1.2 Phenomena, constructs and operationalization

In science, we are generally interested in describing and explaining phenomena. Phenomena are observable events or facts. For example, a person might quit their job, or might get married, or might give money to charity. In general, phenomena can be observed somewhat directly if you can find a way to record them.

Psychology is not just concerned with phenomena. It also deals with psychological constructs. Constructs are not in themselves directly observable, but are used to describe and categorize patterns of cognition or behaviour. Common constructs in psychology are things like empathy, stereotypes, self-esteem, and personality. Consider the example of intelligence. The actual phenomena that we can observe are speed of answering maths problems, or the propensity to solve difficult mental rotation tasks correctly. We never directly see the intelligence itself (which is latent), just the behaviours that we take to reflect it (which are manifest phenomena).

In order to do science, phenomena and constructs have to be turned into variables. A variable is, roughly speaking, something that can be entered into a column of a spreadsheet. A variable can take on several or many different values.

The step of turning the phenomena and constructs of interest into variables that we can measure is known as operationalization. This is a very difficult and important stage in psychological research. Your conclusions are only as good as your operationalization. Say you want to study racial prejudice. You could operationalize this in all kinds of different ways: perhaps the amount of money allocated to someone with an minority-sounding name as opposed to a majority-sounding name in a money allocation task; perhaps the participant’s difference in reaction time for responding to a positive word after having seen an African American versus a White American face appear on the screen. Perhaps it is their reported willingness to live in a majority African American neighbourhood. These operationalizations are all very different, and will have different issues relating to their validity (do they really capture the phenomenon or construct of interest?), convenience, measurement error, and so on.