Black Only Pilot

Analyses

Interaction

Selected Black candidate

summary(glm(selectedblackcand~cond_end*cond_selection, noselection_clean, family = "binomial"))
## 
## Call:
## glm(formula = selectedblackcand ~ cond_end * cond_selection, 
##     family = "binomial", data = noselection_clean)
## 
## Coefficients:
##                                                            Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                                                  -1.119      0.288   -3.89   0.0001 ***
## cond_endStrongEndorsement                                     1.030      0.378    2.73   0.0064 ** 
## cond_selectionSawSelectionOption                              0.426      0.389    1.10   0.2730    
## cond_endStrongEndorsement:cond_selectionSawSelectionOption   -0.883      0.532   -1.66   0.0968 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 336.14  on 257  degrees of freedom
## Residual deviance: 328.18  on 254  degrees of freedom
##   (59 observations deleted due to missingness)
## AIC: 336.2
## 
## Number of Fisher Scoring iterations: 4

Selected Black candidate

summary(glm(didnotselectanyone~cond_end*cond_selection, noselection_clean, family = "binomial"))
## 
## Call:
## glm(formula = didnotselectanyone ~ cond_end * cond_selection, 
##     family = "binomial", data = noselection_clean)
## 
## Coefficients:
##                                                            Estimate Std. Error z value    Pr(>|z|)    
## (Intercept)                                                  -1.776      0.326   -5.45 0.000000051 ***
## cond_endStrongEndorsement                                     0.211      0.439    0.48        0.63    
## cond_selectionSawSelectionOption                             -0.111      0.471   -0.23        0.81    
## cond_endStrongEndorsement:cond_selectionSawSelectionOption    0.578      0.612    0.94        0.35    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 290.99  on 312  degrees of freedom
## Residual deviance: 286.58  on 309  degrees of freedom
##   (4 observations deleted due to missingness)
## AIC: 294.6
## 
## Number of Fisher Scoring iterations: 4

Controls

Selected Black Candidate

summary(glm(selectedblackcand~cond_end+cond_selection, noselection_clean, family = "binomial"))
## 
## Call:
## glm(formula = selectedblackcand ~ cond_end + cond_selection, 
##     family = "binomial", data = noselection_clean)
## 
## Coefficients:
##                                  Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                       -0.8723     0.2330   -3.74  0.00018 ***
## cond_endStrongEndorsement          0.5922     0.2633    2.25  0.02453 *  
## cond_selectionSawSelectionOption  -0.0454     0.2628   -0.17  0.86296    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 336.14  on 257  degrees of freedom
## Residual deviance: 330.96  on 255  degrees of freedom
##   (59 observations deleted due to missingness)
## AIC: 337
## 
## Number of Fisher Scoring iterations: 4

Did not select anyone

summary(glm(didnotselectanyone~cond_end+cond_selection, noselection_clean, family = "binomial"))
## 
## Call:
## glm(formula = didnotselectanyone ~ cond_end + cond_selection, 
##     family = "binomial", data = noselection_clean)
## 
## Coefficients:
##                                  Estimate Std. Error z value        Pr(>|z|)    
## (Intercept)                        -1.952      0.286   -6.83 0.0000000000085 ***
## cond_endStrongEndorsement           0.515      0.305    1.69           0.091 .  
## cond_selectionSawSelectionOption    0.233      0.299    0.78           0.437    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 290.99  on 312  degrees of freedom
## Residual deviance: 287.48  on 310  degrees of freedom
##   (4 observations deleted due to missingness)
## AIC: 293.5
## 
## Number of Fisher Scoring iterations: 4

Within participants who saw a selection option

Selected Black candidate

summary(glm(selectedblackcand~cond_end, noselection_clean %>% filter(cond_selection == "SawSelectionOption"), family = "binomial"))
## 
## Call:
## glm(formula = selectedblackcand ~ cond_end, family = "binomial", 
##     data = noselection_clean %>% filter(cond_selection == "SawSelectionOption"))
## 
## Coefficients:
##                           Estimate Std. Error z value Pr(>|z|)   
## (Intercept)                 -0.693      0.261   -2.65   0.0079 **
## cond_endStrongEndorsement    0.147      0.374    0.39   0.6951   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 163.03  on 125  degrees of freedom
## Residual deviance: 162.88  on 124  degrees of freedom
##   (32 observations deleted due to missingness)
## AIC: 166.9
## 
## Number of Fisher Scoring iterations: 4

