Operationalisation in qualitative research

Published by at November 4th, 2021 , Revised On November 10, 2021

Operationalisation – Definition

Operationalisation is a process where abstract ideas or concepts are defined so they can be measured empirically. The process(es) used themselves help define the concept they are measuring.

Importance of operationalisation in research

Research prefers ‘what can be observed’ over ‘what cannot be observed.’ Therefore, quantifying a construct by measuring it:

  1. helps others understand it better.
  2. shows what that construct is and is not influenced by.
  3. makes the overall research claim(s) more believable.
  4. reduces the development of ‘sweeping statements,’ statements that are over-generalisations and might not be backed up by enough evidence.
  5. increases the specificity of the research claims.

Terms used in operationalisation 

Operationalisation is a critical part of the data analysis step in research. As such, it is based on some critical concepts as well, and they are:

  • Variables or Constructs

These are the concepts one intends to operationalise. They can be abstract (such as spirituality) or concrete (such as a physical disease, like thyroid). In either case, being a variable in itself, that concept will in turn be affected by other ‘constant’ factors. Factors that influence the variable.

  • Indicators

These are exactly what they sound like. A concept will have certain features or characteristics that help identify it. For instance, if one is using anxiety as a construct, what will be the indications that one suffers from anxiety? Everything one writes in response to that question will count as indicators of anxiety.

Dimensions and Indicators: Some indicators may have more than one aspect or ‘attributes.’ these multiple facets of a single indicator are also called dimensions. Another important term to familiarise oneself with here is the interchangeability of indicators. It represents the idea that if multiple attributes or dimensions represent the same concept, they should all behave in the same way as the concept does itself.

Examples of indicators, variables and dimensions

A 1996 study conducted by Medina and others is a good example for dimensions versus indicators. The study compared Mexican-American and Anglo-American attitudes toward money. There were four different dimensions in the study, with thirty-one total indicators. One of the dimensions was power and prestige. Following were the eight indicators of this dimension:

  1. I tend to judge people by their money rather than their deeds
  2. I behave as if money were the ultimate symbol of success I behave as if money were the ultimate symbol of success
  3. I find that I seem to show more respect to those people who possess more money than I do
  4. I own nice things to impress others
  5. I purchase things because I know they will impress others
  6. People that know me tell me that I place too much emphasis on the amount of money people have, as a sign of their success. amount of money people have, as a sign of their success.
  7. I enjoy telling people about the money I make. I enjoy telling people about the money I make.
  8. I try to find out if other people make more money than I do.

So, in this way, it becomes apparent to note that dimensions are the ‘family’ while indicators can be thought of as the ‘members’ that make up the family.

Another example containing indicators’ use is that of Gallup. It asks 1000 randomly selected Americans to ask them about their well-being. In this case, ‘well-being’ is a variable. The poll consists of six factors, factors that Gallup considers are ‘indicators’ of well-being. They are emotional health, physical health, participants’ self-evaluation of life, healthy behaviours, work environment and participants’ access to basic life necessities.

These factors or ‘indicators’ all have varying degrees of influence on the variable being measures, well-being.

Identification, conceptualisation and operationalisation

Before delving deep into the steps of operationalisation, it is important to note the relationship between identification, conceptualisation and operationalisation.

involves identifying the specific research procedures we will use to gather data about our concepts. Of course, this process requires that we know what research method(s) we will employ to learn about our concepts, and we’ll examine specific research methods later.

Identification Conceptualisation Operationalisation
Determining what concept one wishes to research on. Defining the concept that will be measured (theory) Specifying the method(s) that will be used to measure the identified concept (practice).
Determining research procedures that will be used to collect data. Defining certain terms that will be used in the research process. Defining research terms that will be used in the empirical observation of that concept.
Determining which research methods will be used to learn about the concept. Mainly aims to specify abstract concepts and define them. Mainly aims to make even abstract concepts measurable.
Mainly aims to narrow down the research aims from the beginning. Comes first in the process of data collection or measurement. Comes second in the process of data collection or measurement.

Levels of conceptualisation and operationalisation

To better understand how these two concepts work together, they are depicted in the form of ‘levels’ where there is a ‘one leads to the other’ situation. It is represented as follows:

Here, attributes are qualities that indicators possess. For instance, if fatherhood is an indicator of masculinity, what exactly are the attributes of a father?

Concepts vs Conception

Suppose an individual A lists the following items when asked what they think ‘masculinity’ means:

  • The breadwinner of the family
  • Father
  • Physically strong

However, individual B lists these items when asked to define the same thing:

  • Emotionally stable and reserved
  • Quiet
  • Nurturing and patient
  • Responsible

According to research terminology, individual A has a different CONCEPTION of the CONCEPT of masculinity than individual B. The two items, although very closely related, are not the same, in operationalisation.

Tip: A case study of operationalisation of management variables and indicators in UK private finance contracts is a good example to understand how these aspects work together in the real world.

Steps involved in the process of operationalisation

Step # 1 – Identify the concept to be measured

During this initial step, it is important to be as specific as possible. The narrower the research concept, the easier its indicators will be to identify in later steps. Here, identifying a concept means identifying the variable to be measured. Chances are, the research question one needs to answer or the research problem one needs to resolve will contain the variable within it.

The concept to be measured should have an independent variable (the cause) and a dependent one (the effect).

Let’s take an example and discuss it with every step. Suppose a researcher is trying to find the answer to the question, “Does sleep increase or decrease with age?” In this question, age is the independent variable whereas sleep is the dependent variable.

