1 DESCRIPTIVES

1.1 sample size

## [1] 248

1.2 gender

## 
##    custom       man     woman 
##  3.278689 36.885246 59.836066

1.3 race

## 
##     asian     black     latin     other     white 
## 10.245902  3.278689  6.557377  3.278689 76.639344

1.4 income

## Warning in describeBy(d$income): no grouping variable requested

##    vars   n  mean   sd median trimmed  mad min max range  skew kurtosis   se
## X1    1 240 12.67 5.43     16   13.57 1.48   1  17    16 -1.13    -0.19 0.35

1.5 age

## Warning in describeBy(d$age): no grouping variable requested

##    vars   n  mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 242 18.84 1.07     19   18.66 1.48  18  25     7 1.63     4.16 0.07

1.6 climate belief

## Warning in describeBy(d$climateBelief.0): no grouping variable requested

##    vars   n mean   sd median trimmed mad min max range  skew kurtosis   se
## X1    1 244 2.47 1.06      3    2.73   0  -3   3     6 -3.07    10.96 0.07

1.7 need for cognition

## Warning in describeBy(d$NFC): no grouping variable requested

##    vars   n mean   sd median trimmed  mad   min max range  skew kurtosis   se
## X1    1 244 0.65 0.91   0.67    0.66 0.74 -2.67   3  5.67 -0.21     0.55 0.06

1.8 party Identity

## 
##    Democrat Independent  Republican 
##         164          15          46

1.9 political Interest (-3 = no interest, +3 = a lot of interest)

## 
## -3 -2 -1  0  1  2  3 
## 13 24 33 68 57 37 12

## [1] 0.192623

## [1] 1.504379

1.10 party importance

## [1] 0.192623

## [1] 1.504379

## 
## -3 -2 -1  0  1  2  3 
## 13 24 33 68 57 37 12

1.11 party continuous

## 
## -3 -2 -1  0  1  2  3 
## 72 64 28 15 20 22  4

## [1] -1.315556

## [1] 1.750759

1.12 ideology

## 
##                 -3  -2.66666666666667  -2.33333333333333                 -2 
##                 11                  9                 12                 32 
##  -1.66666666666667  -1.33333333333333                 -1 -0.666666666666667 
##                 19                 15                 26                 16 
## -0.333333333333333                  0  0.333333333333333  0.666666666666667 
##                 19                 37                  5                 12 
##                  1   1.33333333333333   1.66666666666667                  2 
##                 11                  7                  7                  6

## [1] -0.7786885

## [1] 1.276291

1.12.1 *party continuous - ideology correlation

## 
##  Pearson's product-moment correlation
## 
## data:  d$partyCont and d$ideology
## t = 20.456, df = 220, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.7587967 0.8505828
## sample estimates:
##      cor 
## 0.809582

2 Manipulation (No media = did not follow experimental procedures)

2.1 conditions sample sizes

## 
## control    high     low noMedia 
##     160      31      24      33

2.2 Media consumption - mean, avg, tally (num of outlets consumed)

## # A tibble: 4 × 7
##   condition M_biasAvg SD_biasAvg M_biasSD SD_biasSD M_tally SD_tally
##   <chr>         <dbl>      <dbl>    <dbl>     <dbl>   <dbl>    <dbl>
## 1 control       -0.07       0.11     0.65      0.37    7.04     3.91
## 2 high          -0.07       0.06     0.29      0.19    6.39     2.76
## 3 low           -0.07       0.13     0.39      0.25    7.21     3.09
## 4 noMedia      NaN         NA      NaN        NA       0        0

3 Correlation tables

3.1 confidence in institutions vs. model predictors

3.2 election outcomes vs. model predictors

3.3 affective polarization

3.4 bias/perspective taking vs. predictors

3.5 policy support vs. predictors

4 descriptives - outcomes

4.1 trust in institutions

4.1.1 general confidence

4.1.2 by party identity

## Warning: Removed 11 rows containing missing values or values outside the scale range
## (`geom_bar()`).

4.1.3 by condition

4.1.4 party x condition (branches of fed govt + science)

## `summarise()` has grouped output by 'party_factor'. You can override using the
## `.groups` argument.

4.1.5 party x condition (federal agencies)

## `summarise()` has grouped output by 'party_factor'. You can override using the
## `.groups` argument.

4.2 policy support

4.2.1 personal policy support

4.2.1.1 by party

## Warning: Removed 8 rows containing missing values or values outside the scale range
## (`geom_bar()`).

4.2.1.2 by condition

4.2.1.3 party x condition (republican policies)

## `summarise()` has grouped output by 'party_factor'. You can override using the
## `.groups` argument.

## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_bar()`).

4.3 self vs. ingroup social norms

4.3.1 Make federal government carbon neutral by 2050

4.3.1.1 x party

4.3.1.2 party x condition

## `summarise()` has grouped output by 'party_factor'. You can override using the
## `.groups` argument.

4.3.2 Support for withdraw from Paris Climate Agreement

4.3.2.1 x party

4.3.2.2 party x condition

## `summarise()` has grouped output by 'party_factor'. You can override using the
## `.groups` argument.

4.3.3 Support for 20% Tariffs

4.3.3.1 by party

4.3.3.2 party x condition

## `summarise()` has grouped output by 'party_factor'. You can override using the
## `.groups` argument.

4.3.4 Support child tax credit

4.3.4.1 by party

4.3.4.2 party x condition

## `summarise()` has grouped output by 'party_factor'. You can override using the
## `.groups` argument.

4.3.5 Support minimum billionaire tax

4.3.5.1 by party

4.3.5.2 party x condition

## `summarise()` has grouped output by 'party_factor'. You can override using the
## `.groups` argument.

4.3.6 Support for investing in South America

4.3.6.1 by party

4.3.6.2 party x condition

## `summarise()` has grouped output by 'party_factor'. You can override using the
## `.groups` argument.

4.3.7 Support for revoking birthright citizenship

4.3.7.1 by party

4.3.7.2 party x condition

## `summarise()` has grouped output by 'party_factor'. You can override using the
## `.groups` argument.

4.3.8 Support for removing tax on tips

4.3.8.1 by party

4.3.8.2 party x condition

## `summarise()` has grouped output by 'party_factor'. You can override using the
## `.groups` argument.

4.4 perceived bias

4.4.0.1 by party

4.4.0.2 party x condition

## `summarise()` has grouped output by 'party_factor'. You can override using the
## `.groups` argument.

4.4.0.3 party continuous x condition

## `summarise()` has grouped output by 'partyCont'. You can override using the
## `.groups` argument.

## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

4.5 perspective taking

4.5.0.1 by party

4.5.0.2 party x condition

## `summarise()` has grouped output by 'party_factor'. You can override using the
## `.groups` argument.

4.5.0.3 party continuous x condition

## `summarise()` has grouped output by 'partyCont'. You can override using the
## `.groups` argument.

4.6 Affective Polarization

4.6.1 political parties

4.6.1.1 by party

4.6.1.2 party x condition

## `summarise()` has grouped output by 'party_factor'. You can override using the
## `.groups` argument.

4.6.1.3 party continuous x condition

## `summarise()` has grouped output by 'partyCont'. You can override using the
## `.groups` argument.

4.6.2 experts

4.6.2.1 by party

4.6.2.2 party x condition

## `summarise()` has grouped output by 'party_factor'. You can override using the
## `.groups` argument.

4.6.2.3 party continuous x condition

## `summarise()` has grouped output by 'partyCont'. You can override using the
## `.groups` argument.

5 ANALYSES

5.1 Categorical Manipulations

5.1.1 Institutional Trust

5.1.1.1 science (NS)

## Model matrix is rank deficient. Parameters `hiVlow:white_.5` were not
##   estimable.

	trustSci
Predictors	Estimates	CI	p
(Intercept)	0.46	0.21 – 0.70	<0.001
txVcontrol	0.31	-0.08 – 0.71	0.120
hiVlow	-0.31	-0.80 – 0.19	0.224
politics z	-0.30	-0.50 – -0.11	0.002
NFC z	0.09	-0.09 – 0.27	0.308
income z	0.06	-0.10 – 0.22	0.441
MvOther	-0.12	-0.44 – 0.21	0.480
white 5	0.31	-0.18 – 0.80	0.214
txVcontrol × politics z	0.15	-0.16 – 0.46	0.331
txVcontrol × NFC z	0.05	-0.23 – 0.33	0.717
txVcontrol × income z	-0.01	-0.27 – 0.24	0.931
txVcontrol × MvOther	0.05	-0.48 – 0.58	0.853
txVcontrol × white 5	-0.34	-1.12 – 0.44	0.386
hiVlow × politics z	-0.00	-0.57 – 0.56	0.999
hiVlow × NFC z	0.30	-0.22 – 0.82	0.260
hiVlow × income z	-0.04	-0.50 – 0.43	0.882
hiVlow × MvOther	0.90	-0.05 – 1.84	0.062
Observations	185
R² / R² adjusted	0.182 / 0.104

