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1000 simulated data sets stored in a list of R dataframes used in support of Reisetter et al. (submitted) 'Mixture model normalization for non-targeted gas chromatography / mass spectrometry metabolomics data'. These are results after normalization using Batch Normalizer (Wang et al. 2012).
Data Types:
  • Software/Code
1000 simulated data sets stored in a list of R dataframes used in support of Reisetter et al. (submitted) 'Mixture model normalization for non-targeted gas chromatography / mass spectrometry metabolomics data'. These are simulated data sets that include batch effects and data truncation and are not yet normalized.
Data Types:
  • Software/Code
1000 simulated data sets stored in a list of R dataframes used in support of Reisetter et al. (submitted) 'Mixture model normalization for non-targeted gas chromatography / mass spectrometry metabolomics data'. These are results after normalization using EigenMS (Karpievitch et al. 2014).
Data Types:
  • Software/Code
1000 simulated data sets stored in a list of R dataframes used in support of Reisetter et al. (submitted) 'Mixture model normalization for non-targeted gas chromatography / mass spectrometry metabolomics data'. These are results after normalization using mean centering as described in Reisetter et al.
Data Types:
  • Software/Code
1000 simulated data sets stored in a list of R dataframes used in support of Reisetter et al. (submitted) 'Mixture model normalization for non-targeted gas chromatography / mass spectrometry metabolomics data'. These are results after normalization using median scaling as described in Reisetter et al.
Data Types:
  • Software/Code
1000 simulated data sets stored in a list of R dataframes used in support of Reisetter et al. (submitted) 'Mixture model normalization for non-targeted gas chromatography / mass spectrometry metabolomics data'. These are results after normalization using mixnorm as described in Reisetter et al.
Data Types:
  • Software/Code
1000 simulated data sets stored in a list of R dataframes used in support of Reisetter et al. (submitted) 'Mixture model normalization for non-targeted gas chromatography / mass spectrometry metabolomics data'. These are results after normalization using quantile normalization (Bolstad et al. 2003).
Data Types:
  • Software/Code
1000 simulated data sets stored in a list of R dataframes used in support of Reisetter et al. (submitted) 'Mixture model normalization for non-targeted gas chromatography / mass spectrometry metabolomics data'. These are results after normalization using quantile + ComBat (Johnson et al. 2007).
Data Types:
  • Software/Code
In recent years, Americans have become more affectively polarized: that is, ordinary Democrats and Republicans increasingly dislike and distrust members of the opposing party. Such polarization is normatively troubling, as it exacerbates gridlock and dissensus in Washington. Given these negative consequences, I investigate whether it is possible to ameliorate this partisan discord. Building on the Common Ingroup Identity Model from social psychology, I show that by heightening subjects’ sense of American national identity, they come to see members of the opposing party as fellow Americans, rather than rival partisans. As a result, they like the opposing party more, thereby reducing affective polarization. Using several original experiments, as well as a natural experiment surrounding the July 4th holiday and the 2008 Summer Olympics, I find strong support for my argument. I conclude by discussing the implications of these findings for efforts to reduce polarization more generally.
Data Types:
  • Software/Code
  • Tabular Data
  • Document
Review of Economics and Statistics: Forthcoming
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  • Tabular Data
  • Document
  • Text
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