because the goal of individual differences investigation should always be confirmatory, rather than exploratory, it is critical to formulate a priori hypotheses regarding putative variables associated with differential intervention effects ( Caspi and Bell, 2004 ). Employing such a theoretically oriented and hypothesis-driven approach would greatly mitigate the concerns of spurious statistical associations that can result from exploratory, “fishing expedition”-type analyses in which a myriad of putative dispositional traits are queried.

Big Five Dimensions of Personality

The Big Five dimensions of personality have been theoretically hypothesized and empirically shown in previous work to influence the extent to which individuals benefit from psychological intervention including mindfulness training. According to several theoretical frameworks relating personality traits to intervention outcomes ( Anderson, 1998 ; Chapman et al., 2014 ), all five dimensions of the Big Five theory have relevance to intervention effectiveness. Here, we extend this framework to discuss how the Big Five dimensions of personality could have an impact on mindfulness training.

Neuroticism

Neuroticism reflects a level of affective instability, in terms of anger, depression and other forms of negative affect, which is generally associated with low responsiveness to a wide range of psychological interventions. This is thought to be the case because high levels of neuroticism are frequently related to difficulties in intervention adherence ( Anderson, 1998 ). One randomized controlled trial (RCT) study comparing the effectiveness of CBT and pharmacotherapy (PHT) did suggest that for patients ( N = 275) with major depressive disorder, higher neuroticism was associated with more improvement in depressive severity post-treatment, if PHT was administered, but not if CBT was used ( Bagby et al., 2008 ). However, such moderating effects of neuroticism seem to be dependent on the population group. In a recent RCT study of 288 students who underwent either 8 weeks of MBSR or served as waitlist controls, neuroticism was examined as one of the moderators of mindfulness training effects; individuals with higher level of neuroticism exhibited greater improvement in subjective well-being and psychological distress ( de Vibe et al., 2015 ). Likewise, in a longitudinal study of 167 participants with general stress complaints, individuals with higher neuroticism level showed a greater reduction in anxiety symptoms after 8-weeks of MBSR. Although controlling for baseline anxiety levels reduced this immediate effect, it still revealed a larger reduction in anxiety levels among such individuals between post-intervention and the 3-month follow-up ( Nyklíček and Irrmischer, 2017 ). Similarly, in a RCT study of 267 police officers who underwent either mindfulness-based intervention or education course, higher neuroticism was also found to be positively related to improvement in self-care, as well as reduction in negative affect and psychological strain after 6 weeks ( Krick and Felfe, 2019 ). These studies not only indicate the high utility of neuroticism in predicting psychological outcomes of mindfulness training, but also suggest that at least in populations with sub-clinical levels of anxiety and depression, high baseline neuroticism may actually lead to greater beneficial outcomes associated with mindfulness training.

In addition to the effects of neuroticism on psychological outcomes, one study investigated the moderating effects of personality traits and mood states on enhanced creativity following mindfulness practice. In a RCT study, 84 undergraduate students participated in either a week of 30 min daily mindfulness practices or relaxation practices. It was found that lower depression, lower fatigue, and higher anger at baseline predicted enhanced creative performance after mindfulness training ( Ding et al., 2015 ). Furthermore, there were interactive effects of mood states and personality traits observed, such that individuals with lower neuroticism or extraversion exhibited greater improvement when they also reported a more positive and aroused mood ( Ding et al., 2015 ). These predictors together explained 57% of variance in creativity improvement, suggesting not only that the extent of improvement was largely influenced by individual differences in baseline personality traits and mood states, but also that these pre-existing differences in individual characteristics should be important components to consider in understanding the mechanism of mindfulness interventions. However, this study offered a different perspective on the role of neuroticism; that is, lower neuroticism was associated with greater improvement in cognitive outcome, which is somewhat contrary to what have been found with regard to other psychological outcomes. Consequently, it suggests a need to further elucidate the exact role and mechanisms of personality traits in affecting different outcomes of mindfulness training in different populations.

Although it is unclear as to why neuroticism could have distinct effects on mindfulness training outcomes, one likely explanation is that for psychological outcomes, mindfulness training may work by improving the suboptimal emotion regulation and self-awareness capacities in individuals with high neuroticism, which together help them better cope with stress and negative affect ( Hölzel et al., 2011 ). We believe that neuroticism is likely to be a personality trait that could have influence over a majority of psychological interventions, and thus could also be a promising target trait for future mindfulness investigations. However, more studies are needed to determine and confirm: (1) if higher neuroticism generally leads to better outcomes in psychological well-being or symptoms for populations without clinically diagnosed anxiety and depression disorders; and (2) if neuroticism is useful in predicting other mindfulness training-related behavioral outcomes, such as enhanced cognitive function.

Agreeableness and Extraversion

Although these two personality dimensions may not seem to be intuitive moderators of mindfulness training outcomes, evidence from other psychological interventions suggest that these traits could still be relevant. A quasi-experimental study conducted among 86 women with childhood sexual abuse histories found that women low in both agreeableness and extraversion had better treatment outcomes in self-report symptoms of psychological distress if they received a highly structured and skill-focused intervention – Women’s Safety in Recovery (WSIR), which focuses on problem solving exercises – rather than treatment as usual (TAU) devoted largely to group therapy, in which general strategies of crisis resolution and symptom reduction are discussed ( Talbot et al., 2003 ). According to the Big Five framework, extraversion tends to manifest as talkative and outgoing behavior that facilitates interpersonal interaction and relationship ( McCrae and Costa, 1991 ). Prior work suggests that extraverted individuals may prefer an intervention approach involving interpersonal interactions, while introverted individuals may prefer a more structured and goal-directed approach ( Sanderson and Clarkin, 1994 ; Anderson, 1998 ). Similarly, agreeableness entails levels of friendliness, warmth and cooperativeness ( McCrae and Costa, 1991 ), and has also been shown to associate with preferences for interpersonal approaches and group therapy during intervention ( Bliwise et al., 1995 ; Anderson, 1998 ). Taken together, these results suggest that agreeableness and extraversion could potentially be key players in influencing the outcomes of group-based interventions, perhaps through preferences in intervention approaches. A practical implication of these personality-related preferences is that psychological interventions might be rendered more effective if they were administered in the manner that individuals prefer.

