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Rape Myths - Study 5

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library(papaja)
library(xtable)
library(tidyr)
library(purrr)
library(ggplot2)
library(brms)
library(bayestestR)
library(rstan)
library(readxl)
library(sjPlot)
library(cmdstanr)
library(plotly)
library(corrplot)
library(htmlwidgets)
library(bayestestR)
library(formatR)
library(kableExtra)
library(tidybayes)
library(blavaan)
library(rmarkdown)
library(tidySEM)
library(ggcorrplot)
library(ggprism)
library(htmlTable)
library(table1)
library(data.table)
library(semPlot)
library(correlation)
library(dplyr)
library(lavaan)
library(dplyr)
library(tibble)
library(stringi)
library(tidyr)
library(kableExtra)
library(sjPlot)
library(purrr)
library(stringi)
library(ggplot2)
library(tidyverse)
locfunc <- function(data, to) {
 which(colnames({{ data }}) == {{ to }})
}
load("Experiment_4_Analysis.RData")
rape_myth_analysis_df <- read.csv("rape_myths_df.csv")
rape_myth_analysis_df$DEMO_GENDER <- as.factor(rape_myth_analysis_df$DEMO_GENDER)

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d2 <- rape_myths_df %>%
 mutate_at(vars(locfunc(rape_myths_df, "DEMO_GENDER")), ~ as.factor(recode(., "Female" = "1", "Male" = "2", "Trans Male" = "5", "Trans Female" = "6"))) %>%
 mutate_at(vars(locfunc(rape_myths_df, "DEMO_ETHNICITY")), ~ as.factor(recode(.,
  "1" = "White",
  "2" = "Mixed  or  Multi-ethnic",
  "3" = "Asian  or  Asian Scottish  or  Asian British",
  "4" = "African",
  "5" = "Caribbean  or  Black",
  "6" = "Arab ",
  "7" = "Other ethnicity",
  "8" = "Prefer not  to respond"
 ))) %>%
 mutate_at(vars(locfunc(rape_myths_df, "DEMO_ETHNIC_ORIGIN")), ~ as.factor(recode(.,
  "1" = "Scottish",
  "2" = "English",
  "3" = "European",
  "4" = "Latin American",
  "5" = "Asian",
  "6" = "Arab",
  "7" = "African",
  "8" = "Other",
  "9" = "Prefer not to respond"
 ))) %>%
 mutate_at(vars(locfunc(rape_myths_df, "DEMO_EDUCATION")), ~ as.factor(recode(.,
  "1" = "Primary School ",
  "2" = "GCSEs  or  Equivalent",
  "3" = "A-Levels  or  Equivalent",
  "4" = "University  Undergraduate  Program",
  "5" = "University  Post-Graduate  Program",
  "6" = "Doctoral  Degree",
  "7" = "Prefer not  to respond"
 )))

Analysis 1 - DoPL predicting overall rape myth Acceptance

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 m1_prior <- c(
  prior(normal(0.4, .3), class = "b", coef = "Dominance_z"),
  prior(normal(0, 0.7), class = "b", coef = "Prestige_z"),
  prior(normal(-0.2, .6), class = "b", coef = "Leadership_z"),
  prior(normal(0, 1), class = "b", coef = "DEMO_GENDER2"),
  prior(normal(0, 1), class = "b", coef = "DEMO_Age")
 )
m1 <- brm(acceptance_z ~ Dominance_z + Prestige_z + Leadership_z + DEMO_GENDER + DEMO_Age, data = rape_myth_analysis_df, chains = 4, iter = 40000, warmup = 1000, cores = 4, control = list(adapt_delta = 0.99), backend = "cmdstanr", prior = m1_prior, save_pars = save_pars(all = TRUE))

Model Summary Table Full

For Full Summary Output Click Here

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summary(m1)

 Family: gaussian 
  Links: mu = identity; sigma = identity 
Formula: acceptance_z ~ Dominance_z + Prestige_z + Leadership_z + DEMO_GENDER + DEMO_Age 
   Data: rape_myth_analysis_df (Number of observations: 197) 
  Draws: 4 chains, each with iter = 40000; warmup = 1000; thin = 1;
         total post-warmup draws = 156000

Population-Level Effects: 
             Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept       -0.64      0.24    -1.10    -0.18 1.00   173630   117174
Dominance_z      0.18      0.07     0.05     0.31 1.00   151521   118692
Prestige_z       0.10      0.07    -0.04     0.24 1.00   113886   112828
Leadership_z    -0.05      0.07    -0.19     0.09 1.00   130676   115811
DEMO_GENDER2     0.80      0.13     0.54     1.06 1.00   143987   112781
DEMO_Age         0.01      0.01    -0.00     0.03 1.00   174745   116938

Family Specific Parameters: 
      Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sigma     0.89      0.05     0.81     0.99 1.00   139956   111490

Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).

