empirical research is theoretical

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.

Table of Contents

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.

Read more articles

Henry Whittemore Library

Research skills hub.

• Click on a section, choose a topic. On computers, page opens on the right. On mobile, scroll past menu to read.
• Who are Our 'Experts'?
• The Information Timeline
• Peer Review
• The Internet vs. Academic Databases
• What kind of Info is in Databases?
• What's in OUR Library's Collection? This link opens in a new window
• Data vs Information
• Popular vs. Scholarly
• Grey Literature
• Primary, Secondary, Tertiary Sources
• What KIND of Info do You Need?
• Find & Narrow Topics
• Keywords from your Topic
• Concept / Mind Mapping
• Advanced Database Features
• Use Boolean Connectors
• Use Phrases
• Use Database Fields
• Use Truncation
• Use Database Limiters
• Use Synonyms
• Use Wildcards
• Use Database Subjects
• Find Better Keywords DURING Your Search
• Google Advanced Search Tricks
• Search Google Scholar
• Reference Mining
• Citation Tracking
• About Call Numbers
• Periodical's Content Not the Same as the Periodical's Website!
• From a scholarly journal - But what IS it??
• Anatomy of a Scientific Research Article
• Quantitative vs Qualitative Research Articles
• Theoretical vs Empirical Research Articles

Theoretical vs Empirical Articles

• Types of Review Articles
• Identify the Research Study Type
• Spotting Bad Science
• Types of Scientific Evidence
• Found it in a newspaper - What is it??
• More About News Evaluation
• Webste Evaluation
• Fear & Loathing on Social Media
• Pseudoscience on Social Media -the Slippery Slope
• Getting Full Text During Library Site Searches
• Find Free Full Text on the Internet
• Got a Citation? Check the Library for Full Text
• Make an Interlibrary Loan (ILL) Request
• Better Organize What You Found
• Organize with Zotero
• Organize with Google Docs
• Organize using personal accounts in databases
• Just store stuff on your laptop?
• Print / Save as PDF Website Info Without the Ads
• Avoid Plagiarism
• Why SO MANY citation systems?
• Why Citations in the Text?
• Citing vs Attribution
• Paraphrase Correctly
• Create a Bibliography
• Create an Annotated Bibliography
• Our Complete Citation Guide This link opens in a new window

Theoretical Research  is a logical exploration of a system of beliefs and assumptions, working with abstract principles related to a field of knowledge.

• Essentially...theorizing

Empirical Research is    based on real-life direct or indirect observation and measurement of phenomena by a researcher.

• Basically... Collecting data by Observing or Experimenting

• << Previous: Quantitative vs Qualitative Research Articles
• Next: Types of Review Articles >>
• Last Updated: Sep 12, 2024 1:19 PM
• URL: https://library.framingham.edu/research-skills-hub

Library Socials

Contact us:.

[email protected] Phone: (508) 626-4650 [email protected] Phone: (508) 626-4654

Search this site

Empirical Research

• Reference work entry
• First Online: 01 January 2020
• Cite this reference work entry

• Emeka Thaddues Njoku 3  

252 Accesses

1 Citations

The term “empirical” entails gathered data based on experience, observations, or experimentation. In empirical research, knowledge is developed from factual experience as opposed to theoretical assumption and usually involved the use of data sources like datasets or fieldwork, but can also be based on observations within a laboratory setting. Testing hypothesis or answering definite questions is a primary feature of empirical research. Empirical research, in other words, involves the process of employing working hypothesis that are tested through experimentation or observation. Hence, empirical research is a method of uncovering empirical evidence.

Through the process of gathering valid empirical data, scientists from a variety of fields, ranging from the social to the natural sciences, have to carefully design their methods. This helps to ensure quality and accuracy of data collection and treatment. However, any error in empirical data collection process could inevitably render such...

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

Access this chapter

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime
• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Durable hardcover edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Bibliography

Bhattacherjee, A. (2012). Social science research: Principles, methods, and practices. Textbooks Collection . Book 3.

Google Scholar  

Comte, A., & Bridges, J. H. (Tr.) (1865). A general view of positivism . Trubner and Co. (reissued by Cambridge University Press, 2009).

Dilworth, C. B. (1982). Empirical research in the literature class. English Journal, 71 (3), 95–97.

Article   Google Scholar  

Heisenberg, W. (1971). Positivism, metaphysics and religion. In R. N. Nanshen (Ed.), Werner Heisenberg – Physics and beyond – Encounters and conversations , World Perspectives. 42. Translator: Arnold J. Pomerans. New York: Harper and Row.

Hossain, F. M. A. (2014). A critical analysis of empiricism. Open Journal of Philosophy, 2014 (4), 225–230.

Kant, I. (1783). Prolegomena to any future metaphysic (trans: Bennett, J.). Early Modern Texts. www.earlymoderntexts.com

Koch, S. (1992). Psychology’s Bridgman vs. Bridgman’s Bridgman: An essay in reconstruction. Theory and Psychology, 2 (3), 261–290.

Matin, A. (1968). An outline of philosophy . Dhaka: Mullick Brothers.

Mcleod, S. (2008). Psychology as science. http://www.simplypsychology.org/science-psychology.html

Popper, K. (1963). Conjectures and refutations: The growth of scientific knowledge . London: Routledge.

Simmel, G. (1908). The problem areas of sociology in Kurt H. Wolf: The sociology of Georg Simmel . London: The Free Press.

Weber, M. (1991). The nature of social action. In W. G. Runciman (Ed.), Weber: Selections in translation . Cambridge: Cambridge University Press.

Download references

Author information

Authors and affiliations.

Department of Political Science, University of Ibadan, Ibadan, Oyo, Nigeria

Emeka Thaddues Njoku

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Emeka Thaddues Njoku .

Editor information

Editors and affiliations.

University of Connecticut, Storrs, CT, USA

David A. Leeming

Blanton-Peale Institute, New York, NY, USA

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this entry

Cite this entry.

Njoku, E.T. (2020). Empirical Research. In: Leeming, D.A. (eds) Encyclopedia of Psychology and Religion. Springer, Cham. https://doi.org/10.1007/978-3-030-24348-7_200051

Download citation

DOI : https://doi.org/10.1007/978-3-030-24348-7_200051

Published : 12 June 2020

Publisher Name : Springer, Cham

Print ISBN : 978-3-030-24347-0

Online ISBN : 978-3-030-24348-7

eBook Packages : Behavioral Science and Psychology Reference Module Humanities and Social Sciences Reference Module Business, Economics and Social Sciences

Share this entry

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

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

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

• Table of Contents
• Random Entry
• Chronological
• Editorial Information
• About the SEP
• Editorial Board
• How to Cite the SEP
• Special Characters
• Advanced Tools
• Support the SEP
• PDFs for SEP Friends
• Make a Donation
• SEPIA for Libraries
• Entry Contents

Bibliography

Academic tools.

• Friends PDF Preview
• Author and Citation Info
• Back to Top

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.

• Anderson, E., 2004, “Uses of Value Judgments in Science: A General Argument, with Lessons from a Case Study of Feminist Research on Divorce,” Hypatia , 19(1): 1–24.
• Aristotle(a), Generation of Animals in Complete Works of Aristotle (Volume 1), J. Barnes (ed.), Princeton: Princeton University Press, 1995, pp. 774–993
• Aristotle(b), History of Animals in Complete Works of Aristotle (Volume 1), J. Barnes (ed.), Princeton: Princeton University Press, 1995, pp. 1111–1228.
• Azzouni, J., 2004, “Theory, Observation, and Scientific Realism,” British Journal for the Philosophy of Science , 55(3): 371–92.
• Bacon, Francis, 1620, Novum Organum with other parts of the Great Instauration , P. Urbach and J. Gibson (eds. and trans.), La Salle: Open Court, 1994.
• Bogen, J., 2016, “Empiricism and After,”in P. Humphreys (ed.), Oxford Handbook of Philosophy of Science , Oxford: Oxford University Press, pp. 779–795.
• Bogen, J, and Woodward, J., 1988, “Saving the Phenomena,” Philosophical Review , XCVII (3): 303–352.
• Bokulich, A., 2020, “Towards a Taxonomy of the Model-Ladenness of Data,” Philosophy of Science , 87(5): 793–806.
• Borrelli, A., 2012, “The Case of the Composite Higgs: The Model as a ‘Rosetta Stone’ in Contemporary High-Energy Physics,” Studies in History and Philosophy of Science (Part B: Studies in History and Philosophy of Modern Physics), 43(3): 195–214.
• Boyd, N. M., 2018, “Evidence Enriched,” Philosophy of Science , 85(3): 403–21.
• Boyle, R., 1661, The Sceptical Chymist , Montana: Kessinger (reprint of 1661 edition).
• Bridgman, P., 1927, The Logic of Modern Physics , New York: Macmillan.
• Chang, H., 2005, “A Case for Old-fashioned Observability, and a Reconstructive Empiricism,” Philosophy of Science , 72(5): 876–887.
• Collins, H. M., 1985 Changing Order , Chicago: University of Chicago Press.
• Conant, J.B., 1957, (ed.) “The Overthrow of the Phlogiston Theory: The Chemical Revolution of 1775–1789,” in J.B.Conant and L.K. Nash (eds.), Harvard Studies in Experimental Science , Volume I, Cambridge: Harvard University Press, pp. 65–116).
• Daston, L., and P. Galison, 2007, Objectivity , Brooklyn: Zone Books.
• Douglas, H., 2000, “Inductive Risk and Values in Science,” Philosophy of Science , 67(4): 559–79.
• –––, 2013, “The Value of Cognitive Values,” Philosophy of Science , 80(5): 796–806.
• Duhem, P., 1906, The Aim and Structure of Physical Theory , P. Wiener (tr.), Princeton: Princeton University Press, 1991.
• Earman, J., 1992, Bayes or Bust? , Cambridge: MIT Press.
• Feest, U., 2005, “Operationism in psychology: what the debate is about, what the debate should be about,” Journal of the History of the Behavioral Sciences , 41(2): 131–149.
• Feyerabend, P.K., 1969, “Science Without Experience,” in P.K. Feyerabend, Realism, Rationalism, and Scientific Method (Philosophical Papers I), Cambridge: Cambridge University Press, 1985, pp. 132–136.
• Franklin, A., 1986, The Neglect of Experiment , Cambridge: Cambridge University Press.
• Galison, P., 1987, How Experiments End , Chicago: University of Chicago Press.
• –––, 1990, “Aufbau/Bauhaus: logical positivism and architectural modernism,” Critical Inquiry , 16 (4): 709–753.
• Goodman, A., et al., 2014, “Ten Simple Rules for the Care and Feeding of Scientific Data,” PLoS Computational Biology , 10(4): e1003542.
• Hacking, I., 1981, “Do We See Through a Microscope?,” Pacific Philosophical Quarterly , 62(4): 305–322.
• –––, 1983, Representing and Intervening , Cambridge: Cambridge University Press.
• Hanson, N.R., 1958, Patterns of Discovery , Cambridge, Cambridge University Press.
• Hempel, C.G., 1952, “Fundamentals of Concept Formation in Empirical Science,” in Foundations of the Unity of Science , Volume 2, O. Neurath, R. Carnap, C. Morris (eds.), Chicago: University of Chicago Press, 1970, pp. 651–746.
• Herschel, J. F. W., 1830, Preliminary Discourse on the Study of Natural Philosophy , New York: Johnson Reprint Corp., 1966.
• Hooke, R., 1705, “The Method of Improving Natural Philosophy,” in R. Waller (ed.), The Posthumous Works of Robert Hooke , London: Frank Cass and Company, 1971.
• Horowitz, P., and W. Hill, 2015, The Art of Electronics , third edition, New York: Cambridge University Press.
• Intemann, K., 2021, “Feminist Perspectives on Values in Science,” in S. Crasnow and L. Intemann (eds.), The Routledge Handbook of Feminist Philosophy of Science , New York: Routledge, pp. 201–15.
• Kuhn, T.S., The Structure of Scientific Revolutions , 1962, Chicago: University of Chicago Press, reprinted,1996.
• Latour, B., 1999, “Circulating Reference: Sampling the Soil in the Amazon Forest,” in Pandora’s Hope: Essays on the Reality of Science Studies , Cambridge, MA: Harvard University Press, pp. 24–79.
• Latour, B., and Woolgar, S., 1979, Laboratory Life, The Construction of Scientific Facts , Princeton: Princeton University Press, 1986.
• Laymon, R., 1988, “The Michelson-Morley Experiment and the Appraisal of Theories,” in A. Donovan, L. Laudan, and R. Laudan (eds.), Scrutinizing Science: Empirical Studies of Scientific Change , Baltimore: The Johns Hopkins University Press, pp. 245–266.
• Leonelli, S., 2009, “On the Locality of Data and Claims about Phenomena,” Philosophy of Science , 76(5): 737–49.
• Leonelli, S., and N. Tempini (eds.), 2020, Data Journeys in the Sciences , Cham: Springer.
• Lipton, P., 1991, Inference to the Best Explanation , London: Routledge.
• Lloyd, E.A., 1993, “Pre-theoretical Assumptions In Evolutionary Explanations of Female Sexuality,” Philosophical Studies , 69: 139–153.
• –––, 2012, “The Role of ‘Complex’ Empiricism in the Debates about Satellite Data and Climate Models,”, Studies in History and Philosophy of Science (Part A), 43(2): 390–401.
• Longino, H., 1979, “Evidence and Hypothesis: An Analysis of Evidential Relations,” Philosophy of Science , 46(1): 35–56.
• –––, 2020, “Afterward:Data in Transit,” in S. Leonelli and N. Tempini (eds.), Data Journeys in the Sciences , Cham: Springer, pp. 391–400.
• Lupyan, G., 2015, “Cognitive Penetrability of Perception in the Age of Prediction – Predictive Systems are Penetrable Systems,” Review of Philosophical Psychology , 6(4): 547–569. doi:10.1007/s13164-015-0253-4
• Mill, J. S., 1872, System of Logic , London: Longmans, Green, Reader, and Dyer.
• Norton, J., 2003, “A Material Theory of Induction,” Philosophy of Science , 70(4): 647–70.
• –––, 2021, The Material Theory of Induction , http://www.pitt.edu/~jdnorton/papers/material_theory/Material_Induction_March_14_2021.pdf .
• Nyquist, H., 1928, “Thermal Agitation of Electric Charge in Conductors,” Physical Review , 32(1): 110–13.
• O’Connor, C. and J. O. Weatherall, 2019, The Misinformation Age: How False Beliefs Spread , New Haven: Yale University Press.
• Olesko, K.M. and Holmes, F.L., 1994, “Experiment, Quantification and Discovery: Helmholtz’s Early Physiological Researches, 1843–50,” in D. Cahan, (ed.), Hermann Helmholtz and the Foundations of Nineteenth Century Science , Berkeley: UC Press, pp. 50–108.
• Osiander, A., 1543, “To the Reader Concerning the Hypothesis of this Work,” in N. Copernicus On the Revolutions , E. Rosen (tr., ed.), Baltimore: Johns Hopkins University Press, 1978, p. XX.
• Parker, W. S., 2016, “Reanalysis and Observation: What’s the Difference?,” Bulletin of the American Meteorological Society , 97(9): 1565–72.
• –––, 2017, “Computer Simulation, Measurement, and Data Assimilation,” The British Journal for the Philosophy of Science , 68(1): 273–304.
• Popper, K.R.,1959, The Logic of Scientific Discovery , K.R. Popper (tr.), New York: Basic Books.
• Rheinberger, H. J., 1997, Towards a History of Epistemic Things: Synthesizing Proteins in the Test Tube , Stanford: Stanford University Press.
• Roush, S., 2005, Tracking Truth , Cambridge: Cambridge University Press.
• Rudner, R., 1953, “The Scientist Qua Scientist Makes Value Judgments,” Philosophy of Science , 20(1): 1–6.
• Schlick, M., 1935, “Facts and Propositions,” in Philosophy and Analysis , M. Macdonald (ed.), New York: Philosophical Library, 1954, pp. 232–236.
• Schottky, W. H., 1918, “Über spontane Stromschwankungen in verschiedenen Elektrizitätsleitern,” Annalen der Physik , 362(23): 541–67.
• Shapere, D., 1982, “The Concept of Observation in Science and Philosophy,” Philosophy of Science , 49(4): 485–525.
• Stanford, K., 1991, Exceeding Our Grasp: Science, History, and the Problem of Unconceived Alternatives , Oxford: Oxford University Press.
• Stephenson, F. R., L. V. Morrison, and C. Y. Hohenkerk, 2016, “Measurement of the Earth’s Rotation: 720 BC to AD 2015,” Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences , 472: 20160404.
• Stuewer, R.H., 1985, “Artificial Disintegration and the Cambridge-Vienna Controversy,” in P. Achinstein and O. Hannaway (eds.), Observation, Experiment, and Hypothesis in Modern Physical Science , Cambridge, MA: MIT Press, pp. 239–307.
• Suppe, F., 1977, in F. Suppe (ed.) The Structure of Scientific Theories , Urbana: University of Illinois Press.
• Van Fraassen, B.C, 1980, The Scientific Image , Oxford: Clarendon Press.
• Ward, Z. B., 2021, “On Value-Laden Science,” Studies in History and Philosophy of Science Part A , 85: 54–62.
• Whewell, W., 1858, Novum Organon Renovatum , Book II, in William Whewell Theory of Scientific Method , R.E. Butts (ed.), Indianapolis: Hackett Publishing Company, 1989, pp. 103–249.
• Woodward, J. F., 2010, “Data, Phenomena, Signal, and Noise,” Philosophy of Science , 77(5): 792–803.
• –––, 2011, “Data and Phenomena: A Restatement and Defense,” Synthese , 182(1): 165–79.
• Wylie, A., 2020, “Radiocarbon Dating in Archaeology: Triangulation and Traceability,” in S. Leonelli and N. Tempini (eds.), Data Journeys in the Sciences , Cham: Springer, pp. 285–301.
• Yap, A., 2016, “Feminist Radical Empiricism, Values, and Evidence,” Hypatia , 31(1): 58–73.

