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Learn about the 4 levels of measurement - nominal, ordinal, interval and ratio. Includes loads of practical examples and analogies.
1. Nominal. 2. Ordinal. 3. Interval. 4. Ratio. In this post, we define each measurement scale and provide examples of variables that can be used with each scale. Nominal. The simplest measurement scale we can use to label variables is a nominal scale. Nominal scale: A scale used to label variables that have no quantitative values.
The primary difference between interval and ratio scales is that, while interval scales are void of absolute or true zero, ratio scales have an absolute zero point. Understanding these differences is the key to getting the most appropriate research data.
Two commonly used scales are interval and ratio. While they share some similarities, they also have distinct attributes that set them apart. In this article, we will explore the characteristics of interval and ratio scales, their applications, and the implications they have on statistical analysis.
Interval: the data can be categorized, ranked, and evenly spaced. Ratio: the data can be categorized, ranked, evenly spaced, and has a natural zero. Depending on the level of measurement of the variable, what you can do to analyze your data may be limited.
While interval and ratio data can both be categorized, ranked, and have equal spacing between adjacent values, only ratio scales have a true zero. For example, temperature in Celsius or Fahrenheit is at an interval scale because zero is not the lowest possible temperature.
The nominal, ordinal, interval, and ratio scales are levels of measurement in statistics. These scales are broad classifications describing the type of information recorded within the values of your variables. Variables take on different values in your data set.