Types of data


    Different data require different methods of summarising, describing and analysing.

    There are four main types of data Nominal, Ordinal, Interval and Ratio. It is important to be able to identify which type of data you have in order to choose appropriate statistical methods. Take a look at the examples below for a quick refresher of the four types of data:    

  • Nominal data are named variables. Nominal data is unordered, categorical and mutually exclusive - which means that each category is separate and cannot occur at the same time.

    Examples of nominal data are:

    • Different countries: Afghanistan, Brazil, China etc.
    • Yes or No answers.
      When a variable only takes only two values, we call this variable dichotomous.

    Ordinal data are named variables that have a meaningful order. Ordinal data is ordered, categorical and mutually exclusive (cannot happen at the same time).

    Examples of ordinal data are:

    • Body Mass Index (BMI):
      Underweight, normal weight, overweight, obese.
    • Responses to questionnaires:
       Strongly Disagree, Disagree, Neutral, Agree and Strongly Agree.
      This is known as a Likert scale, and is often coded using numbers where:
      1 = Strongly Disagree
      2 = Disagree
      3 = Neutral
      4 = Agree
      5 = Strongly Agree.

    How should I analyse Likert data?

    Take a look at the following article Analysing LIkert Scale/Type Data from Maths Support at St Andrews University for some ideas.


    Interval data is quantitative (numbered data) that has a meaningful interval between data points but does not have a meaningful zero, where zero means nothing.

    Examples of interval data are:

    • Temperature (in degrees Celsius, 0oC is cold, not no temperature).
    • Size of shoes (Size 0 shoe?). 

    Ratio data is quantitative (numbered data) that has a meaningful interval between data points. Unlike Interval data, ratio data has a meaningful zero - known as a true zero.

    Examples of ratio data are:

    • Length (in cm, where 0 cm has no length).
    • Weight (in kg, where 0 kg has no weight).
  • When reading about different types of data, you might find that different terminology is used. The boxes below introduce additional statistical terminology that is used to summarise the types of data detailed above.


    Categorical data is named data that can either be ordered or unordered. Nominal and ordinal data are often referred to as categorical data.


    Scale data is used to describe numerical data that has a meaningful scale. Interval and ratio data are often referred to as scale data.


    Discrete data only takes certain values. For example, the number of patients in a trial, the role of a dice 1, 2, 3, 4, 5 or 6.


    Continuous data is numbered data that can take any value within a range. Examples include, height (cm) weight (kg) and race times (seconds). 


    Quantitative data is data that can be quantified. This can be as simple as reporting a percentage of yes to no responses or more complex, reporting if results are statistically significantly different. These pages focus on quantitative data.


    Qualitative data refers to data that represent opinions. This data cannot be captured as a quantity, but can be used to provide deeper insight into a topic. Examples of qualitative data are open-ended questions, responses from focus groups, interviews and observations.

    Once you are confident in identifying the type of data you have, take a look at the descriptive statistics page for ideas on how to present and summarize your data set.