research operational definition examples

Operational Definition Psychology – Definition, Examples, and How to Write One

Elizabeth Research

Every good psychology study contains an operational definition for the variables in the research. An operational definition allows the researchers to describe in a specific way what they mean when they use a certain term. Generally, operational definitions are concrete and measurable. Defining variables in this way allows other people to see if the research has validity . Validity here refers to if the researchers are actually measuring what they intended to measure.

Definition: An operational definition is the statement of procedures the researcher is going to use in order to measure a specific variable.

We need operational definitions in psychology so that we know exactly what researchers are talking about when they refer to something. There might be different definitions of words depending on the context in which the word is used. Think about how words mean something different to people from different cultures. To avoid any confusion about definitions, in research we explain clearly what we mean when we use a certain term.

Operational Definition Examples

Example one:.

A researcher wants to measure if age is related to addiction. Perhaps their hypothesis is: the incidence of addiction will increase with age. Here we have two variables, age and addiction. In order to make the research as clear as possible, the researcher must define how they will measure these variables. Essentially, how do we measure someone’s age and how to we measure addiction?

Variable One: Age might seem straightforward. You might be wondering why we need to define age if we all know what age is. However, one researcher might decide to measure age in months in order to get someone’s precise age, while another researcher might just choose to measure age in years. In order to understand the results of the study, we will need to know how this researcher operationalized age. For the sake of this example lets say that age is defined as how old someone is in years.

Variable Two: The variable of addiction is slightly more complicated than age. In order to operationalize it the researcher has to decide exactly how they want to measure addiction. They might narrow down their definition and say that addiction is defined as going through withdrawal when the person stops using a substance. Or the researchers might decide that the definition of addiction is: if someone currently meets the DSM-5 diagnostic criteria for any substance use disorder. For the sake of this example, let’s say that the researcher chose the latter.

Final Definition: In this research study age is defined as participant’s age measured in years and the incidence of addiction is defined as whether or not the participant currently meets the DSM-5 diagnostic criteria for any substance use disorder.

Example Two

A researcher wants to measure if there is a correlation between hot weather and violent crime. Perhaps their guiding hypothesis is: as temperature increases so will violent crime. Here we have two variables, weather and violent crime. In order to make this research precise the researcher will have to operationalize the variables.

Variable One: The first variable is weather. The researcher needs to decide how to define weather. Researchers might chose to define weather as outside temperature in degrees Fahrenheit. But we need to get a little more specific because there is not one stable temperature throughout the day. So the researchers might say that weather is defined as the high recorded temperature for the day measured in degrees Fahrenheit.

Variable Two: The second variable is violent crime. Again, the researcher needs to define how violent crime is measured. Let’s say that for this study it they use the FBI’s definition of violent crime . This definition describes violent crime as “murder and nonnegligent manslaughter, forcible rape, robbery, and aggravated assault”.

However, how do we actually know how many violent crimes were committed on a given day? Researchers might include in the definition something like: the number of people arrested that day for violent crimes as recorded by the local police.

Final Definition: For this study temperature was defined as high recorded temperature for the day measured in degrees Fahrenheit. Violent crime was defined as the number of people arrested in a given day for murder, forcible rape, robbery, and aggravated assault as recorded by the local police.

How to Write an Operational Definition

For the last example take the opportunity to see if you can write a clear operational definition for yourself. Imagine that you are creating a research study and you want to see if group therapy is helpful for treating social anxiety.

Variable One: How are you going to define group therapy? here are some things you might want to consider when creating your operational definition:

• What type of group therapy?
• Who is leading the therapy group?
• How long do people participate in the therapy group for?
• How can you “measure” group therapy?

There is no one way to write the operational definition for this variable. You could say something like group therapy was defined as a weekly cognitive behavioral therapy group led by a licensed MFT held over the course of ten weeks. Remember there are many ways to write an operational definition. You know you have written an effective one if another researcher could pick it up and create a very similar variable based on your definition.

Variable Two: The second variable you need to define is “effective treatment social anxiety”. Again, see if you can come up with an operational definition of this variable. This is a little tricky because you will need to be specific about what an effective treatment is as well as what social anxiety is. Here are some things to consider when writing your definition:

• How do you know a treatment is effective?
• How do you measure the effectiveness of treatment?
• Who provides a reliable definition of social anxiety?
• How can you measure social anxiety?

