We are receiving a lot of questions about relevant analyses in the Analyse-it Method Validation edition to help in evaluating new diagnostic tests in the fight against COVID-19. Below are some quick links that will help, but contact us if you have questions - we are working as normal.
Also see our latest blog post: Sensitivity/Specificity and The Importance of Predictive Values for a COVID-19 test
Five different scales are used to classify measurements based on how much information each measurement conveys.
The different levels of measurement involve different properties of the numbers or symbols that constitute the measurements and also an associated set of permissible transformations.
For example, Gender (Male, Female); Religion (coded as 0=None, 1=Christian, 2=Buddhist).
For example, Moh's scale for the hardness of minerals; academic performance grades (A, B, C, ...).
For example, Temperature measured in degrees Fahrenheit or Celsius.
For example, Temperature measured in degrees Kelvin scale; Length in centimeters.
For example, Number of children in a family, Frequency of occurrence.
While the measurement scale cannot determine a single best statistical method appropriate for data analysis, it does define which statistical methods are inappropriate. Where possible the use of a variable is restricted when its measurement scale is not appropriate for the analysis. For example, a nominal variable cannot be used in a t-test.
When the measurement scale of a variable is unknown, the scale is inferred from its role in the analysis and the type of data in the variable. If the measurement scale cannot be inferred, you must set the measurement scale.