Quantity does not always mean quality

I'm excited to announce that our new journal article on qualitative methods for conservation has been published in Society & Natural Resources! Here we talk about the quantitative / qualitative divide in conservation and explain the importance of appreciating the benefits of qualitative studies when trying to understand complex, under-researched areas.

Most conservation studies are quantitative in nature. They use numbers, percentages, statistics and modelling to empirically test predefined hypotheses. Whilst there is merit in this approach when you already know a fair amount about a topic, it's unhelpful when studying a new subject - or when you want to challenge conventional thinking.

That's where qualitative methods come in

Qualitative methods are exploratory in nature, where the goal is dive deeply into a specific topic to garner as much information as possible about it. Hypotheses are not usually used here because the researcher doesn't want to start with a preconceived bias. Instead, they let the data speak for itself.

As the goal of a qualitative study is to develop a rich narrative around a phenomenon, it's inappropriate to then conduct statistical analyses on the results - indeed, due to the focus on case studies rather than numbers, there are often no quantitative data to analyse.  Instead, data are often in the form of quotes, reportages of events or descriptions of media (photos, videos, documents, etc.).

Stats or it didn't happen?

Just because qualitative studies do not use stats does not mean that their results are meaningless.  How often have you used stats to prove that you prefer one type of beer over another? I'd guess never. This is because it's your subjective take, through experience, that helped inform your decision. Does that make your preference invalid? Of course not.  You don't need science to tell you that you prefer stouts over lagers because you can make up your own mind based on your taste buds.  Sure, you could do a quantitative study predicting the type of beer someone preferred based on socioeconomic data, but that doesn't really help you decide which beer you like because you are your own person, complete with your own wonderful idiosyncracies (which quantitative researchers interestingly call "outliers" and discard).  And it is delving deeply into understanding each individual case (in this example, you as a beer drinker) that helps qualitative researchers gather rich data on an experience.

Qualitative researchers focus more on the hows and whys rather than the whats, whos and whens.  Quantitative research focuses more on the latter, which is fine, because together qualitative and quantitative data make up two sides of the same coin.

Is there such as thing as objectivity?

To discredit a qualitative study because it didn't use stats does not only highlight the fact that embarrassingly the complainant doesn't understand qualitative methods, but it also ignores an important aspect of research - the exploratory study of subjectivity.

The world is an incredibly complex place.  To reduce it to a discrete number of preconceived variables ignores the intricate colours that help shape who we are as individuals and how society works.

Quantitative scientists need to get off their R-induced high horse to see the wood for the trees.

Citation: Niki A. Rust, Amber Abrams, Daniel W. S. Challender, Guillaume Chapron, Arash Ghoddousi, Jenny A. Glikman, Catherine H. Gowan, Courtney Hughes, Archi Rastogi, Alicia Said, Alexandra Sutton, Nik Taylor, Sarah Thomas, Hita Unnikrishnan, Amanda D. Webber, Gwen Wordingham & Catherine M. Hill (2017). Quantity Does Not Always Mean Quality: The Importance of Qualitative Social Science in Conservation Research. Society and Natural Resources. DOI: http://dx.doi.org/10.1080/08941920.2017.1333661


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