25_12.01-2Cond-Racism

Load data

Sexism

Racism

datalist <- vector("list")
datalist[["vignracismclean"]] <- vignracismclean
datalist[["vignsexismclean"]] <- vignsexismclean

Pre-registration is here:

Demographic information

Raw

Sexism

Age

Gender

## gender_text
## Non-binary/Other            Woman 
##                1              279

Race

## racef
##                      White Traditionally Marginalized                Multiracial                      Asian 
##                        193                         35                         25                         16
## racef
##                      White Traditionally Marginalized                Multiracial                      Asian 
##    0.681978798586572398932    0.123674911660777389621    0.088339222614840992587    0.056537102473498232480

Failed attention check

## filterout
## Exclude  Retain 
##      36     244

Racism

Age

Gender

## gender_text
##              Man Non-binary/Other            Woman 
##              187                4               89

Race

## racef
## Traditionally Marginalized                Multiracial                      Asian                      White 
##                        165                         34                         64                         19
## racef
## Traditionally Marginalized                Multiracial                      Asian                      White 
##    0.583038869257950564950    0.120141342756183738816    0.226148409893992929920    0.067137809187279157142

Failed attention check

## filterout
## Exclude  Retain 
##      35     246

Clean

Sexism

Age

vignsexismclean %>% ungroup() %>% dplyr::summarize(mean_age = mean(age, na.rm = TRUE), sd_age = sd(age, na.rm = TRUE))

Gender

## gender_text
## Woman 
##   243

Race

## racef
##                      White Traditionally Marginalized                Multiracial                      Asian 
##                        170                         29                         19                         14
## racef
##                      White Traditionally Marginalized                Multiracial                      Asian 
##    0.699588477366255179213    0.119341563786008228454    0.078189300411522638656    0.057613168724279836819

Racism

Age

vignracismclean %>% ungroup() %>% dplyr::summarize(mean_age = mean(age, na.rm = TRUE), sd_age = sd(age, na.rm = TRUE))

Gender

## gender_text
##              Man Non-binary/Other            Woman 
##              155                2               72

Race

## racef
## Traditionally Marginalized                Multiracial                      Asian 
##                        146                         29                         54
## racef
## Traditionally Marginalized                Multiracial                      Asian 
##     0.63478260869565217295     0.12608695652173912416     0.23478260869565217850

Alphas

Sexism

Need for significance

## [1] 0.99045317992164860765

Status

## [1] 0.92970625551126451214

Rewards

## 
##  Pearson's product-moment correlation
## 
## data:  reward1 and reward2
## t = 16.6257888234635978, df = 241, p-value < 0.000000000000000222044604925031
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.66635392353719791192 0.78458465597080939702
## sample estimates:
##                    cor 
## 0.73090675290524020902

Social Rewards

## [1] 0.95121896261361182923

Racism

Need for significance

## [1] 0.98578877093160588441

Status

## [1] 0.93596260020119326217

Rewards

## 
##  Pearson's product-moment correlation
## 
## data:  reward1 and reward2
## t = 26.4438041375674011, df = 228, p-value < 0.000000000000000222044604925031
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.83256769017715981374 0.89699450502945576869
## sample estimates:
##                    cor 
## 0.86839978733800593513

Social Rewards

## [1] 0.91286747648896104934

Breakdown of race perceptions

Jeff’s Ethnicity

with(vignracismclean, table(jeff_ethn, instigation_type))
##                  instigation_type
## jeff_ethn         prejudice traditional
##   Asian                   1           0
##   Black                  20          11
##   Latino/Hispanic         7           3
##   NativeAmerican          0           1
##   White                  90          96
chisq.test(with(vignracismclean, table(jeff_ethn, instigation_type)))
## Warning in chisq.test(with(vignracismclean, table(jeff_ethn, instigation_type))): Chi-squared approximation may be incorrect
## 
##  Pearson's Chi-squared test
## 
## data:  with(vignracismclean, table(jeff_ethn, instigation_type))
## X-squared = 6.19826937380245013, df = 4, p-value = 0.18482260839810441

Paul’s Ethnicity

with(vignracismclean, table(paul_ethn, instigation_type))
##                  instigation_type
## paul_ethn         prejudice traditional
##   Asian                   0           1
##   Black                   6           4
##   Latino/Hispanic         3           1
##   NativeAmerican          1           1
##   White                 108         104
chisq.test(with(vignracismclean, table(paul_ethn, instigation_type)))
## Warning in chisq.test(with(vignracismclean, table(paul_ethn, instigation_type))): Chi-squared approximation may be incorrect
## 
##  Pearson's Chi-squared test
## 
## data:  with(vignracismclean, table(paul_ethn, instigation_type))
## X-squared = 2.26361298138560763, df = 4, p-value = 0.68740148605213713

