15 Operationalization Examples

operationalization example and definition

Operationalization is the process of connecting abstract concepts to variables so they can then be measured or observed.

It involves assigning specific definitions or characteristics to a concept to quantify or test it. 

Operationalization is an important part of empirical research, as it helps researchers to reformulate abstract terms into measurable components so that data can be collected and analyzed. 

Operationalizing concepts also enables researchers to refine their hypotheses and develop an understanding of the relationships between variables.

An example of operationalization is when a philosopher needs to make spirituality measurable, so they might choose to design a survey asking participants questions about their religious beliefs, frequency of church attendance, and other related variables.

By doing so, the researcher can accurately measure the impact of a specific research question and determine the most appropriate form of data collection. 

Operationalization Definition

Operationalization involves assigning specific definitions or characteristics to a concept so that it can be quantified or tested.

According to Aragon and colleagues (2022),

“…operationalization is the process of defining the measurement of a phenomenon that is not directly measurable, though its existence is inferred by other phenomena (p. 159).

Potter (1996) believes that:

“…unless theoretical concepts are operationalized, they remain general abstract terms with no link to the real world” (p. 258).

Operationalization is an important part of empirical research. It helps researchers reformulate abstract terms into measurable components to collect and analyze data.

For instance, when exploring the concept of “trust,” a researcher might operationalize it by asking survey questions such as “you trust your partner/friends?” Then, on a scale of 1 to 10, how much do you trust your partner/friends?

These questions are measurable and help the researcher understand the research concept more concretely.

Simply, operationalization is the process of converting an abstract concept into measurable variables that can be tested.

Operationalization Examples

  • Making Spirituality Measurable – Operationalization can involve assigning metrics and scales to measure spiritual beliefs or experiences. For example, a researcher might assign numerical values or ratings to various questions measuring the spiritual intensity or connection.
  • Measuring Attitudes – Operationalization makes it possible to measure attitudes and opinions by attaching specific criteria to the concept. It can include creating scales with definite values (e.g., strongly agree, agree, neutral, disagree, strongly disagree) so that attitudes can be measured objectively.
  • Assessing Team Dynamics – Operationalizing team dynamics can involve creating specific criteria to measure aspects such as communication, collaboration, and conflict resolution. This can include using surveys or observation tools that have been developed based on specific definitions for each of these dynamics.
  • Constructing Social Norms – To operationalize social norms and behaviors, researchers can attach metrics such as frequency of engagement in an activity (e.g., how often people attend church services) or the strength of the norm in a particular culture (e.g., how important respect is seen to be within a society).
  • Assessing Competencies – Competencies are difficult to define without resorting to operationalization, as they require defining specific traits and characteristics that make up a capable individual in a given area. It could involve breaking down core skills into measurable components (e.g., problem-solving ability) and using tools like tests, interviews, or surveys to assess competency levels in each component area.
  • Quantifying Environmental Sustainability – To measure environmental sustainability, researchers and policymakers may use various operational definitions, such as assigning numerical values to measures like carbon footprint or creating standards for energy efficiency in buildings.
  • Identifying Mental Health Issues – Operationalizing mental health can involve assigning values or labels to observable symptoms or behaviors (e.g., sadness = level 4-5 on the depression scale), as well as creating concrete criteria for diagnosis (e.g., 6 out of 10 on the anxiety scale).
  • Myers-Briggs Personality Test: Measuring a person’s personality is hugely subjective. That’s why it needs to be operationalized. To do this, we often give people tests like the Myers-Briggs test, which asks them questions about what they’d do in different situations. This is put onto a scale and results in placing person into one of 16 different personality types.
  • Quantifying Happiness – Researchers have developed numerous metrics for measuring happiness that rely on operationalization; it includes assigning scores based on responses to survey questions about life satisfaction and creating scales that reflect different happiness levels in individuals (e.g., very happy = 7-10 on the happiness scale).
  • Learning Styles – Operationalizing learning styles involves self-reported testing where people look at their approaches to learning in a variety of contexts. This then results in categorizing people into learning styles like kinesthetic, mathematical, musical, etc. This type of testing is widely debunked in academic research but still used by carer councilors, for example, who might give careers advice for people who are musical, and so forth.
  • Measuring Educational Outcome – To measure the educational outcomes of students, teachers may use rubrics that rate performance across different areas, such as reading comprehension and critical thinking skills. These rubrics rely heavily on operational definitions for each skill set being assessed so that performance can be judged accurately against an objective standard.
  • Developing Psychological Tests – Operationalization is also used when constructing psychological tests which measure personality traits, intelligence, and aptitude levels. These tests typically feature clear instructions for participants and precise scoring protocols, which depend on careful consideration of test item content and response accuracy during the assessment stages.
  • Assessing Resilience – Operationalizing resilience involves defining specific factors that contribute to a person’s ability to cope with adversity. This can include measuring factors such as emotional regulation, social support, and problem-solving ability through various surveys or assessments.
  • Gauging Political Ideology – Political ideology is very difficult to measure without having precise definitions assigned to concepts like conservatism, liberalism, or radicalism so that they can be tested through survey questions or experiments.
  • Defining Successful Aging – Successful aging has been studied extensively over recent years to understand what constitutes effective aging when considering physical health indicators, the cognitive functioning capacity, and emotional well-being. Proposing specific metrics for each dimension requires operationalizing concepts to be measurable rather than subjective definitions based purely on opinion.

