Asking many similar questions when trying to measure a concept is done to:

First is the semantic scale. You want to choose options that are simple and unambiguous. Among the most common: Agree—Disagree, Helpful—Not Helpful, Excellent—Poor, Satisfied—Dissatisfied, Always—Never. But just because they’re popular doesn’t mean they are clear.

And make sure the differences between the categories are valid and useful. Let’s say you want to measure how often a person gets up from their desk at work. You choose Never—Seldom—Sometimes—Often—Always. How do you quantify the difference between seldom and sometimes?

If a scale is potentially ambiguous, either explain the meanings in your introduction or change the scale. Don’t use ‘Sometimes’ when you really mean ‘Once a week.’

Second is the number of response choices. Likert-type responses often have an odd number, so respondents have a neutral option. The jury is still out on whether that is necessary or even desirable.

Most researchers agree that, at a minimum, you should use a 5-point Likert scale survey. But other research shows that the more choices there are, the less often respondents use the middle or neutral category. What seems clear is that a 7-point Likert scale approaches the upper limits of reliability—so adding more options is likely to give you worse, not better, Likert scale data.

Think about those HappyOrNot terminals you find in airports, shopping malls, and even in toilets. You know, the ones with the happy and angry faces on them? Most of them use an even number of response choices. Why? Because it forces people to choose a side, making it easier to collapse responses into two categories (positive vs. negative experience).

Which you choose depends on how you plan to evaluate the responses.

Here are a few more tips for creating your Likert scale responses:

Be creative! Just because you’re collecting data doesn’t mean you need to sound like a robot. Survey tools like Typeform let you edit the labels, so your brand voice can shine through your questions. Try out Interesting—Not Interesting, or even No Way—Meh—Totally! to keep people engaged.

Use unipolar responses. The aim of a Likert scale is to get at a larger concept with a series of questions. Don’t name that concept with polar opposites like Safe—Dangerous or Strong—Weak. Instead, measure in degrees: Very Safe—Not Safe and Very Strong—Not at All Strong. They’re just easier for people to understand, and you can be sure that one extreme is the exact opposite of the other.

Stay consistent with your scales. Creating a Likert scale involves summing or averaging the responses to measure a concept or phenomenon. Without consistent scales, you can’t be certain you are measuring the same thing with each statement.

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

Operationalization exampleThe concept of social anxiety can’t be directly measured, but it can be operationalized in many different ways. For example:

• self-rating scores on a social anxiety scale
• number of recent behavioral incidents of avoidance of crowded places
• intensity of physical anxiety symptoms in social situations

Table of contents

1. Why operationalization matters
2. How to operationalize concepts
3. Strengths of operationalization
4. Limitations of operationalization
5. Frequently asked questions about operationalization

Why operationalization matters

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

Without transparent and specific operational definitions, researchers may measure irrelevant concepts or inconsistently apply methods. Operationalization reduces subjectivity, minimizes the potential for research bias, 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 behavioral avoidance of crowded places. This means that your results are context-specific, and may not generalize to different real-life settings.

Generally, abstract concepts can be operationalized 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.

ConceptExamples of operationalizationOverconfidence

• The difference between how well people think they did on a test and how well they actually did (overestimation).
• The difference between where people rank themselves compared to others and where they actually rank (overplacement).

Creativity

• The number of uses for an object (e.g., a paperclip) that participants can come up with in 3 minutes.
• Average ratings of the originality of uses of an object that participants come up with in 3 minutes.

Perception of threat

• Physiological responses of higher sweat gland activity and increased heart rate when presented with threatening images.
• Participants’ reaction times after being presented with threatening images.

Customer loyalty

• Customer ratings on a questionnaire assessing satisfaction and intention to purchase again.
• Records of products purchased by repeat customers in a three-month period.

If you test a hypothesis using multiple operationalizations 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.”

How to operationalize concepts

There are 3 main steps for operationalization:

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

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.

Research question exampleIs there a relation between sleep and social media behavior in teenagers?

There are two main concepts in your research question:

• Sleep
• Social media behavior

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?

ConceptVariablesSleepAmount of sleepQuality of sleepSocial media behaviorFrequency of social media useSocial media platform preferencesNight-time social media useTo decide on which variables to use, review previous studies to identify the most relevant or underused variables. This will highlight any gaps in the existing literature that your research study can fill.Hypothesis exampleBased on your literature review, you choose to measure the variables quality of sleep and night-time social media use. You predict a relationship between these variables, and state it as a null and alternate hypothesis.

• Alternate hypothesis (Ha or H1): Lower quality of sleep is related to higher night-time social media use in teenagers.
• Null hypothesis (H0): There is no relation between quality of sleep and night-time social media use in teenagers.

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 (e.g., Likert 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.

ConceptVariableIndicatorSleepAmountAverage number of hours of sleep per nightQualitySleep activity tracker of sleep phasesSocial media behaviorFrequencyNumber of logins during the dayPreferenceMost frequently used social media platformNight-time useAmount of time spent using social media before sleepIndicators example

• 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 operationalizing 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 operationalization may have affected your results or interpretations in the discussion section.

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Strengths of operationalization

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

• Empiricism

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

• Objectivity

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

• Reliability

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

Limitations of operationalization

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 operationalizations help preserve real-life experiences, but make it 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.

Frequently asked questions about operationalization

What is operationalization?

Operationalization 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, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

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

What’s the difference between concepts, variables, and indicators?

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 operationalization.

What’s the difference between reliability and validity?

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.

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Bhandari, P. (2022, December 02). Operationalization | A Guide with Examples, Pros & Cons. Scribbr. Retrieved January 3, 2023, from https://www.scribbr.com/dissertation/operationalization/

When interrogating the construct validity of a measure which question should a researcher ask?

In interrogating the construct validity of a measure, which question should a researcher ask? Is there enough evidence that this measure is valid? Before using the measure in her study, Dr. Valencia gives the measure to a group of students on Tuesday.

What is the term for a researcher's definition of the variable in question at a theoretical level?

The conceptual definition, or construct, is the researcher's definition of the variable in question on a theoretical level. The operational definition represents a researcher's specific decision about how to measure of manipulate the conceptual variable.

Which of the following is an example of physiological measure?

any of a set of instruments that convey precise information about an individual's bodily functions, such as heart rate, skin conductance, skin temperature, cortisol level, palmar sweat, and eye tracking.

What type of validity involves comparing the conceptual definition of a variable to the operational definition of that variable?

Construct validity defines how well a test or experiment measures up to its claims. It refers to whether the operational definition of a variable actually reflect the true theoretical meaning of a concept.