Confusion may creep in however when we use numbers as names. For instance, if we were comparing the performance of men and women on some task, we might for ease of computation refer to all men as 0 and all women as 1 . Such numbers are still really just names, however, and would have no more computational value than the simple label men or women . A psychologist may want to rank children in a class on the basis of how skilled they are at reading. The resultant data will tell us who is the best reader, who is the second best, and so on, but gives us no information on how much the children differ from each other in terms of reading skill.
Interval data is perhaps the most commonly-collected type of data in Psychology.
In interval data, the differences between any two adjacent numbers is equal to the difference between any other two adjacent numbers. In other words, an interval scale allows one to measure differences in size or magnitude. This may seem a confusing concept, but bear in mind that the IQ scale, for instance, is an interval scale. We know that Jim's score is the greater of the two, and we know by how much it is greater. However it is not possible to say that Jim is one-third more intelligent than Tom.