5.1.1.2 president (NS)

	confPresident
Predictors	Estimates	CI	p
(Intercept)	3.31	3.02 – 3.60	<0.001
txVcontrol	0.12	-0.25 – 0.49	0.523
hiVlow	0.26	-0.40 – 0.92	0.433
politics z	0.66	0.47 – 0.86	<0.001
NFC z	0.10	-0.06 – 0.25	0.232
income z	0.02	-0.14 – 0.18	0.829
MvOther	-0.15	-0.49 – 0.19	0.377
white 5	-0.14	-0.60 – 0.33	0.561
Observations	185
R² / R² adjusted	0.254 / 0.224

5.1.1.3 congress (tx x politics int)

## Model matrix is rank deficient. Parameters `hiVlow:white_.5` were not
##   estimable.

	confCongress
Predictors	Estimates	CI	p
(Intercept)	3.81	3.36 – 4.26	<0.001
txVcontrol	-0.30	-1.02 – 0.41	0.402
hiVlow	0.12	-0.77 – 1.01	0.794
politics z	0.31	-0.04 – 0.66	0.086
NFC z	0.09	-0.24 – 0.41	0.600
income z	-0.11	-0.40 – 0.18	0.445
MvOther	0.02	-0.57 – 0.60	0.953
white 5	-0.09	-0.97 – 0.79	0.840
txVcontrol × politics z	-0.20	-0.76 – 0.37	0.492
txVcontrol × NFC z	-0.18	-0.69 – 0.33	0.487
txVcontrol × income z	0.30	-0.16 – 0.77	0.197
txVcontrol × MvOther	0.11	-0.84 – 1.07	0.817
txVcontrol × white 5	-0.01	-1.42 – 1.40	0.989
hiVlow × politics z	-1.19	-2.22 – -0.17	0.022
hiVlow × NFC z	-0.21	-1.16 – 0.73	0.659
hiVlow × income z	0.60	-0.24 – 1.45	0.159
hiVlow × MvOther	-0.50	-2.21 – 1.20	0.560
Observations	185
R² / R² adjusted	0.087 / 0.000

5.1.1.4 supreme court (tx x politics int)

## Model matrix is rank deficient. Parameters `hiVlow:white_.5` were not
##   estimable.

	confSupremeCourt
Predictors	Estimates	CI	p
(Intercept)	3.91	3.52 – 4.30	<0.001
txVcontrol	-1.00	-1.62 – -0.37	0.002
hiVlow	0.05	-0.73 – 0.82	0.906
politics z	0.40	0.10 – 0.71	0.010
NFC z	0.11	-0.17 – 0.39	0.442
income z	-0.02	-0.27 – 0.23	0.886
MvOther	-0.18	-0.68 – 0.33	0.498
white 5	-0.41	-1.18 – 0.36	0.291
txVcontrol × politics z	-0.22	-0.71 – 0.27	0.377
txVcontrol × NFC z	-0.28	-0.72 – 0.17	0.218
txVcontrol × income z	0.23	-0.18 – 0.63	0.271
txVcontrol × MvOther	-0.27	-1.10 – 0.56	0.527
txVcontrol × white 5	1.73	0.51 – 2.96	0.006
hiVlow × politics z	-1.22	-2.11 – -0.33	0.008
hiVlow × NFC z	-0.26	-1.08 – 0.56	0.536
hiVlow × income z	0.37	-0.37 – 1.10	0.326
hiVlow × MvOther	-0.32	-1.80 – 1.16	0.672
Observations	185
R² / R² adjusted	0.206 / 0.130

5.1.1.5 military (NS)

	confMilitary
Predictors	Estimates	CI	p
(Intercept)	4.24	3.91 – 4.56	<0.001
txVcontrol	0.14	-0.28 – 0.56	0.519
hiVlow	0.39	-0.35 – 1.14	0.302
politics z	0.52	0.30 – 0.74	<0.001
NFC z	0.16	-0.01 – 0.34	0.072
income z	0.09	-0.09 – 0.27	0.301
MvOther	-0.16	-0.54 – 0.23	0.418
white 5	0.47	-0.05 – 0.99	0.079
Observations	185
R² / R² adjusted	0.185 / 0.153

5.1.1.6 DOJ (NS)

	confDOJ
Predictors	Estimates	CI	p
(Intercept)	3.60	3.30 – 3.90	<0.001
txVcontrol	-0.30	-0.69 – 0.09	0.125
hiVlow	-0.31	-1.00 – 0.38	0.382
politics z	-0.02	-0.22 – 0.18	0.846
NFC z	-0.07	-0.23 – 0.10	0.432
income z	0.14	-0.03 – 0.30	0.106
MvOther	-0.09	-0.44 – 0.27	0.630
white 5	0.68	0.20 – 1.17	0.006
Observations	185
R² / R² adjusted	0.078 / 0.041

5.1.1.7 DOE (NS)

	confDOE
Predictors	Estimates	CI	p
(Intercept)	3.74	3.42 – 4.06	<0.001
txVcontrol	-0.24	-0.65 – 0.18	0.265
hiVlow	-0.67	-1.40 – 0.07	0.076
politics z	-0.27	-0.48 – -0.05	0.017
NFC z	0.03	-0.14 – 0.21	0.713
income z	0.01	-0.17 – 0.19	0.930
MvOther	0.35	-0.03 – 0.73	0.069
white 5	0.55	0.03 – 1.06	0.039
Observations	185
R² / R² adjusted	0.124 / 0.089

5.1.1.8 FEMA (NS)

	confFEMA
Predictors	Estimates	CI	p
(Intercept)	3.96	3.66 – 4.26	<0.001
txVcontrol	-0.10	-0.48 – 0.28	0.609
hiVlow	0.00	-0.68 – 0.68	0.997
politics z	-0.08	-0.28 – 0.12	0.448
NFC z	0.08	-0.09 – 0.24	0.362
income z	0.07	-0.10 – 0.23	0.413
MvOther	-0.21	-0.56 – 0.14	0.231
white 5	0.33	-0.14 – 0.81	0.167
Observations	185
R² / R² adjusted	0.031 / -0.007

5.1.1.9 ICE (NS)

	confICE
Predictors	Estimates	CI	p
(Intercept)	3.12	2.82 – 3.43	<0.001
txVcontrol	-0.24	-0.64 – 0.16	0.229
hiVlow	0.19	-0.51 – 0.90	0.590
politics z	0.40	0.19 – 0.61	<0.001
NFC z	0.05	-0.12 – 0.22	0.552
income z	0.23	0.06 – 0.40	0.009
MvOther	-0.13	-0.49 – 0.24	0.491
white 5	0.56	0.06 – 1.05	0.028
Observations	185
R² / R² adjusted	0.173 / 0.140

5.1.2 Bias

5.1.2.1 ingroup bias (ME hi v low)

## Model matrix is rank deficient. Parameters `hiVlow:white_.5` were not
##   estimable.

	bias_ingroup
Predictors	Estimates	CI	p
(Intercept)	4.09	3.78 – 4.41	<0.001
txVcontrol	-0.12	-0.61 – 0.38	0.638
hiVlow	-0.70	-1.40 – -0.01	0.047
politics z	0.22	-0.02 – 0.47	0.073
NFC z	-0.04	-0.25 – 0.17	0.694
white 5	0.45	-0.20 – 1.11	0.172
MvOther	-0.08	-0.50 – 0.33	0.687
txVcontrol × politics z	-0.39	-0.78 – 0.00	0.051
txVcontrol × NFC z	0.00	-0.33 – 0.34	0.995
txVcontrol × white 5	-0.31	-1.33 – 0.72	0.558
txVcontrol × MvOther	-0.22	-0.89 – 0.44	0.509
hiVlow × politics z	-0.27	-0.99 – 0.45	0.461
hiVlow × NFC z	-0.07	-0.68 – 0.55	0.835
hiVlow × MvOther	0.99	-0.22 – 2.20	0.107
Observations	177
R² / R² adjusted	0.071 / -0.004

5.1.2.2 outgroup bias (tx x politics)

