Operational Definition in Research

Operational Definition in ResearchOne of the keys to successful research, in addition to careful planning, is the use of operational definitions in measuring the concepts and variables we are studying or the terms we are using in our research documents.

The specific way in which a variable is measured in a particular study is called the operational definition.

It is critical to operationally define a variable in order to lend credibility to the methodology and to ensure the reproducibility of the results of the study. Another study may identify the same variable differently, making it difficult to compare the results of these two studies.

To begin with, the operational definition is different from the dictionary definition, which is often conceptual, descriptive, and consequently imprecise.

In contrast, an operational definition gives an obvious, precise, and communicable meaning to a concept that is used to ensure comprehensive knowledge of the idea by specifying how the idea is measured and applied within a particular set of circumstances.

This definition highlights two important things about an operational definition:

  • It gives a precise meaning to the spoken or written word, forming a ‘common language’ between two or more people.
  • It defines how a term, word or phrase is used when it is applied in a specific context. This implies that a word may have different meanings when used in different situations.

An operational definition must be valid, which implies that it should measure what it is supposed to measure. It must also be reliable, meaning that the results should be the same even when done by different people or by one person at different times.

An operational definition ensures a succinct description of concepts and terms as applied to a specific situation to facilitate the collection of meaningful and standardized data.

When collecting data, it is important to define every term very clearly in order to assure all those who collect and analyze the data have the same understanding.

Therefore, operational definitions should be very precise and be framed to avoid variation and confusion in interpretation.

Suppose, for example, and we want to know whether a professional journal may be considered as a ‘standard journal’ or not. Here is a possible operational definition of a standard journal.

We set in advance that a journal is considered standard if

  • It contains an ISSN number.
  • It is officially published from a public or private university or from an internationally recognized research organization;
  • It is peer-reviewed;
  • It has a recognized editorial /advisory board;
  • It is published on a regular basis at least once a year,
  • It has an impact factor.

Thus, the researcher knows exactly what to look for when determining whether a published journal is standard or not.

The operational definition of literacy rate as adopted by the Bangladesh Bureau of Statistics (BBS) in their Vital Registration System is as follows:

“Percentage of the population of age 7 years and above who can write a letter among the total population.”

In sum, an operational definition serves four purposes:

  • It establishes the rules and procedures the researcher uses to measure the variable.
  • It provides unambiguous and consistent meaning to terms/variables that otherwise can be interpreted in different ways.
  • It makes the collection of data as well as the analysis more focused and efficient.
  • It guides what type of data and information we are looking for.

By operationally defining a variable, a researcher is able to communicate a common methodology to another researcher.

Operational definitions lay down the ground rules and procedures that the investigator will use to observe and record behavior and write down facts without bias.

The sole purpose of defining the variables operationally is to keep them unambiguous, thereby reducing errors.

How to operationalize a variable?

In fact, there is no hard and first rule for operationally defining a variable. Operational definitions may vary, depending on your purpose and the way you choose to measure them.

Neither are there any universally accepted definitions of all the variables. A researcher can logically choose a definition of a variable that will serve his or her purpose.

Whenever possible, operational definitions used by others in their work of good standing could be used so that the results can be compared.

Suppose a study classifies students according to the grades they received: A, B, C, etc. But the task is not that easy if you must determine which students fall in which grade since there is seldom any universal rule for grades. To do this, you need an operational definition.

In the goiter prevalence survey of 2004a person was classified as iodine deficient for a urinary iodine excretion (IUE) <100 pg/L and severely iodine-deficient for a urinary iodine excretion (IUE) <20 pg/L. One may choose a different threshold, too, in defining the iodine deficiency.

As another example, suppose it is intended to assess the knowledge of mothers on family planning. A set of 20 questions has been designed such that for every correct answer, a score of 1 will be given to the respondents.

Suppose further that we want to make 4 categories of knowledge: ‘no knowledge,’ ‘low knowledge,’ ‘medium knowledge’ and ‘high knowledge.’ We decide to define these knowledge levels as follows:

High knowledge = 15 or more correct answers.

