What is additive effects?

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This refers to the independent variables in a model being added together to then determine the effects of the dependent variables. The predictors in a study are put together to explain the the resulting effects on the dependent variable.

Example / Application

Example: Stephen Colbert's Fancy Feast study

Application: In the study,"Can people distinguish pate from dog?", the majority of the participants were not able to correctly identify what they were eating. Suppose that in the study, 20% of women were more likely to distinguish the dog food then the other participants. We can further suppose that 40% of participants who owned a dog as a pet were more likely to recognize the dog food then those that do not own a pet. If these independent variables have an additive effect, women who owned a dog as a pet were 60% more likely to recognize the dog food than the other participants, (20% + 40%).

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