AI Trust S2 - Analysis (3x2)

ai_training	stakes	n	percentage
yes_ai	low	58	17.31%
yes_ai	high	58	17.31%
control	low	51	15.22%
control	high	55	16.42%
no_ai	low	58	17.31%
no_ai	high	55	16.42%

ai_training	n	percentage
yes_ai	116	34.63%
control	106	31.64%
no_ai	113	33.73%

stakes	n	percentage
low	167	49.85%
high	168	50.15%

Comp Check 2

Single item, 0-6 scale (0 = “Not serious at all”, 6 = “Extremely serious”); manipulation check for the stakes (high/low) factor.

“On a scale of 0 to 6, where 0 = Not serious at all and 6 = Extremely serious, how serious is the medical condition referred to in the story?”

Summary

Both factors

stakes	ai_training	variable	n	mean	sd	se	ci
low	yes_ai	comp_check_2	58	1.690	1.273	0.167	0.335
high	yes_ai	comp_check_2	58	5.172	1.028	0.135	0.270
low	control	comp_check_2	51	2.118	1.532	0.214	0.431
high	control	comp_check_2	55	5.327	1.090	0.147	0.295
low	no_ai	comp_check_2	58	2.224	1.351	0.177	0.355
high	no_ai	comp_check_2	55	5.345	0.844	0.114	0.228

By ai_training

ai_training	variable	n	mean	sd	se	ci
yes_ai	comp_check_2	116	3.431	2.094	0.194	0.385
control	comp_check_2	106	3.783	2.079	0.202	0.400
no_ai	comp_check_2	113	3.743	1.931	0.182	0.360

By stakes

stakes	variable	n	mean	sd	se	ci
low	comp_check_2	167	2.006	1.395	0.108	0.213
high	comp_check_2	168	5.280	0.991	0.076	0.151

Two-Way ANOVA

## $output1
## Anova Table (Type II tests)
## 
## Response: comp_check_2
##                    Sum Sq  Df  F value  Pr(>F)    
## ai_training          8.20   2   2.8295 0.06048 .  
## stakes             897.37   1 619.4389 < 2e-16 ***
## ai_training:stakes   2.03   2   0.7012 0.49674    
## Residuals          476.62 329                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $output2
## # Effect Size for ANOVA (Type II)
## 
## Parameter          | Eta2 (partial) |       95% CI
## --------------------------------------------------
## ai_training        |           0.02 | [0.00, 1.00]
## stakes             |           0.65 | [0.61, 1.00]
## ai_training:stakes |       4.24e-03 | [0.00, 1.00]
## 
## - One-sided CIs: upper bound fixed at [1.00].
## $output3
## # Effect Size for ANOVA (Type II)
## 
## Parameter          | Cohen's f (partial) |      95% CI
## ------------------------------------------------------
## ai_training        |                0.13 | [0.00, Inf]
## stakes             |                1.37 | [1.25, Inf]
## ai_training:stakes |                0.07 | [0.00, Inf]
## 
## - One-sided CIs: upper bound fixed at [Inf].

Plots Two-Way

stakes	group1	group2	n1	n2	p	p.signif	p.adj	p.adj.signif	effsize	conf.low	conf.high	Cohens’ d
low	yes_ai	control	58	51	0.109	ns	0.545	ns	-0.3057	-0.68	0.05	small
low	yes_ai	no_ai	58	58	0.039		0.234	ns	-0.4071	-0.77	-0.01	small
low	control	no_ai	51	58	0.689	ns	1.000	ns	-0.0740	-0.47	0.32	negligible
high	yes_ai	control	58	55	0.409	ns	1.000	ns	-0.1463	-0.57	0.25	negligible
high	yes_ai	no_ai	58	55	0.356	ns	1.000	ns	-0.1835	-0.57	0.18	negligible
high	control	no_ai	55	55	0.924	ns	1.000	ns	-0.0187	-0.37	0.39	negligible

ai_training	group1	group2	n1	n2	p.signif	p.adj.signif	effsize	conf.low	conf.high	Cohens’ d
yes_ai	low	high	58	58	****	****	-3.0093	-3.85	-2.41	large
control	low	high	51	55	****	****	-2.4302	-3.32	-1.80	large
no_ai	low	high	58	55	****	****	-2.7546	-3.60	-2.18	large

One-Way ANOVAs

## $output1
## Anova Table (Type II tests)
## 
## Response: comp_check_2
##              Sum Sq  Df F value Pr(>F)
## ai_training    8.42   2  1.0158 0.3632
## Residuals   1376.02 332               
## 
## $output2
## # Effect Size for ANOVA
## 
## Parameter   |     Eta2 |       95% CI
## -------------------------------------
## ai_training | 6.08e-03 | [0.00, 1.00]
## 
## - One-sided CIs: upper bound fixed at [1.00].
## $output3
## # Effect Size for ANOVA
## 
## Parameter   | Cohen's f |      95% CI
## -------------------------------------
## ai_training |      0.08 | [0.00, Inf]
## 
## - One-sided CIs: upper bound fixed at [Inf].

## $output1
## Anova Table (Type II tests)
## 
## Response: comp_check_2
##           Sum Sq  Df F value    Pr(>F)    
## stakes    897.59   1  613.95 < 2.2e-16 ***
## Residuals 486.85 333                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $output2
## # Effect Size for ANOVA
## 
## Parameter | Eta2 |       95% CI
## -------------------------------
## stakes    | 0.65 | [0.60, 1.00]
## 
## - One-sided CIs: upper bound fixed at [1.00].
## $output3
## # Effect Size for ANOVA
## 
## Parameter | Cohen's f |      95% CI
## -----------------------------------
## stakes    |      1.36 | [1.23, Inf]
## 
## - One-sided CIs: upper bound fixed at [Inf].

