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Title: Replication data for: Aggregation Among Binary, Count, and Duration Models: Estimating the Same Quantities from Different Levels of Data      
dateReleased:
03-04-2010
downloadURL: http://hdl.handle.net/1902.1/14000
ID:
hdl:1902.1/14000
description:
Binary, count, and duration data all code discrete events occurring at points in time. Although a single data generation process can produce all of these three data types, the statistical literature is not very helpful in providing methods to estimate parameters of the same process from each. In fact, only a single theoretical process exists for which known statistical methods can estimate the same parameters—and it is generally used only for count and duration data. The result is that seemingly trivial decisions about which level of data to use can have important consequences for substantive interpretations. We describe the theoretical event process for which results exist, based on time independence. We also derive a set of models for a time-dependent process and compare their predictions to those of a commonly used model. Any hope of understanding and avoiding the more serious problems of aggregation bias in events data is contingent on first deriving a much wider arsenal of statistical models and theoretical processes that are not constrained by the particular forms of data that happen to be available. We discuss these issues and suggest an agenda for political methodologists interested in this very large class of aggregation problems.
description:
James E. Alt; Gary King; Curtis S. Signorino, 2010, "Replication data for: Aggregation Among Binary, Count, and Duration Models: Estimating the Same Quantities from Different Levels of Data", http://hdl.handle.net/1902.1/14000, Harvard Dataverse, V1
name:
James E. Alt
Gary King
Curtis S. Signorino
homePage: http://www.harvard.edu/
name:
Harvard University
ID:
SCR:011273
abbreviation:
DataVerse
homePage: http://thedata.org/
name:
Dataverse Network Project
ID:
SCR:001997