One component often overlooked in the ‘Define & Design’ phase of a study, is writing the analysis plan. The statistical analysis plan integrates a lot of information about the study including the research question, study design, variables and data used, and the type of statistical analysis that will be conducted.
Before you can write a data analysis plan, you have to choose the best statistical test or model. You have to integrate a lot of information about your research question, your design, your variables, and the data itself.
This year I hired a Quickbooks consultant to bring my bookkeeping up from the stone age. (I had been using Excel).
She had asked for some documents with detailed data, and I tried to send her something else as a shortcut. I thought it was detailed enough. It wasn’t, so she just fudged it. The bottom line was all correct, but the data that put it together was all wrong.
I hit the roof. Internally, only—I realized it was my own fault for not giving her the info she needed. She did a fabulous job.
But I could not leave the data fudged, even if it all added up to the right amount, and already reconciled. I had to go in and spend hours fixing it. Truthfully, I was a bit of a compulsive nut about it.
And then I had to ask myself why I was so uptight—if accountants think the details aren’t important, why do I? Statisticians are all about approximations and accountants are exact, right?
As it turns out, not so much.
But I realized I’ve had 20 years of training about the importance of data integrity. Sure, the results might be inexact, the analysis, the estimates, the conclusions. But not the data. The data must be clean.
Sparkling, if possible.
In research, it’s okay if the bottom line is an approximation. Because we’re never really measuring the whole population. And we can’t always measure precisely what we want to measure. But in the long run, it all averages out.
But only if the measurements we do have are as accurate as they possibly can be.