One of the advantages of having really smart colleagues — the kind who exhibit genuine intellectual curiosity, and who are truly interested in doing things well — is that you get to learn a lot from them.
I was recently having a conversation with my colleague and next-door office neighbor Joe Ritter in which we were discussing the possibility that the (binary) treatment variable in a paper I am working on might suffer from some misclassification. That is, my variable D = 1 if an individual has received the treatment and D = 0 otherwise, but it is possible that some people for whom D = 1 actually report D = 0, and that some people for whom D = 0 actually report D = 1.
When the possibility that my treatment variable might suffer from misclassification (or measurement error) arose, Joe recalled that he’d read a paper by Christopher R. Bollinger about this a while back. A few hours later, he sent me an email to which he’d attached the paper. Here is the abstract: Continue reading →