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

Copyright © 2021 by Nora Mills Boyd < nboyd @ siena . edu > James Bogen

• Accessibility

Support SEP

Mirror sites.

View this site from another server:

• Info about mirror sites

The Stanford Encyclopedia of Philosophy is copyright © 2023 by The Metaphysics Research Lab , Department of Philosophy, Stanford University

Library of Congress Catalog Data: ISSN 1095-5054

• University of Memphis Libraries
• Research Guides

Empirical Research: Defining, Identifying, & Finding

Defining empirical research, what is empirical research, quantitative or qualitative.

• Introduction
• Database Tools
• Search Terms
• Image Descriptions

Calfee & Chambliss (2005)  (UofM login required) describe empirical research as a "systematic approach for answering certain types of questions."  Those questions are answered "[t]hrough the collection of evidence under carefully defined and replicable conditions" (p. 43). 

The evidence collected during empirical research is often referred to as "data." 

Characteristics of Empirical Research

Emerald Publishing's guide to conducting empirical research identifies a number of common elements to empirical research: 

• A  research question , which will determine research objectives.
• A particular and planned  design  for the research, which will depend on the question and which will find ways of answering it with appropriate use of resources.
• The gathering of  primary data , which is then analysed.
• A particular  methodology  for collecting and analysing the data, such as an experiment or survey.
• The limitation of the data to a particular group, area or time scale, known as a sample [emphasis added]: for example, a specific number of employees of a particular company type, or all users of a library over a given time scale. The sample should be somehow representative of a wider population.
• The ability to  recreate  the study and test the results. This is known as  reliability .
• The ability to  generalize  from the findings to a larger sample and to other situations.

If you see these elements in a research article, you can feel confident that you have found empirical research. Emerald's guide goes into more detail on each element. 

Empirical research methodologies can be described as quantitative, qualitative, or a mix of both (usually called mixed-methods).

Ruane (2016)  (UofM login required) gets at the basic differences in approach between quantitative and qualitative research:

• Quantitative research  -- an approach to documenting reality that relies heavily on numbers both for the measurement of variables and for data analysis (p. 33).
• Qualitative research  -- an approach to documenting reality that relies on words and images as the primary data source (p. 33).

Both quantitative and qualitative methods are empirical . If you can recognize that a research study is quantitative or qualitative study, then you have also recognized that it is empirical study. 

Below are information on the characteristics of quantitative and qualitative research. This video from Scribbr also offers a good overall introduction to the two approaches to research methodology: 

Characteristics of Quantitative Research 

Researchers test hypotheses, or theories, based in assumptions about causality, i.e. we expect variable X to cause variable Y. Variables have to be controlled as much as possible to ensure validity. The results explain the relationship between the variables. Measures are based in pre-defined instruments.

Examples: experimental or quasi-experimental design, pretest & post-test, survey or questionnaire with closed-ended questions. Studies that identify factors that influence an outcomes, the utility of an intervention, or understanding predictors of outcomes. 

Characteristics of Qualitative Research

Researchers explore “meaning individuals or groups ascribe to social or human problems (Creswell & Creswell, 2018, p3).” Questions and procedures emerge rather than being prescribed. Complexity, nuance, and individual meaning are valued. Research is both inductive and deductive. Data sources are multiple and varied, i.e. interviews, observations, documents, photographs, etc. The researcher is a key instrument and must be reflective of their background, culture, and experiences as influential of the research.

Examples: open question interviews and surveys, focus groups, case studies, grounded theory, ethnography, discourse analysis, narrative, phenomenology, participatory action research.

Calfee, R. C. & Chambliss, M. (2005). The design of empirical research. In J. Flood, D. Lapp, J. R. Squire, & J. Jensen (Eds.),  Methods of research on teaching the English language arts: The methodology chapters from the handbook of research on teaching the English language arts (pp. 43-78). Routledge.  http://ezproxy.memphis.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=125955&site=eds-live&scope=site .

Creswell, J. W., & Creswell, J. D. (2018).  Research design: Qualitative, quantitative, and mixed methods approaches  (5th ed.). Thousand Oaks: Sage.

How to... conduct empirical research . (n.d.). Emerald Publishing.  https://www.emeraldgrouppublishing.com/how-to/research-methods/conduct-empirical-research .

Scribbr. (2019). Quantitative vs. qualitative: The differences explained  [video]. YouTube.  https://www.youtube.com/watch?v=a-XtVF7Bofg .

Ruane, J. M. (2016).  Introducing social research methods : Essentials for getting the edge . Wiley-Blackwell.  http://ezproxy.memphis.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=1107215&site=eds-live&scope=site .  

• << Previous: Home
• Next: Identifying Empirical Research >>
• Last Updated: Apr 2, 2024 11:25 AM
• URL: https://libguides.memphis.edu/empirical-research

• Ask a Librarian

Research: Overview & Approaches

• Getting Started with Undergraduate Research
• Planning & Getting Started
• Building Your Knowledge Base
• Locating Sources
• Reading Scholarly Articles
• Creating a Literature Review
• Productivity & Organizing Research
• Scholarly and Professional Relationships

Introduction to Empirical Research

Databases for finding empirical research, guided search, google scholar, examples of empirical research, sources and further reading.

• Interpretive Research
• Action-Based Research
• Creative & Experimental Approaches

Your Librarian

• Introductory Video This video covers what empirical research is, what kinds of questions and methods empirical researchers use, and some tips for finding empirical research articles in your discipline.

• Guided Search: Finding Empirical Research Articles This is a hands-on tutorial that will allow you to use your own search terms to find resources.

• Study on radiation transfer in human skin for cosmetics
• Long-Term Mobile Phone Use and the Risk of Vestibular Schwannoma: A Danish Nationwide Cohort Study
• Emissions Impacts and Benefits of Plug-In Hybrid Electric Vehicles and Vehicle-to-Grid Services
• Review of design considerations and technological challenges for successful development and deployment of plug-in hybrid electric vehicles
• Endocrine disrupters and human health: could oestrogenic chemicals in body care cosmetics adversely affect breast cancer incidence in women?

• << Previous: Scholarly and Professional Relationships
• Next: Interpretive Research >>
• Last Updated: Aug 13, 2024 12:18 PM
• URL: https://guides.lib.purdue.edu/research_approaches

Canvas | University | Ask a Librarian

• Library Homepage
• Arrendale Library

Empirical & Non-Empirical Research

• Empirical Research

Introduction: What is Empirical Research?

Quantitative methods, qualitative methods.

• Quantitative vs. Qualitative
• Reference Works for Social Sciences Research
• What is Non-Empirical Research?
• Contact Us!

 Call us at 706-776-0111

  Chat with a Librarian

  Send Us Email

  Library Hours

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

Key characteristics of empirical research include:

• Specific research questions to be answered;
• Definitions of the population, behavior, or phenomena being studied;
• Description of the methodology or research design used to study this population or phenomena, including selection criteria, controls, and testing instruments (such as surveys);
• Two basic research processes or methods in empirical research: quantitative methods and qualitative methods (see the rest of the guide for more about these methods).

(based on the original from the Connelly LIbrary of LaSalle University)

Empirical Research: Qualitative vs. Quantitative

Learn about common types of journal articles that use APA Style, including empirical studies; meta-analyses; literature reviews; and replication, theoretical, and methodological articles.

Academic Writer

© 2024 American Psychological Association.

• More about Academic Writer ...

Quantitative Research

A quantitative research project is characterized by having a population about which the researcher wants to draw conclusions, but it is not possible to collect data on the entire population.

• For an observational study, it is necessary to select a proper, statistical random sample and to use methods of statistical inference to draw conclusions about the population. 
• For an experimental study, it is necessary to have a random assignment of subjects to experimental and control groups in order to use methods of statistical inference.

Statistical methods are used in all three stages of a quantitative research project.

For observational studies, the data are collected using statistical sampling theory. Then, the sample data are analyzed using descriptive statistical analysis. Finally, generalizations are made from the sample data to the entire population using statistical inference.

For experimental studies, the subjects are allocated to experimental and control group using randomizing methods. Then, the experimental data are analyzed using descriptive statistical analysis. Finally, just as for observational data, generalizations are made to a larger population.

Iversen, G. (2004). Quantitative research . In M. Lewis-Beck, A. Bryman, & T. Liao (Eds.), Encyclopedia of social science research methods . (pp. 897-898). Thousand Oaks, CA: SAGE Publications, Inc.

Qualitative Research

What makes a work deserving of the label qualitative research is the demonstrable effort to produce richly and relevantly detailed descriptions and particularized interpretations of people and the social, linguistic, material, and other practices and events that shape and are shaped by them.

Qualitative research typically includes, but is not limited to, discerning the perspectives of these people, or what is often referred to as the actor’s point of view. Although both philosophically and methodologically a highly diverse entity, qualitative research is marked by certain defining imperatives that include its case (as opposed to its variable) orientation, sensitivity to cultural and historical context, and reflexivity. 

In its many guises, qualitative research is a form of empirical inquiry that typically entails some form of purposive sampling for information-rich cases; in-depth interviews and open-ended interviews, lengthy participant/field observations, and/or document or artifact study; and techniques for analysis and interpretation of data that move beyond the data generated and their surface appearances. 

Sandelowski, M. (2004).  Qualitative research . In M. Lewis-Beck, A. Bryman, & T. Liao (Eds.),  Encyclopedia of social science research methods . (pp. 893-894). Thousand Oaks, CA: SAGE Publications, Inc.

• Next: Quantitative vs. Qualitative >>
• Last Updated: Jul 24, 2024 12:04 PM
• URL: https://library.piedmont.edu/empirical-research
• Ebooks & Online Video
• New Materials
• Renew Checkouts
• Faculty Resources
• Library Friends
• Library Services
• Our Mission
• Library History
• Ask a Librarian!
• Making Citations
• Working Online

Arrendale Library Piedmont University 706-776-0111

Empirical Research: What is Empirical Research?

• What is Empirical Research?
• Finding Empirical Research in Library Databases
• Designing Empirical Research

Introduction

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 (Introduction – Method – Results – and – Discussion), 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
• 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

Empirical research  is published in books and in  scholarly, peer-reviewed journals .

Make sure to select the  peer-review box  within each database!

• Next: Finding Empirical Research in Library Databases >>
• Last Updated: Nov 21, 2022 8:55 AM
• URL: https://libguides.lahc.edu/empirical

Identify Empirical Research Articles

• What is empirical research?
• Finding empirical research in library databases
• Research design
• Need additional help?

Getting started

According to the APA , empirical research is defined as the following: "Study based on facts, systematic observation, or experiment, rather than theory or general philosophical principle." Empirical research articles are generally located in scholarly, peer-reviewed journals and often follow a specific layout known as IMRaD: 1) Introduction - This provides a theoretical framework and might discuss previous studies related to the topic at hand. 2) Methodology - This describes the analytical tools used, research process, and the populations included. 3) Results - Sometimes this is referred to as findings, and it typically includes statistical data.  4) Discussion - This can also be known as the conclusion to the study, this usually describes what was learned and how the results can impact future practices.

In addition to IMRaD, it's important to see a conclusion and references that can back up the author's claims.

Characteristics to look for

In addition to the IMRaD format mentioned above, empirical research articles contain several key characteristics for identification purposes:

• The length of empirical research is often substantial, usually eight to thirty pages long.
• You should see data of some kind, this includes graphs, charts, or some kind of statistical analysis.
• There is always a bibliography found at the end of the article.

Publications

Empirical research articles can be found in scholarly or academic journals. These types of journals are often referred to as "peer-reviewed" publications; this means qualified members of an academic discipline review and evaluate an academic paper's suitability in order to be published. 

The CRAAP Checklist should be utilized to help you examine the currency, relevancy, authority, accuracy, and purpose of an information resource. This checklist was developed by California State University's Meriam Library . 

This page has been adapted from the Sociology Research Guide: Identify Empirical Articles at Cal State Fullerton Pollak Library.

• << Previous: Home
• Next: Finding empirical research in library databases >>
• Last Updated: Feb 22, 2024 10:12 AM
• URL: https://paloaltou.libguides.com/empiricalresearch

Penn State University Libraries

Empirical research in the social sciences and education.

• What is Empirical Research and How to Read It
• Finding Empirical Research in Library Databases
• Designing Empirical Research
• Ethics, Cultural Responsiveness, and Anti-Racism in Research
• Citing, Writing, and Presenting Your Work

Contact the Librarian at your campus for more help!

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:

• CRAAP Checklist A frequently-used checklist that helps you examine the currency, relevance, authority, accuracy, and purpose of an information source.
• 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
• Credo Tutorial: Evaluating Information
• Credo Video: Evaluating Statistics (4 min.)
• Credo Tutorial: Evaluating for Diverse Points of View
• Next: Finding Empirical Research in Library Databases >>
• Last Updated: Aug 13, 2024 3:16 PM
• URL: https://guides.libraries.psu.edu/emp

• Form Builder
• Survey Maker
• AI Form Generator
• AI Survey Tool
• AI Quiz Maker
• Store Builder
• WordPress Plugin

HubSpot CRM

Google Sheets

Google Analytics

Microsoft Excel

• Popular Forms
• Job Application Form Template
• Rental Application Form Template
• Hotel Accommodation Form Template
• Online Registration Form Template
• Employment Application Form Template
• Application Forms
• Booking Forms
• Consent Forms
• Contact Forms
• Donation Forms
• Customer Satisfaction Surveys
• Employee Satisfaction Surveys
• Evaluation Surveys
• Feedback Surveys
• Market Research Surveys
• Personality Quiz Template
• Geography Quiz Template
• Math Quiz Template
• Science Quiz Template
• Vocabulary Quiz Template

Try without registration Quick Start

Read engaging stories, how-to guides, learn about forms.app features.

Inspirational ready-to-use templates for getting started fast and powerful.

Spot-on guides on how to use forms.app and make the most out of it.

See the technical measures we take and learn how we keep your data safe and secure.

• Integrations
• Help Center
• Sign In Sign Up Free
• What is empirical research: Methods, types & examples

Defne Çobanoğlu

Having opinions on matters based on observation is okay sometimes. Same as having theories on the subject you want to solve. However, some theories need to be tested. Just like Robert Oppenheimer says, “Theory will take you only so far .” 

In that case, when you have your research question ready and you want to make sure it is correct, the next step would be experimentation. Because only then you can test your ideas and collect tangible information. Now, let us start with the empirical research definition:

• What is empirical research?

Empirical research is a research type where the aim of the study is based on finding concrete and provable evidence . The researcher using this method to draw conclusions can use both quantitative and qualitative methods. Different than theoretical research, empirical research uses scientific experimentation and investigation. 

Using experimentation makes sense when you need to have tangible evidence to act on whatever you are planning to do. As the researcher, you can be a marketer who is planning on creating a new ad for the target audience, or you can be an educator who wants the best for the students. No matter how big or small, data gathered from the real world using this research helps break down the question at hand. 

• When to use empirical research?

Empirical research methods are used when the researcher needs to gather data analysis on direct, observable, and measurable data. Research findings are a great way to make grounded ideas. Here are some situations when one may need to do empirical research:

1. When quantitative or qualitative data is needed

There are times when a researcher, marketer, or producer needs to gather data on specific research questions to make an informed decision. And the concrete data gathered in the research process gives a good starting point.

2. When you need to test a hypothesis

When you have a hypothesis on a subject, you can test the hypothesis through observation or experiment. Making a planned study is a great way to collect information and test whether or not your hypothesis is correct.

3. When you want to establish causality

Experimental research is a good way to explore whether or not there is any correlation between two variables. Researchers usually establish causality by changing a variable and observing if the independent variable changes accordingly.

• Types of empirical research

The aim of empirical research is to collect information about a subject from the people by doing experimentation and other data collection methods. However, the methods and data collected are divided into two groups: one collects numerical data, and the other one collects opinion-like data. Let us see the difference between these two types:

Quantitative research

Quantitative research methods are used to collect data in a numerical way. Therefore, the results gathered by these methods will be numbers, statistics, charts, etc. The results can be used to quantify behaviors, opinions, and other variables. Quantitative research methods are surveys, questionnaires, and experimental research.

Qualitiative research

Qualitative research methods are not used to collect numerical answers, instead, they are used to collect the participants’ reasons, opinions, and other meaningful aspects. Qualitative research methods include case studies, observations, interviews, focus groups, and text analysis.

• 5 steps to conduct empirical research

Necessary steps for empirical research

When you want to collect direct and concrete data on a subject, empirical research is a great way to go. And, just like every other project and research, it is best to have a clear structure in mind. This is even more important in studies that may take a long time, such as experiments that take years. Let us look at a clear plan on how to do empirical research:

1. Define the research question

The very first step of every study is to have the question you will explore ready. Because you do not want to change your mind in the middle of the study after investing and spending time on the experimentation.

