There is a total of 353 entries in the dataset. 18 participants failed the attention check or the comprehension check. The valid number of entries is n = 335.
Per the preregistration (section 5), mediation analyses examine whether the effect of training history on comfort is explained by intervening variables. Two separate datasets are used, each with its own reference category:
yes_ai,
reference) vs. AI Negative (no_ai) vs. Controlyes_human, reference) vs. Human Negative
(no_human) vs. ControlCandidate mediators (composite or individual DVs, per section 5): ability, benevolence, integrity, autonomy.
Description
##
## Reliability analysis
## Call: psych::alpha(x = df_comp, na.rm = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.94 0.95 0.93 0.86 18 0.0052 4.8 1.1 0.85
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.93 0.94 0.95
## Duhachek 0.93 0.94 0.95
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## ability1 0.91 0.92 0.85 0.85 11 0.0090 NA 0.85
## ability2 0.94 0.94 0.88 0.88 15 0.0069 NA 0.88
## ability3 0.91 0.91 0.84 0.84 11 0.0096 NA 0.84
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## ability1 333 0.95 0.95 0.92 0.89 4.9 1.1
## ability2 335 0.95 0.94 0.89 0.87 4.7 1.3
## ability3 334 0.95 0.96 0.93 0.90 4.9 1.1
##
## Non missing response frequency for each item
## 0 1 2 3 4 5 6 miss
## ability1 0.01 0.00 0.03 0.05 0.17 0.41 0.34 0.01
## ability2 0.01 0.02 0.06 0.04 0.18 0.43 0.26 0.00
## ability3 0.01 0.01 0.02 0.06 0.19 0.43 0.29 0.00##
## Reliability analysis
## Call: psych::alpha(x = df_comp, na.rm = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.89 0.89 0.86 0.73 8.2 0.01 4.7 0.98 0.69
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.87 0.89 0.91
## Duhachek 0.87 0.89 0.91
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## benevolence1 0.80 0.79 0.66 0.66 3.9 0.0224 NA 0.66
## benevolence2 0.92 0.92 0.85 0.85 11.0 0.0091 NA 0.85
## benevolence3 0.82 0.82 0.69 0.69 4.5 0.0198 NA 0.69
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## benevolence1 335 0.93 0.93 0.91 0.85 4.6 1.1
## benevolence2 335 0.86 0.86 0.73 0.70 4.9 1.1
## benevolence3 333 0.92 0.92 0.88 0.82 4.5 1.1
##
## Non missing response frequency for each item
## 0 2 3 4 5 6 miss
## benevolence1 0 0.00 0.21 0.18 0.38 0.23 0.00
## benevolence2 0 0.00 0.15 0.13 0.39 0.33 0.00
## benevolence3 0 0.01 0.25 0.20 0.35 0.20 0.01##
## Reliability analysis
## Call: psych::alpha(x = df_comp, na.rm = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.88 0.88 0.83 0.71 7.3 0.011 4.4 1.1 0.7
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.85 0.88 0.9
## Duhachek 0.86 0.88 0.9
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## integrity1 0.85 0.85 0.74 0.74 5.7 0.016 NA 0.74
## integrity2 0.82 0.82 0.70 0.70 4.7 0.019 NA 0.70
## integrity3 0.81 0.81 0.69 0.69 4.4 0.020 NA 0.69
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## integrity1 333 0.89 0.89 0.79 0.74 3.9 1.2
## integrity2 334 0.90 0.90 0.83 0.77 4.6 1.2
## integrity3 332 0.90 0.91 0.84 0.78 4.6 1.1
##
## Non missing response frequency for each item
## 0 1 2 3 4 5 6 miss
## integrity1 0 0.03 0.00 0.44 0.16 0.25 0.12 0.01
## integrity2 0 0.01 0.04 0.15 0.20 0.37 0.23 0.00
## integrity3 0 0.01 0.01 0.18 0.22 0.37 0.21 0.01##
## Reliability analysis
## Call: psych::alpha(x = df_comp, na.rm = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.84 0.84 0.8 0.64 5.4 0.015 4.4 1.1 0.59
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.80 0.84 0.87
## Duhachek 0.81 0.84 0.87
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## autonomy1 0.69 0.71 0.55 0.55 2.5 0.032 NA 0.55
## autonomy2 0.74 0.74 0.59 0.59 2.9 0.029 NA 0.59
## autonomy3 0.87 0.88 0.78 0.78 7.3 0.014 NA 0.78
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## autonomy1 331 0.91 0.91 0.86 0.79 4.5 1.3
## autonomy2 334 0.91 0.89 0.84 0.75 4.2 1.5
## autonomy3 335 0.80 0.82 0.64 0.60 4.5 1.1
##
## Non missing response frequency for each item
## 0 1 2 3 4 5 6 miss
## autonomy1 0.00 0.02 0.03 0.17 0.20 0.33 0.25 0.01
## autonomy2 0.02 0.03 0.07 0.22 0.15 0.29 0.23 0.00
## autonomy3 0.00 0.01 0.00 0.27 0.15 0.38 0.19 0.00##
## ********************* PROCESS for R Version 4.1.1 *********************
##
## 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 2022 by Andrew F. Hayes ALL RIGHTS RESERVED
## Workshop schedule at http://haskayne.ucalgary.ca/CCRAM
## Reference category: yes_ai (X1 = no_ai
vs. yes_ai, X2 = control
vs. yes_ai).
| training | n | percentage |
|---|---|---|
| yes_ai | 67 | 33.84% |
| no_ai | 69 | 34.85% |
| control | 62 | 31.31% |
##
## Call:
## lm(formula = comfort ~ training, data = df_ai)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.9697 -0.5652 0.1803 0.4348 2.0303
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.9697 0.1445 27.475 < 2e-16 ***
## trainingno_ai 1.5955 0.2021 7.895 2.14e-13 ***
## trainingcontrol 0.8500 0.2085 4.077 6.66e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.174 on 193 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.2442, Adjusted R-squared: 0.2364
