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An operational definition is the articulation of operationalization (or statement of procedures) used in defining the terms of a process (or set of validation tests) needed to determine the nature of an item or phenomenon (a variable, term, or object) and its properties such as duration, quantity, extension in space, chemical composition, etc. Since the degree of operationalization can vary itself, it can result in a more or less operational definition. The procedures included in definitions should be repeatable by anyone or at least by peers.
An example of operational definition of the term weight of an object, operationalized to a degree, would be the following: "weight is the numbers that appear when that object is placed on a weighing scale". According to it, the weight can be any of the numbers shown on the scale after, including the very moment the object is put on it. Clearly, the inclusion of the moment when one can start reading the numbers on the scale would make it more fully an operational definition. Nonetheless, it is still in contrast to those purely theoretical definitions.
- 1 Overview
- 2 Application
- 3 Examples
- 4 Theoretical vs operational definition
- 5 See also
- 6 References
- 7 Further reading
Properties described in this manner must be sufficiently accessible, so that persons other than the definer may independently measure or test for them at will. An operational definition is generally designed to model a theoretical definition. The most operational definition is a process for identification of an object by distinguishing it from its background of empirical experience.
The binary version produces either the result that the object exists, or that it doesn't, in the experiential field to which it is applied. The classifier version results in discrimination between what is part of the object and what is not part of it. This is also discussed in terms of semantics, pattern recognition, and operational techniques, such as regression.
Operationalize means to put into operation. Operational definitions are also used to define system states in terms of a specific, publicly accessible process of preparation or validation testing, which is repeatable at will. For example, 100 degrees Celsius may be crudely defined by describing the process of heating water at sea level until it is observed to boil. An item like a brick, or even a photograph of a brick, may be defined in terms of how it can be made. Likewise, iron may be defined in terms of the results of testing or measuring it in particular ways.
Vandervert (1980/1988) described in scientific detail a simple, everyday illustration of an operational definition in terms of making a cake (i.e., its recipe is an operational definition used in a specialized laboratory known as the household kitchen). Similarly, the saying, if it walks like a duck and quacks like a duck, it must be some kind of duck, may be regarded as involving a sort of measurement process or set of tests (see duck test).
Despite the controversial philosophical origins of the concept, particularly its close association with logical positivism, operational definitions have undisputed practical applications. This is especially so in the social and medical sciences, where operational definitions of key terms are used to preserve the unambiguous empirical testability of hypothesis and theory. Operational definitions are also important in the physical sciences.
The Stanford Encyclopedia of Philosophy entry on scientific realism, written by Richard Boyd, indicates that the modern concept owes its origin in part to Percy Williams Bridgman, who felt that the expression of scientific concepts was often abstract and unclear. Inspired by Ernst Mach, in 1914 Bridgman attempted to redefine unobservable entities concretely in terms of the physical and mental operations used to measure them. Accordingly, the definition of each unobservable entity was uniquely identified with the instrumentation used to define it. From the beginning objections were raised to this approach, in large part around the inflexibility. As Boyd notes, "In actual, and apparently reliable, scientific practice, changes in the instrumentation associated with theoretical terms are routine, and apparently crucial to the progress of science. According to a 'pure' operationalist conception, these sorts of modifications would not be methodologically acceptable, since each definition must be considered to identify a unique 'object' (or class of objects)." However, this rejection of operationalism as a general project destined ultimately to define all experiential phenomena uniquely did not mean that operational definitions ceased to have any practical use or that they could not be applied in particular cases.
The special theory of relativity can be viewed as the introduction of operational definitions for simultaneity of events and of distance, that is, as providing the operations needed to define these terms.
Operational definitions are often most challenging in the fields of psychology and psychiatry, where intuitive concepts, such as intelligence need to be operationally defined before they become amenable to scientific investigation, for example, through processes such as IQ tests.
On October 15, 1970, the West Gate Bridge in Melbourne, Australia collapsed, killing 35 construction workers. The subsequent enquiry found that the failure arose because engineers had specified the supply of a quantity of flat steel plate. The word flat in this context lacked an operational definition, so there was no test for accepting or rejecting a particular shipment or for controlling quality.
In his managerial and statistical writings, W. Edwards Deming placed great importance on the value of using operational definitions in all agreements in business. As he said:
- "An operational definition is a procedure agreed upon for translation of a concept into measurement of some kind." – W. Edwards Deming
- "There is no true value of any characteristic, state, or condition that is defined in terms of measurement or observation. Change of procedure for measurement (change of operational definition) or observation produces a new number." – W. Edwards Deming
Operational, in a process context, also can denote a working method or a philosophy that focuses principally on cause and effect relationships (or stimulus/response, behavior, etc.) of specific interest to a particular domain at a particular point in time. As a working method, it does not consider issues related to a domain that are more general, such as the ontological, etc.
Science uses computing. Computing uses science. We have seen the development of Computer Science. There are not many who can bridge all three of these. One effect is that, when results are obtained using a computer, the results can be impossible to replicate if the code is poorly documented, contains errors, or if parts are omitted entirely. 