Again, there is no one right way to write this operational definition. If someone else could recreate the study using your definition it is probably an effective one. Here as one example of how you could operationalize the variable: social anxiety was defined as meeting the DSM-5 criteria for social anxiety and the effectiveness of treatment was defined as the reduction of social anxiety symptoms over the 10 week treatment period.

Final Definition: Take your definition for variable one and your definition for variable two and write them in a clear and succinct way. It is alright for your definition to be more than one sentence.

Why We Need Operational Definitions

There are a number of reasons why researchers need to have operational definitions including:

• Replicability
• Generalizability
• Dissemination

The first reason was mentioned earlier in the post when reading research others should be able to assess the validity of the research. That is, did the researchers measure what they intended to measure? If we don’t know how researchers measured something it is very hard to know if the study had validity.

The next reason it is important to have an operational definition is for the sake of replicability . Research should be designed so that if someone else wanted to replicate it they could. By replicating research and getting the same findings we validate the findings. It is impossible to recreate a study if we are unsure about how they defined or measured the variables.

Another reason we need operational definitions is so that we can understand how generalizable the findings are. In research, we want to know that the findings are true not just for a small sample of people. We hope to get findings that generalize to the whole population. If we do not have operational definitions it is hard to generalize the findings because we don’t know who they generalize to.

Finally, operational definitions are important for the dissemination of information. When a study is done it is generally published in a peer-reviewed journal and might be read by other psychologists, students, or journalists. Researchers want people to read their research and apply their findings. If the person reading the article doesn’t know what they are talking about because a variable is not clear it will be hard to them to actually apply this new knowledge.

Receive updates from my blog!

Scientific Research and Methodology

2.2 conceptual and operational definitions.

Research studies usually include terms that must be carefully and precisely defined, so that others know exactly what has been done and there are no ambiguities. Two types of definitions can be given: conceptual definitions and operational definitions .

Loosely speaking, a conceptual definition explains what to measure or observe (what a word or a term means for your study), and an operational definitions defines exactly how to measure or observe it.

For example, in a study of stress in students during a university semester. A conceptual definition would describe what is meant by ‘stress.’ An operational definition would describe how the ‘stress’ would be measured.

Sometimes the definitions themselves aren’t important, provided a clear definition is given. Sometimes, commonly-accepted definitions exist, so should be used unless there is a good reason to use a different definition (for example, in criminal law, an ‘adult’ in Australia is someone aged 18 or over ).

Sometimes, a commonly-accepted definition does not exist, so the definition being used should be clearly articulated.

Example 2.2 (Operational and conceptual definitions) Players and fans have become more aware of concussions and head injuries in sport. A Conference on concussion in sport developed this conceptual definition ( McCrory et al. 2013 ) :

Concussion is a brain injury and is defined as a complex pathophysiological process affecting the brain, induced by biomechanical forces. Several common features that incorporate clinical, pathologic and biomechanical injury constructs that may be utilised in defining the nature of a concussive head injury include: Concussion may be caused either by a direct blow to the head, face, neck or elsewhere on the body with an “impulsive” force transmitted to the head. Concussion typically results in the rapid onset of short-lived impairment of neurological function that resolves spontaneously. However, in some cases, symptoms and signs may evolve over a number of minutes to hours. Concussion may result in neuropathological changes, but the acute clinical symptoms largely reflect a functional disturbance rather than a structural injury and, as such, no abnormality is seen on standard structural neuroimaging studies. Concussion results in a graded set of clinical symptoms that may or may not involve loss of consciousness. Resolution of the clinical and cognitive symptoms typically follows a sequential course. However, it is important to note that in some cases symptoms may be prolonged.

While this is all helpful… it does not explain how to identify a player with concussion during a game.

Rugby decided on this operational definition ( Raftery et al. 2016 ) :

… a concussion applies with any of the following: The presence, pitch side, of any Criteria Set 1 signs or symptoms (table 1)… [ Note : This table includes symptoms such as ‘convulsion,’ ‘clearly dazed,’ etc.]; An abnormal post game, same day assessment…; An abnormal 36–48 h assessment…; The presence of clinical suspicion by the treating doctor at any time…

Example 2.3 (Operational and conceptual definitions) Consider a study requiring water temperature to be measured.

An operational definition would explain how the temperature is measured: the thermometer type, how the thermometer was positioned, how long was it left in the water, and so on.

Example 2.4 (Operational definitions) Consider a study measuring stress in first-year university students.

