##
## ********************* 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
## 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 0.94 0.89 0.89 16 0.0079 4.8 1.4 0.89
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.92 0.94 0.95
## Duhachek 0.93 0.94 0.96
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## comfort 0.96 0.89 0.79 0.89 7.9 NA 0 0.89
## confidence 0.82 0.89 0.79 0.89 7.9 NA 0 0.89
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## comfort 213 0.97 0.97 0.92 0.89 4.7 1.4
## confidence 215 0.97 0.97 0.92 0.89 4.8 1.3
##
## Non missing response frequency for each item
## 0 1 2 3 4 5 6 miss
## comfort 0.03 0.02 0.02 0.09 0.16 0.31 0.36 0.01
## confidence 0.02 0.01 0.03 0.07 0.19 0.34 0.34 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
## 0.95 0.96 0.94 0.88 22 0.0054 4.9 1.2 0.87
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.94 0.95 0.96
## Duhachek 0.94 0.95 0.97
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## ability2 0.92 0.93 0.87 0.87 13 0.0099 NA 0.87
## ability3 0.96 0.96 0.92 0.92 22 0.0056 NA 0.92
## ability4 0.92 0.92 0.85 0.85 12 0.0106 NA 0.85
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## ability2 215 0.96 0.96 0.94 0.92 5.0 1.2
## ability3 216 0.95 0.95 0.89 0.88 4.7 1.3
## ability4 214 0.96 0.97 0.95 0.93 4.9 1.1
##
## Non missing response frequency for each item
## 0 1 2 3 4 5 6 miss
## ability2 0.01 0.01 0.02 0.06 0.11 0.41 0.37 0.00
## ability3 0.01 0.03 0.02 0.07 0.15 0.42 0.29 0.00
## ability4 0.01 0.01 0.03 0.05 0.13 0.43 0.34 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
## 0.89 0.89 0.85 0.73 8.2 0.013 4.5 1.1 0.71
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.86 0.89 0.91
## Duhachek 0.87 0.89 0.92
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## benevolence1 0.83 0.83 0.71 0.71 4.9 0.023 NA 0.71
## benevolence2 0.89 0.89 0.80 0.80 7.9 0.015 NA 0.80
## benevolence4 0.82 0.82 0.69 0.69 4.4 0.025 NA 0.69
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## benevolence1 214 0.91 0.91 0.86 0.81 4.5 1.2
## benevolence2 216 0.88 0.88 0.77 0.74 4.7 1.2
## benevolence4 216 0.92 0.92 0.87 0.82 4.4 1.2
##
## Non missing response frequency for each item
## 0 1 2 3 4 5 6 miss
## benevolence1 0.01 0.01 0.00 0.22 0.16 0.41 0.18 0.01
## benevolence2 0.01 0.01 0.00 0.19 0.12 0.42 0.25 0.00
## benevolence4 0.01 0.01 0.02 0.28 0.12 0.39 0.17 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 0.74 8.5 0.013 4.1 1.2 0.75
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.87 0.89 0.92
## Duhachek 0.87 0.89 0.92
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## integrity1 0.90 0.90 0.81 0.81 8.6 0.014 NA 0.81
## integrity2 0.79 0.79 0.66 0.66 3.9 0.028 NA 0.66
## integrity3 0.85 0.85 0.75 0.75 5.9 0.020 NA 0.75
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## integrity1 216 0.88 0.88 0.78 0.74 3.7 1.3
## integrity2 215 0.94 0.94 0.91 0.86 4.4 1.3
## integrity3 216 0.90 0.91 0.84 0.79 4.3 1.3
##
## Non missing response frequency for each item
## 0 1 2 3 4 5 6 miss
## integrity1 0.02 0.02 0.03 0.47 0.14 0.20 0.11 0
## integrity2 0.01 0.02 0.04 0.21 0.17 0.35 0.20 0
## integrity3 0.01 0.01 0.01 0.26 0.15 0.37 0.18 0##
## 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
## 0.91 0.91 0.87 0.77 9.8 0.011 4.2 1.3 0.78
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.88 0.91 0.93
## Duhachek 0.88 0.91 0.93
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## autonomy1 0.83 0.83 0.71 0.71 4.9 0.023 NA 0.71
