##
## ********************* PROCESS for R Version 4.0.1 *********************
##
## Written by Andrew F. Hayes, Ph.D. www.afhayes.com
## Documentation available in Hayes (2022). www.guilford.com/p/hayes3
##
## ***********************************************************************
##
## PROCESS is now ready for use.
## Copyright 2021 by Andrew F. Hayes ALL RIGHTS RESERVED
## Main analyses
| Cand. | ID | Rating |
|---|---|---|
| Bill | W | 9 |
| Sam | B | 9 |
| Fred | W | 2 |
| Name | ~ | 4 |
| Paula | W | 3 |
| Jack | W | 5 |
| Jane | W | 4 |
H1 - Likert
Regression
##
## Call:
## lm(formula = mediator ~ condition_f * assignment_f, data = black1_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.660 -1.460 0.245 1.540 3.555
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.660 0.280 20.21 < 0.0000000000000002 ***
## condition_fcontrol -1.686 0.382 -4.41 0.000014 ***
## assignment_fother_white -0.500 0.391 -1.28 0.20
## assignment_fother_black -0.405 0.402 -1.01 0.32
## condition_fcontrol:assignment_fother_white 0.175 0.534 0.33 0.74
## condition_fcontrol:assignment_fother_black -0.124 0.549 -0.23 0.82
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2 on 317 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.16, Adjusted R-squared: 0.147
## F-statistic: 12.1 on 5 and 317 DF, p-value: 0.0000000000968Estimated Marginal Means
## condition_f assignment_f emmean SE df lower.CL upper.CL
## race self 5.7 0.280 317 5.1 6.2
## control self 4.0 0.260 317 3.5 4.5
## race other_white 5.2 0.272 317 4.6 5.7
## control other_white 3.7 0.256 317 3.1 4.2
## race other_black 5.3 0.289 317 4.7 5.8
## control other_black 3.4 0.267 317 2.9 4.0
##
## Confidence level used: 0.95Pairwise comparisons: conditions across candidates
## assignment_f = self:
## contrast estimate SE df t.ratio p.value
## race - control 1.69 0.38 317 4.400 <.0001
##
## assignment_f = other_white:
## contrast estimate SE df t.ratio p.value
## race - control 1.51 0.37 317 4.000 0.0001
##
## assignment_f = other_black:
## contrast estimate SE df t.ratio p.value
## race - control 1.81 0.39 317 4.600 <.0001Pairwise comparisons: candidates across condition
## condition_f = race:
## contrast estimate SE df t.ratio p.value
## self - other_white 0.50 0.39 317 1.280 0.6100
## self - other_black 0.40 0.40 317 1.010 0.9500
## other_white - other_black -0.09 0.40 317 -0.240 1.0000
##
## condition_f = control:
## contrast estimate SE df t.ratio p.value
## self - other_white 0.32 0.36 317 0.890 1.0000
## self - other_black 0.53 0.37 317 1.420 0.4700
## other_white - other_black 0.20 0.37 317 0.550 1.0000
##
## P value adjustment: bonferroni method for 3 testsGraphs
Ordinal Ranking
Regression
## formula: rank_diversity ~ condition_f * assignment_f
## data: black1_clean
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 323 -224.04 462.09 6(1) 4.78e-13 2.4e+02
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## condition_fcontrol -1.5713 0.4869 -3.23 0.0012 **
## assignment_fother_white 0.5579 0.3796 1.47 0.1417
## assignment_fother_black 0.1942 0.3935 0.49 0.6217
## condition_fcontrol:assignment_fother_white -0.5579 0.6837 -0.82 0.4145
## condition_fcontrol:assignment_fother_black 0.0699 0.6803 0.10 0.9181
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold coefficients:
## Estimate Std. Error z value
## 2|4 0.423 0.275 1.54
## 4|3 0.925 0.281 3.29
## (2 observations deleted due to missingness)Graphs
Binary (based on ranking)
Regression
##
## Call:
## glm(formula = bin_rank_div ~ condition_f * assignment_f, family = binomial(),
## data = black1_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.516 0.368 -4.12 0.000038 ***
## condition_fcontrol -1.392 0.698 -2.00 0.046 *
## assignment_fother_white -0.542 0.569 -0.95 0.341
## assignment_fother_black -0.405 0.571 -0.71 0.478
## condition_fcontrol:assignment_fother_white -0.627 1.301 -0.48 0.630
## condition_fcontrol:assignment_fother_black -0.675 1.302 -0.52 0.604
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 180.87 on 322 degrees of freedom
## Residual deviance: 164.26 on 317 degrees of freedom
## (2 observations deleted due to missingness)
## AIC: 176.3
##
## Number of Fisher Scoring iterations: 6## (Intercept) condition_fcontrol assignment_fother_white assignment_fother_black
## 0.24 0.18 0.51 0.58## 2.5 % 97.5 %
## (Intercept) 0.118 0.44
## condition_fcontrol 0.059 0.46
## assignment_fother_white 0.179 1.35
## assignment_fother_black 0.203 1.53Estimated Marginal Means
## condition_f assignment_f emmean SE df asymp.LCL asymp.UCL
## race self -1.5 0.37 Inf -2.2 -0.79
## control self -2.9 0.59 Inf -4.1 -1.75
## race other_white -2.1 0.43 Inf -2.9 -1.21
## control other_white -4.1 1.01 Inf -6.1 -2.10
## race other_black -1.9 0.44 Inf -2.8 -1.07
## control other_black -4.0 1.01 Inf -6.0 -2.01
##
## Results are given on the logit (not the response) scale.
## Confidence level used: 0.95Pairwise comparisons: conditions across candidates
## assignment_f = self:
## contrast estimate SE df z.ratio p.value
## race - control 1.39 0.7 Inf 2.000 0.0460
##
## assignment_f = other_white:
## contrast estimate SE df z.ratio p.value
## race - control 2.02 1.1 Inf 1.840 0.0660
##
## assignment_f = other_black:
## contrast estimate SE df z.ratio p.value
## race - control 2.07 1.1 Inf 1.880 0.0600
##
## Results are given on the log odds ratio (not the response) scale.Pairwise comparisons: candidates across condition
## condition_f = race:
## contrast estimate SE df z.ratio p.value
## self - other_white 0.54 0.57 Inf 0.950 1.0000
## self - other_black 0.41 0.57 Inf 0.710 1.0000
## other_white - other_black -0.14 0.62 Inf -0.220 1.0000
##
## condition_f = control:
## contrast estimate SE df z.ratio p.value
## self - other_white 1.17 1.17 Inf 1.000 0.9500
## self - other_black 1.08 1.17 Inf 0.920 1.0000
## other_white - other_black -0.09 1.43 Inf -0.060 1.0000
##
## Results are given on the log odds ratio (not the response) scale.
## P value adjustment: bonferroni method for 3 testsGraphs
H3a: Selecting unqualified candidate
Regression
##
## Call:
## glm(formula = chosename ~ condition_f * assignment_f, family = binomial(),
## data = black1_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.386 0.354 -3.92 0.000088 ***
## condition_fcontrol -0.811 0.557 -1.46 0.145
## assignment_fother_white -1.852 0.803 -2.31 0.021 *
## assignment_fother_black -0.536 0.562 -0.95 0.341
## condition_fcontrol:assignment_fother_white 0.682 1.161 0.59 0.557
## condition_fcontrol:assignment_fother_black -0.120 0.924 -0.13 0.897
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 195.49 on 324 degrees of freedom
## Residual deviance: 182.81 on 319 degrees of freedom
## AIC: 194.8
##
## Number of Fisher Scoring iterations: 6## (Intercept) condition_fcontrol assignment_fother_white assignment_fother_black