For mindfulness-based intervention, such as the most popular variants of mindfulness training (e.g., MBSR), group discussion and activities are involved in almost every class session ( Santorelli et al., 2017 ). It is possible that those low in agreeableness and extraversion may find highly structured, one-on-one, and/or skill-focused mindfulness training more appealing than programs that involve regular group discussion and engagement to acquire the mindfulness skills, as these individuals are likely to feel intimidated in such group scenarios. However, it should also be noted that agreeableness has the potential to influence mindfulness training outcomes through a completely different mechanism. One study of 100 older adults who underwent 8 weeks of MBSR did find that individuals with high agreeableness tended to practice more mindfulness during the period of intervention than those low in agreeableness, but did not continue to show more practice after intervention completion ( Barkan et al., 2016 ). This finding is particularly interesting as it suggests that individuals with high agreeableness may be more cooperative and compliant to instructor’s instructions of completing daily practice outside of the class, but less so when no instructions were given after intervention completion ( Barkan et al., 2016 ). Although the study did not directly investigate whether or not varying amount of practice time was associated with differential training effects on outcomes of interest, previous research in mindfulness training has suggested a positive relationship between practice time and improvement in various outcome measures, such as cognitive function and brain functional connectivity ( Chan and Woollacott, 2007 ; Jha et al., 2010 ; Brewer et al., 2011 ). Overall, this preliminary evidence suggests that it is worth considering extraversion and agreeableness as moderators of intervention effects in future investigations of mindfulness training.

Openness to Experience

Openness to experience represents a sense of curiosity, acceptance and open-mindedness to diverse experiences. This is highly relevant for mindfulness training, since individuals high in openness to experience may be more likely to seek out and practice complementary and alternative medicine (CAM) techniques, such as yoga and mindfulness-based practices in daily life ( van den Hurk et al., 2011 ; Thomson et al., 2014 ). As evident from one study (mentioned briefly above) exploring the influence of personality traits on the frequency of practicing mindfulness techniques, individuals with high openness to experience were found to engage in more mindfulness practices in their daily life, both during and after an 8-week mindfulness intervention program ( Barkan et al., 2016 ). The results held even when demographic differences, such as age, educational level, and sex were statistically controlled, suggesting the unique impact of openness to experience on practice behavior. Relatedly, openness to experience was found to moderate heart rate variability response in police officers following 6-week of mindfulness-based intervention, such that individuals with high openness to experience exhibited more improvement in heart rate variability ( Krick and Felfe, 2019 ).

Based on these findings, openness to experience seems to have a special relevance to mindfulness training, as this trait is likely to determine how much individuals would engage in the actual practices that may lead to observable improvement in any training-related outcomes. This is not surprising since previous evidence suggests that individuals high in openness tend to benefit more from approaches focusing on self-exploration and discovery ( Anderson, 1998 ) and they are also more likely to be curious about their internal experiences ( Barkan et al., 2016 ), leading to further exploration and practice of mindfulness techniques involving self-awareness and interoception. This hypothesis also poses both a challenge and an interesting opportunity for future mindfulness training research; that is, how to adequately account for the effects of openness in influencing training outcomes in volunteer participants who get compensated for their participation, but may nonetheless have an inherent preferred bias toward mindfulness training.

Conscientiousness

Conscientiousness has long been implicated in intervention adherence and compliance, since it encompasses qualities such as persistence, self-control, industriousness and a sense of responsibility that facilitate such behavior ( Sanderson and Clarkin, 1994 ; Chapman et al., 2014 ). A nationwide study of conscientiousness has demonstrated that this personality dimension is positively related to treatment adherence, potentially leading to different health outcomes ( Hill and Roberts, 2011 ). Relatedly, a meta-analysis found that conscientiousness is positively related to motivation to learn, which in and of itself correlates with intervention effectiveness ( Colquitt et al., 2000 ). This seems to suggest that motivation may be one of the underlying factors contributing to adherence to intervention instructions and protocols in high conscientious individuals, which may ultimately manifest as improvement in intervention outcomes. In a study of cognitive training of working memory ( N = 47), high conscientiousness was positively associated with overall improved working memory performance at post-training in conjunction with high training enjoyment reported by participants ( Studer-Luethi et al., 2012 ). This finding further corroborates the notion that high conscientious individuals are more motivated to commit to the goals of training and may also find it more enjoyable to comply with training instructions ( Komarraju and Karau, 2005 ).

However, conscientiousness has been associated with mixed results when examined as a moderator of mindfulness training outcomes. For example, in the abovementioned study where neuroticism and openness to experience were found to separately moderate psychological and physiological outcomes, conscientiousness showed no significant moderating effect ( Krick and Felfe, 2019 ). Furthermore, conscientiousness also had no association with practice time in the study that found two other dimensions (agreeableness and openness) to be related to practice time ( Barkan et al., 2016 ). Nonetheless, in the previously mentioned study of MBSR effects on psychological outcomes, individuals with high conscientiousness showed greater reduction in stress after intervention ( de Vibe et al., 2015 ), suggesting that conscientiousness may be a moderator of intervention effects. Therefore, while conscientiousness is likely to indirectly impact the effectiveness and outcomes of most interventions, including mindfulness training, through its interaction with other mental processes such as motivation to learn and adherence, further research is needed to address these mixed findings and elucidate precisely what behavioral outcomes of mindfulness training are most likely to be moderated by conscientiousness.

