What
do I mean by data cleaning? There are many definitions, but I am talking about
a two-step process. First, double-checking that all data points (cells) are filled, you will
probably discover some are not and decisions will need to be on this. The second step is carefully checking the
statistical assumptions of your variables and looking for extreme scores.
Why are these steps necessary? Because the results of your
study will only be as accurate as the data you analyze. Therefore, it is very
important to take the time to check your data carefully, so that you know that
your results are valid and accurate.
I want to refer you to a great book that much of my advice
over the next few posts will be based:
Osborn, J. W. (2013). Best
practices in data cleaning. DC: Sage.
Next time we will look at the issue of missing data. Do you have an issue or a question that you
would like me to discuss in a future post? Would you like to be a guest writer?
Send me your ideas! leann.stadtlander@waldenu.edu
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