Part 5: How do you prepare the data? · Module 9
Three kinds of data CNA accepts
What Must the Data Look Like?
CNA reads simple tables. The only real craft is "calibrating" each condition, which is a fancy word for turning messy reality into clean, comparable values. There are three flavours, and the cna package happily handles all of them.
Every condition is either present ( 1 ) or absent ( 0 ). "Was the cold chain broken?" becomes 1 or 0. This is where beginners should start, and it is what we use throughout this course, because life is hard enough already.
What You Will Be Able to Do
- Tell crisp-set, multi-value, and fuzzy-set data apart.
- Decide which calibration a condition needs.
- Explain why beginners should usually start crisp.
In This Module
- Crisp-set, the easiest, plain yes or no
- Multi-value, for more than two levels
- Fuzzy-set, for degrees of membership