Stress cannot be measured directly, but could be assessed using a survey (like the Perceived Stress Scale (PSS) ( Cohen et al. 1983 ) ).

The operational definition of stress is the score on the ten-question PSS. Other means of measuring stress are also possible (such as heart rate or blood pressure).

Meline ( 2006 ) discusses five studies about stuttering, each using a different operational definition:

• Study 1: As diagnosed by speech-language pathologist.
• Study 2: Within-word disfluences greater than 5 per 150 words.
• Study 3: Unnatural hesitation, interjections, restarted or incomplete phrases, etc.
• Study 4: More than 3 stuttered words per minute.
• Study 5: State guidelines for fluency disorders.

A study of snacking in Australia ( Fayet-Moore et al. 2017 ) used this operational definition of ‘snacking’:

…an eating occasion that occurred between meals based on time of day. — Fayet-Moore et al. ( 2017 ) (p. 3)

A study examined the possible relationship between the ‘pace of life’ and the incidence of heart disease ( Levine 1990 ) in 36 US cities. The researchers used four different operational definitions for ‘pace of life’ (remember the article was published in 1990!):

• The walking speed of randomly chosen pedestrians.
• The speed with which bank clerks gave ‘change for two $20 bills or [gave] two $20 bills for change.’
• The talking speed of postal clerks.
• The proportion of men and women wearing a wristwatch.

None of these perfectly measure ‘pace of life,’ of course. Nonetheless, the researchers found that, compared to people on the West Coast,

… people in the Northeast walk faster, make change faster, talk faster and are more likely to wear a watch… — Levine ( 1990 ) (p. 455)

15 Operationalization Examples

Viktoriya Sus (MA)

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.

Learn about our Editorial Process

Chris Drew (PhD)

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.

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:

• 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.
• 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.
• 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 (MA) #molongui-disabled-link Cognitive Dissonance Theory: Examples and Definition
• Viktoriya Sus (MA) #molongui-disabled-link 15 Free Enterprise Examples
• Viktoriya Sus (MA) #molongui-disabled-link 21 Sunk Costs Examples (The Fallacy Explained)
• Viktoriya Sus (MA) #molongui-disabled-link Price Floor: 15 Examples & Definition

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 23 Achieved Status Examples
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 15 Ableism Examples
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 25 Defense Mechanisms Examples
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 15 Theory of Planned Behavior Examples

Leave a Comment Cancel Reply

Your email address will not be published. Required fields are marked *

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

Unit 12: Variables.

37 Variables; Operational and Conceptual Definitions

Listen, this whole “conceptual and operational definition” stuff might seem painfully boring but it’s actually one of the most useful Superpowers in your SYBI toolbox. The disconnect between the actual concept, the conceptual definition, and the operational definition is more prevalent than you think! And the disconnect between the scholar’s ConceptConceptualDefinitionOperationalDefinition and the average journalist’s perception? Oi ve! It’s enough to make you want to laterally read EVERYTHING that comes your way. At least, I hope it does. Let’s start in nice and slow and think about what are variables anyway? Student textbook authors: Take it away!

Learning Objectives

What is a variable?

Variables; Operational and Conceptual Definitions

Many of you have probably heard of or know what a variable from other classes like algebra. Variables are important in research because they help define and measure what is being researched. In this unit you should be able to define a variable and know the two main components of  variable.

Variables in social scientific research are similar to what you have learned in math classes, meaning they change depending on another element.

There are two components of a variable:

• A conceptual definition
• An operational definition

Conceptual Definitions- How we define something. It is the foundation of your research question because you must know  what  something is before you study its’ impact.

Example: How do Americans define the term freedom?

Operational Definitions- How we measure the variable. This is what you would typically think of when asked about the relationship between research and the research question. It relies on the conceptual definition.

Example: How do we measure what it means to have freedom?

Find the variables memory game .

Link to the “test” I mention in the video below:

https://www.idrlabs.com/gender-coordinates/test.php

“Researchers H. Heilman, Ph.D. and C. Peus, Ph.D. used a multidimensional framework to assess how people view men and women respectively. Their research results found that men and women consistently ascribe the same characteristics to each gender.”

Give it a whirl , take the “test.” What do YOU think about how they have operationalized the concept of gender?

Humor me and read the information below the start of the questions when you visit that link [1] .

