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I Approve This Candidate: The Strategic Use of Ad Endorsements in the 2008 Election

Published online by Cambridge University Press:  19 October 2015

Newly Paul
Affiliation:
Appalachian State University
Chance York
Affiliation:
Kent State University
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Abstract

Contextual and strategic factors that influence the use of endorsements in political advertisements represent an underexplored area of the scholarly literature, resulting in a gap between the art and science of campaigning and the advertising-effects literature. This article uses data from the Wisconsin Ad Project to study endorsements in ads aired during the 2008 election cycle. We show that endorsements are used strategically by campaigns and that four factors—candidate characteristics, campaign characteristics, ad format, and timing—affect whether an ad containing an endorsement is aired. Specifically, endorsements are more likely to appear in positive ads, are primarily used by incumbents and female candidates, are less likely to appear in competitive races, and tend to air less frequently as a campaign progresses.

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Copyright © American Political Science Association 2015 
Figure 0

Table 1Descriptive Statistics

Figure 1

Table 2Logistic Regression Predicting Use of Endorsement in 2008 Campaign Ad Airings

Figure 2

Figure 1Proportion of Ad Airings with Endorsements for 2008 House, Senate, and Gubernatorial RacesNote: n = 1,312,192. The blue line represents the proportion of ad airings containing endorsements using a one-day moving average from January 1 to November 4, 2008. The trend line is shown in red for comparison.

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Table 3Prototypical Candidate, Campaign, and Ad Characteristics That Affect Use of Endorsements

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Figure 2Path Model Showing Direct and Indirect Effects of Incumbent Status on Use of EndorsementsNote: n = 1,312,192. *** p<0.001. Standardized estimates are reported. Fit statistics are based on a structural-equation model using a randomly selected 1% subsample of the original WiscAd Project dataset to adjust for overidentification. These statistics indicate a good fit with these data: X2 (2, n = 13,113) = 2.34, p = 0.31; Root Mean Square Error of Approximation (RMSEA) = 0.004; Comparative Fit Index (CFI) = 1.00; Tucker-Lewis index (TLI) = 0.99. According to Acock (2013), a CFI value greater than 0.90 and an RMSEA value less than 0.05 indicate an adequate fit.