## F-statistic: 31.18 on 2 and 193 DF, p-value: 1.842e-12Testing each candidate mediator individually (Y = comfort).
##
## ********************* PROCESS for R Version 4.1.1 *********************
##
## Written by Andrew F. Hayes, Ph.D. www.afhayes.com
## Documentation available in Hayes (2022). www.guilford.com/p/hayes3
##
## ***********************************************************************
##
## Model : 4
## Y : comfort
## X : ai_training_1
## M : ability
##
## Sample size: 196
##
## Custom seed: 1234
##
## Coding of categorical X variable for analysis:
## ai_training_1 X1 X2
## 1.0000 0.0000 0.0000
## 2.0000 1.0000 0.0000
## 3.0000 0.0000 1.0000
##
## ***********************************************************************
## Outcome Variable: ability
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.5136 0.2638 0.9083 34.5844 2.0000 193.0000 0.0000
##
## Model:
## coeff se t p LLCI ULCI
## constant 4.1364 0.1173 35.2594 0.0000 3.9050 4.3677
## X1 1.3564 0.1641 8.2660 0.0000 1.0327 1.6800
## X2 0.8281 0.1693 4.8923 0.0000 0.4943 1.1620
##
## ***********************************************************************
## Outcome Variable: comfort
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.8750 0.7656 0.4295 209.0710 3.0000 192.0000 0.0000
##
## Model:
## coeff se t p LLCI ULCI
## constant -0.2617 0.2201 -1.1893 0.2358 -0.6957 0.1723
## X1 0.2080 0.1313 1.5839 0.1149 -0.0510 0.4669
## X2 0.0028 0.1234 0.0229 0.9817 -0.2406 0.2462
## ability 1.0230 0.0495 20.6675 0.0000 0.9254 1.1206
##
## ************************ TOTAL EFFECT MODEL ***************************
## Outcome Variable: comfort
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.4942 0.2442 1.3778 31.1821 2.0000 193.0000 0.0000
##
## Model:
## coeff se t p LLCI ULCI
## constant 3.9697 0.1445 27.4751 0.0000 3.6847 4.2547
## X1 1.5955 0.2021 7.8948 0.0000 1.1969 1.9941
## X2 0.8500 0.2085 4.0771 0.0001 0.4388 1.2612
##
## ***********************************************************************
## Bootstrapping in progress. Please wait.
##
## ************ TOTAL, DIRECT, AND INDIRECT EFFECTS OF X ON Y ************
##
## Relative total effects of X on Y:
## effect se t p LLCI ULCI
## X1 1.5955 0.2021 7.8948 0.0000 1.1969 1.9941
## X2 0.8500 0.2085 4.0771 0.0001 0.4388 1.2612
##
## Omnibus test of total effect of X on Y:
## R2-chng F df1 df2 p
## 0.2442 31.1821 2.0000 193.0000 0.0000
## ----------
##
## Relative direct effects of X on Y:
## effect se t p LLCI ULCI
## X1 0.2080 0.1313 1.5839 0.1149 -0.0510 0.4669
## X2 0.0028 0.1234 0.0229 0.9817 -0.2406 0.2462
##
## Omnibus test of direct effect of X on Y:
## R2-chng F df1 df2 p
## 0.0045 1.8323 2.0000 192.0000 0.1628
##
## ----------
##
## Relative indirect effects of X on Y:
##
## ai_training_1 -> ability -> comfort
##
## Effect BootSE BootLLCI BootULCI
## X1 1.3876 0.1903 1.0288 1.7758
## X2 0.8471 0.1966 0.4671 1.2379
##
## Normal theory test for relative indirect effects:
## Effect se Z p
## X1 1.3876 0.1810 7.6672 0.0000
## X2 0.8471 0.1781 4.7554 0.0000
##
## ******************** ANALYSIS NOTES AND ERRORS ************************
##
## Level of confidence for all confidence intervals in output: 95
##
## Number of bootstraps for percentile bootstrap confidence intervals: 10000
##
## NOTE: Some cases with missing data were deleted. The number of deleted cases was: 2
##
## RESULT: BOTH relative indirect effects' bootstrap CIs exclude zero -- `ability` IS a candidate for the parallel model.##
## ********************* PROCESS for R Version 4.1.1 *********************
##
## Written by Andrew F. Hayes, Ph.D. www.afhayes.com
## Documentation available in Hayes (2022). www.guilford.com/p/hayes3
##
## ***********************************************************************
##
## Model : 4
## Y : comfort
## X : ai_training_1
## M : benevolence
##
## Sample size: 196
##
## Custom seed: 1234
##
## Coding of categorical X variable for analysis:
## ai_training_1 X1 X2
## 1.0000 0.0000 0.0000
## 2.0000 1.0000 0.0000
## 3.0000 0.0000 1.0000
##
## ***********************************************************************
## Outcome Variable: benevolence
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.1884 0.0355 1.0214 3.5503 2.0000 193.0000 0.0306
##
## Model:
## coeff se t p LLCI ULCI
## constant 4.4091 0.1244 35.4416 0.0000 4.1637 4.6545
## X1 0.4605 0.1740 2.6462 0.0088 0.1173 0.8037
## X2 0.1865 0.1795 1.0392 0.3000 -0.1675 0.5406
##
## ***********************************************************************
## Outcome Variable: comfort
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.6033 0.3639 1.1656 36.6181 3.0000 192.0000 0.0000
##
## Model:
## coeff se t p LLCI ULCI
## constant 1.9317 0.3641 5.3048 0.0000 1.2135 2.6499
## X1 1.3827 0.1892 7.3070 0.0000 1.0094 1.7559
## X2 0.7638 0.1923 3.9720 0.0001 0.3845 1.1430
## benevolence 0.4622 0.0769 6.0114 0.0000 0.3106 0.6139
##
## ************************ TOTAL EFFECT MODEL ***************************
## Outcome Variable: comfort
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.4942 0.2442 1.3778 31.1821 2.0000 193.0000 0.0000