## 0.24 0.48 0.21 0.56## 2.5 % 97.5 %
## (Intercept) 0.125 0.44
## condition_fcontrol 0.209 1.04
## assignment_fother_white 0.058 0.60
## assignment_fother_black 0.226 1.32Estimated Marginal Means
## condition_f assignment_f emmean SE df asymp.LCL asymp.UCL
## race self -1.4 0.35 Inf -2.1 -0.69
## control self -2.2 0.43 Inf -3.0 -1.35
## race other_white -3.2 0.72 Inf -4.7 -1.83
## control other_white -3.4 0.72 Inf -4.8 -1.96
## race other_black -1.9 0.44 Inf -2.8 -1.07
## control other_black -2.9 0.59 Inf -4.0 -1.69
##
## Results are given on the logit (not the response) scale.
## Confidence level used: 0.95Pairwise comparisons: conditions across candidates
## assignment_f = self:
## contrast estimate SE df z.ratio p.value
## race - control 0.81 0.56 Inf 1.460 0.1500
##
## assignment_f = other_white:
## contrast estimate SE df z.ratio p.value
## race - control 0.13 1.02 Inf 0.130 0.9000
##
## assignment_f = other_black:
## contrast estimate SE df z.ratio p.value
## race - control 0.93 0.74 Inf 1.260 0.2100
##
## Results are given on the log odds ratio (not the response) scale.Pairwise comparisons: candidates across condition
## condition_f = race:
## contrast estimate SE df z.ratio p.value
## self - other_white 1.85 0.80 Inf 2.310 0.0600
## self - other_black 0.54 0.56 Inf 0.950 1.0000
## other_white - other_black -1.32 0.84 Inf -1.560 0.3500
##
## condition_f = control:
## contrast estimate SE df z.ratio p.value
## self - other_white 1.17 0.84 Inf 1.400 0.4900
## self - other_black 0.66 0.73 Inf 0.890 1.0000
## other_white - other_black -0.51 0.93 Inf -0.550 1.0000
##
## Results are given on the log odds ratio (not the response) scale.
## P value adjustment: bonferroni method for 3 testsGraphs
H3b: Mediation
Likert mediator
##
## Mediation/Moderation Analysis
## Call: psych::mediate(y = chosename ~ cond_num + (mediator), data = black1_clean,
## z = "assignment_num")
##
## The DV (Y) was chosename . The IV (X) was cond_num . The mediating variable(s) = mediator .
##
## Total effect(c) of cond_num on chosename = 0.06 S.E. = 0.03 t = 1.8 df= 323 with p = 0.072
## Direct effect (c') of cond_num on chosename removing mediator = 0 S.E. = 0.03 t = 0.03 df= 322 with p = 0.97
## Indirect effect (ab) of cond_num on chosename through mediator = 0.06
## Mean bootstrapped indirect effect = 0.06 with standard error = 0.02 Lower CI = 0.03 Upper CI = 0.09
## R = 0.25 R2 = 0.06 F = 11 on 2 and 322 DF p-value: 0.00000063
##
## To see the longer output, specify short = FALSE in the print statement or ask for the summaryOrdinal mediator
##
## Mediation/Moderation Analysis
## Call: psych::mediate(y = chosename ~ cond_num + (diversityr), data = black1_clean,
## z = "assignment_num")
##
## The DV (Y) was chosename . The IV (X) was cond_num . The mediating variable(s) = diversityr .
##
## Total effect(c) of cond_num on chosename = 0.06 S.E. = 0.03 t = 1.8 df= 323 with p = 0.072
## Direct effect (c') of cond_num on chosename removing diversityr = -0.02 S.E. = 0.03 t = -0.55 df= 322 with p = 0.58
## Indirect effect (ab) of cond_num on chosename through diversityr = 0.07
## Mean bootstrapped indirect effect = 0.07 with standard error = 0.02 Lower CI = 0.04 Upper CI = 0.12
## R = 0.35 R2 = 0.13 F = 23 on 2 and 322 DF p-value: 0.00000000000013
##
## To see the longer output, specify short = FALSE in the print statement or ask for the summaryBinary mediator
##
## Mediation/Moderation Analysis
## Call: psych::mediate(y = chosename ~ cond_num + (bin_rank_div), data = black1_clean,
## z = "assignment_num")
##
## The DV (Y) was chosename . The IV (X) was cond_num . The mediating variable(s) = bin_rank_div .
##
## Total effect(c) of cond_num on chosename = 0.06 S.E. = 0.03 t = 1.8 df= 323 with p = 0.072
## Direct effect (c') of cond_num on chosename removing bin_rank_div = 0.01 S.E. = 0.03 t = 0.42 df= 322 with p = 0.67
## Indirect effect (ab) of cond_num on chosename through bin_rank_div = 0.04
## Mean bootstrapped indirect effect = 0.04 with standard error = 0.02 Lower CI = 0.02 Upper CI = 0.08
## R = 0.39 R2 = 0.15 F = 28 on 2 and 322 DF p-value: 0.0000000000000003
##
## To see the longer output, specify short = FALSE in the print statement or ask for the summaryExploratory
Moderated Mediation
Likert Mediator
Long Summary
## Call: psych::mediate(y = chosename ~ cond_num * assignment_num + (mediator),
## data = black1_clean)
##
## Direct effect estimates (traditional regression) (c') X + M on Y
## chosename se t df Prob
## Intercept 0.00 0.02 0.00 320 0.998000
## cond_num 0.00 0.03 0.08 320 0.935000
## assignment_num 0.02 0.02 1.15 320 0.253000
## cond_num*assignment_num 0.02 0.04 0.43 320 0.668000
## mediator 0.03 0.01 4.19 320 0.000036
##
## R = 0.26 R2 = 0.07 F = 5.9 on 4 and 320 DF p-value: 0.00014
##
## Total effect estimates (c) (X on Y)
## chosename se t df Prob
## Intercept 0.00 0.02 0.00 321 1.00
## cond_num 0.06 0.03 1.81 321 0.07
## assignment_num 0.03 0.02 1.53 321 0.13
## cond_num*assignment_num 0.01 0.04 0.36 321 0.72
##
## 'a' effect estimates (X on M)
## mediator se t df Prob
## Intercept 0.00 0.11 0.00 321 0.99900000000000
## cond_num 1.67 0.22 7.58 321 0.00000000000038
## assignment_num 0.24 0.14 1.79 321 0.07370000000000
## cond_num*assignment_num -0.07 0.27 -0.25 321 0.80200000000000
##
## 'b' effect estimates (M on Y controlling for X)
## chosename se t df Prob
## mediator 0.03 0.01 4.2 320 0.000036
##
## 'ab' effect estimates (through all mediators)
## chosename boot sd lower upper
## cond_num 0.05 0.06 0.01 0.03 0.09
## assignment_num 0.01 0.01 0.00 0.03 0.09
## cond_num*assignment_num 0.00 0.00 0.01 0.03 0.09Process
##
## ********************* PROCESS for R Version 4.0.1 *********************
##
## Written by Andrew F. Hayes, Ph.D. www.afhayes.com
## Documentation available in Hayes (2022). www.guilford.com/p/hayes3
##
## ***********************************************************************
##
## Model : 7
## Y : chosename
## X : cond_num
## M : mediator
## W : assignment_num
##
## Sample size: 323
##
## Random seed: 728995
##
##
## ***********************************************************************
## Outcome Variable: mediator
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.3977 0.1581 3.9088 19.9734 3.0000 319.0000 0.0000
##
## Model:
## coeff se t p LLCI ULCI
## constant 3.6890 0.1503 24.5367 0.0000 3.3932 3.9848
## cond_num 1.6635 0.2206 7.5397 0.0000 1.2294 2.0976
## assignment_num 0.2649 0.1860 1.4239 0.1554 -0.1011 0.6309
## Int_1 -0.0593 0.2737 -0.2166 0.8286 -0.5978 0.4792
##
## Product terms key:
## Int_1 : cond_num x assignment_num
##
## Test(s) of highest order unconditional interaction(s):
## R2-chng F df1 df2 p
## X*W 0.0001 0.0469 1.0000 319.0000 0.8286
##
## ***********************************************************************
## Outcome Variable: chosename
##
## Coding of binary Y for logistic regression analysis:
## chosename Analysis
## 0.0000 0.0000
## 1.0000 1.0000
##
## Model Summary:
## -2LL ModelLL df p McFadden CoxSnell Nagelkrk
## 168.9435 26.1718 2.0000 0.0000 0.1341 0.0778 0.1717
##
## Model:
## coeff se Z p LLCI ULCI
## constant -5.8756 1.0422 -5.6375 0.0000 -7.9184 -3.8329
## cond_num 0.0518 0.4225 0.1227 0.9024 -0.7763 0.8800
## mediator 0.6497 0.1697 3.8279 0.0001 0.3170 0.9824
##
## These results are expressed in a log-odds metric.
##
## ***********************************************************************
## Bootstrapping progress:
##
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##
## **************** DIRECT AND INDIRECT EFFECTS OF X ON Y ****************
##
## Direct effect of X on Y:
## effect se Z p LLCI ULCI
## 0.0518 0.4225 0.1227 0.9024 -0.7763 0.8800
##
## Conditional indirect effects of X on Y:
##
## INDIRECT EFFECT:
##
## cond_num -> mediator -> chosename
##
## assignment_num Effect BootSE BootLLCI BootULCI
## -1.0000 1.1193 0.4745 0.5089 2.3166
## 0.0000 1.0808 0.4364 0.5266 2.2410
## 1.0000 1.0423 0.4795 0.4379 2.2886
##
## Index of moderated mediation:
## Index BootSE BootLLCI BootULCI
## assignment_num -0.0385 0.1925 -0.4265 0.3515
##
## ---
##
## ******************** ANALYSIS NOTES AND ERRORS ************************
##
## Level of confidence for all confidence intervals in output: 95
##
## Number of bootstraps for percentile bootstrap confidence intervals: 5000
##
## W values in conditional tables are the 16th, 50th, and 84th percentiles.
##
## NOTE: Some cases with missing data were deleted. The number of deleted cases was: 2Plot
##
## Mediation/Moderation Analysis
## Call: psych::mediate(y = chosename ~ cond_num * assignment_num + (mediator),
## data = black1_clean)
##
## The DV (Y) was chosename . The IV (X) was cond_num assignment_num cond_num*assignment_num . The mediating variable(s) = mediator .