	Parameter	Estimate	HDI (95%)	ROPE_Percentage
Intercept	Intercept	-0.64	-1.1, -0.18	0%
Dominance	Dominance	0.18	0.05, 0.31	8%
Prestige	Prestige	0.10	-0.04, 0.24	50%
Leadership	Leadership	-0.05	-0.19, 0.09	76%
Gender	Gender	0.80	0.54, 1.06	0%
Age	Age	0.01	0, 0.03	100%

Interaction model of dopl predicting overall rape myth acceptance

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m1_interaction_prior <- c(
 prior(normal(0.4, .3), class = "b", coef = "Dominance_z"),
 prior(normal(0, 0.7), class = "b", coef = "Prestige_z"),
 prior(normal(-0.2, .6), class = "b", coef = "Leadership_z"),
 prior(normal(0, 1), class = "b", coef = "DEMO_GENDER2"),
 prior(normal(0, 1), class = "b", coef = "DEMO_Age"),
 prior(normal(0, 1), class = "b", coef = "Dominance_z:DEMO_GENDER2"),
 prior(normal(0, 1), class = "b", coef = "DEMO_GENDER2:Prestige_z"),
 prior(normal(0, 1), class = "b", coef = "DEMO_GENDER2:Leadership_z")
)
m1_interaction <- brm(acceptance_z ~ Dominance_z * DEMO_GENDER + Prestige_z * DEMO_GENDER + Leadership_z * DEMO_GENDER + DEMO_Age, data = rape_myth_analysis_df, chains = 4, iter = 40000, warmup = 1000, cores = 4, control = list(adapt_delta = 0.99), backend = "cmdstanr", prior = m1_interaction_prior, save_pars = save_pars(all = TRUE))

bfs_m1 <- bayesfactor_models(m1, m1_interaction, denominator = 2)

Model Summary Interaction Full

For Full Summary Output Click Here

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summary(m1_interaction)

 Family: gaussian 
  Links: mu = identity; sigma = identity 
Formula: acceptance_z ~ Dominance_z * DEMO_GENDER + Prestige_z * DEMO_GENDER + Leadership_z * DEMO_GENDER + DEMO_Age 
   Data: rape_myth_analysis_df (Number of observations: 197) 
  Draws: 4 chains, each with iter = 40000; warmup = 1000; thin = 1;
         total post-warmup draws = 156000

Population-Level Effects: 
                          Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
Intercept                    -0.67      0.24    -1.13    -0.20 1.00   149651
Dominance_z                   0.21      0.08     0.05     0.37 1.00   113225
DEMO_GENDER2                  0.79      0.13     0.53     1.06 1.00   142618
Prestige_z                    0.05      0.10    -0.15     0.25 1.00    91374
Leadership_z                  0.02      0.09    -0.16     0.19 1.00    97301
DEMO_Age                      0.01      0.01    -0.00     0.03 1.00   150323
Dominance_z:DEMO_GENDER2     -0.12      0.14    -0.39     0.15 1.00   109657
DEMO_GENDER2:Prestige_z       0.10      0.15    -0.19     0.39 1.00    95305
DEMO_GENDER2:Leadership_z    -0.21      0.15    -0.50     0.08 1.00   105868
                          Tail_ESS
Intercept                   117673
Dominance_z                 111651
DEMO_GENDER2                115343
Prestige_z                  104222
Leadership_z                107308
DEMO_Age                    116432
Dominance_z:DEMO_GENDER2    111699
DEMO_GENDER2:Prestige_z     108171
DEMO_GENDER2:Leadership_z   112433

Family Specific Parameters: 
      Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sigma     0.89      0.05     0.81     0.99 1.00   136637   111866

Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).

