Data measurement describes the nature of values allocated to different variables in a given statistical set. There are several data measurement scales which include the nominal, and their main purpose is labeling different variables with the absence of quantitative importance. These labels are jointly exclusive, and none of these has numerical importance (Novikov, 2020). For instance, nominal sounds are an object’s name, while nominal scales mean labels. (M) representing male, and (F) standing for female. It can also be numbers representing different variables, like (1, 2, 3, 4, and 5) representing different colors (1 to represent yellow, 2 for blue, 3 for pink).
Ordinal scales are simply the non-numeric models; examples are discomfort and contentment. The systematic order of values is critical, but there exists a difference between them. For instance, one cannot quantify the level of happiness. They offer important information about a different order of choices. The data can be put into categories and also ranked. Some different variables which use these scales include military ranks and movie grading.
Interval scales represent the numerical data where we relate the difference between available orders and the values. For instance, Fahrenheit temperature, pH scale, and the difference between the intervals are the same. The scales like the mean and mode of different statistical samples. The absolute zero provides a wide range of graphic and inferential data statistics (Lin & Chen, 2020). For instance, objects’ height, weight, and time are taken in a given task. These statistical variables can be successfully added, multiplied by other factors, and subtracted from other components. The measures of dispersion which include the coefficient of variation and the standard deviation, can be obtained from ratios.
References
Lin, B., & Chen, K. (2020). Hybrid LLC Converter with a wide range of zero-voltage switching and wide input voltage operation. Applied Sciences, 10(22), 8250. Web.
Novikov, S. (2020). Hygienic regulation for jointly acting harmful factors. Hygiene and Sanitation, 99(2), 222-223. Web.