2. Go through relevant literature

This is the step where you sit down and do a desk research where you gather relevant data and see if other researchers have tried to explore similar research questions. If so, you can see how well they were able to answer the question or what kind of difficulties they faced during the research process.

3. Decide on the methodology

Once you are done going through the relevant literature, you can decide on which method or methods you can use. The appropriate methods are observation, experimentation, surveys, interviews, focus groups, etc.

4. Do data analysis

When you get to this step, it means you have successfully gathered enough data to make a data analysis. Now, all you need to do is look at the data you collected and make an informed analysis.

5. Conclusion

This is the last step, where you are finished with the experimentation and data analysis process. Now, it is time to decide what to do with this information. You can publish a paper and make informed decisions about whatever your goal is.

• Empirical research methodologies

Some essential methodologies to conduct empirical research

The aim of this type of research is to explore brand-new evidence and facts. Therefore, the methods should be primary and gathered in real life, directly from the people. There is more than one method for this goal, and it is up to the researcher to use which one(s). Let us see the methods of empirical research: 

• Observation

The method of observation is a great way to collect information on people without the effect of interference. The researcher can choose the appropriate area, time, or situation and observe the people and their interactions with one another. The researcher can be just an outside observer or can be a participant as an observer or a full participant.

• Experimentation

The experimentation process can be done in the real world by intervening in some elements to unify the environment for all participants. This method can also be done in a laboratory environment. The experimentation process is good for being able to change the variables according to the aim of the study.

The case study method is done by making an in-depth analysis of already existing cases. When the parameters and variables are similar to the research question at hand, it is wise to go through what was researched before.

• Focus groups

The case study method is done by using a group of individuals or multiple groups and using their opinions, characteristics, and responses. The scientists gather the data from this group and generalize it to the whole population.

Surveys are an effective way to gather data directly from people. It is a systematic approach to collecting information. If it is done in an online setting as an online survey , it would be even easier to reach out to people and ask their opinions in open-ended or close-ended questions.

Interviews are similar to surveys as you are using questions to collect information and opinions of the people. Unlike a survey, this process is done face-to-face, as a phone call, or as a video call.

• Advantages of empirical research

Empirical research is effective for many reasons, and helps researchers from numerous fields. Here are some advantages of empirical research to have in mind for your next research:

• Empirical research improves the internal validity of the study.
• Empirical evidence gathered from the study is used to authenticate the research question.
• Collecting provable evidence is important for the success of the study.
• The researcher is able to make informed decisions based on the data collected using empirical research.
• Disadvantages of empirical research

After learning about the positive aspects of empirical research, it is time to mention the negative aspects. Because this type may not be suitable for everyone and the researcher should be mindful of the disadvantages of empirical research. Here are the disadvantages of empirical research:

• As it is similar to other research types, a case study where experimentation is included will be time-consuming no matter what. It has more steps and variables than concluding a secondary research.
• There are a lot of variables that need to be controlled and considered. Therefore, it may be a challenging task to be mindful of all the details.
• Doing evidence-based research can be expensive if you need to complete it on a large scale.
• When you are conducting an experiment, you may need some waivers and permissions.
• Frequently asked questions about empirical research

Empirical research is one of the many research types, and there may be some questions in mind about its similarities and differences to other research types.

Is empirical research qualitative or quantitative?

The data collected by empirical research can be qualitative, quantitative, or a mix of both. It is up to the aim of researcher to what kind of data is needed and searched for.

Is empirical research the same as quantitative research?

As quantitative research heavily relies on data collection methods of observation and experimentation, it is, in nature, an empirical study. Some professors may even use the terms interchangeably. However, that does not mean that empirical research is only a quantitative one.

What is the difference between theoretical and empirical research?

Empirical studies are based on data collection to prove theories or answer questions, and it is done by using methods such as observation and experimentation. Therefore, empirical research relies on finding evidence that backs up theories. On the other hand, theoretical research relies on theorizing on empirical research data and trying to make connections and correlations.

What is the difference between conceptual and empirical research?

Conceptual research is about thoughts and ideas and does not involve any kind of experimentation. Empirical research, on the other hand, works with provable data and hard evidence.

What is the difference between empirical vs applied research?

Some scientists may use these two terms interchangeably however, there is a difference between them. Applied research involves applying theories to solve real-life problems. On the other hand, empirical research involves the obtaining and analysis of data to test hypotheses and theories.

• Final words

Empirical research is a good means when the goal of your study is to find concrete data to go with. You may need to do empirical research when you need to test a theory, establish causality, or need qualitative/quantitative data. For example, you are a scientist and want to know if certain colors have an effect on people’s moods, or you are a marketer and want to test your theory on ad places on websites. 

In both scenarios, you can collect information by using empirical research methods and make informed decisions afterward. These are just the two of empirical research examples. This research type can be applied to many areas of work life and social sciences. Lastly, for all your research needs, you can visit forms.app to use its many useful features and over 1000 form and survey templates!

Defne is a content writer at forms.app. She is also a translator specializing in literary translation. Defne loves reading, writing, and translating professionally and as a hobby. Her expertise lies in survey research, research methodologies, content writing, and translation.

• Form Features
• Data Collection

Table of Contents

Related posts.

A full guide to the customer lifetime value (LTV)

Elif N. Çifçi

45+ Best event satisfaction survey questions to ask

Ayşegül Nacu

8 awesome tips: How to get more form submissions

• MAY 16, 2024

What Is Empirical Research? Definition, Types & Samples in 2024

by Imed Bouchrika, Phd

Co-Founder and Chief Data Scientist

How was the world formed? Are there parallel universes? Why does time move forward but never in reverse? These are longstanding questions that have yet to receive definitive answers up to now.

In research, these are called empirical questions, which ask about how the world is, how the world works, etc. Such questions are addressed by a corresponding type of study—called empirical research or the empirical method—which is concerned with actual events and phenomena.

What is an empirical study? Research is empirical if it seeks to find a general story or explanation, one that applies to various cases and across time. The empirical approach functions to create new knowledge about the way the world actually works. This article discusses the empirical research definition, concepts, types, processes, and other important aspects of this method. It also tackles the importance of identifying evidence in research .

I. What is Empirical Research?

A. definitions.

What is empirical evidence? Empirical research is defined as any study whose conclusions are exclusively derived from concrete, verifiable evidence. The term empirical basically means that it is guided by scientific experimentation and/or evidence. Likewise, a study is empirical when it uses real-world evidence in investigating its assertions.

This research type is founded on the view that direct observation of phenomena is a proper way to measure reality and generate truth about the world (Bhattacharya, 2008). And by its name, it is a methodology in research that observes the rules of empiricism and uses quantitative and qualitative methods for gathering evidence.

For instance, a study is being conducted to determine if working from home helps in reducing stress from highly-demanding jobs. An experiment is conducted using two groups of employees, one working at their homes, the other working at the office. Each group was observed. The outcomes derived from this research will provide empirical evidence if working from home does help reduce stress or not. This also applies to entrepreneurs when they use a small business idea generator instead of manual procedures.

It was the ancient Greek medical practitioners who originated the term empirical ( empeirikos which means “experienced") when they began to deviate from the long-observed dogmatic principles to start depending on observed phenomena. Later on, empiricism pertained to a theory of knowledge in philosophy, which follows the belief that knowledge comes from evidence and experience derived particularly using the senses.

What ancient philosophers considered empirical research pertained to the reliance on observable data to design and test theories and reach conclusions. As such, empirical research is used to produce knowledge that is based on experience. At present, the word “empirical" pertains to the gathering of data using evidence that is derived through experience or observation or by using calibrated scientific tools.

Most of today’s outstanding empirical research outputs are published in prestigious journals. These scientific publications are considered high-impact journals because they publish research articles that tend to be the most cited in their fields.

II. Types and Methodologies of Empirical Research

Empirical research is done using either qualitative or quantitative methods.

Qualitative research Qualitative research methods are utilized for gathering non-numerical data. It is used to determine the underlying reasons, views, or meanings from study participants or subjects. Under the qualitative research design, empirical studies had evolved to test the conventional concepts of evidence and truth while still observing the fundamental principles of recognizing the subjects beings studied as empirical (Powner, 2015).

This method can be semi-structured or unstructured. Results from this research type are more descriptive than predictive. It allows the researcher to write a conclusion to support the hypothesis or theory being examined.

Due to realities like time and resources, the sample size of qualitative research is typically small. It is designed to offer in-depth information or more insight regarding the problem. Some of the most popular forms of methods are interviews, experiments, and focus groups.

Quantitative research   Quantitative research methods are used for gathering information via numerical data. This type is used to measure behavior, personal views, preferences, and other variables. Quantitative studies are in a more structured format, while the variables used are predetermined.

Data gathered from quantitative studies is analyzed to address the empirical questions. Some of the commonly used quantitative methods are polls, surveys, and longitudinal or cohort studies.

There are situations when using a single research method is not enough to adequately answer the questions being studied. In such cases, a combination of both qualitative and quantitative methods is necessary. Also, papers can also make use of both primary and secondary research methods

III. Qualitative Empirical Research Methods

Some research question examples need to be gathered and analyzed qualitatively or quantitatively, depending on the nature of the study. These not only supply answers to empirical questions but also outline one’s scope of work . Here are the general types of qualitative research methods.

Observational Method

This involves observing and gathering data from study subjects. As a qualitative approach, observation is quite personal and time-intensive. It is often used in ethnographic studies to obtain empirical evidence.

The observational method is a part of the ethnographic research design, e.g., archival research, survey, etc. However, while it is commonly used for qualitative purposes, observation is also utilized for quantitative research, such as when observing measurable variables like weight, age, scale, etc.

One remarkable observational research was conducted by Abbott et al. (2016), a team of physicists from the Advanced Laser Interferometer Gravitational-Wave Observatory who examined the very first direct observation of gravitational waves. According to Google Scholar’s (2019) Metrics ranking, this study is among the most highly cited articles from the world’s most influential journals (Crew, 2019).

This method is exclusively qualitative and is one of the most widely used (Jamshed, 2014). Its popularity is mainly due to its ability to allow researchers to obtain precise, relevant information if the correct questions are asked.

This method is a form of a conversational approach, where in-depth data can be obtained. Interviews are commonly used in the social sciences and humanities, such as for interviewing resource persons.

This method is used to identify extensive information through an in-depth analysis of existing cases. It is typically used to obtain empirical evidence for investigating problems or business studies.

When conducting case studies, the researcher must carefully perform the empirical analysis, ensuring the variables and parameters in the current case are similar to the case being examined. From the findings of a case study, conclusions can be deduced about the topic being investigated.

Case studies are commonly used in studying the experience of organizations, groups of persons, geographic locations, etc.

Textual Analysis

This primarily involves the process of describing, interpreting, and understanding textual content. It typically seeks to connect the text to a broader artistic, cultural, political, or social context (Fairclough, 2003).

A relatively new research method, textual analysis is often used nowadays to elaborate on the trends and patterns of media content, especially social media. Data obtained from this approach are primarily used to determine customer buying habits and preferences for product development, and designing marketing campaigns.

Focus Groups

A focus group is a thoroughly planned discussion guided by a moderator and conducted to derive opinions on a designated topic. Essentially a group interview or collective conversation, this method offers a notably meaningful approach to think through particular issues or concerns (Kamberelis & Dimitriadis, 2011).

This research method is used when a researcher wants to know the answers to “how," “what," and “why" questions. Nowadays, focus groups are among the most widely used methods by consumer product producers for designing and/or improving products that people prefer.

IV. Quantitative Empirical Research Methods

Quantitative methods primarily help researchers to better analyze the gathered evidence. Here are the most common types of quantitative research techniques:

A research hypothesis is commonly tested using an experiment, which involves the creation of a controlled environment where the variables are maneuvered. Aside from determining the cause and effect, this method helps in knowing testing outcomes, such as when altering or removing variables.

Traditionally, experimental, laboratory-based research is used to advance knowledge in the physical and life sciences, including psychology. In recent decades, more and more social scientists are also adopting lab experiments (Falk & Heckman, 2009).

Survey research is designed to generate statistical data about a target audience (Fowler, 2014). Surveys can involve large, medium, or small populations and can either be a one-time event or a continuing process

Governments across the world are among the heavy users of continuing surveys, such as for census of populations or labor force surveys. This is a quantitative method that uses predetermined sets of closed questions that are easy to answer, thus enabling the gathering and analysis of large data sets.

In the past, surveys used to be expensive and time-consuming. But with the advancement in technology, new survey tools like social media and emails have made this research method easier and cheaper.

Causal-Comparative research

This method leverages the strength of comparison. It is primarily utilized to determine the cause and effect relationship among variables (Schenker & Rumrill, 2004).

For instance, a causal-comparative study measured the productivity of employees in an organization that allows remote work setup and compared that to the staff of another organization that does not offer work from home arrangements.

Cross-sectional research

While the observation method considers study subjects at a given point in time, cross-sectional research focuses on the similarity in all variables except the one being studied. 

This type does not allow for the determination of cause-effect relationships since subjects are now observed continuously. A cross-sectional study is often followed by longitudinal research to determine the precise causes. It is used mainly by pharmaceutical firms and retailers.

Longitudinal study

A longitudinal method of research is used for understanding the traits or behavior of a subject under observation after repeatedly testing the subject over a certain period of time. Data collected using this method can be qualitative or quantitative in nature. 

A commonly-used form of longitudinal research is the cohort study. For instance, in 1951, a cohort study called the British Doctors Study (Doll et al., 2004) was initiated, which compared smokers and non-smokers in the UK. The study continued through 2001. As early as 1956, the study gave undeniable proof of the direct link between smoking and the incidence of lung cancer.

Correlational research

This method is used to determine the relationships and prevalence among variables (Curtis et al., 2016). It commonly employs regression as the statistical treatment for predicting the study’s outcomes, which can only be a negative, neutral, or positive correlation.

A classic example of empirical research with correlational research is when studying if high education helps in obtaining better-paying jobs. If outcomes indicate that higher education does allow individuals to have high-salaried jobs, then it follows that people with less education tend to have lower-paying jobs.

V. Steps for Conducting Empirical Research

Since empirical research is based on observation and capturing experiences, it is important to plan the steps to conduct the experiment and how to analyze it. This will enable the researcher to resolve problems or obstacles, which can occur during the experiment.

Step #1: Establishing the research objective

In this initial step, the researcher must be clear about what he or she precisely wants to do in the study. He or she should likewise frame the problem statement, plans of action, and determine any potential issues with the available resources, schedule, etc. for the research.

Most importantly, the researcher must be able to ascertain whether the study will be more beneficial than the cost it will incur.

Step #2: Reviewing relevant literature and supporting theories

The researcher must determine relevant theories or models to his or her research problem. If there are any such theories or models, they must understand how it can help in supporting the study outcomes.

Relevant literature must also be consulted. The researcher must be able to identify previous studies that examined similar problems or subjects, as well as determine the issues encountered.

Step #3: Framing the hypothesis and measurement

The researcher must frame an initial hypothesis or educated guess that could be the likely outcome. Variables must be established, along with the research context.

Units of measurements should also be defined, including the allowable margin of errors. The researcher must determine if the selected measures will be accepted by other scholars.

Step #4: Defining the research design, methodology, and data collection techniques

Before proceeding with the study, the researcher must establish an appropriate approach for the research. He or she must organize experiments to gather data that will allow him or her to frame the hypothesis.

The researcher should also decide whether he or she will use a nonexperimental or experimental technique to perform the study. Likewise, the  type of research design will depend on the type of study being conducted.

Finally, the researcher must determine the parameters that will influence the validity of the research design. Data gathering must be performed by selecting suitable samples based on the research question. After gathering the empirical data, the analysis follows.

Step #5: Conducting data analysis and framing the results

Data analysis is done either quantitatively or qualitatively. Depending on the nature of the study, the researcher must determine which method of data analysis is the appropriate one, or whether a combination of the two is suitable.

The outcomes of this step determine if the hypothesis is supported or rejected. This is why data analysis is considered as one of the most crucial steps in any research undertaking.

Step #6: Making conclusions

A report must be prepared in that it presents the findings and the entire research proceeding. If the researcher intends to disseminate his or her findings to a wider audience, the report will be converted into an article for publication. Aside from including the typical parts from the introduction and literature view, up to the methods, analysis, and conclusions, the researcher should also make recommendations for further research on his or her topic.

To ensure the originality and credibility of the report or research, it is essential to employ a plagiarism checker. By using a reliable plagiarism checker, the researcher can verify the uniqueness of their work and avoid any unintentional instances of plagiarism. This step helps maintain the integrity of the research and ensures that the recommendations for further research are based on the researcher’s own original insights. Incorporating a plagiarism checker into the writing process provides an additional layer of assurance and professionalism, enhancing the impact of the report or article in the academic community. Educators can also check the originality of their students’ research by utilizing a free plagiarism checker for teachers .

VI. Empirical Research Cycle

The empirical research cycle is composed of five phases, with each one considered as important as the next phase (de Groot, 1969). This rigorous and systematic method can consistently capture the process of framing hypotheses on how certain subjects behave or function and then testing them versus empirical data. It is considered to typify the deductive approach to science.

These are the five phases of the empirical research cycle:

1. Observation

During this initial phase, an idea is triggered for presenting a hypothesis. It involves the use of observation to gather empirical data. For example :

Consumers tend to consult first their smartphones before buying something in-store .