This second link takes you to a different test but of the same basic concept. This is the one I referenced as “Bem’s Sex Role Inventory [2] .” https://www.idrlabs.com/gender/test.php

Ok, so take this one too (it really doesn’t take long, I promise). What do you think about the questions? Did you “score” the same? If not, why do you think that is? What does that say about operationalizing the concept? In future chapters I’ll ask you to think about what this would say about results and implications! I know – you are so excited!!

Also. Was not exaggerating my results:

First image is coordinates (IN the blue box), second is Bem’s (under the blue box)

Got ideas for questions to include on the exam?

Click this link to add them!

… Unit 1 … Unit 2 …. Unit 3 … Unit 4 … Unit 5 … Unit 6 … Unit 7 … Unit 8 … Unit 9 … Unit 10 … Unit 11 … Unit 12 … Unit 13 … Unit 14 … Unit 15 … Unit 16 …

VIII . Unit 8: Theory…and Research…and Methods (oh my!)

28. Logical Systems: Induction and Deduction

29. Variables; Operational and Conceptual Definitions

30. Variable oh variable! Wherefore art thou o’ variable?

31. On being skeptical [about concepts and variables]

Gender Coordinates Test

Based on the work of heilman and peus, question 1 of 35.

Self-confident

• "Drawing on the work of Dr. Sandra Lipsitz Bem, this test classifies your personality as masculine or feminine. Though gender stereotyping is controversial, it is important to note that Bem's work has been tested in several countries and has repeatedly been shown to have high levels of validity and test-retest reliability. The test exclusively tests for immanent conceptions of gender (meaning that it doesn't theorize about whether gender roles are biological, cultural, or both). Consequently, the test has been used both by feminists as an instrument of cultural criticism and by gender traditionalists who seek to confirm that gender roles are natural and heritable." ↵

Communication Research in Real Life Copyright © 2023 by Kate Magsamen-Conrad. All Rights Reserved.

Share This Book

Research Methods Course Pack

Chapter 3 operational definitions & measurement, 3.1 designing research.

We saw from the last section that conducting a research study involves forming a hypothesis, collecting evidence to confirm or disconfirm the hypothesis, and then interpreting the evidence. Imagine you wanted to see if a placebo (a treatment with no effect) would cause people to experience less pain. This was the question of David J. Scott and his colleagues (2007 ). The study involved injecting participants (with their informed consent) with a saline solution that caused pain. Participants were given either fake pain reliever or no treatment. To support the claim that the placebo reduces pain, the placebo participants should report lower pain than the non-placebo participants. Pain was measured using self-report surveys. Let’s look at the building blocks of this study.

3.2 Constructs versus Measures

The first concept is what the research is about. There is an important distinction between constructs and measures. A construct is a “concept, model, or schematic idea” (Shadish, Cook, & Campbell, 2002, p. 506). Constructs are the big ideas that researchers are interested in measuring: depression, patient outcomes, prevalence of cumulative trauma disorders, or even sales. For constructs in the social sciences, there is often disagreement and debate about how to define a construct. To do science, we must be able to quantify our observations (collect data) on the constructs. To go from a construct (the idea) to a measure requi res an operational definition. An operational definition describes how a construct is measured.

Constructs are what the study is about. The example study is about placebos and the reduction of pain. It isn’t really about saline solution or the Total Mood Disturbance measure as described in the article (Scott et al., 2007). The constructs of interest are placebos and pain. Pain was measured using the Total Mood Disturbance measure. Placebos were manipulated (the researcher controlled which participants were given a placebo and which were not).

3.3 IVs and DVs: Variables in Your Study

Another term for the measure in a study is the dependent variable (DV). Researchers look for a change in the DV that is due to a manipulation (the administration of the placebo or none). We call the manipulation the independent variable (IV). A quick mnemonic (memory aid) for the IV is that it is the variable that “I control”. The IV is also sometimes called the treatment. Researchers look for IVs (the causes) that cause changes in DVs (the effects). Thus, if you are designing a strong study, you want your IV and DV to be strongly related to each other.

So far, we have seen that studies have constructs, at least an IV and a DV. Another term for DV is dependent measure or outcome. All studies need an operational definition that explains how the DV construct is represented as a measure.

But what about the IV? The researcher manipulated the IV; they did not measure it. The construct behind the IV in this example is the placebo. Studies also need an operational definition that explains how the IV construct is represented as a manipulation. Here, the placebo was manipulated by creating two groups; one received the placebo and the other one did not.

