Data Visualisations Lecture 2 – Data Types
Nominal – pertaining to names. It remains unordered and refers to categories. An average cannot be calculated from nominal data however percentages can be determined. When there are only two categories available the data is referred to as dichotomous such as yes or no.
Ordinal – refers to order.
Survey questions which have answers such as Disagree – Neutral – Agree pertaining to the numbers 1, 2 and 3 representing each category is ordinal as long as they remain in order.
Interval – relates to measurement of time or period of time. does not have a meaningful zero point, can refer to zero as the start of a new day when discussing time rather than 0am.
Ratio – Data is numeric and has a meaningful zero point, referring to an absence. Zero means you don’t have anything. Examples could be height, weight or age.
Qualitative vs Quantitative
Qualitative Data – Descriptive information
“I drink coffee everyday”
Quantitative Data – Numerical information
Discrete (counted) – I drink 4 coffees everyday)
Continuous (measured) – I drink 80 grams of coffee everyday
Data types allows various forms of information to be placed as Numerical or Categorical. If one was to use their shopping list as data they could refer to their items as being Ratio, Interval, Ordinal and Nominal. The section of the shop is Nominal, the aisle is Ordinal, quantity of items are interval and ratio is the calculated cost of each item. We are using different forms of data in our everyday life whether we may be aware or not. Placing data within context allows greater understanding of information presented.