A quick side note: as anyone who has been handed a bunch of analytic results can tell you, there is still a lot of work to be done to translate those results into clear and engaging tables, charts, presentations and reports, but that is (figuratively) the icing on the cake and we'll cover that at another time.
For now, we want to focus on what happens during the mixing. What gets done to data before it's ready to go into the oven? Here I've compiled a fairly comprehensive list of things to look for/do with raw data prior to the actual analyses. It is by no means exhaustive, nor does it apply to every data situation, but it's a great place to start the discussion.
Do a quick review to see if the data 'make sense', if the data are complete, in the data are what was expected.
Review the analysis plan (the recipe!) and make sure what is planned is feasible with the data you have.
Check the data structure to make sure it is set up correctly for the type of analysis that is planned. Reconfigure if necessary.
Examine the inclusion/exclusion criteria for the project and make sure all observations in the dataset actually belong there.
Check each variable - frequency if categorical, univariate if continuous - is it complete, is it formattedly correctly.
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