Operationalizing means turning abstract concepts into measurable observations. Although some concepts, such as height or age, are easy to measure, others, such as spirituality or anxiety, are not. Operationalization is the process of strictly defining variables into measurable factors. This process defines fuzzy concepts and allows them to be measured empirically and quantitatively. Operationalization makes it possible to systematically collect data on processes and phenomena that are not directly observable.
In the case of experimental research, in which interval or proportion measures are used, the scales are usually well defined and strict. Operationalization also establishes exact definitions of each variable, which increases the quality of the results and improves the soundness of the design. For many fields, such as the social sciences, which often use ordinal measures, operationalization is essential. Determine how researchers will measure an emotion or concept, such as the level of distress or aggression.
These measurements are arbitrary, but allow others to replicate the research, as well as perform a statistical analysis of the results.
Fuzzy concepts are vague ideas, concepts that lack clarity or are only partially true. They are often referred to as "conceptual variables." It is important to define the variables to facilitate the exact reproduction of the research process. The fuzzy concept has already gone through a period of operationalization, and the hypothesis acquires a testable format.
Without transparent and specific operational definitions, researchers may measure irrelevant concepts or apply methods inconsistently. Operationalization reduces subjectivity and increases the reliability of the study.
The choice of operational definition can sometimes affect the results. An experimental intervention for social anxiety may reduce self-rated anxiety scores but not behavioral avoidance of crowded places. This means that their results are context-specific and may not generalize to different real-life environments.
For example, the concept of social anxiety cannot be measured directly, but it can be operationalized in many different ways. In this case we could consider:
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
Why operationalization is important
In quantitative research, it is important to precisely define the variables to be studied.
In general, abstract concepts can be operationalized in many different ways. These differences mean that you can measure slightly different aspects of a concept, so it's important to be specific about what you're measuring.
If you test a hypothesis using multiple operationalizations of a concept, you can see if its results depend on the type of measure you use. If your results don't vary when you use different measures, they're said to be "robust."
Of course, strictly speaking, concepts such as seconds, kilograms and degrees Celsius are artificial constructions, a way of defining variables.
Pounds and Fahrenheit grades are no less accurate, but they were scrapped in favor of the metric system. A researcher must justify his scientific measurement scale.
Operationalization defines the exact measurement method used and allows other scientists to follow exactly the same methodology. An example of the dangers of non-operationalization is the failure of the Mars Climate Orbiter.
This expensive satellite was lost, somewhere over Mars, and the mission completely failed. Subsequent research found that engineers at the subcontractor, Lockheed, had used imperial units instead of metric units of force.
A failure in operability meant that the units used during construction and simulations were not standardized. American engineers used the pound of force, while other engineers and software designers correctly used Newtons' metric system.
This led to a huge error in thrust calculations, and the spacecraft ended up in a lower orbit around Mars, burning from atmospheric friction. This commissioning failure cost hundreds of millions of dollars, and years of planning and construction were wasted.
How Variables Should Be Operationalized
A scientist could propose the hypothesis:
"Children grow faster if they eat vegetables." Below we can analyze this hypothesis in several parts
What does the statement mean by "children"? Are they from America or Africa? How old are they? Are they boys or girls? There are billions of children in the world, so how are sample groups defined? How is "growth" defined? Is it weight, height, mental growth, or strength? The statement does not strictly define the measurable dependent variable.
What does the term "faster" mean? What units and time scale will be used to measure it? A short-term, month-long experiment can give very different results than a long-term study. Sampling frequency is also important for operationalization.
If the experiment were carried out for a year, it would not be practical to test the weight every 5 minutes, or even every month. The former is impractical, and the latter will not generate enough analyzable data points.
What are "vegetables"? There are hundreds of different types of vegetables, each of which contains different levels of vitamins and minerals. Are children fed raw or cooked vegetables? How does the researcher standardize diets and make sure children eat their vegetables? The researcher could narrow down the group of children, specifying age, sex, nationality, or a combination of attributes. As long as the sample group is representative of the larger group, the statement will be more clearly defined.
Growth can be defined as height or weight. The researcher must select a definable and measurable variable, which will be part of the research problem and hypothesis. Again, "more quickly" would be redefined as a period of time, and the frequency of sampling would be stipulated. The initial design of the research could specify three months or one year, giving a reasonable timescale and taking into account time and budget constraints.
Each group in the sample could be fed the same diet, or different combinations of vegetables. The researcher might decide that the hypothesis could revolve around vitamin C intake, so the average vitamin content of vegetables could be analyzed. Another possibility is that the researcher decides to use an ordinal measurement scale, asking subjects to fill out a questionnaire about their eating habits.
Procedure for the operationalization of concepts
Operationalization consists of three main steps:
Identify the main concepts you are interested in studying
Based on your research interests and goals, define your topic and ask an initial research question.
Example of a research question
Is there a relationship between sleep and social media behavior in teens?
There are two main concepts in your research question:
Sleep and social media behavior
Choose a variable to represent each of the concepts
Your main concepts may each have many variables, or properties, that you can measure.
For example, are you going to measure the quantity or quality of sleep? And are you going to measure how often teens use social media, what social media they use, or when they use it?
To decide which variables to use, review previous studies to identify the most relevant or underutilized variables. This will highlight gaps in the existing literature that your research study can fill.
Example of hypotheses
Based on his review of the literature, he decides to measure the variables sleep quality and nightly use of social networks. You predict a relationship between these variables and you propose it as a null and alternative hypothesis.
Alternative hypothesis: Lower sleep quality is linked to increased nighttime use of social media in adolescents.
Null hypothesis: There is no relationship between sleep quality and nightly use of social media in adolescents.
Select indicators for each of your variables
To measure your variables, decide which indicators 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.
Practical ideas on how to measure variables can be provided based on previously published studies. They can be scales or set 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.
Example of indicators
To measure sleep quality, participants are given bracelets that record sleep phases.
To measure nightly social media use, create a questionnaire asking participants to record time spent using social media in bed.
After putting the concepts into practice, it is important to report the variables and indicators of the study when writing the methodology section. In the discussion section you can evaluate how the choice of operationalization may have affected the results or interpretations.
Operationalization allows variables to be measured consistently in different contexts.
Scientific research is based on observable and measurable results. Operational definitions break down intangible concepts into recordable characteristics.
A standardized approach to data collection leaves little room for subjective or biased personal interpretations of observations.
Good operationalization can be used systematically by other researchers. If other people measure the same thing using their operational definition, they should get the same results.
Limitations of operationalization
Operational definitions of concepts can sometimes be problematic.
Many concepts vary in different time periods and social settings.
For example, poverty is a global phenomenon, but the exact level of income that determines poverty can differ significantly between countries.
Operational definitions can easily overlook meaningful and subjective perceptions of concepts when trying to reduce complex concepts to numbers.
For example, asking consumers to rate their satisfaction with a service on a 5-point scale won't tell you anything about why they feel this way.
Lack of universality
Context-specific operationalizations help preserve real-life experiences, but make it difficult to compare studies if measures differ significantly.
For example, corruption can be operationalized in many ways (e.g., perception of corrupt business practices or frequency of bribe requests by public officials), but measurements may not consistently reflect the same concept.
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McLeod, S. A. (2019, August 01). What are independent and dependent variables. Simply Psychology. https://www.simplypsychology.org/variables.html