Means + SDs

Correlations

library(datscience)

racismcorrelations <- corstars(vignracismclean %>% select(c(createdcolumns, "rudeness1", "rudeness2"))) %>% rownames_to_column("scale")
## Warning: Using an external vector in selections was deprecated in tidyselect 1.1.0.
## ℹ Please use `all_of()` or `any_of()` instead.
##   # Was:
##   data %>% select(createdcolumns)
## 
##   # Now:
##   data %>% select(all_of(createdcolumns))
## 
## See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
sexismcorrelations <- corstars(vignsexismclean %>% select(c(createdcolumns, "rudeness1", "rudeness2"))) %>% rownames_to_column("scale")
racismcorr <- racismcorrelations %>% pivot_longer(nfs:rudeness1) %>% filter(value != "") %>% rename(cor = value)
sexismcorr <- sexismcorrelations %>% pivot_longer(nfs:rudeness1) %>% filter(value != "") %>% rename(cor = value)

f1 <- function(df1, df2) {
  # found here: https://stackoverflow.com/questions/57179449/merge-two-correlation-tables-into-a-symmetrical-named-matrix
    col <- unique(c(t(df1[1:2])))
    rn <- unique(c(t(df2[1:2])))
    mat <- matrix(NA, length(rn), length(col), dimnames = list(rn, col))
    mat[lower.tri(mat)] <- df2$cor
    mat[upper.tri(mat)] <- df1$cor
    diag(mat) <- "-"
    return(mat)
}
(combined_corr <- f1(racismcorr, sexismcorr))
##            status                         nfs                            rewards                        socreward                      disruptive                     harder                         badvibes                       learn1                         learn2                         learn3                         rudeness1                      rudeness2                     
## status     "-"                            " 0.589999999999999968914*** " " 0.569999999999999951150*** " " 0.589999999999999968914*** " "-0.390000000000000013323*** " "-0.330000000000000015543*** " "-0.429999999999999993339*** " "-0.050000000000000002776    " " 0.179999999999999993339**  " " 0.270000000000000017764*** " "-0.130000000000000004441    " " 0.390000000000000013323*** "
## nfs        " 0.650000000000000022204*** " "-"                            " 0.849999999999999977796*** " " 0.869999999999999995559*** " "-0.409999999999999975575*** " "-0.369999999999999995559*** " "-0.450000000000000011102*** " "-0.119999999999999995559    " " 0.140000000000000013323*   " " 0.239999999999999991118*** " "-0.200000000000000011102**  " " 0.409999999999999975575*** "
## rewards    " 0.569999999999999951150*** " "-0.359999999999999986677*** " "-"                            " 0.880000000000000004441*** " "-0.429999999999999993339*** " "-0.409999999999999975575*** " "-0.440000000000000002220*** " "-0.059999999999999997780    " " 0.140000000000000013323*   " " 0.270000000000000017764*** " "-0.209999999999999992228**  " " 0.380000000000000004441*** "
## socreward  " 0.780000000000000026645*** " "-0.230000000000000009992*** " "-0.140000000000000013323*   " "-"                            "-0.440000000000000002220*** " "-0.429999999999999993339*** " "-0.460000000000000019984*** " "-0.080000000000000001665    " " 0.130000000000000004441    " " 0.250000000000000000000*** " "-0.170000000000000012212*   " " 0.400000000000000022204*** "
## disruptive " 0.630000000000000004441*** " "-0.320000000000000006661*** " "-0.050000000000000002776    " "-0.029999999999999998890    " "-"                            " 0.810000000000000053291*** " " 0.869999999999999995559*** " " 0.209999999999999992228**  " " 0.059999999999999997780    " "-0.179999999999999993339**  " " 0.020000000000000000416    " "-0.209999999999999992228**  "
## harder     " 0.849999999999999977796*** " " 0.859999999999999986677*** " "-0.010000000000000000208    " "-0.029999999999999998890    " " 0.190000000000000002220**  " "-"                            " 0.819999999999999951150*** " " 0.200000000000000011102**  " " 0.040000000000000000833    " "-0.140000000000000013323*   " " 0.029999999999999998890    " "-0.190000000000000002220**  "
## badvibes   " 0.829999999999999960032*** " "-0.369999999999999995559*** " "-0.040000000000000000833    " "-0.029999999999999998890    " " 0.209999999999999992228*** " "-0.160000000000000003331*   " "-"                            " 0.200000000000000011102**  " " 0.070000000000000006661    " "-0.200000000000000011102**  " " 0.010000000000000000208    " "-0.200000000000000011102**  "
## learn1     "-0.280000000000000026645*** " "-0.429999999999999993339*** " " 0.340000000000000024425*** " " 0.029999999999999998890    " "-0.200000000000000011102**  " "-0.190000000000000002220**  " " 0.029999999999999998890    " "-"                            "-0.209999999999999992228**  " "-0.469999999999999973355*** " "-0.059999999999999997780    " " 0.040000000000000000833    "
## learn2     "-0.380000000000000004441*** " "-0.309999999999999997780*** " " 0.280000000000000026645*** " "-0.029999999999999998890    " "-0.110000000000000000555    " "-0.089999999999999996669    " "-0.080000000000000001665    " " 0.530000000000000026645*** " "-"                            " 0.460000000000000019984*** " " 0.059999999999999997780    " " 0.010000000000000000208    "
## learn3     "-0.239999999999999991118*** " "-0.400000000000000022204*** " " 0.309999999999999997780*** " "-0.280000000000000026645*** " "-0.230000000000000009992*** " "-0.140000000000000013323*   " " 0.179999999999999993339**  " " 0.419999999999999984457*** " "-0.299999999999999988898*** " "-"                            " 0.179999999999999993339**  " " 0.010000000000000000208    "
## rudeness1  "-0.309999999999999997780*** " " 0.829999999999999960032*** " " 0.100000000000000005551    " " 0.289999999999999980016*** " "-0.419999999999999984457*** " " 0.059999999999999997780    " " 0.100000000000000005551    " " 0.450000000000000011102*** " "-0.400000000000000022204*** " " 0.040000000000000000833    " "-"                            " 0.000000000000000000000    "
## rudeness2  "-0.270000000000000017764*** " " 0.829999999999999960032*** " "-0.029999999999999998890    " " 0.179999999999999993339**  " " 0.579999999999999960032*** " " 0.020000000000000000416    " " 0.409999999999999975575*** " "-0.390000000000000013323*** " "-0.089999999999999996669    " " 0.130000000000000004441*   " " 0.040000000000000000833    " "-"
write.csv(combined_corr, "combined_corr.csv")