2. Induction

Inductive reasoning is then conducted to frame a general conclusion from the data gathered through observation. For example:

As mentioned earlier, most consumers tend to consult first their smartphones before buying something in-store .

A researcher may pose the question, “Does the tendency to use a smartphone indicate that today’s consumers need to be informed before making purchasing decisions?" The researcher can assume that is the case. Nonetheless, since it is still just a supposition, an experiment must be conducted to support or reject this hypothesis.

The researcher decided to conduct an online survey to inquire about the buying habits of a certain sample population of buyers at brick-and-mortar stores. This is to determine whether people always look at their smartphones first before making a purchase.

3. Deduction

This phase enables the researcher to figure out a conclusion out of the experiment. This must be based on rationality and logic in order to arrive at particular, unbiased outcomes. For example:

In the experiment, if a shopper consults first his or her smartphone before buying in-store, then it can be concluded that the shopper needs information to help him or her make informed buying decisions .

This phase involves the researcher going back to the empirical research steps to test the hypothesis. There is now the need to analyze and validate the data using appropriate statistical methods.

If the researcher confirms that in-store shoppers do consult their smartphones for product information before making a purchase, the researcher has found support for the hypothesis. However, it should be noted that this is just support of the hypothesis, not proof of a reality.

5. Evaluation

This phase is often neglected by many but is actually a crucial step to help keep expanding knowledge. During this stage, the researcher presents the gathered data, the supporting contention/s, and conclusions.

The researcher likewise puts forth the limitations of the study and his hypothesis. In addition, the researcher makes recommendations for further studies on the same topic with expanded variables.

VII. Advantages and Disadvantages of Empirical Research

Since the time of the ancient Greeks, empirical research had been providing the world with numerous benefits. The following are a few of them:

• Empirical research is used to validate previous research findings and frameworks.
• It assumes a critical role in enhancing internal validity.
• The degree of control is high, which enables the researcher to manage numerous variables.
• It allows a researcher to comprehend the progressive changes that can occur, and thus enables him to modify an approach when needed.
• Being based on facts and experience makes a research project more authentic and competent.

Disadvantages

Despite the many benefits it brings, empirical research is far from perfect. The following are some of its drawbacks:

• Being evidence-based, data collection is a common problem especially when the research involves different sources and multiple methods.
• It can be time-consuming, especially for longitudinal research.
• Requesting permission to perform certain methods can be difficult, especially when a study involves human subjects.
• Conducting research in multiple locations can be very expensive.
• The propensity of even seasoned researchers to incorrectly interpret the statistical significance. For instance, Amrhein et al. (2019) made an analysis of 791 articles from five journals and found that half incorrectly interpreted that non-significance indicates zero effect.

VIII. Samples of Empirical Research

There are many types of empirical research. And, they can take many formsfrom basic research to action research like community project efforts. Here are some notable empirical research examples:

Professional Research

• Research on Information Technology
• Research on Infectious Diseases
• Research on Occupational Health Psychology
• Research on Infection Control
• Research on Cancer
• Research on Mathematical Science
• Research on Environmental Science
• Research on Genetics
• Research on Climate Change
• Research on Economics

Student Research

• Thesis for B.S. in Computer Science & Engineering  
• Thesis for B.S. in Geography
• Thesis for B.S. in Architecture
• Thesis for Master of Science in Electrical Engineering & Computer Science
• Thesis for Master of Science in Artificial Intelligence
• Thesis for Master of Science in Food Science and Nutrition
• Dissertation for Ph.D. in Marketing  
• Dissertation for Ph.D. in Social Work
• Dissertation for Ph.D. in Urban Planning

Since ancient times until today, empirical research remains one of the most useful tools in man’s collective endeavor to unlock life’s mysteries. Using meaningful experience and observable evidence, this type of research will continue helping validate myriad hypotheses, test theoretical models, and advance various fields of specialization.

With new forms of deadly diseases and other problems continuing to plague man’s existence, finding effective medical interventions and relevant solutions had never been more important. This is among the reasons why empirical research had assumed a more prominent role in today’s society.

This article was able to discuss the different empirical research methods, the steps for conducting empirical research, the empirical research cycle, and notable examples. All of these contribute to supporting the larger societal cause to help understand how the world really works and make it a better place. Furthermore, being factually accurate is a big part of the criteria of good research , and it serves as the heart of empirical research.

Key Insights

• Definition of Empirical Research: Empirical research is based on verifiable evidence derived from observation and experimentation, aiming to understand how the world works.
• Origins: The concept of empirical research dates back to ancient Greek medical practitioners who relied on observed phenomena rather than dogmatic principles.
• Types and Methods: Empirical research can be qualitative (e.g., interviews, case studies) or quantitative (e.g., surveys, experiments), depending on the nature of the data collected and the research question.
• Empirical Research Cycle: Consists of observation, induction, deduction, testing, and evaluation, forming a systematic approach to generating and testing hypotheses.
• Steps in Conducting Empirical Research: Includes establishing objectives, reviewing literature, framing hypotheses, designing methodology, collecting data, analyzing data, and making conclusions.
• Advantages: Empirical research validates previous findings, enhances internal validity, allows for high control over variables, and is fact-based, making it authentic and competent.
• Disadvantages: Data collection can be challenging and time-consuming, especially in longitudinal studies, and interpreting statistical significance can be problematic.
• Applications: Used across various fields such as IT, infectious diseases, occupational health, environmental science, and economics. It is also prevalent in student research for theses and dissertations.
• What is the primary goal of empirical research? The primary goal of empirical research is to generate knowledge about how the world works by relying on verifiable evidence obtained through observation and experimentation.
• How does empirical research differ from theoretical research? Empirical research is based on observable and measurable evidence, while theoretical research involves abstract ideas and concepts without necessarily relying on real-world data.
• What are the main types of empirical research methods? The main types of empirical research methods are qualitative (e.g., interviews, case studies, focus groups) and quantitative (e.g., surveys, experiments, cross-sectional studies).
• Why is the empirical research cycle important? The empirical research cycle is important because it provides a structured and systematic approach to generating and testing hypotheses, ensuring that the research is thorough and reliable.
• What are the steps involved in conducting empirical research? The steps involved in conducting empirical research include establishing the research objective, reviewing relevant literature, framing hypotheses, defining research design and methodology, collecting data, analyzing data, and making conclusions.
• What are the advantages of empirical research? The advantages of empirical research include validating previous findings, enhancing internal validity, allowing for high control over variables, and being based on facts and experiences, making the research authentic and competent.
• What are some common challenges in conducting empirical research? Common challenges in conducting empirical research include difficulties in data collection, time-consuming processes, obtaining permissions for certain methods, high costs, and potential misinterpretation of statistical significance.
• In which fields is empirical research commonly used? Empirical research is commonly used in fields such as information technology, infectious diseases, occupational health, environmental science, economics, and various academic disciplines for student theses and dissertations.
• Can empirical research use both qualitative and quantitative methods? Yes, empirical research can use both qualitative and quantitative methods, often combining them to provide a comprehensive understanding of the research problem.
• What role does empirical research play in modern society? Empirical research plays a crucial role in modern society by validating hypotheses, testing theoretical models, and advancing knowledge across various fields, ultimately contributing to solving complex problems and improving the quality of life.
• Abbott, B., Abbott, R., Abbott, T., Abernathy, M., & Acernese, F. (2016). Observation of Gravitational Waves from a Binary Black Hole Merger. Physical Review Letters, 116 (6), 061102. https://doi.org/10.1103/PhysRevLett.116.061102
• Akpinar, E. (2014). Consumer Information Sharing: Understanding Psychological Drivers of Social Transmission . (Unpublished Ph.D. dissertation). Erasmus University Rotterdam, Rotterdam, The Netherlands.  http://hdl.handle.net/1765/1
• Altmetric (2020). The 2019 Altmetric top 100. Altmetric .
• Amrhein, V., Greenland, S., & McShane, B. (2019). Scientists rise up against statistical significance. Nature, 567 , 305-307.  https://doi.org/10.1038/d41586-019-00857-9
• Amrhein, V., Trafimow, D., & Greenland, S. (2019). Inferential statistics as descriptive statistics: There is no replication crisis if we don’t expect replication. The American Statistician, 73 , 262-270. https://doi.org/10.1080/00031305.2018.1543137
• Arute, F., Arya, K., Babbush, R. et al. (2019). Quantum supremacy using a programmable superconducting processor. Nature, 574 , 505510. https://doi.org/10.1038/s41586-019-1666-5
• Bhattacharya, H. (2008). Empirical Research. In L. M. Given (ed.), The SAGE Encyclopedia of Qualitative Research Methods . Thousand Oaks, CA: Sage, 254-255.  https://dx.doi.org/10.4135/9781412963909.n133
• Cohn, A., Maréchal, M., Tannenbaum, D., & Zund, C. (2019). Civic honesty around the globe. Science, 365 (6448), 70-73. https://doi.org/10.1126/science.aau8712
• Corbin, J., & Strauss, A. (2015). Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory, 4th ed . Thousand Oaks, CA: Sage. ISBN 978-1-4129-9746-1
• Crew, B. (2019, August 2). Google Scholar reveals its most influential papers for 2019. Nature Index .
• Curtis, E., Comiskey, C., & Dempsey, O. (2016). Importance and use of correlational research. Nurse Researcher, 23 (6), 20-25. https://doi.org/10.7748/nr.2016.e1382
• Dashti, H., Jones, S., Wood, A., Lane, J., & van Hees, V., et al. (2019). Genome-wide association study identifies genetic loci for self-reported habitual sleep duration supported by accelerometer-derived estimates. Nature Communications, 10 (1).  https://doi.org/10.1038/s41467-019-08917-4
• de Groot, A.D. (1969). Methodology: foundations of inference and research in the behavioral sciences. In  Psychological Studies, 6 . The Hague & Paris: Mouton & Co. Google Books
• Doll, R., Peto, R., Boreham, J., & Isabelle Sutherland, I. (2004). Mortality in relation to smoking: 50 years’ observations on male British doctors. BMJ, 328  (7455), 1519-33. https://doi.org/10.1136/bmj.38142.554479.AE
• Fairclough, N. (2003). Analyzing Discourse: Textual Analysis for Social Research . Abingdon-on-Thames: Routledge. Google Books
• Falk, A., & Heckman, J. (2009). Lab experiments are a major source of knowledge in the social sciences. Science, 326 (5952), pp. 535-538. https://doi.org/10.1126/science.1168244
• Fowler, F.J. (2014). Survey Research Methods, 5th ed . Thousand Oaks, CA: Sage. WorldCat
• Gabriel, A., Manalo, M., Feliciano, R., Garcia, N., Dollete, U., & Paler J. (2018). A Candida parapsilosis inactivation-based UV-C process for calamansi (Citrus microcarpa) juice frink. LWT Food Science and Technology, 90 , 157-163. https://doi.org/10.1016/j.lwt.2017.12.020
• Gallus, S., Bosetti, C., Negri, E., Talamini, R., Montella, M., et al. (2003). Does pizza protect against cancer? International Journal of Cancer, 107 (2), pp. 283-284. https://doi.org/10.1002/ijc.11382
• Ganna, A., Verweij, K., Nivard, M., Maier, R., & Wedow, R. (2019). Large-scale GWAS reveals insights into the genetic architecture of same-sex sexual behavior. Science, 365 (6456). https://doi.org/10.1126/science.aat7693
• Gedik, H., Voss, T., & Voss, A. (2013). Money and Transmission of Bacteria. Antimicrobial Resistance and Infection Control, 2 (2).  https://doi.org/10.1186/2047-2994-2-22
• Gonzalez-Morales, M. G., Kernan, M. C., Becker, T. E., & Eisenberger, R. (2018). Defeating abusive supervision: Training supervisors to support subordinates. Journal of Occupational Health Psychology, 23  (2), 151-162. https://dx.doi.org/10.1037/ocp0000061
• Google (2020). The 2019 Google Scholar Metrics Ranking . Google Scholar
• Greenberg, D., Warrier, V., Allison, C., & Baron-Cohen, S. (2018). Testing the Empathizing-Systemising theory of sex differences and the Extreme Male Brain theory of autism in half a million people. PNAS, 115 (48), 12152-12157. https://doi.org/10.1073/pnas.1811032115
• Grullon, D. (2019). Disentangling time constant and time-dependent hidden state in time series with variational Bayesian inference . (Unpublished master’s thesis). Massachusetts Institute of Technology, Cambridge, MA.  https://hdl.handle.net/1721.1/124572
• He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 770-778. https://doi.org/10.1109/CVPR.2016.90
• Hviid, A., Hansen, J., Frisch, M., & Melbye, M. (2019). Measles, mumps, rubella vaccination, and autism: A nationwide cohort study. Annals of Internal Medicine, 170 (8), 513-520. https://doi.org/10.7326/M18-2101
• Jamshed, S. (2014). Qualitative research method-interviewing and observation. Journal of Basic and Clinical Pharmacy, 5 (4), 87-88. https://doi.org/10.4103/0976-0105.141942
• Jamshidnejad, A. (2017). Efficient Predictive Model-Based and Fuzzy Control for Green Urban Mobility . (Unpublished Ph.D. dissertation). Delft University of Technology, Delft, Netherlands.  DUT
• Kamberelis, G., & Dimitriadis, G. (2011). Focus groups: Contingent articulations of pedagogy, politics, and inquiry. In N. Denzin & Y. Lincoln (Eds.), The SAGE Handbook of Qualitative Research  (pp. 545-562). Thousand Oaks, CA: Sage. ISBN 978-1-4129-7417-2
• Knowles-Smith, A. (2017). Refugees and theatre: an exploration of the basis of self-representation . (Unpublished undergraduate thesis). University College London, London, UK. UCL
• Kulp, S.A., & Strauss, B.H. (2019). New elevation data triple estimates of global vulnerability to sea-level rise and coastal flooding. Nature Communications, 10 (4844), 1-12.  https://doi.org/10.1038/s41467-019-12808-z
• LeCun, Y., Bengio, Y. & Hinton, G. (2015). Deep learning. Nature, 521 , 436444. https://doi.org/10.1038/nature14539
• Levitt, H. M., Bamberg, M., Creswell, J. W., Frost, D. M., Josselson, R., & Suarez-Orozco, C. (2018). Journal article reporting standards for qualitative primary, qualitative meta-analytic, and mixed methods research in psychology: The APA Publications and Communications Board task force report.  American Psychologist, 73 (1), 26-46. https://doi.org/10.1037/amp0000151
• Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 3431-3440. https://doi.org/10.1109/CVPR.2015.7298965
• Martindell, N. (2014). DCDN: Distributed content delivery for the modern web . (Unpublished undergraduate thesis). University of Washington, Seattle, WA. CSE-UW
• Mora, T. (2019). Transforming Parking Garages Into Affordable Housing . (Unpublished undergraduate thesis). University of Arkansas-Fayetteville, Fayetteville, AK. UARK
• Ng, M., Fleming, T., Robinson, M., Thomson, B., & Graetz, N. (2014). Global, regional, and national prevalence of overweight and obesity in children and adults during 19802013: a systematic analysis for the Global Burden of Disease Study 2013. The Lancet, 384  (9945), 766-781. https://doi.org/10.1016/S0140-6736(14)60460-8
• Ogden, C., Carroll, M., Kit, B., & Flegal, K. (2014). Prevalence of Childhood and Adult Obesity in the United States, 2011-2012. JAMA, 311 (8), 806-14. https://doi.org/10.1001/jama.2014.732
• Powner, L. (2015). Empirical Research and Writing: A Political Science Student’s Practical Guide . Thousand Oaks, CA: Sage, 1-19.  https://dx.doi.org/10.4135/9781483395906
• Ripple, W., Wolf, C., Newsome, T., Barnard, P., & Moomaw, W. (2020). World scientists’ warning of a climate emergency. BioScience, 70 (1), 8-12. https://doi.org/10.1093/biosci/biz088
• Schenker, J., & Rumrill, P. (2004). Causal-comparative research designs. Journal of Vocational Rehabilitation, 21 (3), 117-121.
• Shereen, M., Khan, S., Kazmi, A., Bashir, N., & Siddique, R. (2020). COVID-19 infection: Origin, transmission, and characteristics of human coronaviruses. Journal of Advanced Research, 24 , 91-98.  https://doi.org/10.1016/j.jare.2020.03.005
• Sipola, C. (2017). Summarizing electricity usage with a neural network . (Unpublished master’s thesis). University of Edinburgh, Edinburgh, Scotland. Project-Archive
• Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015). Going deeper with convolutions. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 1-9. https://doi.org/10.1109/CVPR.2015.7298594
• Taylor, S. (2017). Effacing and Obscuring Autonomy: the Effects of Structural Violence on the Transition to Adulthood of Street Involved Youth . (Unpublished Ph.D. dissertation). University of Ottawa, Ottawa, Canada. UOttawa
• Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. Science, 359 (6380), 1146-1151. https://doi.org/10.1126/science.aap9559

Related Articles

How to Write a Thesis Statement for a Research Paper in 2024: Steps and Examples

How to Write a Research Paper Abstract in 2024: Guide With Examples

72 Scholarship Statistics: 2024 Data, Facts & Analysis

What Is a University Dissertation: 2024 Structure, Challenges & Writing Tips

Web-Based Research: Tips For Conducting Academic Research

How to Write Research Methodology in 2024: Overview, Tips, and Techniques

EasyChair : Tutorial of how to request an installation for Conference Management System

Levels of Evidence in Research: Examples, Hierachies & Practice in 2024

How to Write a Research Question in 2024: Types, Steps, and Examples

How to Write a Research Proposal in 2024: Structure, Examples & Common Mistakes

Needs Analysis in 2024: Definition, Importance & Implementation

Importing references from google scholar to bibtex.