Did not select anyone

summary(glm(didnotselectanyone~cond_end, noselection_clean %>% filter(cond_selection == "SawSelectionOption"), family = "binomial"))
## 
## Call:
## glm(formula = didnotselectanyone ~ cond_end, family = "binomial", 
##     data = noselection_clean %>% filter(cond_selection == "SawSelectionOption"))
## 
## Coefficients:
##                           Estimate Std. Error z value    Pr(>|z|)    
## (Intercept)                 -1.887      0.339   -5.56 0.000000027 ***
## cond_endStrongEndorsement    0.788      0.426    1.85       0.064 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 152.74  on 155  degrees of freedom
## Residual deviance: 149.16  on 154  degrees of freedom
##   (2 observations deleted due to missingness)
## AIC: 153.2
## 
## Number of Fisher Scoring iterations: 4

Within participants who did not see a selection option

Selected Black candidate

summary(glm(selectedblackcand~cond_end, noselection_clean %>% filter(cond_selection == "DidNotSeeSelectionOption"), family = "binomial"))
## 
## Call:
## glm(formula = selectedblackcand ~ cond_end, family = "binomial", 
##     data = noselection_clean %>% filter(cond_selection == "DidNotSeeSelectionOption"))
## 
## Coefficients:
##                           Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                 -1.119      0.288   -3.89   0.0001 ***
## cond_endStrongEndorsement    1.030      0.378    2.73   0.0064 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 173.05  on 131  degrees of freedom
## Residual deviance: 165.30  on 130  degrees of freedom
##   (27 observations deleted due to missingness)
## AIC: 169.3
## 
## Number of Fisher Scoring iterations: 4

Did not select anyone

summary(glm(didnotselectanyone~cond_end, noselection_clean %>% filter(cond_selection == "DidNotSeeSelectionOption"), family = "binomial"))
## 
## Call:
## glm(formula = didnotselectanyone ~ cond_end, family = "binomial", 
##     data = noselection_clean %>% filter(cond_selection == "DidNotSeeSelectionOption"))
## 
## Coefficients:
##                           Estimate Std. Error z value    Pr(>|z|)    
## (Intercept)                 -1.776      0.326   -5.45 0.000000051 ***
## cond_endStrongEndorsement    0.211      0.439    0.48        0.63    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 137.66  on 156  degrees of freedom
## Residual deviance: 137.43  on 155  degrees of freedom
##   (2 observations deleted due to missingness)
## AIC: 141.4
## 
## Number of Fisher Scoring iterations: 4

Considerations

dvs <- c("considerations_1", "considerations_2", "considerations_3", "considerations_4", "considerations_5", "considerations_6")
dvsname <- c("Qualifications", "Skills", "Fit", "Diversity", "Race", "Wrkld")
considerationslist <- vector("list")
considerationsmeans <- vector("list")
library(emmeans)
## Welcome to emmeans.
## Caution: You lose important information if you filter this package's results.
## See '? untidy'
for(variable in 1:length(dvs)){
  index <- match(dvs[variable], colnames(noselection_clean))
  colnames(noselection_clean)[index] <- "y"
  considerationsregression <- lm(y~cond_end*cond_selection, noselection_clean)
  considerations_emm <- emmeans::emmeans(considerationsregression, pairwise~cond_end*cond_selection)
  considerationsmeans[[variable]] <- considerations_emm$emmeans %>% data.frame() %>% mutate(value = paste0(round(emmean, 2), " (", round(SE, 2), ")"),
                                                                                variablename = dvsname[variable]) %>% select(cond_end, cond_selection, variablename, value)
  considerationslist[[variable]] <- considerations_emm$contrasts %>%
    as.data.frame() %>%
    separate(contrast, into = c("G1", "G2"), sep = " - ") %>%
    separate(G1, into = c("End1", "See1")) %>% separate(G2, into = c("End2", "See2")) %>%
    mutate(asterix = case_when(p.value > .05 ~ "", p.value < .05 & p.value > .01 ~ "*", p.value < .01 & p.value > .001 ~ "**", p.value < .001 ~ "***")) %>% select(End1:See2, asterix) %>% mutate(variablename = dvsname[variable])
  colnames(noselection_clean)[index] <- dvs[variable]
}

Means

bind_rows(considerationsmeans) %>% pivot_wider(names_from = variablename, values_from = value)

Differences

bind_rows(considerationslist) %>% pivot_wider(names_from = variablename, values_from = asterix)