This post will walk you through the process of operationalizing variables. Operationalization is a technique for making the theory more concrete and useful in research or application by naming, defining, measuring, and/or creating a procedure for executing them.
Variables are the concepts, ideas, or characteristics that you want to measure in your research. Variables can range from something concrete like age or gender (ex: a study on gender) to something more abstract such as power distance or emotional intelligence (ex: a study on culture).
By operationalizing a variable, you make it measurable and more concrete so that it can be used for comparison between samples.
Operationalizing a variable is the process of defining and measuring it so that other researchers can understand it. To do so, you must define what exactly your variable is and then measure its level on some scale. For example, in a survey research, if you are studying people’s sleep habits, you might operationalize “sleep” as the number of hours slept each night and “bedtime” as when someone goes to bed each night.
In a research design, you might operationalize control variables by defining them and measuring their value.
For example, in an experiment on cognitive dissonance theory, your independent variable would be the persuasion message. Your dependent variable would be how much people change their behavior or feel about a topic after reading the message.
1) It must be clear, concise, and unambiguous
This characteristic is important because you want other researchers to understand your codebook and your variables.
2) It must represent the entity being defined independently of any kind or degree of measurement or judgment
This is important because if you are talking about something that cannot be measured, then the variable isn’t useful for research!
For example, researchers operationalizing sleep might say: “Sleep is the total amount of hours an individual spends in bed.”
3) It should describe an action that can feasibly be done in a lab/ verifiable.
This characteristic makes sense in light of your operationalization goal: you want to create a procedure for measuring whatever it is that you’re studying.
If your operational definition says something like “sleep is the urge to sleep when you aren’t sleepy,” then it’s too vague – can anyone in a lab accurately observe that?
Operationalizing a variable involves three steps:
1) Define the variable (what exactly you’re studying). This can be done by making an analogy or defining it in terms of another concept.
2) Measure the level of your variable on some scale so that researchers/practitioners can observe and understand its differences more easily (e.g., 0-10)
3) Provide a procedure for researching in the lab or field to produce reliable data relevant to your variable. (E.g., this is where you might explain how the survey would be conducted if you were trying to study sleep). Without this step, it’s harder to prove that there are “substantive and practical consequences for our understanding.”
1. Identify the topic you are researching (example: gender, culture) and a couple of relevant variables.
2. Choose one of your variables to operationalize now and explain why that variable is important.
3. Define your codebook – what exactly does this variable mean? What are you measuring, and how will it be measured?
4. Decide if your variable is important to research and why/how it might be relevant to practitioners or researchers working in this field.
5. Explain how operationalizing a variable can benefit future researchers/practitioners in this area.
In qualitative research, you may not be able to operationalize your variables in the same way that we do it in experimental or quantitative research. In qualitative methods, you must think critically about how you will operationalize your variables. It may not be possible to create a scale as we do in experimental research that has numbers assigned to each of your dependent and independent variables.
In this case, it is important that when defining your variables, you are still clear and concise in telling the reader what you mean. For example, if your research is focused on sexism and gender roles can’t be operationalized using a scale (such as 1=male gendered behavior; 2=female gendered behavior), it is still possible to ensure clarity by giving explicit examples of what these behaviors might look like.
For example, you could define “male-gendered behavior” as engaging in stereotypically male activities such as playing sports or watching science fiction movies and “female-gendered behavior” as: using make-up or engaging in traditionally female activities like sewing. Then, when your research is being conducted, you can note whether the participants were doing male or female gendered activities. In this way, you have operationalized your variables without using a scale that allows for the coding of behavior along a continuum from one end to the other, with every kind of behavior somewhere in-between.
One way to operationalize your variables is through a body count. For example, if you wanted to conduct a research project to determine the effects of gender on leadership perceptions, you could count up the number of males and females in groups where a leader was present. This method illustrates how you can operationalize variables to make them quantifiable.
