Ordinal data are discrete or discontinuous consisting of categories of variable that are ranked, ordered or compared according to a predetermined standard clothing size, teaching ranks, contest winner, are all ordinal data. A ranking of patient behaviours according to how often they occur during a given period is another example. Although there are no equal intervals between points, rank orders do have a relative order between categories, like first, second, third ranks or ranked as "always", "mostly", "sometimes", "rarely" and "never".
The interval or ratio data can be converted to ordinal data according to magnitude of each score, e.g. score, of 81 to 100 may be called 'excellent', 61 to 80 as 'good', 41 to 60 as 'average', 21 to 40 as 'poor' and below 21 as 'very poor'. However, ordinal data cannot be converted to interval or ratio level data. Let us see how ordinal data would look in tabular form Take Abde1lah and Levine7s attempt to create a graphic rating for assessing nursing care. The categories are ranked in order from "care of the highest quality" to "very poor care" with varying degree of service between.