Are “qualitative” and “quantitative” useful terms for describing research?

We examine the concepts of quantitative research and qualitative research, and argue that this dichotomy has several dimensions which are often, erroneously, assumed to coincide. We analyse two of the important dimensions – statistical versus non-statistical, and hypothesis testing versus induction. The crude quantitative-qualitative dichotomy omits many potentially useful possibilities, such as non-statistical hypothesis testing and statistical induction. We also argue that the first dimension can be extended to include establishing deterministic laws and the consideration of fictional scenarios; and the second to include ‘normal science’ research based on questions defined by an established paradigm. These arguments mean that the possible types of research methods are more diverse than is often assumed, and that the terms ‘quantitative’ and ‘qualitative’ are best avoided, although other, more specific, terms are useful. One important sense in which the term ‘qualitative’ is used is simply to refer to the use of data which yields a deep and detailed picture of the subject matter: we suggest the use of the word ‘rich’ to describe such data.