##
## Model:
## coeff se t p LLCI ULCI
## constant 3.9697 0.1445 27.4751 0.0000 3.6847 4.2547
## X1 1.5955 0.2021 7.8948 0.0000 1.1969 1.9941
## X2 0.8500 0.2085 4.0771 0.0001 0.4388 1.2612
##
## ***********************************************************************
## Bootstrapping in progress. Please wait.
##
## ************ TOTAL, DIRECT, AND INDIRECT EFFECTS OF X ON Y ************
##
## Relative total effects of X on Y:
## effect se t p LLCI ULCI
## X1 1.5955 0.2021 7.8948 0.0000 1.1969 1.9941
## X2 0.8500 0.2085 4.0771 0.0001 0.4388 1.2612
##
## Omnibus test of total effect of X on Y:
## R2-chng F df1 df2 p
## 0.2442 31.1821 2.0000 193.0000 0.0000
## ----------
##
## Relative direct effects of X on Y:
## effect se t p LLCI ULCI
## X1 1.3827 0.1892 7.3070 0.0000 1.0094 1.7559
## X2 0.7638 0.1923 3.9720 0.0001 0.3845 1.1430
##
## Omnibus test of direct effect of X on Y:
## R2-chng F df1 df2 p
## 0.1773 26.7658 2.0000 192.0000 0.0000
##
## ----------
##
## Relative indirect effects of X on Y:
##
## ai_training_1 -> benevolence -> comfort
##
## Effect BootSE BootLLCI BootULCI
## X1 0.2128 0.0913 0.0497 0.4080
## X2 0.0862 0.0880 -0.0763 0.2724
##
## Normal theory test for relative indirect effects:
## Effect se Z p
## X1 0.2128 0.0889 2.3944 0.0166
## X2 0.0862 0.0853 1.0105 0.3123
##
## ******************** ANALYSIS NOTES AND ERRORS ************************
##
## Level of confidence for all confidence intervals in output: 95
##
## Number of bootstraps for percentile bootstrap confidence intervals: 10000
##
## NOTE: Some cases with missing data were deleted. The number of deleted cases was: 2
##
## RESULT: at least one relative indirect effect's bootstrap CI includes zero -- `benevolence` is NOT included in the parallel model.##
## ********************* PROCESS for R Version 4.1.1 *********************
##
## Written by Andrew F. Hayes, Ph.D. www.afhayes.com
## Documentation available in Hayes (2022). www.guilford.com/p/hayes3
##
## ***********************************************************************
##
## Model : 4
## Y : comfort
## X : ai_training_1
## M : integrity
##
## Sample size: 196
##
## Custom seed: 1234
##
## Coding of categorical X variable for analysis:
## ai_training_1 X1 X2
## 1.0000 0.0000 0.0000
## 2.0000 1.0000 0.0000
## 3.0000 0.0000 1.0000
##
## ***********************************************************************
## Outcome Variable: integrity
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.3572 0.1276 1.1036 14.1103 2.0000 193.0000 0.0000
##
## Model:
## coeff se t p LLCI ULCI
## constant 3.8460 0.1293 29.7421 0.0000 3.5909 4.1010
## X1 0.9608 0.1809 5.3120 0.0000 0.6041 1.3175
## X2 0.4819 0.1866 2.5828 0.0105 0.1139 0.8499
##
## ***********************************************************************
## Outcome Variable: comfort
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.6791 0.4611 0.9874 54.7708 3.0000 192.0000 0.0000
##
## Model:
## coeff se t p LLCI ULCI
## constant 1.6675 0.2890 5.7693 0.0000 1.0974 2.2375
## X1 1.0204 0.1832 5.5706 0.0000 0.6591 1.3817
## X2 0.5615 0.1795 3.1279 0.0020 0.2074 0.9156
## integrity 0.5986 0.0681 8.7918 0.0000 0.4643 0.7329
##
## ************************ TOTAL EFFECT MODEL ***************************
## Outcome Variable: comfort
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.4942 0.2442 1.3778 31.1821 2.0000 193.0000 0.0000
##
## Model:
## coeff se t p LLCI ULCI
## constant 3.9697 0.1445 27.4751 0.0000 3.6847 4.2547
## X1 1.5955 0.2021 7.8948 0.0000 1.1969 1.9941
## X2 0.8500 0.2085 4.0771 0.0001 0.4388 1.2612
##
## ***********************************************************************
## Bootstrapping in progress. Please wait.
##
## ************ TOTAL, DIRECT, AND INDIRECT EFFECTS OF X ON Y ************
##
## Relative total effects of X on Y:
## effect se t p LLCI ULCI
## X1 1.5955 0.2021 7.8948 0.0000 1.1969 1.9941
## X2 0.8500 0.2085 4.0771 0.0001 0.4388 1.2612
##
## Omnibus test of total effect of X on Y:
## R2-chng F df1 df2 p
## 0.2442 31.1821 2.0000 193.0000 0.0000
## ----------
##
## Relative direct effects of X on Y:
## effect se t p LLCI ULCI
## X1 1.0204 0.1832 5.5706 0.0000 0.6591 1.3817
## X2 0.5615 0.1795 3.1279 0.0020 0.2074 0.9156
##
## Omnibus test of direct effect of X on Y:
## R2-chng F df1 df2 p
## 0.0873 15.5519 2.0000 192.0000 0.0000
##
## ----------
##
## Relative indirect effects of X on Y:
##
## ai_training_1 -> integrity -> comfort
##
## Effect BootSE BootLLCI BootULCI
## X1 0.5752 0.1311 0.3314 0.8422
## X2 0.2885 0.1224 0.0640 0.5417
##
## Normal theory test for relative indirect effects:
## Effect se Z p
## X1 0.5752 0.1271 4.5252 0.0000
## X2 0.2885 0.1171 2.4635 0.0138
##
## ******************** ANALYSIS NOTES AND ERRORS ************************
##
## Level of confidence for all confidence intervals in output: 95
##
## Number of bootstraps for percentile bootstrap confidence intervals: 10000
##
## NOTE: Some cases with missing data were deleted. The number of deleted cases was: 2
##
## RESULT: BOTH relative indirect effects' bootstrap CIs exclude zero -- `integrity` IS a candidate for the parallel model.##
## ********************* PROCESS for R Version 4.1.1 *********************
##
## Written by Andrew F. Hayes, Ph.D. www.afhayes.com
## Documentation available in Hayes (2022). www.guilford.com/p/hayes3
##
## ***********************************************************************
##
## Model : 4
## Y : comfort
## X : ai_training_1
## M : autonomy
##
## Sample size: 196
##
## Custom seed: 1234
##
## Coding of categorical X variable for analysis:
## ai_training_1 X1 X2
## 1.0000 0.0000 0.0000
## 2.0000 1.0000 0.0000
## 3.0000 0.0000 1.0000
##
## ***********************************************************************
## Outcome Variable: autonomy
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.5322 0.2833 1.1100 38.1389 2.0000 193.0000 0.0000
##
## Model:
## coeff se t p LLCI ULCI
## constant 3.5177 0.1297 27.1253 0.0000 3.2619 3.7735
## X1 1.5838 0.1814 8.7311 0.0000 1.2260 1.9415
## X2 0.7747 0.1871 4.1400 0.0001 0.4056 1.1437
##
## ***********************************************************************
## Outcome Variable: comfort
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.7100 0.5041 0.9087 65.0633 3.0000 192.0000 0.0000
##
## Model:
## coeff se t p LLCI ULCI
## constant 1.6714 0.2574 6.4934 0.0000 1.1637 2.1791
## X1 0.5608 0.1938 2.8928 0.0043 0.1784 0.9431
## X2 0.3438 0.1767 1.9463 0.0531 -0.0046 0.6923
## autonomy 0.6533 0.0651 10.0315 0.0000 0.5249 0.7818
##
## ************************ TOTAL EFFECT MODEL ***************************
## Outcome Variable: comfort
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.4942 0.2442 1.3778 31.1821 2.0000 193.0000 0.0000
