In my final data set I have a high proportion of missing data (1/4 are NAs, 35/52 variables have NAs), because of my codebook design: I only coded features (how things were discussed) that occurred in the text and I had a few conditional questions (e.g. Did they perceive a threat? What was the nature of the threat?). To analyze the data, I want to use classification trees (CART). However, the high proportion of missing values make excluding observations with NAs or imputation impossible.
I believe I should probably deal with that problem on a conceptual level and came up with two tactics:
Do not take the values coded -77 other, -88 uncertain, or -99 not applicable as missing values (like usual) but as their own values (also because they are different NAs). Thus, they would become part of the model. Edit: And if I include them, how would that work when the variable is best conceived as ordinal?
Have default values instead of coding something as -99 not applicable. For conditional question, for instance, "What was the nature of the threat?", a questioned conditioned on a "yes" in "Did they perceive a threat?", would get a category of "no threat". Some normal questions could also receive a default value based on the assumption that another value would have been mentioned in the text because it would affect the outcome (a decision by the body).
Are any of those two tactics an adequate way of dealing with the problem? Are there better ways?
