The distinctions between quantitative and qualitative data can perhaps appear to be a bit of a dull subject. Does it really matter? Don’t we just pick a method that answers our research questions and off we go? Well, yes and no. Entire careers have been fought over exactly these questions. What counts as data, or more specifically what counts as meaningful data? What's going to tell us something that’s worth us knowing? So, yes, we have choices, and in one sense those choices are inevitably going to entail some kind of compromise. There will always be strengths and weaknesses, opportunities and limitations, to different approaches. Different methods will generate different kinds of data.
Quantitative data are concerned with numbers, well, with quantities. We’re taking large amounts of information, such as people’s responses to a host of personality tests, or attitude questionnaires and transforming it into numerical values so that these can quickly be compared across large samples of people, like the scores on the scales devised by Adorno and his colleagues in the authoritarian personality study.
Qualitative data is a bit harder to describe. It might draw on interviews or rich details of observations or case studies. Or examine people’s talk, how they make sense of the world and, so on. It might try to capture people’s experiences, their feelings, the way meanings are built up in terms of their sense of self and the wider world. Qualitative data is rich in description and we can discover all sorts of things about the psychological world by exploring what people say and do in a holistic way, trying to hold on to the richness and depth of psychological life. But, of course, it's precisely that richness which makes it hard to make straightforward comparisons.
With quantitative data we are able to give numerical values to behaviours and this makes it possible to carry out direct explicit and precise comparisons between groups.
So we know we have some seemingly obvious choices. But clearly the methods we choose have to be capable of answering the research question we've posed.
If we want to understand people’s experiences, what it feels like to live through a particular event then there is probably little point in trying to set up an experiment in a laboratory. Why would we try to reduce feeling to a numerical value? Even if we could, why would we want to? By turning participants’ feelings into numbers undoubtedly we lose something about the quality of their experience. But if we want to find out about what people do, for example, whether speaking on a mobile phone impairs people’s driving, then there is not much point in simply asking people about their experiences of phones and driving. They may well be able to give us some interesting information, but in terms of finding out what actually happens, rather than what people think happens or what they might want to tell us happens, rather than relying on what people say, it makes more sense to test it out. We might put people in a driving simulator in a laboratory and compare their driving skills whilst using the phone and when not using the phone.
So, yes, we have some choices. Some researchers will adopt an entirely pragmatic approach to this business. They make their choices between quantitative and qualitative research entirely on a fit for purpose basis. This is pretty much what Adorno and his colleagues did. They wanted to be able to compare broad samples so that they could say something about America and Americans in 1950’s USA. They used quantitative measures to get information they could apply to the population in general. And