Nuclear Partisan attitudes

read main data files

Includes full dataset

setwd("C:/Users/rilo6668/OneDrive - UCB-O365/research/nuclear/literature/Data")
NUC <- read.csv("NUC_comb_TOT.csv" , header = T, na.strings = ".")

create morality scales

Participants rated the wrongness of moral violations (developed by Clifford and colleagues, 2015) on a 1-5 scale. Higher scores indicate great belief in wrongness of a violation. Particpants answered 3 items per foundation. Ratings for these 3 items were average for an overall moral vigilance score for each foundation. Average score indicates average vigilance for particular violations.

names(NUC)

##  [1] "StartDate"            "EndDate"              "RecordedDate"        
##  [4] "ResponseId"           "Q78"                  "ethnicity"           
##  [7] "age"                  "gender"               "party"               
## [10] "party_leaning"        "dem_strength"         "rep_leaning"         
## [13] "party_ID_strength"    "SES_child_1"          "SES_child_2"         
## [16] "Income"               "Mor_F_1"              "Mor_C_1"             
## [19] "Mor_Loy_1"            "Mor_L_2"              "Mor_P_1"             
## [22] "Mor_A_1"              "Mor_lib_1"            "Mor_F_2"             
## [25] "Mor_Loy_3"            "Mor_F_3"              "Mor_C_2"             
## [28] "Mor_P_2"              "Mor_Lib_3"            "Mor_A_2"             
## [31] "Mor_P_3"              "Mor_A_3"              "Mor_Lib_3.1"         
## [34] "Mor_C_3"              "funel1"               "funnel2"             
## [37] "funnel3"              "funnel4"              "funnel5"             
## [40] "funnel6"              "funnel7"              "Condition"           
## [43] "TOT_psychic_num_1mil" "TO_psychic_numb2_20k" "TOT_Nuc_endorse"     
## [46] "TOT_nuc_moral"        "TOT_nuc_prosec"       "TOT_ADV_iran"        
## [49] "TOT_ADV_US"           "TOT_closeness"        "sample"              
## [52] "authoritarianism_1"   "authoritarianism_2"   "authoritarianism_3"  
## [55] "authoritarianism_4"   "authoritarianism_5"   "authoritarianism_6"  
## [58] "authoritarianism_7"   "authoritarianism_8"   "authoritarianism_9"  
## [61] "authoritarianism_10"

#Fairness
NUC$Fairness = rowMeans(subset(NUC, select = c(Mor_F_1, Mor_F_2, Mor_F_3)), na.rm = T)

#Care
NUC$Care = rowMeans(subset(NUC, select = c(Mor_C_1,Mor_C_2,Mor_C_3)), na.rm = T)

#Authority 
NUC$Authority = rowMeans(subset(NUC, select = c(Mor_A_1,Mor_A_2,Mor_A_3)), na.rm = T)

#Loyalty
NUC$Loyalty = rowMeans(subset(NUC, select = c(Mor_L_2,Mor_Loy_1,Mor_Loy_3)), na.rm = T)

#liberty
NUC$Liberty = rowMeans(subset(NUC, select = c(Mor_lib_1,Mor_Lib_3,Mor_Lib_3.1)), na.rm = T)

#Purity
NUC$Purity = rowMeans(subset(NUC, select = c(Mor_P_1,Mor_P_2,Mor_P_3)), na.rm = T)


#Binding 
NUC$bind = rowMeans(subset(NUC, select = c(Mor_P_1,Mor_P_2,Mor_P_3,Mor_L_2,Mor_Loy_1,Mor_Loy_3,Mor_A_1,Mor_A_2,Mor_A_3)), na.rm = T)

NUC$Indiv = rowMeans(subset(NUC, select = c(Mor_lib_1,Mor_Lib_3,Mor_Lib_3.1,Mor_C_1,Mor_C_2,Mor_C_3,Mor_F_1, Mor_F_2, Mor_F_3)), na.rm = T)

Party variable creation

We grouped participants into party identity based on several items. Firstly, participants who answered Democrat of Republicans were sorted into those groups, Democrats coded as 1, Republicans coded as 2.Those who answered Independent were sorted into the Independent group, coded as 3. If participants answered no preference, they were asked if they leaned more towards Democrats, Republicans, or neither. If they answered Democrat or Republican they were assigned to the respective group. If they maintained no preference they were sorted into the Independent category

The match variable was based on the participants party identitt and their assigned framing conditions. Republicans assigned to the Republican framed condition and Democrats assigned to the Democrat framed condition were coded as matching. Republicans assigned to the Democrat framed condition and Democrats assigned to the Republican framed condition were coded as mismatchmatched. Independents in all conditions were coded as controls. Any participants in the nonpartisan framed conditions were coded as controls.

NUC$party.full <- NA
NUC$party.full[NUC$party == 1] <- "dem"
NUC$party.full[NUC$party == 2] <- "rep"
NUC$party.full[NUC$party_leaning == 2] <- "rep"
NUC$party.full[NUC$party_leaning == 1] <- "dem"
NUC$party.full[NUC$party_leaning == 3] <- "ind"
NUC$party.full[NUC$party == 3] <- "ind"


NUC$match[NUC$party.full == "dem" & NUC$Condition == "dem"] <- "yes"
NUC$match[NUC$party.full == "rep" & NUC$Condition == "rep"] <- "yes"
NUC$match[NUC$party.full == "dem" & NUC$Condition == "rep"] <- "no"
NUC$match[NUC$party.full == "rep" & NUC$Condition == "dem"] <- "no"
NUC$match[NUC$party.full == "dem" & NUC$Condition == "control"] <- "control"
NUC$match[NUC$party.full == "rep" & NUC$Condition == "control"] <- "control"
NUC$match[NUC$party.full == "ind" & NUC$Condition == "rep"] <- "control"
NUC$match[NUC$party.full == "ind" & NUC$Condition == "dem"] <- "control"
NUC$match[NUC$party.full == "ind" & NUC$Condition == "control"] <- "control"

Here we mean center the MFQ

mean center morals

NUC$Authority.c <- NUC$Authority - mean(NUC$Authority, na.rm = T)
NUC$Liberty.c <- NUC$Liberty - mean(NUC$Liberty, na.rm = T)
NUC$Loyalty.c <- NUC$Loyalty - mean(NUC$Loyalty, na.rm = T)
NUC$Purity.c <- NUC$Purity - mean(NUC$Purity,na.rm = T)
NUC$Care.c <- NUC$Care - mean(NUC$Care, na.rm = T)
NUC$Fairness.c <- NUC$Fairness - mean(NUC$Fairness, na.rm = T)

Midpoint Center Strike Endorsement

We wanted zero (neutral) as the midpoint so we centered around 4. The scale was originally coded as 1-7 and is tranformed to -3-3

# create index
morrev <- ("TOT_nuc_moral")
NUC[,46] <- 8-NUC[,46]

#center
NUC$TOT_Nuc_endorse <- (NUC$TOT_Nuc_endorse - 4)
NUC$TOT_nuc_moral <- (NUC$TOT_nuc_moral - 4)
NUC$TOT_nuc_prosec <- (NUC$TOT_nuc_prosec -4)


#create extra average variable of moralityof strike + endorsement 
NUC$nuc_avg = (NUC$TOT_Nuc_endorse + NUC$TOT_nuc_moral)/2

Catgorical outcome

Supplementary analysis. Any scores above midpoint code as endorseing the strike for categorical outcomes. any scores at midpoint are neutral and any below midpoint were coded as opposition.

NUC$cat[NUC$TOT_Nuc_endorse <= -1 ] <- "oppose"
NUC$cat[NUC$TOT_Nuc_endorse == 0] <- "neutral"
NUC$cat[NUC$TOT_Nuc_endorse >= 1] <- "support"

DATA PREP

Contrast Coding

partisan framing contrast code

Code fCvP compares the nonpartisan/control framing condition to both partisan framed conditions. Code fDvR compares the democrat framed condition to the republican framed condition

code matchmismatch is another supplemental code to assess partisan framing effects. It compares groups where participant’s identities match or do not match the framing condition controlvs match compares control to all other conditions

Generally the interaction between the party code and the condition code which follows is used to assess the framing effect but the matchmismatch codes are used for simple effect/dummy code models where the interaction cannot easily be interpreted

#Control vs Partisan 
NUC$fCvP <- (2/3)*(NUC$Condition == "control") + ((-1/3))*(NUC$Condition == "rep") + ((-1/3))*(NUC$Condition == "dem") 

#Dem vs rep
NUC$fDvR <- 0*(NUC$Condition == "control") + (-1/2)*(NUC$Condition == "dem") + ((1/2))*(NUC$Condition == "rep") 

# Partisan match vs. no match
NUC$matchmismatch <- 0*(NUC$match == "control") + (1/2)*(NUC$match == "yes") + (-(1/2))*(NUC$match == "no") 

#control vs partisan framed
NUC$othervsmatch <- (2/3)*(NUC$match == "control") + (-(1/3))*(NUC$match == "yes") + (-(1/3))*(NUC$match == "no") 

party group contrast code

Orthogonal codes Set 1 Code pDvR compares democrats to republicans code pIvp compares independents to the other groups

Orthoonal codes set 2 Code pIvR independents and republicans Code DvIR compare Democrats against the other groups

Orthogonal codes set 3 code PDvI compares Democrats and independents Code IvDR compares Republicans to other groups

#dem vs rep
NUC$pDvR <- (-(1/2))*(NUC$party.full == "dem") + (1/2)*(NUC$party.full == "rep") + 0*(NUC$party.full == "ind")

#ind vs partisan 
NUC$pIvP <- ((-1/3))*(NUC$party.full == "dem") + ((-1/3))*(NUC$party.full == "rep") + (2/3)*(NUC$party.full == "ind")

# ind vs rep
NUC$pIvR <- (0)*(NUC$party.full == "dem") + (1/2)*(NUC$party.full == "rep") + (-1/2)*(NUC$party.full == "ind")

#dem versus ind & rep 
NUC$DvIR <- ((-2/3)*(NUC$party.full == "dem")) + ((1/3)*(NUC$party.full == "rep")) + ((1/3)*(NUC$party.full == "ind"))

#dem vs ind
NUC$pDvI <- (-(1/2)*(NUC$party.full == "dem")) + ((0)*(NUC$party.full == "rep")) + ((1/2)*(NUC$party.full == "ind"))

#rep versus ind & dem
NUC$IvDR <- ((1/3)*(NUC$party.full == "dem")) + ((-2/3)*(NUC$party.full == "rep")) + ((1/3)*(NUC$party.full == "ind"))

dummy code party ID

Dummy codes for simple effects by party ID

#rep dummy code
#demd + in_d = republicans as intercept 
#demd + repd = indepents as intercept
#repd + in_d = Democrats as intercept

NUC$demd <- (1)*(NUC$party.full == "dem") + (0)*(NUC$party.full == "rep") + 0*(NUC$party.full == "ind")

NUC$in_d <- (0)*(NUC$party.full == "dem") + (0)*(NUC$party.full == "rep") + 1*(NUC$party.full == "ind")

NUC$repd <- (0)*(NUC$party.full == "dem") + (1)*(NUC$party.full == "rep") + 0*(NUC$party.full == "ind")

STUDY 1

creat sample 1 subset

this file includes only participants for study 1

sub <- NUC[!(NUC$sample=='m2'),]
part <- sub[(sub$pIvP < .5 ),]

Main Models _ Party ID and Framing Condition/Nuclear Endorsement

describeBy(sub$TOT_Nuc_endorse, group = sub$party.full)

## 
##  Descriptive statistics by group 
## group: dem
##    vars   n  mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 698 -1.15 1.32     -1   -1.23 1.48  -3   3     6 0.48    -0.23 0.05
## ------------------------------------------------------------ 
## group: ind
##    vars   n  mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 347 -0.95 1.45     -1   -1.02 1.48  -3   3     6  0.4    -0.22 0.08
## ------------------------------------------------------------ 
## group: rep
##    vars   n  mean   sd median trimmed  mad min max range skew kurtosis  se
## X1    1 263 -0.29 1.68      0   -0.31 1.48  -3   3     6 0.16    -0.83 0.1

describeBy(sub$TOT_Nuc_endorse, list(sub$party.full,sub$Condition))

## 
##  Descriptive statistics by group 
## : dem
## : control
##    vars   n  mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 237 -1.12 1.32     -1   -1.19 1.48  -3   3     6 0.46    -0.24 0.09
## ------------------------------------------------------------ 
## : ind
## : control
##    vars   n  mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 120 -1.02 1.38     -1   -1.11 1.48  -3   3     6 0.58     0.36 0.13
## ------------------------------------------------------------ 
## : rep
## : control
##    vars  n  mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 83 -0.29 1.62     -1    -0.3 1.48  -3   3     6 0.18    -0.76 0.18
## ------------------------------------------------------------ 
## : dem
## : dem
##    vars   n  mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 237 -1.03 1.31     -1   -1.08 1.48  -3   3     6 0.45    -0.17 0.08
## ------------------------------------------------------------ 
## : ind
## : dem
##    vars   n  mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 111 -0.95 1.46     -1      -1 1.48  -3   3     6 0.29    -0.48 0.14
## ------------------------------------------------------------ 
## : rep
## : dem
##    vars  n  mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 84 -0.35 1.76     -1    -0.4 2.22  -3   3     6  0.2       -1 0.19
## ------------------------------------------------------------ 
## : dem
## : rep
##    vars   n  mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 224 -1.31 1.33     -1   -1.43 1.48  -3   3     6 0.56    -0.28 0.09
## ------------------------------------------------------------ 
## : ind
## : rep
##    vars   n  mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 116 -0.89 1.51     -1   -0.95 1.48  -3   3     6 0.32    -0.53 0.14
## ------------------------------------------------------------ 
## : rep
## : rep
##    vars  n  mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 96 -0.23 1.67      0   -0.24 1.48  -3   3     6 0.11    -0.81 0.17

Democrats versus Republicans

MAIN MODEL

library(sjPlot)

m1 <- lm(TOT_Nuc_endorse ~ ((pDvR + pIvP)) * (fDvR + fCvP)+((bind+Indiv) * (fDvR + fCvP)),data = sub)
summary(m1)

## 
## Call:
## lm(formula = TOT_Nuc_endorse ~ ((pDvR + pIvP)) * (fDvR + fCvP) + 
##     ((bind + Indiv) * (fDvR + fCvP)), data = sub)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.1303 -0.9753 -0.0941  0.9290  4.4390 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.48423    0.32296  -4.596 4.73e-06 ***
## pDvR         0.66298    0.10450   6.344 3.08e-10 ***
## pIvP        -0.14695    0.09111  -1.613   0.1070    
## fDvR        -0.92706    0.78505  -1.181   0.2379    
## fCvP        -1.15705    0.69028  -1.676   0.0939 .  
## bind         0.62040    0.07663   8.096 1.30e-15 ***
## Indiv       -0.35886    0.08478  -4.233 2.47e-05 ***
## pDvR:fDvR    0.55024    0.25472   2.160   0.0309 *  
## pDvR:fCvP   -0.09773    0.22276  -0.439   0.6609    
## pIvP:fDvR    0.09435    0.22412   0.421   0.6738    
## pIvP:fCvP   -0.10398    0.19245  -0.540   0.5891    
## fDvR:bind   -0.23790    0.18808  -1.265   0.2061    
## fCvP:bind    0.38046    0.16223   2.345   0.0192 *  
## fDvR:Indiv   0.43166    0.20479   2.108   0.0352 *  
## fCvP:Indiv  -0.03934    0.18231  -0.216   0.8292    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.395 on 1292 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.1075, Adjusted R-squared:  0.09783 
## F-statistic: 11.12 on 14 and 1292 DF,  p-value: < 2.2e-16

tab_model(m1)

	Dependent variable
Predictors	Estimates	CI	p
(Intercept)	-1.48	-2.12 – -0.85	<0.001
pDvR	0.66	0.46 – 0.87	<0.001
pIvP	-0.15	-0.33 – 0.03	0.107
fDvR	-0.93	-2.47 – 0.61	0.238
fCvP	-1.16	-2.51 – 0.20	0.094
bind	0.62	0.47 – 0.77	<0.001
Indiv	-0.36	-0.53 – -0.19	<0.001
pDvR × fDvR	0.55	0.05 – 1.05	0.031
pDvR × fCvP	-0.10	-0.53 – 0.34	0.661
pIvP × fDvR	0.09	-0.35 – 0.53	0.674
pIvP × fCvP	-0.10	-0.48 – 0.27	0.589
fDvR × bind	-0.24	-0.61 – 0.13	0.206
fCvP × bind	0.38	0.06 – 0.70	0.019
fDvR × Indiv	0.43	0.03 – 0.83	0.035
fCvP × Indiv	-0.04	-0.40 – 0.32	0.829
Observations	1307
R2 / R2 adjusted	0.108 / 0.098

non partisan model (coded as party match vs. mismatch)

m1 <- lm(TOT_Nuc_endorse ~ ((matchmismatch)*(pDvR)+(bind+Indiv)) , data = part )
summary(m1)

## 
## Call:
## lm(formula = TOT_Nuc_endorse ~ ((matchmismatch) * (pDvR) + (bind + 
##     Indiv)), data = part)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3094 -0.9625 -0.0784  0.9980  4.2715 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        -1.51130    0.38427  -3.933 9.00e-05 ***
## matchmismatch       0.22174    0.12264   1.808  0.07092 .  
## pDvR                0.66514    0.10531   6.316 4.11e-10 ***
## bind                0.60595    0.08871   6.831 1.50e-11 ***
## Indiv              -0.32683    0.10023  -3.261  0.00115 ** 
## matchmismatch:pDvR -0.13681    0.24528  -0.558  0.57715    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.393 on 954 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.1157, Adjusted R-squared:  0.1111 
## F-statistic: 24.97 on 5 and 954 DF,  p-value: < 2.2e-16

etaSquared(m1)

##                          eta.sq  eta.sq.part
## matchmismatch      0.0048415150 0.0054454266
## pDvR               0.0367480658 0.0399000054
## bind               0.0432507659 0.0466312152
## Indiv              0.0098556042 0.0110227902
## matchmismatch:pDvR 0.0002883354 0.0003259706

tab_model(m1)

	TOT_Nuc_endorse
Predictors	Estimates	CI	p
(Intercept)	-1.51	-2.27 – -0.76	<0.001
matchmismatch	0.22	-0.02 – 0.46	0.071
pDvR	0.67	0.46 – 0.87	<0.001
bind	0.61	0.43 – 0.78	<0.001
Indiv	-0.33	-0.52 – -0.13	0.001
matchmismatch × pDvR	-0.14	-0.62 – 0.34	0.577
Observations	960
R2 / R2 adjusted	0.116 / 0.111

MODEL OPTIMIZED FOR PLOTS - DO NOT USE FOR ANALYSIS

main model including party by MFQ int

tab_model(m1)

	TOT_Nuc_endorse
Predictors	Estimates	CI	p
(Intercept)	-1.51	-2.27 – -0.76	<0.001
matchmismatch	0.22	-0.02 – 0.46	0.071
pDvR	0.67	0.46 – 0.87	<0.001
bind	0.61	0.43 – 0.78	<0.001
Indiv	-0.33	-0.52 – -0.13	0.001
matchmismatch × pDvR	-0.14	-0.62 – 0.34	0.577
Observations	960
R2 / R2 adjusted	0.116 / 0.111

m1 <- lm(TOT_Nuc_endorse ~ ((pDvR + pIvP)) * (fDvR + fCvP)+((bind+Indiv) * (fDvR + fCvP))+((pDvR+pIvP) * (bind+Indiv)),data = sub)
summary(m1)

## 
## Call:
## lm(formula = TOT_Nuc_endorse ~ ((pDvR + pIvP)) * (fDvR + fCvP) + 
##     ((bind + Indiv) * (fDvR + fCvP)) + ((pDvR + pIvP) * (bind + 
##     Indiv)), data = sub)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.9316 -0.9505 -0.0899  0.9481  4.4593 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.303759   0.340659  -3.827 0.000136 ***
## pDvR         1.675570   0.835143   2.006 0.045030 *  
## pIvP        -0.021133   0.729589  -0.029 0.976896    
## fDvR        -0.934301   0.785019  -1.190 0.234202    
## fCvP        -1.246023   0.697384  -1.787 0.074219 .  
## bind         0.512891   0.089077   5.758 1.06e-08 ***
## Indiv       -0.303709   0.093139  -3.261 0.001140 ** 
## pDvR:fDvR    0.520732   0.254786   2.044 0.041177 *  
## pDvR:fCvP   -0.096321   0.222651  -0.433 0.665372    
## pIvP:fDvR    0.107201   0.223999   0.479 0.632319    
## pIvP:fCvP   -0.094876   0.192329  -0.493 0.621887    
## fDvR:bind   -0.235413   0.188114  -1.251 0.211002    
## fCvP:bind    0.348340   0.163108   2.136 0.032897 *  
## fDvR:Indiv   0.429119   0.204870   2.095 0.036403 *  
## fCvP:Indiv   0.007918   0.184203   0.043 0.965718    
## pDvR:bind   -0.553685   0.221306  -2.502 0.012476 *  
## pDvR:Indiv   0.239992   0.231324   1.037 0.299711    
## pIvP:bind    0.166548   0.187263   0.889 0.373965    
## pIvP:Indiv  -0.187177   0.196391  -0.953 0.340726    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.394 on 1288 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.1121, Adjusted R-squared:  0.09972 
## F-statistic: 9.037 on 18 and 1288 DF,  p-value: < 2.2e-16

etaSquared(m1)

