Operationalize Variables (A complete guide)

In this brief guide, we will look at how to operationalize variables, and other statistical procedures related to variables that are often used in qualitative and quantitative research.

What does it mean to Operationalize Variables?

To operationalize variables means the process in any social sciences that a researcher employs in their study, to map out the measurable version of the abstract concepts they are studying or trying to quantify.

In other words, when a researcher wants to figure out the best way to measure concepts that cannot be observed as is or studied as they are in the real-world scenario, one goes through the process of mapping out and defining the variables at the outset so that there is structure to the research.

Operationalizing variables is different from setting a research design as when someone is defining the research design, they define all aspects of the research study, including what kind of study it is (qualitative or quantitative), whereas operationalizing variables may come even before one finalizes the research design.

Operationalizing variables is also specific mostly to quantitative studies, as it involves specifying variables that are meant to measure abstract concepts.

Operationalizing variables may not be as proper a process as other things in the research design, in that it may not be a part of the written paper that the researcher eventually prepares, or the dissertation they write about whatever study they have done.

Information about variables is included under the method or briefly in the introduction and the review of literature is done according to what the variables are, that is, studies that may have used the same variables in the past are read, researched and included as evidence for why the researcher is undertaking a study for these particular variables and what they are aiming at proving, improving upon or invalidating from those previous studies about the variables.

Operationalization may also be defined, simply, as the process by which researchers conducting quantitative research spell or mention precisely how a concept will be measured and which involves identifying the specific research procedures they will use to gather data about their concepts. 

The process of operationalization of variables requires that the researcher already knows what research method(s) they will employ to learn about the concepts they are studying, and they will examine specific research methods for that later. 

Measurement of variables in qualitative research is different, but even in qualitative research one needs to specify the kind of variables they will be studying and what kind of measurement systems they will be using for the study of those variables.

In some cases, the researcher might find that multiple or competing alternative operationalizations for the same phenomenon are available in which case they may want to repeat the analysis with one operationalization after the other so that they can determine whether the results are affected by different operationalizations and this process is known as checking robustness. 

If the researchers find that the results of Checking Robustness are significantly unchanged, the results are said to be robust against certain alternative operationalizations of the checked variables.

Types of Variables

The operationalization of variables depends on what variables are, and there are two main types of variables, Independent variable and dependent variable.

Both these types of variables are found in experimental research, primarily, and they may sometimes also be found in the types of research that is done with the purpose of comparing the means of two populations, particularly for checking the reliability of helpfulness of an intervention.

Independent Variable

An independent variable is the variable that is changed or manipulated by the experimenter. 

Implicitly, it is the variable that we think causes a change in the other variable. In our studying experiment, we manipulate study time because we think that longer studying causes fewer errors. 

Thus, the amount of study time is our independent variable or, in an experiment to determine whether eating more chocolate causes people to blink more, the experimenter would manipulate the independent variable of the amount of chocolate a person eats. 

You can remember the independent variable as the variable that occurs independently of the participants’ wishes.

Technically, a true independent variable is manipulated by doing something to participants.

However, there are many variables that an experimenter cannot manipulate in this way. For example, we might hypothesize that growing older causes a change in some behavior.But we can’t make some people 20 years old and make others 40 year old.

Instead, we would manipulate the variable by selecting one sample of 20-year-olds and one sample of 40-year-olds. Similarly, if we want to examine whether gender is related to some behavior, we would select a sample of females and a sample of males. In our discussions, we will call such variables independent variables because the experimenter controls them by controlling a characteristic of the samples. 

Statistically, all independent variables are treated the same. Thus, the experimenter is always in control of the independent variable, either by determining what is done to each sample or by determining a characteristic of the individuals in each sample. 

In essence, a participant’s “score” on the independent variable is assigned by the experimenter. In our examples, we, the researchers, decided that one group of students will have a score of 1 hour on the variable of study time or that one group of people will have a score of 20 on the variable of age.

A condition is a specific amount or category of the independent variable that creates the specific situation under which participants are examined. Thus, although our independent variable is amount of study time.

Dependent Variable

The dependent variable is used to measure a participant’s behavior under each condition. A participant’s high or low score is supposedly caused or influenced by—depends on—the condition that is present. 

