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Part 1: Document Description
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Citation |
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Title: |
Replication data for: Aggregation Among Binary, Count, and Duration Models: Estimating the Same Quantities from Different Levels of Data |
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Identification Number: |
doi:10.7910/DVN/UDRSQ6 |
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Distributor: |
Harvard Dataverse |
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Date of Distribution: |
2010-03-04 |
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Version: |
1 |
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Bibliographic Citation: |
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", https://doi.org/10.7910/DVN/UDRSQ6, Harvard Dataverse, V1 |
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Citation |
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Title: |
Replication data for: Aggregation Among Binary, Count, and Duration Models: Estimating the Same Quantities from Different Levels of Data |
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Identification Number: |
doi:10.7910/DVN/UDRSQ6 |
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Authoring Entity: |
James E. Alt (Department of Government, Harvard University) |
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Gary King (Department of Government, Harvard University and World Health Organization) |
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Curtis S. Signorino (Department of Political Science, University of Rochester) |
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Producer: |
Political Analysis |
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Date of Production: |
2000 |
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Distributor: |
Harvard Dataverse |
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Distributor: |
Murray Research Archive |
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Date of Deposit: |
2010 |
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Holdings Information: |
https://doi.org/10.7910/DVN/UDRSQ6 |
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Study Scope |
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Abstract: |
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. |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Notes: |
<a href="http://creativecommons.org/publicdomain/zero/1.0">CC0 1.0</a> |
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Other Study Description Materials |
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Related Publications |
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Citation |
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Title: |
James E. Alt, Gary King and Curtis S. Signorino. 2000. "Aggregation Among Binary, Count, and Duration Models: Estimating the Same Quantities from Different Levels of Data." Political Analysis, 9(1), 21-44. <a href= "http://polmeth.wustl.edu/analysis/vol/9/PA91-21-44.pdf" target= "_new">article available here</a> |
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Bibliographic Citation: |
James E. Alt, Gary King and Curtis S. Signorino. 2000. "Aggregation Among Binary, Count, and Duration Models: Estimating the Same Quantities from Different Levels of Data." Political Analysis, 9(1), 21-44. <a href= "http://polmeth.wustl.edu/analysis/vol/9/PA91-21-44.pdf" target= "_new">article available here</a> |
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Label: |
AggregationAmongBinary.pdf |
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Text: |
Published Article |
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Notes: |
application/pdf |