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Analyzing Exposure and Attention Variables in Media Effects Research
Unformatted Document Text:  Exposure and Attention 14 221) = 1.19, p = .316, as was the multiplicative term of exposure and attention. The next model to be tested was the additive model including the control variables and the main effects of exposure and attention. This model showed significant improvement in the amount of variance accounted for ( ) R 2 = .115) and the model was significant, F (5, 220) = 6.94, p = .000. As was expected, both exposure and attention were predictive of memory for cigarette advertising. Finally, the additive + multiplicative model was tested, but this model did not show significant improvement in predictive value over the additive model ( ) R 2 = .001). The model was significant, F (6, 219) = 5.78, p = .000. However, only the individual effects of exposure and attention were significant, the multiplicative effect was not. To summarize, there was no improvement in variance accounted for by adding the multiplicative term to the model. Across analyses only the main effects of exposure and attention significantly predicted memory for cigarette advertisements. These results clearly point to the validity of an additive model of exposure and attention, rather than a model that includes a contingent relationship between exposure and attention. Discussion Clearly, there is good reason for debate about operationalization of exposure and attention measures. Measures of recognition and recall are fairly highly correlated, as one might expect. Correlations with self-reports of exposure and attention, however, are low. It is unclear what the underlying difference is in what is being measured conceptually by each operationalization. It is tempting to claim that the memory measures are superior, in that they are based on actual responses to messages by research participants. However, such a claim may be premature,

Authors: Aloise-Young, Patricia. and Slater, Michael.
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Exposure and Attention
14

221) = 1.19, p = .316, as was the multiplicative term of exposure and attention. The next model
to be tested was the additive model including the control variables and the main effects of
exposure and attention. This model showed significant improvement in the amount of variance
accounted for (
)
R
2
= .115) and the model was significant, F (5, 220) = 6.94, p = .000. As was
expected, both exposure and attention were predictive of memory for cigarette advertising.
Finally, the additive + multiplicative model was tested, but this model did not show significant
improvement in predictive value over the additive model (
)
R
2
= .001). The model was
significant, F (6, 219) = 5.78, p = .000. However, only the individual effects of exposure and
attention were significant, the multiplicative effect was not.
To summarize, there was no improvement in variance accounted for by adding the
multiplicative term to the model. Across analyses only the main effects of exposure and
attention significantly predicted memory for cigarette advertisements. These results clearly point
to the validity of an additive model of exposure and attention, rather than a model that includes a
contingent relationship between exposure and attention.
Discussion
Clearly, there is good reason for debate about operationalization of exposure and
attention measures. Measures of recognition and recall are fairly highly correlated, as one might
expect. Correlations with self-reports of exposure and attention, however, are low. It is unclear
what the underlying difference is in what is being measured conceptually by each
operationalization.
It is tempting to claim that the memory measures are superior, in that they are based on
actual responses to messages by research participants. However, such a claim may be premature,


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