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Analyzing Exposure and Attention Variables in Media Effects Research
Unformatted Document Text:  Exposure and Attention 13 criterion. Our assumption in this analysis follows McGuire’s information processing model (McGuire, 1985). Exposure and attention to advertising should jointly affect attitudes and behavior, and they should also increase retention or memory for advertisements. Memory should also serve as a more robust outcome measure than impact on attitudes or behavior, as it is more proximal to the attention and exposure variables. Thus, recognition of cigarette ads was used as the outcome variable in our tests of the additive and multiplicative models of exposure. This is similar to Chaffee and Schleuder’s (1986) approach in using political knowledge as the criterion for assessing the impact of exposure and attention, though the recognition measure probably suffers fewer problems with causal order than do attention and political knowledge measures. For example, Chaffee, et al. (2001) use political knowledge to predict attention. The baseline model (Model 0) contained only the following control variables as predictors: gender, ethnicity and grade. The multiplicative model (Model 1) included the control variables and the multiplicative measure of exposure and attention (formed from mean-centered variables). The additive model (Model 2) included the control variables and the individual mean-centered measures of exposure and attention. The final model to be tested (Model 3) was the additive + multiplicative model and contained all of the aforementioned predictors. Regression statistics for the models are presented in Table 3. The model including only the control variables explained a small amount of variance (.021) and was not significant, F (3, 224) = 1.63, p = .184. Only grade was a significant predictor of memory for magazine advertising for cigarettes. Next, we tested the multiplicative model of exposure and attention. Adding the exposure x attention variable to the control model did not result in improvement in prediction ( ) R 2 = .000). The model was nonsignificant, F (4,

Authors: Aloise-Young, Patricia. and Slater, Michael.
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Exposure and Attention
13

criterion. Our assumption in this analysis follows McGuire’s information processing model
(McGuire, 1985). Exposure and attention to advertising should jointly affect attitudes and
behavior, and they should also increase retention or memory for advertisements. Memory
should also serve as a more robust outcome measure than impact on attitudes or behavior, as it is
more proximal to the attention and exposure variables. Thus, recognition of cigarette ads was
used as the outcome variable in our tests of the additive and multiplicative models of exposure.
This is similar to Chaffee and Schleuder’s (1986) approach in using political knowledge as the
criterion for assessing the impact of exposure and attention, though the recognition measure
probably suffers fewer problems with causal order than do attention and political knowledge
measures. For example, Chaffee, et al. (2001) use political knowledge to predict attention.
The baseline model (Model 0) contained only the following control variables as
predictors: gender, ethnicity and grade. The multiplicative model (Model 1) included the control
variables and the multiplicative measure of exposure and attention (formed from mean-centered
variables). The additive model (Model 2) included the control variables and the individual
mean-centered measures of exposure and attention. The final model to be tested (Model 3) was
the additive + multiplicative model and contained all of the aforementioned predictors.
Regression statistics for the models are presented in Table 3.
The model including only the control variables explained a small amount of variance
(.021) and was not significant, F (3, 224) = 1.63, p = .184. Only grade was a significant
predictor of memory for magazine advertising for cigarettes. Next, we tested the multiplicative
model of exposure and attention. Adding the exposure x attention variable to the control model
did not result in improvement in prediction (
)
R
2
= .000). The model was nonsignificant, F (4,


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