There are lies, damn lies, and statistics

It’s easy to knock statistics for being misleading, or even misused to support spurious findings. In fact, there seems to be a growing backlash at the automatic way that significance tests in scientific papers are assumed to be the basis for proving findings (an article neatly rebutted here in the aptly named post “Give p a chance!”). However, I think most of the time statistics are actually undervalued. They are extremely good at conveying succinct summaries about large numbers of things. Not that there isn’t room for more public literacy about statistics, a charge that can be levied at many academic researchers too.

But there is a clear limit to how far statistics can take us, especially when dealing with complex and messy social issues. These are often the result of intricately entangled factors, decided by fickle and seemingly irrational human beings. Statistics can give you an overview of what is happening, but they can’t tell you why. To really understand the behaviour and decisions of an individual, or a group of actors, we need to get an in-depth knowledge: one data point in a distribution isn’t going to be enough power.

Sometimes, to understand a public health issue like obesity, we need to know about everything from supermarket psychology that promotes unhealthy food, to how childhood depression can be linked with obesity. When done well, qualitative research allows us to look across societal and personal factors, integrating individuals stories into a social narrative that can explain important issues.

To do this, we can observe the behaviour of people in a supermarket, or interview people about their lives. But one of the key factors in some qualitative research, is that we don’t always know what we are looking for. If we explicitly go into a supermarket with the idea that watching shoppers will prove that supermarket two-for-one offers are causing obesity, we might miss other issues: the shelf placement of junk food, or the high cost of fresh vegetables. In the same way, if we interview someone with set questions about childhood depression, we might miss factors like time needed for food preparation, or cuts to welfare benefits.

This open ended, sometimes called ‘semi-structured’, or inductive analytical approach is one of the most difficult, but most powerful methods of qualitative research. Collecting data first, and then using grounded theory in the analytic phase to discover underlying themes from which can build hypotheses, sometimes seems like backward thinking. But when you don’t know what the right questions are, it’s difficult to find the right answers.

More on all this soon…

Tags : groundedinductiveobesityobservationquantitativestatistics