## autonomy3 0.89 0.89 0.81 0.81 8.3 0.015 NA 0.81
## autonomy4 0.88 0.88 0.78 0.78 7.0 0.017 NA 0.78
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## autonomy1 216 0.94 0.94 0.90 0.86 4.2 1.4
## autonomy3 214 0.90 0.90 0.82 0.78 4.2 1.4
## autonomy4 216 0.92 0.91 0.85 0.81 4.1 1.6
##
## Non missing response frequency for each item
## 0 1 2 3 4 5 6 miss
## autonomy1 0.02 0.03 0.06 0.20 0.18 0.33 0.19 0.00
## autonomy3 0.02 0.02 0.03 0.28 0.11 0.38 0.16 0.01
## autonomy4 0.03 0.04 0.06 0.27 0.13 0.25 0.22 0.00##
## Call:
## lm(formula = trust ~ ai_training, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.8333 -0.5423 0.1667 0.5643 2.1667
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.8333 0.1332 28.775 < 2e-16 ***
## ai_trainingcontrol 1.1024 0.1917 5.750 3.07e-08 ***
## ai_trainingno_ai 1.7089 0.1910 8.946 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.154 on 213 degrees of freedom
## Multiple R-squared: 0.2795, Adjusted R-squared: 0.2728
## F-statistic: 41.32 on 2 and 213 DF, p-value: 6.847e-16##
## ********************* 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 : trust
## X : ai_training_1
## M : ability
##
## Sample size: 216
##
## 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
## 0.5087 0.2588 1.0028 37.1896 2.0000 213.0000 0.0000
##
## Model:
## coeff se t p LLCI ULCI
## constant 4.1289 0.1156 35.7080 0.0000 3.9010 4.3568
## X1 0.8902 0.1664 5.3489 0.0000 0.5621 1.2182
## X2 1.4110 0.1658 8.5098 0.0000 1.0842 1.7379
##
## ***********************************************************************
## Outcome Variable: trust
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.9115 0.8308 0.3141 346.9345 3.0000 212.0000 0.0000
##
## Model:
## coeff se t p LLCI ULCI
## constant -0.3276 0.1710 -1.9150 0.0568 -0.6647 0.0096
## X1 0.2053 0.0992 2.0698 0.0397 0.0098 0.4009
## X2 0.2870 0.1074 2.6713 0.0081 0.0752 0.4987
## ability 1.0078 0.0383 26.2792 0.0000 0.9322 1.0833
##
## ************************ TOTAL EFFECT MODEL ***************************
## Outcome Variable: trust
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.5287 0.2795 1.3310 41.3220 2.0000 213.0000 0.0000
##
## Model:
## coeff se t p LLCI ULCI
## constant 3.8333 0.1332 28.7753 0.0000 3.5707 4.0959
## X1 1.1024 0.1917 5.7496 0.0000 0.7244 1.4803
## X2 1.7089 0.1910 8.9458 0.0000 1.3324 2.0855
##
## ***********************************************************************
## 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 1.1024 0.1917 5.7496 0.0000 0.7244 1.4803
## X2 1.7089 0.1910 8.9458 0.0000 1.3324 2.0855
##
## Omnibus test of total effect of X on Y:
## R2-chng F df1 df2 p
## 0.2795 41.3220 2.0000 213.0000 0.0000
## ----------
##
## Relative direct effects of X on Y:
## effect se t p LLCI ULCI
## X1 0.2053 0.0992 2.0698 0.0397 0.0098 0.4009
## X2 0.2870 0.1074 2.6713 0.0081 0.0752 0.4987
##
## Omnibus test of direct effect of X on Y:
## R2-chng F df1 df2 p
## 0.0061 3.7963 2.0000 212.0000 0.0240
##
## ----------
##
## Relative indirect effects of X on Y:
##
## ai_training_1 -> ability -> trust
##
## Effect BootSE BootLLCI BootULCI
## X1 0.8971 0.1950 0.5243 1.2878
## X2 1.4220 0.1921 1.0565 1.8014
##
## Normal theory test for relative indirect effects:
## Effect se Z p
## X1 0.8971 0.1713 5.2378 0.0000
## X2 1.4220 0.1758 8.0906 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##
## ********************* 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 : trust
## X : ai_training_1
## M : benevolence
##
## Sample size: 216
##
## 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
## 0.2759 0.0761 1.1279 8.7755 2.0000 213.0000 0.0002
##
## Model:
## coeff se t p LLCI ULCI
## constant 4.1644 0.1226 33.9589 0.0000 3.9227 4.4062
## X1 0.2522 0.1765 1.4290 0.1545 -0.0957 0.6001
## X2 0.7276 0.1759 4.1374 0.0001 0.3809 1.0742
##
## ***********************************************************************
## Outcome Variable: trust
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.6704 0.4495 1.0218 57.7002 3.0000 212.0000 0.0000
