Most existing models of ecological inference are based on the assumption that there is no aggregation bias. Few studies have focused on how to correct/ model aggregation bias. This articletakes advantage of a unique opportunity to compare the controversial ecological inference methods by using aggregate as well as individual-level data from an actual election. Furthermore, the true quantities of interest are also available, which guarantees the accuracy of the empirical tests. Our mean squared error analyses show that King’s Ecological Inference (EI) basic model does not always outperform the traditional ecological regression and neighborhood methods when aggregation bias does exist. However, using an appropriate covariate in the King’s EI extended model to correct the aggregation bias problem can drastically improve the estimation accuracy at both the precinct and district levels. This article also makes suggestions on how to use a covariate in a King’s extended model.
Key Words: EI • ecological fallacy • ecological inferences • aggregation bias • contextual effect