Do you see the pattern? Studies exist at two levels. The construct level describes the themes of the study. Constructs are how researchers tie studies together. If you were reading research reports on this topic, you would probably look for “placebo” and “pain.” You would not search for “sugar pill” and “Total Mood Disturbance Measure.” The second level is the measurement level (more generally, the operation level). The operation level is exactly what happened in the study. Constructs are what we investigate, operations are what we do.

Psychologists are operationalists because they use study operations to represent constructs of interest. Is it possible for two psychologists to disagree on the link between study operations and constructs? Yes, this happens all the time. What if participants did not believe they were taking a “real” pain pill? Or, what if the sugar pill actually had effects on pain? Psychologists do argue about whether study operations are a good match for study constructs (this concept is called construct validity, and we’ll revisit it later). But psychologists understand that there is no way to perfectly capture a construct using a measure. If we had to perfectly agree on all measures for all constructs, we would be essentialists. Psychologists also understand that we do not have access to constructs except through study operations. Thus, we don’t argue about the “true nature” of constructs (which would be essentialism). We define constructs based on the measures we use to capture them (which is operationalism).

3.4 Other Variables: Samples and Populations

What is the role of the cause of the pain in this study? You’ll notice it is neither a DV nor an IV. It is best described as part of the study’s setting. Researchers must also make decisions about the settings they represent in their study. Therefore, the setting of the study is another source of constructs. Finally, the participants in the study are also a construct. Who is the study about? This is the population of interest. Because most studies are about large populations, the study is conducted with a sample, a subset of the population. Again, researchers draw conclusions about the study constructs (the population) through observation of study operations (the sample).

Now that you can see the difference between constructs and operations, we will look closer at how we measure.

3.5 Classifying Measurement Scales

We can classify measures in three ways: according to their level of measurement, whether or not they are continuous or discrete, and whether they represent qualitative or quantitative data.

3.5.1 Level of Measurement

A stair diagram is used because higher levels of measurement satisfy all the requirements of the levels below.

Notice that these levels are stair steps. Each level has all the characteristics of the level below it. So interval scales meet all the requirements of ordinal and nominal scales as well (plus they meet the additional requirement for interval scales).

To determine the level of measurement, ask yourself these questions:

• Can you rank/order the numbers? (if no, nominal scale. if yes, keep going) example: kinds of fish. can you rank halibut and mullet? (no, nominal scale) example: Olympic medals, can you rank gold, silver, and bronze? (yes, keep going)
• If you add/subtract the numbers, does the result have meaning? (if no, ordinal scale. if yes, keep going) example: 30 degrees F plus 10 degrees equals 40 degrees (yes, keep going) example: 1st place plus 2 equals 3rd place? (no, this doesn’t make sense, ordinal scale)
• Does the score have a value of 0 that means ‘none’ or ‘nothing’? (if no, interval scale. if yes, ratio scale) example: counting people; 0 people means no people (yes, ratio scale) example: 0 degrees F means no heat? (no, interval scale)

Continuous or Discrete

Separately, decide if your variable is continuous or discrete. If you can have an infinite number of fractions of a value, it’s continuous. If you cannot, the measure is discrete. example: 5 yards, 5.0005 yards, 5.5 years, and 5.500001 yards are all valid measurements (continuous) example: Olympic medals; the measurement between gold and silver does not exist (discrete)

There may be instances where a grey area exists; at some level, all variables are discrete. For example, you could subdivide a measurement of length down to the molecule. At that point, you cannot have fractional values. Try to avoid over-thinking this issue. If you can reasonably talk about fractional values (half seconds; twenty-five cents are a fraction of a dollar) then the measure is continuous. If you cannot (there is no such thing as half a dog or an eighth of an employee), then the measure is discrete.

3.5.2 Qualitative or Quantitative

Quant itative data is associated with a numerical value. Qual itative data is associated with labels that have no numerical value. Nominal and ordinal data are qualitative. Interval and ratio data are quantitative.

3.6 Measurement in SPSS

See the handout “SPSS Basics” for how to represent measures in SPSS.

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

• Knowledge Base
• Dissertation
• Operationalisation | A Guide with Examples, Pros & Cons

Operationalisation | A Guide with Examples, Pros & Cons

Published on 6 May 2022 by Pritha Bhandari . Revised on 10 October 2022.

Operationalisation means turning abstract concepts into measurable observations. Although some concepts, like height or age, are easily measured, others, like spirituality or anxiety, are not.

Through operationalisation, you can systematically collect data on processes and phenomena that aren’t directly observable.