How to Become a Private School Teacher in Vermont: Requirements & Certification in 2024

How to Become a Private School Teacher in Delaware: Requirements & Certification in 2024

How to Become a Private School Teacher in Florida: Requirements & Certification in 2024

How to Become a Private School Teacher in Virginia: Requirements & Certification in 2024

How to Become a Private School Teacher in New Jersey: Requirements & Certification in 2024

How to Become a Private School Teacher in Indiana: Requirements & Certification in 2024

How to Become a Private School Teacher in Ohio: Requirements & Certification in 2024

How to Become a Private School Teacher in Nevada: Requirements & Certification in 2024

How to Become a Private School Teacher in Arizona: Requirements & Certification in 2024

Recently published articles.

Top 50 US Colleges that Pay Off the Most in 2024

Most Affordable Accelerated Executive MBA Online Programs for 2024

Best Online Curriculum and Instruction Doctoral Programs in 2024

Most Affordable Online Marriage and Family Therapy Degree Programs for 2024

Best Fast Online Degree Programs for 2024

Best Psychology Schools in New Jersey – 2024 Accredited Colleges & Programs

Radiology Careers: 2024 Guide to Career Paths, Options & Salary

21 Easiest Online College Degrees and Majors for 2024

Best Online DNP Programs in 2024

5 Shortest EdD Online Degree Fast-Track Programs for 2024

What Is the Average Cost of a Master’s Degree 2024?

Most Affordable Online Construction Management Degree Programs for 2024

12 Fastest Doctor of Nursing Practice (DNP) Degree Programs for 2024

Best Nursing Schools in San Diego, CA 2024 – Accredited Nursing Degree Programs Online

Best Nursing Schools in Sacramento, CA 2024 – Accredited Nursing Degree Programs Online

Best Psychology Schools in New York – 2024 Accredited Colleges & Programs

What Can You Do With a Master’s in Medical Science Degree in 2024?

Best Nursing Schools in San Antonio, TX 2024 – Accredited Nursing Degree Programs Online

10 Best Nationally Accredited Online Colleges & Universities for 2024

Best Psychology Schools in Texas – 2024 Accredited Colleges & Programs

Best Psychology Schools in Michigan – 2024 Accredited Colleges & Programs

Newsletter & conference alerts.

Research.com uses the information to contact you about our relevant content. For more information, check out our privacy policy .

Thank you for subscribing!

Confirmation email sent. Please click the link in the email to confirm your subscription.

How to... Conduct empirical research

Share this content

Empirical research is research that is based on observation and measurement of phenomena, as directly experienced by the researcher. The data thus gathered may be compared against a theory or hypothesis, but the results are still based on real life experience. The data gathered is all primary data, although secondary data from a literature review may form the theoretical background.

On this page

What is empirical research, the research question, the theoretical framework, sampling techniques, design of the research.

• Methods of empirical research
• Techniques of data collection & analysis
• Reporting the findings of empirical research
• Further information

Typically, empirical research embodies the following elements:

• A  research question , which will determine research objectives.
• A particular and planned  design  for the research, which will depend on the question and which will find ways of answering it with appropriate use of resources.
• The gathering of  primary data , which is then analysed.
• A particular  methodology  for collecting and analysing the data, such as an experiment or survey.
• The limitation of the data to a particular group, area or time scale, known as a sample: for example, a specific number of employees of a particular company type, or all users of a library over a given time scale. The sample should be somehow representative of a wider population.
• The ability to  recreate  the study and test the results. This is known as  reliability .
• The ability to  generalise  from the findings to a larger sample and to other situations.

The starting point for your research should be your research question. This should be a formulation of the issue which is at the heart of the area which you are researching, which has the right degree of breadth and depth to make the research feasible within your resources. The following points are useful to remember when coming up with your research question, or RQ:

• your doctoral thesis;
• reading the relevant literature in journals, especially literature reviews which are good at giving an overview, and spotting interesting conceptual developments;
• looking at research priorities of funding bodies, professional institutes etc.;
• going to conferences;
• looking out for calls for papers;
• developing a dialogue with other researchers in your area.
• To narrow down your research topic, brainstorm ideas around it, possibly with your colleagues if you have decided to collaborate, noting all the questions down.
• Come up with a "general focus" question; then develop some other more specific ones.
• they are not too broad;
• they are not so narrow as to yield uninteresting results;
• will the research entailed be covered by your resources, i.e. will you have sufficient time and money;
• there is sufficient background literature on the topic;
• you can carry out appropriate field research;
• you have stated your question in the simplest possible way.

Let's look at some examples:

Bisking et al. examine whether or not gender has an influence on disciplinary action in their article  Does the sex of the leader and subordinate influence a leader's disciplinary decisions?  ( Management Decision , Volume 41 Number 10) and come up with the following series of inter-related questions:

• Given the same infraction, would a male leader impose the same disciplinary action on male and female subordinates?
• Given the same infraction, would a female leader impose the same disciplinary action on male and female subordinates?
• Given the same infraction, would a female leader impose the same disciplinary action on female subordinates as a male leader would on male subordinates?
• Given the same infraction, would a female leader impose the same disciplinary action on male subordinates as a male leader would on female subordinates?
• Given the same infraction, would a male and female leader impose the same disciplinary action on male subordinates?
• Given the same infraction, would a male and female leader impose the same disciplinary action on female subordinates?
• Do female and male leaders impose the same discipline on subordinates regardless of the type of infraction?
• Is it possible to predict how female and male leaders will impose disciplinary actions based on their respective BSRI femininity and masculinity scores?

Motion et al. examined co-branding in  Equity in Corporate Co-branding  ( European Journal of Marketing , Volume 37 Number 7/8) and came up with the following RQs:

RQ1:  What objectives underpinned the corporate brand?

RQ2:  How were brand values deployed to establish the corporate co-brand within particular discourse contexts?

RQ3:  How was the desired rearticulation promoted to shareholders?

RQ4:  What are the sources of corporate co-brand equity?

Note, the above two examples state the RQs very explicitly; sometimes the RQ is implicit:

Qun G. Jiao, Anthony J. Onwuegbuzie are library researchers who examined the question:  "What is the relationship between library anxiety and social interdependence?"  in a number of articles, see  Dimensions of library anxiety and social interdependence: implications for library services   ( Library Review , Volume 51 Number 2).

Or sometimes the RQ is stated as a general objective:

Ying Fan describes outsourcing in British companies in  Strategic outsourcing: evidence from British companies  ( Marketing Intelligence & Planning , Volume 18 Number 4) and states his research question as an objective:

The main objective of the research was to explore the two key areas in the outsourcing process, namely:

• pre-outsourcing decision process; and
• post-outsourcing supplier management.

or as a proposition:

Karin Klenke explores issues of gender in management decisions in  Gender influences in decision-making processes in top management teams   ( Management Decision , Volume 41 Number 10).

Given the exploratory nature of this research, no specific hypotheses were formulated. Instead, the following general propositions are postulated:

P1.  Female and male members of TMTs exercise different types of power in the strategic decision making process.

P2.  Female and male members of TMTs differ in the extent in which they employ political savvy in the strategic decision making process.

P3.  Male and female members of TMTs manage conflict in strategic decision making situations differently.

P4.  Female and male members of TMTs utilise different types of trust in the decision making process.

Sometimes, the theoretical underpinning (see next section) of the research leads you to formulate a hypothesis rather than a question:

Martin et al. explored the effect of fast-forwarding of ads (called zipping) in  Remote control marketing: how ad fast-forwarding and ad repetition affect consumers  ( Marketing Intelligence & Planning , Volume 20 Number 1) and his research explores the following hypotheses:

The influence of zipping H1. Individuals viewing advertisements played at normal speed will exhibit higher ad recall and recognition than those who view zipped advertisements.

Ad repetition effects H2. Individuals viewing a repeated advertisement will exhibit higher ad recall and recognition than those who see an advertisement once.

Zipping and ad repetition H3. Individuals viewing zipped, repeated advertisements will exhibit higher ad recall and recognition than those who see a normal speed advertisement that is played once.

Empirical research is not divorced from theoretical considerations; and a consideration of theory should form one of the starting points of your research. This applies particularly in the case of management research which by its very nature is practical and applied to the real world. The link between research and theory is symbiotic: theory should inform research, and the findings of research should inform theory.

There are a number of different theoretical perspectives; if you are unfamiliar with them, we suggest that you look at any good research methods textbook for a full account (see Further information), but this page will contain notes on the following:

This is the approach of the natural sciences, emphasising total objectivity and independence on the part of the researcher, a highly scientific methodology, with data being collected in a value-free manner and using quantitative techniques with some statistical measures of analysis. Assumes that there are 'independent facts' in the social world as in the natural world. The object is to generalise from what has been observed and hence add to the body of theory.

Very similar to positivism in that it has a strong reliance on objectivity and quantitative methods of data collection, but with less of a reliance on theory. There is emphasis on data and facts in their own right; they do not need to be linked to theory.

Interpretivism

This view criticises positivism as being inappropriate for the social world of business and management which is dominated by people rather than the laws of nature and hence has an inevitable subjective element as people will have different interpretations of situations and events. The business world can only be understood through people's interpretation. This view is more likely to emphasise qualitative methods such as participant observation, focus groups and semi-structured interviewing.

While reality exists independently of human experience, people are not like objects in the natural world but are subject to social influences and processes. Like  empiricism  and  positivism , this emphasises the importance of explanation, but is also concerned with the social world and with its underlying structures.

Inductive and deductive approaches

At what point in your research you bring in a theoretical perspective will depend on whether you choose an:

• Inductive approach  – collect the data, then develop the theory.
• Deductive approach  – assume a theoretical position then test it against the data.

It should be emphasised that none of the above approaches are mutually exclusive and can be used in combination.

Sampling may be done either:

• On a  random  basis – a given number is selected completely at random.
• On a  systematic  basis – every  n th element  of the population is selected.
• On a  stratified random  basis – the population is divided into segments, for example, in a University, you could divide the population into academic, administrators, and academic related. A random number of each group is then selected.
• On a  cluster  basis – a particular subgroup is chosen at random.
• Convenience  – being present at a particular time e.g. at lunch in the canteen.
• Purposive  – people can be selected deliberately because their views are relevant to the issue concerned.
• Quota  – the assumption is made that there are subgroups in the population, and a quota of respondents is chosen to reflect this diversity.

Useful articles

Richard Laughlin in  Empirical research in accounting: alternative approaches and a case for "middle-range" thinking  provides an interesting general overview of the different perspectives on theory and methodology as applied to accounting. ( Accounting, Auditing & Accountability Journal,  Volume 8 Number 1).

D. Tranfield and K. Starkey in  The Nature, Social Organization and Promotion of Management Research: Towards Policy  look at the relationship between theory and practice in management research, and develop a number of analytical frameworks, including looking at Becher's conceptual schema for disciplines and Gibbons et al.'s taxonomy of knowledge production systems. ( British Journal of Management , vol. 9, no. 4 – abstract only).

Research design is about how you go about answering your question: what strategy you adopt, and what methods do you use to achieve your results. In particular you should ask yourself... 

There's a lot more to this article; just fill in the form below to instantly see the complete content.

Read the complete article

What's in the rest?

• Continuation of 'Design of the research'
• Books & websites for further information

Your data will be used, alongside feedback we may request, only to help inform and improve our 'How to' section – thank you.

What is Empirical Research? Definition, Methods, Examples

Appinio Research · 09.02.2024 · 36min read

Ever wondered how we gather the facts, unveil hidden truths, and make informed decisions in a world filled with questions? Empirical research holds the key.

In this guide, we'll delve deep into the art and science of empirical research, unraveling its methods, mysteries, and manifold applications. From defining the core principles to mastering data analysis and reporting findings, we're here to equip you with the knowledge and tools to navigate the empirical landscape.

What is Empirical Research?

Empirical research is the cornerstone of scientific inquiry, providing a systematic and structured approach to investigating the world around us. It is the process of gathering and analyzing empirical or observable data to test hypotheses, answer research questions, or gain insights into various phenomena. This form of research relies on evidence derived from direct observation or experimentation, allowing researchers to draw conclusions based on real-world data rather than purely theoretical or speculative reasoning.

Characteristics of Empirical Research

Empirical research is characterized by several key features:

• Observation and Measurement : It involves the systematic observation or measurement of variables, events, or behaviors.
• Data Collection : Researchers collect data through various methods, such as surveys, experiments, observations, or interviews.
• Testable Hypotheses : Empirical research often starts with testable hypotheses that are evaluated using collected data.
• Quantitative or Qualitative Data : Data can be quantitative (numerical) or qualitative (non-numerical), depending on the research design.
• Statistical Analysis : Quantitative data often undergo statistical analysis to determine patterns , relationships, or significance.
• Objectivity and Replicability : Empirical research strives for objectivity, minimizing researcher bias . It should be replicable, allowing other researchers to conduct the same study to verify results.
• Conclusions and Generalizations : Empirical research generates findings based on data and aims to make generalizations about larger populations or phenomena.

Importance of Empirical Research

Empirical research plays a pivotal role in advancing knowledge across various disciplines. Its importance extends to academia, industry, and society as a whole. Here are several reasons why empirical research is essential:

• Evidence-Based Knowledge : Empirical research provides a solid foundation of evidence-based knowledge. It enables us to test hypotheses, confirm or refute theories, and build a robust understanding of the world.
• Scientific Progress : In the scientific community, empirical research fuels progress by expanding the boundaries of existing knowledge. It contributes to the development of theories and the formulation of new research questions.
• Problem Solving : Empirical research is instrumental in addressing real-world problems and challenges. It offers insights and data-driven solutions to complex issues in fields like healthcare, economics, and environmental science.
• Informed Decision-Making : In policymaking, business, and healthcare, empirical research informs decision-makers by providing data-driven insights. It guides strategies, investments, and policies for optimal outcomes.
• Quality Assurance : Empirical research is essential for quality assurance and validation in various industries, including pharmaceuticals, manufacturing, and technology. It ensures that products and processes meet established standards.
• Continuous Improvement : Businesses and organizations use empirical research to evaluate performance, customer satisfaction , and product effectiveness. This data-driven approach fosters continuous improvement and innovation.
• Human Advancement : Empirical research in fields like medicine and psychology contributes to the betterment of human health and well-being. It leads to medical breakthroughs, improved therapies, and enhanced psychological interventions.
• Critical Thinking and Problem Solving : Engaging in empirical research fosters critical thinking skills, problem-solving abilities, and a deep appreciation for evidence-based decision-making.

Empirical research empowers us to explore, understand, and improve the world around us. It forms the bedrock of scientific inquiry and drives progress in countless domains, shaping our understanding of both the natural and social sciences.

How to Conduct Empirical Research?

So, you've decided to dive into the world of empirical research. Let's begin by exploring the crucial steps involved in getting started with your research project.

1. Select a Research Topic

Selecting the right research topic is the cornerstone of a successful empirical study. It's essential to choose a topic that not only piques your interest but also aligns with your research goals and objectives. Here's how to go about it:

• Identify Your Interests : Start by reflecting on your passions and interests. What topics fascinate you the most? Your enthusiasm will be your driving force throughout the research process.
• Brainstorm Ideas : Engage in brainstorming sessions to generate potential research topics. Consider the questions you've always wanted to answer or the issues that intrigue you.
• Relevance and Significance : Assess the relevance and significance of your chosen topic. Does it contribute to existing knowledge? Is it a pressing issue in your field of study or the broader community?
• Feasibility : Evaluate the feasibility of your research topic. Do you have access to the necessary resources, data, and participants (if applicable)?

2. Formulate Research Questions

Once you've narrowed down your research topic, the next step is to formulate clear and precise research questions . These questions will guide your entire research process and shape your study's direction. To create effective research questions:

• Specificity : Ensure that your research questions are specific and focused. Vague or overly broad questions can lead to inconclusive results.
• Relevance : Your research questions should directly relate to your chosen topic. They should address gaps in knowledge or contribute to solving a particular problem.
• Testability : Ensure that your questions are testable through empirical methods. You should be able to gather data and analyze it to answer these questions.
• Avoid Bias : Craft your questions in a way that avoids leading or biased language. Maintain neutrality to uphold the integrity of your research.

3. Review Existing Literature

Before you embark on your empirical research journey, it's essential to immerse yourself in the existing body of literature related to your chosen topic. This step, often referred to as a literature review, serves several purposes:

• Contextualization : Understand the historical context and current state of research in your field. What have previous studies found, and what questions remain unanswered?
• Identifying Gaps : Identify gaps or areas where existing research falls short. These gaps will help you formulate meaningful research questions and hypotheses.
• Theory Development : If your study is theoretical, consider how existing theories apply to your topic. If it's empirical, understand how previous studies have approached data collection and analysis.
• Methodological Insights : Learn from the methodologies employed in previous research. What methods were successful, and what challenges did researchers face?