##
## Total effect(c) of cond_num on chosename = 0.06 S.E. = 0.03 t = 1.8 df= 321 with p = 0.071
## Direct effect (c') of cond_num on chosename removing mediator = 0 S.E. = 0.03 t = 0.08 df= 320 with p = 0.93
## Indirect effect (ab) of cond_num on chosename through mediator = 0.05
## Mean bootstrapped indirect effect = 0.06 with standard error = 0.01 Lower CI = 0.03 Upper CI = 0.09
##
## Total effect(c) of assignment_num on chosename = 0.03 S.E. = 0.02 t = 1.5 df= 321 with p = 0.13
## Direct effect (c') of assignment_num on chosename removing mediator = 0.02 S.E. = 0.02 t = 1.1 df= 320 with p = 0.25
## Indirect effect (ab) of assignment_num on chosename through mediator = 0.01
## Mean bootstrapped indirect effect = 0.01 with standard error = 0 Lower CI = 0 Upper CI = 0.02
##
## Total effect(c) of cond_num*assignment_num on chosename = 0.01 S.E. = 0.04 t = 0.36 df= 321 with p = 0.72
## Direct effect (c') of cond_num*assignment_num on chosename removing mediator = 0.02 S.E. = 0.04 t = 0.43 df= 320 with p = 0.67
## Indirect effect (ab) of cond_num*assignment_num on chosename through mediator = 0
## Mean bootstrapped indirect effect = 0 with standard error = 0.01 Lower CI = -0.02 Upper CI = 0.01
## R = 0.26 R2 = 0.07 F = 5.9 on 4 and 320 DF p-value: 0.000031
##
## To see the longer output, specify short = FALSE in the print statement or ask for the summaryOrdinal Mediator
Long Summary
## Call: psych::mediate(y = chosename ~ cond_num * assignment_num + (diversityr),
## data = black1_clean)
##
## Direct effect estimates (traditional regression) (c') X + M on Y
## chosename se t df Prob
## Intercept 0.00 0.01 0.00 320 0.99800000000
## cond_num -0.02 0.03 -0.54 320 0.59100000000
## assignment_num 0.03 0.02 1.54 320 0.12500000000
## cond_num*assignment_num 0.01 0.04 0.37 320 0.71500000000
## diversityr 0.17 0.03 6.51 320 0.00000000028
##
## R = 0.36 R2 = 0.13 F = 12 on 4 and 320 DF p-value: 0.0000000029
##
## Total effect estimates (c) (X on Y)
## chosename se t df Prob
## Intercept 0.00 0.02 0.00 321 1.00
## cond_num 0.06 0.03 1.81 321 0.07
## assignment_num 0.03 0.02 1.53 321 0.13
## cond_num*assignment_num 0.01 0.04 0.36 321 0.72
##
## 'a' effect estimates (X on M)
## diversityr se t df Prob
## Intercept 0.00 0.03 0.00 321 1.00000000000
## cond_num 0.45 0.07 6.89 321 0.00000000003
## assignment_num 0.01 0.04 0.25 321 0.80300000000
## cond_num*assignment_num 0.00 0.08 0.05 321 0.96000000000
##
## 'b' effect estimates (M on Y controlling for X)
## chosename se t df Prob
## diversityr 0.17 0.03 6.5 320 0.00000000028
##
## 'ab' effect estimates (through all mediators)
## chosename boot sd lower upper
## cond_num 0.07 0.07 0.02 0.04 0.12
## assignment_num 0.00 0.00 0.01 0.04 0.12
## cond_num*assignment_num 0.00 0.00 0.01 0.04 0.12Plot
##
## Mediation/Moderation Analysis
## Call: psych::mediate(y = chosename ~ cond_num * assignment_num + (diversityr),
## data = black1_clean)
##
## The DV (Y) was chosename . The IV (X) was cond_num assignment_num cond_num*assignment_num . The mediating variable(s) = diversityr .
##
## Total effect(c) of cond_num on chosename = 0.06 S.E. = 0.03 t = 1.8 df= 321 with p = 0.071
## Direct effect (c') of cond_num on chosename removing diversityr = -0.02 S.E. = 0.03 t = -0.54 df= 320 with p = 0.59
## Indirect effect (ab) of cond_num on chosename through diversityr = 0.07
## Mean bootstrapped indirect effect = 0.07 with standard error = 0.02 Lower CI = 0.03 Upper CI = 0.12
##
## Total effect(c) of assignment_num on chosename = 0.03 S.E. = 0.02 t = 1.5 df= 321 with p = 0.13
## Direct effect (c') of assignment_num on chosename removing diversityr = 0.03 S.E. = 0.02 t = 1.5 df= 320 with p = 0.13
## Indirect effect (ab) of assignment_num on chosename through diversityr = 0
## Mean bootstrapped indirect effect = 0 with standard error = 0.01 Lower CI = -0.01 Upper CI = 0.02
##
## Total effect(c) of cond_num*assignment_num on chosename = 0.01 S.E. = 0.04 t = 0.36 df= 321 with p = 0.72
## Direct effect (c') of cond_num*assignment_num on chosename removing diversityr = 0.01 S.E. = 0.04 t = 0.37 df= 320 with p = 0.71
## Indirect effect (ab) of cond_num*assignment_num on chosename through diversityr = 0
## Mean bootstrapped indirect effect = 0 with standard error = 0.01 Lower CI = -0.03 Upper CI = 0.03
## R = 0.36 R2 = 0.13 F = 12 on 4 and 320 DF p-value: 0.000000000074
##
## To see the longer output, specify short = FALSE in the print statement or ask for the summaryProcess
##
## ********************* PROCESS for R Version 4.0.1 *********************
##
## Written by Andrew F. Hayes, Ph.D. www.afhayes.com
## Documentation available in Hayes (2022). www.guilford.com/p/hayes3
##
## ***********************************************************************
##
## Model : 7
## Y : chosename
## X : cond_num
## M : diversityr
## W : assignment_num
##
## Sample size: 323
##
## Random seed: 282103
##
##
## ***********************************************************************
## Outcome Variable: diversityr
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.3587 0.1287 0.3466 15.7044 3.0000 319.0000 0.0000
