For average treatment effect on the treated (ATT), the estimates and SEs from individual studies are divided by proportion reach before meta-analyzing. Proportion reach for the four studies: - 0.5600, 0.6180, 0.6429, 0.6254 - main text prior: `t(df = 3, mu = 0, sigma = 0.05)` # 0) descriptives * plot of proportion of active days pre-campaign by "wave" * distribution of demrep (pooling over studies 1 3 4) - [[230719_164713 descriptives]] # 1) Main effect (and moderation by t0) for thresholds in [40:5:80]: * quasipoisson predicting W95 # bad link RTs using treated * scale(t0) for studies 1, 3, 4 -> frequentist meta-analysis of treated; and treated * scale(t0) -> Treatment effect on the treated version (divide coeff and SE for each study by p(treated) then do meta) - [[230718_135343 frequentist fixed-effects meta for studies 1 3 4]] - [[230719_184036 bayesian fixed-effects meta for studies 1 3 4]] For comparison to FB analyses: * LMP predicting # badlinkRTs > 0 using treated x scale(t0) for studies 1, 3, 4 -> frequentist meta-analysis of treated; and treated x scale(t0) - [[230719_183343 linear probability model - compare FB analyses - bayesian and frequentist]] To assess robustness to winsorizing * quasipoisson predicting W99 # bad link RTs using treated * scale(t0) for studies 1, 3, 4 -> frequentist meta-analysis of treated; and treated * scale(t0) * quasipoisson predicting # bad links using treated * scale(t0) for studies 1, 3, 4 -> frequentist meta-analysis of treated; and treated * scale(t0) - [[230719_183842 robustness to winsorizing-99]] - [[230719_183914 robustness to no winsorizing]] # 2) Moderation for thresholds in [40:5:80]: * quasipoisson predicting W95 # bad link RTs using treated * scale(t0) + treated * day# for studies 1, 3, 4, restricting to days 1-8 for all campaigns -> frequentist meta-analysis of treated * day# * quasipoisson predicting W95 # bad link RTs using treated* scale(t0) + treated * day# for studies 1, 3, 4 -> frequentist meta-analysis of treated * day # - [[230727_120706 treatment effect moderation by day - days 1 to 8]] - [[230728_10706 treatment effect moderation by day - all days]] * quasipoisson predicting W95 # bad link RTs using treated * rep for studies 1, 3, 4 -> frequentist meta-analysis of treated * rep * quasipoisson predicting W95 # bad link RTs using treated * scale(t0) + treated * rep for studies 1, 3, 4 -> frequentist meta-analysis of treated * rep - [[230727_150706 treatment effect moderation by repub]] - quasipoisson predicting W95 # bad link RTs using treated * political (or not) for studies 1, 3, 4 - [[230729_140939 treatment effect moderation by political or not]] # 3) Effect on all RT * quasipoisson predicting W95 # RTs using treated * scale(total # all RTs in t0) for studies 1, 3, 4 -> frequentist meta-analysis of treated - [[230723_184513 total retweets - bayesian and frequentist]] # 4) Low-activity users experiment * quasipoisson predicting W95 # bad link RTs using treated * scale(t0) for study 2 * LMP predicting # badlinkRTs>0 using treated * scale(t0) for study 2 - [[230723_190621 study 2 count models and linear probability models]] --- # other analyses - heterogeneity of treatment effect across campaigns? not possible with fixed-effects models, since any variation between studies is due to sampling error alone - [ ] check study2 t1 and t0; compare with other studies