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 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.

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