The perils of cherry picking low frequency events in large sample surveys

• Stephen Ansolabehere ^[1] ; Samantha Luks ; Brian F. Schaffner ^[2]
  1. [1] Harvard University

     Harvard University

     City of Cambridge, Estados Unidos

     
  2. [2] University of Massachusetts
• Localización: Electoral Studies: An international Journal, ISSN 0261-3794, Nº 40, 2015, págs. 409-410
• Idioma: inglés
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• Resumen

  • The advent of large sample surveys, such as the Cooperative Congressional Election Study (CCES), has opened the possibility of measuring very low frequency events, characteristics, and behaviors in the population. This paper documents how low-level measurement error for survey questions generally agreed to be highly reliable can lead to large prediction errors in large sample surveys, such as the CCES. The example for this analysis is Richman et al. (2014), which presents a biased estimate of the rate at which non-citizens voted in recent elections. The results, we show, are completely accounted for by very low frequency measurement error; further, the likely percent of non-citizen voters in recent US elections is 0.