![[20241107195325.png]]
```r
Statistics (16.05% NAs)
N Ndist Mean SD Min Max Skew Kurt
1936 1933 41.14 6.85 29.07 71.91 0.69 4.63
Quantiles
1% 5% 10% 25% 50% 75% 90% 95% 99%
30.32 31.08 31.35 37.3 42.29 43.88 48.02 52.18 63.16
```
What if we median split? Focus those who completed follow-up under the median no. of days
```r
> med <- 42.29 # median days
> summ(lm(lean_bidentrump_3 ~ condition + scale(lean_bidentrump_1), data = d0[days <= med]))
term result
<char> <char>
1: (Intercept) b = 46.68 [45.53, 47.83], p < .001
2: conditionproTrump b = 1.49 [-0.17, 3.15], p = .079 # almost significant
3: scale(lean_bidentrump_1) b = 40.88 [40.05, 41.71], p < .001
# for simplicity, coded treatment as increase vote (1), decrease vote (0)
> summ(lm(vote_chance_3 ~ conditionVoteMore + scale(vote_chance_1), data = d0[days <= med]))
term result
<char> <char>
1: (Intercept) b = 88.67 [87.43, 89.91], p < .001
2: conditionVoteMore b = 0.09 [-1.66, 1.84], p = .919 # not at all
3: scale(vote_chance_1) b = 19.23 [18.35, 20.10], p < .001
> summ(lm(vote_harris_3 ~ condition + scale(vote_harris_1), data = d0[days <= med]))
term result
<char> <char>
1: (Intercept) b = 0.49 [0.47, 0.50], p < .001
2: conditionproTrump b = -0.03 [-0.06, 0.00], p = .020 # significant
3: scale(vote_harris_1) b = 0.45 [0.44, 0.47], p < .001
> summ(lm(vote_trump_3 ~ condition + scale(vote_trump_1), data = d0[days <= med]))
term result
<char> <char>
1: (Intercept) b = 0.43 [0.41, 0.46], p < .001
2: conditionproTrump b = 0.02 [-0.02, 0.05], p = .348 # not significant
3: scale(vote_trump_1) b = 0.43 [0.41, 0.45], p < .001
```
# other stuff
```r
> summ(lm(lean_bidentrump_3 ~ (lean_bidentrump_2 + lean_bidentrump_1) * days, data = d0))
term result
<char> <char>
1: (Intercept) b = 2.70 [-2.45, 7.84], p = .304
2: lean_bidentrump_2 b = -0.03 [-0.38, 0.31], p = .862
3: lean_bidentrump_1 b = 0.98 [0.64, 1.33], p < .001
4: days b = -0.01 [-0.14, 0.11], p = .823
5: lean_bidentrump_2 × days b = 0.01 [0.00, 0.02], p = .034
6: lean_bidentrump_1 × days b = -0.01 [-0.02, 0.00], p = .037
> summ(lm(vote_chance_3 ~ (vote_chance_2 + vote_chance_1) * days, data = d0))
term result
<char> <char>
1: (Intercept) b = -8.17 [-22.45, 6.12], p = .262
2: vote_chance_2 b = 0.45 [0.04, 0.87], p = .032
3: vote_chance_1 b = 0.62 [0.24, 1.00], p = .001
4: days b = 0.59 [0.25, 0.92], p = .001
5: vote_chance_2 × days b = 0.00 [-0.01, 0.01], p = .840
6: vote_chance_1 × days b = -0.01 [-0.01, 0.00], p = .257
> summ(lm(vote_harris_3 ~ (vote_harris_2 + vote_harris_1) * days, data = d0))
term result
<char> <char>
1: (Intercept) b = 0.02 [-0.06, 0.09], p = .703
2: vote_harris_2 b = 0.21 [-0.14, 0.55], p = .244
3: vote_harris_1 b = 0.74 [0.39, 1.08], p < .001
4: days b = 0.00 [0.00, 0.00], p = .444
5: vote_harris_2 × days b = 0.01 [0.00, 0.02], p = .088
6: vote_harris_1 × days b = -0.01 [-0.02, 0.00], p = .067
> summ(lm(vote_trump_3 ~ (vote_trump_2 + vote_trump_1) * days, data = d0))
term result
<char> <char>
1: (Intercept) b = 0.09 [0.01, 0.17], p = .025
2: vote_trump_2 b = 0.26 [-0.14, 0.67], p = .202
3: vote_trump_1 b = 0.61 [0.21, 1.02], p = .003
4: days b = 0.00 [0.00, 0.00], p = .294
5: vote_trump_2 × days b = 0.01 [0.00, 0.02], p = .074
6: vote_trump_1 × days b = -0.01 [-0.02, 0.00], p = .095
```