1. What assumptions do we make about the distribution of residuals in a negative binomial regression? (surely they're not integers, so not negative binomially distributed themselves?)
i dunno whether you've already had looked into this book or not, but i believe joseph hilbe wrote something specifically on negative binomial regression, right? i would like to think he either provides some guidance about how the residuals should look like or at least a few references... he's always such a pleasure to read!! (i've never done a negative binomial regression myself as i suffer from poissonitis and prefer to model my count data as poisson RVs whenever possible, lolz)
2. Why are peer reviewers in psych more interested in pushing their pet theories than, I don't know, actually making useful comments about data analysis?
oh! i like TOTALLY hear you there... i think we're both in very similar areas, right? (i'm in educational measurement/psychometrics) and i do get this feeling that people in our areas just love to jump over the data analysis and engage in bizarre philosophising while people like us kind of tap on their shoulders and say "well, yes but the data says... excuse me, sir/madam, the DATA... THE DATAAAA!" [/QUOTE]
3. There's a conference coming up in a nice snowy place, and I'd like to go, but I can't quite decide which topic to try and present about.
from what i read on your posts, you seem to be very porficient in structural equation modeling. what about something on latent grow modeling? i'm not sure what's hot in stats for psychology nowadays but in the field of educational measurement, oh boy, if you're into hierarchical linear models/growth curve modeling then you're part of the IN crowd... and if you do
both at the same time they worship you...
happy easter to you too!! see? since you taught me the "/quote trick" i cant stop using it nowwww..
feelting thought of mine:
there's this new stats fad in the U.S. called value added models which people (read politicians) are using to measure how effective teachers are. it's HIGHLY controversial... to the point that a teacher in LA committed suicide after being ranked as "ineffective". i guess it's because, if the people behind this thing have their way, results from these models could be used to fire people, grant promotions, accommodate funding for schools, etc... now if the Obama administration through its "Race to the Top" program endorses these models, i can only wonder what a republican administration would do with them (these models were developed by economists so you get the idea).
there's been talk here in british columbia, canada to bring the models up north and since i am working very, very hard in becoming an expert in both hierarchical and latent growth models i didnt know until the summer holidays started that i was a de-facto member of the team who's brining them up here... which makes me wonder... does one say yes? no? maybe? there's lots of pluses to this (the funding $$$ is so good i could probably quit my stats consulting job and only do research, i'd pretty much come out of this experience with a dissertation already written, etc.) but now i read about people committing suicide and stuff and i'm like... "darn... what do responsible/ethical researchers do in these cases?"
He seems to assume a fairly sturdy background in mathematical stats and calculus, which is a pity because negative binomial regression is a solution (or set of solutions) to a really practical applied research problem (overdispersed count data). The book could potentially be useful to anyone from dentists to criminologists and lots of other researchers not particularly expert in stats, but he seems to have decided to pitch it at a level that cuts out a lot of the potential market. I might need to give it another go though!
