Measures and metrics are valuable tools that can be used to gain insights into business performance. They help to define strategies to achieve desired outcomes.
A measurement is an act of ascertaining the extent, dimensions or quantity of something especially by comparison with a standard. Using various methods and instruments, scientists attempt to reduce uncertainty in measurements.
Level of Measurement
Level of measurement determines the types of statistical analysis that can be applied to a variable’s data. Each level of measurement builds on the one preceding it, adding more precision and mathematical possibilities. Understanding these levels of measurement is important for ensuring accurate data collection, interpretation and reporting.
At the lowest level, a nominal scale categorizes data into mutually exclusive groups without any sort of order or numerical value (e.g., gender, eye color). In contrast, an interval scale allows for a ranking of data within groups and between groups with consistent and meaningful differences but no true zero point (e.g., temperature in Celsius and Fahrenheit). Finally, ratio-level measures allow for a true zero point, meaningful and consistent differences between categories, and the ability to add, subtract, multiply and divide data points. For example, age falls under the ratio-level category of measurement. It’s important to understand how these levels of measurement work so you can collect and analyze the most valuable data possible.
Measurement Procedures
A measurement procedure is the set of tools and techniques for collecting and analyzing data. The decision about which tool to use is referred to as instrumentation and is an important part of the process of operationalization.
There are many different measurement procedures. Some are designed to capture continuous data, such as a latency recorder that provides information about a specific temporal dimension and can be converted to a rate measure. Other measures capture only samples of behavior, such as a number of occurrences or the frequency with which a particular behavior occurs.
Most commonly, however, measurement procedures are norm or criterion referenced. Norm-referenced measures are designed by experts for wide use and have prescribed content, specified directions for administration and scoring, and established norms to compare scores. Criterion-referenced measures are based on a comparison with a set of standards, such as the International System of Units, which defines seven fundamental units. Both types of measurement procedures can be used to evaluate behavioral research and practice, but they require different approaches to establishing reliability and validity.
Measurement Errors
The measurement of an object’s physical characteristics can be subject to many types of errors. These errors can be either random or systematic. Systematic errors skew the measurements away from the truth in one direction, while random error affects the measurement results in unpredictable ways.
Examples of random errors include parallax (the distance between the pointer or indicator on a scale and the mark or scale reading), zero offsets in mechanical devices such as a micrometre calliper, and instrument drift over time. Examples of systematic errors include incorrect initial calibration, lack of attention to proper technique when performing a measurement, and bias in expectations.
To improve the accuracy of measurements, it is important to understand the causes of error. Different measurement properties, such as reliability and measurement error, can be investigated using a variety of experimental designs. These designs are influenced by choices about which potential sources of variation are varied or kept stable over repeated measurements in stable patients, and about whether the entire instrument or measurement procedure is replicated.
Measurement Variability
Variability affects the accuracy of measurements. Ideally, you want your data points to be tightly clustered around the mean. But, in reality, the spread of scores is affected by factors like read-out precision of measuring instruments, human error, and natural fluctuations in the environment in which the measurement takes place.
Measures of variability, such as range and standard deviation, provide insights into how far away your data points are from the mean. They also offer insight into whether you’re dealing with a normal or skewed distribution.
The most common measure of variability is the range, which simply subtracts the lowest and highest score in a dataset. Range is a crude measure of variability because it depends on two extreme values and ignores the rest of the data, but it works well for many purposes. For a more robust measure that works for skewed distributions, try the interquartile range (IQR). This is similar to the median but is more accurate because it doesn’t depend on any individual data point.