ABSTRACT
The interval scale introduces a metric – a regular and equal interval between each data point – as well as keeping the features of the previous two scales, classification and order. This lets us know ‘precisely how far apart are the individuals, the objects or the events that form the focus of our inquiry’ (Cohen and Holliday 1996: 9). As there is an exact and same interval between each data point, interval level data are sometimes called equal-interval scales (e.g. the distance between 3 degrees Celsius and 4 degrees Celsius is the same as the distance between 98 degrees Celsius and 99 degrees Celsius). However, in interval data, there is no true zero. Let us give two examples. In Fahrenheit degrees the freezing point of water is 32 degrees, not zero, so we cannot say, for example, that 100 degrees Fahrenheit is twice as hot as 50 degrees Fahrenheit, because the measurement of Fahrenheit did not start at zero. In fact twice as hot as 50 degrees Fahrenheit is 68 degrees Fahrenheit (({50 − 32} × 2) + 32). Let us give another example. Many IQ tests commence their scoring at point 70, i.e. the lowest score possible is 70. We cannot say that a person with an IQ of 150 has twice the measured intelligence as a person with an IQ of 75 because the starting point is 70; a person with an IQ of 150 has twice the measured intelligence as a person with an IQ of 110, as one has to subtract the initial starting point of 70 ({150 − 70} ÷ 2). In practice, the interval scale is rarely used, and the statistics that one can use with this scale are, to all extents and purposes, the same as for the fourth scale: the ratio scale.
