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An operational definition is a result of the process of operationalization and is used to define something (e.g. a variable, term, or object) in terms of a process (or set of validation tests) needed to determine its existence, duration, and quantity. Since the degree of operationalization can vary itself, it can result in a more or less operational definitions. 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.
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, every day 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 idea originally arises in the operationalist philosophy of P. W. Bridgman and others. By 1914, Bridgman was dismayed by the abstraction and lack of clarity with which, he argued, many scientific concepts were expressed. Inspired by logical positivism and the phenomenalism of Ernst Mach, in 1914 he declared that the meaning of a theoretical term (or unobservable entity), such as electron density, lay in the operations, physical and mental, performed in its measurement. The goal was to eliminate all reference to theoretical entities by "rationally reconstructing" them in terms of the particular operations of laboratory procedures and experimentation.
Hence, the term electron density could be analyzed into a statement of the following form:
- (*) The electron density of an object, O, is given by the value, x, if and only if P applied to O yields the value x,
where P stands for an instrument that scientists take as a procedure for measuring electron density.
Operationalism, defined in this way, was rejected even by the logical positivists, due to inherent problems: defining terms operationally necessarily implied the analytic necessity of the definition. The analyticity of operational definitions like (*) is essential to the project of rational reconstruction. Operationalism is not, for example, the idea that electron density is defined as whatever magnitude instruments of the sort P reliably measure. On that conception (*) would represent an empirical discovery about how to measure electron density, but -- since electrons are unobservables -- that's a realist conception not an empiricist one. What the project of rational reconstruction requires is that (*) be true purely as a matter of linguistic stipulation about how the term "electron density" is to be used.
Since (*) is supposed to be analytic, it's supposed to be unrevisable. There is supposed to be no such thing as discovering, about P, that some other instrument provides a more accurate value for electron density, or provides values for electron density under conditions where P doesn't function. Here again, thinking that there could be such an improvement in P with respect to electron density requires thinking of electron density as a real feature of the world which P (perhaps only approximately) measures. But that's the realist conception that operationalism is designed rationally to do away with!
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). In practice, however, an 'operationally defined' object is often taken to be that object which is determined by a constellation of different unique 'operational procedures.'
Most logical empiricists were not willing to accept the conclusion that operational definitions must be unique (in contradiction to 'established' scientific practice). So they felt compelled to reject operationalism. In the end, it reduces to a reductio ad absurdum, since each measuring instrument must itself be operationally defined, in infinite regress... But this was also a failure of the logical positivist approach generally.
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 at their most controversial 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. Such definitions are used as a follow up to a theoretical definition, in which the specific concept is defined as a measurable occurrence. John Stuart Mill pointed out the dangers of believing that anything that could be given a name must refer to a thing and Stephen Jay Gould and others have criticized psychologists for doing just that. A committed operationalist would respond that speculation about the thing in itself, or noumenon, should be resisted as meaningless, and would comment only on phenomena using operationally defined terms and tables of operationally defined measurements.
A behaviorist psychologist might (operationally) define intelligence as that score obtained on a specific IQ test (e.g., the Wechsler Adult Intelligence Scale test) by a human subject. The theoretical underpinnings of the WAIS would be completely ignored. This WAIS measurement would only be useful to the extent it could be shown to be related to other operationally defined measurements, e.g., to the measured probability of graduation from university.
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:
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.
The term can be used strictly within the realm of the interactions of humans with advanced computational systems. In this sense, an AI system cannot be entirely operational (this issue can be used to discuss strong versus weak AI) if learning is involved.
Given that one motive for the operational approach is stability, systems that relax the operational factor can be problematic, for several reasons, as the operational is a means to manage complexity. There will be differences in the nature of the operational as it pertains to degrees along the end-user computing axis.
For instance, a knowledge-based engineering system can enhance its operational aspect and thereby its stability through more involvement by the SME, thereby opening up issues of limits that are related to being human, in the sense that, 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.
Many domains, with a numerics focus, use limits logic to overcome the duck test necessity with varying degrees of success. Complex situations may require logic to be more non-monotonic than not raising concerns related to the qualification, frame, and ramification problems.
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:
Of these, indentation hardness itself leads to many operational definitions, the most important of which are:
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 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.
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.
|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|
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)