Origins of Operationalization

Operationalization is a concept that originated in the early 20th century. It was first introduced by British physicist Norman Campbell in his 1920 book Physics: The Elements.

Campbell (2015) suggested that scientific concepts should be defined and measured in terms of their observable consequences rather than their abstract properties.

American physicist Percy W. Bridgman further developed this idea in his 1927 book The Logic of Modern Physics.

Bridgman (1993) argued that all scientific concepts should be operationalized, meaning they should be defined and measured regarding their observable effects or outcomes.

Since then, operationalization has become an important part of the methodology and philosophy of science, as it allows for precise measurement and analysis of complex phenomena.

Operationalization is used to define and measure variables such as temperature, pressure, speed, distance, time, etc., as well as more abstract concepts such as intelligence or happiness.

By operationalizing these variables, researchers can accurately measure them and draw meaningful conclusions from their data.

Steps in Operationalization

Operationalization is the process of transforming abstract concepts into measurable observations. It involves creating operational definitions describing how a variable should be observed or measured (Van Thiel, 2014).

There are three main steps involved in the operationalization process:

  1. Defining the Concept – The first step is to define the concept you want to operationalize clearly. It includes identifying its key components, relating it to other concepts, and describing how it will be observed or measured.
  2. Establishing Operational Definitions – The second step is to develop operational definitions for the variables the researcher wants to measure. An operational definition must accurately capture the essence of a concept’s essence and provide clear instructions on how it should be observed or measured.
  3. Measuring Variables – Finally, the researcher needs to measure your variables using scales that best reflect their meaning and accurately capture their values. For example, if they want to measure someone’s level of happiness, they could use a 5-point Likert scale or visual analog scale with endpoints “very happy” and “not at all happy.”

By following these steps, researchers can effectively operationalize complex concepts and accurately measure them to draw meaningful conclusions from their findings.

Benefits of Operationalization

Operationalization has numerous benefits in the study of science and research since it allows for precise and accurate measurement of complex phenomena.

Operationalization is important when conducting experiments or studies as it ensures that all variables are measured accurately, allowing for reliable conclusions to be drawn.

Besides, operationalization helps to eliminate bias from the research process by providing clear guidelines on how a variable should be observed and measured.

By following strict guidelines, researchers can avoid skewed results due to their own misconceptions or expectations about a particular concept.

Importantly, operationalization allows researchers to compare data across different fields and disciplines. This enables them to determine relationships between concepts that may not be immediately apparent.

For example, operationalizing happiness could allow researchers to measure differences in well-being between different populations or understand how various environmental factors impact levels of contentment.

Ultimately, operationalization is essential for conducting valid and reliable research that accurately reflects reality and leads to meaningful findings.

Weaknesses of Operationalization

One of the main drawbacks to operationalizing concepts is that it can lead to oversimplification or distortion of a complex idea.

While operationalizing concepts allows for standardization and consistency, it also means that all nuances and characteristics of a concept may be lost in the process.

As a result, findings from research may overlook important aspects of a concept and fail to fully capture its true essence.

Besides, operationalization can lead to measurement errors if variables are not properly defined or scales are inappropriate for capturing their values accurately. It can cause inaccurate conclusions or results that do not reflect reality.

Finally, operationalization requires much upfront effort as researchers must thoroughly define and measure each variable before beginning their work.

It can be time-consuming and expensive, especially when conducting studies with large sample sizes or multiple variables.


Operationalization is a crucial aspect of empirical research, allowing researchers to convert abstract concepts into measurable variables that can be tested and analyzed.

It enables them to refine hypotheses, develop an understanding of relationships between variables, and accurately measure the impact of a specific research question. 

Despite the benefits of operationalization, there are also drawbacks, including oversimplification, measurement errors, and the requirement for upfront effort.

Nonetheless, operationalization remains essential to valid and reliable research that accurately reflects reality and leads to meaningful findings.

By defining the concept, establishing operational definitions, and measuring variables, researchers can operationalize complex concepts and draw meaningful conclusions from their data.


Aragon, C., Guha, S., Kogan, M., Muller, M., & Neff, G. (2022). Human-Centered data science. MIT Press.

Bridgman, P. W. (1993). The logic of modern physics. Ayer Co.

Campbell, N. R. (2015). Physics: The Elements. Scholar’s Choice.

Potter, W. J. (1996). An analysis of thinking and research about qualitative methods. Erlbaum.

Van Thiel, S. (2014). Research methods in public administration and public management. Routledge.

Viktoriya Sus

Viktoriya Sus (MA)

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Viktoriya Sus is an academic writer specializing mainly in economics and business from Ukraine. She holds a Master’s degree in International Business from Lviv National University and has more than 6 years of experience writing for different clients. Viktoriya is passionate about researching the latest trends in economics and business. However, she also loves to explore different topics such as psychology, philosophy, and more.

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This article was peer-reviewed and edited by Chris Drew (PhD). The review process on Helpful Professor involves having a PhD level expert fact check, edit, and contribute to articles. Reviewers ensure all content reflects expert academic consensus and is backed up with reference to academic studies. Dr. Drew has published over 20 academic articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education and holds a PhD in Education from ACU.

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