Plots One-Way

group1	group2	n1.x	n2.x	p	p.signif	p.adj	p.adj.signif	effsize	n1.y	n2.y	conf.low	conf.high	Cohens’ d
yes_ai	control	116	106	0.199	ns	0.597	ns	-0.1686	116	106	-0.44	0.11	negligible
yes_ai	no_ai	116	113	0.247	ns	0.597	ns	-0.1550	116	113	-0.41	0.12	negligible
control	no_ai	106	113	0.886	ns	0.886	ns	0.0198	106	113	-0.24	0.29	negligible

group1	group2	n1.x	n2.x	p	p.signif	p.adj	p.adj.signif	effsize	n1.y	n2.y	conf.low	conf.high	Cohens’ d
low	high	167	168	0	****	0	****	-2.7075	167	168	-3.19	-2.31	large

Regression (exploratory)

Trust

2-item composite, each on its own 0-6 scale.

comfort: “On a scale of 0 to 6, where 0 = Not comfortable at all and 6 = Very comfortable, how comfortable would you be having Dr. Smith as your doctor?”
confidence: “On a scale of 0 to 6, where 0 = Not confident at all and 6 = Very confident, how confident are you that Dr. Smith provides his patients with high-quality medical care?”

Summary

Both factors

stakes	ai_training	variable	n	mean	sd	se	ci
low	yes_ai	trust	58	4.241	1.499	0.197	0.394
high	yes_ai	trust	58	3.845	1.499	0.197	0.394
low	control	trust	51	5.078	0.731	0.102	0.205
high	control	trust	55	4.709	1.003	0.135	0.271
low	no_ai	trust	58	5.414	0.663	0.087	0.174
high	no_ai	trust	55	5.318	0.790	0.106	0.214

By ai_training

ai_training	variable	n	mean	sd	se	ci
yes_ai	trust	116	4.043	1.506	0.140	0.277
control	trust	106	4.887	0.898	0.087	0.173
no_ai	trust	113	5.367	0.726	0.068	0.135

By stakes

stakes	variable	n	mean	sd	se	ci
low	trust	167	4.904	1.156	0.089	0.177
high	trust	168	4.610	1.291	0.100	0.197

Two-Way ANOVA

## $output1
## Anova Table (Type II tests)
## 
## Response: trust
##                    Sum Sq  Df F value  Pr(>F)    
## ai_training        102.61   2 42.6246 < 2e-16 ***
## stakes               6.87   1  5.7048 0.01748 *  
## ai_training:stakes   1.56   2  0.6486 0.52342    
## Residuals          396.01 329                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $output2
## # Effect Size for ANOVA (Type II)
## 
## Parameter          | Eta2 (partial) |       95% CI
## --------------------------------------------------
## ai_training        |           0.21 | [0.14, 1.00]
## stakes             |           0.02 | [0.00, 1.00]
## ai_training:stakes |       3.93e-03 | [0.00, 1.00]
## 
## - One-sided CIs: upper bound fixed at [1.00].
## $output3
## # Effect Size for ANOVA (Type II)
## 
## Parameter          | Cohen's f (partial) |      95% CI
## ------------------------------------------------------
## ai_training        |                0.51 | [0.41, Inf]
## stakes             |                0.13 | [0.04, Inf]
## ai_training:stakes |                0.06 | [0.00, Inf]
## 
## - One-sided CIs: upper bound fixed at [Inf].

Plots Two-Way

stakes	group1	group2	n1	n2	p	p.signif	p.adj	p.adj.signif	effsize	conf.low	conf.high	Cohens’ d
low	yes_ai	control	58	51	0.0001	****	0.0002	***	-0.6959	-1.03	-0.39	moderate
low	yes_ai	no_ai	58	58	0.0000	****	0.0000	****	-1.0114	-1.37	-0.73	large
low	control	no_ai	51	58	0.0972	ns	0.0972	ns	-0.4822	-0.90	-0.10	small
high	yes_ai	control	58	55	0.0001	****	0.0003	***	-0.6741	-1.07	-0.27	moderate
high	yes_ai	no_ai	58	55	0.0000	****	0.0000	****	-1.2204	-1.66	-0.87	large
high	control	no_ai	55	55	0.0059	**	0.0118	*	-0.6747	-1.11	-0.34	moderate

ai_training	group1	group2	n1	n2	p	p.signif	p.adj	p.adj.signif	effsize	conf.low	conf.high	Cohens’ d
yes_ai	low	high	58	58	0.1570	ns	0.3140	ns	0.2645	-0.09	0.67	small
control	low	high	51	55	0.0337		0.1011	ns	0.4184	0.04	0.77	small
no_ai	low	high	58	55	0.4860	ns	0.4860	ns	0.1314	-0.23	0.50	negligible

One-Way ANOVAs

## $output1
## Anova Table (Type II tests)
## 
## Response: trust
##             Sum Sq  Df F value    Pr(>F)    
## ai_training 102.99   2  42.271 < 2.2e-16 ***
## Residuals   404.43 332                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $output2
## # Effect Size for ANOVA
## 
## Parameter   | Eta2 |       95% CI
## ---------------------------------
## ai_training | 0.20 | [0.14, 1.00]
## 
## - One-sided CIs: upper bound fixed at [1.00].
## $output3
## # Effect Size for ANOVA
## 
## Parameter   | Cohen's f |      95% CI
## -------------------------------------
## ai_training |      0.50 | [0.41, Inf]
## 
## - One-sided CIs: upper bound fixed at [Inf].

## $output1
## Anova Table (Type II tests)
## 
## Response: trust
##           Sum Sq  Df F value  Pr(>F)  
## stakes      7.24   1  4.8218 0.02879 *
## Residuals 500.18 333                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $output2
## # Effect Size for ANOVA
## 
## Parameter | Eta2 |       95% CI
## -------------------------------
## stakes    | 0.01 | [0.00, 1.00]
## 
## - One-sided CIs: upper bound fixed at [1.00].
## $output3
## # Effect Size for ANOVA
## 
## Parameter | Cohen's f |      95% CI
## -----------------------------------
## stakes    |      0.12 | [0.03, Inf]
## 
## - One-sided CIs: upper bound fixed at [Inf].

Plots One-Way

group1	group2	n1.x	n2.x	p	p.signif	p.adj	p.adj.signif	effsize	n1.y	n2.y	conf.low	conf.high	Cohens’ d
yes_ai	control	116	106	0.0000	****	0.0000	****	-0.6733	116	106	-0.93	-0.44	moderate
yes_ai	no_ai	116	113	0.0000	****	0.0000	****	-1.1156	116	113	-1.37	-0.89	large
control	no_ai	106	113	0.0014	**	0.0014	**	-0.5905	106	113	-0.89	-0.33	moderate

group1	group2	n1.x	n2.x	p	p.signif	p.adj	p.adj.signif	effsize	n1.y	n2.y	conf.low	conf.high	Cohens’ d
low	high	167	168	0.0288		0.0288	*	0.2399	167	168	0.02	0.47	small

Regression (exploratory)

Ability

3-item composite. Agree-disagree scale (0 = Strongly disagree, 6 = Strongly agree).

ability1: “Dr. Smith is well qualified to treat patients.”
ability2: “I feel confident in Dr. Smith’s clinical skills.”
ability3: “Dr. Smith is highly capable of performing his medical duties.”