Step # 2 – List the indicators for the given variable(s)

In this step, research specifies the measure he/she will be used to measure the selected concept. A list of indicators representing the concept can either be formulated from scratch. Or they can be duplicated from other sources.

  • Listing indicators from scratch: This is easy to do when an easily observed concept is being measured. However, the more ‘involved’ a concept is with the real world, the more intricate it will become. So, if concepts like depression or happiness are to be measured, their indicators are not that easy to come up with, mainly because there are many different dimensions to these concepts. Multiple things from real life can be affecting one’s happiness or depression.
  • Duplicating indicators from external sources: When concepts like happiness or depression are being measured, researchers prefer to use an inventory of indicators already invented by other researchers. To account for the different dimensions of a concept and how real-world situations affect them, indexes, scales, or typologies are used.

– Indexes: They are a type of measure that contain a list of indicators, which, collectively, paint a general overview of that concept. For instance, an index of depression might comprise these questions as part of its indicators: continually feeling hopeless, strong urges to self-harm, under- and/or over-eating, feeling demotivated and so on.

Even though indexes consist of individual statements/questions, their responses, when combined, give an overall view of the respondent’s experience. That is why indexes are often the first go-to for many researchers during this step of operationalisation. Administering indexes is also easier than the following two sources; it is a lot like administering a questionnaire.

– Scales: A researcher might take things one step further by ranking the different indicators of a concept. Continuing with the same example of depression as mentioned above, a researcher could rank suicidal thoughts above feeling demotivated.

There can even be questions on scales. However, the only difference between scales and indexes is that the former are created to account for each indicator’s varying levels of intensity.

– Typologies: The third way to measure a multidimensional variable in research is through typology. It categorises concepts by their themes. The most commonly used version of a typology in research is the micro-meso-macro framework. In this framework, one classifies certain aspects of the social world by their ecological relationship with the individual at hand.

Taking the example of depression that has been discussed so far, one might classify the ‘lack of sleep’ indicator at a micro-level. ‘Financial trouble’ might be considered a macro-level indicator.

Essentially, typologies are a highly reliable way of measuring a variable. They require researchers to clearly define rules for how data (or parts of it) are organised into specific categories. It is operationalisation at its truest core, one might say. Furthermore, it should also be noted that some scales and indexes come with their way of interpreting them.

For example, Beck’s Depression Inventory (BDI-II) contains 21 indicator statements to measure depression. Respondents are required to rate their level of agreement on a scale of 0 to 3. Every response is added and categorised into one of three categories or labels: low levels of depression (score of 1-16), moderate levels of depression (score of 17-30) and severe levels of depression (score of 31 and above).

Step # 3 – Defining how the measuring tool works

This is a very important step of operationalisation, for this is where a researcher justifies their use of a certain measuring tool over another. Why was a typological measure used to empirically observe the relationship between sleep and age? Why not a simple Likert scale? Would the same results be obtained if another measuring tool was used?

All such questions need to be tackled critically during this step. Those answers are the ones that will convince the target audience whether research made use of appropriate tools to measure a variable. They should not be left to wonder about ‘what could have happened if the researcher did this or that differently.’ Rather, it is the researcher’s job to convince them that what he/she did was the right way to go about measuring the given variable.

Tip: Take the online operationalisation course, offered by the University of Reading. It is a great practice to familiarise oneself with in-depth knowledge of the process of operationalisation.

Step # 4 – Interpreting the results

In this final step of operationalisation, data needs to be interpreted in a way that is easily understandable by even the general public. Just as in the beginning, every abstract term is identified, in this step, too, every term and concept needs to be reiterated, re-defined and explained in light of observed findings.

Limitations of operationalisation

Like everything else in the world, operationalisation has its pros and cons. It is a very crucial step in research. However, some of its disadvantages are:

  1. Too much specificity creates disagreements between different stakeholders involved in the research. One person’s conception of a concept like happiness, for instance, might not be others’ conception of it. Such narrow definitions might not be agreed upon by others. The resulting disagreements cannot be resolved by further operationalisation, but moral, ethical and/or political arguments.
  2. Too much narrowing down of a concept might result in loss of data. For instance, asking respondents to rank their satisfaction levels for a certain product on a scale of 1-10 will not show why they ranked the way they did.
  3. Since the same concept might be measured differently, with different indicators and variables, by different researchers, the universality of that concept will be reduced. For instance, depression might be operationalised in various ways (effects of depression on future success; genetics of depression; older and younger generations’ views about reducing depression, etc). However, measuring a variable like this will detract the researcher from looking at the same concept (depression).

Conclusion

Operationalisation is a process where terms, both abstract and otherwise, are specified, defined and measured using empirical research methods. It is a tedious, yet crucial, part of qualitative research. The concept to be defined comprises variables. Variables, in turn, can be studied through their indicators.

Similar indicators are grouped into larger, broader ‘themes’ called dimensions. Measuring multidimensional aspects, such as power and prestige, can be tricky. That is why researchers make use of measuring instruments like indexes, scales and/or typologies to study a variable.

1.What is the main point behind operationalisation?

It is to define the variables so measuring them becomes empirical, practical and not just theoretical.

2. Why is it so important to operationalise variables?

Operationalisation serves the biggest purpose of providing a very concise, clear, rational and practical definition of even complex, multidimensional and/or abstract variables (concepts). It also enables other researchers to easily replicate a study and confirm its reliability.

3. What does it mean to operationalise a research question?

It means concept-forming for research questions. One needs to determine the operations and techniques that will be used to gauge a research question’s salient features.