Many times, issues are related to persistence and clarity of use of variables, functions, and so forth. Also, systems dependence is an issue. In brief, length (as a standard) has matter as its definitional basis. What pray tell can be used when standards are to be computationally framed?
Hence, operational definition can be used within the realm of the interactions of humans with advanced computational systems. In this sense, one area of discourse deals with computational thinking in, and with how it might influence, the sciences. To quote the American Scientist:
- The computer revolution has profoundly affected how we think about science, experimentation, and research.
One referenced project pulled together fluid experts, including some who were expert in the numeric modeling related to computational fluid dynamics, in a team with computer scientists. Essentially, it turned out that the computer guys did not know enough to weigh in as much as they would have liked. Thus, their role, to their chagrin, many times was "mere" programmer.
Some knowledge-based engineering projects experienced similarly that there is a trade-off between trying to teach programming to a domain expert versus getting a programmer to understand the intricacies of a domain. That, of course, depends upon the domain. In short, any team member has to decide which side of the coin to spend one's time.
The International Society for Technology in Education has a brochure detailing an "operational definition" of computational thinking. At the same time, the ISTE made an attempt at defining related skills.
A recognized skill is tolerance for ambiguity and being able to handle open-ended problems. For instance, a knowledge-based engineering system can enhance its operational aspect and thereby its stability through more involvement by the subject matter expert, thereby opening up issues of limits that are related to being human. As in, many times, computational results have to be taken at face value due to several factors (hence the duck test's necessity arises) that even an expert cannot overcome. The end proof may be the final results (reasonable facsimile by simulation or artifact, working design, etc.) that are not guaranteed to be repeatable, may have been costly to attain (time and money), and so forth.
In advanced modeling, with the requisite computational support such as knowledge-based engineering, mappings must be maintained between a real-world object, its abstracted counterparts as defined by the domain and its experts, and the computer models. Mismatches between domain models and their computational mirrors can raise issues that are apropos to this topic. Techniques that allow the flexible modeling required for many hard problems must resolve issues of identity, type, etc. which then lead to methods, such as duck typing. Many domains, with a numerics focus, use limit theory, of various sorts, to overcome the duck test necessity with varying degrees of success. Yet, with that, issues still remain as representational frameworks bear heavily on what we can know.
In arguing for an object-based methodology, Peter Wegner suggested that "positivist scientific philosophies, such as operationalism in physics and behaviorism in psychology" were powerfully applied in the early part of the 20th century. However, computation has changed the landscape. He notes that we need to distinguish four levels of "irreversible physical and computational abstraction" (Platonic abstraction, computational approximation, functional abstraction, and value computation). Then, we must rely on interactive methods, that have behavior as their focus (see duck test).
The thermodynamic definition of temperature, due to Nicolas Léonard Sadi Carnot, refers to heat "flowing" between "infinite reservoirs". This is all highly abstract and unsuited for the day-to-day world of science and trade. In order to make the idea concrete, temperature is defined in terms of operations with the gas thermometer. However, these are sophisticated and delicate instruments, only adapted to the national standardization laboratory.
For day-to-day use, the International Temperature Scale of 1990 (ITS) is used, defining temperature in terms of characteristics of the several specific sensor types required to cover the full range. One such is the electrical resistance of a thermistor, with specified construction, calibrated against operationally defined fixed points.
Electric current is defined in terms of the force between two infinite parallel conductors, separated by a specified distance. This definition is too abstract for practical measurement, so a device known as a current balance is used to define the ampere operationally.
Unlike temperature and electric current, there is no abstract physical concept of the hardness of a material. It is a slightly vague, subjective idea, somewhat like the idea of intelligence. In fact, it leads to three more specific ideas:
- Scratch hardness measured on Mohs' scale;
- Indentation hardness; and
- Rebound, or dynamic, hardness measured with a Shore scleroscope.
Of these, indentation hardness itself leads to many operational definitions, the most important of which are:
- Brinell hardness test – using a 10 mm steel ball;
- Vickers hardness test – using a pyramidal diamond indenter; and
- Rockwell hardness test – using a diamond cone indenter.
In all these, a process is defined for loading the indenter, measuring the resulting indentation and calculating a hardness number. Each of these three sequences of measurement operations produces numbers that are consistent with our subjective idea of hardness. The harder the material to our informal perception, the greater the number it will achieve on our respective hardness scales. Furthermore, experimental results obtained using these measurement methods has shown that the hardness number can be used to predict the stress required to permanently deform steel, a characteristic that fits in well with our idea of resistance to permanent deformation. However, there is not always a simple relationship between the various hardness scales. Vickers and Rockwell hardness numbers exhibit qualitatively different behaviour when used to describe some materials and phenomena.