##                  eta.sq  eta.sq.part
## pDvR       2.853635e-02 3.113929e-02
## pIvP       3.023352e-03 3.393606e-03
## fDvR       9.962705e-04 1.120829e-03
## fCvP       2.740027e-05 3.085961e-05
## bind       4.540432e-02 4.865039e-02
## Indiv      1.051754e-02 1.170708e-02
## pDvR:fDvR  2.879462e-03 3.232617e-03
## pDvR:fCvP  1.290101e-04 1.452813e-04
## pIvP:fDvR  1.578863e-04 1.777937e-04
## pIvP:fCvP  1.677461e-04 1.888945e-04
## fDvR:bind  1.079579e-03 1.214439e-03
## fCvP:bind  3.144064e-03 3.528623e-03
## fDvR:Indiv 3.024352e-03 3.394724e-03
## fCvP:Indiv 1.273847e-06 1.434715e-06
## pDvR:bind  4.314929e-03 4.836344e-03
## pDvR:Indiv 7.419717e-04 8.349752e-04
## pIvP:bind  5.452684e-04 6.137517e-04
## pIvP:Indiv 6.261790e-04 7.047601e-04

tab_model(m1)

	Dependent variable
Predictors	Estimates	CI	p
(Intercept)	-1.30	-1.97 – -0.64	<0.001
pDvR	1.68	0.04 – 3.31	0.045
pIvP	-0.02	-1.45 – 1.41	0.977
fDvR	-0.93	-2.47 – 0.61	0.234
fCvP	-1.25	-2.61 – 0.12	0.074
bind	0.51	0.34 – 0.69	<0.001
Indiv	-0.30	-0.49 – -0.12	0.001
pDvR × fDvR	0.52	0.02 – 1.02	0.041
pDvR × fCvP	-0.10	-0.53 – 0.34	0.665
pIvP × fDvR	0.11	-0.33 – 0.55	0.632
pIvP × fCvP	-0.09	-0.47 – 0.28	0.622
fDvR × bind	-0.24	-0.60 – 0.13	0.211
fCvP × bind	0.35	0.03 – 0.67	0.033
fDvR × Indiv	0.43	0.03 – 0.83	0.036
fCvP × Indiv	0.01	-0.35 – 0.37	0.966
pDvR × bind	-0.55	-0.99 – -0.12	0.012
pDvR × Indiv	0.24	-0.21 – 0.69	0.300
pIvP × bind	0.17	-0.20 – 0.53	0.374
pIvP × Indiv	-0.19	-0.57 – 0.20	0.341
Observations	1307
R2 / R2 adjusted	0.112 / 0.100

#model 
sub$pDvRxfDvR   <-sub$pDvR*sub$fDvR
sub$pDvRxfCVP   <-sub$pDvR*sub$fCvP
sub$pIVPxfDvR   <-sub$pIvP*sub$fDvR
sub$pIVPxfCvP   <-sub$pIvP*sub$fCvP
sub$fDvRxbind   <-sub$bind*sub$fDvR
sub$fCvpxbind   <-sub$bind*sub$fCvP
sub$fDvRxindiv  <-sub$Indiv*sub$fDvR
sub$fCvpindiv   <-sub$Indiv*sub$fCvP
sub$pDvRxbind   <-sub$bind*sub$pDvR
sub$pDvRxindiv  <-sub$Indiv*sub$pDvR
sub$pIvpxbind   <-sub$bind*sub$pIvP
sub$pIvpxindiv  <-sub$Indiv*sub$pIvP

## PLOT EFFECTS 
## MAIN MODEL WITH MFQ interaction EDITED FOR PLOTTING 
plot.1 <- lm(TOT_Nuc_endorse ~ (pDvRxfDvR + pDvR + pIvP + fDvR + fCvP + bind+Indiv + pDvRxfCVP + pIVPxfDvR + pIVPxfCvP + fDvRxbind + fCvpxbind + fDvRxindiv + fCvpindiv + pDvRxbind + pDvRxindiv + pIvpxbind + pIvpxindiv) , data = sub)
summary(plot.1)

## 
## Call:
## lm(formula = TOT_Nuc_endorse ~ (pDvRxfDvR + pDvR + pIvP + fDvR + 
##     fCvP + bind + Indiv + pDvRxfCVP + pIVPxfDvR + pIVPxfCvP + 
##     fDvRxbind + fCvpxbind + fDvRxindiv + fCvpindiv + pDvRxbind + 
##     pDvRxindiv + pIvpxbind + pIvpxindiv), data = sub)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.9316 -0.9505 -0.0899  0.9481  4.4593 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.303759   0.340659  -3.827 0.000136 ***
## pDvRxfDvR    0.520732   0.254786   2.044 0.041177 *  
## pDvR         1.675570   0.835143   2.006 0.045030 *  
## pIvP        -0.021133   0.729589  -0.029 0.976896    
## fDvR        -0.934301   0.785019  -1.190 0.234202    
## fCvP        -1.246023   0.697384  -1.787 0.074219 .  
## bind         0.512891   0.089077   5.758 1.06e-08 ***
## Indiv       -0.303709   0.093139  -3.261 0.001140 ** 
## pDvRxfCVP   -0.096321   0.222651  -0.433 0.665372    
## pIVPxfDvR    0.107201   0.223999   0.479 0.632319    
## pIVPxfCvP   -0.094876   0.192329  -0.493 0.621887    
## fDvRxbind   -0.235413   0.188114  -1.251 0.211002    
## fCvpxbind    0.348340   0.163108   2.136 0.032897 *  
## fDvRxindiv   0.429119   0.204870   2.095 0.036403 *  
## fCvpindiv    0.007918   0.184203   0.043 0.965718    
## pDvRxbind   -0.553685   0.221306  -2.502 0.012476 *  
## pDvRxindiv   0.239992   0.231324   1.037 0.299711    
## pIvpxbind    0.166548   0.187263   0.889 0.373965    
## pIvpxindiv  -0.187177   0.196391  -0.953 0.340726    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.394 on 1288 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.1121, Adjusted R-squared:  0.09972 
## F-statistic: 9.037 on 18 and 1288 DF,  p-value: < 2.2e-16

tab_model(plot.1, show.std = )

	Dependent variable
Predictors	Estimates	CI	p
(Intercept)	-1.30	-1.97 – -0.64	<0.001
pDvRxfDvR	0.52	0.02 – 1.02	0.041
pDvR	1.68	0.04 – 3.31	0.045
pIvP	-0.02	-1.45 – 1.41	0.977
fDvR	-0.93	-2.47 – 0.61	0.234
fCvP	-1.25	-2.61 – 0.12	0.074
bind	0.51	0.34 – 0.69	<0.001
Indiv	-0.30	-0.49 – -0.12	0.001
pDvRxfCVP	-0.10	-0.53 – 0.34	0.665
pIVPxfDvR	0.11	-0.33 – 0.55	0.632
pIVPxfCvP	-0.09	-0.47 – 0.28	0.622
fDvRxbind	-0.24	-0.60 – 0.13	0.211
fCvpxbind	0.35	0.03 – 0.67	0.033
fDvRxindiv	0.43	0.03 – 0.83	0.036
fCvpindiv	0.01	-0.35 – 0.37	0.966
pDvRxbind	-0.55	-0.99 – -0.12	0.012
pDvRxindiv	0.24	-0.21 – 0.69	0.300
pIvpxbind	0.17	-0.20 – 0.53	0.374
pIvpxindiv	-0.19	-0.57 – 0.20	0.341
Observations	1307
R2 / R2 adjusted	0.112 / 0.100

PLOT main model including party by MFQ int

m1 <- lm(TOT_Nuc_endorse ~ ((pDvR + pIvP)) * (fDvR + fCvP)*((bind+Indiv)),data = sub)
summary(m1)

## 
## Call:
## lm(formula = TOT_Nuc_endorse ~ ((pDvR + pIvP)) * (fDvR + fCvP) * 
##     ((bind + Indiv)), data = sub)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.0798 -0.9694 -0.0789  0.9392  4.3646 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     -1.39552    0.34569  -4.037 5.74e-05 ***
## pDvR             1.54276    0.84633   1.823  0.06855 .  
## pIvP            -0.05105    0.73369  -0.070  0.94454    
## fDvR            -0.94491    0.83593  -1.130  0.25853    
## fCvP            -1.62529    0.74258  -2.189  0.02880 *  
## bind             0.50487    0.09147   5.520 4.11e-08 ***
## Indiv           -0.27413    0.09652  -2.840  0.00458 ** 
## pDvR:fDvR        1.12733    1.96484   0.574  0.56624    
## pDvR:fCvP       -2.49044    1.88441  -1.322  0.18654    
## pIvP:fDvR       -2.92765    1.84218  -1.589  0.11226    
## pIvP:fCvP        2.76469    1.51642   1.823  0.06851 .  
## pDvR:bind       -0.57618    0.22871  -2.519  0.01188 *  
## pDvR:Indiv       0.29344    0.24244   1.210  0.22636    
## pIvP:bind        0.19198    0.18990   1.011  0.31222    
## pIvP:Indiv      -0.20137    0.19940  -1.010  0.31275    
## fDvR:bind       -0.36699    0.21227  -1.729  0.08407 .  
## fDvR:Indiv       0.55820    0.21948   2.543  0.01110 *  
## fCvP:bind        0.36341    0.20371   1.784  0.07468 .  
## fCvP:Indiv       0.09239    0.21843   0.423  0.67238    
## pDvR:fDvR:bind  -0.78041    0.50983  -1.531  0.12609    
## pDvR:fDvR:Indiv  0.54283    0.52389   1.036  0.30032    
## pDvR:fCvP:bind   0.08317    0.52521   0.158  0.87421    
## pDvR:fCvP:Indiv  0.54196    0.56847   0.953  0.34059    
## pIvP:fDvR:bind   0.30811    0.45889   0.671  0.50207    
## pIvP:fDvR:Indiv  0.48344    0.47719   1.013  0.31120    
## pIvP:fCvP:bind   0.04450    0.40818   0.109  0.91321    
## pIvP:fCvP:Indiv -0.76388    0.43250  -1.766  0.07760 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.393 on 1280 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.1189, Adjusted R-squared:  0.101 
## F-statistic: 6.645 on 26 and 1280 DF,  p-value: < 2.2e-16

etaSquared(m1)

##                       eta.sq  eta.sq.part
## pDvR            2.904212e-02 3.191007e-02
## pIvP            2.955685e-03 3.343393e-03
## fDvR            1.008516e-03 1.143325e-03
## fCvP            1.885716e-05 2.140182e-05
## bind            4.432608e-02 4.789896e-02
## Indiv           1.074399e-02 1.204719e-02
## pDvR:fDvR       2.704976e-03 3.060666e-03
## pDvR:fCvP       4.902092e-05 5.563409e-05
## pIvP:fDvR       5.775522e-06 6.554991e-06
## pIvP:fCvP       1.449365e-04 1.644713e-04
## pDvR:bind       4.839709e-03 5.462909e-03
## pDvR:Indiv      6.872767e-04 7.794292e-04
## pIvP:bind       6.704087e-04 7.603140e-04
## pIvP:Indiv      5.611565e-04 6.364894e-04
## fDvR:bind       1.105718e-03 1.253382e-03
## fDvR:Indiv      3.230551e-03 3.653178e-03
## fCvP:bind       2.969330e-03 3.358776e-03
## fCvP:Indiv      3.054356e-06 3.466585e-06
## pDvR:fDvR:bind  1.612837e-03 1.827174e-03
## pDvR:fDvR:Indiv 7.390290e-04 8.380715e-04
## pDvR:fCvP:bind  1.725951e-05 1.958862e-05
## pDvR:fCvP:Indiv 6.256397e-04 7.095773e-04
## pIvP:fDvR:bind  3.103098e-04 3.520678e-04
## pIvP:fDvR:Indiv 7.065013e-04 8.012140e-04
## pIvP:fCvP:bind  8.180983e-06 9.285069e-06
## pIvP:fCvP:Indiv 2.147282e-03 2.431172e-03

tab_model(m1)

	Dependent variable
Predictors	Estimates	CI	p
(Intercept)	-1.40	-2.07 – -0.72	<0.001
pDvR	1.54	-0.12 – 3.20	0.069
pIvP	-0.05	-1.49 – 1.39	0.945
fDvR	-0.94	-2.58 – 0.70	0.259
fCvP	-1.63	-3.08 – -0.17	0.029
bind	0.50	0.33 – 0.68	<0.001
Indiv	-0.27	-0.46 – -0.08	0.005
pDvR × fDvR	1.13	-2.73 – 4.98	0.566
pDvR × fCvP	-2.49	-6.19 – 1.21	0.187
pIvP × fDvR	-2.93	-6.54 – 0.69	0.112
pIvP × fCvP	2.76	-0.21 – 5.74	0.069
pDvR × bind	-0.58	-1.02 – -0.13	0.012
pDvR × Indiv	0.29	-0.18 – 0.77	0.226
pIvP × bind	0.19	-0.18 – 0.56	0.312
pIvP × Indiv	-0.20	-0.59 – 0.19	0.313
fDvR × bind	-0.37	-0.78 – 0.05	0.084
fDvR × Indiv	0.56	0.13 – 0.99	0.011
fCvP × bind	0.36	-0.04 – 0.76	0.075
fCvP × Indiv	0.09	-0.34 – 0.52	0.672
(pDvR × fDvR) × bind	-0.78	-1.78 – 0.22	0.126
(pDvR × fDvR) × Indiv	0.54	-0.48 – 1.57	0.300
(pDvR × fCvP) × bind	0.08	-0.95 – 1.11	0.874
(pDvR × fCvP) × Indiv	0.54	-0.57 – 1.66	0.341
(pIvP × fDvR) × bind	0.31	-0.59 – 1.21	0.502
(pIvP × fDvR) × Indiv	0.48	-0.45 – 1.42	0.311
(pIvP × fCvP) × bind	0.04	-0.76 – 0.85	0.913
(pIvP × fCvP) × Indiv	-0.76	-1.61 – 0.08	0.078
Observations	1307
R2 / R2 adjusted	0.119 / 0.101

plot.1 <- lm(TOT_Nuc_endorse ~ (pDvRxfDvR + pDvR + pIvP + fDvR + fCvP + bind+Indiv + pDvRxfCVP + pIVPxfDvR + pIVPxfCvP + fDvRxbind + fCvpxbind + fDvRxindiv + fCvpindiv + pDvRxbind + pDvRxindiv + pIvpxbind + pIvpxindiv) , data = sub)
summary(plot.1)

## 
## Call:
## lm(formula = TOT_Nuc_endorse ~ (pDvRxfDvR + pDvR + pIvP + fDvR + 
##     fCvP + bind + Indiv + pDvRxfCVP + pIVPxfDvR + pIVPxfCvP + 
##     fDvRxbind + fCvpxbind + fDvRxindiv + fCvpindiv + pDvRxbind + 
##     pDvRxindiv + pIvpxbind + pIvpxindiv), data = sub)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.9316 -0.9505 -0.0899  0.9481  4.4593 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.303759   0.340659  -3.827 0.000136 ***
## pDvRxfDvR    0.520732   0.254786   2.044 0.041177 *  
## pDvR         1.675570   0.835143   2.006 0.045030 *  
## pIvP        -0.021133   0.729589  -0.029 0.976896    
## fDvR        -0.934301   0.785019  -1.190 0.234202    
## fCvP        -1.246023   0.697384  -1.787 0.074219 .  
## bind         0.512891   0.089077   5.758 1.06e-08 ***
## Indiv       -0.303709   0.093139  -3.261 0.001140 ** 
## pDvRxfCVP   -0.096321   0.222651  -0.433 0.665372    
## pIVPxfDvR    0.107201   0.223999   0.479 0.632319    
## pIVPxfCvP   -0.094876   0.192329  -0.493 0.621887    
## fDvRxbind   -0.235413   0.188114  -1.251 0.211002    
## fCvpxbind    0.348340   0.163108   2.136 0.032897 *  
## fDvRxindiv   0.429119   0.204870   2.095 0.036403 *  
## fCvpindiv    0.007918   0.184203   0.043 0.965718    
## pDvRxbind   -0.553685   0.221306  -2.502 0.012476 *  
## pDvRxindiv   0.239992   0.231324   1.037 0.299711    
## pIvpxbind    0.166548   0.187263   0.889 0.373965    
## pIvpxindiv  -0.187177   0.196391  -0.953 0.340726    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.394 on 1288 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.1121, Adjusted R-squared:  0.09972 
## F-statistic: 9.037 on 18 and 1288 DF,  p-value: < 2.2e-16

tab_model(plot.1)

	Dependent variable
Predictors	Estimates	CI	p
(Intercept)	-1.30	-1.97 – -0.64	<0.001
pDvRxfDvR	0.52	0.02 – 1.02	0.041
pDvR	1.68	0.04 – 3.31	0.045
pIvP	-0.02	-1.45 – 1.41	0.977
fDvR	-0.93	-2.47 – 0.61	0.234
fCvP	-1.25	-2.61 – 0.12	0.074
bind	0.51	0.34 – 0.69	<0.001
Indiv	-0.30	-0.49 – -0.12	0.001
pDvRxfCVP	-0.10	-0.53 – 0.34	0.665
pIVPxfDvR	0.11	-0.33 – 0.55	0.632
pIVPxfCvP	-0.09	-0.47 – 0.28	0.622
fDvRxbind	-0.24	-0.60 – 0.13	0.211
fCvpxbind	0.35	0.03 – 0.67	0.033
fDvRxindiv	0.43	0.03 – 0.83	0.036
fCvpindiv	0.01	-0.35 – 0.37	0.966
pDvRxbind	-0.55	-0.99 – -0.12	0.012
pDvRxindiv	0.24	-0.21 – 0.69	0.300
pIvpxbind	0.17	-0.20 – 0.53	0.374
pIvpxindiv	-0.19	-0.57 – 0.20	0.341
Observations	1307
R2 / R2 adjusted	0.112 / 0.100

fp <- plot_model(
  plot.1,
  type = c("std"), ci.style = c("bar"),axis.labels = c("Independents vs. other identities x neutral frame vs. other conditions","Independents vs. other identities x Democrat vs. Republican framed conditions","Democrat vs. Republican identity x neutral frame vs. other conditions ","Individualizing Foundation Vigilance","Binding Foundation Vigilance ","Framing condition (Neutral framed vs. Democrat or Republican framed)", "Framing condition (Democrat framed vs. Republican framed)","Party Identity (Independents vs. other identities)","Party Identity (Democrat vs. Republican)", "Democrat vs. Republican identity x Democrat vs. Republican framed conditions"), show.values = T, terms = c("pDvRxfDvR","pDvR","pIvP","fDvR","fCvP","bind","Indiv","pDvRxfCVP","pIVP×fDvR","pIVP×fCvP") , value.offset = .35, wrap.labels = 30, axis.lim = c(-1,1), p.threshold = c(0.05, 0.01, 0.001))


fp <- fp + theme_sjplot()+ geom_point(shape=19, size=2.5, color="darkred")+ scale_color_manual(values=c("black","black")) +labs(title = element_text(size = 20,"Study 1 - Effect Estimates in Model of Nuclear endorsement")) + theme( axis.text.y = element_text(size = 15, colour = "black"), axis.text.x.bottom = element_text(size = 15,color = "black"))

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

fp

ggsave("fp.jpg", plot = fp , width = 9, height =9, dpi = 400)

fp

### main model including party by MFQ int

m1 <- lm(TOT_Nuc_endorse ~ ((pDvR + pIvP)) * (fDvR + fCvP)+((bind+Indiv)*(pDvR + pIvP)),data = sub)
summary(m1)

