Likert scales, often used to measure attitudes, frequently approximate interval data by assuming that the difference between "Strongly Disagree" and "Disagree" is the same as between "Agree" and "Strongly Agree. The difference between 20°C and 30°C is exactly the same as the difference between 30°C and 40°C, representing a consistent 10-degree increment, even though 40°C does not imply "twice as hot" in a mathematical sense due to the arbitrary zero point.
Clear Definitions and Examples of Interval Level Data
A classic example of interval level of measurement is the Fahrenheit or Celsius temperature scale. In contrast, interval data lacks this true zero; for example, 0°F is simply a point on the scale and does not indicate the absence of thermal energy.
Statistical Analysis for Interval Variables The mathematical properties of interval data allow for a wide range of descriptive and inferential statistics. While it shares the property of ranking from ordinal data, the key distinction is the presence of equal intervals.
Clear Definitions and Examples of Interval Level Measurement
This scale of measurement implies that the differences between values on the scale are equal and meaningful, allowing for mathematical operations such as addition and subtraction. Parametric tests such as the Pearson correlation, t-tests, and ANOVA are suitable for analyzing this type of data, as they assume the equal intervals necessary for these calculations to be valid.
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