5.1.2.3 self - ingroup (tx x politics int, hi v low ME)

there is a main effect of media diversity and an interaction for media diversity manipulation by politics

	bias_self_in
Predictors	Estimates	CI	p
(Intercept)	-0.55	-0.72 – -0.37	<0.001
txVcontrol	0.02	-0.27 – 0.30	0.909
hiVlow	0.52	0.01 – 1.03	0.045
politics z	-0.39	-0.61 – -0.17	0.001
txVcontrol × politics z	0.49	0.14 – 0.84	0.006
hiVlow × politics z	0.63	-0.02 – 1.28	0.057
Observations	195
R² / R² adjusted	0.078 / 0.053

5.1.2.4 self - outgroup (NS)

	bias_self_out
Predictors	Estimates	CI	p
(Intercept)	-1.26	-1.65 – -0.87	<0.001
txVcontrol	-0.16	-0.68 – 0.36	0.537
hiVlow	-0.04	-0.98 – 0.90	0.931
politics z	0.18	-0.22 – 0.58	0.379
NFC z	-0.27	-0.47 – -0.08	0.006
income z	-0.08	-0.28 – 0.11	0.391
white 5	0.03	-0.54 – 0.60	0.918
MvOther	-0.02	-0.45 – 0.40	0.911
txVcontrol × politics z	-0.06	-0.68 – 0.57	0.859
hiVlow × politics z	-0.80	-1.96 – 0.37	0.177
Observations	173
R² / R² adjusted	0.085 / 0.034

5.1.3 Perspective taking

## 
##  Pearson's product-moment correlation
## 
## data:  d$repPTcog and d$repPTemo
## t = 12.835, df = 220, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.5719753 0.7236844
## sample estimates:
##       cor 
## 0.6543656

## 
##  Pearson's product-moment correlation
## 
## data:  d$demPTcog and d$demPTemo
## t = 18.655, df = 220, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.7258715 0.8289684
## sample estimates:
##       cor 
## 0.7827327

## 
##  Pearson's product-moment correlation
## 
## data:  d$PTcog_ingroup and d$PTemo_ingroup
## t = 15.306, df = 205, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.6596195 0.7881729
## sample estimates:
##       cor 
## 0.7302975

## 
##  Pearson's product-moment correlation
## 
## data:  d$PTcog_outgroup and d$PTemo_outgroup
## t = 10.449, df = 205, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.4927586 0.6718715
## sample estimates:
##       cor 
## 0.5895147

5.1.3.1 Republican (NS)

## Model matrix is rank deficient. Parameters `hiVlow:white_.5` were not
##   estimable.

	repPTgen
Predictors	Estimates	CI	p
(Intercept)	3.40	3.03 – 3.77	<0.001
txVcontrol	0.40	-0.19 – 0.99	0.183
hiVlow	0.16	-0.58 – 0.90	0.673
politics z	0.73	0.44 – 1.02	<0.001
NFC z	0.12	-0.14 – 0.39	0.360
income z	0.12	-0.12 – 0.36	0.326
white 5	0.26	-0.47 – 0.99	0.481
MvOther	-0.22	-0.70 – 0.27	0.379
txVcontrol × politics z	0.11	-0.36 – 0.57	0.652
txVcontrol × NFC z	0.42	-0.01 – 0.84	0.054
txVcontrol × income z	-0.06	-0.44 – 0.33	0.772
txVcontrol × white 5	-0.32	-1.49 – 0.85	0.585
txVcontrol × MvOther	0.21	-0.58 – 1.00	0.606
hiVlow × politics z	-0.01	-0.86 – 0.84	0.986
hiVlow × NFC z	-0.15	-0.93 – 0.63	0.706
hiVlow × income z	-0.10	-0.80 – 0.60	0.778
hiVlow × MvOther	0.92	-0.49 – 2.34	0.199
Observations	185
R² / R² adjusted	0.425 / 0.370

5.1.3.2 Democrat (NS)

	demPTgen
Predictors	Estimates	CI	p
(Intercept)	5.16	4.92 – 5.39	<0.001
txVcontrol	-0.24	-0.55 – 0.07	0.126
hiVlow	-0.06	-0.61 – 0.50	0.844
politics z	-0.72	-0.96 – -0.48	<0.001
NFC z	0.21	0.09 – 0.34	0.001
income z	-0.02	-0.15 – 0.11	0.777
white 5	-0.02	-0.39 – 0.36	0.934
MvOther	0.32	0.05 – 0.60	0.020
txVcontrol × politics z	-0.20	-0.58 – 0.18	0.304
hiVlow × politics z	-0.29	-0.98 – 0.40	0.410
Observations	185
R² / R² adjusted	0.513 / 0.488

5.1.3.3 in-group party (tx x politics int)

look into this one

	PTgen_ingroup
Predictors	Estimates	CI	p
(Intercept)	4.94	4.67 – 5.20	<0.001
txVcontrol	0.40	0.04 – 0.75	0.028
hiVlow	0.78	0.14 – 1.42	0.017
politics z	-0.86	-1.13 – -0.58	<0.001
NFC z	0.30	0.17 – 0.43	<0.001
income z	0.01	-0.13 – 0.14	0.939
white 5	0.10	-0.29 – 0.49	0.615
MvOther	0.51	0.22 – 0.80	0.001
txVcontrol × politics z	0.67	0.24 – 1.10	0.002
hiVlow × politics z	0.88	0.09 – 1.67	0.030
Observations	173
R² / R² adjusted	0.410 / 0.378

5.1.3.4 out-group party (tx x politics int)

look into this one

## Model matrix is rank deficient. Parameters `hiVlow:white_.5` were not
##   estimable.

	PTgen_outgroup
Predictors	Estimates	CI	p
(Intercept)	3.43	3.01 – 3.85	<0.001
txVcontrol	0.08	-0.58 – 0.75	0.806
hiVlow	-0.36	-1.35 – 0.64	0.480
politics z	0.78	0.44 – 1.11	<0.001
NFC z	0.04	-0.24 – 0.31	0.799
income z	0.01	-0.25 – 0.27	0.919
white 5	0.32	-0.55 – 1.20	0.467
MvOther	-0.30	-0.87 – 0.27	0.301
txVcontrol × politics z	-0.66	-1.19 – -0.12	0.016
txVcontrol × NFC z	0.35	-0.10 – 0.79	0.123
txVcontrol × income z	0.14	-0.28 – 0.56	0.504
txVcontrol × white 5	-0.67	-2.06 – 0.71	0.338
txVcontrol × MvOther	-0.02	-0.94 – 0.90	0.964
hiVlow × politics z	-0.78	-1.76 – 0.20	0.118
hiVlow × NFC z	-0.07	-0.89 – 0.74	0.857
hiVlow × income z	-0.44	-1.21 – 0.32	0.252
hiVlow × MvOther	0.51	-1.16 – 2.18	0.545
Observations	173
R² / R² adjusted	0.268 / 0.193

5.1.3.5 difference in understanind in- vs. out-group (tx x politics)

look into this one

## Model matrix is rank deficient. Parameters `hiVlow:white_.5` were not
##   estimable.

	PT_in_out
Predictors	Estimates	CI	p
(Intercept)	1.58	1.06 – 2.11	<0.001
txVcontrol	0.10	-0.73 – 0.94	0.805
hiVlow	1.21	-0.03 – 2.46	0.056
politics z	-1.64	-2.06 – -1.22	<0.001
NFC z	0.21	-0.14 – 0.56	0.229
income z	0.03	-0.29 – 0.36	0.833
white 5	-0.49	-1.59 – 0.61	0.380
MvOther	0.95	0.24 – 1.67	0.010
txVcontrol × politics z	1.35	0.68 – 2.01	<0.001
txVcontrol × NFC z	-0.21	-0.77 – 0.34	0.449
txVcontrol × income z	-0.23	-0.75 – 0.29	0.384
txVcontrol × white 5	1.33	-0.40 – 3.07	0.132
txVcontrol × MvOther	-0.15	-1.31 – 1.00	0.793
hiVlow × politics z	1.83	0.60 – 3.06	0.004
hiVlow × NFC z	0.35	-0.68 – 1.37	0.506
hiVlow × income z	0.30	-0.66 – 1.25	0.541
hiVlow × MvOther	-0.01	-2.10 – 2.09	0.996
Observations	173
R² / R² adjusted	0.476 / 0.422

5.1.4 Affective Polarization

5.1.4.1 Republican (tx x politics)

look into this

	AP_rep
Predictors	Estimates	CI	p
(Intercept)	2.77	2.56 – 2.98	<0.001
txVcontrol	0.18	-0.16 – 0.53	0.300
hiVlow	-0.14	-0.76 – 0.47	0.646
politics z	0.86	0.59 – 1.13	<0.001
txVcontrol × politics z	0.56	0.13 – 0.99	0.011
hiVlow × politics z	-1.00	-1.78 – -0.21	0.013
Observations	209
R² / R² adjusted	0.505 / 0.493

