As part of quality improvement, measurement helps healthcare professionals make decisions based on data. When done well, it can also help them feel confident in those decisions and move away from anecdotes and one person’s view of the world.
In mathematics, a measure is a functional on a locally convex Riemannian manifold. Lebesgue measures are s-finite and generalize to a more general form called the Hausdorff measure.
Units of Measure
A measurement is a number that identifies a physical quantity. A measurement cannot be conveyed without the underlying units of measure, and different systems are used for measuring different quantities.
The metric system is the most widely used, but there are other systems as well. The metric system is based on a small number of basic units, each of which has a defined meaning and all of the other measures are related to these base units by powers of 10.
For example, the unit for length is the metre (symbol m), but it can be derived from other units as well. These include the foot, ounce, and gallon. In the United States, US customary units are still heavily used despite a partial move to metrication. Frequently it is necessary to perform calculations on measurements that use different units, and these can be converted using conversion factors. The results of these calculations should be reported in both the original unit and the resulting unit.
Measurement Concepts
A measurement is an empirical process that produces quantitative information about a physical property of objects. It must be both an empirical transformation and a designed process in order to be called a measurement.
This characterization of measurement aims to differentiate it from other types of designed processes, such as computation and logical inference, or from thought experiments that produce values but are not designed. In particular, it aims to distinguish measurement from processes such as manufacturing that are designed to transform inputs into outputs according to production plans, but which are not necessarily measurements.
It also aims to distinguish measurement from counting and the estimation of quantities that can be calculated in other ways, such as by adding up known values (like the number of blocks and dogs in a collection) or by subtracting one value from another (like the difference between distances and weights). In addition, it seeks to establish necessary but not sufficient conditions for a process to be identified as a measurement.
Measurement Instruments
Measurement instruments are tools that help us assign a known standardized numerical value to some property of an object or event. These measurement instruments may be mechanical, chemical, electrical or optical.
Various factors can cause errors in measurement. Some of these are related to the instrument itself, such as sensitivity drift and calibration. Other factors are related to the person using the instrument, such as operator error or environmental conditions during the measurement.
Researchers use many different instruments in the study of a range of variables, from physical functioning to psychosocial well being. The instruments vary in format from formal questionnaires to observational tools. The Library has collections of measurement tools, both published in books and available online. These include rating scales, tests and questionnaires arranged by topic. The PsycInfo database also contains a thesaurus of terms that helps in finding tests and ratings scales by name, synonyms and broader or narrower words. This can be useful to researchers looking for specific test items that are correlated with constructs they are studying.
Measurement Uncertainty
Measurement uncertainty is the statistical dispersion of values that may be attributed to a measured quantity. It is important to note that measurement uncertainty always has a positive value and can be determined using statistics or comparative interlaboratory comparisons/suitability tests.
The difference between error and uncertainty is that error refers to a single discrepancy while uncertainty is the effect of many errors acting conjointly. When a measurand y is calculated from other input quantities x1, x2,… xn through a functional relationship, uncertainties in those input quantities will propagate through the calculation to produce an uncertainty in y. This is the principle behind a common technique called the GUM (General Rules for the Expression of Uncertainty in Measurement) methodology. The GUM provides the general rules for evaluating and expressing measurement uncertainty for functions that contain multiple input quantities including correction factors. It is important to know how to calculate this type of measurement uncertainty correctly. This will ensure that you get the most accurate and precise results possible.