Trait Mindfulness

Finally, there is one additional noteworthy dispositional trait unique to mindfulness training effectiveness and outcomes. Trait mindfulness generally refers to the innate ability to focus attention on present moment experiences and to maintain present moment awareness with a non-judgmental and non-reactive attitude ( Brown and Ryan, 2003 ). There are multiple questionnaires for assessing trait mindfulness; two of the most popular are the Mindful Attention Awareness Scale (MAAS) ( Brown and Ryan, 2003 ) and the Five Facet Mindfulness Questionnaire ( Baer et al., 2008 ). Some theorists hypothesize that mindfulness programs which repeatedly induce mindfulness states may lead to more stable and trait-level changes in mindfulness ( Kiken et al., 2015 ). Not surprisingly, one RCT study conducted in 30 people (15 in each group) showed that individuals who underwent an 8-week MBSR significantly enhanced trait mindfulness compared to those in a control group, and that people with high trait mindfulness at baseline exhibited a greater increase in trait mindfulness at post-intervention ( Shapiro et al., 2011 ). Despite the preliminary nature of this finding, individual differences in trait mindfulness seem to be highly relevant to predicting mindfulness training effects on psychological well-being, since trait mindfulness is positively related to self-report measures of psychological health ( Brown and Ryan, 2003 ; Keng et al., 2011 ).

The positive correlations between individual differences in trait mindfulness and cognitive performance in attention and memory observed in previous studies ( Schmertz et al., 2009 ; Anicha et al., 2012 ; Ruocco and Direkoglu, 2013 ), suggest that trait mindfulness could also exert an influence over the effects of mindfulness training on cognitive function. Specifically, there is some evidence illustrating that trait mindfulness may potentially mediate the effects of mindfulness training on cognitive function, by simultaneously reducing emotional interference and enhancing attentional control to foster optimal performance ( Ortner et al., 2007 ; Shahar et al., 2010 ). For example, in an RCT study of 45 patients with recurrent depression, patients who received 8 weeks of mindfulness-based cognitive therapy (MBCT) showed an improvement in trait mindfulness, which also mediated the reduction in depressive symptoms at post-intervention, suggesting that trait mindfulness is not only an outcome of MBIs with pre-existing individual differences, but is also a critical mediator of affective outcomes associated with MBIs ( Shahar et al., 2010 ). Likewise, one recent study ( N = 60) showed that the positive relationship between trait mindfulness and executive attention performance is mediated by the brain P300 event-related potential component, as assessed via electroencephalography (EEG), suggesting a neuropsychological basis for such relationship ( Lin et al., 2019 ). Thus, trait mindfulness could well be one of the most important contributors to differential training effects, as well as a vital player in the underlying mechanisms and processes supporting training-related improvement in cognition and mental health. However, there is still a poor understanding of the directionality of the effects of trait mindfulness on different training outcomes. Indeed, studies of personality traits seem to indicate the contrary directional pattern, in that individuals with lower psychological well-being (and also likely to have lower trait mindfulness) at baseline are those who tend to experience more improvement in psychological outcomes.

Overall, there is sufficient evidence to support the assumption that individual differences in the Big Five personality traits and trait mindfulness will contribute to the extent to which individuals benefit from mindfulness training. Additionally, it is evident that the influence of some of the dispositional traits may not be limited to a particular psychological intervention or outcome. Lastly, these individual differences variables may interact with global level processes and mechanisms associated with affecting habitual behavioral and psychological patterns, which in turn could contribute to differential effects on outcomes of interest in individuals who undergo some form of intervention ( Anderson, 1998 ; Chapman et al., 2014 ).

Could Pre-Existing Differences in Emotion Regulation Affect Mindfulness Training Outcomes?

Emotion regulation, a core psychological process thought to be critical for maintaining well-being, has also been associated with considerable individual variability in terms of preferred regulatory strategies and styles ( Gross and John, 2003 ). Conceptually, emotion regulation refers to the process of exerting control over one’s emotion through a wide range of strategies to influence the type of emotions that one has, experiences, or expresses ( Gross, 2001 ). Previous literature has suggested that successful and effective emotion regulation is achieved by engaging adaptive strategies that often take place early on in the trajectory of individual’s emotional experience ( Peña-Sarrionandia et al., 2015 ). Moreover, emotion regulation is often targeted as a key component of mindfulness training ( Tang et al., 2015 ). In this section, we focus on a well-established theoretical model of emotion regulation to discuss how individual differences in two specific emotion regulation strategies may impact mindfulness training outcomes.

Cognitive Reappraisal and Expressive Suppression

According to Gross’s influential model of emotion regulation, individuals differ in terms of how they control emotion by employing two different regulatory strategies: cognitive reappraisal and expressive suppression ( Gross and John, 2003 ). Cognitive reappraisal refers to a form of cognitive process that reconstructs an emotionally aroused situation in a way that reduces its emotional impact, which typically occurs before the emotional response has been fully generated ( Gross and John, 2003 ). For instance, one might perceive a job interview failure as an opportunity to learn from experience, rather than as a test of one’s worth. In contrast, expressive suppression is a form of modulatory process that inhibits an ongoing emotional response or expression, which primarily modifies the behavioral expression of the emotion, but leaves the internal emotional experience intact ( Gross, 2001 ). For example, a person might be offended by another individual and feel angry at the moment, but then decide to suppress their emotion, so as not to express it externally.