##
## Model:
## coeff se t p LLCI ULCI
## constant 2.1560 0.0448 48.1571 0.0000 2.0679 2.2441
## cond_num 0.4504 0.0657 6.8560 0.0000 0.3212 0.5797
## assignment_num 0.0047 0.0554 0.0850 0.9323 -0.1043 0.1137
## Int_1 0.0075 0.0815 0.0916 0.9271 -0.1529 0.1678
##
## Product terms key:
## Int_1 : cond_num x assignment_num
##
## Test(s) of highest order unconditional interaction(s):
## R2-chng F df1 df2 p
## X*W 0.0000 0.0084 1.0000 319.0000 0.9271
##
## ***********************************************************************
## Outcome Variable: chosename
##
## Coding of binary Y for logistic regression analysis:
## chosename Analysis
## 0.0000 0.0000
## 1.0000 1.0000
##
## Model Summary:
## -2LL ModelLL df p McFadden CoxSnell Nagelkrk
## 163.9747 31.1406 2.0000 0.0000 0.1596 0.0919 0.2027
##
## Model:
## coeff se Z p LLCI ULCI
## constant -6.1369 0.7911 -7.7571 0.0000 -7.6875 -4.5863
## cond_num -0.1656 0.4678 -0.3539 0.7234 -1.0825 0.7514
## diversityr 1.4839 0.2874 5.1630 0.0000 0.9206 2.0472
##
## These results are expressed in a log-odds metric.
##
## ***********************************************************************
## Bootstrapping progress:
##
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##
## **************** DIRECT AND INDIRECT EFFECTS OF X ON Y ****************
##
## Direct effect of X on Y:
## effect se Z p LLCI ULCI
## -0.1656 0.4678 -0.3539 0.7234 -1.0825 0.7514
##
## Conditional indirect effects of X on Y:
##
## INDIRECT EFFECT:
##
## cond_num -> diversityr -> chosename
##
## assignment_num Effect BootSE BootLLCI BootULCI
## -1.0000 0.6573 0.2213 0.3032 1.1550
## 0.0000 0.6684 0.1826 0.3577 1.0773
## 1.0000 0.6795 0.2331 0.2937 1.2218
##
## Index of moderated mediation:
## Index BootSE BootLLCI BootULCI
## assignment_num 0.0111 0.1353 -0.2585 0.2877
##
## ---
##
## ******************** ANALYSIS NOTES AND ERRORS ************************
##
## Level of confidence for all confidence intervals in output: 95
##
## Number of bootstraps for percentile bootstrap confidence intervals: 5000
##
## W values in conditional tables are the 16th, 50th, and 84th percentiles.
##
## NOTE: Some cases with missing data were deleted. The number of deleted cases was: 2Binary Mediator
Long Summary
## Call: psych::mediate(y = chosename ~ cond_num * assignment_num + (bin_rank_div),
## data = black1_clean)
##
## Direct effect estimates (traditional regression) (c') X + M on Y
## chosename se t df Prob
## Intercept 0.00 0.01 0.00 320 0.9990000000000
## cond_num 0.01 0.03 0.45 320 0.6560000000000
## assignment_num 0.02 0.02 1.17 320 0.2430000000000
## cond_num*assignment_num 0.01 0.04 0.29 320 0.7720000000000
## bin_rank_div 0.40 0.06 7.16 320 0.0000000000056
##
## R = 0.39 R2 = 0.15 F = 14 on 4 and 320 DF p-value: 0.000000000071
##
## Total effect estimates (c) (X on Y)
## chosename se t df Prob
## Intercept 0.00 0.02 0.00 321 1.00
## cond_num 0.06 0.03 1.81 321 0.07
## assignment_num 0.03 0.02 1.53 321 0.13
## cond_num*assignment_num 0.01 0.04 0.36 321 0.72
##
## 'a' effect estimates (X on M)
## bin_rank_div se t df Prob
## Intercept 0.00 0.01 0.00 321 0.99900
## cond_num 0.11 0.03 3.74 321 0.00021
## assignment_num 0.02 0.02 1.19 321 0.23500
## cond_num*assignment_num 0.01 0.04 0.25 321 0.80400
##
## 'b' effect estimates (M on Y controlling for X)
## chosename se t df Prob
## bin_rank_div 0.4 0.06 7.2 320 0.0000000000056
##
## 'ab' effect estimates (through all mediators)
## chosename boot sd lower upper
## cond_num 0.04 0.04 0.02 0.02 0.08
## assignment_num 0.01 0.01 0.01 0.02 0.08
## cond_num*assignment_num 0.00 0.00 0.02 0.02 0.08Plot
##
## Mediation/Moderation Analysis
## Call: psych::mediate(y = chosename ~ cond_num * assignment_num + (bin_rank_div),
## data = black1_clean)
##
## The DV (Y) was chosename . The IV (X) was cond_num assignment_num cond_num*assignment_num . The mediating variable(s) = bin_rank_div .
##
## Total effect(c) of cond_num on chosename = 0.06 S.E. = 0.03 t = 1.8 df= 321 with p = 0.071
## Direct effect (c') of cond_num on chosename removing bin_rank_div = 0.01 S.E. = 0.03 t = 0.45 df= 320 with p = 0.66
## Indirect effect (ab) of cond_num on chosename through bin_rank_div = 0.04
## Mean bootstrapped indirect effect = 0.04 with standard error = 0.02 Lower CI = 0.02 Upper CI = 0.08
##
## Total effect(c) of assignment_num on chosename = 0.03 S.E. = 0.02 t = 1.5 df= 321 with p = 0.13
## Direct effect (c') of assignment_num on chosename removing bin_rank_div = 0.02 S.E. = 0.02 t = 1.2 df= 320 with p = 0.24
## Indirect effect (ab) of assignment_num on chosename through bin_rank_div = 0.01
## Mean bootstrapped indirect effect = 0.01 with standard error = 0.01 Lower CI = -0.01 Upper CI = 0.03
##
## Total effect(c) of cond_num*assignment_num on chosename = 0.01 S.E. = 0.04 t = 0.36 df= 321 with p = 0.72
## Direct effect (c') of cond_num*assignment_num on chosename removing bin_rank_div = 0.01 S.E. = 0.04 t = 0.29 df= 320 with p = 0.77
## Indirect effect (ab) of cond_num*assignment_num on chosename through bin_rank_div = 0
## Mean bootstrapped indirect effect = 0 with standard error = 0.02 Lower CI = -0.03 Upper CI = 0.04
## R = 0.39 R2 = 0.15 F = 14 on 4 and 320 DF p-value: 0.00000000000087
##
## To see the longer output, specify short = FALSE in the print statement or ask for the summarySelecting the candidates
Bill (High qualified, White)
Summary
##
## Call:
## glm(formula = chosebill ~ condition_f * assignment_f, family = binomial(),
## data = black1_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.266 0.341 3.71 0.00021 ***
## condition_fcontrol 0.759 0.528 1.44 0.15036
## assignment_fother_white 0.793 0.552 1.44 0.15084
## assignment_fother_black 0.175 0.504 0.35 0.72886
## condition_fcontrol:assignment_fother_white -0.419 0.827 -0.51 0.61235
## condition_fcontrol:assignment_fother_black 1.790 1.197 1.49 0.13499
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 238.50 on 324 degrees of freedom