	Parameter	Estimate	HDI (95%)	ROPE_Percentage
Intercept	Intercept	-0.67	-1.13, -0.2	0%
Dominance	Dominance	0.21	0.05, 0.37	6%
Gender	Gender	0.79	0.53, 1.06	0%
Prestige	Prestige	0.05	-0.15, 0.25	64%
Leadership	Leadership	0.02	-0.16, 0.19	76%
Age	Age	0.01	0, 0.03	100%
Dominance : Gender	Dominance : Gender	-0.12	-0.39, 0.15	41%
Gender : Prestige	Gender : Prestige	0.10	-0.19, 0.39	43%
Gender : Leadership	Gender : Leadership	-0.21	-0.5, 0.08	21%

Model Comparison Click To show results

Click here

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bfs_m1

Bayes Factors for Model Comparison

    Model                                                               BF
[1] Dominance_z + Prestige_z + Leadership_z + DEMO_GENDER + DEMO_Age 93.15

* Against Denominator: [2] Dominance_z * DEMO_GENDER + Prestige_z * DEMO_GENDER + Leadership_z * DEMO_GENDER + DEMO_Age
*   Bayes Factor Type: marginal likelihoods (bridgesampling)

Exploratory

Individual rape myth acceptance subscales predicted by DoPL

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m2_prior <- c(
 prior(normal(0, 1), coef = "DEMO_Age", resp = "Didntmeantointoxicationz"),
prior(normal(0, 1), coef = "DEMO_GENDER2", resp = "Didntmeantointoxicationz"),
prior(normal(0.4, .3), coef = "Dominance_z", resp = "Didntmeantointoxicationz"),
prior(normal(0, 1), coef = "Leadership_z", resp = "Didntmeantointoxicationz"),
prior(normal(0, 1), coef = "Prestige_z", resp = "Didntmeantointoxicationz"),
prior(normal(0, 1), class = "Intercept", resp = "Didntmeantointoxicationz"),
prior(normal(0, 1), class = "sigma", resp = "Didntmeantointoxicationz"),
prior(normal(0, 1), coef = "DEMO_Age", resp = "DidntMeantoz"),
prior(normal(0, 1), coef = "DEMO_GENDER2", resp = "DidntMeantoz"),
prior(normal(0.4, .3), coef = "Dominance_z", resp = "DidntMeantoz"),
prior(normal(0, 1), coef = "Leadership_z", resp = "DidntMeantoz"),
prior(normal(0, 1), coef = "Prestige_z", resp = "DidntMeantoz"),
prior(normal(0, 1), class = "Intercept", resp = "DidntMeantoz"),
prior(normal(0, 1), class = "sigma", resp = "DidntMeantoz"),
prior(normal(0, 1), coef = "DEMO_Age", resp = "VictimAskedforitz"),
prior(normal(0, 1), coef = "DEMO_GENDER2", resp = "VictimAskedforitz"),
prior(normal(0.4, .3), coef = "Dominance_z", resp = "VictimAskedforitz"),
prior(normal(0, 1), coef = "Leadership_z", resp = "VictimAskedforitz"),
prior(normal(0, 1), coef = "Prestige_z", resp = "VictimAskedforitz"),
prior(normal(0, 1), class = "Intercept", resp = "VictimAskedforitz"),
prior(normal(0, 1), class = "sigma", resp = "VictimAskedforitz"),
prior(normal(0, 1), coef = "DEMO_Age", resp = "VictimLiedz"),
prior(normal(0, 1), coef = "DEMO_GENDER2", resp = "VictimLiedz"),
prior(normal(0.4, .3), coef = "Dominance_z", resp = "VictimLiedz"),
prior(normal(0, 1), coef = "Leadership_z", resp = "VictimLiedz"),
prior(normal(0, 1), coef = "Prestige_z", resp = "VictimLiedz"),
prior(normal(0, 1), class = "Intercept", resp = "VictimLiedz"),
prior(normal(0, 1), class = "sigma", resp = "VictimLiedz"),
prior(normal(0, 1), coef = "DEMO_Age", resp = "WasntRapez"),
prior(normal(0, 1), coef = "DEMO_GENDER2", resp = "WasntRapez"),
prior(normal(0.4, .3), coef = "Dominance_z", resp = "WasntRapez"),
prior(normal(0, 1), coef = "Leadership_z", resp = "WasntRapez"),
prior(normal(0, 1), coef = "Prestige_z", resp = "WasntRapez"),
prior(normal(0, 1), class = "Intercept", resp = "WasntRapez"),
prior(normal(0, 1), class = "sigma", resp = "WasntRapez")
)

m2 <- brm(mvbind(Victim.Asked.for.it_z, Victim.Lied_z, Didn.t.Mean.to_z, Didn.t.mean.to..intoxication_z, Wasn.t.Rape_z ) ~ Dominance_z + Prestige_z + Leadership_z + DEMO_GENDER + DEMO_Age, data = rape_myth_analysis_df, chains = 4, iter = 40000, warmup = 1000, cores = 4, control = list(adapt_delta = 0.99), backend = "cmdstanr", prior = m2_prior, save_pars = save_pars(all = TRUE))

