Do you think your study is sufficiently well powered to detect very small effect sizes on meat consumption? It seems plausible that effects on meat consumption would be very small in expectation plus many people would not reduce meat no matter what, so you may be needing to detect a small shift within a small subpoulation.
It looks like you have 80% power to detect an effect size of d=0.24 - which is actually substantially larger than the effects we usually find for animal interventions even on more moveable things like attitudes/signing a petition/agreeing that "factory farms aren't great". Their null result on effect on meat consumption was not at all tightly bounded: -0.3oz [-6.12oz to + 5.46oz]
So I think the different results here seem possibly explained just by the fact that you could find effects on the moveable attitudes but were underpowered to detect differences in meat consumption. I'd be curious to estimate what effect size would we be looking at if say 3-5% of people stopped eating meat (an optimistic estimate IMO).
This is perhaps further confounded by a large amount of probable noise - how good are people at estimates oz of meat eaten in different time periods, is oz of meat something that is distributed in a way to corresponds to what a t-test is assessing?
Thanks for including the CI bounds, that makes it much more interpretable as an 'actual tight null' rather than an underpowered study.
CF I think that is relative to an average US consumption of about 80 oz per week ... so under +/- 10% with 95% CIs ... and the 80% CI would be obviously even tighter
(Or am I wrong, maybe this isn't that tight)
Disclaimer: Jacob and I both work at Rethink Priorities