Knowing the difference between nominal, ordinal, interval and ratio data is important because these influence the way in which you can analyse data from experiments. For example, when data is collected from an experiment, the experimenter will run a statistical test on the data to see whether the results are significant. However, many statistical tests only work with certain types of data so it is important to identify what type of data you are working with.
(Plus, questions regarding the different types of data are very common in research methods papers, so if you are able to remember the differences it is an easy way to pick up marks in your exam!)
Nominal data is named data which can be separated into discrete categories which do not overlap. A common example of nominal data is gender; male and female. Other examples include eye colour and hair colour. An easy way to remember this type of data is that nominal sounds like named, nominal = named.
Ordinal data is data which is placed into some kind of order or scale. (Again, this is easy to remember because ordinal sounds like order). An example of ordinal data is rating happiness on a scale of 1-10.
In scale data there is no standardised value for the difference from one score to the next. This can be explained in terms of positions in a race (1st, 2nd, 3rd etc). This is ordinal data because the runners are placed in order of who completed the race in the fastest time to the slowest time, but there is no standardised difference in time between the scores. For example the difference in time between the runners in first place and second place is by no means the same as the difference in time between the runners in second and third place.
Interval data is data which comes in the form of a numerical value where the difference between points is standardised and meaningful. The most common example of interval data is temperature, the difference in temperature between 10-20 degrees is the same as the difference in temperature between 20-30 degrees.
Ratio data is much like interval data – it must be numerical values where the difference between points is standardised and meaningful. However, in order for data to be considered ratio data it must have a true zero, meaning it is not possible to have negative values in ratio data. An example of ratio data is measurements of height be that centimetres, metres, inches or feet. It is not possible to have a negative height. When comparing this to temperature it is easy to consider the difference between interval and ratio (which may be a little confusing at first!), as it is possible for the temperature to be -10 degrees, but nothing can be – 10 inches tall.