Summary

Both factors

stakes	ai_training	variable	n	mean	sd	se	ci
low	yes_ai	ability	58	4.385	1.407	0.185	0.370
high	yes_ai	ability	58	4.043	1.498	0.197	0.394
low	control	ability	51	5.163	0.675	0.094	0.190
high	control	ability	55	4.897	0.751	0.101	0.203
low	no_ai	ability	58	5.511	0.653	0.086	0.172
high	no_ai	ability	55	5.494	0.684	0.092	0.185

By ai_training

ai_training	variable	n	mean	sd	se	ci
yes_ai	ability	116	4.214	1.457	0.135	0.268
control	ability	106	5.025	0.724	0.070	0.139
no_ai	ability	113	5.503	0.665	0.063	0.124

By stakes

stakes	variable	n	mean	sd	se	ci
low	ability	167	5.014	1.093	0.085	0.167
high	ability	168	4.798	1.208	0.093	0.184

Two-Way ANOVA

## $output1
## Anova Table (Type II tests)
## 
## Response: ability
##                    Sum Sq  Df F value  Pr(>F)    
## ai_training         97.03   2 46.4708 < 2e-16 ***
## stakes               3.64   1  3.4900 0.06263 .  
## ai_training:stakes   1.63   2  0.7828 0.45796    
## Residuals          343.48 329                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $output2
## # Effect Size for ANOVA (Type II)
## 
## Parameter          | Eta2 (partial) |       95% CI
## --------------------------------------------------
## ai_training        |           0.22 | [0.16, 1.00]
## stakes             |           0.01 | [0.00, 1.00]
## ai_training:stakes |       4.74e-03 | [0.00, 1.00]
## 
## - One-sided CIs: upper bound fixed at [1.00].
## $output3
## # Effect Size for ANOVA (Type II)
## 
## Parameter          | Cohen's f (partial) |      95% CI
## ------------------------------------------------------
## ai_training        |                0.53 | [0.43, Inf]
## stakes             |                0.10 | [0.00, Inf]
## ai_training:stakes |                0.07 | [0.00, Inf]
## 
## - One-sided CIs: upper bound fixed at [Inf].

Plots Two-Way

stakes	group1	group2	n1	n2	p	p.signif	p.adj	p.adj.signif	effsize	conf.low	conf.high	Cohens’ d
low	yes_ai	control	58	51	0.0001	****	0.0002	***	-0.6914	-1.02	-0.39	moderate
low	yes_ai	no_ai	58	58	0.0000	****	0.0000	****	-1.0271	-1.37	-0.74	large
low	control	no_ai	51	58	0.0681	ns	0.0681	ns	-0.5251	-0.98	-0.16	moderate
high	yes_ai	control	58	55	0.0000	****	0.0001	***	-0.7149	-1.08	-0.37	moderate
high	yes_ai	no_ai	58	55	0.0000	****	0.0000	****	-1.2350	-1.63	-0.91	large
high	control	no_ai	55	55	0.0034	**	0.0069	**	-0.8313	-1.28	-0.42	large

ai_training	group1	group2	n1	n2	p	p.signif	p.adj	p.adj.signif	effsize	conf.low	conf.high	Cohens’ d
yes_ai	low	high	58	58	0.208	ns	0.416	ns	0.2353	-0.1200	0.62	small
control	low	high	51	55	0.058	ns	0.174	ns	0.3726	0.0018	0.77	small
no_ai	low	high	58	55	0.889	ns	0.889	ns	0.0263	-0.3500	0.40	negligible

One-Way ANOVAs

## $output1
## Anova Table (Type II tests)
## 
## Response: ability
##             Sum Sq  Df F value    Pr(>F)    
## ai_training  97.31   2  46.316 < 2.2e-16 ***
## Residuals   348.75 332                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $output2
## # Effect Size for ANOVA
## 
## Parameter   | Eta2 |       95% CI
## ---------------------------------
## ai_training | 0.22 | [0.15, 1.00]
## 
## - One-sided CIs: upper bound fixed at [1.00].
## $output3
## # Effect Size for ANOVA
## 
## Parameter   | Cohen's f |      95% CI
## -------------------------------------
## ai_training |      0.53 | [0.43, Inf]
## 
## - One-sided CIs: upper bound fixed at [Inf].

## $output1
## Anova Table (Type II tests)
## 
## Response: ability
##           Sum Sq  Df F value  Pr(>F)  
## stakes      3.92   1  2.9525 0.08667 .
## Residuals 442.14 333                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $output2
## # Effect Size for ANOVA
## 
## Parameter |     Eta2 |       95% CI
## -----------------------------------
## stakes    | 8.79e-03 | [0.00, 1.00]
## 
## - One-sided CIs: upper bound fixed at [1.00].
## $output3
## # Effect Size for ANOVA
## 
## Parameter | Cohen's f |      95% CI
## -----------------------------------
## stakes    |      0.09 | [0.00, Inf]
## 
## - One-sided CIs: upper bound fixed at [Inf].

Plots One-Way

group1	group2	n1.x	n2.x	p	p.signif	p.adj	p.adj.signif	effsize	n1.y	n2.y	conf.low	conf.high	Cohens’ d
yes_ai	control	116	106	0e+00	****	0e+00	****	-0.6955	116	106	-0.93	-0.48	moderate
yes_ai	no_ai	116	113	0e+00	****	0e+00	****	-1.1331	116	113	-1.39	-0.91	large
control	no_ai	106	113	6e-04	***	6e-04	***	-0.6882	106	113	-0.97	-0.41	moderate

group1	group2	n1.x	n2.x	p	p.signif	p.adj	p.adj.signif	effsize	n1.y	n2.y	conf.low	conf.high	Cohens’ d
low	high	167	168	0.0867	ns	0.0867	ns	0.1878	167	168	-0.05	0.4	negligible

Regression (exploratory)

Benevolence

3-item composite. Agree-disagree scale (0 = Strongly disagree, 6 = Strongly agree).

benevolence1: “Dr. Smith is genuinely concerned about his patients’ welfare.”
benevolence2: “Dr. Smith would never knowingly do anything to harm a patient.”
benevolence3: “Dr. Smith truly looks out for what matters most to his patients.”

