types of data

• Raw Data – Unprocessed. Just been collected. Needs to be ordered, grouped, rounded, cleaned.
• Qualitative – Non-numerical, descriptive data such as eye/hair colour or gender. Often subjective so usually more difficult to analyse.
• Quantitative – Numerical data. Can be measured with numbers. Easier to analyse than qualitative data. Example, height, weights, marks in an exam etc.
• Discrete – Only takes particular values (not necessarily whole numbers) such as shoe size or number of people.
• Continuous - Can take any value e.g. height, weight.
• Categorical – data that can be sorted into non-overlapping categories such as gender. Used for qualitative data so that it can be more easily processed.
• Ordinal (rank) – quantitative data that can be given an order or ranked on a rating scale, e.g. marks in an exam.
• Bivariate – Involves measuring 2 variables. Can be qualitative or quantitative, grouped or ungrouped. Usually used with scatter diagrams where the two axes represent the two different variables. One variable is often called the explanatory variable and the other the response variable.
• Multivariate – Made up of more than 2 variables e.g. comparing height, weight, age and shoe size together.