View source for Qualitiative data analysis


{{expert|statistics}}
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 [[science|scientific research]] than quantitative data, due to their [[Subject (philosophy)|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."

==See also==
[[Level of measurement]]
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