##
## Model:
## coeff se t p LLCI ULCI
## constant 3.9697 0.1445 27.4751 0.0000 3.6847 4.2547
## X1 1.5955 0.2021 7.8948 0.0000 1.1969 1.9941
## X2 0.8500 0.2085 4.0771 0.0001 0.4388 1.2612
##
## ***********************************************************************
## Bootstrapping in progress. Please wait.
##
## ************ TOTAL, DIRECT, AND INDIRECT EFFECTS OF X ON Y ************
##
## Relative total effects of X on Y:
## effect se t p LLCI ULCI
## X1 1.5955 0.2021 7.8948 0.0000 1.1969 1.9941
## X2 0.8500 0.2085 4.0771 0.0001 0.4388 1.2612
##
## Omnibus test of total effect of X on Y:
## R2-chng F df1 df2 p
## 0.2442 31.1821 2.0000 193.0000 0.0000
## ----------
##
## Relative direct effects of X on Y:
## effect se t p LLCI ULCI
## X1 0.5608 0.1938 2.8928 0.0043 0.1784 0.9431
## X2 0.3438 0.1767 1.9463 0.0531 -0.0046 0.6923
##
## Omnibus test of direct effect of X on Y:
## R2-chng F df1 df2 p
## 0.0221 4.2694 2.0000 192.0000 0.0153
##
## ----------
##
## Relative indirect effects of X on Y:
##
## ai_training_1 -> autonomy -> comfort
##
## Effect BootSE BootLLCI BootULCI
## X1 1.0348 0.1606 0.7385 1.3629
## X2 0.5061 0.1447 0.2306 0.8018
##
## Normal theory test for relative indirect effects:
## Effect se Z p
## X1 1.0348 0.1576 6.5674 0.0000
## X2 0.5061 0.1328 3.8108 0.0001
##
## ******************** ANALYSIS NOTES AND ERRORS ************************
##
## Level of confidence for all confidence intervals in output: 95
##
## Number of bootstraps for percentile bootstrap confidence intervals: 10000
##
## NOTE: Some cases with missing data were deleted. The number of deleted cases was: 2
##
## RESULT: BOTH relative indirect effects' bootstrap CIs exclude zero -- `autonomy` IS a candidate for the parallel model.| mediator | significant_indirect_effect |
|---|---|
| ability | TRUE |
| benevolence | FALSE |
| integrity | TRUE |
| autonomy | TRUE |
** 3 candidate mediators ( ability, integrity, autonomy ) had BOTH relative indirect effects’ bootstrap CIs exclude zero in the AI dataset . Per the preregistration, a parallel mediation model follows below.**
##
## ********************* PROCESS for R Version 4.1.1 *********************
##
## Written by Andrew F. Hayes, Ph.D. www.afhayes.com
## Documentation available in Hayes (2022). www.guilford.com/p/hayes3
##
## ***********************************************************************
##
## Model : 4
## Y : comfort
## X : ai_training_1
## M1 : ability
## M2 : integrity
## M3 : autonomy
##
## Sample size: 196
##
## Custom seed: 1234
##
## Coding of categorical X variable for analysis:
## ai_training_1 X1 X2
## 1.0000 0.0000 0.0000
## 2.0000 1.0000 0.0000
## 3.0000 0.0000 1.0000
##
## ***********************************************************************
## Outcome Variable: ability
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.5136 0.2638 0.9083 34.5844 2.0000 193.0000 0.0000
##
## Model:
## coeff se t p LLCI ULCI
## constant 4.1364 0.1173 35.2594 0.0000 3.9050 4.3677
## X1 1.3564 0.1641 8.2660 0.0000 1.0327 1.6800
## X2 0.8281 0.1693 4.8923 0.0000 0.4943 1.1620
##
## ***********************************************************************
## Outcome Variable: integrity
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.3572 0.1276 1.1036 14.1103 2.0000 193.0000 0.0000
##
## Model:
## coeff se t p LLCI ULCI
## constant 3.8460 0.1293 29.7421 0.0000 3.5909 4.1010
## X1 0.9608 0.1809 5.3120 0.0000 0.6041 1.3175
## X2 0.4819 0.1866 2.5828 0.0105 0.1139 0.8499
##
## ***********************************************************************
## Outcome Variable: autonomy
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.5322 0.2833 1.1100 38.1389 2.0000 193.0000 0.0000
##
## Model:
## coeff se t p LLCI ULCI
## constant 3.5177 0.1297 27.1253 0.0000 3.2619 3.7735
## X1 1.5838 0.1814 8.7311 0.0000 1.2260 1.9415
## X2 0.7747 0.1871 4.1400 0.0001 0.4056 1.1437
##
## ***********************************************************************
## Outcome Variable: comfort
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.8770 0.7691 0.4276 126.5747 5.0000 190.0000 0.0000
##
## Model:
## coeff se t p LLCI ULCI
## constant -0.2963 0.2275 -1.3021 0.1945 -0.7451 0.1525
## X1 0.1374 0.1388 0.9897 0.3236 -0.1364 0.4112
## X2 -0.0210 0.1246 -0.1687 0.8662 -0.2667 0.2247
## ability 0.9603 0.0663 14.4834 0.0000 0.8295 1.0911
## integrity -0.0302 0.0756 -0.4001 0.6895 -0.1793 0.1188
## autonomy 0.1166 0.0778 1.4990 0.1355 -0.0368 0.2700
##
## ************************ TOTAL EFFECT MODEL ***************************
## Outcome Variable: comfort
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.4942 0.2442 1.3778 31.1821 2.0000 193.0000 0.0000
