A measure is a quantity that can be used to describe the amount of something. Examples include length, weight, and power.
Norman Campbell defines measurement as the process of assigning numbers to qualities that admit of non-arbitrary ordering. This entry will survey the central philosophical standpoints on the nature of measurable quantities and related epistemological issues.
Definition
In a data context, measures are values that can be summed and aggregated. A measure can be a number, value or other type of data point that is tracked and reported. A metric is a specific type of measurement, such as sales or employee productivity, and is used to assess the progress towards a business goal. A useful metric should be accurate (in that it accurately reports the data points) and aligned with your goals.
Measurement is an essential part of modern science, engineering, commerce, and daily life. Despite its importance, there is no consensus on the elements and conditions that make something measurable.
Early measurement theorists like Helmholtz and Holder argued that fundamentally measurable magnitudes share structural features with algebraic operations on numbers. For example, the qualitative relation “longer than” among rigid rods has an analog in the end-to-end concatenation of rods that is transitive and asymmetrical. This characterization of measurement is flawed, however, because it also fits perceptual and linguistic activities that are not typically considered to be measurements.
Harmonization
The technical implementation of a harmonization program requires that commutable reference materials and a panel of clinical samples be available. Ideally, the assessment will be performed using samples from healthy people and those with disease. The panel should contain enough physicochemically different molecular forms of the measurand to evaluate the current degree of measurement equivalency and, experimentally, to determine whether or not harmonization is feasible. These reference materials should be made readily available to clinical laboratories and IVD manufacturers.
However, it is important to consider the tradeoffs involved in pursuing this strategy. Combining datasets that ultimately measure different concepts can lead to false or inappropriate equivalences and nonsensical measures. This can be avoided if the underlying datasets are carefully explored and understood before attempting to harmonize them. This can be achieved by creating a library of common data definitions, which can then be used as a basis for standardized outcome reporting across different systems. However, the time and resources needed to complete this process are substantial.
Reliability
The reliability of a measure is the extent to which it provides consistent results. It is a measure of how consistently a test yields the same result, and it is important for making valid comparisons between data sets. For example, a color blindness test that yields the same result every time is considered to be reliable.
A key aspect of reliability is that it helps identify the causes of failures to enable improvement. This is done by using processes such as FMEA, fault tree analysis and hazard logs.
Reliability is sometimes confused with validity, but the two concepts are distinct. A test can be reliable and not necessarily valid, for example a thermometer that always registers a certain temperature is reliable but not valid because it does not actually tell you whether the temperature is correct. The reliability of a measurement can also be improved by making it dynamic, meaning that calculations change based on user actions like filtering or slicing. This allows for more accurate and efficient calculation.
Maintenance
Measures allow for dynamic calculations that change based on user actions, such as filtering or slicing. Measures can handle a variety of calculations, from simple sums to complex ratios and forecasts. They are useful for data analysis because they can be reused across visualizations and reports. Unlike calculated columns, which use RAM memory even when they’re not part of a report, measures are only computed when added to a visual or data section.
Creating a new measure is easy in Power BI Desktop by selecting the table where you want to create it, then choosing New measure. The new measure is added to the VALUES area and the field list for that table. You can also use Quick measures, which write DAX formulas for you based on your input in a window. Name your new measures so they’re easier to identify and find later. In a future release, you may be able to set a default table for your measures.