## 
## Call:
## lm(formula = TOT_Nuc_endorse ~ ((pDvR + pIvP)) * (fDvR + fCvP) + 
##     ((bind + Indiv) * (pDvR + pIvP)), data = sub)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.9022 -0.9651 -0.0589  0.9542  4.4429 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.30534    0.34119  -3.826 0.000137 ***
## pDvR         1.73464    0.83654   2.074 0.038315 *  
## pIvP        -0.22713    0.72309  -0.314 0.753487    
## fDvR        -0.03301    0.10281  -0.321 0.748205    
## fCvP        -0.02930    0.08947  -0.327 0.743345    
## bind         0.50732    0.08911   5.693 1.54e-08 ***
## Indiv       -0.29740    0.09308  -3.195 0.001431 ** 
## pDvR:fDvR    0.41536    0.24639   1.686 0.092085 .  
## pDvR:fCvP   -0.02841    0.21710  -0.131 0.895894    
## pIvP:fDvR    0.14933    0.22272   0.670 0.502676    
## pIvP:fCvP   -0.14676    0.19156  -0.766 0.443736    
## pDvR:bind   -0.58490    0.22063  -2.651 0.008123 ** 
## pIvP:bind    0.21810    0.18699   1.166 0.243676    
## pDvR:Indiv   0.25271    0.23045   1.097 0.273011    
## pIvP:Indiv  -0.17986    0.19530  -0.921 0.357259    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.397 on 1292 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.1049, Adjusted R-squared:  0.0952 
## F-statistic: 10.82 on 14 and 1292 DF,  p-value: < 2.2e-16

etaSquared(m1)

##                  eta.sq  eta.sq.part
## pDvR       2.819913e-02 3.054180e-02
## pIvP       3.114045e-03 3.466941e-03
## fDvR       9.493152e-04 1.059449e-03
## fCvP       2.396973e-05 2.677820e-05
## bind       4.502148e-02 4.788915e-02
## Indiv      1.050826e-02 1.160358e-02
## pDvR:fDvR  1.968772e-03 2.194680e-03
## pDvR:fCvP  1.186668e-05 1.325725e-05
## pIvP:fDvR  3.114368e-04 3.478154e-04
## pIvP:fCvP  4.066479e-04 4.540997e-04
## pDvR:bind  4.868890e-03 5.410083e-03
## pIvP:bind  9.425152e-04 1.051868e-03
## pDvR:Indiv 8.331470e-04 9.299241e-04
## pIvP:Indiv 5.875726e-04 6.560039e-04

tab_model(m1, show.std = T)

	Dependent variable
Predictors	Estimates	std. Beta	CI	standardized CI	p	std. p
(Intercept)	-1.31	0.02	-1.97 – -0.64	-0.04 – 0.07	<0.001	0.533
pDvR	1.73	0.21	0.09 – 3.38	0.15 – 0.27	0.038	<0.001
pIvP	-0.23	-0.06	-1.65 – 1.19	-0.12 – -0.01	0.753	0.027
fDvR	-0.03	-0.03	-0.23 – 0.17	-0.08 – 0.02	0.748	0.238
fCvP	-0.03	-0.00	-0.20 – 0.15	-0.06 – 0.05	0.743	0.859
bind	0.51	0.23	0.33 – 0.68	0.17 – 0.29	<0.001	<0.001
Indiv	-0.30	-0.11	-0.48 – -0.11	-0.17 – -0.06	0.001	<0.001
pDvR × fDvR	0.42	0.05	-0.07 – 0.90	-0.01 – 0.10	0.092	0.092
pDvR × fCvP	-0.03	-0.00	-0.45 – 0.40	-0.06 – 0.05	0.896	0.896
pIvP × fDvR	0.15	0.02	-0.29 – 0.59	-0.04 – 0.07	0.503	0.503
pIvP × fCvP	-0.15	-0.02	-0.52 – 0.23	-0.07 – 0.03	0.444	0.444
pDvR × bind	-0.58	-0.09	-1.02 – -0.15	-0.16 – -0.02	0.008	0.008
pIvP × bind	0.22	0.04	-0.15 – 0.58	-0.03 – 0.10	0.244	0.244
pDvR × Indiv	0.25	0.03	-0.20 – 0.70	-0.03 – 0.10	0.273	0.273
pIvP × Indiv	-0.18	-0.03	-0.56 – 0.20	-0.09 – 0.03	0.357	0.357
Observations	1307
R2 / R2 adjusted	0.105 / 0.095

plot.2 <- lm(TOT_Nuc_endorse ~ (pDvRxfDvR + pDvR + pIvP + fDvR + fCvP + bind+Indiv + pDvRxfCVP + pIVPxfDvR + pIVPxfCvP + fDvRxbind + fCvpxbind + fDvRxindiv + fCvpindiv) , data = sub)
summary(plot.1)

## 
## Call:
## lm(formula = TOT_Nuc_endorse ~ (pDvRxfDvR + pDvR + pIvP + fDvR + 
##     fCvP + bind + Indiv + pDvRxfCVP + pIVPxfDvR + pIVPxfCvP + 
##     fDvRxbind + fCvpxbind + fDvRxindiv + fCvpindiv + pDvRxbind + 
##     pDvRxindiv + pIvpxbind + pIvpxindiv), data = sub)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.9316 -0.9505 -0.0899  0.9481  4.4593 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.303759   0.340659  -3.827 0.000136 ***
## pDvRxfDvR    0.520732   0.254786   2.044 0.041177 *  
## pDvR         1.675570   0.835143   2.006 0.045030 *  
## pIvP        -0.021133   0.729589  -0.029 0.976896    
## fDvR        -0.934301   0.785019  -1.190 0.234202    
## fCvP        -1.246023   0.697384  -1.787 0.074219 .  
## bind         0.512891   0.089077   5.758 1.06e-08 ***
## Indiv       -0.303709   0.093139  -3.261 0.001140 ** 
## pDvRxfCVP   -0.096321   0.222651  -0.433 0.665372    
## pIVPxfDvR    0.107201   0.223999   0.479 0.632319    
## pIVPxfCvP   -0.094876   0.192329  -0.493 0.621887    
## fDvRxbind   -0.235413   0.188114  -1.251 0.211002    
## fCvpxbind    0.348340   0.163108   2.136 0.032897 *  
## fDvRxindiv   0.429119   0.204870   2.095 0.036403 *  
## fCvpindiv    0.007918   0.184203   0.043 0.965718    
## pDvRxbind   -0.553685   0.221306  -2.502 0.012476 *  
## pDvRxindiv   0.239992   0.231324   1.037 0.299711    
## pIvpxbind    0.166548   0.187263   0.889 0.373965    
## pIvpxindiv  -0.187177   0.196391  -0.953 0.340726    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.394 on 1288 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.1121, Adjusted R-squared:  0.09972 
## F-statistic: 9.037 on 18 and 1288 DF,  p-value: < 2.2e-16

tab_model(plot.2, show.std = T)

	Dependent variable
Predictors	Estimates	std. Beta	CI	standardized CI	p
(Intercept)	-1.48	0.00	-2.12 – -0.85	-0.05 – 0.05	<0.001
pDvRxfDvR	0.55	0.07	0.05 – 1.05	0.01 – 0.13	0.031
pDvR	0.66	0.18	0.46 – 0.87	0.12 – 0.23	<0.001
pIvP	-0.15	-0.04	-0.33 – 0.03	-0.10 – 0.01	0.107
fDvR	-0.93	-0.26	-2.47 – 0.61	-0.68 – 0.17	0.238
fCvP	-1.16	-0.37	-2.51 – 0.20	-0.81 – 0.06	0.094
bind	0.62	0.24	0.47 – 0.77	0.18 – 0.30	<0.001
Indiv	-0.36	-0.13	-0.53 – -0.19	-0.18 – -0.07	<0.001
pDvRxfCVP	-0.10	-0.01	-0.53 – 0.34	-0.07 – 0.05	0.661
pIVPxfDvR	0.09	0.01	-0.35 – 0.53	-0.04 – 0.07	0.674
pIVPxfCvP	-0.10	-0.01	-0.48 – 0.27	-0.07 – 0.04	0.589
fDvRxbind	-0.24	-0.22	-0.61 – 0.13	-0.57 – 0.12	0.206
fCvpxbind	0.38	0.42	0.06 – 0.70	0.07 – 0.77	0.019
fDvRxindiv	0.43	0.48	0.03 – 0.83	0.03 – 0.93	0.035
fCvpindiv	-0.04	-0.05	-0.40 – 0.32	-0.51 – 0.41	0.829
Observations	1307
R2 / R2 adjusted	0.108 / 0.098

fp1 <- plot_model(
  plot.2,
  type = "std",
  ci.style = "bar",
  axis.labels = c(
    "Interaction of Democrat vs. Republican identity with neutral frame vs. other conditions",
    "Individualizing Foundation Vigilance",
    "Binding Foundation Vigilance",
    "Framing condition (Neutral framed vs. Democrat or Republican framed)",
    "Framing condition (Democrat framed vs. Republican framed)",
    "Party Identity (Independents vs. other identities)",
    "Party Identity (Democrat vs. Republican)",
    "Interaction of Democrat vs. Republican identity with Democrat vs. Republican framed conditions"
  ),
  show.values = TRUE,
  value.size = 6.7,  # Bigger effect size text
  terms = c(
    "pDvR", "pIvP", "fDvR", "fCvP", "bind", "Indiv",
    "pDvRxfDvR", "pDvRxfCVP", "pIVP×fDvR", "pIVP×fCvP"
  ),
  value.offset = 0.35,
  wrap.labels = 45,
  axis.lim = c(-1, 1),
  p.threshold = c(0.05, 0.01, 0.001), vline.color = NA)

fp1 <- fp1 +
  theme_sjplot() +
  geom_point(shape = 19, size = 2.5, color = "darkred") +
  scale_color_manual(values = c("black", "black")) +
  labs(
    y = "Standardized Beta Value",
    title = "Study 1 - Effect Estimates in Model of Nuclear Endorsement"
  ) + 
  geom_vline(xintercept = 0, linetype = "solid", color = "black", size = 1.2) + # Thick solid line at 0
  theme(
    axis.text.y = element_text(size = 19, colour = "black"),
    axis.text.x = element_text(size = 19, colour = "black"),
    axis.title.x = element_text(size = 20),
    plot.title = element_text(size = 20, face = "bold")
  )

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

## 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.

ggsave("fp1.jpg", plot = fp1 , width = 9, height =9, dpi = 400)

fp1

Republicans vs. Independent

extra version of the model change party comparison groups

#party ID difs
m1 <- lm(TOT_Nuc_endorse ~ ((pIvR + DvIR)) * (fDvR + fCvP)+((bind+Indiv) * (fDvR + fCvP)),data = sub)
summary(m1)

## 
## Call:
## lm(formula = TOT_Nuc_endorse ~ ((pIvR + DvIR)) * (fDvR + fCvP) + 
##     ((bind + Indiv) * (fDvR + fCvP)), data = sub)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.1303 -0.9753 -0.0941  0.9290  4.4390 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.48423    0.32296  -4.596 4.73e-06 ***
## pIvR         0.47844    0.11663   4.102 4.35e-05 ***
## DvIR         0.42376    0.07930   5.344 1.07e-07 ***
## fDvR        -0.92706    0.78505  -1.181   0.2379    
## fCvP        -1.15705    0.69028  -1.676   0.0939 .  
## bind         0.62040    0.07663   8.096 1.30e-15 ***
## Indiv       -0.35886    0.08478  -4.233 2.47e-05 ***
## pIvR:fDvR    0.18077    0.28572   0.633   0.5270    
## pIvR:fCvP    0.05512    0.24740   0.223   0.8237    
## DvIR:fDvR    0.45986    0.19408   2.369   0.0180 *  
## DvIR:fCvP   -0.12529    0.16836  -0.744   0.4569    
## fDvR:bind   -0.23790    0.18808  -1.265   0.2061    
## fCvP:bind    0.38046    0.16223   2.345   0.0192 *  
## fDvR:Indiv   0.43166    0.20479   2.108   0.0352 *  
## fCvP:Indiv  -0.03934    0.18231  -0.216   0.8292    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.395 on 1292 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.1075, Adjusted R-squared:  0.09783 
## F-statistic: 11.12 on 14 and 1292 DF,  p-value: < 2.2e-16

etaSquared(m1)

##                  eta.sq  eta.sq.part
## pIvR       1.179920e-02 1.304792e-02
## DvIR       2.005655e-02 2.197847e-02
## fDvR       9.102612e-04 1.018864e-03
## fCvP       5.582815e-06 6.255230e-06
## bind       4.536930e-02 4.837497e-02
## Indiv      1.208381e-02 1.335845e-02
## pIvR:fDvR  2.765224e-04 3.097338e-04
## pIvR:fCvP  3.428291e-05 3.841084e-05
## DvIR:fDvR  3.878209e-03 4.326542e-03
## DvIR:fCvP  3.825515e-04 4.284465e-04
## fDvR:bind  1.105293e-03 1.236895e-03
## fCvP:bind  3.799384e-03 4.238977e-03
## fDvR:Indiv 3.069195e-03 3.427097e-03
## fCvP:Indiv 3.217356e-05 3.604759e-05

tab_model(m1)

	Dependent variable
Predictors	Estimates	CI	p
(Intercept)	-1.48	-2.12 – -0.85	<0.001
pIvR	0.48	0.25 – 0.71	<0.001
DvIR	0.42	0.27 – 0.58	<0.001
fDvR	-0.93	-2.47 – 0.61	0.238
fCvP	-1.16	-2.51 – 0.20	0.094
bind	0.62	0.47 – 0.77	<0.001
Indiv	-0.36	-0.53 – -0.19	<0.001
pIvR × fDvR	0.18	-0.38 – 0.74	0.527
pIvR × fCvP	0.06	-0.43 – 0.54	0.824
DvIR × fDvR	0.46	0.08 – 0.84	0.018
DvIR × fCvP	-0.13	-0.46 – 0.21	0.457
fDvR × bind	-0.24	-0.61 – 0.13	0.206
fCvP × bind	0.38	0.06 – 0.70	0.019
fDvR × Indiv	0.43	0.03 – 0.83	0.035
fCvP × Indiv	-0.04	-0.40 – 0.32	0.829
Observations	1307
R2 / R2 adjusted	0.108 / 0.098

#party ID dif +  morality of strike
m1 <- lm(TOT_Nuc_endorse ~ (((pIvR + DvIR)) * (fDvR + fCvP)*TOT_nuc_moral)+((bind+Indiv) * (fDvR + fCvP)),data = sub)
summary(m1)

## 
## Call:
## lm(formula = TOT_Nuc_endorse ~ (((pIvR + DvIR)) * (fDvR + fCvP) * 
##     TOT_nuc_moral) + ((bind + Indiv) * (fDvR + fCvP)), data = sub)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5399 -0.8560 -0.0431  0.8688  4.9592 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             -1.98419    0.27485  -7.219 8.93e-13 ***
## pIvR                     0.18042    0.10536   1.712  0.08707 .  
## DvIR                     0.19585    0.06934   2.824  0.00481 ** 
## fDvR                     0.15316    0.66917   0.229  0.81900    
## fCvP                    -0.43822    0.58654  -0.747  0.45512    
## TOT_nuc_moral            0.83549    0.03692  22.629  < 2e-16 ***
## bind                     0.33595    0.06612   5.081 4.31e-07 ***
## Indiv                   -0.03050    0.07312  -0.417  0.67664    
## pIvR:fDvR                0.21229    0.25924   0.819  0.41298    
## pIvR:fCvP                0.03009    0.22251   0.135  0.89245    
## DvIR:fDvR                0.33671    0.16995   1.981  0.04778 *  
## DvIR:fCvP               -0.03304    0.14702  -0.225  0.82222    
## pIvR:TOT_nuc_moral      -0.03350    0.09562  -0.350  0.72613    
## DvIR:TOT_nuc_moral      -0.01262    0.07074  -0.178  0.85848    
## fDvR:TOT_nuc_moral       0.09707    0.09262   1.048  0.29479    
## fCvP:TOT_nuc_moral      -0.09291    0.07639  -1.216  0.22411    
## fDvR:bind               -0.30532    0.16153  -1.890  0.05895 .  
## fCvP:bind                0.40567    0.14063   2.885  0.00398 ** 
## fDvR:Indiv               0.21371    0.17796   1.201  0.23002    
## fCvP:Indiv              -0.24691    0.15608  -1.582  0.11391    
## pIvR:fDvR:TOT_nuc_moral -0.19837    0.24095  -0.823  0.41051    
## pIvR:fCvP:TOT_nuc_moral  0.11091    0.19683   0.563  0.57322    
## DvIR:fDvR:TOT_nuc_moral -0.18412    0.17348  -1.061  0.28874    
## DvIR:fCvP:TOT_nuc_moral  0.01590    0.14988   0.106  0.91551    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.172 on 1283 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.3744, Adjusted R-squared:  0.3632 
## F-statistic: 33.39 on 23 and 1283 DF,  p-value: < 2.2e-16

etaSquared(m1)

##                               eta.sq  eta.sq.part
## pIvR                    1.372286e-03 2.188795e-03
## DvIR                    3.870057e-03 6.148247e-03
## fDvR                    8.933332e-04 1.425956e-03
## fCvP                    3.397511e-04 5.427969e-04
## TOT_nuc_moral           2.642148e-01 2.969367e-01
## bind                    1.251768e-02 1.961697e-02
## Indiv                   5.171911e-05 8.266610e-05
## pIvR:fDvR               1.715731e-04 2.741843e-04
## pIvR:fCvP               4.720701e-05 7.545465e-05
## DvIR:fDvR               1.682188e-03 2.681764e-03
## DvIR:fCvP               2.157244e-05 3.448233e-05
## pIvR:TOT_nuc_moral      2.890567e-05 4.620355e-05
## DvIR:TOT_nuc_moral      4.992688e-06 7.980742e-06
## fDvR:TOT_nuc_moral      8.937354e-04 1.426597e-03
## fCvP:TOT_nuc_moral      7.647750e-04 1.220999e-03
## fDvR:bind               1.742147e-03 2.777086e-03
## fCvP:bind               4.057390e-03 6.443940e-03
## fDvR:Indiv              7.031648e-04 1.122746e-03
## fCvP:Indiv              1.220245e-03 1.946762e-03
## pIvR:fDvR:TOT_nuc_moral 3.304754e-04 5.279856e-04
## pIvR:fCvP:TOT_nuc_moral 1.548050e-04 2.473943e-04
## DvIR:fDvR:TOT_nuc_moral 5.492329e-04 8.771781e-04
## DvIR:fCvP:TOT_nuc_moral 5.490511e-06 8.776497e-06

tab_model(m1)

	Dependent variable
Predictors	Estimates	CI	p
(Intercept)	-1.98	-2.52 – -1.44	<0.001
pIvR	0.18	-0.03 – 0.39	0.087
DvIR	0.20	0.06 – 0.33	0.005
fDvR	0.15	-1.16 – 1.47	0.819
fCvP	-0.44	-1.59 – 0.71	0.455
TOT nuc moral	0.84	0.76 – 0.91	<0.001
bind	0.34	0.21 – 0.47	<0.001
Indiv	-0.03	-0.17 – 0.11	0.677
pIvR × fDvR	0.21	-0.30 – 0.72	0.413
pIvR × fCvP	0.03	-0.41 – 0.47	0.892
DvIR × fDvR	0.34	0.00 – 0.67	0.048
DvIR × fCvP	-0.03	-0.32 – 0.26	0.822
pIvR × TOT nuc moral	-0.03	-0.22 – 0.15	0.726
DvIR × TOT nuc moral	-0.01	-0.15 – 0.13	0.858
fDvR × TOT nuc moral	0.10	-0.08 – 0.28	0.295
fCvP × TOT nuc moral	-0.09	-0.24 – 0.06	0.224
fDvR × bind	-0.31	-0.62 – 0.01	0.059
fCvP × bind	0.41	0.13 – 0.68	0.004
fDvR × Indiv	0.21	-0.14 – 0.56	0.230
fCvP × Indiv	-0.25	-0.55 – 0.06	0.114
(pIvR × fDvR) × TOT nuc moral	-0.20	-0.67 – 0.27	0.411
(pIvR × fCvP) × TOT nuc moral	0.11	-0.28 – 0.50	0.573
(DvIR × fDvR) × TOT nuc moral	-0.18	-0.52 – 0.16	0.289
(DvIR × fCvP) × TOT nuc moral	0.02	-0.28 – 0.31	0.916
Observations	1307
R2 / R2 adjusted	0.374 / 0.363

Democrats vs. Independents

extra version of the model change party comparison groups

#party ID dif
m1 <- lm(TOT_Nuc_endorse ~ (((pDvI + IvDR)) * (fDvR + fCvP))+((bind+Indiv) * (fDvR + fCvP)),data = sub)
summary(m1)

## 
## Call:
## lm(formula = TOT_Nuc_endorse ~ (((pDvI + IvDR)) * (fDvR + fCvP)) + 
##     ((bind + Indiv) * (fDvR + fCvP)), data = sub)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.1303 -0.9753 -0.0941  0.9290  4.4390 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.48423    0.32296  -4.596 4.73e-06 ***
## pDvI         0.18454    0.09197   2.007   0.0450 *  
## IvDR        -0.57071    0.10073  -5.666 1.81e-08 ***
## fDvR        -0.92706    0.78505  -1.181   0.2379    
## fCvP        -1.15705    0.69028  -1.676   0.0939 .  
## bind         0.62040    0.07663   8.096 1.30e-15 ***
## Indiv       -0.35886    0.08478  -4.233 2.47e-05 ***
## pDvI:fDvR    0.36947    0.22642   1.632   0.1030    
## pDvI:fCvP   -0.15285    0.19410  -0.787   0.4312    
## IvDR:fDvR   -0.36551    0.24585  -1.487   0.1373    
## IvDR:fCvP    0.02131    0.21447   0.099   0.9209    
## fDvR:bind   -0.23790    0.18808  -1.265   0.2061    
## fCvP:bind    0.38046    0.16223   2.345   0.0192 *  
## fDvR:Indiv   0.43166    0.20479   2.108   0.0352 *  
## fCvP:Indiv  -0.03934    0.18231  -0.216   0.8292    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.395 on 1292 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.1075, Adjusted R-squared:  0.09783 
## F-statistic: 11.12 on 14 and 1292 DF,  p-value: < 2.2e-16

etaSquared(m1)