##
## Model:
## coeff se t p LLCI ULCI
## constant 3.9697 0.1445 27.4751 0.0000 3.6847 4.2547
## X1 1.5955 0.2021 7.8948 0.0000 1.1969 1.9941
## X2 0.8500 0.2085 4.0771 0.0001 0.4388 1.2612
##
## ***********************************************************************
## Bootstrapping in progress. Please wait.
##
## ************ TOTAL, DIRECT, AND INDIRECT EFFECTS OF X ON Y ************
##
## Relative total effects of X on Y:
## effect se t p LLCI ULCI
## X1 1.5955 0.2021 7.8948 0.0000 1.1969 1.9941
## X2 0.8500 0.2085 4.0771 0.0001 0.4388 1.2612
##
## Omnibus test of total effect of X on Y:
## R2-chng F df1 df2 p
## 0.2442 31.1821 2.0000 193.0000 0.0000
## ----------
##
## Relative direct effects of X on Y:
## effect se t p LLCI ULCI
## X1 0.1374 0.1388 0.9897 0.3236 -0.1364 0.4112
## X2 -0.0210 0.1246 -0.1687 0.8662 -0.2667 0.2247
##
## Omnibus test of direct effect of X on Y:
## R2-chng F df1 df2 p
## 0.0022 0.9070 2.0000 190.0000 0.4055
##
## ----------
##
## Relative indirect effects of X on Y:
##
## ai_training_1 -> ability -> comfort
##
## Effect BootSE BootLLCI BootULCI
## X1 1.3025 0.1919 0.9351 1.6915
## X2 0.7952 0.1904 0.4317 1.1814
##
## Normal theory test for relative indirect effects:
## Effect se Z p
## X1 1.3025 0.1818 7.1662 0.0000
## X2 0.7952 0.1719 4.6251 0.0000
##
## ai_training_1 -> integrity -> comfort
##
## Effect BootSE BootLLCI BootULCI
## X1 -0.0291 0.0731 -0.1804 0.1113
## X2 -0.0146 0.0389 -0.0958 0.0632
##
## Normal theory test for relative indirect effects:
## Effect se Z p
## X1 -0.0291 0.0741 -0.3921 0.6949
## X2 -0.0146 0.0395 -0.3693 0.7119
##
## ai_training_1 -> autonomy -> comfort
##
## Effect BootSE BootLLCI BootULCI
## X1 0.1846 0.1163 -0.0312 0.4266
## X2 0.0903 0.0619 -0.0148 0.2279
##
## Normal theory test for relative indirect effects:
## Effect se Z p
## X1 0.1846 0.1258 1.4680 0.1421
## X2 0.0903 0.0657 1.3744 0.1693
##
## ******************** ANALYSIS NOTES AND ERRORS ************************
##
## Level of confidence for all confidence intervals in output: 95
##
## Number of bootstraps for percentile bootstrap confidence intervals: 10000
##
## NOTE: Some cases with missing data were deleted. The number of deleted cases was: 2
##
## ========================================================================
## SUMMARY: which mediator(s) survived the parallel model
## ========================================================================
## Criterion: BOTH relative indirect effects (X1 and X2) must have
## bootstrap CIs excluding zero, controlling for the other mediator(s).
##
## - ability branch X1 (no_ai vs. yes_ai (reference)): indirect effect = 1.3025, p = 0.0000 (normal-theory test), 95% bootstrap CI [0.9351, 1.6915]
## - ability branch X2 (control vs. yes_ai (reference)): indirect effect = 0.7952, p = 0.0000 (normal-theory test), 95% bootstrap CI [0.4317, 1.1814]Reference category: yes_human (X1 =
no_human vs. yes_human, X2 =
control vs. yes_human).
| training | n | percentage |
|---|---|---|
| yes_human | 71 | 35.68% |
| no_human | 66 | 33.17% |
| control | 62 | 31.16% |
##
## Call:
## lm(formula = comfort ~ training, data = df_human)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.8028 -0.8028 0.1803 1.1803 1.7121
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.80282 0.14958 32.108 <2e-16 ***
## trainingno_human -0.51494 0.21551 -2.389 0.0178 *
## trainingcontrol 0.01686 0.22004 0.077 0.9390
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.26 on 195 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.03739, Adjusted R-squared: 0.02751
## F-statistic: 3.787 on 2 and 195 DF, p-value: 0.02435Testing each candidate mediator individually (Y = comfort).
##
## ********************* PROCESS for R Version 4.1.1 *********************
##
## Written by Andrew F. Hayes, Ph.D. www.afhayes.com
## Documentation available in Hayes (2022). www.guilford.com/p/hayes3
##
## ***********************************************************************
##
## Model : 4
## Y : comfort
## X : human_training_1
## M : ability
##
## Sample size: 198
##
## Custom seed: 1234
##
## Coding of categorical X variable for analysis:
## human_training_1 X1 X2
## 1.0000 0.0000 0.0000
## 2.0000 1.0000 0.0000
## 3.0000 0.0000 1.0000
##
## ***********************************************************************
## Outcome Variable: ability
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.1422 0.0202 0.9246 2.0135 2.0000 195.0000 0.1363
##
## Model:
## coeff se t p LLCI ULCI
## constant 4.8920 0.1141 42.8680 0.0000 4.6670 5.1171
## X1 -0.2506 0.1644 -1.5242 0.1291 -0.5749 0.0737
## X2 0.0725 0.1679 0.4317 0.6665 -0.2586 0.4035
##
## ***********************************************************************
## Outcome Variable: comfort
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.8454 0.7147 0.4733 161.9922 3.0000 194.0000 0.0000
##
## Model:
## coeff se t p LLCI ULCI
## constant -0.5760 0.2636 -2.1851 0.0301 -1.0959 -0.0561
## X1 -0.2394 0.1183 -2.0232 0.0444 -0.4728 -0.0060
## X2 -0.0628 0.1202 -0.5228 0.6017 -0.2998 0.1742
## ability 1.0995 0.0512 21.4605 0.0000 0.9985 1.2006
##
## ************************ TOTAL EFFECT MODEL ***************************
## Outcome Variable: comfort
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.1934 0.0374 1.5886 3.7869 2.0000 195.0000 0.0244
