Interval data is data that can be assigned a numeric value. This allows us to compute quantitative differences between values.
Consider temperature readings recorded in Fahrenheit for five consecutive days, with readings of 31, 33, 39, 32, and 26. The temperature rose by two degrees from the first day to the second, and by six degrees from the second to the third day, which means that the second temperature rise was three times higher than the first. Such calculations are not possible with ordinal and nominal data (e.g., we cannot say with any confidence that the difference between Unhappy and Somewhat happy is the same as the difference between Somewhat happy and Happy).
Interval data’s real power is that it allows the calculation of averages and variance, which are at the heart of most statistical and analysis calculations (e.g., correlation, linear regression).
There are a number of special cases of interval-scale data, including:
- Log-scale data.
Interval data is sometimes referred to as being metric or numeric.
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