Model Summary rape myth subscales Table Full

For Full Summary Output Click Here

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summary(m2)

 Family: MV(gaussian, gaussian, gaussian, gaussian, gaussian) 
  Links: mu = identity; sigma = identity
         mu = identity; sigma = identity
         mu = identity; sigma = identity
         mu = identity; sigma = identity
         mu = identity; sigma = identity 
Formula: Victim.Asked.for.it_z ~ Dominance_z + Prestige_z + Leadership_z + DEMO_GENDER + DEMO_Age 
         Victim.Lied_z ~ Dominance_z + Prestige_z + Leadership_z + DEMO_GENDER + DEMO_Age 
         Didn.t.Mean.to_z ~ Dominance_z + Prestige_z + Leadership_z + DEMO_GENDER + DEMO_Age 
         Didn.t.mean.to..intoxication_z ~ Dominance_z + Prestige_z + Leadership_z + DEMO_GENDER + DEMO_Age 
         Wasn.t.Rape_z ~ Dominance_z + Prestige_z + Leadership_z + DEMO_GENDER + DEMO_Age 
   Data: rape_myth_analysis_df (Number of observations: 197) 
  Draws: 4 chains, each with iter = 40000; warmup = 1000; thin = 1;
         total post-warmup draws = 156000

Population-Level Effects: 
                                      Estimate Est.Error l-95% CI u-95% CI Rhat
VictimAskedforitz_Intercept              -0.57      0.25    -1.06    -0.09 1.00
VictimLiedz_Intercept                    -0.43      0.24    -0.91     0.04 1.00
DidntMeantoz_Intercept                   -0.82      0.25    -1.32    -0.32 1.00
Didntmeantointoxicationz_Intercept       -0.43      0.26    -0.93     0.07 1.00
WasntRapez_Intercept                     -0.19      0.25    -0.69     0.30 1.00
VictimAskedforitz_Dominance_z             0.19      0.07     0.06     0.32 1.00
VictimAskedforitz_Prestige_z              0.14      0.08    -0.01     0.29 1.00
VictimAskedforitz_Leadership_z           -0.11      0.07    -0.26     0.03 1.00
VictimAskedforitz_DEMO_GENDER2            0.64      0.14     0.37     0.91 1.00
VictimAskedforitz_DEMO_Age                0.01      0.01    -0.01     0.03 1.00
VictimLiedz_Dominance_z                   0.24      0.07     0.11     0.37 1.00
VictimLiedz_Prestige_z                    0.07      0.08    -0.08     0.21 1.00
VictimLiedz_Leadership_z                 -0.04      0.07    -0.18     0.10 1.00
VictimLiedz_DEMO_GENDER2                  0.69      0.14     0.43     0.96 1.00
VictimLiedz_DEMO_Age                      0.01      0.01    -0.01     0.02 1.00
DidntMeantoz_Dominance_z                  0.11      0.07    -0.02     0.25 1.00
DidntMeantoz_Prestige_z                   0.11      0.08    -0.05     0.26 1.00
DidntMeantoz_Leadership_z                -0.03      0.08    -0.18     0.11 1.00
DidntMeantoz_DEMO_GENDER2                 0.51      0.14     0.24     0.79 1.00
DidntMeantoz_DEMO_Age                     0.02      0.01     0.01     0.04 1.00
Didntmeantointoxicationz_Dominance_z      0.20      0.07     0.07     0.34 1.00
Didntmeantointoxicationz_Prestige_z      -0.06      0.08    -0.22     0.10 1.00
Didntmeantointoxicationz_Leadership_z    -0.05      0.08    -0.20     0.10 1.00
Didntmeantointoxicationz_DEMO_GENDER2     0.44      0.14     0.15     0.72 1.00
Didntmeantointoxicationz_DEMO_Age         0.01      0.01    -0.01     0.03 1.00
WasntRapez_Dominance_z                    0.09      0.07    -0.05     0.22 1.00
WasntRapez_Prestige_z                    -0.00      0.08    -0.16     0.15 1.00
WasntRapez_Leadership_z                   0.05      0.07    -0.10     0.20 1.00
WasntRapez_DEMO_GENDER2                   0.54      0.14     0.27     0.81 1.00
WasntRapez_DEMO_Age                      -0.00      0.01    -0.02     0.02 1.00
                                      Bulk_ESS Tail_ESS
VictimAskedforitz_Intercept             122036   121907
VictimLiedz_Intercept                   122530   118831
DidntMeantoz_Intercept                  125158   122884
Didntmeantointoxicationz_Intercept      114331   120849
WasntRapez_Intercept                    122524   121424
VictimAskedforitz_Dominance_z           115372   117866
VictimAskedforitz_Prestige_z             95480   113932
VictimAskedforitz_Leadership_z          100650   114981
VictimAskedforitz_DEMO_GENDER2          108411   116859
VictimAskedforitz_DEMO_Age              124200   122863
VictimLiedz_Dominance_z                 117922   117456
VictimLiedz_Prestige_z                  100360   114610
VictimLiedz_Leadership_z                101205   115680
VictimLiedz_DEMO_GENDER2                107199   115587
VictimLiedz_DEMO_Age                    126136   117764
DidntMeantoz_Dominance_z                109765   114582
DidntMeantoz_Prestige_z                  95607   112845
DidntMeantoz_Leadership_z                98017   111163
DidntMeantoz_DEMO_GENDER2               106643   116185
DidntMeantoz_DEMO_Age                   127183   123282
Didntmeantointoxicationz_Dominance_z    107110   112795
Didntmeantointoxicationz_Prestige_z      91288   111936
Didntmeantointoxicationz_Leadership_z    96625   114771
Didntmeantointoxicationz_DEMO_GENDER2   100397   115393
Didntmeantointoxicationz_DEMO_Age       117999   122433
WasntRapez_Dominance_z                  115654   115296
WasntRapez_Prestige_z                    91446   110911
WasntRapez_Leadership_z                 100748   116859
WasntRapez_DEMO_GENDER2                 105666   116177
WasntRapez_DEMO_Age                     124187   119380