## Residual deviance: 223.68 on 319 degrees of freedom
## AIC: 235.7
##
## Number of Fisher Scoring iterations: 6Estimated Marginal Means
## condition_f assignment_f emmean SE df asymp.LCL asymp.UCL
## race self 1.3 0.34 Inf 0.60 1.9
## control self 2.0 0.40 Inf 1.24 2.8
## race other_white 2.1 0.43 Inf 1.21 2.9
## control other_white 2.4 0.47 Inf 1.48 3.3
## race other_black 1.4 0.37 Inf 0.71 2.2
## control other_black 4.0 1.01 Inf 2.01 6.0
##
## Results are given on the logit (not the response) scale.
## Confidence level used: 0.95Pairwise comparisons: conditions across candidates
## assignment_f = self:
## contrast estimate SE df z.ratio p.value
## race - control -0.76 0.53 Inf -1.440 0.1500
##
## assignment_f = other_white:
## contrast estimate SE df z.ratio p.value
## race - control -0.34 0.64 Inf -0.530 0.5900
##
## assignment_f = other_black:
## contrast estimate SE df z.ratio p.value
## race - control -2.55 1.08 Inf -2.370 0.0200
##
## Results are given on the log odds ratio (not the response) scale.Pairwise comparisons: candidates across condition
## condition_f = race:
## contrast estimate SE df z.ratio p.value
## self - other_white -0.79 0.55 Inf -1.440 0.4500
## self - other_black -0.17 0.50 Inf -0.350 1.0000
## other_white - other_black 0.62 0.57 Inf 1.080 0.8400
##
## condition_f = control:
## contrast estimate SE df z.ratio p.value
## self - other_white -0.37 0.62 Inf -0.610 1.0000
## self - other_black -1.96 1.09 Inf -1.810 0.2100
## other_white - other_black -1.59 1.11 Inf -1.430 0.4600
##
## Results are given on the log odds ratio (not the response) scale.
## P value adjustment: bonferroni method for 3 testsSam (High qualified, Black)
Summary
##
## Call:
## glm(formula = chosesam ~ condition_f * assignment_f, family = binomial(),
## data = black1_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.89 1.01 3.85 0.00012 ***
## condition_fcontrol -1.69 1.10 -1.54 0.12273
## assignment_fother_white -1.08 1.17 -0.92 0.35750
## assignment_fother_black -1.21 1.17 -1.03 0.30386
## condition_fcontrol:assignment_fother_white 2.25 1.44 1.56 0.11864
## condition_fcontrol:assignment_fother_black 2.29 1.44 1.59 0.11296
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 133.42 on 324 degrees of freedom
## Residual deviance: 128.90 on 319 degrees of freedom
## AIC: 140.9
##
## Number of Fisher Scoring iterations: 6Estimated Marginal Means
## condition_f assignment_f emmean SE df asymp.LCL asymp.UCL
## race self 3.9 1.01 Inf 1.91 5.9
## control self 2.2 0.43 Inf 1.35 3.0
## race other_white 2.8 0.59 Inf 1.65 4.0
## control other_white 3.4 0.72 Inf 1.96 4.8
## race other_black 2.7 0.60 Inf 1.52 3.9
## control other_black 3.3 0.72 Inf 1.87 4.7
##
## Results are given on the logit (not the response) scale.
## Confidence level used: 0.95Pairwise comparisons: conditions across candidates
## assignment_f = self:
## contrast estimate SE df z.ratio p.value
## race - control 1.69 1.10 Inf 1.540 0.1200
##
## assignment_f = other_white:
## contrast estimate SE df z.ratio p.value
## race - control -0.55 0.93 Inf -0.590 0.5500
##
## assignment_f = other_black:
## contrast estimate SE df z.ratio p.value
## race - control -0.59 0.94 Inf -0.630 0.5300
##
## Results are given on the log odds ratio (not the response) scale.Pairwise comparisons: candidates across condition
## condition_f = race:
## contrast estimate SE df z.ratio p.value
## self - other_white 1.08 1.17 Inf 0.920 1.0000
## self - other_black 1.21 1.17 Inf 1.030 0.9100
## other_white - other_black 0.13 0.84 Inf 0.150 1.0000
##
## condition_f = control:
## contrast estimate SE df z.ratio p.value
## self - other_white -1.17 0.84 Inf -1.400 0.4900
## self - other_black -1.08 0.84 Inf -1.290 0.5900
## other_white - other_black 0.09 1.02 Inf 0.090 1.0000
##
## Results are given on the log odds ratio (not the response) scale.
## P value adjustment: bonferroni method for 3 testsGraphs
Fred (Low qualified, White)
Summary
##
## Call:
## glm(formula = chosefred ~ condition_f * assignment_f, family = binomial(),
## data = black1_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -22.566068522989 6815.963810967535 0 1
## condition_fcontrol 18.488531079084 6815.963885568100 0 1
## assignment_fother_white 18.614824804408 6815.963885735483 0 1
## assignment_fother_black -0.000000000303 9791.837872799135 0 1
## condition_fcontrol:assignment_fother_white -37.103355883819 9228.858738146075 0 1
## condition_fcontrol:assignment_fother_black -18.488531079084 11752.193668264686 0 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 24.350 on 324 degrees of freedom
## Residual deviance: 20.094 on 319 degrees of freedom
## AIC: 32.09
##
## Number of Fisher Scoring iterations: 21Estimated Marginal Means
## condition_f assignment_f emmean SE df asymp.LCL asymp.UCL
## race self -22.6 6816 Inf -13382 13336
## control self -4.1 1 Inf -6 -2
## race other_white -4.0 1 Inf -6 -2
## control other_white -22.6 6222 Inf -12218 12173
## race other_black -22.6 7030 Inf -13801 13756
## control other_black -22.6 6499 Inf -12760 12715
##
## Results are given on the logit (not the response) scale.
## Confidence level used: 0.95Pairwise comparisons: conditions across candidates
## assignment_f = self:
## contrast estimate SE df z.ratio p.value
## race - control -18.5 6816 Inf -0.003 1.0000
##
## assignment_f = other_white:
## contrast estimate SE df z.ratio p.value
## race - control 18.6 6222 Inf 0.003 1.0000
##
## assignment_f = other_black:
## contrast estimate SE df z.ratio p.value
## race - control 0.0 9574 Inf 0.000 1.0000
##
## Results are given on the log odds ratio (not the response) scale.Pairwise comparisons: candidates across condition
## condition_f = race:
## contrast estimate SE df z.ratio p.value
## self - other_white -18.6 6816 Inf -0.003 1.0000
## self - other_black 0.0 9792 Inf 0.000 1.0000
## other_white - other_black 18.6 7030 Inf 0.003 1.0000
##
## condition_f = control:
## contrast estimate SE df z.ratio p.value
## self - other_white 18.5 6222 Inf 0.003 1.0000
## self - other_black 18.5 6499 Inf 0.003 1.0000
## other_white - other_black 0.0 8997 Inf 0.000 1.0000
##
## Results are given on the log odds ratio (not the response) scale.
## P value adjustment: bonferroni method for 3 testsGraphs
Paula (Low qualified, White)
Summary
##
## Call:
## glm(formula = chosepaula ~ condition_f * assignment_f, family = binomial(),
## data = black1_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -20.6 2507.5 -0.01 0.99
## condition_fcontrol 16.5 2507.5 0.01 0.99
## assignment_fother_white 17.8 2507.5 0.01 0.99
## assignment_fother_black 16.7 2507.5 0.01 0.99
## condition_fcontrol:assignment_fother_white -17.0 2507.5 -0.01 0.99
## condition_fcontrol:assignment_fother_black -33.2 3464.5 -0.01 0.99
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 67.579 on 324 degrees of freedom
## Residual deviance: 60.445 on 319 degrees of freedom
## AIC: 72.45
##
## Number of Fisher Scoring iterations: 19Estimated Marginal Means
## condition_f assignment_f emmean SE df asymp.LCL asymp.UCL
## race self -20.6 2507 Inf -4935 4894
## control self -4.1 1 Inf -6 -2
## race other_white -2.8 1 Inf -4 -2
## control other_white -3.4 1 Inf -5 -2
## race other_black -3.8 1 Inf -6 -2
## control other_black -20.6 2391 Inf -4706 4665
##
## Results are given on the logit (not the response) scale.
## Confidence level used: 0.95Pairwise comparisons: conditions across candidates
## assignment_f = self:
## contrast estimate SE df z.ratio p.value
## race - control -16.5 2507 Inf -0.010 0.9900
##
## assignment_f = other_white:
## contrast estimate SE df z.ratio p.value
## race - control 0.6 1 Inf 0.590 0.5500
##
## assignment_f = other_black:
## contrast estimate SE df z.ratio p.value
## race - control 16.7 2391 Inf 0.010 0.9900
##
## Results are given on the log odds ratio (not the response) scale.Pairwise comparisons: candidates across condition
## condition_f = race:
## contrast estimate SE df z.ratio p.value
## self - other_white -17.8 2507 Inf -0.010 1.0000
## self - other_black -16.7 2507 Inf -0.010 1.0000
## other_white - other_black 1.0 1 Inf 0.870 1.0000
##
## condition_f = control:
## contrast estimate SE df z.ratio p.value
## self - other_white -0.7 1 Inf -0.570 1.0000
## self - other_black 16.5 2391 Inf 0.010 1.0000
## other_white - other_black 17.2 2391 Inf 0.010 1.0000
##
## Results are given on the log odds ratio (not the response) scale.
## P value adjustment: bonferroni method for 3 testsGraphs
Jack (Low qualified, White)
Summary
##
## Call:
## glm(formula = chosejack ~ condition_f * assignment_f, family = binomial(),
## data = black1_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.178 0.722 -4.40 0.000011 ***
## condition_fcontrol -17.388 2288.981 -0.01 0.99
## assignment_fother_white 0.365 0.935 0.39 0.70
## assignment_fother_black 1.050 0.863 1.22 0.22
## condition_fcontrol:assignment_fother_white 17.257 2288.981 0.01 0.99
## condition_fcontrol:assignment_fother_black -1.050 3309.861 0.00 1.00
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 109.164 on 324 degrees of freedom
