```
> d2[, n_distinct(user_id), condition]
condition V1
1: -0.5 136
2: 0.5 112
```
# correlations between DVs
```r
> cors
topic r
1: climate 0.8818548
2: election 0.7929602
3: johnny 0.6816853
```
![[s20220523_095637.png]]
# manipulation check: how many % climate cards seen
![[s20220523_095719.png]]
# main results
No effect of treatment on climate importance, newsworthiness. Looks like ceiling effects?
```r
> m1_imp <- lm(importance ~ condition, d3[topic == "climate"])
> summaryh(m1_imp)
term results
1: (Intercept) b = 5.04, SE = 0.08, t(246) = 60.51, p < .001, r = 0.97
2: condition b = 0.02, SE = 0.17, t(246) = 0.14, p = .885, r = 0.01
BF = 0.14, 0.051
> m1_newsw <- lm(newsworthiness ~ condition, d3[topic == "climate"])
> summaryh(m1_newsw)
term results
1: (Intercept) b = 4.81, SE = 0.09, t(246) = 56.03, p < .001, r = 0.96
2: condition b = −0.005, SE = 0.17, t(246) = −0.03, p = .976, r = 0
BF = 0.1397504, 0.051
> summaryh(m1_combine)
term results
1: (Intercept) b = 4.92, SE = 0.08, t(246) = 60.03, p < .001, r = 0.97
2: condition b = 0.009, SE = 0.16, t(246) = 0.06, p = .954, r = 0
BF = 0.1399083, 0.051
```
But potential transfer effects? Treatment group thinks election is less important/newsworthy?
```r
> summaryh(lm(importance ~ condition, d3[topic == "election"]))
term results
1: (Intercept) b = 4.82, SE = 0.07, t(246) = 72.54, p < .001, r = 0.98
2: condition b = −0.22, SE = 0.13, t(246) = −1.65, p = .100, r = −0.10
> summaryh(lm(newsworthiness ~ condition, d3[topic == "election"]))
term results
1: (Intercept) b = 4.74, SE = 0.07, t(246) = 70.69, p < .001, r = 0.98
2: condition b = −0.29, SE = 0.13, t(246) = −2.19, p = .030, r = −0.14
> summaryh(lm(rating_combined ~ condition, d3[topic == "election"]))
term results
1: (Intercept) b = 4.78, SE = 0.06, t(246) = 75.69, p < .001, r = 0.98
2: condition b = −0.26, SE = 0.13, t(246) = −2.03, p = .043, r = −0.13
```
![[main_effect.png]]
## perform analysis on subset of subjects who reported seeing "correct" no. of headlines
```r
d3a <- d3[(condition == -0.5 & climate_perc < 20) | (condition == 0.5 & climate_perc > 33)]
condition V1
1: -0.5 82
2: 0.5 95
```
![[manipulation_check_subset 1.png]]
**Reduced** importance, newsworthiness, problem, human activity, concern? Potential backfire?
![[main_effect_subset.png]]
## feed engagement associated with outcomes
x-axis: proportion of climate headlines shared/liked/bookmarked (only in the treatment group)
![[s20220523_105854.png]]
![[s20220523_110049.png]]
![[s20220523_110118.png]]
![[s20220523_110145.png]]
![[s20220523_110222.png]]
![[s20220523_110250.png]]
![[s20220523_110310.png]]
![[s20220523_110430.png]]
![[s20220523_110636.png]]