- Nominal (Categorical) data
- Ordinal (Ranked) data
- Interval (Scale) data
- Ratio (Scale) data
Polgar, S., & Thomas, S. A. (2008). Introduction to research in the health sciences (5th edn.). PA, USA: Churchill Livingstone
Nominal data are categorical data, which are separated in different groups. Each of these categories are assumed to be distinct (as in the above picture) and independent of each other. A specific value of a variable either falls into a specific category, or it does not. For example, a value of "male" will only be categorised into the 'male' group and not into the 'female' group.
Ordinal data are ranked data, with the values being ordered in sequence, signifying the rank of the value, e.g. 1st, 2nd, and 3rd. Note that though there is an order, the difference in ranking does not imply the variation of class performance, i.e. the the difference between the 1st and 2nd is not the same as that between the 2nd and 3rd.
Unlike ordinal data, the interval and ratio data has that last characteristic (equal differences between subsequent values) as mentioned. Interval and ratio data are also known as 'scale' data in SPSS, because they are measured on a scale with continuous values. As long as your value can be measured on a 'scale' (e.g. cm, inch, kg, etc.), it would most probably be interval or ratio data.
The main difference between interval and ratio data is that ratio data has an absolute (or non-arbitary) zero. An absolute zero is a "0" that is quite meaningful, as it indicates a value of an absence or non-existence of the value. For example, 0 degree Kelvin represents an absence of heat (ratio data), while 0 degree Celsius is the melting point of water (interval data) with the values of degree Celsius being able to go below zero. Hence if the value can go below the value of zero and consist of a negative value, it would be interval data, and if the lowest value can only reach zero, it would be ratio data.
Are you now clear on which type of data you using? Please note that you might have to decide on the type of data you are using earlier in your research, as it might affect your hypotheses and research questions.