Family Specific Parameters: 
                               Estimate Est.Error l-95% CI u-95% CI Rhat
sigma_VictimAskedforitz            0.93      0.05     0.84     1.03 1.00
sigma_VictimLiedz                  0.92      0.05     0.83     1.01 1.00
sigma_DidntMeantoz                 0.96      0.05     0.87     1.06 1.00
sigma_Didntmeantointoxicationz     0.97      0.05     0.88     1.07 1.00
sigma_WasntRapez                   0.95      0.05     0.86     1.05 1.00
                               Bulk_ESS Tail_ESS
sigma_VictimAskedforitz          129523   118057
sigma_VictimLiedz                141041   117034
sigma_DidntMeantoz               124512   113194
sigma_Didntmeantointoxicationz   126491   117779
sigma_WasntRapez                 131877   115278

Residual Correlations: 
                                                   Estimate Est.Error l-95% CI
rescor(VictimAskedforitz,VictimLiedz)                  0.43      0.06     0.31
rescor(VictimAskedforitz,DidntMeantoz)                 0.35      0.06     0.22
rescor(VictimLiedz,DidntMeantoz)                       0.38      0.06     0.25
rescor(VictimAskedforitz,Didntmeantointoxicationz)     0.25      0.07     0.12
rescor(VictimLiedz,Didntmeantointoxicationz)           0.36      0.06     0.23
rescor(DidntMeantoz,Didntmeantointoxicationz)          0.60      0.05     0.51
rescor(VictimAskedforitz,WasntRapez)                   0.50      0.05     0.39
rescor(VictimLiedz,WasntRapez)                         0.38      0.06     0.26
rescor(DidntMeantoz,WasntRapez)                        0.20      0.07     0.07
rescor(Didntmeantointoxicationz,WasntRapez)            0.43      0.06     0.31
                                                   u-95% CI Rhat Bulk_ESS
rescor(VictimAskedforitz,VictimLiedz)                  0.54 1.00   121259
rescor(VictimAskedforitz,DidntMeantoz)                 0.47 1.00   114839
rescor(VictimLiedz,DidntMeantoz)                       0.49 1.00   120071
rescor(VictimAskedforitz,Didntmeantointoxicationz)     0.38 1.00   108102
rescor(VictimLiedz,Didntmeantointoxicationz)           0.47 1.00   116526
rescor(DidntMeantoz,Didntmeantointoxicationz)          0.69 1.00   118399
rescor(VictimAskedforitz,WasntRapez)                   0.60 1.00   123431
rescor(VictimLiedz,WasntRapez)                         0.50 1.00   137770
rescor(DidntMeantoz,WasntRapez)                        0.33 1.00   127903
rescor(Didntmeantointoxicationz,WasntRapez)            0.53 1.00   141173
                                                   Tail_ESS
rescor(VictimAskedforitz,VictimLiedz)                118732
rescor(VictimAskedforitz,DidntMeantoz)               118212
rescor(VictimLiedz,DidntMeantoz)                     118335
rescor(VictimAskedforitz,Didntmeantointoxicationz)   116294
rescor(VictimLiedz,Didntmeantointoxicationz)         120284
rescor(DidntMeantoz,Didntmeantointoxicationz)        118156
rescor(VictimAskedforitz,WasntRapez)                 117725
rescor(VictimLiedz,WasntRapez)                       122145
rescor(DidntMeantoz,WasntRapez)                      118769
rescor(Didntmeantointoxicationz,WasntRapez)          121788

Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).

	Victim Asked for it			Victim Lied			Didn't Mean to			Didn't Mean to (Intoxication)			Wasn't Rape
Parameter	Estimate	HDI (95%)	ROPE	Estimate	HDI (95%)	ROPE	Estimate	HDI (95%)	ROPE	Estimate	HDI (95%)	ROPE	Estimate	HDI (95%)	ROPE
Intercept	-0.82	-1.32, -0.32	0%	-0.43	-0.93, 0.07	8%	-0.57	-1.06, -0.09	0%	-0.43	-0.91, 0.04	6%	-0.19	-0.69, 0.3	24%
Dominance	0.11	-0.02, 0.25	42%	0.20	0.07, 0.34	5%	0.19	0.06, 0.32	7%	0.24	0.11, 0.37	0%	0.09	-0.05, 0.22	58%
Prestige	0.11	-0.05, 0.26	47%	-0.06	-0.22, 0.1	71%	0.14	-0.01, 0.29	30%	0.07	-0.08, 0.21	69%	0.00	-0.16, 0.15	84%
Leadership	-0.03	-0.18, 0.11	81%	-0.05	-0.2, 0.1	74%	-0.11	-0.26, 0.03	43%	-0.04	-0.18, 0.1	81%	0.05	-0.1, 0.2	76%
Gender2	0.51	0.24, 0.79	0%	0.44	0.15, 0.72	0%	0.64	0.37, 0.91	0%	0.69	0.43, 0.96	0%	0.54	0.27, 0.81	0%
Age	0.02	0.01, 0.04	100%	0.01	-0.01, 0.03	100%	0.01	-0.01, 0.03	100%	0.01	-0.01, 0.02	100%	0.00	-0.02, 0.02	100%

Correlation

Show code

correlation_df <- rape_myth_analysis_df %>%
select(c("acceptance_z", "Dominance_z", "Prestige_z", "Leadership_z", "DEMO_GENDER", "DEMO_Age"))

corr_1 <- correlation::correlation(correlation_df, bayesian = TRUE)


correlation_df_2 <- rape_myth_analysis_df %>%
select(c("Dominance_z", "Prestige_z", "Leadership_z", "DEMO_GENDER", "Victim.Asked.for.it_z", "Victim.Lied_z", "Didn.t.Mean.to_z", "Didn.t.mean.to..intoxication_z", "Wasn.t.Rape_z"))

corr_2 <- correlation::correlation(correlation_df_2, bayesian = TRUE)

Correlation summaries

Parameter	Age	Leadership	Prestige	Dominance	Acceptance
Acceptance	0.08	-0.01	0.04	0.20**	-
Dominance	-0.1	0.16*	0.26**	-
Prestige	-0.09	0.42***	-
Leadership	0.03	-
Age	-

Parameter	Wasn't Rape	Didn't mean to (intoxication)	Didn't Mean to	Victim Lied	Victim Asked for it	Leadership	Prestige	Dominance
Dominance	0.08	0.14*	0.09	0.23**	0.18**	0.16*	0.25***	-
Prestige	-9.90E-03	-0.08	0.05	0.04	0.07	0.42***	-
Leadership	0.03	-0.06	0.01	-8.26E-03	-0.05	-
Victim Asked for it	0.54***	0.33***	0.43***	0.52***	-
Victim Lied	0.45***	0.43***	0.45***	-
Didn't Mean to	0.27***	0.62***	-
Didn't mean to (intoxication)	0.47***	-
Wasn't Rape	-