I’ve recently seen two almost identical charts explaining the difference between

qualitative and quantitative research. One was shown at a conference, the other

presented in a graduate level methods textbook. I didn’t save the charts, unfortunately,

but this is how I recall them. (Similar charts can be found on the Web, e.g.,

here

and or at the bottom of this

page.)

quantitative	qualitative
detached	engaged
value-neutral	partial/committed
claims objectivity	admits subjectivity
seeks general findings	denies that general rules apply in cases
describes the mean	looks for exceptions, complications
pulls factors out of context	describes situations holistically
assumes certainty	presumes uncertainty
distinguishes causes from effects	does not presume to isolate causes

I think this is a very misleading way to draw the distinction. Quantitative

research means mathematical analysis; qualitative research means descriptions

in words. The use of math requires quantification and a large enough sample

to generate statistically meaningful results. The use of descriptive language

requires enough detail about cases to generate insightful narratives or portraits.

Both approaches are useful. Neither method implies positivism (a strict distinction

between facts and values, or between facts and opinions), nor does either method

imply skepticism or postmodernism. One can use quantitative methods–such as

surveys and statistical analysis of the results–in a deeply engaged, critical,

"political" way, without any illusions that one is objective. Or one

can use qualitative methods–such as in-depth interviews–in a highly "positivistic"

spirit (thinking that one has no values or biases and no axes to grind). Charts

like the one above appeal to very general beliefs (or prejudices) about epistemology

and drive us to favor either quantitative or qualitative methods. Instead, I

think we should simply ask what can usefully be counted in particular cases,

and what cannot.