The constellation Virgo
The constellation Virgo is a specific constellation of stars in the sky, hence the process of forming Virgo cannot be an operational definition, since it is historical and not repeatable. Nevertheless, the process whereby we locate Virgo in the sky is repeatable, so in this way, Virgo is operationally defined. In fact, Virgo can have any number of definitions (although we can never prove that we are talking about the same Virgo), and any number may be operational.
- Discipline is a term used to describe a subject matter area when there are more than approximately a dozen university departments using the same name for roughly the same subject matter.
Theoretical vs operational definition
|Theoretical definition||Operational definition|
|Weight: a measurement of gravitational force acting on an object||a result of measurement of an object on a newton spring scale|
- Adanza, Estela G. (1995) Research methods: Principles and Applications p.21
- Sevilla, Consuelo G. et al. (1992) Research methods, revised edition p.20
- Shoemaker, Pamela J.; et al. (2004). How to build social science theories. SAGE. p. 29. ISBN 978-0-7619-2667-2.
- Vandervert, L. (1988). Operational definitions made simple, useful, and lasting. In M. Ware & C. Brewer (Eds.), Handbook for teaching statistics and research methods (pp. 132–134). Hillsdale, NJ: Lawrence Erlbaum Associates. (Original work published 1980)
- Boyd, Richard. "Scientific Realism". Stanford Encyclopedia of Philosophy. Retrieved 8 February 2013.
- NMJ Woodhouse (2003). Special relativity. London: Springer. p. 58. ISBN 1-85233-426-6.
- C. J. Isham (1995). Lectures on Quantum Theory. Imperial College Press. p. 95. ISBN 1-86094-001-3.
- Jiří Blank; Pavel Exner; Miloslav Havlíček (1994). Hilbert Space Operators in Quantum Physics. Springer. p. 252. ISBN 1-56396-142-3.
- Collberg, C., Roebsting, T. (2016) "Repeability in Computer Systems Research" Communications of the ACM, Vol. 59, No. 3, pages 62–69 (via acm.org)
- "Computational Thinking in Science" American Scientist, Jan/Feb 2017 (via My American Scientist)
- "Operational Definition of Computational Thinking" (for K–12 Education) 2011 (via website)
- Wegner, P. () "Beyond Computable Functions" Specification of Parallel Algorithms Page 37 American Mathematical Society (via Google)
- Rustum Roy (1977) "Interdisciplinary research on campus – the elusive dream", Chemical and Engineering News 55(35): 28–40
- Ballantyne, Paul F. History and Theory of Psychology Course, in Langfeld, H.S. (1945) Introduction to the Symposium on Operationism. Psyc. Rev. 32, 241–243.
- Bohm, D. (1996). On dialog. N.Y.: Routledge.
- Boyd, Richard. On the Current Status of the Issue of Scientific Realism in Erkenntnis. 19: 45–90.
- Bridgman, P. W. The way things are. Cambridge: Harvard University Press. (1959)
- Carnap, R. The Elimination of Metaphysics Through Logical Analysis of Language in Ayer, A.J. 1959.
- Churchland, Patricia, Neurophilosophy— Toward a unified science of the mind/brain, MIT Press (1986).
- Churchland, Paul., A Neurocomputational Perspective— The Nature of Mind and the Structure of Science, MIT Press (1989).
- Dennett, Daniel C. Consciousness Explained, Little, Brown & Co.. 1992.
- Depraz, N. (1999). "The phenomenological reduction as praxis." Journal of Consciousness Studies, 6(2-3), 95–110.
- Green, C. D. (1992). "Of Immortal Mythological Beasts: Operationism in Psychology." Theory & Psychology, 2, 291–320 
- Hardcastle, G. L. (1995). "S.S. Stevens and the origins of operationism." Philosophy of Science, 62, 404–424.
- Hermans, H. J. M. (1996). "Voicing the self: from information processing to dialogical interchange." Psychological Bulletin, 119(1), 31–50.
- Hyman, Bronwen, U of Toronto, and Shephard, Alfred H., U of Manitoba, "Zeitgeist: The Development of an Operational Definition", The Journal of Mind and Behavior, 1(2), pps. 227-246 (1980)
- Leahy, Thomas H., Virginia Commonwealth U, The Myth of Operationism, ibid, pps. 127–144 (1980)
- Ribes-Inesta, Emilio "What Is Defined In Operational Definitions? The Case Of Operant Psychology," Behavior and Philosophy, 2003.
- Roepstorff, A. & Jack, A. (2003). "Editorial introduction, Special Issue: Trusting the Subject? (Part 1)." Journal of Consciousness Studies, 10(9–10), v–xx.
- Roepstorff, A. & Jack, A. (2004). "Trust or Interaction? Editorial introduction, Special Issue: Trusting the Subject? (Part 2)." Journal of Consciousness Studies, 11(7–8), v–xxii.
- Stevens, S. S. Operationism and logical positivism, in M. H. Marx (Ed.), Theories in contemporary psychology (pp. 47–76). New York: MacMillan. (1963)
- Thomson — Waddsworth, eds., Learning Psychology: Operational Definitions Research Methods Workshops