##
## Model:
## coeff se t p LLCI ULCI
## constant 4.8028 0.1496 32.1079 0.0000 4.5078 5.0978
## X1 -0.5149 0.2155 -2.3894 0.0178 -0.9400 -0.0899
## X2 0.0169 0.2200 0.0766 0.9390 -0.4171 0.4508
##
## ***********************************************************************
## Bootstrapping in progress. Please wait.
##
## ************ TOTAL, DIRECT, AND INDIRECT EFFECTS OF X ON Y ************
##
## Relative total effects of X on Y:
## effect se t p LLCI ULCI
## X1 -0.5149 0.2155 -2.3894 0.0178 -0.9400 -0.0899
## X2 0.0169 0.2200 0.0766 0.9390 -0.4171 0.4508
##
## Omnibus test of total effect of X on Y:
## R2-chng F df1 df2 p
## 0.0374 3.7869 2.0000 195.0000 0.0244
## ----------
##
## Relative direct effects of X on Y:
## effect se t p LLCI ULCI
## X1 -0.2394 0.1183 -2.0232 0.0444 -0.4728 -0.0060
## X2 -0.0628 0.1202 -0.5228 0.6017 -0.2998 0.1742
##
## Omnibus test of direct effect of X on Y:
## R2-chng F df1 df2 p
## 0.0063 2.1586 2.0000 194.0000 0.1183
##
## ----------
##
## Relative indirect effects of X on Y:
##
## human_training_1 -> ability -> comfort
##
## Effect BootSE BootLLCI BootULCI
## X1 -0.2755 0.1927 -0.6562 0.1035
## X2 0.0797 0.1725 -0.2560 0.4210
##
## Normal theory test for relative indirect effects:
## Effect se Z p
## X1 -0.2755 0.1814 -1.5187 0.1288
## X2 0.0797 0.1848 0.4311 0.6664
##
## ******************** ANALYSIS NOTES AND ERRORS ************************
##
## Level of confidence for all confidence intervals in output: 95
##
## Number of bootstraps for percentile bootstrap confidence intervals: 10000
##
## NOTE: Some cases with missing data were deleted. The number of deleted cases was: 1
##
## RESULT: at least one relative indirect effect's bootstrap CI includes zero -- `ability` is NOT included in the parallel model.##
## ********************* PROCESS for R Version 4.1.1 *********************
##
## Written by Andrew F. Hayes, Ph.D. www.afhayes.com
## Documentation available in Hayes (2022). www.guilford.com/p/hayes3
##
## ***********************************************************************
##
## Model : 4
## Y : comfort
## X : human_training_1
## M : benevolence
##
## Sample size: 198
##
## Custom seed: 1234
##
## Coding of categorical X variable for analysis:
## human_training_1 X1 X2
## 1.0000 0.0000 0.0000
## 2.0000 1.0000 0.0000
## 3.0000 0.0000 1.0000
##
## ***********************************************************************
## Outcome Variable: benevolence
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.1025 0.0105 0.9066 1.0354 2.0000 195.0000 0.3570
##
## Model:
## coeff se t p LLCI ULCI
## constant 4.7934 0.1130 42.4197 0.0000 4.5706 5.0163
## X1 -0.2076 0.1628 -1.2750 0.2038 -0.5287 0.1135
## X2 -0.1978 0.1662 -1.1899 0.2355 -0.5256 0.1300
##
## ***********************************************************************
## Outcome Variable: comfort
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.5008 0.2508 1.2428 21.6502 3.0000 194.0000 0.0000
##
## Model:
## coeff se t p LLCI ULCI
## constant 1.8150 0.4231 4.2895 0.0000 0.9805 2.6495
## X1 -0.3856 0.1914 -2.0143 0.0454 -0.7631 -0.0080
## X2 0.1401 0.1953 0.7175 0.4739 -0.2451 0.5254
## benevolence 0.6233 0.0838 7.4343 0.0000 0.4580 0.7887
##
## ************************ TOTAL EFFECT MODEL ***************************
## Outcome Variable: comfort
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.1934 0.0374 1.5886 3.7869 2.0000 195.0000 0.0244
##
## Model:
## coeff se t p LLCI ULCI
## constant 4.8028 0.1496 32.1079 0.0000 4.5078 5.0978
## X1 -0.5149 0.2155 -2.3894 0.0178 -0.9400 -0.0899
## X2 0.0169 0.2200 0.0766 0.9390 -0.4171 0.4508
##
## ***********************************************************************
## Bootstrapping in progress. Please wait.
##
## ************ TOTAL, DIRECT, AND INDIRECT EFFECTS OF X ON Y ************
##
## Relative total effects of X on Y:
## effect se t p LLCI ULCI
## X1 -0.5149 0.2155 -2.3894 0.0178 -0.9400 -0.0899
## X2 0.0169 0.2200 0.0766 0.9390 -0.4171 0.4508
##
## Omnibus test of total effect of X on Y:
## R2-chng F df1 df2 p
## 0.0374 3.7869 2.0000 195.0000 0.0244
## ----------
##
## Relative direct effects of X on Y:
## effect se t p LLCI ULCI
## X1 -0.3856 0.1914 -2.0143 0.0454 -0.7631 -0.0080
## X2 0.1401 0.1953 0.7175 0.4739 -0.2451 0.5254
##
## Omnibus test of direct effect of X on Y:
## R2-chng F df1 df2 p
## 0.0297 3.8447 2.0000 194.0000 0.0230
##
## ----------
##
## Relative indirect effects of X on Y:
##
## human_training_1 -> benevolence -> comfort
##
## Effect BootSE BootLLCI BootULCI
## X1 -0.1294 0.0996 -0.3248 0.0656
## X2 -0.1233 0.1118 -0.3458 0.0966
##
## Normal theory test for relative indirect effects:
## Effect se Z p
## X1 -0.1294 0.1039 -1.2457 0.2129
## X2 -0.1233 0.1059 -1.1647 0.2441
##
## ******************** ANALYSIS NOTES AND ERRORS ************************
##
## Level of confidence for all confidence intervals in output: 95
##
## Number of bootstraps for percentile bootstrap confidence intervals: 10000
##
## NOTE: Some cases with missing data were deleted. The number of deleted cases was: 1
##
## RESULT: at least one relative indirect effect's bootstrap CI includes zero -- `benevolence` is NOT included in the parallel model.##
## ********************* PROCESS for R Version 4.1.1 *********************
##
## Written by Andrew F. Hayes, Ph.D. www.afhayes.com
## Documentation available in Hayes (2022). www.guilford.com/p/hayes3
##
## ***********************************************************************
##
## Model : 4
## Y : comfort
## X : human_training_1
## M : integrity
##
## Sample size: 198
##
## Custom seed: 1234
##
## Coding of categorical X variable for analysis:
## human_training_1 X1 X2
## 1.0000 0.0000 0.0000
## 2.0000 1.0000 0.0000
## 3.0000 0.0000 1.0000
##
## ***********************************************************************
## Outcome Variable: integrity
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.1988 0.0395 0.8878 4.0111 2.0000 195.0000 0.0196
##
## Model:
## coeff se t p LLCI ULCI
## constant 4.6033 0.1118 41.1669 0.0000 4.3828 4.8238
## X1 -0.4518 0.1611 -2.8042 0.0056 -0.7695 -0.1340
## X2 -0.2754 0.1645 -1.6744 0.0957 -0.5998 0.0490
##
## ***********************************************************************
## Outcome Variable: comfort
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.5196 0.2700 1.2110 23.9189 3.0000 194.0000 0.0000
##
## Model:
## coeff se t p LLCI ULCI
## constant 1.7757 0.4066 4.3677 0.0000 0.9739 2.5775
## X1 -0.2179 0.1919 -1.1352 0.2577 -0.5964 0.1607
## X2 0.1980 0.1935 1.0232 0.3075 -0.1836 0.5796
## integrity 0.6576 0.0836 7.8626 0.0000 0.4926 0.8226
##
## ************************ TOTAL EFFECT MODEL ***************************
## Outcome Variable: comfort
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.1934 0.0374 1.5886 3.7869 2.0000 195.0000 0.0244