Each of these two strategies can result in significant affective consequences for individuals’ well-being. Theoretical models and empirical research suggest that reappraisal is generally more effective than suppression, and that individuals who engage in habitual suppression experience more negative emotion, whereas people who employ reappraisal experience more positive affect and have better psychological health ( Gross and John, 2003 ). With regard to mindfulness training, these pre-existing differences in emotion regulation tendencies could lead to differential training effects on psychological well-being, and even cognitive function, by interacting directly with the training process. Typical mindfulness practices involve observing and attending to any emotion, thoughts, or sensations that arise at the present moment. Such attentive observation of emotional experiences is achieved with a non-emotionally reactive and non-judgmental attitude, which may partly require the engagement of cognitive reappraisal to translate emotionally aroused experiences into emotion-detached events ( Hölzel et al., 2011 ). One recent longitudinal study ( N = 339) of an 8-week of mindfulness intervention further confirms the notion that mindfulness training not only elicits cognitive reappraisal during training, but also increases self-reported positive reappraisal at post-training ( Garland et al., 2011 ). Conversely, suppression is discouraged during mindfulness practices, as the goal of these practices is to openly accept any emotional experiences, regardless of their pleasantness or unpleasantness.

As such, cognitive reappraisers may likely find mindfulness training more intuitive than expressive suppressors, since the latter often regulate their emotion by forcefully suppressing it. This difference could entail two possibilities (not necessarily mutually exclusive) for mindfulness training effects on psychological well-being: (1) cognitive reappraisers would show more improvement since they can easily acquire and practice mindfulness techniques involving reappraisal for emotion regulation, thereby leading to better psychological outcomes and cognitive function; or (2) expressive suppressors would exhibit more enhancement in psychological health and cognition, especially in positive affect, since they could gradually shift away from the previous mentally taxing strategy, to a more beneficial and effective way of emotion regulation through recurrent mindfulness practices. Although there has not been empirical research specifically examining these two hypotheses, the abovementioned inter-relationships among emotion regulation, psychological well-being, and mindfulness training suggest that the tendency for either reappraisal or suppression should theoretically be relevant for influencing the extent of mindfulness training effects on psychological health and cognitive function.

Taken together, individual differences in styles of emotion regulation could potentially interact with intervention processes, by influencing the state of learning and acquisition of relevant techniques to induce differential effects at the individual level. It should be noted that the putative emotional regulatory strategies mentioned above are by no means exhaustive, but they do serve as useful examples to illustrate the importance of examining pre-existing individual differences in emotion regulation tendencies in relation to mindfulness training effectiveness and outcomes.

Could Pre-Existing Differences in Cognitive Function Affect Mindfulness Training Outcomes?

A rapidly growing body of empirical studies has shown three broad domains of cognitive function are often enhanced following mindfulness training: attention, memory, and executive functions ( Chiesa et al., 2011 ). Among them, attentional control (the ability to control attention), working memory (the ability to maintain and manipulate information over short periods of time for ongoing mental processes), and inhibitory control (the capacity to voluntarily regulate and inhibit prepotent responses) are three representative subdomains that have shown consistent improvement. Although there has been some disagreement regarding whether these constructs are each tapping into dissociable cognitive process or rather a common underlying mechanism, researchers generally agree that individual differences are ubiquitous in nearly all cognitive abilities ( Kane and Engle, 2002 ; Braver et al., 2010 ; Miyake and Friedman, 2012 ).

Indeed, individual variability in cognitive function partly appears to be the result of genetic contributions mediated through core neural circuits. Extensive behavioral genetic analyses have found moderate heritability (0.25–0.55) in individual tasks, but this rises to high heritability (0.75) at the level of latent variables, where shared variance across multiple tasks can be extracted to reflect an underlying global process or ability that influences overall performance ( Friedman et al., 2008 ). Similarly, the neural underpinnings of individual differences in attentional control and working memory capacity have been theorized and reliably shown within circuits connected to and centered in the prefrontal cortex ( Kane and Engle, 2002 ; Braver et al., 2010 ; Burgess et al., 2011 ). Given these behavioral and biological sources of evidence regarding individual differences in cognitive function, the interesting question with respect to mindfulness training is whether these pre-existing cognitive dimensions of individual variation have important implications for intervention effectiveness and outcomes.

Attentional Control

Attentional control is one fundamental component of mindfulness training, since cultivating mindfulness requires the ability to control attention in detecting when the mind is wandering, being able to reorient cognitive focus back to the target of concentration, and in sustaining concentration throughout the practice ( Lutz et al., 2008 ; Tang and Posner, 2009 ). Relatedly, individual differences in self-reported attentional control are also positively correlated with trait mindfulness ( Walsh et al., 2009 ). It is interesting that trait mindfulness, is often referred to as a “naturally occurring” aspect of mindfulness, that is nevertheless often reported to be increased following mindfulness training. These converging lines of evidence indicate that attentional control is indispensable to mindfulness practice, both conceptually and empirically, and further imply that the capacity of attentional control would inevitably exert a dominating influence over the process of mindfulness training. More specially, high attentional control ability at baseline would be expected to bolster mindfulness practices, by minimizing the difficulty of concentrating on present moment experiences, by effectively regulating attentional focus to reduce mind-wandering, and by enabling present moment awareness to be more easily maintained. Consequently, high attentional control ability should thereby result in a more stable mindfulness state that leads to greater training benefits.

Following this line of logic, a number of theoretical accounts of mindfulness training mechanisms have strongly argued for the role of attentional control in subserving training-related cognitive improvements in working memory and inhibitory control ( Hölzel et al., 2011 ; Malinowski, 2013 ). This theory coincides with prevalent perspectives in cognitive psychology that describe attentional control as a crucial system, through which goals (such as those in cognitive tasks) are actively maintained, monitored, and executed ( Kane and Engle, 2002 ; Posner and Snyder, 1975 ). In particular, Kane and Engle (2002) , as well as Baddeley (2010) have further proposed an attentional mechanism of working memory capacity, based on evidence showing individuals with low span of working memory experience more visual and cognitive interference in tasks ( Kane et al., 2001 ), and are also more susceptible to lures and distractors ( Conway et al., 2001 ). Together, these findings suggest that low span individuals may have substantial amount of difficulty in effectively controlling their attentional focus to support optimal cognitive performance. Additionally, inhibitory control, another major outcome of mindfulness training, has been shown to be partially contingent on attentional control processes that allocate resources for constraining prepotent responses ( Howard et al., 2014 ). Inhibitory control processes are also thought to be heavily involved in supporting working memory, in combination with attentional control, to maintain goal-related information in the face of interference from irrelevant stimuli ( Redick et al., 2007 ). As such, pre-existing individual differences in attentional control are likely to impact the extent of improvement in major cognitive outcomes of mindfulness training, such as working memory and inhibitory control.

