Could I ask for pointers on how to guide a student in my multilevel
modeling course?
The outcome data is terrorist attack events, with one row per event
(events are listed by country and year). The data also indicates if
each attach is a "success" (I have no idea how that's measured, if it
matters I can find out).
The student says that, in his field, what they would do is aggregate
events at the country/year level to create a "proportion of successful
attacks" variable. If a country has no events, then it is scored as a
0. Then they'd run random intercept models using country as case
identifier, possibly with other country level predictors that vary
across time.
I think we can do better than that. The number of events within
countries varies widely, some have 0 or 1 attack, while in some years
there are 30 or more. Measuring the proportions is, obviously,
sensitive to the number in the denominator. Some countries are scored
on a scale 0, .5, 1, while others are scored as 0, 0.03, 0.06, and so
forth. Other obvious problems are the presence of 0's.
My first idea was to made this a binomial glm and predict successes as
a proportion of attacks. That's a problem because there are lots of 0
attack country/years, but also because I'm
It looks to me like we need to explore this as a two part model, where
part 1 predicts (attacks > 0) and part 2 is binomial among the
countries and places where attacks > 0. I'm not finding discussion of
this particular example while searching (I probably don't know the
magic words). However, we need to insert the country-level intercept
in both models, and perhaps the country effect needs to be correlated
between the two models.
pj
--
Paul E. Johnson http://pj.freefaculty.org
Director, Center for Research Methods and Data Analysis
http://crmda.ku.edu
To write to me directly, please address me at pauljohn at ku.edu.