##
## Model:
## coeff se t p LLCI ULCI
## constant 1.6361 0.2956 5.5347 0.0000 1.0534 2.2188
## X1 0.9693 0.1688 5.7425 0.0000 0.6366 1.3020
## X2 1.3250 0.1740 7.6163 0.0000 0.9821 1.6680
## benevolence 0.5276 0.0652 8.0901 0.0000 0.3991 0.6562
##
## ************************ TOTAL EFFECT MODEL ***************************
## Outcome Variable: trust
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.5287 0.2795 1.3310 41.3220 2.0000 213.0000 0.0000
##
## Model:
## coeff se t p LLCI ULCI
## constant 3.8333 0.1332 28.7753 0.0000 3.5707 4.0959
## X1 1.1024 0.1917 5.7496 0.0000 0.7244 1.4803
## X2 1.7089 0.1910 8.9458 0.0000 1.3324 2.0855
##
## ***********************************************************************
## 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 1.1024 0.1917 5.7496 0.0000 0.7244 1.4803
## X2 1.7089 0.1910 8.9458 0.0000 1.3324 2.0855
##
## Omnibus test of total effect of X on Y:
## R2-chng F df1 df2 p
## 0.2795 41.3220 2.0000 213.0000 0.0000
## ----------
##
## Relative direct effects of X on Y:
## effect se t p LLCI ULCI
## X1 0.9693 0.1688 5.7425 0.0000 0.6366 1.3020
## X2 1.3250 0.1740 7.6163 0.0000 0.9821 1.6680
##
## Omnibus test of direct effect of X on Y:
## R2-chng F df1 df2 p
## 0.1644 31.6549 2.0000 212.0000 0.0000
##
## ----------
##
## Relative indirect effects of X on Y:
##
## ai_training_1 -> benevolence -> trust
##
## Effect BootSE BootLLCI BootULCI
## X1 0.1331 0.1025 -0.0478 0.3565
## X2 0.3839 0.1257 0.1640 0.6587
##
## Normal theory test for relative indirect effects:
## Effect se Z p
## X1 0.1331 0.0953 1.3969 0.1624
## X2 0.3839 0.1048 3.6615 0.0003
##
## ******************** ANALYSIS NOTES AND ERRORS ************************
##
## Level of confidence for all confidence intervals in output: 95
##
## Number of bootstraps for percentile bootstrap confidence intervals: 10000##
## ********************* 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 : trust
## X : ai_training_1
## M : integrity
##
## Sample size: 216
##
## 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
## 0.3490 0.1218 1.2568 14.7703 2.0000 213.0000 0.0000
##
## Model:
## coeff se t p LLCI ULCI
## constant 3.6822 0.1294 28.4456 0.0000 3.4271 3.9374
## X1 0.3940 0.1863 2.1146 0.0356 0.0267 0.7612
## X2 1.0032 0.1856 5.4045 0.0000 0.6373 1.3691
##
## ***********************************************************************
## Outcome Variable: trust
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.7718 0.5957 0.7505 104.1060 3.0000 212.0000 0.0000
##
## Model:
## coeff se t p LLCI ULCI
## constant 1.3232 0.2191 6.0383 0.0000 0.8912 1.7552
## X1 0.8338 0.1455 5.7317 0.0000 0.5471 1.1206
## X2 1.0250 0.1530 6.7010 0.0000 0.7235 1.3266
## integrity 0.6817 0.0529 12.8744 0.0000 0.5773 0.7861
##
## ************************ TOTAL EFFECT MODEL ***************************
## Outcome Variable: trust
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.5287 0.2795 1.3310 41.3220 2.0000 213.0000 0.0000
