I recently finished reading ”Freakonomics”, a book which is based more or less entirely around quantitative research. The authors, Steven D Levitt & Stephen J. Dubner, use data to answer questions such as ”How is The Ku Klux Klan like a group of real estate agents?”, ”Why do drug dealers still live with their moms” and ”What do school teachers and sumo wrestlers have in common?”. It was interesting to see how quantitative methods could bring answers to such rather odd questions and the power of explanation that statistics have.
It can, on one hand, be argued that quantitative research is more objective due to it’s close connection to the real world (after all, quantitative data could be viewed as the real world from a certain perspective transformed into numbers). On the other hand, due to the need to have the correct complementary data, the lack of said data may lead to loss of objectiveness. For example, simply saying that there are less plane-crashes than car-accidents will give the illusion that traveling by air is safer than by road. Taking into account the amount of air and road traffic would instead show that neither of them is a lot more dangerous or safer than the other.
While this example is oversimplified, and both cases are technically objective (after all, numbers don’t lie), the question of the level of objectiveness of quantitative research turns into a philosophical one. If I’d like to convince my audience into believing something, I can choose the right complementary data to make my case. I do realize that this isn’t a scientific approach, nevertheless it’s still a question of objectivity.
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