Results

Bayesian Independent Samples T-Test

jaspTTests::TTestBayesianIndependentSamples(
        version = "0.17.2",
        formula = ~ Age + ICAR + AQ + ASRS + DT_Fluency + DT_Flexibility + Sqrt_DT_Originality + Log_CAQ + BICB + CPS + CSE,
        group = "ASD")

Table 1 and Table 2

Bayesian Independent Samples T-Test
	BF₁₀	error %
Age	0.125	0.120
ICAR	0.127	0.118
AQ	1.040×10⁺⁵⁵	7.035×10^-61
ASRS	1.904×10⁺¹⁷	2.190×10^-24
DT_Fluency	0.165	0.094
DT_Flexibility	0.740	0.025
Sqrt_DT_Originality	0.120	0.125
Log_CAQ	20.956	0.001
BICB	1246.689	2.465×10^-5
CPS	0.141	0.108
CSE	0.122	0.122

Bayesian ANCOVA

jaspAnova::AncovaBayesian(
        version = "0.17.2",
        formula = DT_Fluency ~ Age + ICAR + Sex + ADHD + ASD,
        covariates = list("ICAR", "Age"),
        customPriorSpecification = list(list(components = "ASD", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "ADHD", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "ICAR", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "Sex", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "Age", inclusionProbability = 0.5, scaleFixedEffects = 0.5)),
        effects = TRUE,
        seed = 1234,
        setSeed = TRUE,
        singleModelTerms = ~ ASD * ADHD * Sex + Age + ICAR)

Table 3, Analysis 1, Dependent Variable: DT Fluency

Model Comparison
Models	P(M)	P(M\|data)	BF_M	BF₁₀	error %
ADHD + ICAR + Age	0.031	0.279	12.005	1.000
ICAR + Age	0.031	0.241	9.823	0.862	0.926
Age	0.031	0.065	2.149	0.232	0.926
ADHD + Sex + ICAR + Age	0.031	0.049	1.586	0.174	2.057
ASD + ICAR + Age	0.031	0.047	1.539	0.169	1.643
ASD + ADHD + ICAR + Age	0.031	0.041	1.339	0.148	2.024
ICAR	0.031	0.040	1.306	0.145	0.926
ADHD + Age	0.031	0.039	1.264	0.140	1.319
Sex + ICAR + Age	0.031	0.038	1.239	0.138	1.652
ADHD + ICAR	0.031	0.035	1.137	0.127	1.305

Note. Showing the best 10 out of 32 models.

Analysis of Effects - DT_Fluency
Effects	P(incl)	P(excl)	P(incl\|data)	P(excl\|data)	BF_incl
ASD	0.500	0.500	0.145	0.855	0.170
ADHD	0.500	0.500	0.491	0.509	0.963
Sex	0.500	0.500	0.140	0.860	0.163
ICAR	0.500	0.500	0.812	0.188	4.308
Age	0.500	0.500	0.852	0.148	5.745

Bayesian ANCOVA

jaspAnova::AncovaBayesian(
        version = "0.17.2",
        formula = DT_Flexibility ~ Age + ICAR + Sex + ADHD + ASD,
        covariates = list("ICAR", "Age"),
        customPriorSpecification = list(list(components = "ASD", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "ADHD", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "ICAR", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "Sex", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "Age", inclusionProbability = 0.5, scaleFixedEffects = 0.5)),
        effects = TRUE,
        seed = 1234,
        setSeed = TRUE,
        singleModelTerms = ~ ASD * ADHD * Sex + Age + ICAR)

Table 3, Analysis 1, Dependent Variable: DT Flexibility

Model Comparison
Models	P(M)	P(M\|data)	BF_M	BF₁₀	error %
ADHD + ICAR + Age	0.031	0.448	25.156	1.000
ASD + ICAR + Age	0.031	0.150	5.464	0.334	1.547
ICAR + Age	0.031	0.143	5.152	0.318	1.035
ASD + ADHD + ICAR + Age	0.031	0.117	4.106	0.261	1.778
ADHD + Sex + ICAR + Age	0.031	0.057	1.889	0.128	1.812
Sex + ICAR + Age	0.031	0.020	0.625	0.044	1.617
ASD + Sex + ICAR + Age	0.031	0.019	0.613	0.043	1.935
ASD + ADHD + Sex + ICAR + Age	0.031	0.016	0.492	0.035	3.133
ASD + Age	0.031	0.006	0.202	0.014	1.368
ADHD + Age	0.031	0.006	0.192	0.014	1.342

Note. Showing the best 10 out of 32 models.

