What Is Operationalization?

Abstract concepts are difficult to measure because they do not have digital criteria. However, you can turn these concepts into measurable things with the help of operationalization. Even such difficult for measuring phenomena as anxiety or satisfaction can be measured. They are not immediately observable, but they can produce data that you can use as results of your research.

For instance, you can operationalize the concept of satisfaction via such techniques as:

• ✔️ self-rating results on satisfaction scales
• ✔️ the number of cases that produced satisfaction with the results
• ✔️ intensity of satisfaction when specific services are provided

Why Operationalization Is Important

The variables are usually observable in figures in quantitative research. Therefore, you need clear definitions of measures to avoid measuring concepts that do not relate to each other or using inconsistent research methods. You should reduce subjectivity and bias in this way to increase the validity and reliability of research.

When you choose an operational definition, think about its possible effects on the obtained results. Even when you are conducting an experiment meant to observe different levels of satisfaction, the self-rating results may diverge from the experimental outcomes and real-life settings for the same products or services.

You can operationalize abstract concepts in several ways. You can even measure the aspects that may be slightly different, so specifications while measuring these concepts can matter a lot.

Examples
1. While measuring the concept of creativity, you need to consider the experimental number of object uses a participant can produce in 5 minutes and their average rating of originality.
2. The concept of overconfidence can produce a discrepancy between how strong people think they are while dealing with weight-lifting and how much they can really lift. It is called overestimation. Or the same people can compare and rank themselves differently among others in comparison to where they are actually ranked by those others. It is known as over-placement.
3. When you research the concept of customer loyalty, you may come across the difference between the customers’ intention to use the IT company’s services they have expressed in a questionnaire and their true desire to come back to this company. Or you can see that the repeated customers do not tend to return to the company within fewer than 3-4 months which diverges from what they declared in the answers.
4. Suppose you need to assess the rate of stress perception. You experiment with various stressful situations that can produce anxiety and physiological responses to stress, such as the loss of appetite or apathy. However, the participants’ reactions to stress may diverge in time, so it is impossible to follow the exact cause-and-effect relationships because, if extended, similar reactions can be caused by some other factors which are not part of your experiment.

The results can also depend on the type of measure you have chosen for your experiment, especially if you utilize multiple operationalizations of concepts to test your hypothesis. The results are considered robust if they do not vary too much when various kinds of measurements are used.

Operationalization Steps

There are at least three operationalization steps to apply to the research procedure.

Step 1. Identification of the Main Concepts of Interest

This identification should be based on the goals of the research. You need to start with a research topic and question. For example, your research question can be, ‘Is there a relationship between stress and chocolate consumption in office workers?’ Here, you have two main research concepts - stress and chocolate consumption.

Step 2. The Choice of Variables to Represent the Concepts

Every concept may have different variables and characteristics for research. For example, you can measure either the intensity of teenage playing violent computer games or the time of the day when they prefer to do it and how it influences their mood and well-being. Or you can consider teenagers’ mothers who are housewives and use social media, paying attention to how often they use it, what kind of social media they use, and at what times of the day they prefer to use it.

Sometimes, it may be rather difficult to decide which variable to consider. Therefore, you need to review the previous pieces of research and think about the most helpful variables in them or those that were not included in the study. Seeing gaps in the research is a valuable quality for gaining academic success when you manage to fill them.

Thus, after considering the previous research attempts, you have chosen to measure the time of the day when teenagers prefer to play computer games and the frequency of mothering housewives using social media. You predict that there is a relationship between these variables and put forward null and alternate hypotheses.

The alternate hypothesis is that the intensity of teenagers’ playing violent computer games depends on their mothers’ immersion in the social media world and not taking care of their kids. The null hypothesis here is that there is no relationship between teenage playing violent video games and their mothers being busy with social media communication.

Step 3. Selection of Indicators for Variables

Indicators for variables should represent them in numbers. Sometimes, they are apparent, for example, the amounts of time teenagers play violent video games, which can be expressed in the number of hours per day. However, the variable of how the intensity of playing violent video games influences teenagers’ mood and well-being is difficult to measure.

You should think about some practical indicators for measuring the variables that have been possibly used in previous research works. These indicators may consider the scales or questionnaires that you can use for your participants. Or you may develop your own questionnaires and scales if you have not managed to find any.

For example, you can measure the influence of violent games on teenagers’ moods by giving them the task of assessing their mood before and after playing the games by the use of a specifically developed scale. Or you can ask the teenagers’ mothers to complete the questionnaire about their preferable day times and amounts for using social media.

When you have operationalized the concepts in this way, you need to report these indicators and chosen variables in the methodology section of your paper. You can also discuss now how much the choice of operationalization has influenced the results of the research in a discussion section.

Strengths and Limitations of Operationalization

The most apparent strengths of operationalization are:
✔️ it allows for measuring variables in different contexts; ✔️ it makes research more empirical by observation and measuring findings with the help of operational definitions; ✔️ it produces standardized approaches that make the research objective and free from bias; ✔️ it makes your research reliable and available for use by further researchers with the help of your operational definitions.
As for the limitations, operationalization can produce the following:
❌ finding adequate operational definitions can sometimes be problematic; ❌ many concepts can change over time or with the changes in social settings; ❌ these definitions can miss some meanings or subjective interpretations when reduced to mere numbers; ❌ operational definitions are mostly context-specific, so measures in real life can differ a lot.

Final Thoughts

Nevertheless, operationalization is a great method to research concepts that are not initially countable. This method plays a great role in obtaining reliable and valid results and making consistent conclusions. Though, you need to take into account all its pros and cons to be on the safe side and make your research results valid.