##                  eta.sq  eta.sq.part
## pDvI       2.773052e-03 3.097444e-03
## IvDR       2.285038e-02 2.496359e-02
## fDvR       9.102612e-04 1.018864e-03
## fCvP       5.582815e-06 6.255230e-06
## bind       4.536930e-02 4.837497e-02
## Indiv      1.208381e-02 1.335845e-02
## pDvI:fDvR  1.839343e-03 2.056655e-03
## pDvI:fCvP  4.283605e-04 4.797266e-04
## IvDR:fDvR  1.526873e-03 1.707864e-03
## IvDR:fCvP  6.819076e-06 7.640381e-06
## fDvR:bind  1.105293e-03 1.236895e-03
## fCvP:bind  3.799384e-03 4.238977e-03
## fDvR:Indiv 3.069195e-03 3.427097e-03
## fCvP:Indiv 3.217356e-05 3.604759e-05

tab_model(m1)

	Dependent variable
Predictors	Estimates	CI	p
(Intercept)	-1.48	-2.12 – -0.85	<0.001
pDvI	0.18	0.00 – 0.36	0.045
IvDR	-0.57	-0.77 – -0.37	<0.001
fDvR	-0.93	-2.47 – 0.61	0.238
fCvP	-1.16	-2.51 – 0.20	0.094
bind	0.62	0.47 – 0.77	<0.001
Indiv	-0.36	-0.53 – -0.19	<0.001
pDvI × fDvR	0.37	-0.07 – 0.81	0.103
pDvI × fCvP	-0.15	-0.53 – 0.23	0.431
IvDR × fDvR	-0.37	-0.85 – 0.12	0.137
IvDR × fCvP	0.02	-0.40 – 0.44	0.921
fDvR × bind	-0.24	-0.61 – 0.13	0.206
fCvP × bind	0.38	0.06 – 0.70	0.019
fDvR × Indiv	0.43	0.03 – 0.83	0.035
fCvP × Indiv	-0.04	-0.40 – 0.32	0.829
Observations	1307
R2 / R2 adjusted	0.108 / 0.098

#party ID dif +  morality of strike
m1 <- lm(TOT_Nuc_endorse ~ (((pDvI + IvDR)) * (fDvR + fCvP)*TOT_nuc_moral)+((bind+Indiv) * (fDvR + fCvP)),data = sub)
summary(m1)

## 
## Call:
## lm(formula = TOT_Nuc_endorse ~ (((pDvI + IvDR)) * (fDvR + fCvP) * 
##     TOT_nuc_moral) + ((bind + Indiv) * (fDvR + fCvP)), data = sub)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5399 -0.8560 -0.0431  0.8688  4.9592 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             -1.984189   0.274848  -7.219 8.93e-13 ***
## pDvI                     0.105644   0.077497   1.363  0.17305    
## IvDR                    -0.233241   0.092900  -2.511  0.01217 *  
## fDvR                     0.153158   0.669174   0.229  0.81900    
## fCvP                    -0.438225   0.586537  -0.747  0.45512    
## TOT_nuc_moral            0.835485   0.036921  22.629  < 2e-16 ***
## bind                     0.335955   0.066119   5.081 4.31e-07 ***
## Indiv                   -0.030500   0.073115  -0.417  0.67664    
## pDvI:fDvR                0.230562   0.190904   1.208  0.22737    
## pDvI:fCvP               -0.048086   0.163457  -0.294  0.76867    
## IvDR:fDvR               -0.327574   0.227931  -1.437  0.15091    
## IvDR:fCvP               -0.006046   0.196746  -0.031  0.97549    
## pDvI:TOT_nuc_moral       0.004135   0.085456   0.048  0.96142    
## IvDR:TOT_nuc_moral       0.031434   0.079902   0.393  0.69408    
## fDvR:TOT_nuc_moral       0.097069   0.092615   1.048  0.29479    
## fCvP:TOT_nuc_moral      -0.092912   0.076391  -1.216  0.22411    
## fDvR:bind               -0.305323   0.161528  -1.890  0.05895 .  
## fCvP:bind                0.405670   0.140631   2.885  0.00398 ** 
## fDvR:Indiv               0.213706   0.177958   1.201  0.23002    
## fCvP:Indiv              -0.246909   0.156079  -1.582  0.11391    
## pDvI:fDvR:TOT_nuc_moral -0.084937   0.211957  -0.401  0.68869    
## pDvI:fCvP:TOT_nuc_moral -0.039549   0.178972  -0.221  0.82514    
## IvDR:fDvR:TOT_nuc_moral  0.240837   0.199865   1.205  0.22843    
## IvDR:fCvP:TOT_nuc_moral -0.091133   0.165828  -0.550  0.58272    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.172 on 1283 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.3744, Adjusted R-squared:  0.3632 
## F-statistic: 33.39 on 23 and 1283 DF,  p-value: < 2.2e-16

etaSquared(m1)

##                               eta.sq  eta.sq.part
## pDvI                    8.963020e-04 1.430688e-03
## IvDR                    3.193829e-03 5.079400e-03
## fDvR                    8.933332e-04 1.425956e-03
## fCvP                    3.397511e-04 5.427969e-04
## TOT_nuc_moral           2.642148e-01 2.969367e-01
## bind                    1.251768e-02 1.961697e-02
## Indiv                   5.171911e-05 8.266610e-05
## pDvI:fDvR               6.978748e-04 1.114309e-03
## pDvI:fCvP               4.544501e-05 7.263852e-05
## IvDR:fDvR               5.431097e-04 8.674071e-04
## IvDR:fCvP               3.049006e-05 4.873596e-05
## pDvI:TOT_nuc_moral      1.796790e-06 2.872159e-06
## IvDR:TOT_nuc_moral      2.997561e-05 4.791368e-05
## fDvR:TOT_nuc_moral      8.937354e-04 1.426597e-03
## fCvP:TOT_nuc_moral      7.647750e-04 1.220999e-03
## fDvR:bind               1.742147e-03 2.777086e-03
## fCvP:bind               4.057390e-03 6.443940e-03
## fDvR:Indiv              7.031648e-04 1.122746e-03
## fCvP:Indiv              1.220245e-03 1.946762e-03
## pDvI:fDvR:TOT_nuc_moral 7.829897e-05 1.251451e-04
## pDvI:fCvP:TOT_nuc_moral 2.381026e-05 3.805922e-05
## IvDR:fDvR:TOT_nuc_moral 7.080008e-04 1.130459e-03
## IvDR:fCvP:TOT_nuc_moral 1.472636e-04 2.353453e-04

tab_model(m1)

	Dependent variable
Predictors	Estimates	CI	p
(Intercept)	-1.98	-2.52 – -1.44	<0.001
pDvI	0.11	-0.05 – 0.26	0.173
IvDR	-0.23	-0.42 – -0.05	0.012
fDvR	0.15	-1.16 – 1.47	0.819
fCvP	-0.44	-1.59 – 0.71	0.455
TOT nuc moral	0.84	0.76 – 0.91	<0.001
bind	0.34	0.21 – 0.47	<0.001
Indiv	-0.03	-0.17 – 0.11	0.677
pDvI × fDvR	0.23	-0.14 – 0.61	0.227
pDvI × fCvP	-0.05	-0.37 – 0.27	0.769
IvDR × fDvR	-0.33	-0.77 – 0.12	0.151
IvDR × fCvP	-0.01	-0.39 – 0.38	0.975
pDvI × TOT nuc moral	0.00	-0.16 – 0.17	0.961
IvDR × TOT nuc moral	0.03	-0.13 – 0.19	0.694
fDvR × TOT nuc moral	0.10	-0.08 – 0.28	0.295
fCvP × TOT nuc moral	-0.09	-0.24 – 0.06	0.224
fDvR × bind	-0.31	-0.62 – 0.01	0.059
fCvP × bind	0.41	0.13 – 0.68	0.004
fDvR × Indiv	0.21	-0.14 – 0.56	0.230
fCvP × Indiv	-0.25	-0.55 – 0.06	0.114
(pDvI × fDvR) × TOT nuc moral	-0.08	-0.50 – 0.33	0.689
(pDvI × fCvP) × TOT nuc moral	-0.04	-0.39 – 0.31	0.825
(IvDR × fDvR) × TOT nuc moral	0.24	-0.15 – 0.63	0.228
(IvDR × fCvP) × TOT nuc moral	-0.09	-0.42 – 0.23	0.583
Observations	1307
R2 / R2 adjusted	0.374 / 0.363

Simple effect models

Match framing condition match versus mismatch with party identity are coded hear for the purpose of using party ID dummy codes with out interfere with the interactions of interest.

dummy coded nuc models

mrep <- lm(TOT_Nuc_endorse ~ ((matchmismatch + othervsmatch) * (demd + in_d)), data = sub)
summary(mrep)

## 
## Call:
## lm(formula = TOT_Nuc_endorse ~ ((matchmismatch + othervsmatch) * 
##     (demd + in_d)), data = sub)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.7708 -0.9747  0.0253  1.0253  4.3125 
## 
## Coefficients: (2 not defined because of singularities)
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        -0.287854   0.088511  -3.252 0.001175 ** 
## matchmismatch       0.116071   0.213989   0.542 0.587624    
## othervsmatch       -0.001954   0.190168  -0.010 0.991802    
## demd               -0.864133   0.103804  -8.325  < 2e-16 ***
## in_d               -0.664734   0.175009  -3.798 0.000152 ***
## matchmismatch:demd  0.171112   0.252201   0.678 0.497591    
## matchmismatch:in_d        NA         NA      NA       NA    
## othervsmatch:demd   0.052719   0.221976   0.237 0.812308    
## othervsmatch:in_d         NA         NA      NA       NA    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.432 on 1301 degrees of freedom
## Multiple R-squared:  0.05436,    Adjusted R-squared:   0.05 
## F-statistic: 12.46 on 6 and 1301 DF,  p-value: 1.074e-13

etaSquared(mrep)

##                          eta.sq  eta.sq.part
## matchmismatch      3.244400e-03 3.419170e-03
## othervsmatch       1.019641e-04 1.078137e-04
## demd               5.019518e-02 5.040506e-02
## in_d               1.048632e-02 1.096750e-02
## matchmismatch:demd 3.345927e-04 3.537012e-04
## matchmismatch:in_d 0.000000e+00 0.000000e+00
## othervsmatch:demd  4.099871e-05 4.335359e-05
## othervsmatch:in_d  0.000000e+00 0.000000e+00

tab_model(mrep)

## Model matrix is rank deficient. Parameters `matchmismatch:in_d,
##   othervsmatch:in_d` were not estimable.

	Dependent variable
Predictors	Estimates	CI	p
(Intercept)	-0.29	-0.46 – -0.11	0.001
matchmismatch	0.12	-0.30 – 0.54	0.588
othervsmatch	-0.00	-0.38 – 0.37	0.992
demd	-0.86	-1.07 – -0.66	<0.001
in d	-0.66	-1.01 – -0.32	<0.001
matchmismatch × demd	0.17	-0.32 – 0.67	0.498
othervsmatch × demd	0.05	-0.38 – 0.49	0.812
Observations	1308
R2 / R2 adjusted	0.054 / 0.050

mdem <- lm(TOT_Nuc_endorse ~ ((matchmismatch + othervsmatch) * (repd + in_d)), data = sub)
summary(mdem)

## 
## Call:
## lm(formula = TOT_Nuc_endorse ~ ((matchmismatch + othervsmatch) * 
##     (repd + in_d)), data = sub)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.7708 -0.9747  0.0253  1.0253  4.3125 
## 
## Coefficients: (2 not defined because of singularities)
##                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        -1.15199    0.05423 -21.242   <2e-16 ***
## matchmismatch       0.28718    0.13347   2.152   0.0316 *  
## othervsmatch        0.05076    0.11450   0.443   0.6576    
## repd                0.86413    0.10380   8.325   <2e-16 ***
## in_d                0.16425    0.12070   1.361   0.1738    
## matchmismatch:repd -0.17111    0.25220  -0.678   0.4976    
## matchmismatch:in_d       NA         NA      NA       NA    
## othervsmatch:repd  -0.05272    0.22198  -0.237   0.8123    
## othervsmatch:in_d        NA         NA      NA       NA    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.432 on 1301 degrees of freedom
## Multiple R-squared:  0.05436,    Adjusted R-squared:   0.05 
## F-statistic: 12.46 on 6 and 1301 DF,  p-value: 1.074e-13

etaSquared(mdem)

##                          eta.sq  eta.sq.part
## matchmismatch      3.244400e-03 3.419170e-03
## othervsmatch       1.019641e-04 1.078137e-04
## repd               5.019518e-02 5.040506e-02
## in_d               1.346108e-03 1.421464e-03
## matchmismatch:repd 3.345927e-04 3.537012e-04
## matchmismatch:in_d 0.000000e+00 0.000000e+00
## othervsmatch:repd  4.099871e-05 4.335359e-05
## othervsmatch:in_d  0.000000e+00 0.000000e+00

tab_model(mrep,mdem)

## Model matrix is rank deficient. Parameters `matchmismatch:in_d,
##   othervsmatch:in_d` were not estimable.
## Model matrix is rank deficient. Parameters `matchmismatch:in_d,
##   othervsmatch:in_d` were not estimable.

	Dependent variable			Dependent variable
Predictors	Estimates	CI	p	Estimates	CI	p
(Intercept)	-0.29	-0.46 – -0.11	0.001	-1.15	-1.26 – -1.05	<0.001
matchmismatch	0.12	-0.30 – 0.54	0.588	0.29	0.03 – 0.55	0.032
othervsmatch	-0.00	-0.38 – 0.37	0.992	0.05	-0.17 – 0.28	0.658
demd	-0.86	-1.07 – -0.66	<0.001
in d	-0.66	-1.01 – -0.32	<0.001	0.16	-0.07 – 0.40	0.174
matchmismatch × demd	0.17	-0.32 – 0.67	0.498
othervsmatch × demd	0.05	-0.38 – 0.49	0.812
repd				0.86	0.66 – 1.07	<0.001
matchmismatch × repd				-0.17	-0.67 – 0.32	0.498
othervsmatch × repd				-0.05	-0.49 – 0.38	0.812
Observations	1308			1308
R2 / R2 adjusted	0.054 / 0.050			0.054 / 0.050

######################################################################################################

STUDY 2

data prep

The psychic numbing variables (strike ratings at different levels of casualties) only appear in in sample 4. So, we use and prepare that data set here.

Create varibales

NUC2 <-read.csv("Nuc1D.CSV")
NUC2$party.full <- NA
NUC2$party.full[NUC2$party == 1] <- "dem"
NUC2$party.full[NUC2$party == 2] <- "rep"
NUC2$party.full[NUC2$party_leaning == 2] <- "rep"
NUC2$party.full[NUC2$party_leaning == 1] <- "dem"
NUC2$party.full[NUC2$party_leaning == 3] <- "ind"
NUC2$party.full[NUC2$party == 3] <- "ind"

NUC[,86] <- 8-NUC[,86]

NUC2$TOT_Nuc_endorse <- (NUC2$TOT_Nuc_endorse - 4)
NUC2$TOT_nuc_moral   <- (NUC2$TOT_nuc_moral - 4)
NUC2$TOT_nuc_prosec  <- (NUC2$TOT_nuc_prosec -4)


NUC2$nuc_avg = (NUC2$TOT_Nuc_endorse + NUC2$TOT_nuc_moral)/2

NUC2$TOT_psychic_num_1mil   <- (NUC2$TOT_psychic_num_1mil - 4)
NUC2$TO_psychic_numb2_20k   <- (NUC2$TO_psychic_numb2_20k - 4)

#Binding 
NUC2$bind = rowMeans(subset(NUC2, select = c(Mor_P_1,Mor_P_2,Mor_P_3,Mor_L_2,Mor_Loy_1,Mor_Loy_3,Mor_A_1,Mor_A_2,Mor_A_3)), na.rm = T)

NUC2$Indiv = rowMeans(subset(NUC2, select = c(Mor_lib_1,Mor_Lib_3,Mor_Lib_3.1,Mor_C_1,Mor_C_2,Mor_C_3,Mor_F_1, Mor_F_2, Mor_F_3)), na.rm = T)

NUC2$match[NUC2$party.full == "dem" & NUC2$Condition == "dem"] <- "yes"
NUC2$match[NUC2$party.full == "rep" & NUC2$Condition == "rep"] <- "yes"
NUC2$match[NUC2$party.full == "dem" & NUC2$Condition == "rep"] <- "no"
NUC2$match[NUC2$party.full == "rep" & NUC2$Condition == "dem"] <- "no"
NUC2$match[NUC2$party.full == "dem" & NUC2$Condition == "control"] <- "control"
NUC2$match[NUC2$party.full == "rep" & NUC2$Condition == "control"] <- "control"
NUC2$match[NUC2$party.full == "ind" & NUC2$Condition == "rep"] <- "control"
NUC2$match[NUC2$party.full == "ind" & NUC2$Condition == "dem"] <- "control"
NUC2$match[NUC2$party.full == "ind" & NUC2$Condition == "control"] <- "control"

part group codes

#dem vs rep
NUC2$pDvR <- (-(1/2))*(NUC2$party.full == "dem") + (1/2)*(NUC2$party.full == "rep") + 0*(NUC2$party.full == "ind")

#ind vs part
NUC2$pIvP <- ((1/3))*(NUC2$party.full == "dem") + ((1/3))*(NUC2$party.full == "rep") + (-2/3)*(NUC2$party.full == "ind")

NUC2$fCvP <- (-2/3)*(NUC2$Condition == "control") + ((1/3))*(NUC2$Condition == "rep") + ((1/3))*(NUC2$Condition == "dem") 

#Dem vs rep
NUC2$fDvR <- 0*(NUC2$Condition == "control") + (-1/2)*(NUC2$Condition == "dem") + ((1/2))*(NUC2$Condition == "rep")

NUC2$demd <- (1)*(NUC2$party.full == "dem") + (0)*(NUC2$party.full == "rep") + 0*(NUC2$party.full == "ind")

NUC2$repd <- (0)*(NUC2$party.full == "dem") + (1)*(NUC2$party.full == "rep") + 0*(NUC2$party.full == "ind")

I set up the contrast codes for this dataset in the following sections

Here, I convert the sample 4 data to long form for multilevel models

NUC2<- dplyr::mutate(NUC2, ID = row_number())
df1 <- data.frame(NUC2$ID,NUC2$TO_psychic_numb2_20k, NUC2$TOT_Nuc_endorse, NUC2$TOT_psychic_num_1mil, NUC2$Condition, NUC2$party.full,NUC2$bind, NUC2$Indiv, NUC2$match, NUC2$nuc_avg)
d2 <- melt(df1, id.vars=c("NUC2.ID", "NUC2.Condition", "NUC2.party.full","NUC2.bind", "NUC2.Indiv", "NUC2.match","NUC2.nuc_avg"))

#rename(d2, c("NUC2.ID"="ID", "NUC2.Condition"= "condition", "NUC2.party.full" = "party.full", "variable" = "deaths", "value" = "endorsement"))

I set up the contrast codes for this dataset in the following sections

##partisan contrast
#set 1
#dem vs rep
d2$pDvR <- (-(1/2))*(d2$NUC2.party.full == "dem") + (1/2)*(d2$NUC2.party.full == "rep") + 0*(d2$NUC2.party.full == "ind")

#ind vs part
d2$pIvP <- (-(1/3))*(d2$NUC2.party.full == "dem") + (-(1/3))*(d2$NUC2.party.full == "rep") + (2/3)*(d2$NUC2.party.full == "ind")

#condition
#Control vs Partisan 
d2$fCvP <- (-2/3)*(d2$NUC2.Condition == "control") + ((1/3))*(d2$NUC2.Condition == "rep") + ((1/3))*(d2$NUC2.Condition == "dem") 

#Dem vs rep
d2$fDvR <- 0*(d2$NUC2.Condition == "control") + (-1/2)*(d2$NUC2.Condition == "dem") + ((1/2))*(d2$NUC2.Condition == "rep")

# Partisan match vs. no match
d2$matchvsmismatch <- 0*(d2$NUC2.match == "control") + (1/2)*(d2$NUC2.match == "yes") + (-(1/2))*(d2$NUC2.match == "no") 