While it is logical to assume individuals with high attentional control would gain greater benefits in cognitive function because they can more easily attain a mindfulness state during practices, another important possibility is that of potential ceiling effects in individuals with inherent high levels of cognitive abilities. If an individual is already at the upper limit of his or her attentional control ability, then by theory, this individual would also tend to exhibit an equally high capacity of working memory and inhibitory control, which could indicate that practicing mindfulness would be less likely to result in significant improvement in these three constructs. On the other hand, it is possible that individuals low in attentional control ability may face a more difficult period in the beginning with practicing mindfulness techniques, but would eventually experience more benefits in cognitive function, since they still have room for potential improvement. Indeed, as described earlier, a recent RCT study ( N = 48) demonstrated that improved working memory capacity following 2 weeks of mindfulness training was mediated by attentional control ability at baseline, such that participants who were initially prone to distraction (low attentional control) exhibited greater benefits in working memory capacity ( Mrazek et al., 2013 ). Therefore, just like emotion regulation, attentional control capacity at baseline could influence the extent of improvement in cognitive function following mindfulness training, but the exact directionality of such influence remains to be ascertained by future studies.

Working Memory and Fluid Intelligence

Although attentional control and working memory capacity are two overlapping and inter-correlated constructs with shared variance, as demonstrated by studies employing latent variable analyses ( Kane and Engle, 2002 ; Baddeley, 2010 ), there is also unique variance reflected in each cognitive subdomain (diversity) ( Miyake and Friedman, 2012 ). In fact, inherent differences in working memory capacity may exert their own influence over mindfulness training outcomes through a different mechanism that affects learning abilities. General fluid intelligence is one of the most important factors in learning, which broadly includes problem solving and reasoning abilities that facilitate acquisition of new skills and knowledge ( Conway et al., 2003 ). According to empirical findings, working memory capacity not only correlates highly (0.60–0.80) with level of fluid intelligence ( Kane and Engle, 2002 ), but it also shares common underlying neural substrates with the cognitive processes comprising fluid intelligence ( Conway et al., 2003 ; Burgess et al., 2011 ). Most importantly, such shared variance between fluid intelligence and working memory may have important implications for mindfulness training, particularly by governing skills acquisition, learning ability, and self-efficacy, which are all pertinent processes for influencing the effects of training ( Gully and Chen, 2010 ). For example, having high working memory capacity may suggest a high level of fluid intelligence. In turn, high fluid intelligence may endow individuals with greater efficiency to learn new skills of mindfulness, and more ability to apply such skills outside of the training context to everyday life, likely resulting in a greater increase the extent of cognitive improvement following mindfulness training. However, it should be noted that the ceiling effects discussed above also have relevance for the present discussion, such that exceptionally high levels of working memory capacity and fluid intelligence at baseline may inevitably result in minimal cognitive changes post-training, due to individuals already hitting the upper boundary of their cognitive abilities.

Overall, the common cognitive outcomes of mindfulness training may reflect inter-correlated relationships, and further, considerable individual variability within each construct. Not only are these baseline individual differences in cognitive function likely to exert reciprocal influences over one another, but they may also interact with the learning and training processes to affect the magnitude of improvement induced by mindfulness training, especially for cognitive outcomes. Therefore, it may be important to take into account pre-existing individual differences in these cognitive constructs when studying mindfulness training effects, in order to more accurately evaluate the extent of improvement among individuals, and conversely, to identify individuals for whom mindfulness training is unlikely to be useful in terms of improving cognitive abilities. It should be noted, however, that there are certainly other cognitive outcome measures of mindfulness training that were not mentioned above, but which could also contribute to differential training effectiveness. The present discussion serves only as a starting point for a more in-depth investigation and conversation regarding the role of individual differences in cognitive function, in terms of their potential impact on mindfulness training effects.

How Should We Study Individual Differences in Mindfulness Training?

Any attempts to study these promising effects of individual differences on mindfulness training outcomes must carefully consider common methodological issues and constraints that have been emphasized in individual differences-based studies in psychology. To facilitate future investigation, the following section describes several general theoretical and empirical considerations and addresses potential methodological challenges with suggestive solutions. We also provide an empirical example as an illustration of how an individual differences perspective could influence future mindfulness research investigations. Finally, we discuss potential research strategies for assessing the efficacy of mindfulness-based interventions that are tailored to individuals’ characteristics.

Sample Size

The first issue to consider in research on individual differences is the need for a large enough sample size to have sufficient statistical power to detect individual difference effects. Unfortunately, the sample size in the majority of mindfulness training studies typically fall within the range of 10–50 people per group, with the exception of a few recent large-scale studies, that reach more than 50 participants per group ( Kuyken et al., 2013 ; Engert et al., 2017 ; Hildebrandt et al., 2017 ). In fact, the issue of small sample size has long been criticized within mindfulness research, though not for the reason of individual differences; instead, the concerns are mostly oriented toward the over-inflation of reported training effects and the generalizability of such results ( Baer, 2003 ; Van Dam et al., 2018 ). Regardless of whether researchers are interested in making inferences about population-level or individual-level training effects, a large sample size is necessary to capture a wide range of inter-subject variability from the population, and to have sufficient statistical power to detect effects that are likely to be more subtle in nature ( Goldberg et al., 2017 ; Van Dam et al., 2018 ).