##
## Model:
## coeff se t p LLCI ULCI
## constant 3.8333 0.1332 28.7753 0.0000 3.5707 4.0959
## X1 1.1024 0.1917 5.7496 0.0000 0.7244 1.4803
## X2 1.7089 0.1910 8.9458 0.0000 1.3324 2.0855
##
## ***********************************************************************
## 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 1.1024 0.1917 5.7496 0.0000 0.7244 1.4803
## X2 1.7089 0.1910 8.9458 0.0000 1.3324 2.0855
##
## Omnibus test of total effect of X on Y:
## R2-chng F df1 df2 p
## 0.2795 41.3220 2.0000 213.0000 0.0000
## ----------
##
## Relative direct effects of X on Y:
## effect se t p LLCI ULCI
## X1 0.8338 0.1455 5.7317 0.0000 0.5471 1.1206
## X2 1.0250 0.1530 6.7010 0.0000 0.7235 1.3266
##
## Omnibus test of direct effect of X on Y:
## R2-chng F df1 df2 p
## 0.1001 26.2404 2.0000 212.0000 0.0000
##
## ----------
##
## Relative indirect effects of X on Y:
##
## ai_training_1 -> integrity -> trust
##
## Effect BootSE BootLLCI BootULCI
## X1 0.2686 0.1425 0.0066 0.5646
## X2 0.6839 0.1638 0.3840 1.0178
##
## Normal theory test for relative indirect effects:
## Effect se Z p
## X1 0.2686 0.1291 2.0806 0.0375
## X2 0.6839 0.1376 4.9705 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##
## ********************* 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 : trust
## X : ai_training_1
## M : autonomy
##
## Sample size: 216
##
## 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
## 0.5647 0.3189 1.2170 49.8691 2.0000 213.0000 0.0000
##
## Model:
## coeff se t p LLCI ULCI
## constant 3.2978 0.1274 25.8881 0.0000 3.0467 3.5489
## X1 0.8784 0.1833 4.7912 0.0000 0.5170 1.2398
## X2 1.8243 0.1827 9.9867 0.0000 1.4642 2.1844
##
## ***********************************************************************
## Outcome Variable: trust
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.7701 0.5930 0.7554 102.9610 3.0000 212.0000 0.0000
##
## Model:
## coeff se t p LLCI ULCI
## constant 1.5585 0.2044 7.6262 0.0000 1.1557 1.9614
## X1 0.4965 0.1520 3.2655 0.0013 0.1968 0.7961
## X2 0.4505 0.1744 2.5835 0.0105 0.1068 0.7943
## autonomy 0.6898 0.0540 12.7780 0.0000 0.5834 0.7962
##
## ************************ TOTAL EFFECT MODEL ***************************
## Outcome Variable: trust
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.5287 0.2795 1.3310 41.3220 2.0000 213.0000 0.0000
##
## Model:
## coeff se t p LLCI ULCI
## constant 3.8333 0.1332 28.7753 0.0000 3.5707 4.0959
## X1 1.1024 0.1917 5.7496 0.0000 0.7244 1.4803
## X2 1.7089 0.1910 8.9458 0.0000 1.3324 2.0855
##
## ***********************************************************************
## 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 1.1024 0.1917 5.7496 0.0000 0.7244 1.4803
## X2 1.7089 0.1910 8.9458 0.0000 1.3324 2.0855
##
## Omnibus test of total effect of X on Y:
## R2-chng F df1 df2 p
## 0.2795 41.3220 2.0000 213.0000 0.0000
## ----------
##
## Relative direct effects of X on Y:
## effect se t p LLCI ULCI
## X1 0.4965 0.1520 3.2655 0.0013 0.1968 0.7961
## X2 0.4505 0.1744 2.5835 0.0105 0.1068 0.7943
##
## Omnibus test of direct effect of X on Y:
## R2-chng F df1 df2 p
## 0.0221 5.7583 2.0000 212.0000 0.0037
##
## ----------
##
## Relative indirect effects of X on Y:
##
## ai_training_1 -> autonomy -> trust
##
## Effect BootSE BootLLCI BootULCI
## X1 0.6059 0.1633 0.3076 0.9398
## X2 1.2584 0.2029 0.8758 1.6604
##
## Normal theory test for relative indirect effects:
## Effect se Z p
## X1 0.6059 0.1354 4.4742 0.0000
## X2 1.2584 0.1602 7.8537 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##
## ********************* 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 : trust
## X : ai_training_1
## M : ai_use
##
## Sample size: 215
##
## 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: ai_use
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.6441 0.4149 1.6431 75.1655 2.0000 212.0000 0.0000