	AP_rep
Predictors	Estimates	CI	p
(Intercept)	2.89	2.73 – 3.05	<0.001
condition [high]	-0.11	-0.57 – 0.35	0.632
condition [low]	-0.26	-0.73 – 0.22	0.288
condition [noMedia]	-0.22	-0.80 – 0.37	0.464
politics z	1.24	1.06 – 1.41	<0.001
condition [high] × politics z	-0.06	-0.67 – 0.55	0.846
condition [low] × politics z	-1.06	-1.61 – -0.50	<0.001
condition [noMedia] × politics z	-0.32	-1.00 – 0.35	0.340
Observations	222
R² / R² adjusted	0.501 / 0.485

5.1.4.2 Democrat (NS)

	AP_dem
Predictors	Estimates	CI	p
(Intercept)	4.38	4.09 – 4.67	<0.001
txVcontrol	-0.30	-0.76 – 0.17	0.209
hiVlow	0.22	-0.62 – 1.07	0.602
politics z	-0.77	-1.12 – -0.43	<0.001
NFC z	0.21	-0.10 – 0.53	0.184
income z	-0.14	-0.41 – 0.13	0.319
MvOther	0.26	-0.32 – 0.85	0.377
txVcontrol × politics z	-0.02	-0.57 – 0.53	0.953
txVcontrol × NFC z	-0.27	-0.77 – 0.24	0.295
txVcontrol × income z	0.23	-0.21 – 0.67	0.304
txVcontrol × MvOther	0.08	-0.87 – 1.03	0.864
hiVlow × politics z	-0.91	-1.91 – 0.09	0.075
hiVlow × NFC z	0.46	-0.47 – 1.39	0.333
hiVlow × income z	-0.04	-0.83 – 0.76	0.928
hiVlow × MvOther	-1.65	-3.36 – 0.05	0.058
Observations	205
R² / R² adjusted	0.326 / 0.277

5.1.4.3 Economic Experts (NS)

## Model matrix is rank deficient. Parameters `hiVlow:white_.5` were not
##   estimable.

	AP_econ
Predictors	Estimates	CI	p
(Intercept)	4.56	4.14 – 4.98	<0.001
txVcontrol	0.29	-0.38 – 0.96	0.400
hiVlow	-0.62	-1.45 – 0.22	0.147
politics z	-0.42	-0.75 – -0.09	0.012
NFC z	0.11	-0.20 – 0.41	0.487
income z	-0.06	-0.33 – 0.21	0.653
white 5	0.41	-0.42 – 1.24	0.329
MvOther	-0.11	-0.66 – 0.44	0.696
txVcontrol × politics z	0.17	-0.36 – 0.70	0.522
txVcontrol × NFC z	0.27	-0.21 – 0.75	0.264
txVcontrol × income z	0.17	-0.27 – 0.60	0.446
txVcontrol × white 5	-0.28	-1.60 – 1.04	0.675
txVcontrol × MvOther	-0.33	-1.22 – 0.56	0.468
hiVlow × politics z	-0.76	-1.72 – 0.19	0.117
hiVlow × NFC z	0.15	-0.73 – 1.04	0.731
hiVlow × income z	-0.35	-1.14 – 0.44	0.384
hiVlow × MvOther	-0.38	-1.98 – 1.22	0.638
Observations	185
R² / R² adjusted	0.190 / 0.112

5.1.4.4 Election Experts (NS)

## Model matrix is rank deficient. Parameters `hiVlow:white_.5` were not
##   estimable.

	AP_elect
Predictors	Estimates	CI	p
(Intercept)	4.76	4.38 – 5.15	<0.001
txVcontrol	0.22	-0.39 – 0.83	0.480
hiVlow	-0.48	-1.25 – 0.28	0.215
politics z	-0.52	-0.82 – -0.22	0.001
NFC z	0.12	-0.16 – 0.39	0.406
income z	-0.05	-0.30 – 0.20	0.699
white 5	0.42	-0.34 – 1.17	0.277
MvOther	-0.38	-0.88 – 0.12	0.138
txVcontrol × politics z	0.42	-0.07 – 0.90	0.091
txVcontrol × NFC z	0.24	-0.19 – 0.68	0.273
txVcontrol × income z	0.36	-0.03 – 0.76	0.073
txVcontrol × white 5	-0.69	-1.90 – 0.52	0.260
txVcontrol × MvOther	0.50	-0.32 – 1.32	0.227
hiVlow × politics z	0.00	-0.87 – 0.88	0.997
hiVlow × NFC z	0.35	-0.46 – 1.16	0.399
hiVlow × income z	0.21	-0.51 – 0.93	0.567
hiVlow × MvOther	-0.43	-1.89 – 1.02	0.558
Observations	185
R² / R² adjusted	0.197 / 0.120

5.1.4.5 Climate Scientists (NS)

## Model matrix is rank deficient. Parameters `hiVlow:white_.5` were not
##   estimable.

	AP_clim
Predictors	Estimates	CI	p
(Intercept)	5.25	4.84 – 5.67	<0.001
txVcontrol	-0.00	-0.66 – 0.66	0.998
hiVlow	-0.16	-0.98 – 0.67	0.711
politics z	-0.76	-1.08 – -0.43	<0.001
NFC z	0.11	-0.18 – 0.41	0.453
income z	-0.16	-0.43 – 0.10	0.229
white 5	0.64	-0.18 – 1.45	0.126
MvOther	-0.09	-0.63 – 0.45	0.749
txVcontrol × politics z	0.25	-0.28 – 0.77	0.352
txVcontrol × NFC z	0.32	-0.15 – 0.79	0.185
txVcontrol × income z	0.32	-0.11 – 0.75	0.148
txVcontrol × white 5	-0.22	-1.53 – 1.09	0.741
txVcontrol × MvOther	0.13	-0.76 – 1.01	0.776
hiVlow × politics z	-0.10	-1.05 – 0.85	0.834
hiVlow × NFC z	0.34	-0.53 – 1.22	0.440
hiVlow × income z	-0.06	-0.84 – 0.72	0.877
hiVlow × MvOther	-0.37	-1.95 – 1.21	0.643
Observations	185
R² / R² adjusted	0.324 / 0.259

5.1.4.6 In-group party (tx x politics)

look into this one

	AP_ingroup
Predictors	Estimates	CI	p
(Intercept)	4.25	3.87 – 4.63	<0.001
txVcontrol	0.28	-0.22 – 0.79	0.271
hiVlow	0.10	-0.81 – 1.02	0.824
politics z	-0.63	-1.02 – -0.24	0.002
NFC z	0.13	-0.05 – 0.32	0.164
income z	-0.02	-0.20 – 0.17	0.865
MvOther	0.70	0.29 – 1.11	0.001
white 5	0.20	-0.36 – 0.76	0.482
txVcontrol × politics z	0.74	0.12 – 1.35	0.019
hiVlow × politics z	-0.35	-1.48 – 0.78	0.542
Observations	173
R² / R² adjusted	0.171 / 0.125

5.1.4.7 Out-group party (tx x politics)

look into this

## Model matrix is rank deficient. Parameters `hiVlow:white_.5` were not
##   estimable.

	AP_outgroup
Predictors	Estimates	CI	p
(Intercept)	2.78	2.30 – 3.25	<0.001
txVcontrol	-0.16	-0.92 – 0.59	0.668
hiVlow	0.10	-1.03 – 1.23	0.860
politics z	0.58	0.20 – 0.96	0.003
income z	-0.01	-0.31 – 0.28	0.930
NFC z	-0.02	-0.34 – 0.29	0.889
MvOther	-0.17	-0.82 – 0.48	0.598
white 5	-0.32	-1.32 – 0.68	0.525
txVcontrol × politics z	-0.01	-0.61 – 0.60	0.982
txVcontrol × income z	-0.06	-0.53 – 0.42	0.815
txVcontrol × NFC z	0.09	-0.41 – 0.59	0.724
txVcontrol × MvOther	0.07	-0.98 – 1.11	0.901
txVcontrol × white 5	-0.23	-1.80 – 1.34	0.773
hiVlow × politics z	-1.13	-2.25 – -0.02	0.046
hiVlow × income z	-0.12	-0.98 – 0.75	0.790
hiVlow × NFC z	0.16	-0.77 – 1.09	0.741
hiVlow × MvOther	-0.67	-2.56 – 1.23	0.488
Observations	173
R² / R² adjusted	0.221 / 0.141

