A Review and Proposal for a New Measure of Poll Accuracy
ELIZABETH A. MARTIN is the senior survey methodologist for the U.S. Bureau of the Census. MICHAEL W. TRAUGOTT is professor of communication studies and senior research scientist at the Institute for Social Research, University of Michigan. COURTNEY KENNEDY works at the Pew Research Center for the People and the Press and is a graduate student in the Joint Program in Survey Methodology. A previous version of this article was prepared for presentation at the annual meeting of the American Association for Public Opinion Research, Nashville, TN, May 16–19, 2003. This article reports the results of research and analysis undertaken collaboratively by Census Bureau staff and researchers from the University of Michigan. This report is released to inform interested parties of ongoing research and to encourage discussion of work in progress. The views expressed are those of the authors and not necessarily those of the U.S. Census Bureau or the University of Michigan. Special thanks are due to Bob Fay for assistance on the derivation of the variance. We also thank Pat Cantwell, Diane Colasanto, Bob Fay, Jon Krosnick, Warren Mitofsky, Colm O’Muircheartaigh, Paul Siegel, Eric Slud, and anonymous reviewers for helpful comments on an earlier draft.
Address correspondence to Elizabeth Martin; e-mail: elizabeth.ann.martin{at}census.gov.
This article proposes a new measure of the predictive accuracy (A) of election polls that permits examination of both accuracy and bias, and it applies the new measure to summarize the results of a number of preelection polls. We first briefly review past measures of accuracy, then introduce the new measure. After the new measure is described, the general strategy is to apply it to three presidential elections (1948, 1996, and 2000) and to compare the results derived from it to the results obtained with the Mosteller measures. Then, the new measure is applied to the results of 548 state polls from gubernatorial and senatorial races in the 2002 elections to illustrate its application to a large body of preelection polls conducted in "off-year" races with different outcomes. We believe that this new measure will be useful as a summary measure of accuracy in election forecasts. It is easily computed and summarized, and it can be used as a dependent variable in multivariate statistical analyses of the nature and extent of biases that affect election forecasts and to identify their potential sources. It is comparable across elections with different outcomes and among polls that vary in their treatment or numbers of undecided voters.
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