I am a lonely peon currently researching and playing around with multiple imputation. I am using MICE in R to impute random missing data; however, I run into a problem when attempting to account for conditional or structured NAs in a dataset.
I'll provide a simplistic dataset in an attempt to illustrate my meaning:
TestData <- data.frame(Condition= c(1,1,1,1,2,NA,2,2), Dependent1=c(1,NA,2,3,NA,NA,NA,NA), Dependent2=c(1,12,44,1,NA,NA,NA,NA), Dependent3=c(NA,2,3,5,NA,NA,NA,NA), UnaffiliatedQ=c(1,NA,3,2,27,NA,32,35)) TestData$Condition <- factor(TestData$Condition, levels = c(1,2), labels = c("Yes","No"))
In my example, the variable "Condition" is a gatekeeper question which determines whether a respondent needs to fill the next three questions (Dependent#). If a respondent answers with "No" and he/she does not see the next three questions, then they are marked as NAs - though not technically missing/ they are structurally not applicable.
I've come to ask what CV would do in this type of situation? If I Impute the NA value in the Condition variable, along with those in Dependent1, Dependent2, and Dependent3, how would I ensure that I don't end up with values in Dependent# that don't make sense (constraints)?
I've thought of possible solutions - but none that I think would be valid or a good idea. (e.g., creating a structured missing value like -999/ subsetting the dataframe based on conditional answers). I've looked around for possible literature or walkthroughs for situations like this; however, I've come up empty handed.
The other alternative is that I've simply been running down the rabbit hole of multiple imputation and this is not the correct use of it. I posted on SO a few days ago, and was told to post here :)
I appreciate your thoughts and help all!