The distance between categories is equal across the range of interval/ratio data The data that we gather varies from person to person. People are of different ages, have different income levels and prefer to do some things more than other people. We call these things variables just because their values vary from person to person. Analysis of quantitative data starts by trying to understand what kinds of variables we are dealing with. A person’s age is an example of an interval/ratio variable, because ages are measured in years. We can do a lot of statistical analysis on this kind of variable because the interval (one year) is the same for everybody in our data-set. Some variables are called ‘dichotomous’, meaning all possible answers are of one of two types (male/female, for example). We call those variables ‘nominal’, which we can, literally, only “name”, like many types of job occupation, for example. Finally, we refer to some variables as ‘ordinal’, which means we can only place the values in an order of first, second, third and so on, without considering the gap between the first and second, or whether it was the same as between second and third. Apart from dichotomous variables, all others can be rank-ordered.
Reference: Bryman: Social Research Methods: 5th Edition Page(s) 334,335

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