5.2 Continuous Media Exposure

5.2.1 own Vote (NS)

m.ownVote <- lm(ownVote.0 ~ (biasAvg.z * biasSD.z) + (biasAvg.z + biasSD.z) * 
                   politics.z + MvOther + white_.5, data = d)
tab_model(m.ownVote)

	ownVote.0
Predictors	Estimates	CI	p
(Intercept)	1.22	0.93 – 1.51	<0.001
biasAvg z	-0.01	-0.32 – 0.29	0.923
biasSD z	0.05	-0.18 – 0.29	0.651
politics z	-0.24	-0.51 – 0.02	0.075
MvOther	-0.61	-1.00 – -0.21	0.003
white 5	0.23	-0.28 – 0.74	0.368
biasAvg z × biasSD z	0.11	-0.09 – 0.31	0.285
biasAvg z × politics z	0.04	-0.18 – 0.26	0.730
biasSD z × politics z	0.12	-0.09 – 0.33	0.273
Observations	201
R² / R² adjusted	0.064 / 0.025

5.2.2 national Vote (DMC X CMC)

There is an interaction between CMC and DMC.

d$politics.z <- as.numeric(d$politics.z)
m.natVote <- lm(nationalVote.0 ~ (biasAvg.z * biasSD.z) + (biasAvg.z + biasSD.z) * 
                   politics.z + MvOther + white_.5, data = d)
tab_model(m.natVote)

	nationalVote.0
Predictors	Estimates	CI	p
(Intercept)	1.14	0.82 – 1.45	<0.001
biasAvg z	-0.42	-0.75 – -0.09	0.012
biasSD z	-0.09	-0.35 – 0.16	0.476
politics z	0.23	-0.05 – 0.52	0.110
MvOther	-0.62	-1.05 – -0.19	0.005
white 5	-0.01	-0.56 – 0.54	0.967
biasAvg z × biasSD z	0.26	0.04 – 0.48	0.020
biasAvg z × politics z	-0.08	-0.31 – 0.16	0.521
biasSD z × politics z	0.15	-0.08 – 0.38	0.204
Observations	201
R² / R² adjusted	0.103 / 0.065

summary(m.natVote)

## 
## Call:
## lm(formula = nationalVote.0 ~ (biasAvg.z * biasSD.z) + (biasAvg.z + 
##     biasSD.z) * politics.z + MvOther + white_.5, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8321 -0.7944  0.1592  1.0744  2.9505 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           1.13628    0.15813   7.186 1.44e-11 ***
## biasAvg.z            -0.42245    0.16606  -2.544  0.01175 *  
## biasSD.z             -0.09195    0.12863  -0.715  0.47559    
## politics.z            0.23483    0.14609   1.607  0.10961    
## MvOther              -0.61988    0.21864  -2.835  0.00507 ** 
## white_.5             -0.01151    0.27974  -0.041  0.96721    
## biasAvg.z:biasSD.z    0.25867    0.10988   2.354  0.01957 *  
## biasAvg.z:politics.z -0.07733    0.12015  -0.644  0.52057    
## biasSD.z:politics.z   0.14954    0.11735   1.274  0.20408    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.409 on 192 degrees of freedom
##   (47 observations deleted due to missingness)
## Multiple R-squared:  0.1027, Adjusted R-squared:  0.06527 
## F-statistic: 2.746 on 8 and 192 DF,  p-value: 0.006838

# Create the plot
plot_model(m.natVote, 
                type = "pred", 
                terms = c("biasAvg.z", "biasSD.z [-1, 1]")) +
  ggtitle("") + 
  ylab("Confidence in National Vote") +
  xlab("COnservative Media Consumption") +
  xlim(-3.2, 3.2) +
  ylim(-4, 4) +
  theme(
    panel.grid.major = element_blank(), 
    panel.grid.minor = element_blank(),
    panel.background = element_blank(), 
    axis.line = element_line(colour = "black"),
    legend.position = c(0.7, 0.8), 
    legend.background = element_rect(fill = "white", color = "white"),
    legend.title = element_blank()
  ) + 
  scale_color_manual(
    labels = c("High Diversity", "Low Diversity"), 
    values = c("blue", "purple")
  ) +
  scale_fill_manual(values = c("blue", "purple")) +
  scale_y_continuous(
    breaks = seq(-4, 4, 1), 
    limits = c(-5.4, 5.2)
  )

## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.

## Warning: A numeric `legend.position` argument in `theme()` was deprecated in ggplot2
## 3.5.0.
## ℹ Please use the `legend.position.inside` argument of `theme()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.

## Warning: Removed 2 rows containing missing values or values outside the scale range
## (`geom_line()`).

5.2.3 trust in Science (NS)

m.trustSci <- lm(trustSci ~ (biasAvg.z * biasSD.z) + (biasAvg.z + biasSD.z) * 
                   politics.z + MvOther + white_.5, data = d)
tab_model(m.trustSci)

	trustSci
Predictors	Estimates	CI	p
(Intercept)	0.59	0.45 – 0.73	<0.001
biasAvg z	-0.17	-0.32 – -0.02	0.027
biasSD z	0.07	-0.05 – 0.18	0.241
politics z	-0.24	-0.37 – -0.11	<0.001
MvOther	-0.18	-0.37 – 0.02	0.076
white 5	0.05	-0.20 – 0.30	0.681
biasAvg z × biasSD z	0.05	-0.05 – 0.15	0.289
biasAvg z × politics z	0.11	0.00 – 0.21	0.048
biasSD z × politics z	0.06	-0.05 – 0.16	0.300
Observations	201
R² / R² adjusted	0.192 / 0.158

5.2.4 expected win (NS)

m.expectWin <- lm(expectedWin.0 ~ (biasAvg.z * biasSD.z) + (biasAvg.z + biasSD.z) * 
                   politics.z + MvOther + white_.5, data = d)
tab_model(m.expectWin)

	expectedWin.0
Predictors	Estimates	CI	p
(Intercept)	-0.05	-0.35 – 0.26	0.755
biasAvg z	-0.13	-0.45 – 0.19	0.413
biasSD z	-0.14	-0.39 – 0.10	0.256
politics z	-0.93	-1.21 – -0.65	<0.001
MvOther	0.23	-0.19 – 0.65	0.289
white 5	0.53	-0.00 – 1.07	0.051
biasAvg z × biasSD z	0.09	-0.12 – 0.30	0.412
biasAvg z × politics z	0.01	-0.22 – 0.24	0.933
biasSD z × politics z	0.05	-0.18 – 0.27	0.669
Observations	201
R² / R² adjusted	0.312 / 0.284

5.2.5 vote (NS)

m.voteBehavior <- polr(vote ~ (biasAvg.z * biasSD.z) + (biasAvg.z + biasSD.z) * 
                   politics.z + MvOther + white_.5, data = d,  Hess = T)
tab_model(m.voteBehavior)

	vote
Predictors	Odds Ratios	CI	p
1\|2	0.04	0.02 – 0.10	<0.001
2\|3	0.35	0.19 – 0.67	0.002
biasAvg z	1.09	0.54 – 2.17	0.798
biasSD z	1.43	0.79 – 2.75	0.255
politics z	0.06	0.03 – 0.12	<0.001
MvOther	1.26	0.56 – 2.80	0.572
white 5	0.76	0.22 – 2.33	0.638
biasAvg z × biasSD z	0.81	0.53 – 1.20	0.298
biasAvg z × politics z	1.22	0.65 – 2.52	0.555
biasSD z × politics z	0.90	0.51 – 1.51	0.699
Observations	197
R² Nagelkerke	0.681

5.2.6 President (NS)

m.trustPrez <- lm(confPresident ~ (biasAvg.z * biasSD.z) + (biasAvg.z + biasSD.z) * 
                   politics.z + MvOther + white_.5, data = d)
tab_model(m.trustPrez)

	confPresident
Predictors	Estimates	CI	p
(Intercept)	3.38	3.15 – 3.61	<0.001
biasAvg z	-0.08	-0.33 – 0.16	0.508
biasSD z	-0.08	-0.27 – 0.11	0.427
politics z	0.58	0.37 – 0.80	<0.001
MvOther	-0.29	-0.61 – 0.04	0.081
white 5	-0.14	-0.55 – 0.27	0.503
biasAvg z × biasSD z	0.11	-0.06 – 0.27	0.201
biasAvg z × politics z	-0.02	-0.20 – 0.16	0.829
biasSD z × politics z	0.14	-0.04 – 0.31	0.119
Observations	201
R² / R² adjusted	0.264 / 0.233

