- see [[220331_173513 summed badness|summed badness]], [[220331_183210 fraction bad|fraction badness]] # parameters - bad/goodness measures: fc, mbfc (factual, bias, mean(factual, bias), min(factual_bias)), afm (reliable, bias, mean(reliable, bias), min(reliable, bias)) (see [[different measures of domain quality]]) - goodness thresholds: $[5, 10, 15...90, 95]$ - each domain with value **below** threshold is counted as $1$ (domains above threshold not counted) - outcome: bad domain **count** (range: $[0, \infty]$) - winsorize user summed values (both at t1 and t0): $[0.85, 0.90, 0.95, 0.99, 1.00]$ (1.00 = no winsorizing) - winsorizing applied **before** *filling in missing values with 0* # results models - sum badness, **quasipoisson** model: `feglm(t1 ~ conditionC * t0LC | block, dt1, family = "quasipoisson", vcov = "HC1")` - `conditionC` $[-0.5, 0.5]$ - `t0` time0/pre-campaign badness (not shown because all estimates are highly significant) - sum badness: `log(t0 + 1)` then mean-centered - `block` as fixed effect - red: statistically significant effect (p < .05) ![[_count_winsorize-1.00 1.png]] ![[_count_winsorize-0.99 1.png]] ![[_count_winsorize-0.95 1.png]] ![[_count_winsorize-0.90 1.png]] ![[_count_winsorize-0.85 1.png]]