However, it is worth acknowledging that acquiring data with large sample size is not easily accomplished in psychological intervention research, due to several competing empirical constraints. First, having more than 25 participants per training group could potentially jeopardize the training quality, especially if there is only one instructor assigned to provide instructions, respond to questions, and monitor the progress of each participant. For this reason, previous studies have mostly adopted a sample size of under 30 per group for mindfulness training studies, which means that to accumulate a sufficiently large sample size requires a greater outlay of time and resources. Yet, there are a few alternative solutions that can increase the overall sample size without, overly increasing the time required for data collection: (1) running multiple training groups in parallel with multiple instructors (one per group); and (2) having more teaching assistants with one instructor to guide a larger training group. The former option is an ideal solution when there are adequate financial and instructional resources to support such large-scale study. The latter option would likely reduce the financial burden more than the former, but it is currently an unstudied question as to whether or not having trained teaching assistants would introduce unknown effects to training quality.

A related issue is that of participant attrition. This factor, which is common in intervention research, also makes collecting large sample size data challenging, especially for the types of longitudinal designs becoming more common in mindfulness training research, which require a significant time commitment and persistence from participants throughout the study period. This challenge may be potentially addressed by offering greater incentives for study completion or by providing easily accessible training programs to participants via online platforms. Indeed, web-based mindfulness programs have increasingly been developed and distributed in recent years, but the standardization and effectiveness of such programs warrants further investigation. In particular, a recent review suggests some support for the efficacy of web-based mindfulness training programs on improving psychological well-being, with most studies showing large effect sizes ( Fish et al., 2016 ). Yet methodological concerns including selection bias and lack of control group make it difficult to evaluate and compare the efficacy of these online programs with other web-based health enhancement alternatives.

Reliability and Validity

The second issue to consider is the reliability and validity of measurement tools used for examining mindfulness training effects. Reliability refers to the overall consistency of a measure to produce similar results when administered multiple times in the same individual when all other factors are held constant, whereas validity is the extent to which an assessment measures what it is supposed to measure. While both concepts are fundamental to psychometric theory and are valued highly in psychology and cognitive neuroscience research, they are especially worth underscoring for studies adopting an individual differences approach. Validity is generally less of a concern if the construct of interest is clearly defined and operationalized by the measurement tools, based on established theories, and more importantly, if the measurement tools have previously been examined in empirical studies for validity. However, Van Dam et al. (2018) have pointed out the semantic ambiguity in defining the construct of mindfulness, specifically within the realm of mindfulness research. They emphasized how this can lead to problems of construct validity that are particularly acute when considering the different self-report questionnaires of mindfulness, that incorporate various semantic associations that are commonly being used as outcome measures of mindfulness training ( Black et al., 2012 ; Goldberg et al., 2016 ). Moreover, this issue becomes especially problematic when multiple studies are compared across various dimensions to draw general conclusions regarding effects of training on a construct with a wide range of different semantic definitions. Therefore, investigators should explicitly address the issue of validity in mindfulness research, by being extremely clear about the operationalized definitions of all constructs of interests measured in the study, especially that of mindfulness.

Reliability plays an even more pivotal role in individual differences research, by directly influencing the extent to which individual differences effects can be observed in studies, as well as the stability of such effects, if any. Intuitively, low measurement reliability could lead to fluctuating results, since the assessment would yield fairly different scores each time it is administered to the same individual. For example, low reliability can lead to unstable correlational relationships between the measured variables, resulting in inconsistent conclusions about individual differences in these variables. Another notable, perhaps more serious consequence arises from the reliability paradox, which has recently been shown to be especially pernicious with regard to robust cognitive paradigms. Here, well-established experimental effects are consistently replicated at the group level, simply because these tasks are less sensitive to between-subject variability ( Hedge et al., 2017 ).

The reliability paradox is problematic because most studies of individual differences employ a correlational approach for data analysis; yet if seemingly well-validated tasks that nevertheless have low between-subject variability are employed, it can greatly undermine the correlational relationships observed between theoretically important variables, resulting in misleading conclusions. As a concrete example, the cognitive tasks that are frequently utilized to assess mindfulness training effects on attentional control and related constructs (e.g., Stroop, Flankers, SART), are precisely the ones demonstrated by Hedge et al. (2017) to have rather low test-retest reliabilities, typically less than the conventional standard of 0.70 adopted in psychometric research. Thus, if and when the focus of mindfulness research shifts toward an individual difference perspective, it will be imperative for researchers to be highly aware of such problems in the cognitive paradigms that may be employed, and to avoid potential pitfalls by exercising caution in study planning and task selection.

Although the abovementioned issues are troublesome for individual differences research in mindfulness training, two straightforward solutions could potentially remedy some of the negative impacts. The first solution is to compute and report reliability coefficients for each assessment (behavioral and neuroimaging), and for each new sample, by following guidelines from the psychometric literature ( Braver et al., 2010 ; Cooper et al., 2017 ; Hedge et al., 2017 ; Parsons et al., 2018 ). This quality control step would qualitatively evaluate the extent to which the observed effects can be trusted, adding another source of validation and objective reference when reporting study results. The second straightforward solution is to carefully select assessment tools with excellent reliability (>0.8 reliability coefficient) based on prior research, or measures with at least moderate reliability (0.6–0.7), when there are no other alternatives for measuring the specific construct of interest ( Hedge et al., 2017 ).

Furthermore, it is worth mentioning that for individual differences research, it is imperative to select measurement tools that not only have good reliability but also are sensitive to inter-individual variability. As described above, it is not logically entailed that a paradigm yielding consistent experimental effects at the group-level will also capture unique variance between individuals. Fortunately, there has been new progress in the development of cognitive task batteries sensitive to both group and individual level effects ( Braver, 2012 ; Cooper et al., 2017 ), and some individual-focused batteries are being adapted for on-line testing ( Hicks et al., 2016 ). Together, these should gradually provide more new avenues for assessing cognitive effects in individual differences-based studies of mindfulness training.

