What is operationalisation? (With benefits and steps)

By Indeed Editorial Team

Published 5 July 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

The operationalising process allows professionals to use their theories in real-life situations. It's an important step in a research project that helps focus efforts, avoid unnecessary work and ensure that the results are reliable and valid. Understanding this theory can help you develop your research skills and make you more efficient. In this article, we review the meaning of operationalisation, its benefits, how to use it and the different types, provide an example and answer some FAQ.

What is operationalisation?

Operationalisation is a research process that helps develop a clearly defined and implementable variable. It involves identifying the critical variables of research and expressing them clearly. You can then analyse these variables by observing their categories, dimensions or components. This process aims to help understand the variables and measure them. For example, if a theory states that individuals may react differently to a specific stimulus depending on their mental health, you can operationalise this theory by creating mental health tests.

Benefits of the operationalising process

This process is beneficial for businesses and other organisations. Businesses can use it to do the following:

Increase efficiency

Operationalising your ideas means creating measures for them to be easier for individuals to access and understand. Once employees can easily understand your concept, they can avoid lengthy meetings with you before implementing your idea. They may implement certain ideas as soon as they receive them from you, thereby increasing efficiency.

Related: How to calculate work efficiency and why it matters

Improve communication

It encourages employees across different departments to work together effectively by helping them communicate more easily. Additionally, it enables clear communication between different individuals or departments within an organisation. When employees understand each other better, their working relationships may improve too.

For example, if you're marketing a new product or service, you may want to understand what consumers think about it. Rather than just asking them questions like how they feel about a product or what they believe, it's more helpful to ask them specific questions like why they would buy the product or how likely they are to recommend it. This action makes it easier for both parties to understand each other's views regarding what's important when making decisions about their business.

Related: The importance of good communication in organisations

Test predictions

You can use experimental designs and statistical analyses to test the predictions you've already made. This means that you may not rely on anecdotal evidence or personal impressions about what works best. Instead, you can test your ideas through empirical investigation. For example, if you want to determine if those who smoke cigarettes become addicted faster than those who don't smoke, you can measure how long the subjects smoked. Then, check if there's any correlation between these variables and whether they become addicted.

Related: What are the 3 predictive models and what are their uses?

How to operationalise a concept

Here are some steps to operationalise concepts in your research as a professional:

1. Identify the concepts

The first step to operationalise your variables is to identify the concepts you want to measure. These might include key terms, such as emotional intelligence or learning styles, which you may define more precisely before beginning the next steps. This can be a complex task and it's easy to confuse concepts with variables. You can avoid confusion by thinking about how you would explain the idea to someone unfamiliar with your research area. Explaining it clearly helps others to understand it.

2. Define the concepts

Once you identify the concepts, define them more precisely. You can do this by defining some key terms or measures associated with each idea to become more apparent to everyone involved in the study. In describing any metrics, specify what they mean and don't mean. This process might include using examples from existing literature, such as textbooks or journal articles, or searching for relevant definitions and explanations.

3. Specify relationships between concepts

Once you define all the concepts, you can specify how they relate. For example, if you want to determine whether individuals that drink more alcohol are not as healthy as those who drink less, you can decide how you would measure alcohol consumption and health. You could use a questionnaire asking how much alcohol individuals drink each week and then refer to medical records to determine their health status.

4. Choose appropriate measures for the construct

Several scales collect different data, like how much someone holds an attribute. Your choice may depend on the data you want to gather and the number of variables it uses. If it involves only a few variables, nominal or ordinal scales might be appropriate. When there are many variables, interval or ratio scales might be more appropriate. Measurements can be from self-report or observer reports, but using both is ideal, so you can get some consistency across responses and check for bias.

5. Test the reliability of the measures

This measure refers to whether different individuals get consistent scores when answering the same question or scale. For example, you may want to measure intelligence. If your instrument has a scale from one to 10, this might be a simple task. It may be complex to measure someone's intelligence based on their examination results or IQ score. You could create a questionnaire based on previous research questions on intelligence and ask participants to fill it out before taking an examination. This action could test the instrument's reliability by observing whether participants give similar scores.

Types of operationalising processes

The following are types of operationalising processes:

Nominal operationalising process

This is the process of assigning a name or number to a concept. It involves selecting one or more characteristics of an idea and measuring them as discrete variables. For example, if you're studying the theory of personality, you may want to know what factors contribute most strongly to an individual's personality. You can measure these two concepts with a self-report questionnaire that asks individuals how much they agree or disagree with statements, such as 'I am outgoing or I prefer working on my own'.

Ordinal operationalising process

Ordinal refers to the ranking or arranging of items according to some criteria. For example, a survey instrument asks respondents to rank three brands on their preference scale from lowest to highest. The ordinal operationalising process involves defining things at different levels but not ordering them from the most favourable to the least, for example, quality of life. A typical example would be the Likert scales, where participants rate how much they agree or disagree with statements by ranking them from one to five or one to 10, where one stands for strongly disagree and five stands for strongly agree.

Categorical operationalising process

This is where researchers categorise variables as belonging to one group or another. For example, if you want to measure whether a group of people has depression, you could use a questionnaire which asks them whether they felt depressed at any point in their lives. If they answer yes, you can categorise them as having experienced depression at some point in time, either currently or previously. This categorises individuals into groups based on their answers to questions about their mental state, such as depressed and non-depressed.

Example of operationalisation

A manager wants to understand how their employees experience anxiety in the workplace. They interview six employees who report experiencing anxiety symptoms, including feeling nervous and easily startled. The manager asks each employee to rate their anxiety level on a scale of 1 to 5, meaning low and high. They average the responses across all six interviews to create a composite score for each employee, which they use to compare them. Still, it does not accurately measure individual differences because some employees reported low anxiety levels, while others said high despite holding identical scores on the composite scale.

To improve upon this method, the manager assesses individual differences within each interview by asking participants questions that measure discrete components of anxiety rather than asking them to self-report the overall level on a single scale. For example, the manager asks the participants about their feelings during specific situations, such as speaking in front of others or taking tests at school when they felt anxious and their thoughts about these situations. This process gives them a more accurate measure of how their employees experience anxiety and causes a positive change in the workplace environment.

FAQ about the operationalising process

Here are some FAQ about the operationalising process:

Why do researchers operationalise constructs?

Measuring a construct through its constituent parts is beneficial for any research that aims to provide evidence for the validity of a theory. For example, if you're testing whether the big five personality traits predict job performance, make sure to define these traits. If you don't explain it, you may want to develop a reliable way to measure them before testing this hypothesis.

Why is it important in psychology?

Psychology is a science that explains human behaviours by objectively studying them. Psychologists operationalise every concept they want to examine empirically to achieve this. They use it to specify how they measure each variable before conducting research studies and analysing their results.

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