Another way to operationalize variables is through Likert scales. For example, you could develop questions for leaders like “how assertive do you think this person was?” Then create a five-point Likert scale for your respondents based on how they respond. This would be a much better way to operationalize variables because it’s more specific.
Other examples of operationalized variables include the number of years in a career field, the percentage of time spent on certain activities, and working with other people.
The three major types of variables include independent, dependent, and control.
Independent variables are the antecedents in a research study. In other words, they explain the outcome of the study. The common type of independent variable is a manipulated variable, which is controlled by researchers and changed during an experiment or field study.
For example, in a study looking at the effect of “genital exposure” on behavior, the independent variable would be genital exposure. If researchers wanted to investigate how gender roles affect work performance, gender would be considered an independent variable because it is one of the reasons why performance differs between men and women.
Dependent variables are the outcome or response of a study. In other words, they depend on the independent variable(s) but do not cause them.
The most common type of dependent variable is a measured variable, which researchers discover instead of manipulated like independent variables. For example, in a study looking at the effect of “genital exposure” on behavior, whether or not someone punches another person would be considered a dependent variable because it is the response to genital exposure. Another kind of dependent variable is a quasi-measured variable. These are variables that researchers can observe based on something else.
For example, in an observational study of how employment affects family well-being, the number of hours worked would be considered a quasi-measured dependent variable because it is based on employment, not independent of it.
Independent and dependent variables aren’t always easily distinguished by their labels. But, if you know your independent variable is the cause of some other behavior or outcome, then it’s probably a dependent variable.
Control variables are anything that isn’t an independent variable or dependent variable and yet could change the results of your study.
For example, if I want to know how gender and leadership style affects job satisfaction, participants’ age may influence the results. So, it could be considered a control variable.
Control variables are very important to mention in your research proposal and methods section so that you can take them into account when conducting your study.
What Are Examples of Independent, Dependent, and Control Variables?
The following list shows three different types of variables: independent, dependent, and control. Many of these variables are common in research studies:
Independent Variable: Gender is an example of an independent variable because it’s one of the reasons for differences between men and women. Researchers might manipulate gender to see how it affects a particular behavior or outcome. They could also study how gender stereotypes affect work performance if they’re not manipulating it.
Dependent Variables: Employment is a dependent variable because its value (i.e., the employment rate) is defined by another factor, such as occupation. The more you study this type of variable, the easier it becomes to identify in research papers and articles.
Control Variable: Age and gender are important examples of control variables to discuss in your methods section. Why? Because they could have an effect on the employment rate, even though they’re not causing it.
For example, older workers are less likely to be employed than younger ones because of how age is related to other variables like education and experience.
Yes. Sometimes researchers want to see how something affects the value of a certain variable (i.e., independent), but they also want to know why it has this effect (i.e., dependent). These types of studies are called “cause and effect” experiments, and they’re very common in psychology research.
It would help if you always wrote your independent, dependent, and control variables in italics. You can also use the lowercase letter as a variable name, but it’s not required for each one (i.e., both “age” or “a” would work when discussing how age is a control variable). You should also mention the variables you’re working on within your research methods section if they aren’t controlling factors so that reviewers can better understand what’s happening to cause changes.
In research, there are limitations on how researchers can operationalize variables. Here are three possible problems or areas where operationalizing variables can go wrong:
- Operational measures do not measure the same concept.
- Operational measures allow self-report bias or other kinds of bias.
- There are no good ways of operationalizing concepts or ideas that are too abstract to be measured directly.
When conducting research, it is important to make sure we are using the best tools available. You want your data to be reliable and valid to be confident in the conclusions you can draw from them.
To do this, you must write out explicitly what your variables are and how they are operationalized or defined. If you do not write out your variables and operationalize them, your measurements are probably not valid or reliable – we cannot be sure that you measured what you say you did.
This is why it is important to have a clear operational definition for each of your variables in your research (or at least an explicit definition).
Remember that if you’re writing a research proposal for the first time, it may be difficult, and you’re better of leaving it to our research proposal writing service!