## Residual deviance: 95.528 on 319 degrees of freedom
## AIC: 107.5
##
## Number of Fisher Scoring iterations: 19Estimated Marginal Means
## condition_f assignment_f emmean SE df asymp.LCL asymp.UCL
## race self -3.2 1 Inf -5 -2
## control self -20.6 2289 Inf -4507 4466
## race other_white -2.8 1 Inf -4 -2
## control other_white -2.9 1 Inf -4 -2
## race other_black -2.1 0 Inf -3 -1
## control other_black -20.6 2391 Inf -4706 4665
##
## Results are given on the logit (not the response) scale.
## Confidence level used: 0.95Pairwise comparisons: conditions across candidates
## assignment_f = self:
## contrast estimate SE df z.ratio p.value
## race - control 17.4 2289 Inf 0.008 0.9900
##
## assignment_f = other_white:
## contrast estimate SE df z.ratio p.value
## race - control 0.1 1 Inf 0.156 0.8800
##
## assignment_f = other_black:
## contrast estimate SE df z.ratio p.value
## race - control 18.4 2391 Inf 0.008 0.9900
##
## Results are given on the log odds ratio (not the response) scale.Pairwise comparisons: candidates across condition
## condition_f = race:
## contrast estimate SE df z.ratio p.value
## self - other_white -0.4 1 Inf -0.390 1.0000
## self - other_black -1.0 1 Inf -1.220 0.6700
## other_white - other_black -0.7 1 Inf -0.900 1.0000
##
## condition_f = control:
## contrast estimate SE df z.ratio p.value
## self - other_white -17.6 2289 Inf -0.010 1.0000
## self - other_black 0.0 3310 Inf 0.000 1.0000
## other_white - other_black 17.6 2391 Inf 0.010 1.0000
##
## Results are given on the log odds ratio (not the response) scale.
## P value adjustment: bonferroni method for 3 testsGraphs
Jane (Low qualified, White)
Summary
##
## Call:
## glm(formula = chosejane ~ condition_f * assignment_f, family = binomial(),
## data = black1_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.8918 1.0101 -3.85 0.00012 ***
## condition_fcontrol -0.1857 1.4272 -0.13 0.89646
## assignment_fother_white -0.0594 1.4281 -0.04 0.96681
## assignment_fother_black 0.0632 1.4290 0.04 0.96473
## condition_fcontrol:assignment_fother_white 0.7697 1.8903 0.41 0.68388
## condition_fcontrol:assignment_fother_black 0.0254 2.0191 0.01 0.98997
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 67.579 on 324 degrees of freedom
## Residual deviance: 67.110 on 319 degrees of freedom
## AIC: 79.11
##
## Number of Fisher Scoring iterations: 6Estimated Marginal Means
## condition_f assignment_f emmean SE df asymp.LCL asymp.UCL
## race self -3.9 1.01 Inf -5.9 -1.91
## control self -4.1 1.01 Inf -6.1 -2.10
## race other_white -4.0 1.01 Inf -5.9 -1.97
## control other_white -3.4 0.72 Inf -4.8 -1.96
## race other_black -3.8 1.01 Inf -5.8 -1.85
## control other_black -4.0 1.01 Inf -6.0 -2.01
##
## Results are given on the logit (not the response) scale.
## Confidence level used: 0.95Pairwise comparisons: conditions across candidates
## assignment_f = self:
## contrast estimate SE df z.ratio p.value
## race - control 0.19 1.43 Inf 0.130 0.9000
##
## assignment_f = other_white:
## contrast estimate SE df z.ratio p.value
## race - control -0.58 1.24 Inf -0.470 0.6400
##
## assignment_f = other_black:
## contrast estimate SE df z.ratio p.value
## race - control 0.16 1.43 Inf 0.110 0.9100
##
## Results are given on the log odds ratio (not the response) scale.Pairwise comparisons: candidates across condition
## condition_f = race:
## contrast estimate SE df z.ratio p.value
## self - other_white 0.06 1.43 Inf 0.040 1.0000
## self - other_black -0.06 1.43 Inf -0.040 1.0000
## other_white - other_black -0.12 1.43 Inf -0.090 1.0000
##
## condition_f = control:
## contrast estimate SE df z.ratio p.value
## self - other_white -0.71 1.24 Inf -0.570 1.0000
## self - other_black -0.09 1.43 Inf -0.060 1.0000
## other_white - other_black 0.62 1.24 Inf 0.500 1.0000
##
## Results are given on the log odds ratio (not the response) scale.
## P value adjustment: bonferroni method for 3 testsGraphs
Other considerations
Qualifications - Likert
##
## Call:
## lm(formula = qualifications ~ condition_f * assignment_f, data = black1_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.467 -0.302 0.309 0.698 0.830
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.2800 0.1389 45.22 <0.0000000000000002 ***
## condition_fcontrol 0.4614 0.1895 2.43 0.015 *
## assignment_fother_white 0.0219 0.1936 0.11 0.910
## assignment_fother_black -0.1098 0.1995 -0.55 0.583
## condition_fcontrol:assignment_fother_white -0.2966 0.2649 -1.12 0.264
## condition_fcontrol:assignment_fother_black 0.0593 0.2720 0.22 0.827
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.98 on 317 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.045, Adjusted R-squared: 0.03
## F-statistic: 2.99 on 5 and 317 DF, p-value: 0.0119Estimated Marginal Means
## condition_f assignment_f emmean SE df lower.CL upper.CL
## race self 6.3 0.139 317 6.0 6.6
## control self 6.7 0.129 317 6.5 7.0
## race other_white 6.3 0.135 317 6.0 6.6
## control other_white 6.5 0.127 317 6.2 6.7
## race other_black 6.2 0.143 317 5.9 6.5
## control other_black 6.7 0.132 317 6.4 7.0
##
## Confidence level used: 0.95Pairwise comparisons: conditions across candidates
## assignment_f = self:
## contrast estimate SE df t.ratio p.value
## race - control -0.46 0.190 317 -2.430 0.0200
##
## assignment_f = other_white:
## contrast estimate SE df t.ratio p.value
## race - control -0.16 0.185 317 -0.890 0.3700
##
## assignment_f = other_black:
## contrast estimate SE df t.ratio p.value
## race - control -0.52 0.195 317 -2.670 0.0100Pairwise comparisons: candidates across condition
## condition_f = race:
## contrast estimate SE df t.ratio p.value
## self - other_white -0.022 0.194 317 -0.110 1.0000
## self - other_black 0.110 0.200 317 0.550 1.0000
## other_white - other_black 0.132 0.197 317 0.670 1.0000
##
## condition_f = control:
## contrast estimate SE df t.ratio p.value
## self - other_white 0.275 0.181 317 1.520 0.3900
## self - other_black 0.050 0.185 317 0.270 1.0000
## other_white - other_black -0.224 0.183 317 -1.220 0.6700
##
## P value adjustment: bonferroni method for 3 testsGraphs
Qualifications - Ordinal
Regression
## formula: rank_qualifications ~ condition_f * assignment_f
## data: black1_clean
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 323 -117.85 249.70 8(2) 1.12e-11 4.3e+02
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## condition_fcontrol -1.6777 0.6838 -2.45 0.014 *
## assignment_fother_white -0.3764 0.4987 -0.75 0.450
## assignment_fother_black -0.6785 0.5539 -1.22 0.221
## condition_fcontrol:assignment_fother_white -0.0684 1.0570 -0.06 0.948
## condition_fcontrol:assignment_fother_black 0.7342 1.0051 0.73 0.465
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold coefficients:
## Estimate Std. Error z value
## 4|2 1.237 0.341 3.62
## 2|3 1.504 0.350 4.29
## (2 observations deleted due to missingness)Graphs
Workload - Likert
##
## Call:
## lm(formula = workload ~ condition_f * assignment_f, data = black1_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.532 -1.867 0.133 1.800 3.291
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.2000 0.2910 14.43 <0.0000000000000002 ***
## condition_fcontrol -0.1138 0.3971 -0.29 0.77
## assignment_fother_white -0.1623 0.4057 -0.40 0.69
## assignment_fother_black 0.3319 0.4181 0.79 0.43
## condition_fcontrol:assignment_fother_white -0.0573 0.5552 -0.10 0.92
## condition_fcontrol:assignment_fother_black -0.7090 0.5699 -1.24 0.21
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.1 on 317 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.0151, Adjusted R-squared: -0.000478
