Qualitiative data analysis

The term qualitative is used to describe certain types of information. Qualitative data are described in terms of quality (that is, 'informal' or relative characteristics such as warmth and flavour). This is the converse of quantitative, which more precisely describes data in terms of quantity (that is, using formal numerical measurement).

Quantitative data falls into two broad categories: Discrete (or attribute) data and Continuous (or variable) data. Discrete data generally falls into three categories: Category data (eg. car type), Bi-nomial data (eg. pass/fail), and Count / Poisson data (eg. # of hairs on your head).

Qualitative data are generally (but not always) of less value to scientific research than quantitative data, due to their subjective and intangible nature.

It is possible to etertapproximate quantitative data from qualitative data - for instance, asking people to rate their perception of a sensation on a Likert scale.

Examples
A quantitative way to report room temperature would be "the temperature in this room is 23 degrees Celsius."

A qualitative way to report room temperature would be to say "this room is comfortably warm," or "this room is warmer than it is outside".

A quantitative way to describe a tree would be to say "the tree is 30 feet tall."

A qualitative way to describe a tree would be to say "the tree is taller than the building."