Analysis of Effects - DT_Flexibility
Effects	P(incl)	P(excl)	P(incl\|data)	P(excl\|data)	BF_incl
ASD	0.500	0.500	0.314	0.686	0.457
ADHD	0.500	0.500	0.651	0.349	1.867
Sex	0.500	0.500	0.116	0.884	0.131
ICAR	0.500	0.500	0.976	0.024	40.320
Age	0.500	0.500	0.993	0.007	144.591

Bayesian ANCOVA

jaspAnova::AncovaBayesian(
        version = "0.17.2",
        formula = Sqrt_DT_Originality ~ Age + ICAR + Sex + ADHD + ASD,
        covariates = list("ICAR", "Age"),
        customPriorSpecification = list(list(components = "ASD", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "ADHD", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "ICAR", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "Sex", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "Age", inclusionProbability = 0.5, scaleFixedEffects = 0.5)),
        effects = TRUE,
        seed = 1234,
        setSeed = TRUE,
        singleModelTerms = ~ ASD * ADHD * Sex + Age + ICAR)

Table 3, Analysis 1, Dependent Variable: DT Originality

Model Comparison
Models	P(M)	P(M\|data)	BF_M	BF₁₀	error %
Null model	0.031	0.412	21.708	1.000
Age	0.031	0.114	3.981	0.276	0.003
ADHD	0.031	0.084	2.834	0.203	0.041
ICAR	0.031	0.079	2.665	0.192	0.003
ASD	0.031	0.057	1.875	0.138	0.094
Sex	0.031	0.055	1.798	0.133	0.098
ICAR + Age	0.031	0.035	1.127	0.085	0.008
ADHD + Age	0.031	0.024	0.772	0.059	1.088
ADHD + ICAR	0.031	0.018	0.560	0.043	1.097
ASD + Age	0.031	0.016	0.515	0.040	1.115

Note. Showing the best 10 out of 32 models.

Analysis of Effects - Sqrt_DT_Originality
Effects	P(incl)	P(excl)	P(incl\|data)	P(excl\|data)	BF_incl
ASD	0.500	0.500	0.123	0.877	0.141
ADHD	0.500	0.500	0.174	0.826	0.210
Sex	0.500	0.500	0.118	0.882	0.133
ICAR	0.500	0.500	0.180	0.820	0.220
Age	0.500	0.500	0.234	0.766	0.305

Bayesian ANCOVA

jaspAnova::AncovaBayesian(
        version = "0.17.2",
        formula = Log_CAQ ~ Age + ICAR + Sex + ADHD + ASD,
        covariates = list("ICAR", "Age"),
        customPriorSpecification = list(list(components = "ASD", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "ADHD", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "ICAR", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "Sex", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "Age", inclusionProbability = 0.5, scaleFixedEffects = 0.5)),
        effects = TRUE,
        seed = 1234,
        setSeed = TRUE,
        singleModelTerms = ~ ASD * ADHD * Sex + Age + ICAR)

Table 3, Analysis 1, Dependent Variable: Creative Achievements

Model Comparison
Models	P(M)	P(M\|data)	BF_M	BF₁₀	error %
ADHD + ICAR	0.031	0.246	10.132	1.000
ASD + ADHD + ICAR	0.031	0.135	4.847	0.549	2.488
ADHD + ICAR + Age	0.031	0.095	3.241	0.384	1.328
ASD + ICAR	0.031	0.066	2.200	0.269	1.226
ADHD	0.031	0.064	2.108	0.259	0.803
ASD	0.031	0.055	1.814	0.224	0.803
ASD + ADHD	0.031	0.054	1.781	0.221	1.225
ASD + ADHD + ICAR + Age	0.031	0.052	1.716	0.213	2.025
ADHD + Sex + ICAR	0.031	0.035	1.137	0.144	2.202
ASD + ICAR + Age	0.031	0.027	0.856	0.109	1.467

Note. Showing the best 10 out of 32 models.

Analysis of Effects - Log_CAQ
Effects	P(incl)	P(excl)	P(incl\|data)	P(excl\|data)	BF_incl
ASD	0.500	0.500	0.487	0.513	0.951
ADHD	0.500	0.500	0.787	0.213	3.687
Sex	0.500	0.500	0.125	0.875	0.143
ICAR	0.500	0.500	0.725	0.275	2.640
Age	0.500	0.500	0.274	0.726	0.378

Bayesian ANCOVA

jaspAnova::AncovaBayesian(
        version = "0.17.2",
        formula = BICB ~ Age + ICAR + Sex + ADHD + ASD,
        covariates = list("ICAR", "Age"),
        customPriorSpecification = list(list(components = "ASD", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "ADHD", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "ICAR", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "Sex", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "Age", inclusionProbability = 0.5, scaleFixedEffects = 0.5)),
        effects = TRUE,
        seed = 1234,
        setSeed = TRUE,
        singleModelTerms = ~ ASD * ADHD * Sex + Age + ICAR)

Table 3, Analysis 1, Dependent Variable: Creative Behaviors

Model Comparison
Models	P(M)	P(M\|data)	BF_M	BF₁₀	error %
ADHD	0.031	0.286	12.441	1.000
ASD + ADHD	0.031	0.275	11.786	0.962	0.818
ADHD + Sex	0.031	0.065	2.164	0.228	0.875
ADHD + Age	0.031	0.060	1.978	0.209	0.879
ADHD + ICAR	0.031	0.059	1.928	0.204	0.880
ASD + ADHD + Sex	0.031	0.056	1.827	0.194	2.371
ASD + ADHD + Age	0.031	0.051	1.670	0.178	2.410
ASD + ADHD + ICAR	0.031	0.046	1.491	0.160	2.395
ADHD + ICAR + Age	0.031	0.017	0.522	0.058	1.192
ASD + ADHD + ICAR + Age	0.031	0.013	0.413	0.046	2.129

Note. Showing the best 10 out of 32 models.