4. Define Variables

Variables are fundamental components of empirical research. They are the factors or characteristics that can change or be manipulated during your study. Properly defining and categorizing variables is crucial for the clarity and validity of your research. Here's what you need to know:

• Independent Variables : These are the variables that you, as the researcher, manipulate or control. They are the "cause" in cause-and-effect relationships.
• Dependent Variables : Dependent variables are the outcomes or responses that you measure or observe. They are the "effect" influenced by changes in independent variables.
• Operational Definitions : To ensure consistency and clarity, provide operational definitions for your variables. Specify how you will measure or manipulate each variable.
• Control Variables : In some studies, controlling for other variables that may influence your dependent variable is essential. These are known as control variables.

Understanding these foundational aspects of empirical research will set a solid foundation for the rest of your journey. Now that you've grasped the essentials of getting started, let's delve deeper into the intricacies of research design.

Empirical Research Design

Now that you've selected your research topic, formulated research questions, and defined your variables, it's time to delve into the heart of your empirical research journey – research design . This pivotal step determines how you will collect data and what methods you'll employ to answer your research questions. Let's explore the various facets of research design in detail.

Types of Empirical Research

Empirical research can take on several forms, each with its own unique approach and methodologies. Understanding the different types of empirical research will help you choose the most suitable design for your study. Here are some common types:

• Experimental Research : In this type, researchers manipulate one or more independent variables to observe their impact on dependent variables. It's highly controlled and often conducted in a laboratory setting.
• Observational Research : Observational research involves the systematic observation of subjects or phenomena without intervention. Researchers are passive observers, documenting behaviors, events, or patterns.
• Survey Research : Surveys are used to collect data through structured questionnaires or interviews. This method is efficient for gathering information from a large number of participants.
• Case Study Research : Case studies focus on in-depth exploration of one or a few cases. Researchers gather detailed information through various sources such as interviews, documents, and observations.
• Qualitative Research : Qualitative research aims to understand behaviors, experiences, and opinions in depth. It often involves open-ended questions, interviews, and thematic analysis.
• Quantitative Research : Quantitative research collects numerical data and relies on statistical analysis to draw conclusions. It involves structured questionnaires, experiments, and surveys.

Your choice of research type should align with your research questions and objectives. Experimental research, for example, is ideal for testing cause-and-effect relationships, while qualitative research is more suitable for exploring complex phenomena.

Experimental Design

Experimental research is a systematic approach to studying causal relationships. It's characterized by the manipulation of one or more independent variables while controlling for other factors. Here are some key aspects of experimental design:

• Control and Experimental Groups : Participants are randomly assigned to either a control group or an experimental group. The independent variable is manipulated for the experimental group but not for the control group.
• Randomization : Randomization is crucial to eliminate bias in group assignment. It ensures that each participant has an equal chance of being in either group.
• Hypothesis Testing : Experimental research often involves hypothesis testing. Researchers formulate hypotheses about the expected effects of the independent variable and use statistical analysis to test these hypotheses.

Observational Design

Observational research entails careful and systematic observation of subjects or phenomena. It's advantageous when you want to understand natural behaviors or events. Key aspects of observational design include:

• Participant Observation : Researchers immerse themselves in the environment they are studying. They become part of the group being observed, allowing for a deep understanding of behaviors.
• Non-Participant Observation : In non-participant observation, researchers remain separate from the subjects. They observe and document behaviors without direct involvement.
• Data Collection Methods : Observational research can involve various data collection methods, such as field notes, video recordings, photographs, or coding of observed behaviors.

Survey Design

Surveys are a popular choice for collecting data from a large number of participants. Effective survey design is essential to ensure the validity and reliability of your data. Consider the following:

• Questionnaire Design : Create clear and concise questions that are easy for participants to understand. Avoid leading or biased questions.
• Sampling Methods : Decide on the appropriate sampling method for your study, whether it's random, stratified, or convenience sampling.
• Data Collection Tools : Choose the right tools for data collection, whether it's paper surveys, online questionnaires, or face-to-face interviews.

Case Study Design

Case studies are an in-depth exploration of one or a few cases to gain a deep understanding of a particular phenomenon. Key aspects of case study design include:

• Single Case vs. Multiple Case Studies : Decide whether you'll focus on a single case or multiple cases. Single case studies are intensive and allow for detailed examination, while multiple case studies provide comparative insights.
• Data Collection Methods : Gather data through interviews, observations, document analysis, or a combination of these methods.

Qualitative vs. Quantitative Research

In empirical research, you'll often encounter the distinction between qualitative and quantitative research . Here's a closer look at these two approaches:

• Qualitative Research : Qualitative research seeks an in-depth understanding of human behavior, experiences, and perspectives. It involves open-ended questions, interviews, and the analysis of textual or narrative data. Qualitative research is exploratory and often used when the research question is complex and requires a nuanced understanding.
• Quantitative Research : Quantitative research collects numerical data and employs statistical analysis to draw conclusions. It involves structured questionnaires, experiments, and surveys. Quantitative research is ideal for testing hypotheses and establishing cause-and-effect relationships.

Understanding the various research design options is crucial in determining the most appropriate approach for your study. Your choice should align with your research questions, objectives, and the nature of the phenomenon you're investigating.

Data Collection for Empirical Research

Now that you've established your research design, it's time to roll up your sleeves and collect the data that will fuel your empirical research. Effective data collection is essential for obtaining accurate and reliable results.

Sampling Methods

Sampling methods are critical in empirical research, as they determine the subset of individuals or elements from your target population that you will study. Here are some standard sampling methods:

• Random Sampling : Random sampling ensures that every member of the population has an equal chance of being selected. It minimizes bias and is often used in quantitative research.
• Stratified Sampling : Stratified sampling involves dividing the population into subgroups or strata based on specific characteristics (e.g., age, gender, location). Samples are then randomly selected from each stratum, ensuring representation of all subgroups.
• Convenience Sampling : Convenience sampling involves selecting participants who are readily available or easily accessible. While it's convenient, it may introduce bias and limit the generalizability of results.
• Snowball Sampling : Snowball sampling is instrumental when studying hard-to-reach or hidden populations. One participant leads you to another, creating a "snowball" effect. This method is common in qualitative research.
• Purposive Sampling : In purposive sampling, researchers deliberately select participants who meet specific criteria relevant to their research questions. It's often used in qualitative studies to gather in-depth information.

The choice of sampling method depends on the nature of your research, available resources, and the degree of precision required. It's crucial to carefully consider your sampling strategy to ensure that your sample accurately represents your target population.

Data Collection Instruments

Data collection instruments are the tools you use to gather information from your participants or sources. These instruments should be designed to capture the data you need accurately. Here are some popular data collection instruments:

• Questionnaires : Questionnaires consist of structured questions with predefined response options. When designing questionnaires, consider the clarity of questions, the order of questions, and the response format (e.g., Likert scale , multiple-choice).
• Interviews : Interviews involve direct communication between the researcher and participants. They can be structured (with predetermined questions) or unstructured (open-ended). Effective interviews require active listening and probing for deeper insights.
• Observations : Observations entail systematically and objectively recording behaviors, events, or phenomena. Researchers must establish clear criteria for what to observe, how to record observations, and when to observe.
• Surveys : Surveys are a common data collection instrument for quantitative research. They can be administered through various means, including online surveys, paper surveys, and telephone surveys.
• Documents and Archives : In some cases, data may be collected from existing documents, records, or archives. Ensure that the sources are reliable, relevant, and properly documented.

To streamline your process and gather insights with precision and efficiency, consider leveraging innovative tools like Appinio . With Appinio's intuitive platform, you can harness the power of real-time consumer data to inform your research decisions effectively. Whether you're conducting surveys, interviews, or observations, Appinio empowers you to define your target audience, collect data from diverse demographics, and analyze results seamlessly.

By incorporating Appinio into your data collection toolkit, you can unlock a world of possibilities and elevate the impact of your empirical research. Ready to revolutionize your approach to data collection?

Book a Demo

Data Collection Procedures

Data collection procedures outline the step-by-step process for gathering data. These procedures should be meticulously planned and executed to maintain the integrity of your research.

• Training : If you have a research team, ensure that they are trained in data collection methods and protocols. Consistency in data collection is crucial.
• Pilot Testing : Before launching your data collection, conduct a pilot test with a small group to identify any potential problems with your instruments or procedures. Make necessary adjustments based on feedback.
• Data Recording : Establish a systematic method for recording data. This may include timestamps, codes, or identifiers for each data point.
• Data Security : Safeguard the confidentiality and security of collected data. Ensure that only authorized individuals have access to the data.
• Data Storage : Properly organize and store your data in a secure location, whether in physical or digital form. Back up data to prevent loss.

Ethical Considerations

Ethical considerations are paramount in empirical research, as they ensure the well-being and rights of participants are protected.

• Informed Consent : Obtain informed consent from participants, providing clear information about the research purpose, procedures, risks, and their right to withdraw at any time.
• Privacy and Confidentiality : Protect the privacy and confidentiality of participants. Ensure that data is anonymized and sensitive information is kept confidential.
• Beneficence : Ensure that your research benefits participants and society while minimizing harm. Consider the potential risks and benefits of your study.
• Honesty and Integrity : Conduct research with honesty and integrity. Report findings accurately and transparently, even if they are not what you expected.
• Respect for Participants : Treat participants with respect, dignity, and sensitivity to cultural differences. Avoid any form of coercion or manipulation.
• Institutional Review Board (IRB) : If required, seek approval from an IRB or ethics committee before conducting your research, particularly when working with human participants.

Adhering to ethical guidelines is not only essential for the ethical conduct of research but also crucial for the credibility and validity of your study. Ethical research practices build trust between researchers and participants and contribute to the advancement of knowledge with integrity.

With a solid understanding of data collection, including sampling methods, instruments, procedures, and ethical considerations, you are now well-equipped to gather the data needed to answer your research questions.

Empirical Research Data Analysis

Now comes the exciting phase of data analysis, where the raw data you've diligently collected starts to yield insights and answers to your research questions. We will explore the various aspects of data analysis, from preparing your data to drawing meaningful conclusions through statistics and visualization.

Data Preparation

Data preparation is the crucial first step in data analysis. It involves cleaning, organizing, and transforming your raw data into a format that is ready for analysis. Effective data preparation ensures the accuracy and reliability of your results.

• Data Cleaning : Identify and rectify errors, missing values, and inconsistencies in your dataset. This may involve correcting typos, removing outliers, and imputing missing data.
• Data Coding : Assign numerical values or codes to categorical variables to make them suitable for statistical analysis. For example, converting "Yes" and "No" to 1 and 0.
• Data Transformation : Transform variables as needed to meet the assumptions of the statistical tests you plan to use. Common transformations include logarithmic or square root transformations.
• Data Integration : If your data comes from multiple sources, integrate it into a unified dataset, ensuring that variables match and align.
• Data Documentation : Maintain clear documentation of all data preparation steps, as well as the rationale behind each decision. This transparency is essential for replicability.

Effective data preparation lays the foundation for accurate and meaningful analysis. It allows you to trust the results that will follow in the subsequent stages.

Descriptive Statistics

Descriptive statistics help you summarize and make sense of your data by providing a clear overview of its key characteristics. These statistics are essential for understanding the central tendencies, variability, and distribution of your variables. Descriptive statistics include:

• Measures of Central Tendency : These include the mean (average), median (middle value), and mode (most frequent value). They help you understand the typical or central value of your data.
• Measures of Dispersion : Measures like the range, variance, and standard deviation provide insights into the spread or variability of your data points.
• Frequency Distributions : Creating frequency distributions or histograms allows you to visualize the distribution of your data across different values or categories.

Descriptive statistics provide the initial insights needed to understand your data's basic characteristics, which can inform further analysis.

Inferential Statistics

Inferential statistics take your analysis to the next level by allowing you to make inferences or predictions about a larger population based on your sample data. These methods help you test hypotheses and draw meaningful conclusions. Key concepts in inferential statistics include:

• Hypothesis Testing : Hypothesis tests (e.g., t-tests , chi-squared tests ) help you determine whether observed differences or associations in your data are statistically significant or occurred by chance.
• Confidence Intervals : Confidence intervals provide a range within which population parameters (e.g., population mean) are likely to fall based on your sample data.
• Regression Analysis : Regression models (linear, logistic, etc.) help you explore relationships between variables and make predictions.
• Analysis of Variance (ANOVA) : ANOVA tests are used to compare means between multiple groups, allowing you to assess whether differences are statistically significant.

Chi-Square Calculator :

t-Test Calculator :

One-way ANOVA Calculator :

Inferential statistics are powerful tools for drawing conclusions from your data and assessing the generalizability of your findings to the broader population.

Qualitative Data Analysis

Qualitative data analysis is employed when working with non-numerical data, such as text, interviews, or open-ended survey responses. It focuses on understanding the underlying themes, patterns, and meanings within qualitative data. Qualitative analysis techniques include:

• Thematic Analysis : Identifying and analyzing recurring themes or patterns within textual data.
• Content Analysis : Categorizing and coding qualitative data to extract meaningful insights.
• Grounded Theory : Developing theories or frameworks based on emergent themes from the data.
• Narrative Analysis : Examining the structure and content of narratives to uncover meaning.

Qualitative data analysis provides a rich and nuanced understanding of complex phenomena and human experiences.

Data Visualization

Data visualization is the art of representing data graphically to make complex information more understandable and accessible. Effective data visualization can reveal patterns, trends, and outliers in your data. Common types of data visualization include:

• Bar Charts and Histograms : Used to display the distribution of categorical data or discrete data .
• Line Charts : Ideal for showing trends and changes in data over time.
• Scatter Plots : Visualize relationships and correlations between two variables.
• Pie Charts : Display the composition of a whole in terms of its parts.
• Heatmaps : Depict patterns and relationships in multidimensional data through color-coding.
• Box Plots : Provide a summary of the data distribution, including outliers.
• Interactive Dashboards : Create dynamic visualizations that allow users to explore data interactively.

Data visualization not only enhances your understanding of the data but also serves as a powerful communication tool to convey your findings to others.

As you embark on the data analysis phase of your empirical research, remember that the specific methods and techniques you choose will depend on your research questions, data type, and objectives. Effective data analysis transforms raw data into valuable insights, bringing you closer to the answers you seek.

How to Report Empirical Research Results?

At this stage, you get to share your empirical research findings with the world. Effective reporting and presentation of your results are crucial for communicating your research's impact and insights.

1. Write the Research Paper

Writing a research paper is the culmination of your empirical research journey. It's where you synthesize your findings, provide context, and contribute to the body of knowledge in your field.

• Title and Abstract : Craft a clear and concise title that reflects your research's essence. The abstract should provide a brief summary of your research objectives, methods, findings, and implications.
• Introduction : In the introduction, introduce your research topic, state your research questions or hypotheses, and explain the significance of your study. Provide context by discussing relevant literature.
• Methods : Describe your research design, data collection methods, and sampling procedures. Be precise and transparent, allowing readers to understand how you conducted your study.
• Results : Present your findings in a clear and organized manner. Use tables, graphs, and statistical analyses to support your results. Avoid interpreting your findings in this section; focus on the presentation of raw data.
• Discussion : Interpret your findings and discuss their implications. Relate your results to your research questions and the existing literature. Address any limitations of your study and suggest avenues for future research.
• Conclusion : Summarize the key points of your research and its significance. Restate your main findings and their implications.
• References : Cite all sources used in your research following a specific citation style (e.g., APA, MLA, Chicago). Ensure accuracy and consistency in your citations.
• Appendices : Include any supplementary material, such as questionnaires, data coding sheets, or additional analyses, in the appendices.

Writing a research paper is a skill that improves with practice. Ensure clarity, coherence, and conciseness in your writing to make your research accessible to a broader audience.

2. Create Visuals and Tables

Visuals and tables are powerful tools for presenting complex data in an accessible and understandable manner.

• Clarity : Ensure that your visuals and tables are clear and easy to interpret. Use descriptive titles and labels.
• Consistency : Maintain consistency in formatting, such as font size and style, across all visuals and tables.
• Appropriateness : Choose the most suitable visual representation for your data. Bar charts, line graphs, and scatter plots work well for different types of data.
• Simplicity : Avoid clutter and unnecessary details. Focus on conveying the main points.
• Accessibility : Make sure your visuals and tables are accessible to a broad audience, including those with visual impairments.
• Captions : Include informative captions that explain the significance of each visual or table.

Compelling visuals and tables enhance the reader's understanding of your research and can be the key to conveying complex information efficiently.

3. Interpret Findings

Interpreting your findings is where you bridge the gap between data and meaning. It's your opportunity to provide context, discuss implications, and offer insights. When interpreting your findings:

• Relate to Research Questions : Discuss how your findings directly address your research questions or hypotheses.
• Compare with Literature : Analyze how your results align with or deviate from previous research in your field. What insights can you draw from these comparisons?
• Discuss Limitations : Be transparent about the limitations of your study. Address any constraints, biases, or potential sources of error.
• Practical Implications : Explore the real-world implications of your findings. How can they be applied or inform decision-making?
• Future Research Directions : Suggest areas for future research based on the gaps or unanswered questions that emerged from your study.

Interpreting findings goes beyond simply presenting data; it's about weaving a narrative that helps readers grasp the significance of your research in the broader context.

With your research paper written, structured, and enriched with visuals, and your findings expertly interpreted, you are now prepared to communicate your research effectively. Sharing your insights and contributing to the body of knowledge in your field is a significant accomplishment in empirical research.

