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.
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For attribution, please cite this work as
Collingwood, et al., "The R Journal: eiCompare: Comparing Ecological Inference Estimates across EI and EI:RC", The R Journal, 2016
BibTeX citation
@article{RJ-2016-035,
author = {Collingwood, Loren and Oskooii, Kassra and Garcia-Rios, Sergio and Barreto, Matt},
title = {The R Journal: eiCompare: Comparing Ecological Inference Estimates across EI and EI:RC},
journal = {The R Journal},
year = {2016},
note = {https://doi.org/10.32614/RJ-2016-035},
doi = {10.32614/RJ-2016-035},
volume = {8},
issue = {2},
issn = {2073-4859},
pages = {92-101}
}