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Searching for the Meaning of Negative: Fairness and Believability in Political Advertising
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8
IV. Analysis Elements of Fairness
Voters are more likely to react to fairness than to negativity in political ads (Lawton
2001). This analysis examines ad content that leads to perceptions of fairness. Characteristics often associated with negativity are tested.
Table 2 presents a basic OLS regression estimating the relative effects of each content
characteristic on fairness. Four of the elements produce statistically significant effects. Believability has the strongest effect (B=.553, p<.001), increasing the perception of fairness. Logical arguments (B=.092, p<.001) and issue content (B=.065, p=.002) also increase perceptions of fairness. The use of drama reduces perceived fairness (B=-.091, p<.001). The potential negative effects on perceived fairness of emotional appeals, while not having a statistically significant effect using the .05 standard, merit additional study (B=-.042, p=.051). These results indicate that many of the ad characteristics thought to drive perceptions of negativity also effect perceptions of fairness.
Table 2: Effects of Content Characteristic on Fairness
Unstandardized
Coefficients
Standardized
Coefficients
B
Std. Error
Beta
t
Sig.
(Constant)
2.263
.210
10.779
.000
humor
.018
.147
.003
.124
.901
drama
-.510
.122
-.091
-4.163
.000
issues
.376
.124
.065
3.046
.002
character
-.035
.117
-.006
-.295
.768
logical
.537
.126
.092
4.274
.000
emotion
-.236
.121
-.042
-1.956
.051
believe
.613
.024
.553
25.670
.000
Perceived negativity is not used as an estimator of fairness for two reasons. First, voter
perceptions of negativity and fairness are highly correlated (r=-.518, p<.001). This creates a potential multi-colinearity problem for including negativity in an equation estimating fairness. In addition, the characteristics used here to estimate fairness are considered by the communications literature to be elements of negativity. Thus, to find the effects of these characteristics on fairness, negativity was not included.
Effects of Issues
The effects of issues on these four perceptions are tested in Table 3. A difference of
means test shows statistically significant differences (p<.001) in perceptions of fairness, believability, helpfulness and negativity. Issue content increased perceptions of fairness, believability and helpfulness. It decreased perceptions of negativity.
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| | Authors: Brooks, Stephen. and Farmer, Rick. |
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8
IV. Analysis
Elements of Fairness
Voters are more likely to react to fairness than to negativity in political ads (Lawton
2001). This analysis examines ad content that leads to perceptions of fairness. Characteristics
often associated with negativity are tested.
Table 2 presents a basic OLS regression estimating the relative effects of each content
characteristic on fairness. Four of the elements produce statistically significant effects.
Believability has the strongest effect (B=.553, p<.001), increasing the perception of fairness.
Logical arguments (B=.092, p<.001) and issue content (B=.065, p=.002) also increase
perceptions of fairness. The use of drama reduces perceived fairness (B=-.091, p<.001). The
potential negative effects on perceived fairness of emotional appeals, while not having a
statistically significant effect using the .05 standard, merit additional study (B=-.042, p=.051).
These results indicate that many of the ad characteristics thought to drive perceptions of
negativity also effect perceptions of fairness.
Table 2: Effects of Content Characteristic on Fairness
Unstandardized
Coefficients
Standardized
Coefficients
B
Std. Error
Beta
t
Sig.
(Constant)
2.263
.210
10.779
.000
humor
.018
.147
.003
.124
.901
drama
-.510
.122
-.091
-4.163
.000
issues
.376
.124
.065
3.046
.002
character
-.035
.117
-.006
-.295
.768
logical
.537
.126
.092
4.274
.000
emotion
-.236
.121
-.042
-1.956
.051
believe
.613
.024
.553
25.670
.000
Perceived negativity is not used as an estimator of fairness for two reasons. First, voter
perceptions of negativity and fairness are highly correlated (r=-.518, p<.001). This creates a
potential multi-colinearity problem for including negativity in an equation estimating fairness. In
addition, the characteristics used here to estimate fairness are considered by the communications
literature to be elements of negativity. Thus, to find the effects of these characteristics on
fairness, negativity was not included.
Effects of Issues
The effects of issues on these four perceptions are tested in Table 3. A difference of
means test shows statistically significant differences (p<.001) in perceptions of fairness,
believability, helpfulness and negativity. Issue content increased perceptions of fairness,
believability and helpfulness. It decreased perceptions of negativity.
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