##
## Model:
## coeff se t p LLCI ULCI
## constant 4.5600 0.1480 30.8079 0.0000 4.2682 4.8518
## X1 -1.2412 0.2138 -5.8045 0.0000 -1.6627 -0.8197
## X2 -2.6023 0.2123 -12.2603 0.0000 -3.0206 -2.1839
##
## ***********************************************************************
## Outcome Variable: trust
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.5292 0.2801 1.3373 27.3587 3.0000 211.0000 0.0000
##
## Model:
## coeff se t p LLCI ULCI
## constant 3.7302 0.3125 11.9363 0.0000 3.1141 4.3462
## X1 1.1150 0.2077 5.3693 0.0000 0.7057 1.5244
## X2 1.7678 0.2503 7.0619 0.0000 1.2743 2.2613
## ai_use 0.0226 0.0620 0.3651 0.7154 -0.0995 0.1448
##
## ************************ TOTAL EFFECT MODEL ***************************
## Outcome Variable: trust
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.5288 0.2796 1.3318 41.1396 2.0000 212.0000 0.0000
##
## Model:
## coeff se t p LLCI ULCI
## constant 3.8333 0.1333 28.7660 0.0000 3.5707 4.0960
## X1 1.0870 0.1925 5.6462 0.0000 0.7075 1.4664
## X2 1.7089 0.1911 8.9429 0.0000 1.3322 2.0856
##
## ***********************************************************************
## 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 1.0870 0.1925 5.6462 0.0000 0.7075 1.4664
## X2 1.7089 0.1911 8.9429 0.0000 1.3322 2.0856
##
## Omnibus test of total effect of X on Y:
## R2-chng F df1 df2 p
## 0.2796 41.1396 2.0000 212.0000 0.0000
## ----------
##
## Relative direct effects of X on Y:
## effect se t p LLCI ULCI
## X1 1.1150 0.2077 5.3693 0.0000 0.7057 1.5244
## X2 1.7678 0.2503 7.0619 0.0000 1.2743 2.2613
##
## Omnibus test of direct effect of X on Y:
## R2-chng F df1 df2 p
## 0.1784 26.1398 2.0000 211.0000 0.0000
##
## ----------
##
## Relative indirect effects of X on Y:
##
## ai_training_1 -> ai_use -> trust
##
## Effect BootSE BootLLCI BootULCI
## X1 -0.0281 0.0882 -0.1892 0.1580
## X2 -0.0589 0.1868 -0.4203 0.3184
##
## Normal theory test for relative indirect effects:
## Effect se Z p
## X1 -0.0281 0.0782 -0.3591 0.7195
## X2 -0.0589 0.1618 -0.3637 0.7160
##
## ******************** 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##
## ********************* 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 : trust
## X : ai_training_1
## M1 : ability
## M2 : benevolence
## M3 : integrity
## M4 : autonomy
##
## Sample size: 216
##
## 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
## 0.5087 0.2588 1.0028 37.1896 2.0000 213.0000 0.0000
##
## Model:
## coeff se t p LLCI ULCI
## constant 4.1289 0.1156 35.7080 0.0000 3.9010 4.3568
## X1 0.8902 0.1664 5.3489 0.0000 0.5621 1.2182
## X2 1.4110 0.1658 8.5098 0.0000 1.0842 1.7379
##
## ***********************************************************************
## Outcome Variable: benevolence
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.2759 0.0761 1.1279 8.7755 2.0000 213.0000 0.0002
##
## Model:
## coeff se t p LLCI ULCI
## constant 4.1644 0.1226 33.9589 0.0000 3.9227 4.4062
## X1 0.2522 0.1765 1.4290 0.1545 -0.0957 0.6001
## X2 0.7276 0.1759 4.1374 0.0001 0.3809 1.0742
##
## ***********************************************************************
## Outcome Variable: integrity
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.3490 0.1218 1.2568 14.7703 2.0000 213.0000 0.0000