Thus, in our studying experiment, the number of test errors is the dependent variable because we believe that errors depend on the amount of study. 

If we manipulate the amount of chocolate people consume and measure their eye blinking, eye blinking is our dependent variable, on the other hand, if we studied whether 20- or 40-year-olds are more physically active,then activity level is our dependent variable.

A major component of your statistics course will be for you to read descriptions of various experiments and, for each, to identify its components. 

As shown, from the description, find the variable that the researcher manipulates in order to influence a behavior—it is the independent variable, and the amounts of the variable that are present are the conditions.

The behavior that is to be influenced is measured by the dependent variable, and the amounts of the variable that are present are indicated by the scores. All statistical analyses are applied to only the scores from this variable.

Strengths and Weaknesses of Operationalization of Variables 

While operationalization of variables is important and must be done, it still has strengths and weaknesses that should be kept in mind either way, and these are given below.

Strengths of operationalization of variables:

Operationalization makes it possible for researchers to measure variables consistently across contexts.

Empiricism, which is the quality of research to be based on observable and measurable findings,comes into existence after the Operational definitions of variables have been set so that the variables can be broken down from intangible concepts into recordable characteristics.

Objectivity is another benefit of the operationalization process, and through this because operationalization of variables is a standardized approach for collecting data, it leaves little room for subjective or biased personal interpretations of observations.

A well-done operationalization of variables can be used consistently by other researchers, which is a central tenet of the scientific method, and if operationalization of variables is not done, this is not possible, which makes this process even more necessary.

Weaknesses of operationalization of variables includes:

Sometimes, operationalization of variables may not necessarily take into account the time frame and social context related variability of the concepts.

Operational definitions can sometimes also miss meaningful and subjective perceptions of concepts because they involve breaking things down into small and measurable components, and they seek to reduce complex concepts to numbers, and this may cause things that should only be measures in a qualitative way to be measures in a quantitative way, which may be an inadequate measure of that concept.

Operationalization of variables can sometimes also have a lack of universality, and while context-specific operationalizations can help preserve real-life experiences, they may at the same time make it rather hard to compare studies if the measures differ significantly.

For example, corruption can be operationalized in a wide range of ways (e.g., perceptions of corrupt business practices, or frequency of bribe requests from public officials), but the measures may not consistently reflect the same concept.


In this brief guide, we looked at how to operationalize variables, and other statistical procedures related to variables that are often used in qualitative and quantitative research.

Research is one of the most important parts of many sciences, including humanities subjects like psychology, and even though it was an assumption for the longest time that research was not a part of psychology, it has been widely accepted in the past few decades that research is extremely important in psychology and there are many variables to be studied in this subject.

Research variables in psychology have been a very important measure for psychologists to understand how certain mechanisms of behavior work, and many important theories in psychology have come about as a result of operationalizing behavior.

If you have any questions or comments about operationalizing variables in psychology or other social sciences, please feel free to reach out to us at any time.

Frequently Asked Questions (FAQs): Operationalize Variables

What is the meaning of operationalization in research?

The meaning of Operationalization in research is the process by which researchers or experimenters conducting quantitative research put in place a plan of how the concept will be measured and specifically what methodology and variables will be used.

Operationalization in research involves identifying the specific research procedures that will be employed in gathering data about the concepts under study.

What is an example of operationalization?

An example of operationalization may be studying the symptoms of social anxiety by means of the experiences reported by individuals on a self-rating scale for social anxiety.

Operationalization may be seen in any example where an abstract concept is turned into a valid and measurable theory or group of variables.

Why is operationalization important?

Operationalization is important because through this process, a researcher is able to achieve real-world and measurable variables for a theoretically centered science, which is extremely important in a science like psychology given that it pertains to how human beings work.

The importance of operationalization stems from ust how much it helps in specifying exactly how a concept is being measured or produced and reproduced in a particular study.

How do you operationalize a construct?

To operationalize a construct , one might try to make it more observable and and define the measurable components of a given construct or behavior. 

The process of operationalization of constructs is done more in the social sciences because scientists in that field have to spend so much time creating and validating their constructs of interest because they start out from a very theoretical basis, and they have to go through a process of defining these constructs explicitly before measuring them.





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