Examples of Empirical Research

To solidify your understanding of empirical research, let's delve into some real-world examples across different fields. These examples will illustrate how empirical research is applied to gather data, analyze findings, and draw conclusions.

Social Sciences

In the realm of social sciences, consider a sociological study exploring the impact of socioeconomic status on educational attainment. Researchers gather data from a diverse group of individuals, including their family backgrounds, income levels, and academic achievements.

Through statistical analysis, they can identify correlations and trends, revealing whether individuals from lower socioeconomic backgrounds are less likely to attain higher levels of education. This empirical research helps shed light on societal inequalities and informs policymakers on potential interventions to address disparities in educational access.

Environmental Science

Environmental scientists often employ empirical research to assess the effects of environmental changes. For instance, researchers studying the impact of climate change on wildlife might collect data on animal populations, weather patterns, and habitat conditions over an extended period.

By analyzing this empirical data, they can identify correlations between climate fluctuations and changes in wildlife behavior, migration patterns, or population sizes. This empirical research is crucial for understanding the ecological consequences of climate change and informing conservation efforts.

Business and Economics

In the business world, empirical research is essential for making data-driven decisions. Consider a market research study conducted by a business seeking to launch a new product. They collect data through surveys , focus groups , and consumer behavior analysis.

By examining this empirical data, the company can gauge consumer preferences, demand, and potential market size. Empirical research in business helps guide product development, pricing strategies, and marketing campaigns, increasing the likelihood of a successful product launch.

Psychological studies frequently rely on empirical research to understand human behavior and cognition. For instance, a psychologist interested in examining the impact of stress on memory might design an experiment. Participants are exposed to stress-inducing situations, and their memory performance is assessed through various tasks.

By analyzing the data collected, the psychologist can determine whether stress has a significant effect on memory recall. This empirical research contributes to our understanding of the complex interplay between psychological factors and cognitive processes.

These examples highlight the versatility and applicability of empirical research across diverse fields. Whether in medicine, social sciences, environmental science, business, or psychology, empirical research serves as a fundamental tool for gaining insights, testing hypotheses, and driving advancements in knowledge and practice.

Conclusion for Empirical Research

Empirical research is a powerful tool for gaining insights, testing hypotheses, and making informed decisions. By following the steps outlined in this guide, you've learned how to select research topics, collect data, analyze findings, and effectively communicate your research to the world. Remember, empirical research is a journey of discovery, and each step you take brings you closer to a deeper understanding of the world around you. Whether you're a scientist, a student, or someone curious about the process, the principles of empirical research empower you to explore, learn, and contribute to the ever-expanding realm of knowledge.

How to Collect Data for Empirical Research?

Introducing Appinio , the real-time market research platform revolutionizing how companies gather consumer insights for their empirical research endeavors. With Appinio, you can conduct your own market research in minutes, gaining valuable data to fuel your data-driven decisions.

Appinio is more than just a market research platform; it's a catalyst for transforming the way you approach empirical research, making it exciting, intuitive, and seamlessly integrated into your decision-making process.

Here's why Appinio is the go-to solution for empirical research:

• From Questions to Insights in Minutes : With Appinio's streamlined process, you can go from formulating your research questions to obtaining actionable insights in a matter of minutes, saving you time and effort.
• Intuitive Platform for Everyone : No need for a PhD in research; Appinio's platform is designed to be intuitive and user-friendly, ensuring that anyone can navigate and utilize it effectively.
• Rapid Response Times : With an average field time of under 23 minutes for 1,000 respondents, Appinio delivers rapid results, allowing you to gather data swiftly and efficiently.
• Global Reach with Targeted Precision : With access to over 90 countries and the ability to define target groups based on 1200+ characteristics, Appinio empowers you to reach your desired audience with precision and ease.

Get free access to the platform!

Join the loop 💌

Be the first to hear about new updates, product news, and data insights. We'll send it all straight to your inbox.

Get the latest market research news straight to your inbox! 💌

Wait, there's more

16.09.2024 | 9min read

Creative Checkup – Optimize Advertising Slogans & Creatives for ROI

03.09.2024 | 5min read

Get your brand Holiday Ready: 4 Essential Steps to Smash your Q4

03.09.2024 | 8min read

Beyond Demographics: Psychographics power in target group identification

Identifying Empirical Research Articles

Identifying empirical articles.

• Searching for Empirical Research Articles

What is Empirical Research?

An empirical research article reports the results of a study that uses data derived from actual observation or experimentation. Empirical research articles are examples of primary research. To learn more about the differences between primary and secondary research, see our related guide:

• Primary and Secondary Sources

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

• Identify common elements of an empirical article
• Use a variety of search strategies to search for empirical articles within the library collection

Look for the  IMRaD  layout in the article to help identify empirical research. Sometimes the sections will be labeled differently, but the content will be similar. 

• I ntroduction: why the article was written, research question or questions, hypothesis, literature review
• M ethods: the overall research design and implementation, description of sample, instruments used, how the authors measured their experiment
• R esults: output of the author's measurements, usually includes statistics of the author's findings
• D iscussion: the author's interpretation and conclusions about the results, limitations of study, suggestions for further research

Parts of an Empirical Research Article

Parts of an empirical article.

The screenshots below identify the basic IMRaD structure of an empirical research article. 

Introduction

The introduction contains a literature review and the study's research hypothesis.

The method section outlines the research design, participants, and measures used.

Results 

The results section contains statistical data (charts, graphs, tables, etc.) and research participant quotes.

The discussion section includes impacts, limitations, future considerations, and research.

Learn the IMRaD Layout: How to Identify an Empirical Article

This short video overviews the IMRaD method for identifying empirical research.

• Next: Searching for Empirical Research Articles >>
• Last Updated: Nov 16, 2023 8:24 AM

CityU Home - CityU Catalog

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

• Solutions Industries Gaming Automotive Sports and events Education Government Travel & Hospitality Financial Services Healthcare Cannabis Technology Use Case AskWhy Communities Audience Contactless surveys Mobile LivePolls Member Experience GDPR Positive People Science 360 Feedback Surveys
• Resources Blog eBooks Survey Templates Case Studies Training Help center

Home Market Research Research Tools and Apps

Theoretical Research: Definition, Methods + Examples

Research is the careful study of a particular research problem or concern using the scientific method. A theory is essential for any research project because it gives it direction and helps prove or disprove something. Theoretical basis helps us figure out how things work and why we do certain things.

Theoretical research lets you examine and discuss a research object using philosophical ideas and abstract theoretical structures.

In theoretical research, you can’t look at the research object directly. With the help of research literature, your research aims to define and sketch out the chosen topic’s conceptual models, explanations, and structures.

LEARN ABOUT: Research Process Steps

This blog will cover theoretical research and why it is essential. In addition to that, we are going to go over some examples.

What is the theoretical research?

Theoretical research is the systematic examination of a set of beliefs and assumptions.

It aims to learn more about a subject and help us understand it better. The information gathered in this way is not used for anything in particular because this kind of research aims to learn more.

All professionals, like biologists, chemists, engineers, architects, philosophers, writers, sociologists, historians, etc., can do theoretical research. No matter what field you work in, theoretical research is the foundation for new ideas.

It tries to answer basic questions about people, which is why this kind of research is used in every field of knowledge.

For example , a researcher starts with the idea that we need to understand the world around us. To do this, he begins with a hypothesis and tests it through experiments that will help him develop new ideas. 

What is the theoretical framework?

A theoretical framework is a critical component in research that provides a structured foundation for investigating a specific topic or problem. It encompasses a set of interconnected theories, existing theories, and concepts that guide the entire research process. 

The theoretical framework introduces a comprehensive understanding of the subject matter. Also, the theoretical framework strengthens the research’s validity and specifies the key elements that will be explored. Furthermore, it connects different ideas and theories, forming a cohesive structure that underpins the research endeavor.

A complete theoretical framework consists of a network of theories, existing theories, and concepts that collectively shape the direction of a research study. 

The theoretical framework is the fundamental principle that will be explored, strengthens the research’s credibility by aligning it with established knowledge, specifies the variables under investigation, and connects different aspects of the research to create a unified approach.

Theoretical frameworks are the intellectual scaffolding upon which the research is constructed. It is the lens through which researchers view their subject, guiding their choice of methodologies, data collection, analysis, and interpretation. By incorporating existing theory, and established concepts, a theoretical framework not only grounds the research but also provides a coherent roadmap for exploring the intricacies of the chosen topic.

Benefits of theoretical research

Theoretical research yields a wealth of benefits across various fields, from social sciences to human resource development and political science. Here’s a breakdown of these benefits while incorporating the requested topics:

Predictive power

Theoretical models are the cornerstone of theoretical research. They grant us predictive power, enabling us to forecast intricate behaviors within complex systems, like societal interactions. In political science, for instance, a theoretical model helps anticipate potential outcomes of policy changes.

Understanding human behavior

Drawing from key social science theories, it assists us in deciphering human behavior and societal dynamics. For instance, in the context of human resource development, theories related to motivation and psychology provide insights into how to effectively manage a diverse workforce.

Optimizing workforce

In the realm of human resource development, insights gleaned from theoretical research, along with the research methods knowledge base, help create targeted training programs. By understanding various learning methodologies and psychological factors, organizations can optimize workforce training for better results.

Building on foundations

It doesn’t exist in isolation; it builds upon existing theories. For instance, within the human resource development handbook, theoretical research expands established concepts, refining their applicability to contemporary organizational challenges.

Ethical policy formulation

Within political science, theoretical research isn’t confined to governance structures. It extends to ethical considerations, aiding policymakers in creating policies that balance the collective good with individual rights, ensuring just and fair governance. 

Rigorous investigations

Theoretical research underscores the importance of research methods knowledge base. This knowledge equips researchers in theory-building research methods and other fields to design robust research methodologies, yielding accurate data and credible insights.

Long-term impact

Theoretical research leaves a lasting impact. The theoretical models and insights from key social science theories provide enduring frameworks for subsequent research, contributing to the cumulative growth of knowledge in these fields.

Innovation and practical applications

It doesn’t merely remain theoretical. It inspires innovation and practical applications. By merging insights from diverse theories and fields, practitioners in human resource development devise innovative strategies to foster employee growth and well-being.

Theoretical research method

Researchers follow so many methods when doing research. There are two types of theoretical research methods.

• Scientific methods
• Social science method 

Let’s explore them below:

Scientific method

Scientific methods have some important points that you should know. Let’s figure them out below:

• Observation: Any part you want to explain can be found through observation. It helps define the area of research.
• Hypothesis: The hypothesis is the idea put into words, which helps us figure out what we see.
• Experimentation: Hypotheses are tested through experiments to see if they are true. These experiments are different for each research.
• Theory: When we create a theory, we do it because we believe it will explain hypotheses of higher probability.
• Conclusions: Conclusions are the learnings we derive from our investigation.

Social science methods

There are different methods for social science theoretical research. It consists of polls, documentation, and statistical analysis.

• Polls: It is a process whereby the researcher uses a topic-specific questionnaire to gather data. No changes are made to the environment or the phenomenon where the polls are conducted to get the most accurate results. QuestionPro live polls are a great way to get live audiences involved and engaged.
• Documentation: Documentation is a helpful and valuable technique that helps the researcher learn more about the subject. It means visiting libraries or other specialized places, like documentation centers, to look at the existing bibliography. With the documentation, you can find out what came before the investigated topic and what other investigations have found. This step is important because it shows whether or not similar investigations have been done before and what the results were.
• Statistic analysis : Statistics is a branch of math that looks at random events and differences. It follows the rules that are established by probability. It’s used a lot in sociology and language research. 

Examples of theoretical research

We talked about theoretical study methods in the previous part. We’ll give you some examples to help you understand it better.

Example 1: Theoretical research into the health benefits of hemp

The plant’s active principles are extracted and evaluated, and by studying their components, it is possible to determine what they contain and whether they can potentially serve as a medication.

Example 2: Linguistics research

Investigate to determine how many people in the Basque Country speak Basque. Surveys can be used to determine the number of native Basque speakers and those who speak Basque as a second language.

Example 3: Philosophical research

Research politics and ethics as they are presented in the writings of Hanna Arendt from a theoretical perspective.

LEARN ABOUT: 12 Best Tools for Researchers

From our above discussion, we learned about theoretical research and its methods and gave some examples. It explains things and leads to more knowledge for the sake of knowledge. This kind of research tries to find out more about a thing or an idea, but the results may take time to be helpful in the real world. 

This research is sometimes called basic research. Theoretical research is an important process that gives researchers valuable data with insight.

QuestionPro is a strong platform for managing your data. You can conduct simple surveys to more complex research using QuestionPro survey software.

At QuestionPro, we give researchers tools for collecting data, such as our survey software and a library of insights for any long-term study. Contact our expert team to find out more about it.

LEARN MORE         FREE TRIAL

MORE LIKE THIS

Was The Experience Memorable? (Part II) — Tuesday CX Thoughts

Sep 17, 2024

Data Discovery: What it is, Importance, Process + Use Cases

Sep 16, 2024

Competitive Insights : Importance, How to Get + Usage

Sep 13, 2024

Participant Engagement: Strategies + Improving Interaction

Sep 12, 2024

Other categories

• Academic Research
• Artificial Intelligence
• Assessments
• Brand Awareness
• Case Studies
• Communities
• Consumer Insights
• Customer effort score
• Customer Engagement
• Customer Experience
• Customer Loyalty
• Customer Research
• Customer Satisfaction
• Employee Benefits
• Employee Engagement
• Employee Retention
• Friday Five
• General Data Protection Regulation
• Insights Hub
• Life@QuestionPro
• Market Research
• Mobile diaries
• Mobile Surveys
• New Features
• Online Communities
• Question Types
• Questionnaire
• QuestionPro Products
• Release Notes
• Research Tools and Apps
• Revenue at Risk
• Survey Templates
• Training Tips
• Tuesday CX Thoughts (TCXT)
• Uncategorized
• What’s Coming Up
• Workforce Intelligence

• How to find Psychology Articles
• Using APA Thesaurus

Empirical Articles

• How to Limit to Empirical Articles
• What are they?
• How to Read them?
• Main Sections

Empirical articles are those in which authors report on their own study. The authors will have collected data to answer a research question.  Empirical research contains observed and measured examples that inform or answer the research question. The data can be collected in a variety of ways such as interviews, surveys, questionnaires, observations, and various other quantitative and qualitative research methods. 

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
• 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

General Advice

• Plan to read the article more than once
• Don't read it all the way through in one sitting, read strategically first.
• Identify relevant conclusions and limitations of study

Abstract: Get a sense of the article’s purpose and findings. Use it to assess if the article is useful for your research.

Skim: Review headings to understand the structure and label parts if needed.

Introduction/Literature Review: Identify the main argument, problem, previous work, proposed next steps, and hypothesis.

Methodology: Understand data collection methods, data sources, and variables.

Findings/Results: Examine tables and figures to see if they support the hypothesis without relying on captions.

Discussion/Conclusion: Determine if the findings support the argument/hypothesis and if the authors acknowledge any limitations.

Anatomy of a Research Paper    by Richard D. Branson published in Respir Care.  2004 October;  49(10): 1222–1228.

How to Read a Scholarly Chemistry Artricle -  Rider tutorial.

How to read and understand a scientific paper - a guide for non-scientists  - Violent Metaphors (blog post).

Compare your article to this table to help determine you have located an empirical study/research report.

Look for the following words in the title/abstract: empirical, experiment, research, or study.

• << Previous: Using APA Thesaurus
• Next: How to Limit to Empirical Articles >>
• Last Updated: Sep 16, 2024 2:30 PM
• URL: https://guides.rider.edu/psy

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here .

Loading metrics

Open Access

Peer-reviewed

Research Article

Inequality as determinant of donation: A theoretical modeling and empirical analysis of Korea

Roles Conceptualization, Methodology, Project administration

Affiliation PPS Company, Seoul, Korea

Roles Methodology, Writing – original draft

Affiliation The Credit Finance Association, The Credit Finance Research Institute, Seoul, Korea

Roles Conceptualization, Project administration, Writing – original draft

* E-mail: [email protected]

Affiliation College of Business, Gachon University, Seongnam, Korea

• Sang Hak Sohn, 
• Jong Mun Yoon, 
• Seongmin Jeon

• Published: September 16, 2024
• https://doi.org/10.1371/journal.pone.0306370
• Reader Comments

Recipient financial need is a crucial factor in donation decisions. This study proposes a novel model for determining financial donations, incorporating consumption levels of both donor and recipient within a societal context. Solving our model’s utility maximization problem reveals how consumption, donation, and savings are interlinked. Empirical evidence reinforces these findings, aligning with prior research and showing that larger consumption gaps between donors and recipients lead to increased donations. Our findings point towards an inherent altruistic motivation in donation, where elevating the recipient’s well-being ultimately enhances the donor’s own utility. This reinforces the notion that consideration of the recipient’s financial hardship, as reflected by their consumption patterns, is crucial when making donation decisions. Shifting beyond traditional models, this study introduces a groundbreaking approach to financial donations. Our novel model factors in consumption levels of both the donor and recipient, along with the broader societal context, using utility maximization to unravel the intertwined decisions of consumption, donation, and savings. Real-world data validates our model, confirming known donation factors and revealing a key finding: larger disparities in consumption lead to increased giving, suggesting an altruistic drive where helping others boosts personal satisfaction.

