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Harder Than It Looks: Coding Political Knowledge on the ANES

Published online by Cambridge University Press:  04 January 2017

Matthew DeBell
Matthew DeBell*
Affiliation:
Institute for Research in the Social Sciences, Stanford University, 30 Alta Road, Stanford, CA 94305 e-mail: debell@stanford.edu
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Abstract

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Political knowledge research faces a problem, perhaps even a crisis. For two decades, the American National Election Studies asked open-ended questions about political knowledge and coded answers using procedures that are neither reliable nor replicable and that were never shown to be optimally valid. Consequently, conclusions based on these widely used measures of the public's competence are in doubt. This article presents several new and overdue methodological improvements: coding knowledge data using formal and specific coding rules based on a substantive rationale for the validity of the codes, recognizing partially correct answers, using multiple coders working independently, using machine coding, and testing reliability and validity. The new methods are an improvement because they are transparent and replicable and they produce valid and extremely reliable knowledge data. Further, machine coding produces codes nearly identical to those from a team of human coders, at much lower cost.

Information

Type
Research Article
Information
Political Analysis , Volume 21 , Issue 4 , Autumn 2013, pp. 393 - 406
Copyright
Copyright © The Author 2013. Published by Oxford University Press on behalf of the Society for Political Methodology 

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