5.2.7 congress (NS)

m.trustCongress <- lm(confCongress ~ (biasAvg.z * biasSD.z) + (biasAvg.z + biasSD.z) * 
                   politics.z + MvOther + white_.5, data = d)
tab_model(m.trustCongress)

	confCongress
Predictors	Estimates	CI	p
(Intercept)	3.64	3.39 – 3.90	<0.001
biasAvg z	-0.20	-0.47 – 0.07	0.139
biasSD z	-0.05	-0.25 – 0.16	0.666
politics z	0.22	-0.02 – 0.45	0.067
MvOther	0.10	-0.25 – 0.45	0.575
white 5	0.02	-0.43 – 0.47	0.917
biasAvg z × biasSD z	0.17	-0.01 – 0.34	0.066
biasAvg z × politics z	-0.06	-0.26 – 0.13	0.520
biasSD z × politics z	0.06	-0.13 – 0.25	0.550
Observations	201
R² / R² adjusted	0.042 / 0.002

5.2.8 supreme court (NS)

m.trustSC <- lm(confSupremeCourt ~ (biasAvg.z * biasSD.z) + (biasAvg.z + biasSD.z) * 
                   politics.z + MvOther + white_.5, data = d)
tab_model(m.trustSC)

	confSupremeCourt
Predictors	Estimates	CI	p
(Intercept)	3.47	3.24 – 3.70	<0.001
biasAvg z	-0.16	-0.40 – 0.08	0.195
biasSD z	-0.13	-0.32 – 0.06	0.184
politics z	0.29	0.08 – 0.50	0.008
MvOther	-0.29	-0.61 – 0.03	0.076
white 5	0.42	0.01 – 0.83	0.045
biasAvg z × biasSD z	0.15	-0.01 – 0.31	0.074
biasAvg z × politics z	0.00	-0.17 – 0.18	0.972
biasSD z × politics z	0.11	-0.06 – 0.28	0.201
Observations	201
R² / R² adjusted	0.136 / 0.100

5.2.9 EPA (NS)

m.trustEPA <- lm(confEPA ~ (biasAvg.z * biasSD.z) + (biasAvg.z + biasSD.z) * 
                   politics.z + MvOther + white_.5, data = d)
tab_model(m.trustEPA)

	confEPA
Predictors	Estimates	CI	p
(Intercept)	4.12	3.86 – 4.38	<0.001
biasAvg z	-0.43	-0.71 – -0.15	0.003
biasSD z	-0.17	-0.39 – 0.04	0.119
politics z	-0.09	-0.33 – 0.16	0.480
MvOther	-0.08	-0.44 – 0.29	0.678
white 5	0.31	-0.16 – 0.78	0.192
biasAvg z × biasSD z	0.16	-0.02 – 0.35	0.081
biasAvg z × politics z	0.11	-0.09 – 0.31	0.295
biasSD z × politics z	0.12	-0.07 – 0.32	0.222
Observations	201
R² / R² adjusted	0.082 / 0.044

5.2.10 DOJ (NS)

m.trustDOJ <- lm(confDOJ ~ (biasAvg.z * biasSD.z) + (biasAvg.z + biasSD.z) * 
                   politics.z + MvOther + white_.5, data = d)
tab_model(m.trustDOJ)

	confDOJ
Predictors	Estimates	CI	p
(Intercept)	3.53	3.28 – 3.78	<0.001
biasAvg z	-0.16	-0.42 – 0.10	0.224
biasSD z	-0.03	-0.24 – 0.17	0.742
politics z	0.06	-0.17 – 0.29	0.611
MvOther	0.03	-0.31 – 0.37	0.865
white 5	0.61	0.17 – 1.05	0.007
biasAvg z × biasSD z	0.16	-0.01 – 0.34	0.065
biasAvg z × politics z	0.01	-0.18 – 0.20	0.919
biasSD z × politics z	-0.04	-0.23 – 0.14	0.660
Observations	201
R² / R² adjusted	0.061 / 0.022

5.2.11 DOE (CMC x Politics)

m.trustDOE <- lm(confDOE ~ (biasAvg.z * biasSD.z) + (biasAvg.z + biasSD.z) * 
                   politics.z + MvOther + white_.5, data = d)
tab_model(m.trustDOE)

	confDOE
Predictors	Estimates	CI	p
(Intercept)	3.61	3.34 – 3.88	<0.001
biasAvg z	-0.26	-0.54 – 0.02	0.073
biasSD z	-0.17	-0.38 – 0.05	0.135
politics z	-0.21	-0.45 – 0.04	0.102
MvOther	0.34	-0.03 – 0.71	0.072
white 5	0.47	-0.00 – 0.94	0.050
biasAvg z × biasSD z	0.07	-0.12 – 0.25	0.475
biasAvg z × politics z	0.23	0.03 – 0.44	0.024
biasSD z × politics z	0.05	-0.15 – 0.25	0.616
Observations	201
R² / R² adjusted	0.136 / 0.100

5.2.12 Military (NS)

m.trustMil <- lm(confMilitary ~ (biasAvg.z * biasSD.z) + (biasAvg.z + biasSD.z) * 
                   (politics.z + MvOther + white_.5), data = d)
tab_model(m.trustMil)

	confMilitary
Predictors	Estimates	CI	p
(Intercept)	4.33	4.06 – 4.61	<0.001
biasAvg z	-0.21	-0.60 – 0.18	0.289
biasSD z	0.03	-0.31 – 0.37	0.869
politics z	0.51	0.26 – 0.76	<0.001
MvOther	-0.10	-0.48 – 0.28	0.608
white 5	0.54	0.03 – 1.04	0.039
biasAvg z × biasSD z	0.10	-0.10 – 0.29	0.336
biasAvg z × politics z	-0.20	-0.42 – 0.03	0.092
biasAvg z × MvOther	0.13	-0.31 – 0.57	0.568
biasAvg z × white 5	0.23	-0.44 – 0.90	0.495
biasSD z × politics z	-0.02	-0.23 – 0.19	0.845
biasSD z × MvOther	-0.19	-0.61 – 0.22	0.362
biasSD z × white 5	0.14	-0.53 – 0.82	0.675
Observations	201
R² / R² adjusted	0.169 / 0.116

5.3 Bias

5.3.1 ingroup bias (NS)

tab_model(m.ingroupBias <- lm(bias_ingroup ~ (biasAvg.z * biasSD.z) + (biasAvg.z + biasSD.z) * (politics.z + MvOther + white_.5), data = d))

	bias_ingroup
Predictors	Estimates	CI	p
(Intercept)	4.07	3.90 – 4.24	<0.001
biasAvg z	0.11	-0.13 – 0.35	0.346
biasSD z	-0.14	-0.35 – 0.07	0.200
politics z	-0.02	-0.17 – 0.14	0.845
MvOther	-0.14	-0.38 – 0.10	0.258
white 5	0.17	-0.15 – 0.49	0.289
biasAvg z × biasSD z	-0.01	-0.13 – 0.11	0.919
biasAvg z × politics z	0.03	-0.11 – 0.17	0.632
biasAvg z × MvOther	0.08	-0.19 – 0.35	0.542
biasAvg z × white 5	-0.19	-0.60 – 0.21	0.351
biasSD z × politics z	-0.03	-0.16 – 0.10	0.655
biasSD z × MvOther	-0.04	-0.30 – 0.22	0.775
biasSD z × white 5	0.37	-0.06 – 0.79	0.089
Observations	188
R² / R² adjusted	0.081 / 0.017

5.3.2 outgroup bias (CMC x politcs)

# Generate the model
d$NFC.z <- as.numeric(d$NFC.z)
tab_model(m.outgroupBias <- lm(bias_outgroup ~ (biasAvg.z * biasSD.z) + (biasAvg.z + biasSD.z) *
                   (politics.z + MvOther + white_.5), data = d))

	bias_outgroup
Predictors	Estimates	CI	p
(Intercept)	4.90	4.68 – 5.12	<0.001
biasAvg z	0.02	-0.28 – 0.32	0.913
biasSD z	-0.00	-0.27 – 0.27	0.991
politics z	-0.27	-0.46 – -0.07	0.009
MvOther	0.06	-0.24 – 0.37	0.677
white 5	0.08	-0.32 – 0.47	0.702
biasAvg z × biasSD z	-0.11	-0.26 – 0.04	0.148
biasAvg z × politics z	0.19	0.02 – 0.37	0.033
biasAvg z × MvOther	0.18	-0.15 – 0.52	0.283
biasAvg z × white 5	-0.02	-0.53 – 0.49	0.939
biasSD z × politics z	0.05	-0.11 – 0.21	0.520
biasSD z × MvOther	0.20	-0.12 – 0.53	0.223
biasSD z × white 5	-0.43	-0.96 – 0.11	0.116
Observations	188
R² / R² adjusted	0.152 / 0.094

