Data can be described as quantitative if it can be measured or identified on a numerical scale. Examples include length, height, area, volume, weight, speed, age, distance, cost and so on. However, not all data using numbers is quantitative: Datasets are often classified into categorical data, i.e. using numbers as descriptors. Arithmetic performed on the numbers describing categorical data would produce nonsensical results, for the same reason that you cannot add 6 Acacia Road to 12 Acacia Road to create 18 Acacia Road. Be wary, therefore, when you consider a dataset.
What exactly do the numbers represent? If your numbers answer a question beginning ‘how many’ or ‘how much’, you have quantitative data. If your numbers represent groups or classes, you have qualitative data expressed categorically. The appropriate analytical techniques will vary accordingly.
This new tutorial on PORT and developed by the School of Advanced Study, is designed to give you the ability to approach quantitative work with confidence, even if you have no prior statistical experience. It will provide a grounding in the collection and analysis of numerical data and give you the tools to report your own results and think critically about those of others.