Medium knowledge = 8 to 14 correct answers

Low knowledge = 1 to 7 correct answers

No knowledge = No correct answers.

One might, however, choose a different range of scores to defining the knowledge levels.

Based on the body mass index (BMI), for example, the international health risk classification is operationally defined as follows

ClassificationBMI category (kg/m1)
Underweight=     <18.5
Normal weight=     18.5-24.9
Overweight= 25.0-29.9
Obese=      30 and over

For the classification of nutritional status, internationally accepted categories already exist, which are based on the so-called NCHS/WHO standard growth curves. For the indicator ‘weight-for-age’ for example, children are assessed to be

  • Well-nourished (normal) if they are above 80% of the standard.
  • Moderately malnourished (moderate under-weight) if they are between 60% and 80% of the standard.
  • Severely malnourished (severe under-weight) if they are below 60% of the standard.

Classification of the nutritional status can also be made on the basis of the weight-for-age Z-score (WAZ) values. The Z- score of cut-off values are:

Well-nourished (normal)= < -2.0
Moderately malnourished=     <-3.0 to <-2.01
Severely malnourished=     <-3.0

A farmer may be classified as landless, medium, and big, depending on his possession of landholding size. One such classification is as follows:

CategoryAcres of land
Medium.01 – 0.5

Similarly, a business firm may be classified as being large, medium, or small in terms of its investment, capital, and a number of employees or assets, which may vary widely by type of business firm.

In demographic research, a person may be categorized as a child, those who are under five years of age, adolescent in the age range 12-19, adults aged 20-65, and old aged 65 and over.

Not only that, but variables also need operationally defined, the terms that indicate the relationship between variables needs to be defined.

For example, in many stated hypotheses, we use such terms as ‘frequent,’ ‘greater than,’ ‘less than,’ ‘significant,’ ‘higher than,’ ‘favorable’ ‘different’ ‘efficient’ and the like.

These terms must be clearly and unambiguously defined so that they make sense and allow the researcher to measure variables in question.

Consider the following hypothesis.

  • Visits of Family Welfare Assistants will motivate the women resulting in significantly higher use of contraceptives.

‘Visit’ is the independent variable to which we might associate numbers 0, 1, 2, to mean the frequency of visits made during a stipulated period. The term ‘higher use’ may mean a rate (dependent variable) higher than before.

This can be measured as the difference of the present and the past rate or between a post-test and a pretest measurement:

Difference =.Average CPR (pretest) – Average CPR (posttest)

But how much ‘higher’ will be regarded as significant? Thus the term’ significant’ needs to be defined clearly. We may decide to statistically verify at a 5% level with a probability of at least 95% that the difference in usage level is significant.

Thus the operational definition of terms not only tells us the meaning of their use but also the way of measuring the difference and testing its statistical significance, thereby accepting or rejecting the hypothesis.

In a study on the comparison of the performance of nationalized commercial banks (NCB) and private commercial banks (PCB) by Hasan (1995), one of the hypotheses was of the following form:

  • PCBs are more efficient than NCBs in the private deposit collection.

The term ‘more efficient’ was assessed by testing the statistical significance of the differences in the mean deposits of the two banks in questions at a 5% level.

The concept of operational definition also applies to other technical terms that are not universally defined. Here are some examples of such terms with their operational definitions:

Operational Definition of Terms

CensusThe enumeration of an entire population of a defined area.
PopulationThe universe of units from which a sample is to be selected.
Consent form:A written agreement signed by a subject and by a researcher concerning the terms and conditions of a subject’s voluntary participation in a study.
VignetteA brief description of an event or situation to which respondents are asked to react.
Hypothesis:Informed speculation, which is set up to be tested about the possible relationship between two or more variables.
Balance sheet:Description of the organization in terms of its assets, liabilities, and net worth.
Dependent:(Population aged < 15)+ (Population aged >65).
Reliability:The degree to which a measure of a concept is stable.

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