Defining the Interval Scale
An interval scale is a quantitative measurement scale where the order of values is significant, and the difference between values is meaningful and consistent across the scale. However, it lacks a true or absolute zero point, meaning that a value of zero does not indicate the complete absence of the measured attribute. This arbitrary zero point prevents meaningful ratio comparisons between values.
Key Characteristics and Properties
Data on an interval scale can be ranked, and the intervals between successive values are equal. For example, the difference between 20°C and 30°C is the same as the difference between 30°C and 40°C (10°C). Despite this, you cannot say that 40°C is twice as hot as 20°C, because 0°C does not mean 'no temperature'. Common statistical measures such as mean, median, mode, standard deviation, and range can be calculated for interval data.
Practical Examples in Science
The most widely cited examples of interval scales are temperature measurements in Celsius and Fahrenheit. In both scales, 0° does not signify the absence of heat; it's simply an arbitrary point. Other examples include dates (e.g., years, where year 0 does not mean the absence of time), and often, scores on standardized tests (like IQ scores or SAT scores) are treated as interval data, assuming equal differences between score points.
Importance in Research and Data Analysis
Understanding the interval scale is crucial for selecting appropriate statistical analyses. Because interval data provides meaningful differences, it allows for more sophisticated statistical methods compared to nominal or ordinal scales, such as parametric tests (e.g., t-tests, ANOVA, correlation coefficients). This enables researchers to draw more robust conclusions about relationships and differences within their data, even without the ability to make ratio statements.