• Self-rating scores on a social anxiety scale
• Number of recent behavioural incidents of avoidance of crowded places
• Intensity of physical anxiety symptoms in social situations

Instantly correct all language mistakes in your text

Be assured that you'll submit flawless writing. Upload your document to correct all your mistakes.

Table of contents

Why operationalisation matters, how to operationalise concepts, strengths of operationalisation, limitations of operationalisation, frequently asked questions about operationalisation.

In quantitative research , it’s important to precisely define the variables that you want to study.

Without transparent and specific operational definitions, researchers may measure irrelevant concepts or inconsistently apply methods. Operationalisation reduces subjectivity and increases the reliability  of your study.

Your choice of operational definition can sometimes affect your results. For example, an experimental intervention for social anxiety may reduce self-rating anxiety scores but not behavioural avoidance of crowded places. This means that your results are context-specific and may not generalise to different real-life settings.

Generally, abstract concepts can be operationalised in many different ways. These differences mean that you may actually measure slightly different aspects of a concept, so it’s important to be specific about what you are measuring.

If you test a hypothesis using multiple operationalisations of a concept, you can check whether your results depend on the type of measure that you use. If your results don’t vary when you use different measures, then they are said to be ‘robust’.

The only proofreading tool specialized in correcting academic writing

The academic proofreading tool has been trained on 1000s of academic texts and by native English editors. Making it the most accurate and reliable proofreading tool for students.

Correct my document today

There are three main steps for operationalisation:

• Identify the main concepts you are interested in studying.
• Choose a variable to represent each of the concepts.
• Select indicators for each of your variables.

Step 1: Identify the main concepts you are interested in studying

Based on your research interests and goals, define your topic and come up with an initial research question .

There are two main concepts in your research question:

• Social media behaviour

Step 2: Choose a variable to represent each of the concepts

Your main concepts may each have many variables , or properties, that you can measure.

For instance, are you going to measure the  amount of sleep or the  quality of sleep? And are you going to measure  how often teenagers use social media,  which social media they use, or when they use it?

• Alternate hypothesis: Lower quality of sleep is related to higher night-time social media use in teenagers.
• Null hypothesis: There is no relation between quality of sleep and night-time social media use in teenagers.

Step 3: Select indicators for each of your variables

To measure your variables, decide on indicators that can represent them numerically.

Sometimes these indicators will be obvious: for example, the amount of sleep is represented by the number of hours per night. But a variable like sleep quality is harder to measure.

You can come up with practical ideas for how to measure variables based on previously published studies. These may include established scales or questionnaires that you can distribute to your participants. If none are available that are appropriate for your sample, you can develop your own scales or questionnaires.

• To measure sleep quality, you give participants wristbands that track sleep phases.
• To measure night-time social media use, you create a questionnaire that asks participants to track how much time they spend using social media in bed.

After operationalising your concepts, it’s important to report your study variables and indicators when writing up your methodology section. You can evaluate how your choice of operationalisation may have affected your results or interpretations in the discussion section.

Operationalisation makes it possible to consistently measure variables across different contexts.

Scientific research is based on observable and measurable findings. Operational definitions break down intangible concepts into recordable characteristics.

Objectivity

A standardised approach for collecting data leaves little room for subjective or biased personal interpretations of observations.

Reliability

A good operationalisation can be used consistently by other researchers. If other people measure the same thing using your operational definition, they should all get the same results.

Operational definitions of concepts can sometimes be problematic.

Underdetermination

Many concepts vary across different time periods and social settings.

For example, poverty is a worldwide phenomenon, but the exact income level that determines poverty can differ significantly across countries.

Reductiveness

Operational definitions can easily miss meaningful and subjective perceptions of concepts by trying to reduce complex concepts to numbers.

For example, asking consumers to rate their satisfaction with a service on a 5-point scale will tell you nothing about why they felt that way.

Lack of universality

Context-specific operationalisations help preserve real-life experiences, but make it hard to compare studies if the measures differ significantly.

For example, corruption can be operationalised 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.

Prevent plagiarism, run a free check.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports).

The process of turning abstract concepts into measurable variables and indicators is called operationalisation .

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research , you also have to consider the internal and external validity of your experiment.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

Bhandari, P. (2022, October 10). Operationalisation | A Guide with Examples, Pros & Cons. Scribbr. Retrieved 30 August 2024, from https://www.scribbr.co.uk/thesis-dissertation/operationalisation/

Is this article helpful?

Pritha Bhandari