#control vs partisan framed
d2$othervsmatch <- (2/3)*(d2$NUC2.match == "control") + (-(1/3))*(d2$NUC2.match == "yes") + (-(1/3))*(d2$NUC2.match == "no") 

Psychic numbing dummy code

I generate dummy codes for the long form of the data set with level of causalties as a nominal variable and endorsement as a repeated measure

# SET 1
#mil dummy
d2$mild <- (1)*(d2$variable == "NUC2.TOT_psychic_num_1mil") + (0)*(d2$variable == " NUC2.TOT_Nuc_endorse") + 0*(d2$variable=="NUC2.TO_psychic_numb2_20k")

#20k dummy
d2$d20k <- (0)*(d2$variable == "NUC2.TOT_psychic_num_1mil") + (0)*(d2$variable == "NUC2.TOT_Nuc_endorse") + (1)*(d2$variable =="NUC2.TO_psychic_numb2_20k")

d2$dum100k <- (1)*(d2$variable == "NUC2.TOT_psychic_num_1mil") + (0)*(d2$variable == "NUC2.TOT_Nuc_endorse") + (0)*(d2$variable =="NUC2.TO_psychic_numb2_20k")

alties as a nominal variable and endorsement as a repeated measure

# SET 1
d2$lin <- (1/2)*(d2$variable == "NUC2.TOT_psychic_num_1mil") + (0)*(d2$variable == "    NUC2.TOT_Nuc_endorse") + (-1/2)*(d2$variable=="NUC2.TO_psychic_numb2_20k")

d2$quad<- (-1/3)*(d2$variable == "NUC2.TOT_psychic_num_1mil") + (2/3)*(d2$variable == " NUC2.TOT_Nuc_endorse") + (-1/3)*(d2$variable=="NUC2.TO_psychic_numb2_20k")

Dummy codes for simple effects by party ID

#rep dummy code
#demd + in_d = republicans as intercept 
#demd + repd = indepents as intercept
#repd + in_d = Democrats as intercept

d2$demd <- (1)*(d2$NUC2.party.full == "dem") + (0)*(d2$NUC2.party.full == "rep") + 0*(d2$NUC2.party.full == "ind")

d2$in_d <- (0)*(d2$NUC2.party.full == "dem") + (0)*(d2$NUC2.party.full == "rep") + 1*(d2$NUC2.party.full == "ind")

d2$repd <- (0)*(d2$NUC2.party.full == "dem") + (1)*(d2$NUC2.party.full == "rep") + 0*(d2$NUC2.party.full == "ind")

# match vs mismatch codes for simple effect models
d2$yn[d2$matchvsmismatch == -.5] <- "Condition Identity Mismatch"
d2$yn[d2$matchvsmismatch == .5] <- "Condition Identity Match"
d2$yn[d2$matchvsmismatch == 0] <- "Control"

MAIN STUDY 2 MODEL - full scope models

m1.2 <- lmer(d2$value~ (lin+quad) + ((pDvR + pIvP)*(fDvR + fCvP)) + (NUC2.bind+ NUC2.Indiv)*(fDvR + fCvP) + lin*((pDvR + pIvP)*(fDvR + fCvP)) + (1|NUC2.ID),data = d2)
summary(m1.2)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## d2$value ~ (lin + quad) + ((pDvR + pIvP) * (fDvR + fCvP)) + (NUC2.bind +  
##     NUC2.Indiv) * (fDvR + fCvP) + lin * ((pDvR + pIvP) * (fDvR +  
##     fCvP)) + (1 | NUC2.ID)
##    Data: d2
## 
## REML criterion at convergence: 9456.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.6189 -0.4843 -0.1048  0.4083  5.2690 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  NUC2.ID  (Intercept) 1.7545   1.3246  
##  Residual             0.7206   0.8489  
## Number of obs: 2931, groups:  NUC2.ID, 977
## 
## Fixed effects:
##                   Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)       -1.24736    0.34491  969.99311  -3.616 0.000314 ***
## lin               -0.92211    0.04105 1944.00002 -22.464  < 2e-16 ***
## quad               0.30553    0.09978 1944.00002   3.062 0.002229 ** 
## pDvR               0.63567    0.12210  961.99993   5.206 2.36e-07 ***
## pIvP              -0.10327    0.10359  961.99993  -0.997 0.319078    
## fDvR              -0.36044    0.87124  961.99994  -0.414 0.679182    
## fCvP               0.06891    0.70497  961.99982   0.098 0.922152    
## NUC2.bind          0.66650    0.07859  961.99985   8.481  < 2e-16 ***
## NUC2.Indiv        -0.63289    0.09719  961.99980  -6.512 1.19e-10 ***
## pDvR:fDvR          0.76498    0.29933  961.99993   2.556 0.010752 *  
## pDvR:fCvP         -0.39417    0.25881  961.99993  -1.523 0.128079    
## pIvP:fDvR         -0.01448    0.24945  961.99993  -0.058 0.953732    
## pIvP:fCvP          0.31896    0.22341  961.99994   1.428 0.153710    
## fDvR:NUC2.bind     0.25652    0.19063  961.99986   1.346 0.178733    
## fCvP:NUC2.bind    -0.02017    0.16831  961.99985  -0.120 0.904623    
## fDvR:NUC2.Indiv   -0.12060    0.23475  961.99985  -0.514 0.607568    
## fCvP:NUC2.Indiv   -0.06883    0.20900  961.99983  -0.329 0.741974    
## lin:pDvR          -0.20538    0.09973 1944.00002  -2.059 0.039587 *  
## lin:pIvP           0.05012    0.08778 1944.00002   0.571 0.568053    
## lin:fDvR          -0.10861    0.10000 1944.00002  -1.086 0.277542    
## lin:fCvP           0.18536    0.08755 1944.00002   2.117 0.034370 *  
## lin:pDvR:fDvR     -0.49096    0.24575 1944.00002  -1.998 0.045877 *  
## lin:pDvR:fCvP      0.14534    0.21028 1944.00002   0.691 0.489549    
## lin:pIvP:fDvR     -0.13569    0.21142 1944.00002  -0.642 0.521069    
## lin:pIvP:fCvP      0.24348    0.18926 1944.00002   1.287 0.198412    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 25 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

tab_model(m1.2)

	d2$value
Predictors	Estimates	CI	p
(Intercept)	-1.25	-1.92 – -0.57	<0.001
lin	-0.92	-1.00 – -0.84	<0.001
quad	0.31	0.11 – 0.50	0.002
pDvR	0.64	0.40 – 0.88	<0.001
pIvP	-0.10	-0.31 – 0.10	0.319
fDvR	-0.36	-2.07 – 1.35	0.679
fCvP	0.07	-1.31 – 1.45	0.922
NUC2 bind	0.67	0.51 – 0.82	<0.001
NUC2 Indiv	-0.63	-0.82 – -0.44	<0.001
pDvR × fDvR	0.76	0.18 – 1.35	0.011
pDvR × fCvP	-0.39	-0.90 – 0.11	0.128
pIvP × fDvR	-0.01	-0.50 – 0.47	0.954
pIvP × fCvP	0.32	-0.12 – 0.76	0.153
fDvR × NUC2 bind	0.26	-0.12 – 0.63	0.179
fCvP × NUC2 bind	-0.02	-0.35 – 0.31	0.905
fDvR × NUC2 Indiv	-0.12	-0.58 – 0.34	0.607
fCvP × NUC2 Indiv	-0.07	-0.48 – 0.34	0.742
lin × pDvR	-0.21	-0.40 – -0.01	0.040
lin × pIvP	0.05	-0.12 – 0.22	0.568
lin × fDvR	-0.11	-0.30 – 0.09	0.277
lin × fCvP	0.19	0.01 – 0.36	0.034
(lin × pDvR) × fDvR	-0.49	-0.97 – -0.01	0.046
(lin × pDvR) × fCvP	0.15	-0.27 – 0.56	0.490
(lin × pIvP) × fDvR	-0.14	-0.55 – 0.28	0.521
(lin × pIvP) × fCvP	0.24	-0.13 – 0.61	0.198
Random Effects
σ2	0.72
τ00 NUC2.ID	1.75
ICC	0.71
N NUC2.ID	977
Observations	2931
Marginal R2 / Conditional R2	0.160 / 0.756

STUDY 2 PLOT

plot_model(m1.2, type = "pred", terms = c("lin","pDvR", "fDvR"))

## Warning: Using `$` in model formulas can produce unexpected results. Specify your
##   model using the `data` argument instead.
##   Try: value ~ (lin + quad) +
##   ((pDvR + pIvP) * (fDvR + fCvP)) + (NUC2.bind + NUC2.Indiv) * (fDvR +
##   fCvP) + lin * ((pDvR + pIvP) * (fDvR + fCvP)), data =
## Warning: Using `$` in model formulas can produce unexpected results. Specify your
##   model using the `data` argument instead.
##   Try: value ~ (lin + quad) +
##   ((pDvR + pIvP) * (fDvR + fCvP)) + (NUC2.bind + NUC2.Indiv) * (fDvR +
##   fCvP) + lin * ((pDvR + pIvP) * (fDvR + fCvP)), data =

#interaction terms as vareable for model 
d2$pDvRxfDvR    <-d2$pDvR*d2$fDvR
d2$pDvRxfCVP    <-d2$pDvR*d2$fCvP
d2$pIVPxfDvR    <-d2$pIvP*d2$fDvR
d2$pIVPxfCvP    <-d2$pIvP*d2$fCvP
d2$fDvRxbind    <-d2$NUC2.bind*d2$fDvR
d2$fCvpxbind    <-d2$NUC2.bind*d2$fCvP
d2$fDvRxindiv  <-d2$NUC2.Indiv*d2$fDvR
d2$fCvpindiv   <-d2$NUC2.Indiv*d2$fCvP
d2$pDvRxbind    <-d2$NUC2.bind*d2$pDvR
d2$pDvRxindiv  <-d2$NUC2.Indiv*d2$pDvR
d2$pIvpxbind    <-d2$NUC2.bind*d2$pIvP
d2$pIvpxindiv  <-d2$NUC2.Indiv*d2$pIvP
d2$linxfDvR    <-d2$lin*d2$fDvR
d2$linxfCVP    <-d2$lin*d2$fCvP
d2$pDvRxlin    <-d2$pDvR*d2$lin
d2$pIVPxlin    <-d2$pIvP*d2$lin
d2$linxpDvRxfDvR    <-d2$pDvR*d2$fDvR*d2$lin
d2$linxpDvRxfCVP    <-d2$pDvR*d2$fCvP*d2$lin
d2$linxpIvPxfDvR    <-d2$pIvP*d2$fDvR*d2$lin
d2$linxpIvPxfCVP    <-d2$pIvP*d2$fCvP*d2$lin

plot.1 <- lmer(value ~ (pDvRxfDvR +lin+ quad + pDvR + pIvP + fDvR + fCvP + NUC2.bind+ NUC2.Indiv + pDvRxfCVP + pIVPxfDvR + pIVPxfCvP + fDvRxbind + fCvpxbind + fDvRxindiv + fCvpindiv + pDvRxlin + pIVPxlin + linxfDvR + linxfCVP + linxpDvRxfDvR +linxpDvRxfCVP + linxpIvPxfDvR + linxpIvPxfCVP+ (1|NUC2.ID)) , data = d2)
summary(plot.1)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ (pDvRxfDvR + lin + quad + pDvR + pIvP + fDvR + fCvP +  
##     NUC2.bind + NUC2.Indiv + pDvRxfCVP + pIVPxfDvR + pIVPxfCvP +  
##     fDvRxbind + fCvpxbind + fDvRxindiv + fCvpindiv + pDvRxlin +  
##     pIVPxlin + linxfDvR + linxfCVP + linxpDvRxfDvR + linxpDvRxfCVP +  
##     linxpIvPxfDvR + linxpIvPxfCVP + (1 | NUC2.ID))
##    Data: d2
## 
## REML criterion at convergence: 9456.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.6189 -0.4843 -0.1048  0.4083  5.2690 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  NUC2.ID  (Intercept) 1.7545   1.3246  
##  Residual             0.7206   0.8489  
## Number of obs: 2931, groups:  NUC2.ID, 977
## 
## Fixed effects:
##                 Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)     -1.24736    0.34491  969.99307  -3.616 0.000314 ***
## pDvRxfDvR        0.76498    0.29933  961.99994   2.556 0.010752 *  
## lin             -0.92211    0.04105 1944.00002 -22.464  < 2e-16 ***
## quad             0.30553    0.09978 1944.00003   3.062 0.002229 ** 
## pDvR             0.63567    0.12210  961.99993   5.206 2.36e-07 ***
## pIvP            -0.10327    0.10359  961.99994  -0.997 0.319078    
## fDvR            -0.36044    0.87124  961.99995  -0.414 0.679182    
## fCvP             0.06891    0.70497  961.99997   0.098 0.922152    
## NUC2.bind        0.66650    0.07859  961.99990   8.481  < 2e-16 ***
## NUC2.Indiv      -0.63289    0.09719  961.99987  -6.512 1.19e-10 ***
## pDvRxfCVP       -0.39417    0.25881  961.99994  -1.523 0.128079    
## pIVPxfDvR       -0.01448    0.24945  961.99994  -0.058 0.953732    
## pIVPxfCvP        0.31896    0.22341  961.99994   1.428 0.153710    
## fDvRxbind        0.25652    0.19063  961.99995   1.346 0.178733    
## fCvpxbind       -0.02017    0.16831  961.99995  -0.120 0.904623    
## fDvRxindiv      -0.12060    0.23475  961.99994  -0.514 0.607568    
## fCvpindiv       -0.06883    0.20900  961.99997  -0.329 0.741974    
## pDvRxlin        -0.20538    0.09973 1944.00002  -2.059 0.039587 *  
## pIVPxlin         0.05012    0.08778 1944.00002   0.571 0.568053    
## linxfDvR        -0.10861    0.10000 1944.00002  -1.086 0.277542    
## linxfCVP         0.18536    0.08755 1944.00002   2.117 0.034370 *  
## linxpDvRxfDvR   -0.49096    0.24575 1944.00002  -1.998 0.045877 *  
## linxpDvRxfCVP    0.14534    0.21028 1944.00002   0.691 0.489549    
## linxpIvPxfDvR   -0.13569    0.21142 1944.00002  -0.642 0.521069    
## linxpIvPxfCVP    0.24348    0.18926 1944.00002   1.287 0.198412    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 25 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

tab_model(plot.1, show.std = T)

	value
Predictors	Estimates	std. Beta	CI	standardized CI	p
(Intercept)	-1.25	-0.00	-1.92 – -0.57	-0.05 – 0.05	<0.001
pDvRxfDvR	0.76	0.08	0.18 – 1.35	0.02 – 0.14	0.011
lin	-0.92	-0.22	-1.00 – -0.84	-0.24 – -0.20	<0.001
quad	0.31	0.03	0.11 – 0.50	0.01 – 0.05	0.002
pDvR	0.64	0.15	0.40 – 0.88	0.09 – 0.20	<0.001
pIvP	-0.10	-0.03	-0.31 – 0.10	-0.08 – 0.03	0.319
fDvR	-0.36	-0.09	-2.07 – 1.35	-0.49 – 0.32	0.679
fCvP	0.07	0.02	-1.31 – 1.45	-0.36 – 0.40	0.922
NUC2 bind	0.67	0.27	0.51 – 0.82	0.20 – 0.33	<0.001
NUC2 Indiv	-0.63	-0.20	-0.82 – -0.44	-0.26 – -0.14	<0.001
pDvRxfCVP	-0.39	-0.05	-0.90 – 0.11	-0.11 – 0.01	0.128
pIVPxfDvR	-0.01	-0.00	-0.50 – 0.47	-0.06 – 0.05	0.954
pIVPxfCvP	0.32	0.04	-0.12 – 0.76	-0.01 – 0.09	0.153
fDvRxbind	0.26	0.21	-0.12 – 0.63	-0.09 – 0.51	0.179
fCvpxbind	-0.02	-0.02	-0.35 – 0.31	-0.32 – 0.29	0.905
fDvRxindiv	-0.12	-0.11	-0.58 – 0.34	-0.55 – 0.32	0.607
fCvpindiv	-0.07	-0.07	-0.48 – 0.34	-0.52 – 0.37	0.742
pDvRxlin	-0.21	-0.02	-0.40 – -0.01	-0.04 – -0.00	0.040
pIVPxlin	0.05	0.01	-0.12 – 0.22	-0.01 – 0.02	0.568
linxfDvR	-0.11	-0.01	-0.30 – 0.09	-0.03 – 0.01	0.277
linxfCVP	0.19	0.02	0.01 – 0.36	0.00 – 0.04	0.034
linxpDvRxfDvR	-0.49	-0.02	-0.97 – -0.01	-0.04 – -0.00	0.046
linxpDvRxfCVP	0.15	0.01	-0.27 – 0.56	-0.01 – 0.03	0.490
linxpIvPxfDvR	-0.14	-0.01	-0.55 – 0.28	-0.02 – 0.01	0.521
linxpIvPxfCVP	0.24	0.01	-0.13 – 0.61	-0.01 – 0.03	0.198
Random Effects
σ2	0.72
τ00 NUC2.ID	1.75
ICC	0.71
N NUC2.ID	977
Observations	2931
Marginal R2 / Conditional R2	0.160 / 0.756

fp <- plot_model(
  plot.1,
  type = "std",
  ci.style = "bar",
  show.values = TRUE,
  value.size = 6,  # Bigger numeric labels
  axis.labels = c(
    "Three-way interaction of linear effect of casualties with Democrat vs. Republican identity and Democrat vs. Republican framed conditions",
    "Interaction of linear effect of casualties Democrat vs. Republican identity",
    "Independents vs. other identities x neutral frame vs. other conditions",
    "Independents vs. other identities x Democrat vs. Republican framed conditions",
    "Democrat vs. Republican identity x neutral frame vs. other conditions",
    "Individualizing Foundation Vigilance",
    "Binding Foundation Vigilance",
    "Framing condition (Neutral framed vs. Democrat or Republican framed)",
    "Framing condition (Democrat framed vs. Republican framed)",
    "Party Identity (Independents vs. other identities)",
    "Party Identity (Democrat vs. Republican)",
    "Linear",
    "Democrat vs. Republican identity x Democrat vs. Republican framed conditions"
  ),
  terms = c(
    "pDvRxfDvR", "lin", "pDvR", "pIvP", "fDvR", "fCvP",
    "NUC2.bind", "NUC2.Indiv", "pDvRxfCVP", "pIVPxfDvR",
    "pIVPxfCvP", "pDvRxlin", "linxpDvRxfDvR"
  ),
  value.offset = 0.35,
  wrap.labels = 48,
  axis.lim = c(-1, 1),
  p.threshold = c(0.05, 0.01, 0.001),
  vline.color = NA  # Remove default dashed 0-line
)

fp <- fp +
  theme_sjplot() +
  geom_point(shape = 19, size = 2, color = "darkred") +
  scale_color_manual(values = c("black", "black")) +
  labs(
    y = "Standardized Beta Values",
    title = "Study 2 - Effect Estimates in Model of Nuclear Endorsement"
  ) +
  geom_vline(xintercept = 0, linetype = "solid", color = "black", size = 1.2) + # Bold solid 0-line
  theme(
    axis.text.y = element_text(size = 18, colour = "black"),
    axis.text.x = element_text(size = 18, colour = "black"),
    axis.title.x = element_text(size = 19),
    plot.title = element_text(size = 16, face = "bold")
  )

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

fp

ggsave("fp2.jpg", plot = fp , width = 10, height =10, dpi = 400)

