Ignoramus, ignorabimus? On uncertainty in ecological inference

Abstract: "Models of ecological inference (EI) have to rely on crucial assumptions about the individual-level data-generating process, which cannot be tested because of the unavailability of these data. However, these assumptions may be violated by the unknown data and this may lead to serious bias of estimates and predictions. The amount of bias, however, cannot be assessed without information that is unavailable in typical applications of EI. We therefore construct a model that at least approximately accounts for the additional, nonsampling error that may result from possible bias incurred by an EI procedure, a model that builds on the Principle of Maximum Entropy. By means of a systematic simulation experiment, we examine the performance of prediction intervals based on this second-stage Maximum Entropy model. The results of this simulation study suggest that these prediction intervals are at least approximately correct if all possible configurations of the unknown data are taken into acc

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch
• Notes: Veröffentlichungsversion ; In: Political Analysis ; 16 (2008) 1 ; 70-92

Classification and Topics

• Classification: Politik

Contributors, Places and Time

• Event: Veröffentlichung
• (where): Mannheim
• (when): 2008
• Creator: Elff, Martin ; Gschwend, Thomas ; Johnston, Ron

Further information

• DOI: 10.1093/pan/mpm030
• URN: urn:nbn:de:0168-ssoar-257841
• Rights: Open Access unbekannt; Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 15.08.2025, 7:30 AM CEST