Specification: Levels of measurement: nominal, ordinal and interval. Factors affecting the choice of statistical test, including level of measurement and experimental design. 

Levels of measurement

Quantitative research can generate a range of data, and the type of data that is obtained will be an important factor when it comes to deciding which statistical test should be carried out for the analysis. Each specific data type will fall into one of three levels of measurement: nominal, ordinal, or interval.

Nominal

Nominal data can be referred to as categorical data. For example, if a researcher was interested to know if more students doing A‐level psychology went to a school or a college, the data would be categorised as either ‘school’ or ‘college’: two distinct categories.

If the data is nominal, then each participant will only appear in one category. This is called discrete data. Using the example above, it is not possible to be studying both at school and at college for the same qualification – students are either in one or the other.

Ordinal

Data is ordinal if it is ordered in some way and the intervals between the data are not equal. Typically, this is used to simply rank data where the values assigned have no meaning beyond the purpose of stating where one score appeared in relation to others.

For example, if people were asked to rate their preference of local restaurants, with 1 being their least favourite and 10 being their favourite, a researcher would be able to generate a list of restaurants from this data based upon the average ratings for each. However, they wouldn’t be able to say for sure that the difference between the restaurants ranked in 1st and 2nd place was equal to the difference between the ones rated as 8th and 9th – perhaps it was a very close call between those rated as 1st and 2nd, but there was a much bigger difference between 8th and 9th. With ordinal data, that level of detail cannot be told; it is only possible tell where they lie in relation to each other in an orderly fashion.

Ordinal data often appears in psychology when researchers are investigating a non‐physical entity, such as attitudes. This is subjective, as the ranks that are available will be interpreted as being different by each participant. Ordinal data can therefore be regarded as lacking certainty, due to the lack of objective insight into this type of data. 

Interval

Interval data is like ordinal data in that it also refers to data that is ordered in some way. However, with interval data we are confident that the intervals between each value are equal in measurement. This type of data is much more objective and scientific in nature as a result. Examples of interval data include temperature and time. The difference between 3 and 4 degrees Celsius is the same as the difference between 35 and 36 degrees Celsius. 

Possible exam questions

1. Identify the key term which is used to describe categorical data from the list below. (1 mark)

A Nominal

B Ordinal

C Interval

D Ratio

2. Define nominal data. (1 marks)

3. Suggest an example of ordinal data. (2 marks)

4. Explain one limitation of nominal level data. (2 marks)

5. Explain why interval level data is often considered the most reliable. (2 marks)

6. Name three levels of measurement. (3 marks)

7. Explain what is meant by levels of measurement in psychological research. (3 marks)