Protobi uses the label "[NA]" to represent missing values. This acronym can be interpreted as "Not available," "Not applicable," or "No answer."
For some analyses it may make more sense to ignore missing values and consider only respondents who actually answered the question. The [NA] button in the toolbar toggles whether missing values are shown in charts or not.
Missing values can occur when:
- Someone skips a question due to a skip pattern
- Survey logic doesn't force an answer
- A profile variable has no recorded value
- The analyst removes outliers for individual questions
Consider this hypothetical survey with two questions:
- S1: What is your medical specialty?
- S2: [IF NURSE] What grade nurse are you?
Question S2 is asked only of respondents who indicated they are Practice Nurse in question S1.
Results would appear as follows, where 40% of all respondents are nurses, and 24% of all respondents are Nursing Equivalent band:
Exclude missing values
In the toolbar, press the [NA] button to toggle whether missing values are included in the basis or not. This applies globally to all elements (except those with specific settings.)
Here, the basis is now N=40, reflecting answers only from nurses, and 60% of all nurses are Equivalent band 7+.
You can override the global setting for one or more elements by pressing the edit icon, choosing "More properties..." from the context menu, and selecting a value for "showMissing."
- Yes will always include missing values
- No will always exclude missing values
- Default will defer to the global setting per the toolbar
Recode missing values
Alternatively, your analysis may require missing values to be recoded to another value, such as zero ("zero filling") or a known answer from another question. See the tutorial on replacing missing values.
NA means not asked, not answered, or not applicable. In S2, we can see that 60% of respondents are NA. For General Practitioners, it is 100% NA because it has not been asked. For nurses, it is 0 percent NA.