5.3.3 self - ingroup (NS)

tab_model(m.self_ingroup <- lm(bias_self_in ~ (biasAvg.z * biasSD.z) + (biasAvg.z + biasSD.z) *
                   (politics.z + MvOther + white_.5), data = d))

	bias_self_in
Predictors	Estimates	CI	p
(Intercept)	-0.40	-0.58 – -0.22	<0.001
biasAvg z	0.02	-0.22 – 0.26	0.870
biasSD z	0.04	-0.18 – 0.25	0.735
politics z	-0.13	-0.29 – 0.03	0.115
MvOther	0.17	-0.08 – 0.41	0.184
white 5	-0.28	-0.60 – 0.04	0.090
biasAvg z × biasSD z	0.03	-0.09 – 0.15	0.639
biasAvg z × politics z	-0.06	-0.20 – 0.08	0.424
biasAvg z × MvOther	-0.07	-0.34 – 0.21	0.626
biasAvg z × white 5	-0.05	-0.47 – 0.36	0.794
biasSD z × politics z	0.10	-0.03 – 0.23	0.135
biasSD z × MvOther	0.17	-0.10 – 0.43	0.220
biasSD z × white 5	-0.21	-0.64 – 0.22	0.334
Observations	188
R² / R² adjusted	0.094 / 0.032

5.3.4 self - outgroup (NS)

tab_model(m.self_outgroup <- lm(bias_self_out ~ (biasAvg.z * biasSD.z) + (biasAvg.z + biasSD.z) * (politics.z + MvOther + white_.5), data = d))

	bias_self_out
Predictors	Estimates	CI	p
(Intercept)	-1.23	-1.53 – -0.94	<0.001
biasAvg z	0.12	-0.29 – 0.53	0.569
biasSD z	-0.10	-0.46 – 0.26	0.588
politics z	0.12	-0.15 – 0.39	0.373
MvOther	-0.04	-0.45 – 0.37	0.861
white 5	-0.18	-0.72 – 0.36	0.501
biasAvg z × biasSD z	0.13	-0.07 – 0.34	0.199
biasAvg z × politics z	-0.22	-0.45 – 0.02	0.077
biasAvg z × MvOther	-0.17	-0.63 – 0.29	0.469
biasAvg z × white 5	-0.23	-0.92 – 0.47	0.518
biasSD z × politics z	0.02	-0.20 – 0.23	0.872
biasSD z × MvOther	-0.07	-0.52 – 0.37	0.740
biasSD z × white 5	0.58	-0.14 – 1.30	0.114
Observations	188
R² / R² adjusted	0.125 / 0.065

5.4 Perspective-taking

5.4.1 Republican (ME DMC)

tab_model(lm(repPTgen ~ (biasAvg.z * biasSD.z) + politics.z + MvOther + white_.5, data = d))

	repPTgen
Predictors	Estimates	CI	p
(Intercept)	3.54	3.34 – 3.75	<0.001
biasAvg z	-0.00	-0.24 – 0.23	0.968
biasSD z	0.23	0.06 – 0.40	0.007
politics z	0.60	0.40 – 0.80	<0.001
MvOther	-0.43	-0.74 – -0.13	0.006
white 5	0.16	-0.24 – 0.56	0.426
biasAvg z × biasSD z	0.04	-0.10 – 0.19	0.567
Observations	201
R² / R² adjusted	0.325 / 0.304

5.4.2 Democrat (ME DMC)

tab_model(lm(demPTgen ~ (biasAvg.z * biasSD.z) + (politics.z + MvOther + white_.5), data = d))

	demPTgen
Predictors	Estimates	CI	p
(Intercept)	4.94	4.76 – 5.12	<0.001
biasAvg z	-0.10	-0.30 – 0.10	0.327
biasSD z	0.15	0.00 – 0.30	0.048
politics z	-0.90	-1.08 – -0.73	<0.001
MvOther	0.15	-0.12 – 0.42	0.268
white 5	0.19	-0.16 – 0.54	0.279
biasAvg z × biasSD z	0.03	-0.09 – 0.16	0.597
Observations	201
R² / R² adjusted	0.489 / 0.473

5.4.3 in-group party (DMC x politics int)

look into this one

tab_model(m <- lm(PTgen_ingroup ~ (biasAvg.z * biasSD.z) + (biasAvg.z + biasSD.z) * (politics.z + MvOther + white_.5), data = d))

	PTgen_ingroup
Predictors	Estimates	CI	p
(Intercept)	4.98	4.76 – 5.19	<0.001
biasAvg z	-0.30	-0.60 – -0.00	0.050
biasSD z	0.05	-0.22 – 0.31	0.740
politics z	-0.67	-0.87 – -0.48	<0.001
MvOther	0.17	-0.14 – 0.47	0.279
white 5	0.36	-0.04 – 0.75	0.078
biasAvg z × biasSD z	0.07	-0.08 – 0.22	0.361
biasAvg z × politics z	0.09	-0.08 – 0.27	0.297
biasAvg z × MvOther	-0.03	-0.37 – 0.31	0.843
biasAvg z × white 5	0.35	-0.16 – 0.86	0.182
biasSD z × politics z	0.18	0.02 – 0.34	0.027
biasSD z × MvOther	0.12	-0.21 – 0.44	0.477
biasSD z × white 5	0.19	-0.34 – 0.72	0.484
Observations	188
R² / R² adjusted	0.367 / 0.323

5.4.4 out-group party (ME DMC)

look into this one

tab_model(m <- lm(PTgen_outgroup ~ (biasAvg.z * biasSD.z) + (politics.z + MvOther + white_.5), data = d))

	PTgen_outgroup
Predictors	Estimates	CI	p
(Intercept)	3.34	3.12 – 3.56	<0.001
biasAvg z	0.12	-0.13 – 0.36	0.338
biasSD z	0.21	0.03 – 0.39	0.025
politics z	0.26	0.05 – 0.47	0.017
MvOther	-0.44	-0.77 – -0.12	0.008
white 5	0.09	-0.33 – 0.51	0.667
biasAvg z × biasSD z	-0.04	-0.19 – 0.11	0.570
Observations	188
R² / R² adjusted	0.163 / 0.136

5.4.5 difference in understanind in- vs. out-group (NS)

d$PT_in_out <- d$PTgen_ingroup - d$PTgen_outgroup
tab_model(m <- lm(PT_in_out ~ (biasAvg.z * biasSD.z) + (politics.z + MvOther + white_.5), data = d))

	PT_in_out
Predictors	Estimates	CI	p
(Intercept)	1.74	1.46 – 2.02	<0.001
biasAvg z	-0.28	-0.59 – 0.03	0.075
biasSD z	0.01	-0.22 – 0.24	0.920
politics z	-0.87	-1.13 – -0.60	<0.001
MvOther	0.67	0.25 – 1.08	0.002
white 5	0.19	-0.34 – 0.72	0.491
biasAvg z × biasSD z	0.14	-0.05 – 0.34	0.135
Observations	188
R² / R² adjusted	0.381 / 0.360

5.5 Affective Polarization

5.5.1 Republican (DMC x politics)

look into this

d$politics.z <- as.numeric(d$politics.z)

tab_model(m<-lm(AP_rep ~ (biasAvg.z * biasSD.z) + (biasAvg.z + biasSD.z) * (politics.z + MvOther + white_.5), data = d))

	AP_rep
Predictors	Estimates	CI	p
(Intercept)	2.89	2.65 – 3.13	<0.001
biasAvg z	0.01	-0.34 – 0.35	0.975
biasSD z	0.05	-0.25 – 0.35	0.742
politics z	1.04	0.82 – 1.26	<0.001
MvOther	-0.07	-0.40 – 0.26	0.685
white 5	-0.23	-0.67 – 0.22	0.318
biasAvg z × biasSD z	0.05	-0.13 – 0.22	0.602
biasAvg z × politics z	0.04	-0.16 – 0.24	0.718
biasAvg z × MvOther	0.03	-0.36 – 0.41	0.897
biasAvg z × white 5	-0.33	-0.91 – 0.25	0.264
biasSD z × politics z	0.21	0.03 – 0.39	0.021
biasSD z × MvOther	0.04	-0.32 – 0.41	0.814
biasSD z × white 5	-0.09	-0.68 – 0.51	0.776
Observations	201
R² / R² adjusted	0.479 / 0.446

5.5.2 Democrat (NS)

tab_model(lm(AP_dem ~ (biasAvg.z * biasSD.z) + (politics.z + MvOther + white_.5), data = d))