Analyses

basedatalik <- expand_grid(
  dataset = c("vignsexismclean", "vignracismclean"),
  y = createdcolumns
)

basedatabin <- expand_grid(
  dataset = c("vignsexismclean", "vignracismclean"),
  y = c("reprimanded", "jeffuncivil", "pauluncivil")
)

Main effects

Mediation

Midpoint analyses

Tables

Correlations

Controls

Likert

  nfs nfs status status rewards rewards socreward socreward
Predictors B SE T-value df B SE T-value df B SE T-value df B SE T-value df B SE T-value df B SE T-value df B SE T-value df B SE T-value df
(Intercept) 4.55 *** 0.14 31.40 228.00 2.60 *** 0.62 4.20 219.00 0.91 *** 0.14 6.36 228.00 -0.69 0.60 -1.15 219.00 0.69 *** 0.14 5.08 228.00 -1.33 * 0.57 -2.35 219.00 0.77 *** 0.14 5.48 228.00 -0.54 0.59 -0.91 219.00
age 0.01 0.01 0.60 219.00 0.01 0.01 0.98 219.00 0.01 0.01 0.87 219.00 -0.00 0.01 -0.32 219.00
prejudice Reference Reference Reference Reference Reference Reference Reference Reference
traditional -1.71 *** 0.21 -8.23 228.00 -1.30 *** 0.22 -5.93 219.00 -1.26 *** 0.20 -6.18 228.00 -0.69 ** 0.21 -3.26 219.00 -1.00 *** 0.20 -5.09 228.00 -0.43 * 0.20 -2.15 219.00 -1.15 *** 0.20 -5.73 228.00 -0.62 ** 0.21 -2.98 219.00
learn1 0.06 0.06 0.99 219.00 -0.04 0.06 -0.63 219.00 0.05 0.06 0.81 219.00 0.02 0.06 0.40 219.00
learn2 0.09 0.07 1.30 219.00 0.05 0.06 0.71 219.00 0.02 0.06 0.36 219.00 0.02 0.06 0.28 219.00
learn3 0.28 *** 0.07 4.07 219.00 0.22 ** 0.07 3.26 219.00 0.28 *** 0.06 4.48 219.00 0.25 *** 0.07 3.81 219.00
Traditionally
Marginalized 		Reference Reference Reference Reference Reference Reference Reference Reference
Multiracial 0.02 0.30 0.06 219.00 0.05 0.29 0.17 219.00 -0.23 0.27 -0.82 219.00 -0.27 0.29 -0.94 219.00
Asian -0.00 0.24 -0.00 219.00 0.08 0.23 0.34 219.00 -0.04 0.22 -0.18 219.00 0.18 0.23 0.79 219.00
rudeness1 -0.13 0.08 -1.66 219.00 -0.26 *** 0.08 -3.36 219.00 -0.29 *** 0.07 -3.92 219.00 -0.22 ** 0.08 -2.96 219.00
rudeness2 0.25 *** 0.06 4.02 219.00 0.32 *** 0.06 5.31 219.00 0.29 *** 0.06 5.11 219.00 0.30 *** 0.06 5.06 219.00
Observations 230 229 230 229 230 229 230 229
R2 / R2 adjusted 0.229 / 0.226 0.366 / 0.340 0.144 / 0.140 0.320 / 0.292 0.102 / 0.098 0.306 / 0.278 0.126 / 0.122 0.297 / 0.268
  • p<0.050000000000000002776   ** p<0.010000000000000000208   *** p<0.0010000000000000000208

