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An operational definition, when applied to data collection, is a clear, concise detailed definition of a measure. The need for operational definitions is fundamental when collecting all types of data. It is particularly important when a decision is being made about whether something is correct or incorrect, or when a visual check is being made where there is room for confusion.
For example, data collected will be erroneous if those completing the checks have different views of what constitutes a fault at the end of a glass panel production line. Defective glass panels may be passed and good glass panels may be rejected. Similarly, when invoices are being checked for errors, the data collection will be meaningless if the definition of an error has not been specified.
When collecting data, it is essential that everyone in the system has the same understanding and collects data in the same way. Operational definitions should therefore be made before the collection of data begins.
Any time data is being collected, it is necessary to define how to collect the data. Data that is not defined will usually be inconsistent and will give an erroneous result. It is easy to assume that those collecting the data understand what and how to complete the task. However, people have different opinions and views, and these will affect the data collection. The only way to ensure consistent data collection is by means of a detailed operational definition that eliminates ambiguity.
The above article is an excerpt from the “Operational definition” chapter of Practical Tools for Continuous Improvement: Volume 1 – Statistical Tools. The full chapter provides more details on creating operational definition.