Part 5 · How do you prepare the data?
Module 9 · What must the data look like?
Three kinds of data CNA accepts
- Tell crisp-set, multi-value, and fuzzy-set data apart.
- Decide which calibration a condition needs.
- Explain why beginners should usually start crisp.
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.
Crisp-set, the easiest, plain yes or no
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.
| Facility | Broken cold chain | Meaning |
|---|---|---|
| HF1 | 1 | Cold chain was broken |
| HF2 | 0 | Cold chain was working |
Multi-value, for more than two levels
When a condition has natural steps, say supervision quality runs 0 = poor, 1 = moderate, 2 = good, you can use multi-value coding. CNA reads it fine, as long as the levels are clearly defined and you don't make them up on the spot.
Fuzzy-set, for degrees of membership
Sometimes "present" is a matter of degree. A value of 0.85 means "strongly, but not perfectly, in the set of low-coverage facilities." Fuzzy values run between 0 and 1. It's powerful, but save it for the day crisp-set feels boringly easy.
Start crisp. Graduate later.
Almost every immunization supervision question can be answered well with clean yes or no coding. Reach for multi-value or fuzzy only when a condition genuinely loses its meaning by being squashed into two boxes.
Look at one indicator you collect.
- Is it naturally yes/no, stepped, or a matter of degree?
- What would be lost or gained by squashing it into a simple 0/1?