Citation: Sohn SH, Yoon JM, Jeon S (2024) Inequality as determinant of donation: A theoretical modeling and empirical analysis of Korea. PLoS ONE 19(9): e0306370. https://doi.org/10.1371/journal.pone.0306370

Editor: Paolo Ghinetti, Universita degli Studi del Piemonte Orientale Amedeo Avogadro, ITALY

Received: April 11, 2023; Accepted: June 14, 2024; Published: September 16, 2024

Copyright: © 2024 Sohn et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

This study challenges the assumption of pure self-interest by introducing a novel utility function that incorporates altruism. It shows how individuals, despite potential spending reductions, are motivated to donate, providing empirical evidence for their inherent generosity. While existing theoretical models explore why people donate, they often overlook the recipient’s consumption–a crucial factor influencing the impact and motivation for giving. This study bridges this gap by presenting a novel explanation for donation that incorporates both donor motives and recipient needs. Capitalizing on the stark contrast in marginal utility, the UNICEF campaign demonstrates how even small donations can significantly improve the lives of those in poverty. This effective strategy taps into our empathy and encourages giving by bridging the gap between donor and recipient. Moving beyond existing paradigms, this study analyzes donation through a novel economic utility lens. It proposes a groundbreaking model that incorporates both individual and societal consumption levels into the decision-making process of economic actors, revealing the recipient’s consumption as a key determinant of donation behavior. Moving beyond traditional models, this study pioneers the integration of recipient consumption into an individual’s utility function. This novel framework, encompassing both donor and recipient needs, redefines how we understand donation decisions by explicitly linking the act of giving to its impact on well-being. While individual income and other factors undoubtedly influence donation, existing research often overlooks the underlying mechanisms. This study dives deeper, exploring the decision-making process through the lens of individual utility. It posits that relative income and consumption play crucial roles, offering a novel framework for understanding why and how people donate. Beyond limitations of traditional models, this study unveils hidden levers of altruism by integrating relative consumption into a broader utility function. Armed with cutting-edge models and real-world data, we delve into the intricate dance of decision-making, revealing the true catalysts of individual giving. While European and American philanthropy are widely lauded, less is known about the burgeoning charitable spirit in South Korea. This study delves into the unique world of Korean giving, a relatively young phenomenon that emerged in the late 1980s, to uncover its underlying dynamics and shed light on its growing impact.

Literature review

Building on the argument by Clark et al. (2008) [ 1 ] that relative income, not just absolute wealth, influences happiness and economic behavior, this study investigates how relative income and consumption affect donation decisions. By exploring these factors through the lens of individual utility, we aim to illuminate the underlying mechanisms driving donation behavior. This deeper understanding of donation motivations can potentially contribute to further insights into both happiness and economic choices. Donations are defined as a signal of generosity, motivated by the desire to appear generous and to receive social approval (Harbaugh, 1998 [ 2 ]). The theoretical and empirical literature has tried to identify the determinants of money donations and time volunteered. The literature is grouped into three categories: individual preferences and attitudes, charities behavior, and government behavior (Cappellari et al., 2011 [ 3 ]). Economists have begun to incorporate psychological factors into models of philanthropic behavior. This is because empirical evidence suggests that psychological factors play a role in explaining non-selfish behavior. Unveiling the multifaceted nature of giving, Benabou and Tirolle (2006) [ 4 ] proposes three distinct pathways to donor satisfaction: the inherent joy of generosity, the internal reward of a positive self-image, and the external validation of social respect. In exploring the economics of individual giving, three key models dominate: the public goods model, the private consumption model, and the impure public goods model. Each reveals distinct motivations behind donations, offering a nuanced understanding of why people give. Understanding why people donate hinges on three competing narratives. The public goods model sees individuals driven by a desire to improve society, while the private consumption model views giving as a source of personal satisfaction. Finally, the impure public goods model reconciles these perspectives, suggesting a blend of altruism and self-benefit motivates generosity. Table 1 below summarizes the discussed models.

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

https://doi.org/10.1371/journal.pone.0306370.t001

Son and Park (2008) [ 5 ] laid the groundwork with their insightful analysis of three key models: public goods, private consumption, and impure public goods. Their work offers fertile ground for further exploration, but also identifies potential blind spots. Before embarking on our own investigation, we would carefully examine these existing studies to glean their strengths and weaknesses, building a firmer foundation for our own understanding. While the public goods model predicts donations plummeting with increased government funding, reality paints a different picture, hinting at the model’s narrow view of giving behavior. Similarly, the private consumption model, focusing solely on personal satisfaction, neglects the potential societal impact of donations. Even the impure public goods model, though nuanced, assumes a binary choice between public good and personal satisfaction, limiting its explanatory power. A vast body of research delves into the factors influencing individual giving. Notably, studies consistently reveal a positive link between Christianity, particularly church attendance, and donation levels. Likewise, higher education and age emerge as reliable predictors of increased generosity. Delving deeper into individual characteristics, Wiepking and Bekkers (2012) [ 6 ] surveyed the landscape of research on gender, family, and income. While married couples tend to donate more, the link between children and giving remains nuanced, with some studies finding no clear positive association. In contrast, the connection between income and generosity receives strong backing from research institutions. Interestingly, the perceived visibility of the donation to the recipient also plays a role, with givers more likely to contribute when they feel their effort will resonate directly with the beneficiary. Moving beyond traditional models, this study pioneers the investigation of relative income and consumption as crucial determinants of donation. By focusing on these previously overlooked factors, it unveils a deeper understanding of the motivations and processes underlying charitable giving.

Data and methods

Leveraging data from the 7 th (2004) to 20 th (2017) waves of the Korean Labor and Income Panel Study (KLIPS), a large-scale annual survey of 5,000 households and individuals in non-rural Korea, we delve into the factors influencing charitable giving. While the Korean Labor Panel provides valuable data, it only offers household-level consumption. To overcome this limitation, this study focuses solely on the household head. This enables us to align individual and household characteristics, ensuring consistency in our analysis of donation behavior. This study examines how relative income and relative consumption influence household charitable giving, measured as annual expenditure on donations. The KLIPS used the question to assess household contributions: "How much did your family spend on average per month on tithes and various donations throughout the past year?" Table 2 provides details of the data used in this analysis.

https://doi.org/10.1371/journal.pone.0306370.t002

We use the dependent variable of ‘Engaged in Donation’ in the selection equation and ln (Amount of donation+1) as the dependent variable in the estimation equation. To ensure mathematical validity, we log-transform the dependent variable as ln(donation amount + 1) rather than ln(donation amount). This adjustment is crucial because the logarithm of zero is undefined, and donation amounts can potentially be zero. Similarly, any variables related to the amount also undergo this transformation to maintain mathematical consistency. The variable ’Engaged in Donation’ in the selection equation is set to 1 if a donation is made, and 0 otherwise, with an average of about 32% of all households engaging in donations.

The independent variables are classified into four types. The first set of variables are supposed to affect the equation determining the amount of donation. As stated in theoretical discussion above, we include the variables representing a donor’s relative income and consumption, ln(equivalence-adjusted financial assets+1), ln(equivalence-adjusted current-term income+1), ln(equivalence-adjusted next-term income+1), and the tax price of donations. Moreover, the tax price of donations is used as an independent variable determining the amount of donation. Because a part of donation is returned to the donator in the form of tax deductions, the tax price of donations differs depending on a donor’s income, and the number of household members, etc. Hence, the tax price of donations here means how much a donor can give by donating KRW 1, which is obtained by the following equation: 1- (calculated tax amount/tax base).

The second group of independent variables are concerning individual and household characteristics. In our dataset, marital status is coded as 1 for married individuals and 0 for unmarried individuals (including divorced and single). Similarly, the variable "female" indicates households where the head of the household is female. Variables for individual characteristics include the age, marital status, gender, region of residence, education, employment status, etc., whereas household characteristics refer to the number of members in the household, and the number of household members under age 15 or over 60.

The third are independent variables regarding religion, while the fourth is individual health condition where respondents are asked to rate their health on a scale of 1–5. Also included is the body mass index measured by the value of weight divided by the square of height. In general, the BMI value under 20 is regarded as thinness, and those over 25 are classified into obese class. Table 3 offers a regional breakdown of the equivalence-adjusted Gini coefficient and the equivalence-adjusted average consumption for the bottom 5% of consumers.

https://doi.org/10.1371/journal.pone.0306370.t003

Building on existing models, this study offers a fresh perspective on donation behavior by employing a novel utility function. Unlike traditional approaches, this function explicitly factors in the difference in consumption between donors and recipients, addressing a key issue raised in our research question.

The theoretical model adopted in this study analyzes the impact of several variables on donation by using a utility function taking a differentiated form from theory in previous literature. Our utility function reflects the differences in consumption between a donor and a recipient, mentioned in the research question in the previous section.

To simplify the maximization of the utility function such as Eq ( 1 ), we apply a log utility function to consumption and the endowment one-period model assuming only two parties to finally get the utility maximization problem such as Eqs ( 2 ), ( 3 ), and (3)’ as follows.

To solve the utility maximization problem, we substitute the Eq (3)’ into Eq ( 2 ), and finally obtain Eq ( 4 ).

By solving Eq ( 4 ) using FOC, we have a solution to private consumption ( C i ) as below in Eq ( 5 ).

Finally, we substitute Eq ( 5 ), the solution to private consumption ( C i ) into Eq (3)’ to obtain a solution to individual donation ( D i ) as shown in Eq ( 6 ).

In Eq ( 6 ), an amount of donation could be expressed as a functional relationship between an individual economic actor’s income and a recipient’s consumption. An individual economic actor’s donation that is determined by the utility maximization problem is a function of consumption by a recipient where an actor’s donation decreases as a recipient’s consumption rises. This occurs because the incentive for donation falls when the donor-recipient gap in marginal utility decreases.

As donations are zero when there are no donations, the donation model is similar to a left-censored model. However, if an actor first decides whether or not to donate, and then chooses the amount of donation, this can lead to sample selection bias. To address this issue, most previous studies have used the Tobit model or the Heckman two-stage model. This study also adopts the double-hurdle model in addition to the Heckman two-stage model. Our analysis model can be expressed in Eqs (7) through ( 9 ). Basically, the amount of donation becomes zero when there is no donation, which is similar to the left-censored model. However, if an actor first decides whether to donate or not, and then chooses the amount of donation, this gives rise to sample selection bias. Taking into account that, most previous studies turned to the Tobit model as well as the Heckman two-stage model. On top of the Heckman two-stage model, this study also adopts the double-hurdle model for carrying out analysis. Simply put, out analysis model could be expressed as Eq (7) through ( 9 ).

Eq ( 7 ) is regarding selection, whereas Eq ( 8 ) is for deciding the amount of donation. Eq ( 9 ) refers to independent variables. x it means independent variables related to a decision on how much an individual donates. As explored in the theoretical ground, x it variables are a donator’s income at the current and subsequent terms, a recipient’s income, and the tax price of donations. w it refer to independent variables that explain sample selection, including personal elements including age, education, family composition, place of residence, employment status, religion, physical characteristics, etc.

This equation is a regression model that attempts to explain the amount of money donated by individual i at time t as a function of such independent variables as individual consumption, individual income, individual expected income, individual wealth, and the proxy for individual altruism. The coefficients measure the strength of the relationship between each independent variable and the amount of money donated. The error term ε it captures all other factors that may influence the amount of money donated that are not included in the model.

Finally, the parameters to be estimated are α , β , and σ u , with the log-likelihood function of our double-hurdle model as shown in Eq ( 12 ) below.

Table 4 presents our findings obtained using two models: The Heckman two-stage and the double-hurdle. Interestingly, for the decision to donate, both models yield identical results regardless of the included variables. However, when it comes to the amount donated, the story differs, and only the results from the Heckman model are reported in the left column.

https://doi.org/10.1371/journal.pone.0306370.t004

Our study confirms and expands upon the established link between individual and household characteristics and donation decisions. Examining this relationship in the Korean context, we not only replicate the findings of previous research but also uncover unique patterns and motivations specific to this population. This reinforces the consistency of these factors while adding valuable context and nuance to the existing knowledge base. The sole exception emerged with the equivalence-adjusted average household consumption of the bottom 5% of households. Our first key finding is the positive link between income inequality (measured by the Gini coefficient) and individual donations. It is important to see how a higher Gini coefficient might affect the likelihood of individuals making the initial decision to donate. We may consider factors like increased awareness of inequality, potential for higher returns on donations in high-inequality settings, or moral motivations driven by concerns about fairness. Also, we analyze how the magnitude of the Gini coefficient could influence the amount individuals choose to donate. It is likely whether a larger coefficient might trigger larger donations due to feelings of obligation or guilt, or if potential diminishing returns in situations of extreme inequality could lead to lower contributions. Donors from regions with higher inequality tend to give more generously, supporting our theoretical prediction that relative income drives charitable giving. Reassuringly, our analysis of individual and household characteristics influencing donation decisions mirrors the findings of most previous studies. This agreement bolsters the validity of existing theoretical frameworks and demonstrates consistency between past and present research. Several factors significantly influence individuals’ decisions to donate at the 1% level. Married individuals, women, those with higher education, and religious affiliation generally give more. Age also plays a role, with older adults and those with health conditions (but not BMI) more likely to contribute. Meanwhile, living in urban areas, being self-employed, or engaging in unhealthy habits like drinking and smoking tend to discourage giving. Our analysis of both donation decisions and amounts largely confirms existing research. Table 5 details the close alignment between our findings and theoretical predictions, bolstering the validity of past models and enriching our understanding of charitable giving.

https://doi.org/10.1371/journal.pone.0306370.t005

Conclusions and limitations

This study delves into the factors influencing individual generosity, specifically exploring the role of relative income. We track changes in donations and personal characteristics over time using panel data, allowing us to build a robust model that supports our theoretical framework. This framework incorporates a broader definition of utility compared to past studies, accounting for how differences in income between donors and recipients impact their satisfaction. Our research reveals that how much you spend compared to others (relative consumption) is the main driver of your charitable giving, even more than your current income. This aligns with our theoretical model, which predicts that factors like your relative wealth and future income prospects also play a role, along with the cost of giving itself. Notably, we also find that donors tend to give more when their contribution makes a big difference for the recipient, highlighting the role of altruistic motivation. Breaking new ground, our study offers a more complete model exploring how individuals decide how much to spend, donate, and save. Unlike past research, we consider not just the donor’s own situation but also the recipient’s economic hardships and the broader societal trends in consumer spending. This reveals that helping those struggling financially is a top priority for most donors, making the recipient’s consumption level a key factor in whether a donation is made.

To understand how people decide how much to spend, donate, and save, we create a new model that considers both their own happiness and the impact their donations have on others. This model takes into account how much better off someone is after receiving a donation, helping us explain how generosity works. Past research on donation behavior used models like Tobit and Heckman two-stage, considering factors like income, wealth, and religion. While they found these factors to positively influence giving, their models might have missed some important information. Our study takes a step forward by employing a double-hurdle model, tackling potential biases in both deciding whether to donate and how much to give. This allows us to provide a more accurate picture of what drives generosity. Unlike previous research, our study considers how your spending power compared to others (relative income) influences your desire to donate. This new perspective lets us pinpoint the factors that truly drive charitable giving in both theory and practice. To ensure accurate results, we use sophisticated tools like the Heckman two-stage approach and the double-hurdle model, which account for potential biases and limitations in the data.

While our study sheds light on factors influencing donation decisions, it’s important to acknowledge some limitations. One concerns the use of the regional Gini coefficient to capture recipient circumstances. This measure reflects overall regional inequality, but it doesn’t consider that recipients might compete for resources with individuals from other regions, potentially affecting its accuracy in representing their specific situations. Another study limitation concerns using the ratio of a donor’s income to the bottom 5%’s average as a proxy for recipient need. This measure raises concerns because it implicitly assumes all recipients belong to the lowest income bracket, potentially misrepresenting the diversity of their socioeconomic situations. The measure used in this study overlooks several points. First, many recipients outside the bottom 5% may struggle financially. Second, individual needs vary widely, and this measure doesn’t capture that diversity. Finally, cost-of-living differences across regions further complicate its accuracy. Future research should explore how much of a donation in one region actually stays within that region and benefits local recipients. Our study utilizes a comprehensive annual survey, but capturing donations to rural Korean areas remains a challenge. This is because such donations often flow through informal channels like social networks, making them difficult to track. Understanding the true extent of financial aid reaching rural communities requires addressing these data collection hurdles. Understanding how much of a donation stays within the intended region and benefits local recipients is crucial. Further research on this question can enhance the effectiveness of charitable giving, ensuring support reaches those who need it most.

This study examines how donations impact low-income groups, even though the available data only captures the total amount donated, not its specific purpose. While donations can support various causes like nature restoration and disease eradication, we assume them to be directed towards lower-income individuals due to the data limitations. Ideally, future research could benefit from more detailed data collection on donation usage patterns. However, existing evidence suggests a significant portion of Korean donations support domestic and local communities, as observed by Song (2016) [ 7 ]’s finding that 65.5% of donors contribute to such activities.

Supporting information

https://doi.org/10.1371/journal.pone.0306370.s001

• View Article
• Google Scholar
• 5. Son W., & Park T. (2008). A Study on Private Donation of Korea. KIPF, https://repository.kipf.re.kr/handle/201201/4133 [In Korean].
• 7. Song, H.J. (2016). Analysis of individual donation survey results. In Proceedings of the 16th Donation Culture Symposium , Giving Korea 2016. [In Korean].