Operationalization

In research design, especially in psychology, social sciences, life sciences, and physics, operationalization is a process of defining the measurement of a phenomenon that is not directly measurable, though its existence is inferred by other phenomena. Operationalization thus defines a fuzzy concept so as to make it clearly distinguishable, measurable, and understandable by empirical observation. In a broader sense, it defines the extension of a concept—describing what is and is not an instance of that concept. For example, in medicine, the phenomenon of health might be operationalized by one or more indicators like body mass index or tobacco smoking. As another example, in visual processing the presence of a certain object in the environment could be inferred by measuring specific features of the light it reflects. In these examples, the phenomena are difficult to directly observe and measure because they are general/abstract (as in the example of health) or they are latent (as in the example of the object). Operationalization helps infer the existence, and some elements of the extension, of the phenomena of interest by means of some observable and measurable effects they have. In research design, especially in psychology, social sciences, life sciences, and physics, operationalization is a process of defining the measurement of a phenomenon that is not directly measurable, though its existence is inferred by other phenomena. Operationalization thus defines a fuzzy concept so as to make it clearly distinguishable, measurable, and understandable by empirical observation. In a broader sense, it defines the extension of a concept—describing what is and is not an instance of that concept. For example, in medicine, the phenomenon of health might be operationalized by one or more indicators like body mass index or tobacco smoking. As another example, in visual processing the presence of a certain object in the environment could be inferred by measuring specific features of the light it reflects. In these examples, the phenomena are difficult to directly observe and measure because they are general/abstract (as in the example of health) or they are latent (as in the example of the object). Operationalization helps infer the existence, and some elements of the extension, of the phenomena of interest by means of some observable and measurable effects they have. Sometimes multiple or competing alternative operationalizations for the same phenomenon are available. Repeating the analysis with one operationalization after the other can determine whether the results are affected by different operationalizations. This is called checking robustness. If the results are (substantially) unchanged, the results are said to be robust against certain alternative operationalizations of the checked variables. The concept of operationalization was first presented by the British physicist N. R. Campbell in his 'Physics: The Elements' (Cambridge, 1920). This concept spread to humanities and social sciences. It remains in use in physics. Operationalization is the scientific practice of operational definition, where even the most basic concepts are defined through the operations by which we measure them. The practice originated with the philosophy of science book The Logic of Modern Physics (1927), by Percy Williams Bridgman, whose methodological position is called operationalism. Bridgman wrote that in the theory of relativity a concept like 'duration' can split into multiple different concepts. In refining a physical theory, it may be discovered that what was thought to be one concept is actually two or more distinct concepts. Bridgman proposed that if only operationally defined concepts are used, this will never happen. Bridgman's theory was criticized because 'length' is measured in various ways (e.g. it's impossible to use a measuring rod to measure the distance to the Moon), so 'length' logically isn't one concept but many, some concepts requiring knowledge of geometry. Each concept is to be defined by the measuring operation used. Another example is the radius of a sphere, obtaining different values depending on the way it is measured (say, in metres and in millimeters). Bridgman said the concept is defined on the measurement. So the criticism is that there are potentially infinite concepts, each defined by the methods that measured it, such as angle of sighting, day of the solar year, angular subtense of the moon, etc. which were gathered together, some astronomical observations taken over a period of thousands of years. The practical 'operational definition' is generally understood as relating to the theoretical definitions that describe reality through the use of theory. The importance of careful operationalization can perhaps be more clearly seen in the development of General Relativity. Einstein discovered that there were two operational definitions of 'mass' being used by scientists: inertial, defined by applying a force and observing the acceleration, from Newton's Second Law of Motion; and gravitational, defined by putting the object on a scale or balance. Previously, no one had paid any attention to the different operations used because they always produced the same results, but the key insight of Einstein was to posit the Principle of Equivalence that the two operations would always produce the same result because they were equivalent at a deep level, and work out the implications of that assumption, which is the General Theory of Relativity. Thus, a breakthrough in science was achieved by disregarding different operational definitions of scientific measurements and realizing that they both described a single theoretical concept. Einstein's disagreement with the operationalist approach was criticized by Bridgman as follows: 'Einstein did not carry over into his general relativity theory the lessons and insights he himself has taught us in his special theory.' (p. 335). Operationalization is often used in the social sciences as part of the scientific method and psychometrics. Particular concerns about operationalization arise in cases that deal with complex concepts and complex stimuli (e.g., business research, software engineering) where unique threats to validity of operationalization are believed to exist.