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This is debated. Peter Austin strongly recommends accounting for the paired nature of the matched groups. Other methodologies do not. I think it makes sense to do so. Generally, power will be increased if within-pair distances are low. The differences will not be stark, however.


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I spent some time on this and i have to say it was a bit hard understanding the research question and data structure. So I'm shooting a bit in the dark here. Based on this line: "In other words, an individual is assumed to give a significantly higher score of guilt for guilt-inducing advertisement compared to the shame-score." Shame ads are not needed, ...


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Yes, if subjects participate in the survey multiple times, then their responses are correlated, and you would need to employ RMANOVA.


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Mixed models can be used to test for both between- and within-subjects effects. Both are treated in the same manner as columns of the design matrix for the fixed effects. In the former case, the value of the covariate will be the same for all the measurements of a particular subject (e.g., sex), and in the latter case the covariate will have different values ...


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You should use $\log(\text{exposure time)$ as an offset in the (log link) poisson regression. That way you are comparing rates and not expected counts. For details see How is a Poisson rate regression equal to a Poisson regression with corresponding offset term? (and its links and references.) You do this the same way with a random effects model (I ...


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