Summary

Both factors

stakes	ai_training	variable	n	mean	sd	se	ci
low	yes_ai	benevolence	58	4.563	1.216	0.160	0.320
high	yes_ai	benevolence	58	4.379	0.884	0.116	0.232
low	control	benevolence	51	4.595	1.018	0.143	0.286
high	control	benevolence	55	4.509	1.044	0.141	0.282
low	no_ai	benevolence	57	5.018	0.907	0.120	0.241
high	no_ai	benevolence	55	5.185	0.812	0.109	0.219

By ai_training

ai_training	variable	n	mean	sd	se	ci
yes_ai	benevolence	116	4.471	1.062	0.099	0.195
control	benevolence	106	4.550	1.028	0.100	0.198
no_ai	benevolence	112	5.100	0.862	0.081	0.161

By stakes

stakes	variable	n	mean	sd	se	ci
low	benevolence	166	4.729	1.072	0.083	0.164
high	benevolence	168	4.686	0.978	0.075	0.149

Two-Way ANOVA

## $output1
## Anova Table (Type II tests)
## 
## Response: benevolence
##                    Sum Sq  Df F value    Pr(>F)    
## ai_training         26.27   2 13.4106 2.522e-06 ***
## stakes               0.10   1  0.1042    0.7471    
## ai_training:stakes   1.86   2  0.9479    0.3886    
## Residuals          321.20 328                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $output2
## # Effect Size for ANOVA (Type II)
## 
## Parameter          | Eta2 (partial) |       95% CI
## --------------------------------------------------
## ai_training        |           0.08 | [0.03, 1.00]
## stakes             |       3.17e-04 | [0.00, 1.00]
## ai_training:stakes |       5.75e-03 | [0.00, 1.00]
## 
## - One-sided CIs: upper bound fixed at [1.00].
## $output3
## # Effect Size for ANOVA (Type II)
## 
## Parameter          | Cohen's f (partial) |      95% CI
## ------------------------------------------------------
## ai_training        |                0.29 | [0.19, Inf]
## stakes             |                0.02 | [0.00, Inf]
## ai_training:stakes |                0.08 | [0.00, Inf]
## 
## - One-sided CIs: upper bound fixed at [Inf].

Plots Two-Way

stakes	group1	group2	n1	n2	p	p.signif	p.adj	p.adj.signif	effsize	conf.low	conf.high	Cohens’ d
low	yes_ai	control	58	51	0.8770	ns	0.9080	ns	-0.0280	-0.40	0.34	negligible
low	yes_ai	no_ai	58	58	0.0225		0.0900	ns	NA	NA	NA	NA
low	control	no_ai	51	58	0.0396		0.1188	ns	NA	NA	NA	NA
high	yes_ai	control	58	55	0.4540	ns	0.9080	ns	-0.1345	-0.51	0.22	negligible
high	yes_ai	no_ai	58	55	0.0000	****	0.0000	****	-0.9480	-1.37	-0.56	large
high	control	no_ai	55	55	0.0002	***	0.0008	***	-0.7226	-1.13	-0.35	moderate

ai_training	group1	group2	n1	n2	p	p.signif	p.adj	p.adj.signif	effsize	conf.low	conf.high	Cohens’ d
yes_ai	low	high	58	58	0.353	ns	0.921	ns	0.173	-0.2	0.57	negligible
control	low	high	51	55	0.670	ns	0.921	ns	0.083	-0.3	0.48	negligible
no_ai	low	high	58	55	0.307	ns	0.921	ns	NA	NA	NA	NA

One-Way ANOVAs

## $output1
## Anova Table (Type II tests)
## 
## Response: benevolence
##             Sum Sq  Df F value    Pr(>F)    
## ai_training  26.32   2   13.48 2.356e-06 ***
## Residuals   323.16 331                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $output2
## # Effect Size for ANOVA
## 
## Parameter   | Eta2 |       95% CI
## ---------------------------------
## ai_training | 0.08 | [0.03, 1.00]
## 
## - One-sided CIs: upper bound fixed at [1.00].
## $output3
## # Effect Size for ANOVA
## 
## Parameter   | Cohen's f |      95% CI
## -------------------------------------
## ai_training |      0.29 | [0.19, Inf]
## 
## - One-sided CIs: upper bound fixed at [Inf].

## $output1
## Anova Table (Type II tests)
## 
## Response: benevolence
##           Sum Sq  Df F value Pr(>F)
## stakes      0.16   1  0.1495 0.6993
## Residuals 349.32 332               
## 
## $output2
## # Effect Size for ANOVA
## 
## Parameter |     Eta2 |       95% CI
## -----------------------------------
## stakes    | 4.50e-04 | [0.00, 1.00]
## 
## - One-sided CIs: upper bound fixed at [1.00].
## $output3
## # Effect Size for ANOVA
## 
## Parameter | Cohen's f |      95% CI
## -----------------------------------
## stakes    |      0.02 | [0.00, Inf]
## 
## - One-sided CIs: upper bound fixed at [Inf].

Plots One-Way

group1	group2	n1.x	n2.x	p	p.signif	p.adj	p.adj.signif	effsize	n1.y	n2.y	conf.low	conf.high	Cohens’ d
yes_ai	control	116	106	0.5520	ns	0.5520	ns	-0.0756	116	106	-0.33	0.19	negligible
yes_ai	no_ai	116	113	0.0000	****	0.0000	****	-0.6485	116	112	-0.93	-0.37	moderate
control	no_ai	106	113	0.0001	****	0.0001	***	-0.5806	106	112	-0.87	-0.31	moderate

group1	group2	n1.x	n2.x	p	p.signif	p.adj	p.adj.signif	effsize	n1.y	n2.y	conf.low	conf.high	Cohens’ d
low	high	167	168	0.699	ns	0.699	ns	0.0423	166	168	-0.17	0.26	negligible

Regression (exploratory)

Integrity

3-item composite. Agree-disagree scale (0 = Strongly disagree, 6 = Strongly agree).

integrity1: “Dr. Smith has values I share.”
integrity2: “Dr. Smith’s decisions and actions are guided by sound principles.”
integrity3: “Dr. Smith can be counted on to be honest with his patients.”