Binary

  reprimanded reprimanded jeffuncivil jeffuncivil pauluncivil pauluncivil
Predictors B SE T-value df B SE T-value df B SE T-value df B SE T-value df B SE T-value df B SE T-value df
(Intercept) 0.18 *** 0.05 -6.70 Inf 4.40 14934.55 0.00 Inf 0.18 *** 0.05 -6.70 Inf 0.00 0.00 -0.01 Inf 2.81 *** 0.59 4.93 Inf 0.64 1314.49 -0.00 Inf
age 0.97 0.02 -1.44 Inf 0.99 0.02 -0.82 Inf 0.99 0.02 -0.78 Inf
prejudice Reference Reference Reference Reference Reference Reference
traditional 3.87 *** 1.24 4.23 Inf 2.28 * 0.90 2.09 Inf 2.85 ** 0.93 3.23 Inf 1.69 0.67 1.33 Inf 0.67 0.19 -1.41 Inf 0.75 0.27 -0.80 Inf
jeff_ethnBlack 2700517.69 6480013153.27 0.01 Inf 1508380.03 3619425456.58 0.01 Inf 10593274.51 15417426618.02 0.01 Inf
jeff_ethnLatino/Hispanic 1209053.62 2901178484.99 0.01 Inf 811860.53 1948095813.01 0.01 Inf 12386527.69 18027324652.34 0.01 Inf
jeff_ethnNativeAmerican 0.00 0.00 -0.00 Inf 663825.25 2758946423.95 0.00 Inf 27910680906363789312.00 70357879512255887835136.00 0.02 Inf
jeff_ethnWhite 933838.57 2240787461.12 0.01 Inf 788883.18 1892960519.01 0.01 Inf 9945468.86 14474611307.60 0.01 Inf
learn1 1.11 0.13 0.94 Inf 0.93 0.11 -0.62 Inf 1.19 0.12 1.76 Inf
learn2 1.06 0.13 0.46 Inf 1.11 0.13 0.87 Inf 1.23 0.14 1.83 Inf
learn3 0.88 0.11 -0.99 Inf 0.65 ** 0.10 -2.93 Inf 0.78 * 0.09 -2.20 Inf
paul_ethnBlack 0.00 0.00 -0.01 Inf 74248381.80 178162324920.99 0.01 Inf 0.00 0.00 -0.01 Inf
paul_ethnLatino/Hispanic 0.00 0.00 -0.01 Inf 45867873.75 110062032388.56 0.01 Inf 0.00 0.00 -0.01 Inf
paul_ethnNativeAmerican 2.15 7306.59 0.00 Inf 4.33 14679.57 0.00 Inf 0.00 0.00 -0.01 Inf
paul_ethnWhite 0.00 0.00 -0.01 Inf 15507787.25 37211629614.59 0.01 Inf 0.00 0.00 -0.01 Inf
Traditionally
Marginalized 		Reference Reference Reference Reference Reference Reference
Multiracial 0.81 0.47 -0.37 Inf 1.34 0.74 0.53 Inf 0.47 0.23 -1.53 Inf
Asian 1.13 0.47 0.30 Inf 1.34 0.56 0.70 Inf 0.69 0.26 -0.97 Inf
rudeness1 1.62 ** 0.24 3.29 Inf 1.56 ** 0.24 2.95 Inf 0.68 ** 0.09 -2.92 Inf
rudeness2 0.53 *** 0.08 -4.26 Inf 0.58 *** 0.09 -3.70 Inf 1.00 0.11 -0.04 Inf
Observations 230 228 230 228 230 228
R2 Tjur 0.083 0.271 0.047 0.233 0.009 0.154
  • p<0.050000000000000002776   ** p<0.010000000000000000208   *** p<0.0010000000000000000208