	AP_dem
Predictors	Estimates	CI	p
(Intercept)	4.21	3.97 – 4.45	<0.001
biasAvg z	-0.22	-0.49 – 0.04	0.100
biasSD z	-0.04	-0.24 – 0.15	0.652
politics z	-0.75	-0.99 – -0.52	<0.001
MvOther	0.40	0.05 – 0.76	0.025
white 5	0.03	-0.43 – 0.48	0.905
biasAvg z × biasSD z	0.10	-0.07 – 0.27	0.252
Observations	201
R² / R² adjusted	0.341 / 0.321

5.5.3 Economic Experts (ME CMC)

tab_model(m <-lm(AP_econ ~ (biasAvg.z * biasSD.z) + (biasAvg.z + biasSD.z) + (politics.z + MvOther + white_.5), data = d))

	AP_econ
Predictors	Estimates	CI	p
(Intercept)	4.75	4.52 – 4.97	<0.001
biasAvg z	-0.28	-0.53 – -0.03	0.028
biasSD z	-0.00	-0.18 – 0.18	0.978
politics z	-0.31	-0.52 – -0.09	0.006
MvOther	-0.32	-0.65 – 0.01	0.058
white 5	0.29	-0.14 – 0.71	0.190
biasAvg z × biasSD z	0.12	-0.04 – 0.27	0.146
Observations	201
R² / R² adjusted	0.121 / 0.094

5.5.4 Election Experts (ME CMC)

tab_model(lm(AP_elect ~ (biasAvg.z * biasSD.z) + (biasAvg.z + biasSD.z) + (politics.z + MvOther + white_.5), data = d))

	AP_elect
Predictors	Estimates	CI	p
(Intercept)	4.89	4.68 – 5.10	<0.001
biasAvg z	-0.33	-0.56 – -0.09	0.007
biasSD z	-0.06	-0.23 – 0.11	0.483
politics z	-0.23	-0.43 – -0.03	0.028
MvOther	-0.14	-0.45 – 0.17	0.378
white 5	0.09	-0.31 – 0.50	0.643
biasAvg z × biasSD z	0.14	-0.01 – 0.29	0.065
Observations	201
R² / R² adjusted	0.114 / 0.086

5.5.5 Climate Scientists (DMC x CMC)

look into this

tab_model(lm(AP_clim ~ (biasAvg.z * biasSD.z) + (biasAvg.z + biasSD.z) * (politics.z + MvOther + white_.5), data = d))

	AP_clim
Predictors	Estimates	CI	p
(Intercept)	5.31	5.07 – 5.55	<0.001
biasAvg z	-0.47	-0.81 – -0.14	0.006
biasSD z	0.00	-0.29 – 0.30	0.990
politics z	-0.61	-0.82 – -0.39	<0.001
MvOther	-0.14	-0.47 – 0.19	0.414
white 5	0.55	0.11 – 0.99	0.014
biasAvg z × biasSD z	0.20	0.03 – 0.36	0.024
biasAvg z × politics z	-0.06	-0.25 – 0.14	0.573
biasAvg z × MvOther	-0.21	-0.59 – 0.17	0.271
biasAvg z × white 5	0.22	-0.36 – 0.80	0.449
biasSD z × politics z	0.05	-0.13 – 0.22	0.609
biasSD z × MvOther	-0.07	-0.43 – 0.28	0.682
biasSD z × white 5	0.12	-0.46 – 0.71	0.675
Observations	201
R² / R² adjusted	0.324 / 0.281

5.5.6 In-group party (DMC x politics)

look into this one

tab_model(m.2 <-lm(AP_ingroup ~ (biasAvg.z * biasSD.z) + (biasAvg.z + biasSD.z) * (politics.z + MvOther + white_.5), data = d))

	AP_ingroup
Predictors	Estimates	CI	p
(Intercept)	4.29	4.01 – 4.57	<0.001
biasAvg z	-0.31	-0.71 – 0.08	0.117
biasSD z	0.02	-0.33 – 0.36	0.930
politics z	-0.36	-0.62 – -0.10	0.006
MvOther	0.49	0.09 – 0.88	0.016
white 5	0.17	-0.34 – 0.69	0.510
biasAvg z × biasSD z	0.05	-0.15 – 0.25	0.615
biasAvg z × politics z	0.12	-0.11 – 0.35	0.298
biasAvg z × MvOther	-0.27	-0.71 – 0.18	0.236
biasAvg z × white 5	0.19	-0.48 – 0.85	0.577
biasSD z × politics z	0.32	0.12 – 0.53	0.002
biasSD z × MvOther	0.14	-0.29 – 0.57	0.518
biasSD z × white 5	-0.18	-0.87 – 0.51	0.611
Observations	188
R² / R² adjusted	0.219 / 0.165

5.5.7 out-group party (NS)

tab_model(m.2 <-lm(AP_outgroup ~ (biasAvg.z * biasSD.z) + (biasAvg.z + biasSD.z) * (politics.z + MvOther + white_.5), data = d))

	AP_outgroup
Predictors	Estimates	CI	p
(Intercept)	2.68	2.42 – 2.94	<0.001
biasAvg z	0.10	-0.26 – 0.47	0.566
biasSD z	0.16	-0.15 – 0.48	0.310
politics z	0.54	0.30 – 0.77	<0.001
MvOther	-0.15	-0.51 – 0.22	0.428
white 5	-0.41	-0.89 – 0.06	0.087
biasAvg z × biasSD z	0.11	-0.07 – 0.29	0.235
biasAvg z × politics z	-0.01	-0.23 – 0.20	0.889
biasAvg z × MvOther	-0.07	-0.48 – 0.33	0.730
biasAvg z × white 5	-0.45	-1.06 – 0.16	0.148
biasSD z × politics z	0.06	-0.13 – 0.25	0.547
biasSD z × MvOther	-0.33	-0.72 – 0.06	0.093
biasSD z × white 5	-0.21	-0.85 – 0.42	0.511
Observations	188
R² / R² adjusted	0.218 / 0.164

Fall 2024 Media Experiment Results

Dani Grant

2025-01-15

1 DESCRIPTIVES

1.1 sample size

1.2 gender

1.3 race

1.4 income

1.5 age

1.6 climate belief

1.7 need for cognition

1.8 party Identity

1.9 political Interest (-3 = no interest, +3 = a lot of interest)

1.10 party importance

1.11 party continuous

1.12 ideology

1.12.1 *party continuous - ideology correlation

2 Manipulation (No media = did not follow experimental procedures)

2.1 conditions sample sizes

2.2 Media consumption - mean, avg, tally (num of outlets consumed)

3 Correlation tables

3.1 confidence in institutions vs. model predictors

3.2 election outcomes vs. model predictors

3.3 affective polarization

3.4 bias/perspective taking vs. predictors

3.5 policy support vs. predictors

4 descriptives - outcomes

4.1 trust in institutions

4.1.1 general confidence

4.1.2 by party identity

4.1.3 by condition

4.1.4 party x condition (branches of fed govt + science)

4.1.5 party x condition (federal agencies)

4.2 policy support

4.2.1 personal policy support

4.2.1.1 by party

4.2.1.2 by condition

4.2.1.3 party x condition (republican policies)

4.3 self vs. ingroup social norms

4.3.1 Make federal government carbon neutral by 2050

4.3.1.1 x party

4.3.1.2 party x condition

4.3.2 Support for withdraw from Paris Climate Agreement

4.3.2.1 x party

4.3.2.2 party x condition

4.3.3 Support for 20% Tariffs

4.3.3.1 by party

4.3.3.2 party x condition

4.3.4 Support child tax credit

4.3.4.1 by party

4.3.4.2 party x condition

4.3.5 Support minimum billionaire tax

4.3.5.1 by party

4.3.5.2 party x condition

4.3.6 Support for investing in South America

4.3.6.1 by party

4.3.6.2 party x condition

4.3.7 Support for revoking birthright citizenship

4.3.7.1 by party

4.3.7.2 party x condition

4.3.8 Support for removing tax on tips

4.3.8.1 by party

4.3.8.2 party x condition

4.4 perceived bias

4.4.0.1 by party

4.4.0.2 party x condition

4.4.0.3 party continuous x condition

4.5 perspective taking

4.5.0.1 by party

4.5.0.2 party x condition

4.5.0.3 party continuous x condition

4.6 Affective Polarization

4.6.1 political parties

4.6.1.1 by party

4.6.1.2 party x condition

4.6.1.3 party continuous x condition

4.6.2 experts

4.6.2.1 by party

4.6.2.2 party x condition

4.6.2.3 party continuous x condition