Summary

Both factors

stakes	ai_training	variable	n	mean	sd	se	ci
low	yes_ai	integrity	58	3.954	1.202	0.158	0.316
high	yes_ai	integrity	58	3.718	1.028	0.135	0.270
low	control	integrity	51	4.477	0.902	0.126	0.254
high	control	integrity	55	4.145	0.916	0.123	0.248
low	no_ai	integrity	57	4.906	0.776	0.103	0.206
high	no_ai	integrity	55	5.073	0.886	0.119	0.239

By ai_training

ai_training	variable	n	mean	sd	se	ci
yes_ai	integrity	116	3.836	1.119	0.104	0.206
control	integrity	106	4.305	0.920	0.089	0.177
no_ai	integrity	112	4.988	0.832	0.079	0.156

By stakes

stakes	variable	n	mean	sd	se	ci
low	integrity	166	4.442	1.053	0.082	0.161
high	integrity	168	4.302	1.099	0.085	0.167

Two-Way ANOVA

## $output1
## Anova Table (Type II tests)
## 
## Response: integrity
##                     Sum Sq  Df F value Pr(>F)    
## ai_training         76.085   2 40.9648 <2e-16 ***
## stakes               1.438   1  1.5486 0.2142    
## ai_training:stakes   3.857   2  2.0767 0.1270    
## Residuals          304.603 328                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $output2
## # Effect Size for ANOVA (Type II)
## 
## Parameter          | Eta2 (partial) |       95% CI
## --------------------------------------------------
## ai_training        |           0.20 | [0.14, 1.00]
## stakes             |       4.70e-03 | [0.00, 1.00]
## ai_training:stakes |           0.01 | [0.00, 1.00]
## 
## - One-sided CIs: upper bound fixed at [1.00].
## $output3
## # Effect Size for ANOVA (Type II)
## 
## Parameter          | Cohen's f (partial) |      95% CI
## ------------------------------------------------------
## ai_training        |                0.50 | [0.40, Inf]
## stakes             |                0.07 | [0.00, Inf]
## ai_training:stakes |                0.11 | [0.00, Inf]
## 
## - One-sided CIs: upper bound fixed at [Inf].

Plots Two-Way

stakes	group1	group2	n1	n2	p	p.signif	p.adj	p.adj.signif	effsize	conf.low	conf.high	Cohens’ d
low	yes_ai	control	58	51	0.0061	**	0.0183	*	-0.4878	-0.89	-0.15	small
low	yes_ai	no_ai	58	58	0.0000	****	0.0000	****	NA	NA	NA	NA
low	control	no_ai	51	58	0.0244		0.0352	*	NA	NA	NA	NA
high	yes_ai	control	58	55	0.0176		0.0352	*	-0.4381	-0.81	-0.09	small
high	yes_ai	no_ai	58	55	0.0000	****	0.0000	****	-1.4090	-1.92	-0.98	large
high	control	no_ai	55	55	0.0000	****	0.0000	****	-1.0293	-1.54	-0.62	large

ai_training	group1	group2	n1	n2	p	p.signif	p.adj	p.adj.signif	effsize	conf.low	conf.high	Cohens’ d
yes_ai	low	high	58	58	0.2590	ns	0.5180	ns	0.2108	-0.1600	0.59	small
control	low	high	51	55	0.0634	ns	0.1902	ns	0.3647	-0.0018	0.76	small
no_ai	low	high	58	55	0.2920	ns	0.5180	ns	NA	NA	NA	NA

One-Way ANOVAs

## $output1
## Anova Table (Type II tests)
## 
## Response: integrity
##              Sum Sq  Df F value    Pr(>F)    
## ai_training  76.288   2  40.741 < 2.2e-16 ***
## Residuals   309.898 331                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $output2
## # Effect Size for ANOVA
## 
## Parameter   | Eta2 |       95% CI
## ---------------------------------
## ai_training | 0.20 | [0.14, 1.00]
## 
## - One-sided CIs: upper bound fixed at [1.00].
## $output3
## # Effect Size for ANOVA
## 
## Parameter   | Cohen's f |      95% CI
## -------------------------------------
## ai_training |      0.50 | [0.40, Inf]
## 
## - One-sided CIs: upper bound fixed at [Inf].

## $output1
## Anova Table (Type II tests)
## 
## Response: integrity
##           Sum Sq  Df F value Pr(>F)
## stakes      1.64   1  1.4165 0.2348
## Residuals 384.55 332               
## 
## $output2
## # Effect Size for ANOVA
## 
## Parameter |     Eta2 |       95% CI
## -----------------------------------
## stakes    | 4.25e-03 | [0.00, 1.00]
## 
## - One-sided CIs: upper bound fixed at [1.00].
## $output3
## # Effect Size for ANOVA
## 
## Parameter | Cohen's f |      95% CI
## -----------------------------------
## stakes    |      0.07 | [0.00, Inf]
## 
## - One-sided CIs: upper bound fixed at [Inf].

Plots One-Way

group1	group2	n1.x	n2.x	p	p.signif	p.adj	p.adj.signif	effsize	n1.y	n2.y	conf.low	conf.high	Cohens’ d
yes_ai	control	116	106	4e-04	***	4e-04	***	-0.4555	116	106	-0.74	-0.21	small
yes_ai	no_ai	116	113	0e+00	****	0e+00	****	-1.1649	116	112	-1.49	-0.91	large
control	no_ai	106	113	0e+00	****	0e+00	****	-0.7797	106	112	-1.10	-0.51	moderate

group1	group2	n1.x	n2.x	p	p.signif	p.adj	p.adj.signif	effsize	n1.y	n2.y	conf.low	conf.high	Cohens’ d
low	high	167	168	0.235	ns	0.235	ns	0.1303	166	168	-0.08	0.34	negligible

Regression (exploratory)

Autonomy

3-item composite. Agree-disagree scale (0 = Strongly disagree, 6 = Strongly agree).

autonomy1: “Dr. Smith exercises independent judgment in his clinical decisions.”
autonomy2: “Dr. Smith takes personal responsibility for the consequences of his decisions.”
autonomy3: “Dr. Smith forms his own opinion rather than simply deferring to what a computer-based tool or algorithm recommends.”

