# Results
## Ideology measure: average social and economic conservatism
Model: `share ~ conserv * bfi_c` (only false headlines, clustered within participant)
```r
# only false headlines
m4 <- glm(share ~ conserv * bfi_c, family = binomial, data = dt1[veracity == 0])
m4c <- coeftest(m4, vcovCL(m4, ~ responseid + headline_id, NULL, fix = FALSE))
# study 1
term res
1: (Intercept) b = -0.35 [-0.52, -0.18], p < .001
2: conserv b = -0.05 [-0.19, 0.10], p = .524
3: bfi_c b = -0.34 [-0.47, -0.20], p < .001
4: conserv:bfi_c b = -0.03 [-0.15, 0.09], p = .636
# study 2
term res
1: (Intercept) b = -1.33 [-1.60, -1.06], p < .001
2: conserv b = 0.10 [-0.09, 0.29], p = .296
3: bfi_c b = -0.17 [-0.30, -0.05], p = .006
4: conserv:bfi_c b = -0.05 [-0.16, 0.06], p = .349
# study 3
term res
1: (Intercept) b = -1.45 [-1.76, -1.14], p < .001
2: conserv b = 0.18 [-0.02, 0.38], p = .075
3: bfi_c b = -0.30 [-0.44, -0.17], p < .001
4: conserv:bfi_c b = -0.04 [-0.13, 0.06], p = .450
# study 4
term res
1: (Intercept) b = -1.69 [-1.91, -1.46], p < .001
2: conserv b = 0.30 [0.03, 0.58], p = .032
3: bfi_c b = -0.13 [-0.27, 0.01], p = .059
4: conserv:bfi_c b = -0.05 [-0.18, 0.08], p = .486
```
## Ideology measure: `warm_repub`
Model: `share ~ warm_repub * bfi_c` (only false headlines, clustered within participant and headline)
```r
# only false headlines
m5 <- glm(share ~ warm_repub * bfi_c, family = binomial, data = dt1[veracity == 0])
m5c <- coeftest(m5, vcovCL(m5, ~ responseid + headline_id, NULL, fix = FALSE))
# study 1
term res
1: (Intercept) b = -0.33 [-0.51, -0.16], p < .001
2: warm_repub b = 0.01 [0.01, 0.02], p < .001
3: bfi_c b = -0.33 [-0.47, -0.20], p < .001
4: warm_repub:bfi_c b = -0.00 [-0.01, 0.00], p = .221
# study 2
term res
1: (Intercept) b = -1.34 [-1.62, -1.06], p < .001
2: warm_repub b = 0.01 [0.00, 0.02], p = .027
3: bfi_c b = -0.19 [-0.32, -0.07], p = .003
4: warm_repub:bfi_c b = -0.00 [-0.01, 0.00], p = .068 #
# study 3
1: (Intercept) b = -1.46 [-1.77, -1.14], p < .001
2: warm_repub b = 0.01 [0.00, 0.02], p = .015
3: bfi_c b = -0.31 [-0.44, -0.18], p < .001
4: warm_repub:bfi_c b = -0.00 [-0.01, 0.00], p = .367
# study 4
1: (Intercept) b = -1.71 [-1.94, -1.48], p < .001
2: warm_repub b = 0.01 [0.015, 0.02], p = .003
3: bfi_c b = -0.15 [-0.28, -0.01], p = .035
4: warm_repub:bfi_c b = -0.001 [-0.01, -0.001], p = .605
```
Model: `share ~ warm_repub * (bfi_c + attention_score + ctsq_aot)` (only false headlines, clustered within participant and headline)
```r
# only false headlines
# control for other covariates (attention, thinking style)
m6 <- glm(share ~ warm_repub * (bfi_c + attention_score + ctsq_aot), family = binomial, data = dt1)
m6c <- coeftest(m6, vcovCL(m6, ~ responseid + headline_id, NULL))
# see line 6 for interaction: warm_repub:bfi_c
# study 1
term res
1: (Intercept) b = -0.39 [-0.57, -0.21], p < .001
2: warm_repub b = 0.01 [-0.00, 0.01], p = .081
3: bfi_c b = -0.21 [-0.35, -0.08], p = .002
4: attention_score b = -0.20 [-0.34, -0.07], p = .003
5: ctsq_aot b = -0.65 [-0.82, -0.49], p < .001
6: warm_repub:bfi_c b = -0.00 [-0.01, 0.00], p = .391
7: warm_repub:attention_score b = -0.00 [-0.01, 0.00], p = .652
8: warm_repub:ctsq_aot b = -0.00 [-0.01, 0.00], p = .340
# study 2
term res
1: (Intercept) b = -1.36 [-1.65, -1.06], p < .001
2: warm_repub b = 0.00 [-0.00, 0.01], p = .258
3: bfi_c b = -0.12 [-0.24, 0.01], p = .061
4: attention_score b = -0.14 [-0.25, -0.02], p = .019
5: ctsq_aot b = -0.41 [-0.59, -0.22], p < .001
6: warm_repub:bfi_c b = -0.00 [-0.01, 0.00], p = .263
7: warm_repub:attention_score b = 0.00 [0.00, 0.01], p = .003 #
8: warm_repub:ctsq_aot b = 0.00 [-0.00, 0.01], p = .514
# study 3
1: (Intercept) b = -1.49 [-1.83, -1.16], p < .001
2: warm_repub b = 0.00 [-0.00, 0.01], p = .272
3: bfi_c b = -0.27 [-0.40, -0.15], p < .001
4: attention_score b = -0.11 [-0.22, -0.01], p = .030
5: ctsq_aot b = -0.41 [-0.60, -0.23], p < .001
6: warm_repub:bfi_c b = -0.00 [-0.00, 0.00], p = .678
7: warm_repub:attention_score b = -0.00 [-0.00, 0.00], p = .468
8: warm_repub:ctsq_aot b = 0.00 [-0.00, 0.01], p = .633
# study 4
term res
1: (Intercept) b = -1.76 [-2.01, -1.52], p < .001
2: warm_repub b = 0.01 [0.010, 0.02], p = .061
3: bfi_c b = -0.11 [-0.25, 0.03], p = .126
4: attention_score b = -0.08 [-0.22, 0.05], p = .218
5: ctsq_aot b = -0.47 [-0.66, -0.29], p < .001
6: warm_repub:bfi_c b = -0.001 [-0.01, -0.001], p = .635
7: warm_repub:attention_score b = 0.002 [0.002, 0.01], p = .396
8: warm_repub:ctsq_aot b = 0.001 [0.001, 0.01], p = .751
```