The R Journal: article published in 2016, volume 8:2

eiCompare: Comparing Ecological Inference Estimates across EI and EI:RC
Loren Collingwood, Kassra Oskooii, Sergio Garcia-Rios and Matt Barreto , The R Journal (2016) 8:2, pages 92-101.

Abstract Social scientists and statisticians often use aggregate data to predict individual-level behavior because the latter are not always available. Various statistical techniques have been developed to make inferences from one level (e.g., precinct) to another level (e.g., individual voter) that minimize errors associated with ecological inference. While ecological inference has been shown to be highly problematic in a wide array of scientific fields, many political scientists and analysis employ the techniques when studying voting patterns. Indeed, federal voting rights lawsuits now require such an analysis, yet expert reports are not consistent in which type of ecological inference is used. This is especially the case in the analysis of racially polarized voting when there are multiple candidates and multiple racial groups. The eiCompare package was developed to easily assess two of the more common ecological inference methods: EI and EI:R×C. The package facilitates a seamless comparison between these methods so that scholars and legal practitioners can easily assess the two methods and whether they produce similar or disparate findings.

Received: 2016-01-06; online 2016-09-09
CRAN packages: ei, eiPack, eiCompare


CC BY 4.0
This article is licensed under a Creative Commons Attribution 3.0 Unported license .

@article{RJ-2016-035,
  author = {Loren Collingwood and Kassra Oskooii and Sergio Garcia-Rios
          and Matt Barreto},
  title = {{eiCompare: Comparing Ecological Inference Estimates across
          EI and EI:RC}},
  year = {2016},
  journal = {{The R Journal}},
  url = {https://journal.r-project.org/archive/2016/RJ-2016-035/index.html},
  pages = {92--101},
  volume = {8},
  number = {2}
}