Qualitative and Quantitative

Recently, I've had to speak with various students on the subject of what 'qualitative' and 'quantitative' mean, in the sense of qualitative or quantitative data or research methodology. It's interesting how oversimplication (the subject of my previous post) creeps in.

For instance, one of them said, "Quantitative means you can put a number on it."

I replied, "Well, you can put a number on a marathon runner and it doesn't make her quantitative data. You can have a school survey with ratings on items (a 'Likert scale') from 1 to 5, and that doesn't make it quantitative research either."

Quantitative data is actually data that can be gathered and placed within a scale of measurement, and can legitimately be subjected to mathematical operations. Sometimes this definition works to confuse people.

Take for example the idea of 'an average age'. Suppose that we ask your class how old they are in years (number of birthdays passed), and your class replies "17!" (40%) and "18!" (60%). Does this mean that the average age of the class is (0.4 x 17 + 0.6 x 18) = 17.6? That would make the average member of the class roughly 17 years, 7 months and 6 days old. What do you think? I suspect not. But the problem here is one of insufficient resolution and definition, not one of illegitimacy. Age can indeed be used as quantitative data in some contexts.

Qualitative data is actually data that specifies a kind or a class of property without being subject to scalar manipulation. It cannot be subjected legitimately to mathematical operation, although it is possible to try. This also confuses people.

Take for example the idea of 'colour'. Suppose we ask your class what their favourite colours are, and your class replies "Blue!" (50%) and "Gold!" (50%). Does this mean that the average favourite colour is metallic green? What do you think? I suspect not. The problem here is not numerical, but conceptual. You can't find an 'average' of favourite colours, since the average is unlikely to be anybody's favourite in this context.

The thing about this colour example is that it can be treated both quantitatively and qualitatively, with different kinds of results. A person can say, "My favourite colour can be expressed as that produced by a photon source in which all the radiation has the wavelength 530 nm. It's a kind of green."

Well, the wavelength is a manipulatable scalar, as is the intensity of the source and so on. But the greenness of the colour is what we call an example of the qualia, those sensory occurrences which we find difficult to think about, and which are almost by definition the basis of qualitative data. It is extremely unlikely that any two people will see exactly the same shade of green when exposed to a light source at 530 nm. This can be due to biology, biography or biasedness of some unknown kind. In fact, it is even less likely that the colour will affect them in exactly the same way.

This is why the methodologies that handle quantitative and qualitative data tend to be, respectively, mathematical and social. The former manipulates numbers of the kind that can be manipulated (vectors, scalars, cardinals — but not most ordinals); if any explanatory power resides in numbers, it is of the statistical and correlative kind. The latter tries to come to humanly acceptable consensus on what qualia could possibly have been observed and what kinds of explanation would be sufficient to account for them.

Of course, books have been written on guidelines for research in both kinds of methodologies and their deployment in many different areas of knowledge. I myself used mixed methodologies when doing my 1999 Master's thesis on Why Teachers Quit Teacher Development in Atlantis. Most of it was qualitative though; qualitative data is a lot better at explaining social phenomena and general insanity than quantitative data is. You can find my research online if you know where to look. Enjoy!