Summary

Both factors

stakes	ai_training	variable	n	mean	sd	se	ci
low	yes_ai	autonomy	58	3.649	1.407	0.185	0.370
high	yes_ai	autonomy	58	3.213	1.213	0.159	0.319
low	control	autonomy	51	4.418	0.961	0.135	0.270
high	control	autonomy	55	4.218	0.939	0.127	0.254
low	no_ai	autonomy	57	5.234	0.661	0.087	0.175
high	no_ai	autonomy	55	5.358	0.714	0.096	0.193

By ai_training

ai_training	variable	n	mean	sd	se	ci
yes_ai	autonomy	116	3.431	1.326	0.123	0.244
control	autonomy	106	4.314	0.950	0.092	0.183
no_ai	autonomy	112	5.295	0.687	0.065	0.129

By stakes

stakes	variable	n	mean	sd	se	ci
low	autonomy	166	4.430	1.245	0.097	0.191
high	autonomy	168	4.244	1.315	0.101	0.200

Two-Way ANOVA

## $output1
## Anova Table (Type II tests)
## 
## Response: autonomy
##                    Sum Sq  Df F value Pr(>F)    
## ai_training        197.62   2 94.6409 <2e-16 ***
## stakes               2.52   1  2.4134 0.1213    
## ai_training:stakes   4.50   2  2.1554 0.1175    
## Residuals          342.44 328                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $output2
## # Effect Size for ANOVA (Type II)
## 
## Parameter          | Eta2 (partial) |       95% CI
## --------------------------------------------------
## ai_training        |           0.37 | [0.30, 1.00]
## stakes             |       7.30e-03 | [0.00, 1.00]
## ai_training:stakes |           0.01 | [0.00, 1.00]
## 
## - One-sided CIs: upper bound fixed at [1.00].
## $output3
## # Effect Size for ANOVA (Type II)
## 
## Parameter          | Cohen's f (partial) |      95% CI
## ------------------------------------------------------
## ai_training        |                0.76 | [0.65, Inf]
## stakes             |                0.09 | [0.00, Inf]
## ai_training:stakes |                0.11 | [0.00, Inf]
## 
## - One-sided CIs: upper bound fixed at [Inf].

Plots Two-Way

stakes	group1	group2	n1	n2	p	p.signif	p.adj	p.adj.signif	effsize	conf.low	conf.high	Cohens’ d
low	yes_ai	control	58	51	2e-04	***	2e-04	***	-0.6308	-1.01	-0.27	moderate
low	yes_ai	no_ai	58	58	0e+00	****	0e+00	****	NA	NA	NA	NA
low	control	no_ai	51	58	1e-04	****	2e-04	***	NA	NA	NA	NA
high	yes_ai	control	58	55	0e+00	****	0e+00	****	-0.9237	-1.35	-0.53	large
high	yes_ai	no_ai	58	55	0e+00	****	0e+00	****	-2.1405	-2.75	-1.70	large
high	control	no_ai	55	55	0e+00	****	0e+00	****	-1.3661	-1.88	-0.97	large

ai_training	group1	group2	n1	n2	p	p.signif	p.adj	p.adj.signif	effsize	conf.low	conf.high	Cohens’ d
yes_ai	low	high	58	58	0.076	ns	0.228	ns	0.3325	-0.03	0.72	small
control	low	high	51	55	0.281	ns	0.562	ns	0.2107	-0.21	0.63	small
no_ai	low	high	58	55	0.343	ns	0.562	ns	NA	NA	NA	NA

One-Way ANOVAs

## $output1
## Anova Table (Type II tests)
## 
## Response: autonomy
##             Sum Sq  Df F value    Pr(>F)    
## ai_training 197.98   2  93.758 < 2.2e-16 ***
## Residuals   349.47 331                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $output2
## # Effect Size for ANOVA
## 
## Parameter   | Eta2 |       95% CI
## ---------------------------------
## ai_training | 0.36 | [0.30, 1.00]
## 
## - One-sided CIs: upper bound fixed at [1.00].
## $output3
## # Effect Size for ANOVA
## 
## Parameter   | Cohen's f |      95% CI
## -------------------------------------
## ai_training |      0.75 | [0.65, Inf]
## 
## - One-sided CIs: upper bound fixed at [Inf].

## $output1
## Anova Table (Type II tests)
## 
## Response: autonomy
##           Sum Sq  Df F value Pr(>F)
## stakes      2.88   1  1.7549 0.1862
## Residuals 544.56 332               
## 
## $output2
## # Effect Size for ANOVA
## 
## Parameter |     Eta2 |       95% CI
## -----------------------------------
## stakes    | 5.26e-03 | [0.00, 1.00]
## 
## - One-sided CIs: upper bound fixed at [1.00].
## $output3
## # Effect Size for ANOVA
## 
## Parameter | Cohen's f |      95% CI
## -----------------------------------
## stakes    |      0.07 | [0.00, Inf]
## 
## - One-sided CIs: upper bound fixed at [Inf].

Plots One-Way

group1	group2	n1.x	n2.x	p.signif	p.adj.signif	effsize	n1.y	n2.y	conf.low	conf.high	Cohens’ d
yes_ai	control	116	106	****	****	-0.7602	116	106	-1.02	-0.51	moderate
yes_ai	no_ai	116	113	****	****	-1.7558	116	112	-2.11	-1.47	large
control	no_ai	106	113	****	****	-1.1872	106	112	-1.53	-0.90	large

group1	group2	n1.x	n2.x	p	p.signif	p.adj	p.adj.signif	effsize	n1.y	n2.y	conf.low	conf.high	Cohens’ d
low	high	167	168	0.186	ns	0.186	ns	0.145	166	168	-0.05	0.37	negligible

Regression (exploratory)

Ai Use

Single item. Agree-disagree scale (0 = Strongly disagree, 6 = Strongly agree).

“Dr. Smith often uses computer-based diagnostic tools in his medical practice.”