##
## Model:
## coeff se t p LLCI ULCI
## constant 3.6822 0.1294 28.4456 0.0000 3.4271 3.9374
## X1 0.3940 0.1863 2.1146 0.0356 0.0267 0.7612
## X2 1.0032 0.1856 5.4045 0.0000 0.6373 1.3691
##
## ***********************************************************************
## Outcome Variable: autonomy
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.5647 0.3189 1.2170 49.8691 2.0000 213.0000 0.0000
##
## Model:
## coeff se t p LLCI ULCI
## constant 3.2978 0.1274 25.8881 0.0000 3.0467 3.5489
## X1 0.8784 0.1833 4.7912 0.0000 0.5170 1.2398
## X2 1.8243 0.1827 9.9867 0.0000 1.4642 2.1844
##
## ***********************************************************************
## Outcome Variable: trust
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.9192 0.8449 0.2921 189.7185 6.0000 209.0000 0.0000
##
## Model:
## coeff se t p LLCI ULCI
## constant -0.1331 0.1848 -0.7205 0.4720 -0.4973 0.2311
## X1 0.1653 0.0993 1.6653 0.0974 -0.0304 0.3610
## X2 0.1641 0.1137 1.4435 0.1504 -0.0600 0.3881
## ability 0.8946 0.0545 16.4256 0.0000 0.7873 1.0020
## benevolence -0.1774 0.0610 -2.9103 0.0040 -0.2976 -0.0572
## integrity 0.1431 0.0733 1.9517 0.0523 -0.0014 0.2876
## autonomy 0.1469 0.0541 2.7179 0.0071 0.0404 0.2535
##
## ************************ TOTAL EFFECT MODEL ***************************
## Outcome Variable: trust
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.5287 0.2795 1.3310 41.3220 2.0000 213.0000 0.0000
##
## Model:
## coeff se t p LLCI ULCI
## constant 3.8333 0.1332 28.7753 0.0000 3.5707 4.0959
## X1 1.1024 0.1917 5.7496 0.0000 0.7244 1.4803
## X2 1.7089 0.1910 8.9458 0.0000 1.3324 2.0855
##
## ***********************************************************************
## 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 1.1024 0.1917 5.7496 0.0000 0.7244 1.4803
## X2 1.7089 0.1910 8.9458 0.0000 1.3324 2.0855
##
## Omnibus test of total effect of X on Y:
## R2-chng F df1 df2 p
## 0.2795 41.3220 2.0000 213.0000 0.0000
## ----------
##
## Relative direct effects of X on Y:
## effect se t p LLCI ULCI
## X1 0.1653 0.0993 1.6653 0.0974 -0.0304 0.3610
## X2 0.1641 0.1137 1.4435 0.1504 -0.0600 0.3881
##
## Omnibus test of direct effect of X on Y:
## R2-chng F df1 df2 p
## 0.0023 1.5447 2.0000 209.0000 0.2158
##
## ----------
##
## Relative indirect effects of X on Y:
##
## ai_training_1 -> ability -> trust
##
## Effect BootSE BootLLCI BootULCI
## X1 0.7964 0.1791 0.4577 1.1581
## X2 1.2623 0.1829 0.9203 1.6337
##
## Normal theory test for relative indirect effects:
## Effect se Z p
## X1 0.7964 0.1568 5.0775 0.0000
## X2 1.2623 0.1673 7.5449 0.0000
##
## ai_training_1 -> benevolence -> trust
##
## Effect BootSE BootLLCI BootULCI
## X1 -0.0447 0.0400 -0.1406 0.0165
## X2 -0.1291 0.0633 -0.2718 -0.0262
##
## Normal theory test for relative indirect effects:
## Effect se Z p
## X1 -0.0447 0.0365 -1.2258 0.2203
## X2 -0.1291 0.0553 -2.3352 0.0195
##
## ai_training_1 -> integrity -> trust
##
## Effect BootSE BootLLCI BootULCI
## X1 0.0564 0.0450 -0.0081 0.1639
## X2 0.1435 0.0851 -0.0119 0.3242
##
## Normal theory test for relative indirect effects:
## Effect se Z p
## X1 0.0564 0.0416 1.3547 0.1755
## X2 0.1435 0.0794 1.8085 0.0705
##
## ai_training_1 -> autonomy -> trust
##
## Effect BootSE BootLLCI BootULCI
## X1 0.1291 0.0712 0.0099 0.2888
## X2 0.2680 0.1329 0.0227 0.5479
##
## Normal theory test for relative indirect effects:
## Effect se Z p
## X1 0.1291 0.0555 2.3260 0.0200
## X2 0.2680 0.1027 2.6104 0.0090
##
## ******************** ANALYSIS NOTES AND ERRORS ************************
##
## Level of confidence for all confidence intervals in output: 95
##
## Number of bootstraps for percentile bootstrap confidence intervals: 10000