An Example: Individual Preferences in Mindfulness Training

Identifying predictors or moderators of mindfulness training effects is one approach to investigate the question of individual differences. However, one can also tackle individual variability in response to mindfulness training from a slightly different angle. Previous reports have shown that individuals, especially patients who undergo psychological intervention, have preferences for at least one aspect of their intervention and if such preferences were not met during the intervention, poorer outcomes are observed at post-intervention ( Williams et al., 2016 ). Additionally, one study ( N = 247) also established the presence of individual preferences for specific meditation techniques, but not all techniques, taught within a training program, suggesting that people may not find every training technique to be helpful and may only engage in techniques they find most effective ( Burke, 2012 ). Similarly, preferences for specific modality of mindfulness practice anchors (e.g., using breath, imagery, or auditory-phrase as a focus of attention), were found among novices ( N = 117) who practice mindfulness and such preferences also underwent change over the course of mindfulness training in some individuals ( Anderson and Farb, 2018 ). Unfortunately, most mindfulness training programs tend to present students with a multi-faceted package, encompassing a broad set of different mindfulness techniques, while overlooking the fact that individual preferences may exist with regard to the techniques that are taught; such preferences may have the potential to influence the outcomes of mindfulness training.

Building upon this work of preferences in psychological intervention, we recently conducted a study examining individual preferences in mindfulness training, specifically focusing on whether or not individual differences in personality traits can predict personal preferences for practicing one specific mindfulness technique over the other alternatives ( Tang and Braver, 2020 , PsyArXiv). Relevant dispositional traits, including but not limited to personality, trait mindfulness, emotion profiles, and empathy were assessed among a group of meditation-naïve participants who were exposed to four different mindfulness sessions, via a novel on-line protocol, with each session involving exposure to one of the following four commonly practiced techniques – focused attention, open monitoring, loving kindness, and body scan. Among all four techniques, preferences for open monitoring and loving kindness were found to be predicted by dispositional traits, such that the tendency to rank open monitoring as the most preferred technique was associated with higher level of non-judgment, a facet of trait mindfulness. Conversely, a greater preference for loving kindness was related to higher level of empathic concerns and perspective taking, two facets of empathy. Likewise, participants self-reported stronger mindfulness states as assessed via the State Mindfulness Scale when practicing with their preferred technique, suggesting that individual preferences for specific techniques may lead to differential training outcomes.

This finding from our own work, together with previous reports of individual preferences for different meditation techniques ( Burke, 2012 ) and mindfulness practice anchors modality ( Anderson and Farb, 2018 ) still leave open the question of how such preferences might impact the outcomes of mindfulness training. If individual preferences do indeed have substantial contribution to differential training outcomes, then such information would be critical for effectively applying and tailoring existing mindfulness programs toward individual characteristics, as a means to enhance their overall effectiveness and cost-effectiveness in achieving desirable outcomes. Furthermore, this also leads to another important research direction concerning the outcome and efficacy assessment of mindfulness programs that are tailored to individual characteristics.

One logical first step in approaching this question would be a prospectively designed empirical study that, in the pre-test phase, gathers information about individual characteristics and traits to make predictions about individuals’ preferences for specific mindfulness practices, as well as asking questions that directly probe their general preferences for relevant intervention components, such as delivery format and settings. Once this information is compiled for each individual, researchers are able to assign individuals either to mindfulness practices and programs that are compatible with their preferences and needs, or ones that are incompatible. Consequently, such a prospective design would allow a direct comparison of potential improvement in the targeted outcomes between the two groups, enabling strong inferences about interventions efficacy as a function of intervention compatibility based on individual characteristics. Finally, as more attention and research effort are devoted to individual differences investigation in mindfulness training, more innovative approaches for assessing the effectiveness of individual-tailored mindfulness-based interventions would become available. Our discussion only seeks to provide a concrete example of how to examine individual differences in mindfulness training, and how to build upon these research findings to rigorously evaluate the outcomes of individual-oriented interventions programs.

Future Directions

In this review, we have primarily focused on behavioral studies of individual differences related to mindfulness training, but it is worth noting that studies of mindfulness training effects utilizing cognitive neuroscience approaches, though still in their infancy, would also benefit from adopting an individual differences perspective in future investigation. An impressive body of evidence has demonstrated inter-individual variability in the brain using neuroimaging methods (e.g., fMRI), acquired during both task-evoked and resting states ( Finn et al., 2015 ; Dubois and Adolphs, 2016 ; Gordon et al., 2017a ; Gratton et al., 2018 ), with the latter echoing findings from the older EEG literature demonstrating individual-differences in frontal asymmetries (e.g., Wheeler et al., 1993 ; Davidson et al., 2003 ).

Brain functional connectivity profiles provide a particularly clear example. In particular, connectivity profiles in task and resting states can act as a “fingerprint” that reliably distinguishes individuals from rest of the group ( Finn et al., 2015 ). Likewise, Gratton et al. (2018) illustrated that task-evoked modulation of functional networks primarily behaves in an individual-specific manner in the frontoparietal regions relevant for high-level cognitive processes. These regions, along with the default mode network manifested in resting state, are found to be affected by mindfulness training ( Tang et al., 2015 ), indicating that an individual differences perspective would be highly meaningful for exploring the effects of mindfulness training on the brain.

Neuroimaging studies of individual differences are strongly benefited by a longitudinal design with multiple sessions of scanning data per participant, to most accurately identify features unique to the individuals ( Gordon et al., 2017b ). Fortunately, similar longitudinal research designs (though with fewer sessions) are required in mindfulness training research to compare pre- and post-training changes in brain and behavior. Therefore, exploiting these existing strengths for the purpose of investigating individual differences is not only convenient but also may greatly enrich the scientific understanding of mindfulness training in an unprecedent way.

