Happiness doesn’t have a single definition that is considered operational. Psychology holds the idea that a researcher or data analyst can determine which of a number of variables is appropriate for use in evaluating happiness. For instance, a researcher may determine that smiling is a sign of pleasure and that the best way to gauge happiness is to count how many times a person smiles within a given time period. Understanding what operational definitions are, how they work, and what benefits and drawbacks they can provide can help one better comprehend this idea as it relates to research in psychology and other social scientific domains.
Operational Definitions: What Are They?
Operational definitions are, to put it simply, parameters that specify how to measure or detect something when collecting data. They are frequently employed in studies relating to psychology, sociology, and other social sciences, and the most fruitful research in these areas includes some use of operational definitions. This is because operational definitions can aid in meaningfully quantifying obtained data because they are concrete, measurable, and countable. Operational definitions play a crucial role in research because they specify how a researcher will measure a variable in a study. They are descriptions of the steps a researcher will take to specify and monitor certain variables. It’s critical to have a clear understanding of the variables involved and how they will be measured because variables by their very nature are subject to change. This can help to guarantee that the data the researcher collects are genuinely pertinent to the study and are useful indications of whatever the researcher is measuring in order to determine whether or not their hypothesis is accurate.
A variable, such as an action, is always and explicitly defined in the operational definition. It doesn’t provide a value, which might be a score or a number. Values can be assigned to variables. The description of how those values might be applied to measure variables is known as an operational definition.
Giving the Case of Anxiety
It can be helpful to explore the various ways a notion can be observed, measured, or operationalized in order to better grasp operational definitions. Consider that you are researching the emotional reaction known as anxiety. You already know that anxiety can generate physical symptoms that other people can see, such as trembling, sweaty palms, and a quivering voice. It can also make a person run away from the stressor that is producing the worry. You are also aware that anxiety can result in hidden symptoms like racing thoughts or tightness in the chest that are felt only by the sufferer. All of these factors point to anxiousness.
It’s time to develop your operational definition, or choose how you’ll measure people’s anxiety in your study, bearing the variables in mind. If you want to gauge anything based on observable signals, you could develop an operational definition that counts the number of outward behaviours people display or tracks whether they leave or remain in a difficult scenario. For internal signals, you can have them wear heart rate monitors to determine when their pulses quicken. Or, you might make a survey and ask participants to complete a questionnaire or score their own level of worry on a scale. The act of determining how you’ll measure the anxiety is the establishment of your operational definition, regardless of the method you decide to use.
Benefits and Drawbacks of Using Operational Definitions
The ability of operational definitions to define the reliability of research is one of its most beneficial features. In other words, they assist researchers in determining if they were able to collect the data they needed for their study. Operational definitions also aid in defining a study’s variables. Because the variable was described so precisely, other researchers can set up a comparable study and measure similar outcomes, which makes it simpler for other researchers to comprehend a study and possibly duplicate the results.
It’s vital to consider the drawbacks of using operational definitions. One of the largest is that adopting operational definitions fundamentally entails selecting a measurement strategy with knowledge. You can be operating under an assumption regarding the operationalization, or there might be another element that renders the option incorrect. The term you come up with could distort your views and understanding of the data, leading you to present that data incorrectly.
Let’s go back to the beginning as an illustration. Let’s say your goal is to gauge happiness. You operationalize counting smiles based on your theory that people smile more when they are happy. This describes how you will measure the variable, happiness, and serves as your operational definition. You specify other criteria, such a time range or even the requirement that cheerful people grin 10 times in an hour. Although it seems like you’re on the right road, happiness isn’t actually reflected in a person’s grin count. People are more likely to smile for social reasons than for emotional ones, such as in response to another person’s grin when initiating eye contact in public or when feeling humiliated. Your assumption that the operationalization was a suitable measure in this instance will hinder you from meaningfully understanding your data.
