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Using R for Identification of Data Inconsistency in Electoral Models

Author

Listed:
  • Marius Jula

    (“Nicolae Titulescu” University of Bucharest)

Abstract

When using datasets for various analyses one should test the data for particular situations like existence of outliers or possible data errors. Outliers may indicate bad data and the results may be affected if these points are not identified and/or explained. Also, there are sensitive data, like electoral datasets, which are subject of fraud suspicion. Methods for identifying outliers and data errors are described in this paper, using R support and electoral data.

Suggested Citation

  • Marius Jula, 2015. "Using R for Identification of Data Inconsistency in Electoral Models," Romanian Statistical Review, Romanian Statistical Review, vol. 63(3), pages 101-108, September.
    • Handle: RePEc:rsr:journl:v:63:y:2015:i:3:p:101-108
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    References listed on IDEAS

    as
    1. Beber, Bernd & Scacco, Alexandra, 2012. "What the Numbers Say: A Digit-Based Test for Election Fraud," Political Analysis, Cambridge University Press, vol. 20(2), pages 211-234, April.
    2. Cantú, Francisco & Saiegh, Sebastián M., 2011. "Fraudulent Democracy? An Analysis of Argentina's Infamous Decade Using Supervised Machine Learning," Political Analysis, Cambridge University Press, vol. 19(4), pages 409-433.
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    Keywords

    outlier; Z-score; Benford’s law; R;
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