<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Adam N. Glynn</style></author><author><style face="normal" font="default" size="100%">Jon Wakefield</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Ecological Inference in the Social Sciences</style></title><secondary-title><style face="normal" font="default" size="100%">Statistical Methodology</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><volume><style face="normal" font="default" size="100%">7</style></volume><pages><style face="normal" font="default" size="100%">307-322</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Ecological inference is a problem of partial identification, and
  therefore precise conclusions are rarely possible without
  the collection of individual level (identifying) data. Without such
  data, sensitivity analyses provide the only recourse. In this paper
  we review and critique recent approaches to ecological inference in the
  social sciences, and describe in detail hierarchical models, which
  allow both sensitivity analysis and the incorporation of individual
  level data into an ecological analysis. A crucial element of a
  sensitivity analysis in such models is prior specification, and we
  detail how this may be carried out. Furthermore, we demonstrate how
  the inclusion of a small amount of individual level data from a small number of ecological areas can
  dramatically improve the properties of such estimates. 
</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue></record></records></xml>