##
## Model:
## coeff se t p LLCI ULCI
## constant 4.8028 0.1496 32.1079 0.0000 4.5078 5.0978
## X1 -0.5149 0.2155 -2.3894 0.0178 -0.9400 -0.0899
## X2 0.0169 0.2200 0.0766 0.9390 -0.4171 0.4508
##
## ***********************************************************************
## Bootstrapping in progress. Please wait.
##
## ************ TOTAL, DIRECT, AND INDIRECT EFFECTS OF X ON Y ************
##
## Relative total effects of X on Y:
## effect se t p LLCI ULCI
## X1 -0.5149 0.2155 -2.3894 0.0178 -0.9400 -0.0899
## X2 0.0169 0.2200 0.0766 0.9390 -0.4171 0.4508
##
## Omnibus test of total effect of X on Y:
## R2-chng F df1 df2 p
## 0.0374 3.7869 2.0000 195.0000 0.0244
## ----------
##
## Relative direct effects of X on Y:
## effect se t p LLCI ULCI
## X1 -0.2179 0.1919 -1.1352 0.2577 -0.5964 0.1607
## X2 0.1980 0.1935 1.0232 0.3075 -0.1836 0.5796
##
## Omnibus test of direct effect of X on Y:
## R2-chng F df1 df2 p
## 0.0170 2.2536 2.0000 194.0000 0.1078
##
## ----------
##
## Relative indirect effects of X on Y:
##
## human_training_1 -> integrity -> comfort
##
## Effect BootSE BootLLCI BootULCI
## X1 -0.2971 0.1128 -0.5294 -0.0837
## X2 -0.1811 0.1133 -0.4122 0.0360
##
## Normal theory test for relative indirect effects:
## Effect se Z p
## X1 -0.2971 0.1133 -2.6225 0.0087
## X2 -0.1811 0.1114 -1.6251 0.1041
##
## ******************** ANALYSIS NOTES AND ERRORS ************************
##
## Level of confidence for all confidence intervals in output: 95
##
## Number of bootstraps for percentile bootstrap confidence intervals: 10000
##
## NOTE: Some cases with missing data were deleted. The number of deleted cases was: 1
##
## RESULT: at least one relative indirect effect's bootstrap CI includes zero -- `integrity` is NOT included in the parallel model.##
## ********************* PROCESS for R Version 4.1.1 *********************
##
## Written by Andrew F. Hayes, Ph.D. www.afhayes.com
## Documentation available in Hayes (2022). www.guilford.com/p/hayes3
##
## ***********************************************************************
##
## Model : 4
## Y : comfort
## X : human_training_1
## M : autonomy
##
## Sample size: 198
##
## Custom seed: 1234
##
## Coding of categorical X variable for analysis:
## human_training_1 X1 X2
## 1.0000 0.0000 0.0000
## 2.0000 1.0000 0.0000
## 3.0000 0.0000 1.0000
##
## ***********************************************************************
## Outcome Variable: autonomy
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.2884 0.0832 0.9243 8.8455 2.0000 195.0000 0.0002
##
## Model:
## coeff se t p LLCI ULCI
## constant 4.1831 0.1141 36.6623 0.0000 3.9581 4.4081
## X1 0.6528 0.1644 3.9709 0.0001 0.3286 0.9770
## X2 0.1093 0.1678 0.6509 0.5159 -0.2218 0.4403
##
## ***********************************************************************
## Outcome Variable: comfort
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.4558 0.2077 1.3143 16.9542 3.0000 194.0000 0.0000
##
## Model:
## coeff se t p LLCI ULCI
## constant 2.4959 0.3822 6.5298 0.0000 1.7421 3.2498
## X1 -0.8749 0.2038 -4.2932 0.0000 -1.2769 -0.4730
## X2 -0.0434 0.2004 -0.2166 0.8288 -0.4386 0.3518
## autonomy 0.5515 0.0854 6.4582 0.0000 0.3831 0.7199
##
## ************************ TOTAL EFFECT MODEL ***************************
## Outcome Variable: comfort
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.1934 0.0374 1.5886 3.7869 2.0000 195.0000 0.0244
##
## Model:
## coeff se t p LLCI ULCI
## constant 4.8028 0.1496 32.1079 0.0000 4.5078 5.0978
## X1 -0.5149 0.2155 -2.3894 0.0178 -0.9400 -0.0899
## X2 0.0169 0.2200 0.0766 0.9390 -0.4171 0.4508
##
## ***********************************************************************
## Bootstrapping in progress. Please wait.
##
## ************ TOTAL, DIRECT, AND INDIRECT EFFECTS OF X ON Y ************
##
## Relative total effects of X on Y:
## effect se t p LLCI ULCI
## X1 -0.5149 0.2155 -2.3894 0.0178 -0.9400 -0.0899
## X2 0.0169 0.2200 0.0766 0.9390 -0.4171 0.4508
##
## Omnibus test of total effect of X on Y:
## R2-chng F df1 df2 p
## 0.0374 3.7869 2.0000 195.0000 0.0244
## ----------
##
## Relative direct effects of X on Y:
## effect se t p LLCI ULCI
## X1 -0.8749 0.2038 -4.2932 0.0000 -1.2769 -0.4730
## X2 -0.0434 0.2004 -0.2166 0.8288 -0.4386 0.3518
##
## Omnibus test of direct effect of X on Y:
## R2-chng F df1 df2 p
## 0.0919 11.2497 2.0000 194.0000 0.0000
##
## ----------
##
## Relative indirect effects of X on Y:
##
## human_training_1 -> autonomy -> comfort
##
## Effect BootSE BootLLCI BootULCI
## X1 0.3600 0.1218 0.1551 0.6232
## X2 0.0602 0.1013 -0.1279 0.2774
##
## Normal theory test for relative indirect effects:
## Effect se Z p
## X1 0.3600 0.1073 3.3536 0.0008
## X2 0.0602 0.0941 0.6401 0.5221
##
## ******************** ANALYSIS NOTES AND ERRORS ************************
##
## Level of confidence for all confidence intervals in output: 95
##
## Number of bootstraps for percentile bootstrap confidence intervals: 10000
##
## NOTE: Some cases with missing data were deleted. The number of deleted cases was: 1
##
## RESULT: at least one relative indirect effect's bootstrap CI includes zero -- `autonomy` is NOT included in the parallel model.| mediator | significant_indirect_effect |
|---|---|
| ability | FALSE |
| benevolence | FALSE |
| integrity | FALSE |
| autonomy | FALSE |
No candidate mediator had both relative indirect effects’ bootstrap CIs exclude zero in the Human dataset . Per the preregistration, no parallel model is run.