###axis.labels = c("Three-way interaction of linear effect of casualties with Democrat vs. Republican identity and Democrat vs. Republican framed conditions", "Interaction of linear effect of casualties Democrat vs. Republican identity ","Interaction of Independents vs. other identities with neutral frame vs. other conditions","Interaction of Independents vs. other identities with Democrat vs. Republican framed conditions","Interaction of Democrat vs. Republican identity with neutral frame vs. other conditions ","Interaction of Democrat vs. Republican identity with Democrat vs. Republican framed conditions","Individualizing Foundation Vigilance","Binding Foundation Vigilance ","Framing condition (Neutral framed vs. Democrat or Republican framed)", "Framing condition (Democrat framed vs. Republican framed)","Party Identity (Independents vs. other identities)","Party Identity (Democrat vs. Republican)","linear"),

part2<- d2[(d2$pIvP < .5 ),]

m1.2 <- lmer(d2$value~ (lin+quad)+ (matchvsmismatch)* ((pDvR) +(NUC2.bind+ NUC2.Indiv))+ lin*(pDvR)+ (1|NUC2.ID),data = d2)
summary(m1.2)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: d2$value ~ (lin + quad) + (matchvsmismatch) * ((pDvR) + (NUC2.bind +  
##     NUC2.Indiv)) + lin * (pDvR) + (1 | NUC2.ID)
##    Data: d2
## 
## REML criterion at convergence: 9451.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.5412 -0.5119 -0.1093  0.4106  5.3963 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  NUC2.ID  (Intercept) 1.7623   1.3275  
##  Residual             0.7224   0.8499  
## Number of obs: 2931, groups:  NUC2.ID, 977
## 
## Fixed effects:
##                              Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)                  -1.18790    0.34215  977.20359  -3.472 0.000539
## lin                          -0.92011    0.04101 1951.00003 -22.437  < 2e-16
## quad                          0.30553    0.09991 1951.00003   3.058 0.002258
## matchvsmismatch               0.87305    1.05591  968.99989   0.827 0.408541
## pDvR                          0.60210    0.11844  968.99993   5.083 4.45e-07
## NUC2.bind                     0.65716    0.07767  968.99985   8.461  < 2e-16
## NUC2.Indiv                   -0.64052    0.09685  968.99981  -6.614 6.19e-11
## matchvsmismatch:pDvR         -0.03255    0.30356  968.99993  -0.107 0.914644
## matchvsmismatch:NUC2.bind     0.21828    0.22466  968.99986   0.972 0.331481
## matchvsmismatch:NUC2.Indiv   -0.30419    0.28711  968.99979  -1.059 0.289649
## lin:pDvR                     -0.19477    0.09696 1951.00003  -2.009 0.044706
##                               
## (Intercept)                ***
## lin                        ***
## quad                       ** 
## matchvsmismatch               
## pDvR                       ***
## NUC2.bind                  ***
## NUC2.Indiv                 ***
## matchvsmismatch:pDvR          
## matchvsmismatch:NUC2.bind     
## matchvsmismatch:NUC2.Indiv    
## lin:pDvR                   *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) lin    quad   mtchvs pDvR   NUC2.b NUC2.I mtc:DR mt:NUC2.
## lin          0.000                                                          
## quad         0.065  0.000                                                   
## mtchvsmsmtc -0.060  0.000  0.000                                            
## pDvR         0.058  0.000  0.000 -0.030                                     
## NUC2.bind   -0.222  0.000  0.000  0.005 -0.257                              
## NUC2.Indiv  -0.734  0.000  0.000  0.043  0.167 -0.486                       
## mtchvsms:DR -0.031  0.000  0.000  0.090 -0.035  0.019  0.010                
## mtchv:NUC2.  0.011  0.000  0.000 -0.262  0.022 -0.015  0.003 -0.297         
## mtch:NUC2.I  0.042  0.000  0.000 -0.749  0.008  0.007 -0.039  0.165 -0.432  
## lin:pDvR     0.000  0.347  0.000  0.000  0.000  0.000  0.000  0.000  0.000  
##             m:NUC2.I
## lin                 
## quad                
## mtchvsmsmtc         
## pDvR                
## NUC2.bind           
## NUC2.Indiv          
## mtchvsms:DR         
## mtchv:NUC2.         
## mtch:NUC2.I         
## lin:pDvR     0.000

tab_model(m1.2)

	d2$value
Predictors	Estimates	CI	p
(Intercept)	-1.19	-1.86 – -0.52	0.001
lin	-0.92	-1.00 – -0.84	<0.001
quad	0.31	0.11 – 0.50	0.002
matchvsmismatch	0.87	-1.20 – 2.94	0.408
pDvR	0.60	0.37 – 0.83	<0.001
NUC2 bind	0.66	0.50 – 0.81	<0.001
NUC2 Indiv	-0.64	-0.83 – -0.45	<0.001
matchvsmismatch × pDvR	-0.03	-0.63 – 0.56	0.915
matchvsmismatch × NUC2 bind	0.22	-0.22 – 0.66	0.331
matchvsmismatch × NUC2 Indiv	-0.30	-0.87 – 0.26	0.289
lin × pDvR	-0.19	-0.38 – -0.00	0.045
Random Effects
σ2	0.72
τ00 NUC2.ID	1.76
ICC	0.71
N NUC2.ID	977
Observations	2931
Marginal R2 / Conditional R2	0.153 / 0.754

m1.2 <- lmer(value~ (lin+quad) + ((pDvR + pIvP)*(fDvR + fCvP)) * (NUC2.bind + NUC2.Indiv) + lin*((pDvR + pIvP)*(fDvR + fCvP)) + (1|NUC2.ID),data = d2)
summary(m1.2)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ (lin + quad) + ((pDvR + pIvP) * (fDvR + fCvP)) * (NUC2.bind +  
##     NUC2.Indiv) + lin * ((pDvR + pIvP) * (fDvR + fCvP)) + (1 |      NUC2.ID)
##    Data: d2
## 
## REML criterion at convergence: 9439.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.5471 -0.4874 -0.1055  0.4019  5.2578 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  NUC2.ID  (Intercept) 1.7373   1.3181  
##  Residual             0.7206   0.8489  
## Number of obs: 2931, groups:  NUC2.ID, 977
## 
## Fixed effects:
##                        Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)            -1.42956    0.36971  956.86560  -3.867 0.000118 ***
## lin                    -0.92211    0.04105 1944.00002 -22.464  < 2e-16 ***
## quad                    0.30553    0.09978 1944.00002   3.062 0.002229 ** 
## pDvR                   -0.06407    0.91331  950.00007  -0.070 0.944089    
## pIvP                   -1.49814    0.77467  949.99988  -1.934 0.053422 .  
## fDvR                   -0.52068    0.94272  950.00010  -0.552 0.580860    
## fCvP                    0.23066    0.74778  949.99988   0.308 0.757796    
## NUC2.bind               0.66736    0.09148  949.99989   7.296 6.27e-13 ***
## NUC2.Indiv             -0.58143    0.10749  950.00001  -5.409 8.01e-08 ***
## pDvR:fDvR               0.57981    2.37806  950.00015   0.244 0.807424    
## pDvR:fCvP               0.02849    1.80717  949.99984   0.016 0.987425    
## pIvP:fDvR               1.85096    1.93833  949.99980   0.955 0.339859    
## pIvP:fCvP               2.16408    1.60724  949.99988   1.346 0.178476    
## pDvR:NUC2.bind         -0.12596    0.23108  949.99995  -0.545 0.585812    
## pDvR:NUC2.Indiv         0.30415    0.27247  950.00019   1.116 0.264579    
## pIvP:NUC2.bind          0.37089    0.18778  949.99991   1.975 0.048549 *  
## pIvP:NUC2.Indiv         0.03893    0.21979  949.99992   0.177 0.859465    
## fDvR:NUC2.bind          0.45568    0.21722  949.99993   2.098 0.036191 *  
## fDvR:NUC2.Indiv        -0.26913    0.26085  950.00013  -1.032 0.302454    
## fCvP:NUC2.bind          0.10513    0.19980  949.99985   0.526 0.598896    
## fCvP:NUC2.Indiv        -0.23977    0.23012  949.99986  -1.042 0.297697    
## lin:pDvR               -0.20538    0.09973 1944.00002  -2.059 0.039587 *  
## lin:pIvP                0.05012    0.08778 1944.00002   0.571 0.568053    
## lin:fDvR               -0.10861    0.10000 1944.00002  -1.086 0.277542    
## lin:fCvP                0.18536    0.08755 1944.00002   2.117 0.034370 *  
## pDvR:fDvR:NUC2.bind     1.05128    0.54800  949.99992   1.918 0.055358 .  
## pDvR:fDvR:NUC2.Indiv   -0.89176    0.66668  950.00009  -1.338 0.181346    
## pDvR:fCvP:NUC2.bind     0.89725    0.50532  949.99982   1.776 0.076116 .  
## pDvR:fCvP:NUC2.Indiv   -0.93273    0.57861  949.99979  -1.612 0.107294    
## pIvP:fDvR:NUC2.bind    -0.24936    0.44660  949.99986  -0.558 0.576739    
## pIvP:fDvR:NUC2.Indiv   -0.23411    0.52824  949.99981  -0.443 0.657726    
## pIvP:fCvP:NUC2.bind    -1.02937    0.40961  949.99986  -2.513 0.012133 *  
## pIvP:fCvP:NUC2.Indiv    0.42404    0.47485  949.99992   0.893 0.372082    
## lin:pDvR:fDvR          -0.49096    0.24575 1944.00002  -1.998 0.045877 *  
## lin:pDvR:fCvP           0.14534    0.21028 1944.00002   0.691 0.489549    
## lin:pIvP:fDvR          -0.13569    0.21142 1944.00002  -0.642 0.521069    
## lin:pIvP:fCvP           0.24348    0.18926 1944.00002   1.287 0.198412    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 37 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

tab_model(m1.2, show.std = T)

	value
Predictors	Estimates	std. Beta	CI	standardized CI	p	std. p
(Intercept)	-1.43	0.01	-2.15 – -0.70	-0.05 – 0.06	<0.001	0.819
lin	-0.92	-0.21	-1.00 – -0.84	-0.23 – -0.20	<0.001	<0.001
quad	0.31	0.03	0.11 – 0.50	0.01 – 0.05	0.002	0.002
pDvR	-0.06	0.17	-1.85 – 1.73	0.10 – 0.23	0.944	<0.001
pIvP	-1.50	-0.03	-3.02 – 0.02	-0.09 – 0.02	0.053	0.228
fDvR	-0.52	-0.04	-2.37 – 1.33	-0.09 – 0.02	0.581	0.160
fCvP	0.23	-0.08	-1.24 – 1.70	-0.13 – -0.02	0.758	0.005
NUC2 bind	0.67	0.27	0.49 – 0.85	0.20 – 0.33	<0.001	<0.001
NUC2 Indiv	-0.58	-0.20	-0.79 – -0.37	-0.26 – -0.14	<0.001	<0.001
pDvR × fDvR	0.58	0.05	-4.08 – 5.24	-0.01 – 0.11	0.807	0.088
pDvR × fCvP	0.03	-0.07	-3.51 – 3.57	-0.13 – -0.01	0.987	0.019
pIvP × fDvR	1.85	0.01	-1.95 – 5.65	-0.04 – 0.07	0.340	0.642
pIvP × fCvP	2.16	0.05	-0.99 – 5.32	-0.00 – 0.11	0.178	0.059
pDvR × NUC2 bind	-0.13	-0.02	-0.58 – 0.33	-0.09 – 0.05	0.586	0.595
pDvR × NUC2 Indiv	0.30	0.04	-0.23 – 0.84	-0.03 – 0.10	0.264	0.267
pIvP × NUC2 bind	0.37	0.07	0.00 – 0.74	0.00 – 0.13	0.048	0.048
pIvP × NUC2 Indiv	0.04	0.01	-0.39 – 0.47	-0.06 – 0.07	0.859	0.865
fDvR × NUC2 bind	0.46	0.05	0.03 – 0.88	-0.01 – 0.11	0.036	0.104
fDvR × NUC2 Indiv	-0.27	-0.02	-0.78 – 0.24	-0.08 – 0.04	0.302	0.595
fCvP × NUC2 bind	0.11	0.00	-0.29 – 0.50	-0.06 – 0.07	0.599	0.891
fCvP × NUC2 Indiv	-0.24	-0.02	-0.69 – 0.21	-0.08 – 0.04	0.298	0.559
lin × pDvR	-0.21	-0.02	-0.40 – -0.01	-0.04 – -0.00	0.040	0.037
lin × pIvP	0.05	0.01	-0.12 – 0.22	-0.01 – 0.02	0.568	0.575
lin × fDvR	-0.11	-0.00	-0.30 – 0.09	-0.02 – 0.02	0.277	0.751
lin × fCvP	0.19	0.02	0.01 – 0.36	-0.00 – 0.04	0.034	0.062
(pDvR × fDvR) × NUC2 bind	1.05	0.07	-0.02 – 2.13	-0.00 – 0.14	0.055	0.055
(pDvR × fDvR) × NUC2 Indiv	-0.89	-0.05	-2.20 – 0.42	-0.11 – 0.02	0.181	0.181
(pDvR × fCvP) × NUC2 bind	0.90	0.07	-0.09 – 1.89	-0.01 – 0.14	0.076	0.076
(pDvR × fCvP) × NUC2 Indiv	-0.93	-0.05	-2.07 – 0.20	-0.12 – 0.01	0.107	0.107
(pIvP × fDvR) × NUC2 bind	-0.25	-0.02	-1.13 – 0.63	-0.08 – 0.05	0.577	0.577
(pIvP × fDvR) × NUC2 Indiv	-0.23	-0.01	-1.27 – 0.80	-0.07 – 0.05	0.658	0.658
(pIvP × fCvP) × NUC2 bind	-1.03	-0.09	-1.83 – -0.23	-0.16 – -0.02	0.012	0.012
(pIvP × fCvP) × NUC2 Indiv	0.42	0.03	-0.51 – 1.36	-0.03 – 0.09	0.372	0.372
(lin × pDvR) × fDvR	-0.49	-0.02	-0.97 – -0.01	-0.04 – -0.00	0.046	0.046
(lin × pDvR) × fCvP	0.15	0.01	-0.27 – 0.56	-0.01 – 0.02	0.490	0.490
(lin × pIvP) × fDvR	-0.14	-0.01	-0.55 – 0.28	-0.02 – 0.01	0.521	0.521
(lin × pIvP) × fCvP	0.24	0.01	-0.13 – 0.61	-0.01 – 0.03	0.198	0.198
Random Effects
σ2	0.72
τ00 NUC2.ID	1.74
ICC	0.71
N NUC2.ID	977
Observations	2931
Marginal R2 / Conditional R2	0.173 / 0.758

m1.2 <- lmer(value ~ (lin+quad) + ((pDvR + pIvP)*(fDvR + fCvP)) + (NUC2.bind+ NUC2.Indiv)*(fDvR + fCvP) + lin*((pDvR + pIvP)*(fDvR + fCvP)) + (1|NUC2.ID),data = d2)
summary(m1.2)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ (lin + quad) + ((pDvR + pIvP) * (fDvR + fCvP)) + (NUC2.bind +  
##     NUC2.Indiv) * (fDvR + fCvP) + lin * ((pDvR + pIvP) * (fDvR +  
##     fCvP)) + (1 | NUC2.ID)
##    Data: d2
## 
## REML criterion at convergence: 9456.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.6189 -0.4843 -0.1048  0.4083  5.2690 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  NUC2.ID  (Intercept) 1.7545   1.3246  
##  Residual             0.7206   0.8489  
## Number of obs: 2931, groups:  NUC2.ID, 977
## 
## Fixed effects:
##                   Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)       -1.24736    0.34491  969.99311  -3.616 0.000314 ***
## lin               -0.92211    0.04105 1944.00002 -22.464  < 2e-16 ***
## quad               0.30553    0.09978 1944.00002   3.062 0.002229 ** 
## pDvR               0.63567    0.12210  961.99993   5.206 2.36e-07 ***
## pIvP              -0.10327    0.10359  961.99993  -0.997 0.319078    
## fDvR              -0.36044    0.87124  961.99994  -0.414 0.679182    
## fCvP               0.06891    0.70497  961.99982   0.098 0.922152    
## NUC2.bind          0.66650    0.07859  961.99985   8.481  < 2e-16 ***
## NUC2.Indiv        -0.63289    0.09719  961.99980  -6.512 1.19e-10 ***
## pDvR:fDvR          0.76498    0.29933  961.99993   2.556 0.010752 *  
## pDvR:fCvP         -0.39417    0.25881  961.99993  -1.523 0.128079    
## pIvP:fDvR         -0.01448    0.24945  961.99993  -0.058 0.953732    
## pIvP:fCvP          0.31896    0.22341  961.99994   1.428 0.153710    
## fDvR:NUC2.bind     0.25652    0.19063  961.99986   1.346 0.178733    
## fCvP:NUC2.bind    -0.02017    0.16831  961.99985  -0.120 0.904623    
## fDvR:NUC2.Indiv   -0.12060    0.23475  961.99985  -0.514 0.607568    
## fCvP:NUC2.Indiv   -0.06883    0.20900  961.99983  -0.329 0.741974    
## lin:pDvR          -0.20538    0.09973 1944.00002  -2.059 0.039587 *  
## lin:pIvP           0.05012    0.08778 1944.00002   0.571 0.568053    
## lin:fDvR          -0.10861    0.10000 1944.00002  -1.086 0.277542    
## lin:fCvP           0.18536    0.08755 1944.00002   2.117 0.034370 *  
## lin:pDvR:fDvR     -0.49096    0.24575 1944.00002  -1.998 0.045877 *  
## lin:pDvR:fCvP      0.14534    0.21028 1944.00002   0.691 0.489549    
## lin:pIvP:fDvR     -0.13569    0.21142 1944.00002  -0.642 0.521069    
## lin:pIvP:fCvP      0.24348    0.18926 1944.00002   1.287 0.198412    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation matrix not shown by default, as p = 25 > 12.
## Use print(x, correlation=TRUE)  or
##     vcov(x)        if you need it

tab_model(m1.2)

	value
Predictors	Estimates	CI	p
(Intercept)	-1.25	-1.92 – -0.57	<0.001
lin	-0.92	-1.00 – -0.84	<0.001
quad	0.31	0.11 – 0.50	0.002
pDvR	0.64	0.40 – 0.88	<0.001
pIvP	-0.10	-0.31 – 0.10	0.319
fDvR	-0.36	-2.07 – 1.35	0.679
fCvP	0.07	-1.31 – 1.45	0.922
NUC2 bind	0.67	0.51 – 0.82	<0.001
NUC2 Indiv	-0.63	-0.82 – -0.44	<0.001
pDvR × fDvR	0.76	0.18 – 1.35	0.011
pDvR × fCvP	-0.39	-0.90 – 0.11	0.128
pIvP × fDvR	-0.01	-0.50 – 0.47	0.954
pIvP × fCvP	0.32	-0.12 – 0.76	0.153
fDvR × NUC2 bind	0.26	-0.12 – 0.63	0.179
fCvP × NUC2 bind	-0.02	-0.35 – 0.31	0.905
fDvR × NUC2 Indiv	-0.12	-0.58 – 0.34	0.607
fCvP × NUC2 Indiv	-0.07	-0.48 – 0.34	0.742
lin × pDvR	-0.21	-0.40 – -0.01	0.040
lin × pIvP	0.05	-0.12 – 0.22	0.568
lin × fDvR	-0.11	-0.30 – 0.09	0.277
lin × fCvP	0.19	0.01 – 0.36	0.034
(lin × pDvR) × fDvR	-0.49	-0.97 – -0.01	0.046
(lin × pDvR) × fCvP	0.15	-0.27 – 0.56	0.490
(lin × pIvP) × fDvR	-0.14	-0.55 – 0.28	0.521
(lin × pIvP) × fCvP	0.24	-0.13 – 0.61	0.198
Random Effects
σ2	0.72
τ00 NUC2.ID	1.75
ICC	0.71
N NUC2.ID	977
Observations	2931
Marginal R2 / Conditional R2	0.160 / 0.756

rep <- lmer(value ~ (lin+quad) + ((matchvsmismatch + othervsmatch)*(demd + in_d)) + (1|NUC2.ID),data = d2)

## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients

summary(rep)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ (lin + quad) + ((matchvsmismatch + othervsmatch) * (demd +  
##     in_d)) + (1 | NUC2.ID)
##    Data: d2
## 
## REML criterion at convergence: 9518.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.5634 -0.5320 -0.1229  0.4064  5.3555 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  NUC2.ID  (Intercept) 1.9050   1.3802  
##  Residual             0.7235   0.8506  
## Number of obs: 2931, groups:  NUC2.ID, 977
## 
## Fixed effects:
##                        Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)            -0.97229    0.10461 1062.65404  -9.295  < 2e-16 ***
## lin                    -0.89150    0.03849 1952.00001 -23.165  < 2e-16 ***
## quad                    0.30553    0.09999 1952.00000   3.056  0.00228 ** 
## matchvsmismatch         0.46354    0.25087  969.99999   1.848  0.06494 .  
## othervsmatch            0.57477    0.21642  970.00000   2.656  0.00804 ** 
## demd                   -0.92377    0.12170  969.99999  -7.590 7.48e-14 ***
## in_d                   -1.01686    0.19704  970.00000  -5.161 2.98e-07 ***
## matchvsmismatch:demd   -0.06136    0.29990  969.99999  -0.205  0.83793    
## othervsmatch:demd      -0.44027    0.25662  969.99999  -1.716  0.08654 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) lin    quad   mtchvs othrvs demd   in_d   mtchv:
## lin          0.000                                                 
## quad         0.212  0.000                                          
## mtchvsmsmtc -0.053  0.000  0.000                                   
## othervsmtch -0.005  0.000  0.000  0.038                            
## demd        -0.821  0.000  0.000  0.045  0.005                     
## in_d        -0.503  0.000  0.000  0.000 -0.729  0.432              
## mtchvsmsmt:  0.044  0.000  0.000 -0.837 -0.032 -0.033  0.000       
## othrvsmtch:  0.005  0.000  0.000 -0.032 -0.843 -0.017  0.615  0.024
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients

tab_model(rep)

	value
Predictors	Estimates	CI	p
(Intercept)	-0.97	-1.18 – -0.77	<0.001
lin	-0.89	-0.97 – -0.82	<0.001
quad	0.31	0.11 – 0.50	0.002
matchvsmismatch	0.46	-0.03 – 0.96	0.065
othervsmatch	0.57	0.15 – 1.00	0.008
demd	-0.92	-1.16 – -0.69	<0.001
in d	-1.02	-1.40 – -0.63	<0.001
matchvsmismatch × demd	-0.06	-0.65 – 0.53	0.838
othervsmatch × demd	-0.44	-0.94 – 0.06	0.086
Random Effects
σ2	0.72
τ00 NUC2.ID	1.90
ICC	0.72
N NUC2.ID	977
Observations	2931
Marginal R2 / Conditional R2	0.103 / 0.753

dem <- lmer(value ~ (lin+quad) + ((matchvsmismatch + othervsmatch)*(repd+ in_d)) + (1|NUC2.ID),data = d2)

## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients

summary(dem)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ (lin + quad) + ((matchvsmismatch + othervsmatch) * (repd +  
##     in_d)) + (1 | NUC2.ID)
##    Data: d2
## 
## REML criterion at convergence: 9518.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.5634 -0.5320 -0.1229  0.4064  5.3555 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  NUC2.ID  (Intercept) 1.9050   1.3802  
##  Residual             0.7235   0.8506  
## Number of obs: 2931, groups:  NUC2.ID, 977
## 
## Fixed effects:
##                        Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)            -1.89606    0.06969 1194.33443 -27.206  < 2e-16 ***
## lin                    -0.89150    0.03849 1952.00001 -23.165  < 2e-16 ***
## quad                    0.30553    0.09999 1952.00001   3.056  0.00228 ** 
## matchvsmismatch         0.40218    0.16433  969.99998   2.447  0.01457 *  
## othervsmatch            0.13450    0.13790  969.99999   0.975  0.32962    
## repd                    0.92377    0.12170  969.99998   7.590 7.48e-14 ***
## in_d                    0.20042    0.14136  969.99998   1.418  0.15657    
## matchvsmismatch:repd    0.06136    0.29990  969.99998   0.205  0.83793    
## othervsmatch:repd       0.44027    0.25662  969.99999   1.716  0.08654 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) lin    quad   mtchvs othrvs repd   in_d   mtchv:
## lin          0.000                                                 
## quad         0.319  0.000                                          
## mtchvsmsmtc  0.015  0.000  0.000                                   
## othervsmtch -0.043  0.000  0.000 -0.011                            
## repd        -0.514  0.000  0.000 -0.008  0.025                     
## in_d        -0.415  0.000  0.000  0.000 -0.629  0.238              
## mtchvsmsmt: -0.008  0.000  0.000 -0.548  0.006 -0.033  0.000       
## othrvsmtch:  0.023  0.000  0.000  0.006 -0.537 -0.017  0.338  0.024
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients

tab_model(dem)

	value
Predictors	Estimates	CI	p
(Intercept)	-1.90	-2.03 – -1.76	<0.001
lin	-0.89	-0.97 – -0.82	<0.001
quad	0.31	0.11 – 0.50	0.002
matchvsmismatch	0.40	0.08 – 0.72	0.014
othervsmatch	0.13	-0.14 – 0.40	0.329
repd	0.92	0.69 – 1.16	<0.001
in d	0.20	-0.08 – 0.48	0.156
matchvsmismatch × repd	0.06	-0.53 – 0.65	0.838
othervsmatch × repd	0.44	-0.06 – 0.94	0.086
Random Effects
σ2	0.72
τ00 NUC2.ID	1.90
ICC	0.72
N NUC2.ID	977
Observations	2931
Marginal R2 / Conditional R2	0.103 / 0.753

Descriptives for nuclear endorsement at each level of casualties

describeBy(d2$value, list(d2$NUC2.party.full))

## 
##  Descriptive statistics by group 
## : dem
##    vars    n  mean   sd median trimmed mad min max range skew kurtosis   se
## X1    1 1479 -1.96 1.51     -3   -2.29   0  -3   3     6 1.57     1.59 0.04
## ------------------------------------------------------------ 
## : ind
##    vars   n  mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 834 -1.67 1.69     -2   -1.97 1.48  -3   3     6 1.23     0.41 0.06
## ------------------------------------------------------------ 
## : rep
##    vars   n  mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 618 -1.03 1.97     -2   -1.25 1.48  -3   3     6 0.74    -0.78 0.08

describeBy(d2$value, list(d2$NUC2.party.full, d2$NUC2.Condition))

## 
##  Descriptive statistics by group 
## : dem
## : control
##    vars   n  mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 525 -1.87 1.51     -2   -2.17 1.48  -3   3     6 1.39     0.93 0.07
## ------------------------------------------------------------ 
## : ind
## : control
##    vars   n  mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 249 -1.65 1.75     -2   -1.94 1.48  -3   3     6 1.16     0.09 0.11
## ------------------------------------------------------------ 
## : rep
## : control
##    vars   n  mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 207 -0.66 2.15     -1   -0.81 2.97  -3   3     6 0.41    -1.34 0.15
## ------------------------------------------------------------ 
## : dem
## : dem
##    vars   n  mean   sd median trimmed mad min max range skew kurtosis   se
## X1    1 468 -1.81 1.65     -3   -2.11   0  -3   3     6 1.36     0.75 0.08
## ------------------------------------------------------------ 
## : ind
## : dem
##    vars   n  mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 303 -1.67 1.62     -2   -1.93 1.48  -3   3     6 1.17     0.33 0.09
## ------------------------------------------------------------ 
## : rep
## : dem
##    vars   n  mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 192 -1.46 1.76     -2   -1.75 1.48  -3   3     6 1.11     0.12 0.13
## ------------------------------------------------------------ 
## : dem
## : rep
##    vars   n  mean   sd median trimmed mad min max range skew kurtosis   se
## X1    1 486 -2.21 1.35     -3   -2.53   0  -3   3     6 2.08     3.97 0.06
## ------------------------------------------------------------ 
## : ind
## : rep
##    vars   n mean   sd median trimmed  mad min max range skew kurtosis  se
## X1    1 282 -1.7 1.72     -2   -2.03 1.48  -3   3     6 1.34     0.72 0.1
## ------------------------------------------------------------ 
## : rep
## : rep
##    vars   n mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 219   -1 1.88     -2   -1.21 1.48  -3   3     6 0.75    -0.62 0.13

describeBy(d2$value, list(d2$NUC2.Condition,d2$NUC2.party.full, d2$variable))

## 
##  Descriptive statistics by group 
## : control
## : dem
## : NUC2.TO_psychic_numb2_20k
##    vars   n  mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 175 -1.53 1.62     -2   -1.77 1.48  -3   3     6 1.04     0.05 0.12
## ------------------------------------------------------------ 
## : dem
## : dem
## : NUC2.TO_psychic_numb2_20k
##    vars   n  mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 156 -1.36 1.84     -2   -1.63 1.48  -3   3     6 0.98    -0.21 0.15
## ------------------------------------------------------------ 
## : rep
## : dem
## : NUC2.TO_psychic_numb2_20k
##    vars   n  mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 162 -1.84 1.44     -2   -2.09 1.48  -3   3     6 1.32     1.08 0.11
## ------------------------------------------------------------ 
## : control
## : ind
## : NUC2.TO_psychic_numb2_20k
##    vars  n  mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 83 -1.12 1.95     -2   -1.33 1.48  -3   3     6 0.63    -1.01 0.21
## ------------------------------------------------------------ 
## : dem
## : ind
## : NUC2.TO_psychic_numb2_20k
##    vars   n  mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 101 -1.36 1.65     -2   -1.58 1.48  -3   3     6 0.94    -0.05 0.16
## ------------------------------------------------------------ 
## : rep
## : ind
## : NUC2.TO_psychic_numb2_20k
##    vars  n  mean   sd median trimmed  mad min max range skew kurtosis  se
## X1    1 94 -1.29 1.89     -2   -1.55 1.48  -3   3     6 0.92    -0.39 0.2
## ------------------------------------------------------------ 
## : control
## : rep
## : NUC2.TO_psychic_numb2_20k
##    vars  n  mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 69 -0.13 2.29     -1   -0.16 2.97  -3   3     6 0.14    -1.61 0.28
## ------------------------------------------------------------ 
## : dem
## : rep
## : NUC2.TO_psychic_numb2_20k
##    vars  n  mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 64 -1.09 1.81     -2   -1.29 1.48  -3   3     6 0.79    -0.56 0.23
## ------------------------------------------------------------ 
## : rep
## : rep
## : NUC2.TO_psychic_numb2_20k
##    vars  n  mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 73 -0.48 1.95     -1   -0.59 2.97  -3   3     6 0.37    -1.16 0.23
## ------------------------------------------------------------ 
## : control
## : dem
## : NUC2.TOT_Nuc_endorse
##    vars   n mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 175 -1.7 1.48     -2   -1.95 1.48  -3   2     5 1.16      0.4 0.11
## ------------------------------------------------------------ 
## : dem
## : dem
## : NUC2.TOT_Nuc_endorse
##    vars   n  mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 156 -1.79 1.57     -2   -2.04 1.48  -3   3     6 1.22     0.37 0.13
## ------------------------------------------------------------ 
## : rep
## : dem
## : NUC2.TOT_Nuc_endorse
##    vars   n  mean   sd median trimmed mad min max range skew kurtosis  se
## X1    1 162 -2.22 1.33     -3   -2.52   0  -3   3     6 2.33     5.54 0.1
## ------------------------------------------------------------ 
## : control
## : ind
## : NUC2.TOT_Nuc_endorse
##    vars  n  mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 83 -1.59 1.66     -2   -1.84 1.48  -3   3     6 1.03    -0.14 0.18
## ------------------------------------------------------------ 
## : dem
## : ind
## : NUC2.TOT_Nuc_endorse
##    vars   n  mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 101 -1.61 1.58     -2   -1.84 1.48  -3   3     6 1.03    -0.12 0.16
## ------------------------------------------------------------ 
## : rep
## : ind
## : NUC2.TOT_Nuc_endorse
##    vars  n  mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 94 -1.66 1.72     -2   -1.95 1.48  -3   3     6 1.25      0.5 0.18
## ------------------------------------------------------------ 
## : control
## : rep
## : NUC2.TOT_Nuc_endorse
##    vars  n  mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 69 -0.55 2.05     -1   -0.65 2.97  -3   3     6  0.3    -1.43 0.25
## ------------------------------------------------------------ 
## : dem
## : rep
## : NUC2.TOT_Nuc_endorse
##    vars  n  mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 64 -1.38 1.71     -2   -1.62 1.48  -3   3     6 1.07     0.13 0.21
## ------------------------------------------------------------ 
## : rep
## : rep
## : NUC2.TOT_Nuc_endorse
##    vars  n mean  sd median trimmed  mad min max range skew kurtosis   se
## X1    1 73 -0.9 1.8     -1   -1.08 1.48  -3   3     6  0.7     -0.6 0.21
## ------------------------------------------------------------ 
## : control
## : dem
## : NUC2.TOT_psychic_num_1mil
##    vars   n  mean   sd median trimmed mad min max range skew kurtosis  se
## X1    1 175 -2.39 1.28     -3   -2.74   0  -3   2     5  2.4     4.77 0.1
## ------------------------------------------------------------ 
## : dem
## : dem
## : NUC2.TOT_psychic_num_1mil
##    vars   n  mean   sd median trimmed mad min max range skew kurtosis   se
## X1    1 156 -2.28 1.39     -3   -2.63   0  -3   3     6 2.03     3.23 0.11
## ------------------------------------------------------------ 
## : rep
## : dem
## : NUC2.TOT_psychic_num_1mil
##    vars   n  mean   sd median trimmed mad min max range skew kurtosis   se
## X1    1 162 -2.57 1.17     -3    -2.9   0  -3   3     6 3.19     9.78 0.09
## ------------------------------------------------------------ 
## : control
## : ind
## : NUC2.TOT_psychic_num_1mil
##    vars  n  mean   sd median trimmed mad min max range skew kurtosis   se
## X1    1 83 -2.24 1.45     -3   -2.61   0  -3   3     6 2.16     3.94 0.16
## ------------------------------------------------------------ 
## : dem
## : ind
## : NUC2.TOT_psychic_num_1mil
##    vars   n  mean   sd median trimmed mad min max range skew kurtosis   se
## X1    1 101 -2.03 1.57     -3   -2.38   0  -3   3     6 1.64     1.65 0.16
## ------------------------------------------------------------ 
## : rep
## : ind
## : NUC2.TOT_psychic_num_1mil
##    vars  n  mean   sd median trimmed mad min max range skew kurtosis   se
## X1    1 94 -2.16 1.43     -3   -2.49   0  -3   3     6 2.02     3.54 0.15
## ------------------------------------------------------------ 
## : control
## : rep
## : NUC2.TOT_psychic_num_1mil
##    vars  n  mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 69 -1.29 1.97     -2   -1.53 1.48  -3   3     6 0.77    -0.81 0.24
## ------------------------------------------------------------ 
## : dem
## : rep
## : NUC2.TOT_psychic_num_1mil
##    vars  n  mean   sd median trimmed mad min max range skew kurtosis   se
## X1    1 64 -1.92 1.69     -3   -2.29   0  -3   3     6 1.62     1.46 0.21
## ------------------------------------------------------------ 
## : rep
## : rep
## : NUC2.TOT_psychic_num_1mil
##    vars  n  mean   sd median trimmed  mad min max range skew kurtosis  se
## X1    1 73 -1.62 1.73     -2   -1.93 1.48  -3   3     6 1.35     0.82 0.2

describeBy(NUC2$TOT_psychic_num_1mil, list(NUC2$party.full))

## 
##  Descriptive statistics by group 
## : dem
##    vars   n  mean   sd median trimmed mad min max range skew kurtosis   se
## X1    1 493 -2.41 1.28     -3   -2.78   0  -3   3     6 2.49     5.38 0.06
## ------------------------------------------------------------ 
## : ind
##    vars   n  mean   sd median trimmed mad min max range skew kurtosis   se
## X1    1 278 -2.14 1.49     -3   -2.49   0  -3   3     6 1.93     2.91 0.09
## ------------------------------------------------------------ 
## : rep
##    vars   n mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 206 -1.6 1.81     -2   -1.92 1.48  -3   3     6 1.21     0.25 0.13

describeBy(NUC2$TO_psychic_numb2_20k, list(NUC2$match))

## 
##  Descriptive statistics by group 
## : control
##    vars   n mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 522 -1.2 1.87     -2   -1.45 1.48  -3   3     6 0.85     -0.5 0.08
## ------------------------------------------------------------ 
## : no
##    vars   n  mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 226 -1.63 1.58     -2   -1.88 1.48  -3   3     6 1.18     0.55 0.11
## ------------------------------------------------------------ 
## : yes
##    vars   n  mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 229 -1.08 1.92     -2    -1.3 1.48  -3   3     6 0.76    -0.67 0.13

describeBy(NUC2$TOT_psychic_num_1mil, list(NUC2$match))

## 
##  Descriptive statistics by group 
## : control
##    vars   n  mean   sd median trimmed mad min max range skew kurtosis   se
## X1    1 522 -2.11 1.53     -3   -2.48   0  -3   3     6 1.84     2.35 0.07
## ------------------------------------------------------------ 
## : no
##    vars   n  mean   sd median trimmed mad min max range skew kurtosis   se
## X1    1 226 -2.39 1.37     -3   -2.77   0  -3   3     6 2.54     5.66 0.09
## ------------------------------------------------------------ 
## : yes
##    vars   n  mean   sd median trimmed mad min max range skew kurtosis  se
## X1    1 229 -2.07 1.53     -3   -2.41   0  -3   3     6 1.77     2.26 0.1

describeBy(NUC2$TOT_Nuc_endorse, list(NUC2$match))

## 
##  Descriptive statistics by group 
## : control
##    vars   n  mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 522 -1.51 1.69     -2   -1.77 1.48  -3   3     6 1.04    -0.07 0.07
## ------------------------------------------------------------ 
## : no
##    vars   n  mean   sd median trimmed mad min max range skew kurtosis  se
## X1    1 226 -1.98 1.49     -3   -2.31   0  -3   3     6 1.82     2.81 0.1
## ------------------------------------------------------------ 
## : yes
##    vars   n  mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 229 -1.51 1.69     -2   -1.74 1.48  -3   3     6 1.03    -0.02 0.11

Demographics study 1

nrow(sub)

## [1] 1308

table(sub$sample)

## 
## mturk  sona sona1 
##   499   454   355

#party demographics
table(sub$party.full)

## 
## dem ind rep 
## 698 347 263

partyT <- table(sub$party.full)
round(100*prop.table(partyT), digits = 1)

## 
##  dem  ind  rep 
## 53.4 26.5 20.1

#ethnicity 
table(sub$ethnicity)

## 
##    1    2    3    4    5    6    7 
## 1032   55    3   94   23   74   27

ethnT <- table(sub$ethnicity)
round(100*prop.table(ethnT), digits = 1)

## 
##    1    2    3    4    5    6    7 
## 78.9  4.2  0.2  7.2  1.8  5.7  2.1

#gender
table(sub$gender)

## 
##   1   2   3 
## 589 716   3

gendT <- table(sub$gender)
round(100*prop.table(gendT), digits = 1)

## 
##    1    2    3 
## 45.0 54.7  0.2

#age
range(sub$age, na.rm = T)

## [1] 17 75

 mean(sub$age, na.rm = T)

## [1] 26.98699

   sd(sub$age, na.rm = T)

## [1] 12.54757

#conditions
table(sub$Condition)

## 
## control     dem     rep 
##     440     432     436

table(sub$Condition, sub$party.full)

##          
##           dem ind rep
##   control 237 120  83
##   dem     237 111  84
##   rep     224 116  96

table(sub$cat)

## 
## neutral  oppose support 
##     220     854     234

catT <- table(sub$cat)
round(100*prop.table(catT), digits = 1)

## 
## neutral  oppose support 
##    16.8    65.3    17.9

hist(sub$gender)

Demographics study 2

nrow(NUC2)

## [1] 1002

table(sub$sample)

## 
## mturk  sona sona1 
##   499   454   355

#party demographics
table(NUC2$party.full)

## 
## dem ind rep 
## 493 278 206

partyT <- table(sub$party.full)
round(100*prop.table(partyT), digits = 1)

## 
##  dem  ind  rep 
## 53.4 26.5 20.1

#ethnicity 
table(NUC2$ethnicity)

## 
##   1   2   3   4   5   6   7 
## 748  99   6  84   2  39  24

ethnT <- table(NUC2$ethnicity)
round(100*prop.table(ethnT), digits = 1)

## 
##    1    2    3    4    5    6    7 
## 74.7  9.9  0.6  8.4  0.2  3.9  2.4

#gender
table(NUC2$gender)

## 
##   1   2   3 
## 494 503   5

gendT <- table(NUC2$gender)
round(100*prop.table(gendT), digits = 1)

## 
##    1    2    3 
## 49.3 50.2  0.5

#age
range(NUC2$age, na.rm = T)

## [1] 18 84

 mean(NUC2$age, na.rm = T)

## [1] 40.48902

   sd(NUC2$age, na.rm = T)

## [1] 12.53193

table(sub$Condition)

## 
## control     dem     rep 
##     440     432     436

table(sub$Condition, sub$party.full)

##          
##           dem ind rep
##   control 237 120  83
##   dem     237 111  84
##   rep     224 116  96

Alphas

Study 1

#individualizing foundations alpha
df_indiv <- data.frame(sub$Mor_F_1, sub$Mor_F_2, sub$Mor_F_3, sub$Mor_C_1, sub$Mor_C_2, sub$Mor_C_3, sub$Mor_lib_1, sub$Mor_Lib_3, sub$Mor_Lib_3.1)
psych::alpha(df_indiv)

