I have a question on multilevel analysis. I hope I explain everything clearly, if not please let me know!
I have a dichotomous dependent variable (burglary yes/no), which I try to explain by variables on three levels (house, street and neighbourhood). I am conducting multi level analysis. I just did some cross level interactions, which show a significant influence. With a normally distributed dependent variable, it is now possible to calculate the explained slope variance, to see how much percent of the effect is explained by this variable to see if the influence is indeed relevant. Since for a dichotomous dependent variable it is not useful to calculate the explained variance I used the ICC. But I cannot seem to find anything on how to calculate the ICC for the slope variance. Is this even possible? And if so, how do I do this?
If you are use a multilevel logit model the level 1 variance is usually taken to be the variance of a standard logistic distribution - that is 3.29. The complication is that this cannot go down so you get some quite strange effects as you put variables into the model. The Snijder and Bosker multilevel book is very ggod on this and you may also want to look at the material on the Lemma course on discrete outcomes
http://www.bristol.ac.uk/cmm/learning/online-course/index.html
There is a specific online resource on this topic at
http://www.bristol.ac.uk/cmm/software/support/support-faqs/pval.html
http://r.789695.n4.nabble.com/ICC-for-Binary-data-td791831.html describes some tools in R
A very late response, since other things came up this last month. I will dive into the literature you suggested and look into R more closely!
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