Requested directly rather than arrived at via the simple-mediation
screening above – neither integrity nor
autonomy is the preregistered mediator for the
human-training hypotheses (that’s ability; see S3-mediation-hypotheses.Rmd), so
this is an exploratory follow-up specific to the Human dataset, run
regardless of whether these two happened to “win” the screening
above.
##
## ********************* PROCESS for R Version 4.1.1 *********************
##
## Written by Andrew F. Hayes, Ph.D. www.afhayes.com
## Documentation available in Hayes (2022). www.guilford.com/p/hayes3
##
## ***********************************************************************
##
## Model : 4
## Y : comfort
## X : human_training_1
## M1 : integrity
## M2 : autonomy
##
## Sample size: 198
##
## Custom seed: 1234
##
## Coding of categorical X variable for analysis:
## human_training_1 X1 X2
## 1.0000 0.0000 0.0000
## 2.0000 1.0000 0.0000
## 3.0000 0.0000 1.0000
##
## ***********************************************************************
## Outcome Variable: integrity
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.1988 0.0395 0.8878 4.0111 2.0000 195.0000 0.0196
##
## Model:
## coeff se t p LLCI ULCI
## constant 4.6033 0.1118 41.1669 0.0000 4.3828 4.8238
## X1 -0.4518 0.1611 -2.8042 0.0056 -0.7695 -0.1340
## X2 -0.2754 0.1645 -1.6744 0.0957 -0.5998 0.0490
##
## ***********************************************************************
## Outcome Variable: autonomy
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.2884 0.0832 0.9243 8.8455 2.0000 195.0000 0.0002
##
## Model:
## coeff se t p LLCI ULCI
## constant 4.1831 0.1141 36.6623 0.0000 3.9581 4.4081
## X1 0.6528 0.1644 3.9709 0.0001 0.3286 0.9770
## X2 0.1093 0.1678 0.6509 0.5159 -0.2218 0.4403
##
## ***********************************************************************
## Outcome Variable: comfort
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.5303 0.2812 1.1986 18.8750 4.0000 193.0000 0.0000
##
## Model:
## coeff se t p LLCI ULCI
## constant 1.5978 0.4173 3.8290 0.0002 0.7748 2.4208
## X1 -0.4105 0.2209 -1.8579 0.0647 -0.8462 0.0253
## X2 0.1376 0.1956 0.7033 0.4827 -0.2483 0.5234
## integrity 0.5167 0.1163 4.4415 0.0000 0.2873 0.7462
## autonomy 0.1976 0.1140 1.7328 0.0847 -0.0273 0.4224
##
## ************************ TOTAL EFFECT MODEL ***************************
## Outcome Variable: comfort
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.1934 0.0374 1.5886 3.7869 2.0000 195.0000 0.0244
##
## Model:
## coeff se t p LLCI ULCI
## constant 4.8028 0.1496 32.1079 0.0000 4.5078 5.0978
## X1 -0.5149 0.2155 -2.3894 0.0178 -0.9400 -0.0899
## X2 0.0169 0.2200 0.0766 0.9390 -0.4171 0.4508
##
## ***********************************************************************
## Bootstrapping in progress. Please wait.
##
## ************ TOTAL, DIRECT, AND INDIRECT EFFECTS OF X ON Y ************
##
## Relative total effects of X on Y:
## effect se t p LLCI ULCI
## X1 -0.5149 0.2155 -2.3894 0.0178 -0.9400 -0.0899
## X2 0.0169 0.2200 0.0766 0.9390 -0.4171 0.4508
##
## Omnibus test of total effect of X on Y:
## R2-chng F df1 df2 p
## 0.0374 3.7869 2.0000 195.0000 0.0244
## ----------
##
## Relative direct effects of X on Y:
## effect se t p LLCI ULCI
## X1 -0.4105 0.2209 -1.8579 0.0647 -0.8462 0.0253
## X2 0.1376 0.1956 0.7033 0.4827 -0.2483 0.5234
##
## Omnibus test of direct effect of X on Y:
## R2-chng F df1 df2 p
## 0.0261 3.5029 2.0000 193.0000 0.0320
##
## ----------
##
## Relative indirect effects of X on Y:
##
## human_training_1 -> integrity -> comfort
##
## Effect BootSE BootLLCI BootULCI
## X1 -0.2334 0.1086 -0.4739 -0.0517
## X2 -0.1423 0.0983 -0.3618 0.0255
##
## Normal theory test for relative indirect effects:
## Effect se Z p
## X1 -0.2334 0.1002 -2.3293 0.0198
## X2 -0.1423 0.0928 -1.5331 0.1253
##
## human_training_1 -> autonomy -> comfort
##
## Effect BootSE BootLLCI BootULCI
## X1 0.1290 0.1051 -0.0389 0.3737
## X2 0.0216 0.0474 -0.0518 0.1424
##
## Normal theory test for relative indirect effects:
## Effect se Z p
## X1 0.1290 0.0833 1.5475 0.1218
## X2 0.0216 0.0403 0.5361 0.5919
##
## ******************** ANALYSIS NOTES AND ERRORS ************************
##
## Level of confidence for all confidence intervals in output: 95
##
## Number of bootstraps for percentile bootstrap confidence intervals: 10000
##
## NOTE: Some cases with missing data were deleted. The number of deleted cases was: 1
##
## ========================================================================
## SUMMARY: which mediator(s) survived the parallel model
## ========================================================================
## Criterion: BOTH relative indirect effects (X1 and X2) must have
## bootstrap CIs excluding zero, controlling for the other mediator(s).
##
## None of the mediators in this parallel model survived -- every
## mediator had at least one relative indirect effect whose bootstrap
## CI included zero once the other mediators were controlled for.