How do you classify data based on measurement levels?

Data can be classified into four measurement levels: nominal, ordinal, interval, and ratio.

When we classify data based on measurement levels, we are essentially looking at how the data is organised and what kind of mathematical operations can be performed on it. The four levels of measurement are nominal, ordinal, interval, and ratio.

Nominal data is the most basic level. It involves categorising data without any order. For example, colours (red, blue, green) or types of pets (dog, cat, fish) are nominal data. You can count the frequency of each category, but you can't say one is greater or less than another.

Ordinal data, on the other hand, does have a meaningful order. Think of rankings or scales, like finishing positions in a race (1st, 2nd, 3rd) or levels of satisfaction (satisfied, neutral, dissatisfied). While you can order these data points, the differences between them aren't consistent or measurable.

Interval data takes it a step further by not only having order but also equal intervals between values. A good example is temperature in degrees Celsius. The difference between 20°C and 30°C is the same as between 30°C and 40°C. However, interval data lacks a true zero point, meaning you can't say something is "twice as hot."

Finally, ratio data has all the properties of interval data, but it also has a true zero point, allowing for meaningful comparisons of magnitude. Examples include height, weight, and time. With ratio data, you can say that 4 metres is twice as long as 2 metres.

Understanding these levels helps in choosing the right statistical methods to analyse the data effectively.