Summary

Both factors

stakes	ai_training	variable	n	mean	sd	se	ci
low	yes_ai	ai_use	55	4.636	1.253	0.169	0.339
high	yes_ai	ai_use	58	4.983	0.868	0.114	0.228
low	control	ai_use	50	3.180	0.983	0.139	0.279
high	control	ai_use	54	3.093	0.937	0.128	0.256
low	no_ai	ai_use	57	1.439	1.488	0.197	0.395
high	no_ai	ai_use	55	1.836	1.630	0.220	0.441

By ai_training

ai_training	variable	n	mean	sd	se	ci
yes_ai	ai_use	113	4.814	1.082	0.102	0.202
control	ai_use	104	3.135	0.956	0.094	0.186
no_ai	ai_use	112	1.634	1.565	0.148	0.293

By stakes

stakes	variable	n	mean	sd	se	ci
low	ai_use	162	3.062	1.837	0.144	0.285
high	ai_use	167	3.335	1.765	0.137	0.270

Two-Way ANOVA

## $output1
## Anova Table (Type II tests)
## 
## Response: ai_use
##                    Sum Sq  Df  F value  Pr(>F)    
## ai_training        567.63   2 187.3961 < 2e-16 ***
## stakes               4.23   1   2.7925 0.09567 .  
## ai_training:stakes   3.79   2   1.2496 0.28801    
## Residuals          489.19 323                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $output2
## # Effect Size for ANOVA (Type II)
## 
## Parameter          | Eta2 (partial) |       95% CI
## --------------------------------------------------
## ai_training        |           0.54 | [0.48, 1.00]
## stakes             |       8.57e-03 | [0.00, 1.00]
## ai_training:stakes |       7.68e-03 | [0.00, 1.00]
## 
## - One-sided CIs: upper bound fixed at [1.00].
## $output3
## # Effect Size for ANOVA (Type II)
## 
## Parameter          | Cohen's f (partial) |      95% CI
## ------------------------------------------------------
## ai_training        |                1.08 | [0.96, Inf]
## stakes             |                0.09 | [0.00, Inf]
## ai_training:stakes |                0.09 | [0.00, Inf]
## 
## - One-sided CIs: upper bound fixed at [Inf].

Plots Two-Way

stakes	group1	group2	n1	n2	p.signif	p.adj.signif	effsize	conf.low	conf.high	Cohens’ d
low	yes_ai	control	58	51	****	****	NA	NA	NA	NA
low	yes_ai	no_ai	58	58	****	****	NA	NA	NA	NA
low	control	no_ai	51	58	****	****	NA	NA	NA	NA
high	yes_ai	control	58	55	****	****	NA	NA	NA	NA
high	yes_ai	no_ai	58	55	****	****	2.4273	1.9	3.21	large
high	control	no_ai	55	55	****	****	NA	NA	NA	NA

ai_training	group1	group2	n1	n2	p	p.signif	p.adj	p.adj.signif	effsize	conf.low	conf.high	Cohens’ d
yes_ai	low	high	58	58	0.089	ns	0.267	ns	NA	NA	NA	NA
control	low	high	51	55	0.644	ns	0.644	ns	NA	NA	NA	NA
no_ai	low	high	58	55	0.180	ns	0.360	ns	NA	NA	NA	NA

One-Way ANOVAs

## $output1
## Anova Table (Type II tests)
## 
## Response: ai_use
##             Sum Sq  Df F value    Pr(>F)    
## ai_training 569.56   2  186.72 < 2.2e-16 ***
## Residuals   497.20 326                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $output2
## # Effect Size for ANOVA
## 
## Parameter   | Eta2 |       95% CI
## ---------------------------------
## ai_training | 0.53 | [0.48, 1.00]
## 
## - One-sided CIs: upper bound fixed at [1.00].
## $output3
## # Effect Size for ANOVA
## 
## Parameter   | Cohen's f |      95% CI
## -------------------------------------
## ai_training |      1.07 | [0.95, Inf]
## 
## - One-sided CIs: upper bound fixed at [Inf].

## $output1
## Anova Table (Type II tests)
## 
## Response: ai_use
##            Sum Sq  Df F value Pr(>F)
## stakes       6.16   1  1.8979 0.1693
## Residuals 1060.60 327               
## 
## $output2
## # Effect Size for ANOVA
## 
## Parameter |     Eta2 |       95% CI
## -----------------------------------
## stakes    | 5.77e-03 | [0.00, 1.00]
## 
## - One-sided CIs: upper bound fixed at [1.00].
## $output3
## # Effect Size for ANOVA
## 
## Parameter | Cohen's f |      95% CI
## -----------------------------------
## stakes    |      0.08 | [0.00, Inf]
## 
## - One-sided CIs: upper bound fixed at [Inf].

Plots One-Way

group1	group2	n1.x	n2.x	p.signif	p.adj.signif	effsize	n1.y	n2.y	conf.low	conf.high	Cohens’ d
yes_ai	control	116	106	****	****	1.6410	113	104	1.27	2.09	large
yes_ai	no_ai	116	113	****	****	2.3654	113	112	1.94	2.95	large
control	no_ai	106	113	****	****	1.1473	104	112	0.81	1.58	large

group1	group2	n1.x	n2.x	p	p.signif	p.adj	p.adj.signif	effsize	n1.y	n2.y	conf.low	conf.high	Cohens’ d
low	high	167	168	0.169	ns	0.169	ns	-0.1519	162	167	-0.37	0.06	negligible

Regression (exploratory)

Ai Attitudes

2-item composite (pre-existing individual-difference measure, not an outcome of the manipulation), each on its own 0-6 scale.

ai_attitude: “On a scale of 0 to 6, where 0 = Strongly against, 6 = Strongly in favor, what is your attitude towards the use of AI tools in the training of doctors?”
ai_feeling: “On a scale of 0 to 6, where 0 = Very negative, 6 = Very positive, how do you feel about the use of AI tools in the training of doctors?”