## F-statistic: 0.969 on 5 and 317 DF, p-value: 0.437Estimated Marginal Means
## condition_f assignment_f emmean SE df lower.CL upper.CL
## race self 4.2 0.291 317 3.6 4.8
## control self 4.1 0.270 317 3.6 4.6
## race other_white 4.0 0.283 317 3.5 4.6
## control other_white 3.9 0.266 317 3.3 4.4
## race other_black 4.5 0.300 317 3.9 5.1
## control other_black 3.7 0.277 317 3.2 4.3
##
## Confidence level used: 0.95Pairwise comparisons: conditions across candidates
## assignment_f = self:
## contrast estimate SE df t.ratio p.value
## race - control 0.11 0.40 317 0.290 0.7700
##
## assignment_f = other_white:
## contrast estimate SE df t.ratio p.value
## race - control 0.17 0.39 317 0.440 0.6600
##
## assignment_f = other_black:
## contrast estimate SE df t.ratio p.value
## race - control 0.82 0.41 317 2.010 0.0400Pairwise comparisons: candidates across condition
## condition_f = race:
## contrast estimate SE df t.ratio p.value
## self - other_white 0.16 0.41 317 0.400 1.0000
## self - other_black -0.33 0.42 317 -0.790 1.0000
## other_white - other_black -0.49 0.41 317 -1.200 0.6900
##
## condition_f = control:
## contrast estimate SE df t.ratio p.value
## self - other_white 0.22 0.38 317 0.580 1.0000
## self - other_black 0.38 0.39 317 0.970 0.9900
## other_white - other_black 0.16 0.38 317 0.410 1.0000
##
## P value adjustment: bonferroni method for 3 testsGraphs
Workload - Ordinal
## formula: rank_workload ~ condition_f * assignment_f
## data: black1_clean
##
## link threshold nobs logLik AIC niter max.grad cond.H
## logit flexible 323 -198.03 410.05 6(0) 8.39e-10 9.3e+01
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## condition_fcontrol -1.9260 0.5149 -3.74 0.00018 ***
## assignment_fother_white 0.3562 0.3886 0.92 0.35934
## assignment_fother_black -0.1214 0.4024 -0.30 0.76299
## condition_fcontrol:assignment_fother_white -0.0518 0.6935 -0.07 0.94049
## condition_fcontrol:assignment_fother_black 0.6879 0.6935 0.99 0.32122
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold coefficients:
## Estimate Std. Error z value
## 3|2 0.240 0.282 0.85
## 2|4 3.192 0.436 7.32
## (2 observations deleted due to missingness)Graphs
### Skills {.tabset} #### Summary
##
## Call:
## lm(formula = skills ~ condition_f * assignment_f, data = black1_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.483 -0.280 0.400 0.720 0.755
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.2800 0.1379 45.54 <0.0000000000000002 ***
## condition_fcontrol 0.4097 0.1882 2.18 0.03 *
## assignment_fother_white -0.0347 0.1922 -0.18 0.86
## assignment_fother_black -0.0247 0.1981 -0.12 0.90
## condition_fcontrol:assignment_fother_white -0.1716 0.2631 -0.65 0.51
## condition_fcontrol:assignment_fother_black -0.0650 0.2701 -0.24 0.81
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.97 on 317 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.0322, Adjusted R-squared: 0.0169
## F-statistic: 2.11 on 5 and 317 DF, p-value: 0.0642Estimated Marginal Means
## condition_f assignment_f emmean SE df lower.CL upper.CL
## race self 6.3 0.138 317 6.0 6.6
## control self 6.7 0.128 317 6.4 6.9
## race other_white 6.2 0.134 317 6.0 6.5
## control other_white 6.5 0.126 317 6.2 6.7
## race other_black 6.3 0.142 317 6.0 6.5
## control other_black 6.6 0.131 317 6.3 6.9
##
## Confidence level used: 0.95Pairwise comparisons: conditions across candidates
## assignment_f = self:
## contrast estimate SE df t.ratio p.value
## race - control -0.41 0.188 317 -2.180 0.0300
##
## assignment_f = other_white:
## contrast estimate SE df t.ratio p.value
## race - control -0.24 0.184 317 -1.300 0.1960
##
## assignment_f = other_black:
## contrast estimate SE df t.ratio p.value
## race - control -0.34 0.194 317 -1.780 0.0760Pairwise comparisons: candidates across condition
## condition_f = race:
## contrast estimate SE df t.ratio p.value
## self - other_white 0.035 0.192 317 0.180 1.0000
## self - other_black 0.025 0.198 317 0.120 1.0000
## other_white - other_black -0.010 0.195 317 -0.050 1.0000
##
## condition_f = control:
## contrast estimate SE df t.ratio p.value
## self - other_white 0.206 0.180 317 1.150 0.7500
## self - other_black 0.090 0.184 317 0.490 1.0000
## other_white - other_black -0.117 0.182 317 -0.640 1.0000
##
## P value adjustment: bonferroni method for 3 testsGraphs
### Fit {.tabset} #### Summary
##
## Call:
## lm(formula = fit ~ condition_f * assignment_f, data = black1_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.094 -0.400 0.397 0.767 0.920
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.0800 0.1506 40.38 <0.0000000000000002 ***
## condition_fcontrol 0.5234 0.2055 2.55 0.011 *
## assignment_fother_white 0.0143 0.2099 0.07 0.946
## assignment_fother_black 0.1966 0.2163 0.91 0.364
## condition_fcontrol:assignment_fother_white -0.3845 0.2873 -1.34 0.182
## condition_fcontrol:assignment_fother_black -0.4000 0.2949 -1.36 0.176
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.1 on 317 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.0292, Adjusted R-squared: 0.0139
## F-statistic: 1.9 on 5 and 317 DF, p-value: 0.0932Estimated Marginal Means
## condition_f assignment_f emmean SE df lower.CL upper.CL
## race self 6.1 0.151 317 5.8 6.4
## control self 6.6 0.140 317 6.3 6.9
## race other_white 6.1 0.146 317 5.8 6.4
## control other_white 6.2 0.137 317 6.0 6.5
## race other_black 6.3 0.155 317 6.0 6.6
## control other_black 6.4 0.144 317 6.1 6.7
##
## Confidence level used: 0.95Pairwise comparisons: conditions across candidates
## assignment_f = self:
## contrast estimate SE df t.ratio p.value
## race - control -0.52 0.205 317 -2.550 0.0100
##
## assignment_f = other_white:
## contrast estimate SE df t.ratio p.value
## race - control -0.14 0.201 317 -0.690 0.4900
##
## assignment_f = other_black:
## contrast estimate SE df t.ratio p.value
## race - control -0.12 0.212 317 -0.580 0.5600Pairwise comparisons: candidates across condition
## condition_f = race:
## contrast estimate SE df t.ratio p.value
## self - other_white -0.01 0.210 317 -0.070 1.0000
## self - other_black -0.20 0.216 317 -0.910 1.0000
## other_white - other_black -0.18 0.213 317 -0.850 1.0000
##
## condition_f = control:
## contrast estimate SE df t.ratio p.value
## self - other_white 0.37 0.196 317 1.890 0.1800
## self - other_black 0.20 0.200 317 1.020 0.9300
## other_white - other_black -0.17 0.199 317 -0.840 1.0000
##
## P value adjustment: bonferroni method for 3 testsGraphs
Emotions
| Cand. | ID | Rating |
|---|---|---|
| Bill | W | 9 |
| Sam | B | 9 |
| Fred | W | 2 |
| Name | ~ | 4 |
| Paula | W | 3 |
| Jack | W | 5 |
| Jane | W | 4 |
Positive Afffect
Regression
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: paff ~ assignment_f * condition_f * cand_f + (1 | pid)
## Data: black1_cleanlong
##
## REML criterion at convergence: 6944
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.476 -0.576 0.027 0.627 3.510
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.864 0.929
## Residual 0.966 0.983
## Number of obs: 2237, groups: pid, 322
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.8720 0.1913 941.9573 30.69 < 0.0000000000000002 ***
