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 indicated by other phenomena. Operationalization is thus the process of defining a fuzzy concept so as to make it clearly distinguishable, measurable, and understandable in terms of empirical observations. In a wider sense, it refers to the process of specifying 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. As shown in these examples, two main reasons for which some phenomena are difficult to directly observe and measure are that they are general/abstract (as in the example of health) or they are latent (as in the example of the object). In these cases operationalization leads to 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, when multiple or competing alternative operationalizations for the same phenomenon are available, one can repeat the analysis with all of the operationalizations one after the other, to see if the results are impacted by different operationalizations. This is often called conducting a robustness check. 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 next spread to humanities and social sciences. It remains in use in physics.
Video Operationalization
Theory
History
Operationalization is used to specifically refer to the scientific practice of operationally defining, where even the most basic concepts are defined through the operations by which we measure them. This comes from the philosophy of science book The Logic of Modern Physics (1927), by Percy Williams Bridgman, whose methodological position is called operationalism.
Bridgman notes that in the theory of relativity we see how a concept like "duration" can split into multiple different concepts. As part of the process of refining a physical theory, it may be found that what was one concept is, in fact, two or more distinct concepts. However, Bridgman proposes that if we only stick to operationally defined concepts, this will never happen.
Bridgman's theory was criticized because we measure "length" in various ways (e.g. it's impossible to use a measuring rod if we want to measure the distance to the Moon), "length" logically isn't one concept but many, some concepts requiring knowledge of geometry. Each concept is to be defined by the measuring operations 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 we could potentially end up with endless concepts, each defined by the things that measured the concept, 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.
Operationalization
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).
Maps Operationalization
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.
Anger example
For example, a researcher may wish to measure the concept "anger." Its presence, and the depth of the emotion, cannot be directly measured by an outside observer because anger is intangible. Rather, other measures are used by outside observers, such as facial expression, choice of vocabulary, loudness and tone of voice; after Damasio, Lesion studies.
If a researcher wants to measure the depth of "anger" in various persons, the most direct operation would be to ask them a question, such as "are you angry", or "how angry are you?". This operation is problematic, however, because it depends upon the definition of the individual. Some people might be subjected to a mild annoyance, and become slightly angry, but describe themselves as "extremely angry," whereas others might be subjected to a severe provocation, and become very angry, but describe themselves as "slightly angry." In addition, in many circumstances it is impractical to ask subjects whether they are angry.
Since one of the measures of anger is loudness, the researcher can operationalize the concept of anger by measuring how loudly the subject speaks compared to his normal tone. However, this must assume that loudness is uniform measure. Some might respond verbally while other might respond physically.
Economics objections
One of the main critics of operationalism in social science argues that "the original goal was to eliminate the subjective mentalistic concepts that had dominated earlier psychological theory and to replace them with a more operationally meaningful account of human behavior. But, as in economics, the supporters ultimately ended up "turning operationalism inside out". "Instead of replacing 'metaphysical' terms such as 'desire' and 'purpose'" they "used it to legitimize them by giving them operational definitions." Thus in psychology, as in economics, the initial, quite radical operationalist ideas eventually came to serve as little more than a "reassurance fetish" for mainstream methodological practice."
Tying to conceptual frameworks
The above discussion links operationalization to measurement of concepts. Many scholars have worked to operationalize concepts like job satisfaction, prejudice, anger etc. Scale and index construction are forms of operationalization. There is not one perfect way to operationalize. For example, in the United States the concept distance driven would be operationalized as miles, whereas kilometers would be used in Europe.
Operationalization is part of the empirical research process Take for example an empirical research question: Does job satisfaction influence job turnover? Both job satisfaction and job turnover need to be measured. The concepts and their relationship are important -- operationalization occurs within a larger framework of concepts. When there is a large empirical research question or purpose the conceptual framework that organizes the response to the question must be operationalized before the data collection can begin. If a scholar constructs a questionnaire based on a conceptual framework, they have operationalized the framework. Most serious empirical research should involve operationalization that is transparent and linked to a conceptual framework.
To use an oversimplified example, the hypothesis Job satisfaction reduces job turnover is one way to connect (or frame) two concepts - job satisfaction and job turnover. The process of moving from the idea job satisfaction to the set of questionnaire items that form a job satisfaction scale is operationalization. For most of us, operationalization outside the larger issue of a research question and conceptual framework is just not very interesting.
Operationalization uses a different logic when testing a formal (quantitative) hypothesis and testing working hypothesis (qualitative). For formal hypotheses the concepts are represented empirically (or operationalized) as numeric variables and tested using inferential statistics. Working hypotheses (particularly in the social and administrative sciences), on the other hand, are tested through evidence collection and the assessment of the evidence. The evidence is generally collected within the context of a case study. The researcher asks - Is the evidence sufficient to 'support' the working hypothesis? Formal operationalization would specify the kinds of evidence needed to support the hypothesis as well as evidence which would "fail" to support it. Robert Yin recommends developing a case study protocol as a way to specify the kinds of evidence needed during the data collection phases. He identifies six sources of evidence 1) documentation; 2) archival records; 3) interviews; 4) direct observations; 5) participant observation and 6) physical or cultural artifacts.
In the field of public administration, Shields and Tajalli (2006) have identified five kinds of conceptual frameworks (working hypothesis, descriptive categories, practical ideal type, operations research, and formal hypothesis). They explain and illustrate how each of these conceptual frameworks can be operationalized. They also show how to make conceptualization and operationalization more concrete by demonstrating how to form conceptual framework tables that are tied to the literature and operationalization tables that lay out the specifics of how to operationalize the conceptual framework (measure the concepts).
For examples of research projects that use conceptual framework and operationalization tables.
Notes
Bibliography
- Bridgman, P.W. (1927). "The Logic of Modern Physics".
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