zero-truncated mixed effects logistic regression?
On Tue, 17 Jan 2012, Martin Schmettow wrote:
The problem I have is similar to the capture-recapture approach for estimating abundance. In my case the captured animals are design flaws of software. A given number of testers independently tries to find these flaws, which makes it a binomial problem. However, flaws that were never discovered during the study are not known to the experimenter.
Furthermore this is a crossed mixed-effects situation as discovery trials are repeated over testers and flaws. (1) Does effectiveness of testers increases with years of experience? (2) Are certain classes of flaws easier to find than others? A general finding of previous research is that testers as well as flaws are heterogeneous. Some flaws are less visible than others and testers differ in overall effectiveness. Hence, random effects are needed to account for overdispersion, right?
I may be corrected, but I think your setup is "actually" a Rasch type model with each flaw being an item. Some flaws are just too difficult to see, ie the item is "too hard". I presume, given your research questions, you are not actually interested in estimating the number of undetected flaws from each class, so a missing data type setup is not really needed. http://www.jstatsoft.org/v20/a02/paper is one paper from our esteemed leader ;)
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