What is research?

So, what do we mean by ‘research’?

Well, on a very fundamental level it’s about searching out something that we don’t yet know. It’s about asking questions to find answers, understanding and meaning. From a policing perspective it’s very similar to the National Decision Making Model as it’s about ‘what we know already’ then ‘what we don’t know’ and seeking to ‘find out’ to better inform our decisions. (And, of course, monitoring this in a cyclical fashion and continually learning.)

So let’s stick with that NDM as a reference point, so you can feel comfortable based on something you already know. Having an idea of what you already know is a core point, plus an awareness of what you might be missing, or need to know more about. So it’s about collating and making sense of what you’ve already got ‘in the bag’ (this might be things personally known to you or things others have found out that you’re able to somehow assess as to its trustworthiness) – this in ‘research’ terms is sometimes called a ‘Literature Review’. It’s just really a collation of ‘what do we already know’ to inform where we go next.

Just as in assessing intelligence within the NDM you need a critical mind – you don’t just take things on face value; you seek corroboration and ‘test’ sources. This sometimes means comparing how say Source A adds a bit to the picture but clashes with parts of Source B. That’s just like assessing literature – so you should never just passively read and accept but always engage and be questioning. It’s the same point around presuming things – don’t!

So, you might say this is a bit like investigating – yes it is!

So, let’s also add in that angle of being like an investigation – think primary and secondary sources, like hearsay. Primary sources are like getting something from the ‘horse’s mouth’ (although this is not strictly speaking a technical researcher’s phrase!), whilst secondary data is where you’re looking at something second hand. Doesn’t make any one thing right or wrong – but you are aware of the pros and cons and potential limitations.

Research has its own model, just like the NDM. On a basic overview, it usually fits the convention of:-  identify the query, then rationalise and plan your approach, find the data, analyse it, use it and then into that cycle of continual learning.

You will know with any data enquiry that you need to think abut your question – if you search NICHE for any white male you will spend a long time waiting for a return and then even longer reading a very long list. You might try a speculative but informed guess (in research terms this is called deductive hypothesis testing). You make a stab at a possible insight or explanation and test it out.

You will also know the difference between probability, coincidence (circumstantial) and beyond all reasonable doubt. So, in research terms, if you find that there is a connection between A and B, you know you have to do more to prove something. In research terms, this is about understanding correlation (spotting a connection) doesn’t equate to causation (you have to prove beyond simple coincidences).

And, of course, Sherlock, you know what evidence is. Well it’s just like that in research – we don’t get a conviction on the basis of Mr A thinks Mr B did it – we seek the evidence. Evidence can come in all shapes and sizes, and research, just as disclosure reveals in an investigation, can be a bit messy, with some dead ends, cul de sacs and occasional lucky breaks. But, just like an investigation, research, messy or not, aims to be systematic so there is a clear accountability audit trail of what was done, why that rationale and an honesty that allows a fellow investigator to pick up and review or continue.

In research that accountability is termed ‘ethics’ and means to do no harm in the process.

Time for a break and a bit of reflecting before you move onto the next article maybe. Think on this though – do you see how you do already know these principles already, just under different names? Research  – it’s like police work under an alias!

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