Whether to take a qualitative or quantitative approach to research is often presented as an either-or debate. In reality, the approaches are not only complementary (see Combining methods and triangulation), but are often two sides of the same coin. Data collected, presented and analysed in a quantitative manner is not by its nature any more reliable or generalisable than qualitative information. The assumptions and biases of the researcher inform both types of research, and one type is often convertible to the other. As Trochim (2000) writes:
"Typically data is called quantitative if it is in numerical form and qualitative if it is not. Notice that qualitative data could be much more than just words or text. Photographs, videos, sound recordings, and so on, can be considered qualitative data. … In some areas of social research, the qualitative-quantitative distinction has led to protracted arguments with the proponents of each arguing the superiority of their kind of data over the other. The quantitative types argue that their data is hard, rigorous, credible, and scientific. The qualitative proponents counter that their data is sensitive, nuanced, detailed, and contextual. For many of us in social research, this kind of polarized debate has become less than productive. Additionally, it obscures the fact that qualitative and quantitative data are intimately related to each other. All quantitative data is based upon qualitative judgements; and all qualitative data can be described and manipulated numerically.
For instance, think about a common quantitative measure in social research – a self-esteem scale. The researchers who developed such instruments had to make countless judgements in constructing them: How to define self-esteem; how to distinguish it from other related concepts; how to word potential scale items; how to make sure the items would be understandable to the intended respondents; what kinds of contexts they could be used in; what kinds of cultural and language constraints might be present, and so on. Researchers who decide to use such a scale in their studies have to make another set of judgements: how well the scale measures the intended concept; how reliable or consistent it is; how appropriate it is for the research context and intended respondents; and so on. Believe it or not, even the respondents make many judgements when filling out such a scale: what various terms and phrases mean; why the researcher is giving this scale to them; how much energy and effort they want to expend to complete it, and so on. Even the consumers and readers of the research make judgements about the self-esteem measure and its appropriateness in that research context. What may look like a simple, straightforward, cut-and-dried quantitative measure is actually based on lots of qualitative judgements made by many different people.
On the other hand, all qualitative information can be easily converted into quantitative, and many times doing so would add considerable value to your research. The simplest way to do this is to divide the qualitative information into categories and number them … even that simple nominal enumeration can enable you to organize and process qualitative information more efficiently. As an example, you might take text information (say, excerpts from transcripts) and pile these excerpts into piles of similar statements. When you perform something as easy as this simple grouping or piling task, you can describe the results quantitatively."
Within the CPRC, researchers are both expected and encouraged to collect, present, and analyse both qualitative and quantitative data. Many of the most interesting discoveries occur when findings based on one type of data seem to contradict findings based on the other.
See Report on CPRC Workshop on Panel Surveys and Life History Methods (eds. Baulch and Scott, 2006) (pdf file, opens in new window).