The operational definition of happiness isn’t one definition. It’s a concept in psychology that happiness can be measured or detected based on a variety of factors that a researcher or data analyst decides are sufficient for measuring that happiness. For example, a researcher may decide that smiling is an indicator of happiness and that, to measure happiness, the researcher can count the number of smiles people make over a certain period of time. To better understand this concept as it applies to research in psychology and other social science fields, it helps to learn more about what operational definitions are, what they look like and what advantages and shortcomings they can offer.
What Are Operational Definitions?
In simple terms, operational definitions are parameters that define how to measure or detect something when you’re gathering data. They’re commonly used during research that relates to psychology, sociology and other social sciences, and most effective studies in these fields incorporate operational definitions in some way. This is because operational definitions are concrete and measurable, meaning they’re clear and can be counted, so they can help quantify gathered data in a meaningful way.
Operational definitions are important because they define how a researcher will measure a variable in a study. They’re statements of the procedures a researcher will use to define and track those variables. Because variables, by their very nature, change, it’s important to be able to clearly know what they are and how they’ll be measured. This can help ensure the data the researcher is gathering are actually relevant to the study and are effective indicators of whatever the researcher is measuring when seeing if their hypothesis is correct or incorrect.
The operational definition clearly (and always) defines a variable, such as an action. It doesn’t define a value, which is a number or score. Variables can take on values. An operational definition is the description of how those values might be used to measure variables.
Using Anxiety as an Example
To better understand operational definitions, it can help to take a concept and review the ways that it can be observed and measured — or how it can be operationalized. Imagine that you’re studying anxiety, an emotional response that we’re all familiar with. You know from experiencing anxiety that it can have outward effects that other people can observe, such as shaking, sweaty palms and a cracking voice, or it might cause someone to flee the stressor that’s causing anxiety. You also know that anxiety can cause inward symptoms that other people don’t notice, such as racing thoughts or chest tightness. These are all variables that signal anxiety.
With the variables in mind, it’s time to create your operational definition, or decide on the ways that you’ll measure people’s anxiety in your study. If you want to measure based on observable signals, you might create an operational definition that involves counting the number of outward signs people exhibit or seeing whether they flee a stressful situation or stay. For inward signs, you might have people wear heart rate monitors to determine when their pulses quicken. Or, you might create a survey and ask people to self-rate their anxiety on a scale or answer a questionnaire. No matter which method you choose to measure the anxiety, the act of defining how you’ll measure it is the creation of your operational definition.
Pros and Cons of Relying on Operational Definitions
One of the most helpful facets of operational definitions is their role in clarifying the validity of research. This means they help researchers determine if they measured what they intended to measure during their study. Operational definitions also help clarify the variables of a study. This makes it easier for other researchers to understand a study and potentially replicate the results because the variable was defined clearly enough that other researchers can set up a similar study and measure similar results.
Utilizing operational definitions has downsides that are important to take into account. One of the biggest is that using operational definitions essentially means making an informed choice about how to measure something. You could be making an underlying assumption about the operationalization, or there could be another factor that makes the choice an inappropriate one. The definition you create could skew the perceptions and analysis of data in a way that causes you to misrepresent those data.
As an example, let’s go back to the beginning. Say you want to measure happiness. Your hypothesis is that people smile more when they’re happy, so you operationalize counting smiles. This is your operational definition — it defines how you’ll measure the variable, happiness. You define other parameters, such as a timeframe, maybe that happy people smile 10 times in an hour. This sounds like you’re on the right track, but counting smiles actually doesn’t reflect a person’s happiness. People are more apt to smile primarily for social reasons, such as in response to another person’s smile when making eye contact in public or when feeling embarrassed, than they are because they’re feeling happy. In this case, you made an assumption about the operationalization being an appropriate measure, and it’ll prevent you from interpreting your data in a meaningful way.