Relatedly, recent advances in neuroimaging analytic approaches also hold promise for interrogating individual differences within the context of mindfulness training. Some of these approaches include but not limited to: (1) structural equation modeling, which can directly model between-subject variability and is sensitive to the complexities of brain-behavior relationships, as well as the psychometric properties of data critical for individual differences-type analysis (for a detailed review, see Cooper et al., 2019 ); (2) multi-variate pattern analysis (MVPA) approaches, which can be used to decode different mental states during mindfulness training based on distributed patterns of brain activity, and which may be especially powerful in detecting and identifying differences in mental states across individuals who practice mindfulness ( Weng et al., 2018 ); and (3) network analysis (i.e., graph theory), which captures the functional organization of large-scale brain networks through measures of network properties that can explain individual differences in cognition and behavior (for a detailed review, see Tompson et al., 2018 ), and which is also beginning to be used to study brain-related changes following mindfulness training ( Gard et al., 2014 ). Bridging these new methodologies in future investigation may further deepen our current knowledge regarding the brain mechanisms underlying mindfulness training and associated inter-individual variability.

Finally, the personalized medicine and patient-centered care movements suggest a completely new direction of research that affords tremendous opportunities and progress for bridging psychology and health science. In particular, mindfulness research has yet to investigate the feasibility of modifying and tailoring existing established prevention and treatment programs to individual’s characteristics and needs. Although standardized programs enable a widespread implementation of intervention protocols in an easily controlled and accessible manner, customizing these multifaceted packages could potentially be illuminating at the individual level. For instance, prior work ( Burke, 2012 ; Anderson and Farb, 2018 ; Tang and Braver, 2020 , PsyArXiv) have already shown that individuals may not find every component of a mindfulness training program to be helpful, and that such preferences can be predicted by personality traits. Furthermore, there is also evidence from a recent survey study ( N = 500) indicating individual preferences for certain delivery formats (group, internet, one-on-one) of mindfulness training ( Wahbeh et al., 2014 ), which could potentially influence how well one practices and learns the training. Therefore, these findings, in combination with existing evidence of individual differences in intervention effectiveness, call for more research and development of individual-specific intervention programs.

As this review has highlighted, evidence is accumulating that individual differences in dispositional traits, psychological well-being, and cognitive function could play a critical role in contributing to the heterogeneity in mindfulness training effects across individuals. Consequently, moving toward an individual difference perspective is imperative for accurately evaluating mindfulness training effects and outcomes to inform subsequent application in real-world settings. In this review, we discussed putative traits and characteristics relevant to mindfulness training effectiveness, as well as theoretical considerations and useful examples for future investigations on this topic. It is our hope that researchers will not only be made aware of these critical issues associated with individual differences research within the context of psychological intervention, but will also be encouraged to explore various ideas and approaches to tackle this emerging question in mindfulness training research.

Author Contributions

RT contributed to the conception, writing, and critical revision of the manuscript, and final approval of the version to be published. TB contributed to the writing and critical revision of the manuscript, and final approval of the version to be published.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We would like to thank Dr. Jeff Lin and Dr. Yi-Yuan Tang for their insightful comments and suggestions.

Funding. This work was supported by NCCIH F31 AT010422 to RT and by NCCIH R21 AT009483 to TB.

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‘Interpretation’ and the ‘Empirical’: Similarities between theoretical and empirical political science

• Critical Exchange
• Published: 04 November 2014
• Volume 14 , pages 256–275, ( 2015 )

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• Paul A Passavant 1 &
• John Gunnell 2  

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Acknowledgements

I would like to acknowledge helpful conversations with Peregrine Schwartz-Shea and Dvora Yanow at the inception of this essay. Jodi Dean, Lisa Disch, Jeffrey Dudas and Bert Kritzer provided helpful comments on later drafts. I thank John Gunnell for responding to this essay.

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This Critical Exchange features a contribution by Paul Passavant that engages with an earlier publication by John Gunnell, ‘Professing Political Theory,’ ( Political Research Quarterly 2010), and includes a response by John Gunnell.

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Passavant, P., Gunnell, J. ‘Interpretation’ and the ‘Empirical’: Similarities between theoretical and empirical political science. Contemp Polit Theory 14 , 256–275 (2015). https://doi.org/10.1057/cpt.2014.36

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Towards an Individual Differences Perspective in Mindfulness Training Research: Theoretical and Empirical Considerations

Affiliation.

• 1 Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, United States.
• PMID: 32508702
• PMCID: PMC7248295
• DOI: 10.3389/fpsyg.2020.00818

A growing body of research indicates that mindfulness training can have beneficial effects on critical aspects of psychological well-being, cognitive function, and brain health. Although these benefits have been generalized to the population level, individual variability in observed effects of mindfulness training has not been systematically investigated. Research on other similar forms of psychological intervention demonstrates that individual differences are prominent in terms of intervention responsiveness and outcomes. Furthermore, individual characteristics such as personality traits have been shown to play a crucial role in influencing the effects of intervention. In light of these lines of evidence, we review representative work on individual differences in mindfulness training and advocate for an individual difference perspective in mindfulness training research. We discuss relevant empirical evidence of individual differences potentially influencing behavioral outcomes of mindfulness training, focusing on both cognitive function and psychological well-being. Finally, theoretical considerations and potentially fruitful research strategies and directions for studying individual differences in mindfulness training are discussed, including those involving cognitive neuroscience methods.

Keywords: disposition; individual differences; meditation; mindfulness training; personality traits; personalized medicine; precision medicine.

Copyright © 2020 Tang and Braver.

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• F31 AT010422/AT/NCCIH NIH HHS/United States
• R21 AT009483/AT/NCCIH NIH HHS/United States