Summary

Both factors

stakes	ai_training	variable	n	mean	sd	se	ci
low	yes_ai	ai_attitudes	57	3.518	1.623	0.215	0.431
high	yes_ai	ai_attitudes	58	3.078	1.910	0.251	0.502
low	control	ai_attitudes	50	3.330	1.683	0.238	0.478
high	control	ai_attitudes	54	3.157	1.526	0.208	0.417
low	no_ai	ai_attitudes	57	2.947	1.718	0.228	0.456
high	no_ai	ai_attitudes	55	2.636	1.473	0.199	0.398

By ai_training

ai_training	variable	n	mean	sd	se	ci
yes_ai	ai_attitudes	115	3.296	1.779	0.166	0.329
control	ai_attitudes	104	3.240	1.598	0.157	0.311
no_ai	ai_attitudes	112	2.795	1.603	0.151	0.300

By stakes

stakes	variable	n	mean	sd	se	ci
low	ai_attitudes	164	3.262	1.682	0.131	0.259
high	ai_attitudes	167	2.958	1.660	0.128	0.254

Two-Way ANOVA

## $output1
## Anova Table (Type II tests)
## 
## Response: ai_attitudes
##                    Sum Sq  Df F value  Pr(>F)  
## ai_training         17.29   2  3.1207 0.04545 *
## stakes               8.07   1  2.9136 0.08879 .
## ai_training:stakes   0.98   2  0.1761 0.83865  
## Residuals          900.17 325                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $output2
## # Effect Size for ANOVA (Type II)
## 
## Parameter          | Eta2 (partial) |       95% CI
## --------------------------------------------------
## ai_training        |           0.02 | [0.00, 1.00]
## stakes             |       8.89e-03 | [0.00, 1.00]
## ai_training:stakes |       1.08e-03 | [0.00, 1.00]
## 
## - One-sided CIs: upper bound fixed at [1.00].
## $output3
## # Effect Size for ANOVA (Type II)
## 
## Parameter          | Cohen's f (partial) |      95% CI
## ------------------------------------------------------
## ai_training        |                0.14 | [0.01, Inf]
## stakes             |                0.09 | [0.00, Inf]
## ai_training:stakes |                0.03 | [0.00, Inf]
## 
## - One-sided CIs: upper bound fixed at [Inf].

Plots Two-Way

stakes	group1	group2	n1	n2	p	p.signif	p.adj	p.adj.signif	effsize	conf.low	conf.high	Cohens’ d
low	yes_ai	control	58	51	0.564	ns	1.000	ns	NA	NA	NA	NA
low	yes_ai	no_ai	58	58	0.071	ns	0.426	ns	NA	NA	NA	NA
low	control	no_ai	51	58	0.240	ns	0.720	ns	NA	NA	NA	NA
high	yes_ai	control	58	55	0.799	ns	1.000	ns	NA	NA	NA	NA
high	yes_ai	no_ai	58	55	0.158	ns	0.632	ns	0.2578	-0.13	0.65	small
high	control	no_ai	55	55	0.102	ns	0.510	ns	NA	NA	NA	NA

ai_training	group1	group2	n1	n2	p	p.signif	p.adj	p.adj.signif	effsize	conf.low	conf.high	Cohens’ d
yes_ai	low	high	58	58	0.186	ns	0.558	ns	NA	NA	NA	NA
control	low	high	51	55	0.585	ns	0.614	ns	NA	NA	NA	NA
no_ai	low	high	58	55	0.307	ns	0.614	ns	NA	NA	NA	NA

One-Way ANOVAs

## $output1
## Anova Table (Type II tests)
## 
## Response: ai_attitudes
##             Sum Sq  Df F value  Pr(>F)  
## ai_training  16.87   2  3.0429 0.04905 *
## Residuals   909.21 328                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $output2
## # Effect Size for ANOVA
## 
## Parameter   | Eta2 |       95% CI
## ---------------------------------
## ai_training | 0.02 | [0.00, 1.00]
## 
## - One-sided CIs: upper bound fixed at [1.00].
## $output3
## # Effect Size for ANOVA
## 
## Parameter   | Cohen's f |      95% CI
## -------------------------------------
## ai_training |      0.14 | [0.01, Inf]
## 
## - One-sided CIs: upper bound fixed at [Inf].

## $output1
## Anova Table (Type II tests)
## 
## Response: ai_attitudes
##           Sum Sq  Df F value  Pr(>F)  
## stakes      7.65   1  2.7412 0.09874 .
## Residuals 918.43 329                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $output2
## # Effect Size for ANOVA
## 
## Parameter |     Eta2 |       95% CI
## -----------------------------------
## stakes    | 8.26e-03 | [0.00, 1.00]
## 
## - One-sided CIs: upper bound fixed at [1.00].
## $output3
## # Effect Size for ANOVA
## 
## Parameter | Cohen's f |      95% CI
## -----------------------------------
## stakes    |      0.09 | [0.00, Inf]
## 
## - One-sided CIs: upper bound fixed at [Inf].

Plots One-Way

group1	group2	n1.x	n2.x	p	p.signif	p.adj	p.adj.signif	effsize	n1.y	n2.y	conf.low	conf.high	Cohens’ d
yes_ai	control	116	106	0.8060	ns	0.8060	ns	0.0326	115	104	-0.2500	0.29	negligible
yes_ai	no_ai	116	113	0.0241		0.0723	ns	0.2956	115	112	0.0400	0.56	small
control	no_ai	106	113	0.0501	ns	0.1002	ns	0.2785	104	112	0.0058	0.56	small

group1	group2	n1.x	n2.x	p	p.signif	p.adj	p.adj.signif	effsize	n1.y	n2.y	conf.low	conf.high	Cohens’ d
low	high	167	168	0.0987	ns	0.0987	ns	0.182	164	167	-0.05	0.4	negligible

AI Trust S2 - Analysis (3x2)

Date of study: 6 July 2026. Last compiled on 14 August, 2026

Valid entries and size of groups

Creating composites

trust

ability

benevolence

integrity

autonomy

ai_attitudes

Effect of conditions on DVs (3 x 2 ANOVAs)

Comp Check 2

Summary

Two-Way ANOVA

Plots Two-Way

One-Way ANOVAs

Plots One-Way

Regression (exploratory)

Trust

Summary

Two-Way ANOVA

Plots Two-Way

One-Way ANOVAs

Plots One-Way

Regression (exploratory)

Ability

Summary

Two-Way ANOVA

Plots Two-Way

One-Way ANOVAs

Plots One-Way

Regression (exploratory)

Benevolence

Summary

Two-Way ANOVA

Plots Two-Way

One-Way ANOVAs

Plots One-Way

Regression (exploratory)

Integrity

Summary

Two-Way ANOVA

Plots Two-Way

One-Way ANOVAs

Plots One-Way

Regression (exploratory)

Autonomy

Summary

Two-Way ANOVA

Plots Two-Way

One-Way ANOVAs

Plots One-Way

Regression (exploratory)

Ai Use

Summary

Two-Way ANOVA

Plots Two-Way

One-Way ANOVAs

Plots One-Way

Regression (exploratory)

Ai Attitudes

Summary

Two-Way ANOVA

Plots Two-Way

One-Way ANOVAs

Plots One-Way

Regression (exploratory)