## 
## Reliability analysis   
## Call: psych::alpha(x = df_indiv)
## 
##   raw_alpha std.alpha G6(smc) average_r S/N   ase mean   sd median_r
##       0.73      0.75    0.74      0.25   3 0.011    4 0.51     0.25
## 
##     95% confidence boundaries 
##          lower alpha upper
## Feldt     0.71  0.73  0.75
## Duhachek  0.71  0.73  0.75
## 
##  Reliability if an item is dropped:
##                 raw_alpha std.alpha G6(smc) average_r S/N alpha se  var.r med.r
## sub.Mor_F_1          0.73      0.74    0.73      0.26 2.9    0.011 0.0065  0.25
## sub.Mor_F_2          0.70      0.71    0.71      0.24 2.5    0.013 0.0092  0.23
## sub.Mor_F_3          0.70      0.72    0.71      0.24 2.5    0.012 0.0089  0.24
## sub.Mor_C_1          0.71      0.72    0.72      0.25 2.6    0.012 0.0087  0.24
## sub.Mor_C_2          0.72      0.73    0.73      0.25 2.7    0.012 0.0094  0.27
## sub.Mor_C_3          0.68      0.70    0.70      0.23 2.4    0.013 0.0082  0.23
## sub.Mor_lib_1        0.73      0.74    0.73      0.27 2.9    0.011 0.0070  0.27
## sub.Mor_Lib_3        0.70      0.72    0.71      0.25 2.6    0.012 0.0078  0.26
## sub.Mor_Lib_3.1      0.70      0.73    0.71      0.25 2.6    0.012 0.0077  0.27
## 
##  Item statistics 
##                    n raw.r std.r r.cor r.drop mean   sd
## sub.Mor_F_1     1307  0.49  0.50  0.39   0.31  3.7 0.95
## sub.Mor_F_2     1307  0.60  0.63  0.56   0.48  4.2 0.77
## sub.Mor_F_3     1307  0.58  0.63  0.56   0.48  4.6 0.65
## sub.Mor_C_1     1307  0.56  0.58  0.50   0.42  4.2 0.80
## sub.Mor_C_2     1307  0.56  0.54  0.44   0.38  3.7 1.06
## sub.Mor_C_3     1307  0.68  0.68  0.64   0.55  4.1 0.87
## sub.Mor_lib_1   1307  0.51  0.47  0.36   0.30  3.6 1.11
## sub.Mor_Lib_3   1307  0.60  0.59  0.52   0.44  4.0 0.94
## sub.Mor_Lib_3.1 1307  0.59  0.57  0.50   0.43  3.6 0.92
## 
## Non missing response frequency for each item
##                    1    2    3    4    5 miss
## sub.Mor_F_1     0.02 0.09 0.24 0.45 0.20    0
## sub.Mor_F_2     0.00 0.01 0.15 0.44 0.40    0
## sub.Mor_F_3     0.00 0.01 0.04 0.23 0.72    0
## sub.Mor_C_1     0.00 0.04 0.11 0.47 0.38    0
## sub.Mor_C_2     0.02 0.12 0.26 0.32 0.28    0
## sub.Mor_C_3     0.01 0.03 0.18 0.36 0.41    0
## sub.Mor_lib_1   0.05 0.10 0.28 0.32 0.26    0
## sub.Mor_Lib_3   0.01 0.06 0.21 0.36 0.36    0
## sub.Mor_Lib_3.1 0.02 0.10 0.35 0.37 0.16    0

#binding foundations alpha
df_bind <- data.frame(sub$Mor_A_1, sub$Mor_A_2, sub$Mor_A_3, sub$Mor_L_2, sub$Mor_Loy_1, sub$Mor_Loy_3, sub$Mor_P_1, sub$Mor_P_2, sub$Mor_P_3)
psych::alpha(df_bind)

## 
## Reliability analysis   
## Call: psych::alpha(x = df_bind)
## 
##   raw_alpha std.alpha G6(smc) average_r S/N  ase mean   sd median_r
##       0.75      0.74    0.75      0.24 2.9 0.01  3.4 0.57     0.23
## 
##     95% confidence boundaries 
##          lower alpha upper
## Feldt     0.72  0.75  0.77
## Duhachek  0.73  0.75  0.77
## 
##  Reliability if an item is dropped:
##               raw_alpha std.alpha G6(smc) average_r S/N alpha se  var.r med.r
## sub.Mor_A_1        0.73      0.72    0.74      0.25 2.6    0.011 0.0147  0.23
## sub.Mor_A_2        0.72      0.72    0.72      0.24 2.6    0.012 0.0128  0.23
## sub.Mor_A_3        0.74      0.73    0.73      0.26 2.8    0.011 0.0110  0.24
## sub.Mor_L_2        0.73      0.73    0.73      0.25 2.6    0.011 0.0133  0.23
## sub.Mor_Loy_1      0.72      0.72    0.72      0.24 2.6    0.012 0.0120  0.23
## sub.Mor_Loy_3      0.71      0.71    0.72      0.23 2.4    0.012 0.0128  0.23
## sub.Mor_P_1        0.71      0.71    0.70      0.23 2.4    0.012 0.0092  0.24
## sub.Mor_P_2        0.74      0.74    0.73      0.26 2.8    0.011 0.0070  0.24
## sub.Mor_P_3        0.72      0.72    0.72      0.24 2.5    0.012 0.0122  0.24
## 
##  Item statistics 
##                  n raw.r std.r r.cor r.drop mean   sd
## sub.Mor_A_1   1307  0.55  0.56  0.46   0.41  3.6 0.89
## sub.Mor_A_2   1307  0.57  0.59  0.51   0.43  2.8 0.90
## sub.Mor_A_3   1307  0.49  0.51  0.42   0.34  2.8 0.88
## sub.Mor_L_2   1307  0.57  0.55  0.46   0.40  2.9 1.09
## sub.Mor_Loy_1 1307  0.59  0.58  0.50   0.43  2.5 1.04
## sub.Mor_Loy_3 1307  0.65  0.64  0.57   0.50  2.8 1.13
## sub.Mor_P_1   1307  0.64  0.64  0.60   0.50  4.5 1.04
## sub.Mor_P_2   1306  0.48  0.49  0.41   0.33  4.5 0.90
## sub.Mor_P_3   1307  0.61  0.60  0.54   0.45  3.8 1.08
## 
## Non missing response frequency for each item
##                  1    2    3    4    5 miss
## sub.Mor_A_1   0.02 0.08 0.29 0.48 0.12    0
## sub.Mor_A_2   0.06 0.29 0.46 0.14 0.04    0
## sub.Mor_A_3   0.04 0.36 0.42 0.13 0.04    0
## sub.Mor_L_2   0.10 0.25 0.33 0.24 0.07    0
## sub.Mor_Loy_1 0.18 0.34 0.30 0.15 0.03    0
## sub.Mor_Loy_3 0.14 0.25 0.34 0.20 0.08    0
## sub.Mor_P_1   0.04 0.03 0.07 0.14 0.71    0
## sub.Mor_P_2   0.02 0.03 0.08 0.15 0.72    0
## sub.Mor_P_3   0.03 0.09 0.21 0.34 0.33    0

study 2

#individualizing foundations alpha
df_indiv2 <- data.frame(NUC2$Mor_F_1, NUC2$Mor_F_2, NUC2$Mor_F_3, NUC2$Mor_C_1, NUC2$Mor_C_2, NUC2$Mor_C_3, NUC2$Mor_lib_1, NUC2$Mor_Lib_3, NUC2$Mor_Lib_3.1)
psych::alpha(df_indiv2)

## 
## Reliability analysis   
## Call: psych::alpha(x = df_indiv2)
## 
##   raw_alpha std.alpha G6(smc) average_r S/N   ase mean   sd median_r
##       0.75      0.77    0.77      0.27 3.3 0.012  3.9 0.54     0.26
## 
##     95% confidence boundaries 
##          lower alpha upper
## Feldt     0.73  0.75  0.78
## Duhachek  0.73  0.75  0.78
## 
##  Reliability if an item is dropped:
##                  raw_alpha std.alpha G6(smc) average_r S/N alpha se  var.r
## NUC2.Mor_F_1          0.75      0.76    0.75      0.28 3.2    0.012 0.0075
## NUC2.Mor_F_2          0.72      0.73    0.72      0.25 2.7    0.013 0.0077
## NUC2.Mor_F_3          0.73      0.74    0.73      0.26 2.8    0.013 0.0086
## NUC2.Mor_C_1          0.73      0.74    0.74      0.27 2.9    0.013 0.0104
## NUC2.Mor_C_2          0.74      0.76    0.76      0.28 3.1    0.012 0.0104
## NUC2.Mor_C_3          0.72      0.73    0.73      0.26 2.8    0.013 0.0110
## NUC2.Mor_lib_1        0.74      0.75    0.75      0.27 3.0    0.013 0.0095
## NUC2.Mor_Lib_3        0.72      0.74    0.73      0.26 2.9    0.013 0.0080
## NUC2.Mor_Lib_3.1      0.72      0.74    0.73      0.26 2.8    0.013 0.0092
##                  med.r
## NUC2.Mor_F_1      0.27
## NUC2.Mor_F_2      0.25
## NUC2.Mor_F_3      0.27
## NUC2.Mor_C_1      0.26
## NUC2.Mor_C_2      0.27
## NUC2.Mor_C_3      0.25
## NUC2.Mor_lib_1    0.27
## NUC2.Mor_Lib_3    0.26
## NUC2.Mor_Lib_3.1  0.25
## 
##  Item statistics 
##                     n raw.r std.r r.cor r.drop mean   sd
## NUC2.Mor_F_1     1002  0.47  0.50  0.40   0.31  3.8 0.88
## NUC2.Mor_F_2     1002  0.62  0.66  0.63   0.51  4.2 0.76
## NUC2.Mor_F_3     1002  0.56  0.62  0.56   0.46  4.6 0.65
## NUC2.Mor_C_1     1002  0.55  0.59  0.51   0.43  4.2 0.77
## NUC2.Mor_C_2     1002  0.56  0.52  0.41   0.37  3.7 1.11
## NUC2.Mor_C_3     1002  0.63  0.64  0.57   0.50  3.9 0.88
## NUC2.Mor_lib_1   1002  0.60  0.56  0.47   0.42  3.4 1.13
## NUC2.Mor_Lib_3   1002  0.64  0.60  0.55   0.48  3.8 1.02
## NUC2.Mor_Lib_3.1 1002  0.64  0.61  0.55   0.49  3.4 0.99
## 
## Non missing response frequency for each item
##                     1    2    3    4    5 miss
## NUC2.Mor_F_1     0.01 0.07 0.22 0.50 0.20    0
## NUC2.Mor_F_2     0.00 0.01 0.13 0.44 0.40    0
## NUC2.Mor_F_3     0.00 0.01 0.04 0.28 0.66    0
## NUC2.Mor_C_1     0.00 0.02 0.11 0.45 0.40    0
## NUC2.Mor_C_2     0.03 0.12 0.26 0.31 0.27    0
## NUC2.Mor_C_3     0.01 0.03 0.27 0.39 0.30    0
## NUC2.Mor_lib_1   0.07 0.12 0.30 0.32 0.19    0
## NUC2.Mor_Lib_3   0.02 0.08 0.27 0.34 0.28    0
## NUC2.Mor_Lib_3.1 0.04 0.12 0.40 0.31 0.14    0

#binding foundations alpha
df_bind2 <- data.frame(NUC2$Mor_A_1, NUC2$Mor_A_2, NUC2$Mor_A_3, NUC2$Mor_L_2, NUC2$Mor_Loy_1, NUC2$Mor_Loy_3, NUC2$Mor_P_1, NUC2$Mor_P_2, NUC2$Mor_P_3)
psych::alpha(df_bind2)

## 
## Reliability analysis   
## Call: psych::alpha(x = df_bind2)
## 
##   raw_alpha std.alpha G6(smc) average_r S/N    ase mean   sd median_r
##       0.81      0.81    0.82      0.32 4.3 0.0089  3.3 0.68     0.32
## 
##     95% confidence boundaries 
##          lower alpha upper
## Feldt     0.79  0.81  0.83
## Duhachek  0.79  0.81  0.83
## 
##  Reliability if an item is dropped:
##                raw_alpha std.alpha G6(smc) average_r S/N alpha se  var.r med.r
## NUC2.Mor_A_1        0.80      0.80    0.81      0.33 4.0   0.0096 0.0129  0.33
## NUC2.Mor_A_2        0.79      0.79    0.79      0.32 3.8   0.0099 0.0106  0.31
## NUC2.Mor_A_3        0.79      0.79    0.79      0.32 3.7   0.0100 0.0110  0.29
## NUC2.Mor_L_2        0.80      0.80    0.80      0.33 3.9   0.0096 0.0117  0.32
## NUC2.Mor_Loy_1      0.80      0.80    0.80      0.33 4.0   0.0097 0.0107  0.33
## NUC2.Mor_Loy_3      0.79      0.79    0.79      0.32 3.8   0.0101 0.0119  0.30
## NUC2.Mor_P_1        0.79      0.79    0.78      0.32 3.7   0.0101 0.0086  0.33
## NUC2.Mor_P_2        0.80      0.81    0.80      0.34 4.1   0.0095 0.0070  0.33
## NUC2.Mor_P_3        0.78      0.78    0.79      0.31 3.6   0.0103 0.0106  0.29
## 
##  Item statistics 
##                   n raw.r std.r r.cor r.drop mean   sd
## NUC2.Mor_A_1   1002  0.57  0.60  0.51   0.46  3.7 0.92
## NUC2.Mor_A_2   1002  0.62  0.64  0.59   0.52  2.7 0.93
## NUC2.Mor_A_3   1002  0.65  0.67  0.63   0.55  3.0 0.90
## NUC2.Mor_L_2   1002  0.62  0.61  0.53   0.48  2.8 1.18
## NUC2.Mor_Loy_1 1002  0.61  0.60  0.53   0.47  2.3 1.09
## NUC2.Mor_Loy_3 1002  0.68  0.66  0.60   0.54  2.9 1.24
## NUC2.Mor_P_1   1002  0.68  0.67  0.64   0.55  4.1 1.20
## NUC2.Mor_P_2   1002  0.55  0.55  0.48   0.42  4.5 0.95
## NUC2.Mor_P_3   1002  0.70  0.69  0.65   0.58  3.6 1.19
## 
## Non missing response frequency for each item
##                   1    2    3    4    5 miss
## NUC2.Mor_A_1   0.02 0.07 0.29 0.44 0.17    0
## NUC2.Mor_A_2   0.09 0.33 0.42 0.12 0.04    0
## NUC2.Mor_A_3   0.04 0.23 0.47 0.20 0.05    0
## NUC2.Mor_L_2   0.17 0.25 0.32 0.17 0.09    0
## NUC2.Mor_Loy_1 0.26 0.33 0.26 0.11 0.03    0
## NUC2.Mor_Loy_3 0.17 0.21 0.30 0.20 0.12    0
## NUC2.Mor_P_1   0.06 0.06 0.11 0.21 0.56    0
## NUC2.Mor_P_2   0.02 0.03 0.08 0.19 0.67    0
## NUC2.Mor_P_3   0.06 0.11 0.26 0.28 0.29    0

extra PLOTS

#condition
#Control vs Partisan 
NUC2$fCvP <- (-2/3)*(NUC2$Condition == "control") + ((1/3))*(NUC2$Condition == "rep") + ((1/3))*(NUC2$Condition == "dem") 

#Dem vs rep
NUC2$fDvR <- 0*(NUC2$Condition == "control") + (-1/2)*(NUC2$Condition == "dem") + ((1/2))*(NUC2$Condition == "rep")

# Partisan match vs. no match
NUC2$yesvsno <- 0*(NUC2$match == "control") + (1/2)*(NUC2$match == "yes") + (-(1/2))*(NUC2$match == "no") 

#control vs partisan framed
NUC2$controlvsmatch <- (2/3)*(NUC2$match == "control") + (-(1/3))*(NUC2$match == "yes") + (-(1/3))*(NUC2$match == "no") 

p3 <- ggplot(sub, aes(x=bind, y=TOT_Nuc_endorse)) +
  geom_jitter() +
  geom_smooth(method=lm , color="red", fill="#69b3a2", se=TRUE)  + xlab("Binding Moral Foundations" ) + ylab("Endorsement of Nuclear Strike")
p3<- p3 + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"))
p3

## `geom_smooth()` using formula = 'y ~ x'

## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_smooth()`).

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

p3 <- ggplot(sub, aes(x=Indiv, y=TOT_Nuc_endorse)) +
  geom_jitter() +
  geom_smooth(method=lm , color="red", fill="#69b3a2", se=TRUE)  + xlab("Binding Moral Foundations" ) + ylab("Endorsement of Nuclear Strike")
p3<- p3 + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"))
p3

## `geom_smooth()` using formula = 'y ~ x'

## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_smooth()`).

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

party ID and framing plot nuclear endorsement

NUC2$party.full <- factor(NUC2$party.full, levels = c("dem", "rep", "ind"))
NUC2$Condition <- factor(NUC2$Condition, levels = c("dem", "rep", "control"))

int.plot <- ggplot(NUC2[!is.na(NUC2$party.full),], aes(x = party.full, y = TOT_psychic_num_1mil, fill = Condition)) +
  ggtitle("Support for Nuclear Retaliation- Combined Samples") +
  geom_bar(stat = "summary", fun = "mean" , position = position_dodge(.9))+
  stat_summary(fun.data = mean_cl_normal, geom = "errorbar", position = position_dodge(.9),fun.args = list(mult = 1), width = .2)

int.plot  + theme(
  # Remove panel border
  panel.border = element_blank(),  
  # Remove panel grid lines
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank(),
  # Remove panel background
  panel.background = element_blank(),
  # Add axis line
  axis.line = element_line(colour = "grey")
  ) + xlab("Participant Partisan Identity" ) + ylab("Endorsement of Nuclear Strike")+ scale_fill_manual("Partisan Framing Condition", values = c("dodgerblue","red3", "grey42")) + scale_x_discrete(labels = c("Democrat", "Republican", "Independent") ) + coord_cartesian(ylim = c(-3,3))

party ID and framing plot nuclear endorsement

NUC2$party.full <- factor(NUC2$party.full, levels = c("dem", "rep", "ind"))
NUC2$Condition <- factor(NUC2$Condition, levels = c("dem", "rep", "control"))

int.plot <- ggplot(NUC2[!is.na(NUC2$party.full),], aes(x = party.full, y = TOT_Nuc_endorse, fill = Condition)) +
  ggtitle("Support for Nuclear Retaliation- Combined Samples") +
  geom_bar(stat = "summary", fun = "mean" , position = position_dodge(.9))+
  stat_summary(fun.data = mean_cl_normal, geom = "errorbar", position = position_dodge(.9),fun.args = list(mult = 1), width = .2)

int.plot  + theme(
  # Remove panel border
  panel.border = element_blank(),  # Remove panel grid lines panel.grid.major = element_blank(),
  panel.grid.minor = element_blank(),
  # Remove panel background
  panel.background = element_blank(),
  # Add axis line
  axis.line = element_line(colour = "grey")
  ) + xlab("Participant Partisan Identity" ) + ylab("Endorsement of Nuclear Strike")+ scale_fill_manual("Partisan Framing Condition", values = c("dodgerblue","red3", "grey42")) + scale_x_discrete(labels = c("Democrat", "Republican", "Independent") ) + coord_cartesian(ylim = c(-3,3))

party ID and framing plot nuclear endorsement

NUC2$party.full <- factor(NUC2$party.full, levels = c("dem", "rep", "ind"))
NUC2$Condition <- factor(NUC2$Condition, levels = c("dem", "rep", "control"))

int.plot <- ggplot(NUC2[!is.na(NUC2$party.full),], aes(x = party.full, y = TO_psychic_numb2_20k, fill = Condition)) +
  ggtitle("Support for Nuclear Retaliation- Combined Samples") +
  geom_bar(stat = "summary", fun = "mean" , position = position_dodge(.9))+
  stat_summary(fun.data = mean_cl_normal, geom = "errorbar", position = position_dodge(.9),fun.args = list(mult = 1), width = .2)

int.plot <- int.plot  + theme(
  # Remove panel border
  panel.border = element_blank(),  
  # Remove panel grid lines
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank(),
  # Remove panel background
  panel.background = element_blank(),
  # Add axis line
  axis.line = element_line(colour = "grey")
  ) + xlab("Participant Partisan Identity" ) + ylab("Endorsement of Nuclear Strike")+ scale_fill_manual("Partisan Framing Condition", values = c("dodgerblue","red3", "grey42")) + scale_x_discrete(labels = c("Democrat", "Republican", "Independent") ) + coord_cartesian(ylim = c(-3,3))
int.plot

party ID and framing plot nuclear endorsement

sub$party.full <- factor(sub$party.full, levels = c("dem", "rep", "ind"))
sub$Condition <- factor(sub$Condition, levels = c("dem", "rep", "control"))

int.plot <- ggplot(sub[!is.na(sub$party.full),], aes(x = party.full, y = TOT_Nuc_endorse, fill = Condition)) +
  ggtitle("Support for Nuclear Retaliation- Combined Samples") +
  geom_bar(stat = "summary", fun = "mean" , position = position_dodge(.9))+
  stat_summary(fun.data = mean_cl_normal, geom = "errorbar", position = position_dodge(.9),fun.args = list(mult = 1), width = .2)

int.plot  + theme(
  # Remove panel border
  panel.border = element_blank(),  
  # Remove panel grid lines
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank(),
  # Remove panel background
  panel.background = element_blank(),
  # Add axis line
  axis.line = element_line(colour = "grey")
  ) + xlab("Participant Partisan Identity" ) + ylab("Endorsement of Nuclear Strike")+ scale_fill_manual("Partisan Framing Condition", values = c("dodgerblue","red3", "grey42")) + scale_x_discrete(labels = c("Democrat", "Republican", "Independent") ) + coord_cartesian(ylim = c(-3,3))