## assignment_fother_white -0.1022 0.2667 941.9573 -0.38 0.70168
## assignment_fother_black -0.0301 0.2758 951.4568 -0.11 0.91314
## condition_fcontrol 0.1380 0.2617 948.8460 0.53 0.59809
## cand_ffred -2.8720 0.1966 1878.9874 -14.61 < 0.0000000000000002 ***
## cand_fjack -1.2200 0.1966 1878.9874 -6.21 0.00000000067010285 ***
## cand_fjane -1.6040 0.1966 1878.9874 -8.16 0.00000000000000061 ***
## cand_fpaula -1.9840 0.1966 1878.9874 -10.09 < 0.0000000000000002 ***
## cand_fsam 0.4400 0.1966 1878.9874 2.24 0.02534 *
## cand_ftarg -0.2560 0.1966 1878.9874 -1.30 0.19305
## assignment_fother_white:condition_fcontrol 0.3897 0.3660 948.2948 1.06 0.28723
## assignment_fother_black:condition_fcontrol 0.1988 0.3772 954.2225 0.53 0.59821
## assignment_fother_white:cand_ffred 0.3211 0.2741 1878.9873 1.17 0.24159
## assignment_fother_black:cand_ffred 0.6471 0.2834 1880.5903 2.28 0.02253 *
## assignment_fother_white:cand_fjack -0.1838 0.2741 1878.9873 -0.67 0.50262
## assignment_fother_black:cand_fjack 0.3244 0.2840 1878.9873 1.14 0.25361
## assignment_fother_white:cand_fjane 0.1587 0.2741 1878.9873 0.58 0.56260
## assignment_fother_black:cand_fjane 0.4475 0.2840 1878.9873 1.58 0.11531
## assignment_fother_white:cand_fpaula 0.0670 0.2741 1878.9873 0.24 0.80685
## assignment_fother_black:cand_fpaula 0.5719 0.2834 1880.5903 2.02 0.04376 *
## assignment_fother_white:cand_fsam 0.0430 0.2741 1878.9873 0.16 0.87530
## assignment_fother_black:cand_fsam 0.0032 0.2834 1880.5903 0.01 0.99098
## assignment_fother_white:cand_ftarg -1.3025 0.2741 1878.9873 -4.75 0.00000216508852615 ***
## assignment_fother_black:cand_ftarg -0.3092 0.2840 1878.9873 -1.09 0.27643
## condition_fcontrol:cand_ffred 1.0966 0.2694 1878.9873 4.07 0.00004879771701185 ***
## condition_fcontrol:cand_fjack 0.2100 0.2690 1880.1516 0.78 0.43509
## condition_fcontrol:cand_fjane 0.4777 0.2694 1878.9873 1.77 0.07634 .
## condition_fcontrol:cand_fpaula 0.3636 0.2690 1880.1516 1.35 0.17655
## condition_fcontrol:cand_fsam -0.3224 0.2690 1880.1516 -1.20 0.23074
## condition_fcontrol:cand_ftarg 0.0735 0.2694 1878.9873 0.27 0.78487
## assignment_fother_white:condition_fcontrol:cand_ffred -1.1219 0.3765 1878.9873 -2.98 0.00292 **
## assignment_fother_black:condition_fcontrol:cand_ffred -1.4801 0.3879 1880.2622 -3.82 0.00014 ***
## assignment_fother_white:condition_fcontrol:cand_fjack -0.1960 0.3762 1879.5824 -0.52 0.60242
## assignment_fother_black:condition_fcontrol:cand_fjack -0.4389 0.3884 1879.5459 -1.13 0.25862
## assignment_fother_white:condition_fcontrol:cand_fjane -0.6256 0.3765 1878.9873 -1.66 0.09678 .
## assignment_fother_black:condition_fcontrol:cand_fjane -0.8834 0.3886 1878.9873 -2.27 0.02313 *
## assignment_fother_white:condition_fcontrol:cand_fpaula -0.4772 0.3762 1879.5824 -1.27 0.20489
## assignment_fother_black:condition_fcontrol:cand_fpaula -1.0081 0.3876 1880.8243 -2.60 0.00937 **
## assignment_fother_white:condition_fcontrol:cand_fsam -0.0915 0.3761 1881.6828 -0.24 0.80788
## assignment_fother_black:condition_fcontrol:cand_fsam -0.1921 0.3876 1880.8243 -0.50 0.62013
## assignment_fother_white:condition_fcontrol:cand_ftarg -0.2845 0.3765 1878.9873 -0.76 0.44994
## assignment_fother_black:condition_fcontrol:cand_ftarg -0.9686 0.3883 1879.4053 -2.49 0.01270 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Estimated marginal means
Posthoc tests
Graphs (Black Candidates)
Graphs (All Candidates)
Negative Affect
Regression
## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: naff ~ assignment_f * condition_f * cand_f + (1 | pid)
## Data: black1_cleanlong
##
## REML criterion at convergence: 6668
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.399 -0.606 -0.056 0.539 4.488
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.703 0.838
## Residual 0.862 0.928
## Number of obs: 2237, groups: pid, 322
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.7480 0.1769 995.3959 9.88 < 0.0000000000000002 ***
## assignment_fother_white 0.2294 0.2466 995.3959 0.93 0.3526
## assignment_fother_black 0.2139 0.2551 1005.3727 0.84 0.4020
## condition_fcontrol -0.2455 0.2421 1002.6317 -1.01 0.3108
## cand_ffred 1.5520 0.1857 1878.8861 8.36 < 0.0000000000000002 ***
## cand_fjack 0.8320 0.1857 1878.8861 4.48 0.0000078973179 ***
## cand_fjane 1.0480 0.1857 1878.8861 5.64 0.0000000191859 ***
## cand_fpaula 1.2800 0.1857 1878.8861 6.89 0.0000000000074 ***
## cand_fsam 0.1920 0.1857 1878.8861 1.03 0.3013
## cand_ftarg 0.5160 0.1857 1878.8861 2.78 0.0055 **
## assignment_fother_white:condition_fcontrol -0.0742 0.3384 1001.9595 -0.22 0.8264
## assignment_fother_black:condition_fcontrol 0.0385 0.3489 1008.2973 0.11 0.9122
## assignment_fother_white:cand_ffred -0.1595 0.2589 1878.8861 -0.62 0.5378
## assignment_fother_black:cand_ffred -0.4202 0.2677 1880.5780 -1.57 0.1166
## assignment_fother_white:cand_fjack -0.0207 0.2589 1878.8861 -0.08 0.9363
## assignment_fother_black:cand_fjack -0.4668 0.2683 1878.8861 -1.74 0.0820 .
## assignment_fother_white:cand_fjane 0.1860 0.2589 1878.8861 0.72 0.4726
## assignment_fother_black:cand_fjane -0.0784 0.2683 1878.8861 -0.29 0.7700
## assignment_fother_white:cand_fpaula -0.1555 0.2589 1878.8861 -0.60 0.5482
## assignment_fother_black:cand_fpaula -0.2802 0.2677 1880.5780 -1.05 0.2954
## assignment_fother_white:cand_fsam -0.1618 0.2589 1878.8861 -0.63 0.5320
## assignment_fother_black:cand_fsam -0.3751 0.2677 1880.5780 -1.40 0.1613
## assignment_fother_white:cand_ftarg 0.4048 0.2589 1878.8861 1.56 0.1181
## assignment_fother_black:cand_ftarg 0.1188 0.2683 1878.8861 0.44 0.6580
## condition_fcontrol:cand_ffred 0.2796 0.2544 1878.8861 1.10 0.2720
## condition_fcontrol:cand_fjack 0.1344 0.2540 1880.1150 0.53 0.5967
## condition_fcontrol:cand_fjane 0.3099 0.2544 1878.8861 1.22 0.2234
## condition_fcontrol:cand_fpaula 0.0347 0.2540 1880.1150 0.14 0.8913
## condition_fcontrol:cand_fsam -0.0118 0.2540 1880.1150 -0.05 0.9631
## condition_fcontrol:cand_ftarg 0.4700 0.2544 1878.8861 1.85 0.0649 .
## assignment_fother_white:condition_fcontrol:cand_ffred 0.1822 0.3556 1878.8861 0.51 0.6085
## assignment_fother_black:condition_fcontrol:cand_ffred 0.4486 0.3663 1880.2361 1.22 0.2209
## assignment_fother_white:condition_fcontrol:cand_fjack -0.0508 0.3554 1879.5142 -0.14 0.8862
## assignment_fother_black:condition_fcontrol:cand_fjack 0.4437 0.3668 1879.4757 1.21 0.2265
## assignment_fother_white:condition_fcontrol:cand_fjane -0.2727 0.3556 1878.8861 -0.77 0.4434
## assignment_fother_black:condition_fcontrol:cand_fjane -0.0266 0.3671 1878.8861 -0.07 0.9422
## assignment_fother_white:condition_fcontrol:cand_fpaula 0.4272 0.3554 1879.5142 1.20 0.2295
## assignment_fother_black:condition_fcontrol:cand_fpaula 0.5958 0.3661 1880.8294 1.63 0.1038
## assignment_fother_white:condition_fcontrol:cand_fsam -0.0161 0.3552 1881.6281 -0.05 0.9639
## assignment_fother_black:condition_fcontrol:cand_fsam -0.0451 0.3661 1880.8294 -0.12 0.9019
## assignment_fother_white:condition_fcontrol:cand_ftarg -0.0483 0.3556 1878.8861 -0.14 0.8919
## assignment_fother_black:condition_fcontrol:cand_ftarg 0.3145 0.3667 1879.3316 0.86 0.3913
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Estimated marginal means
Posthoc tests
Graphs (Black Candidates)
Graphs (All Candidates)
