What method do I need to analyse my data?

In the FAQ series we will elaborate on common questions that come across our desk. This is not so you wouldn’t ask us those questions anymore, but to provide you a deeper understanding on how to formulate your questions more specific to your research context.

On the surface it seems like the right question to ask a statistician. “What method do I need to analyse my data?” But don’t be surprised if the answer you get is: “It depends”.

Let’s dig a little bit deeper to understand why this question doesn’t generate a specific answer.

When academics think about their research, they commonly have a linear process in mind. A research question gets translated into a data collection and once the data are collected they need to be analysed. Or, alternatively, certain data become available to use and generate some questions. Then statistics are being used to answer those questions.

In the statistician’s mind, the research question (problem), the data and the analysis are all connected. It might help to think of them as being part of a triangle.

A problem cannot be resolved without data, and an analysis requires data, but to the same extent the analysis also needs to be capable of providing an answer to the problem. When you consider the analysis method after the data have been collected it is possible that it would become rather difficult to provide an analysis method that both can deal with the data characteristics as well as answering the problem.

Or to put it in Ronald Fisher’s words:

To call in the statistician after the experiment is done may be no more than asking him to perform a postmortem examination: he may be able to say what the experiment died of.

~ RA Fisher 1938

So asking the question around which method you need, should come sooner rather than later. A well thought out research plan has considered all possibilities with respect to what data are needed to answer the research questions as well as which analysis is required to provide insight to those answers that suit the data characteristics.

Try to avoid thinking about your research plan as being linear. It is not a sequential timeline. All pieces of the puzzle need to fit before you can plan them out to be executed over time.

Marijke joined the Statistical Consulting Unit in May 2019. She is passionate about explaining statistics, especially to those who deem themselves not statistically gifted.

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