You should’ve followed Rose’s example more thoroughly and chucked the paper off the back of a boat…

Given this discussion you might be interested in this paper where we simulated the return to message pre-testing assuming different values of the correlation (& other params). We also unearth some papers on the survey-field correlation in treatment fx (very thin) www.benmtappin.com/papers/Tappi...

Ah yes I see more where you’re coming from now - many thanks for persisting! I agree and think it’s actually distinct from the decay mechanism. I’m most used to thinking about persuasive message treatments like political ads, so wasn’t considering those that remove barriers or reduce cognitive load

Hence my interest in understanding your mechanism as it hadn’t occurred to me before!

Additional context: I’ve thought about this before and agree the correlation is unlikely to be strong. The mechanisms most plausible to me are a) treatment effect heterogeneity and b) some interventions decay faster than others - not detectable on intentions, which are usually measured immediately.

Thanks! I’m still a little unclear (likely my fault). Say we had 10 ads encouraging eating veggie, with some rank order of impact on intention to eat veggie. You claim the rank order is likely different on actually eating veggie because the ads change the cost-benefit setup of intention vs. action?

Hi Fabian, sympathetic to your paper + this specific point! Interested to hear more re: your reasoning. Say we have 10 interventions. Their effects are correlated across intention A and action A. In your view is there a key mechanism why the value of this correlation depends on the cost of action A?