Analysis of Effects - BICB
Effects	P(incl)	P(excl)	P(incl\|data)	P(excl\|data)	BF_incl
ASD	0.500	0.500	0.484	0.516	0.937
ADHD	0.500	0.500	0.982	0.018	55.628
Sex	0.500	0.500	0.178	0.822	0.216
ICAR	0.500	0.500	0.167	0.833	0.200
Age	0.500	0.500	0.175	0.825	0.212

Bayesian ANCOVA

jaspAnova::AncovaBayesian(
        version = "0.17.2",
        formula = CPS ~ Age + ICAR + Sex + ADHD + ASD,
        covariates = list("ICAR", "Age"),
        customPriorSpecification = list(list(components = "ASD", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "ADHD", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "ICAR", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "Sex", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "Age", inclusionProbability = 0.5, scaleFixedEffects = 0.5)),
        effects = TRUE,
        seed = 1234,
        setSeed = TRUE,
        singleModelTerms = ~ ASD * ADHD * Sex + Age + ICAR)

Table 3, Analysis 1, Dependent Variable: Creative Personality

Model Comparison
Models	P(M)	P(M\|data)	BF_M	BF₁₀	error %
Null model	0.031	0.411	21.601	1.000
Sex	0.031	0.144	5.203	0.350	0.044
ADHD	0.031	0.083	2.811	0.202	0.041
Age	0.031	0.058	1.906	0.141	0.002
ASD	0.031	0.057	1.890	0.140	0.093
ICAR	0.031	0.057	1.874	0.139	0.002
ADHD + Sex	0.031	0.028	0.904	0.069	1.087
Sex + Age	0.031	0.021	0.658	0.051	1.106
ASD + Sex	0.031	0.021	0.650	0.050	1.106
Sex + ICAR	0.031	0.020	0.637	0.049	1.108

Note. Showing the best 10 out of 32 models.

Analysis of Effects - CPS
Effects	P(incl)	P(excl)	P(incl\|data)	P(excl\|data)	BF_incl
ASD	0.500	0.500	0.127	0.873	0.146
ADHD	0.500	0.500	0.171	0.829	0.207
Sex	0.500	0.500	0.258	0.742	0.348
ICAR	0.500	0.500	0.129	0.871	0.148
Age	0.500	0.500	0.130	0.870	0.150

Bayesian ANCOVA

jaspAnova::AncovaBayesian(
        version = "0.17.2",
        formula = CSE ~ Age + ICAR + Sex + ADHD + ASD,
        covariates = list("ICAR", "Age"),
        customPriorSpecification = list(list(components = "ASD", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "ADHD", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "ICAR", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "Sex", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "Age", inclusionProbability = 0.5, scaleFixedEffects = 0.5)),
        effects = TRUE,
        seed = 1234,
        setSeed = TRUE,
        singleModelTerms = ~ ASD * ADHD * Sex + Age + ICAR)

Table 3, Analysis 1, Dependent Variable: Creative Self Efficacy

Model Comparison
Models	P(M)	P(M\|data)	BF_M	BF₁₀	error %
Sex	0.031	0.372	18.363	1.000
Sex + Age	0.031	0.143	5.165	0.384	0.981
ADHD + Sex	0.031	0.084	2.854	0.227	0.994
Null model	0.031	0.074	2.477	0.199	0.004
ASD + Sex	0.031	0.051	1.674	0.138	1.024
Sex + ICAR	0.031	0.051	1.672	0.138	1.024
ADHD + Sex + Age	0.031	0.032	1.013	0.085	2.296
Age	0.031	0.029	0.921	0.078	0.006
Sex + ICAR + Age	0.031	0.029	0.913	0.077	1.533
ADHD	0.031	0.019	0.604	0.051	0.035

Note. Showing the best 10 out of 32 models.

Analysis of Effects - CSE
Effects	P(incl)	P(excl)	P(incl\|data)	P(excl\|data)	BF_incl
ASD	0.500	0.500	0.123	0.877	0.140
ADHD	0.500	0.500	0.191	0.809	0.237
Sex	0.500	0.500	0.829	0.171	4.839
ICAR	0.500	0.500	0.133	0.867	0.153
Age	0.500	0.500	0.288	0.712	0.405

Bayesian ANCOVA

jaspAnova::AncovaBayesian(
        version = "0.17.2",
        formula = DT_Fluency ~ Age + ICAR + ASRS + AQ + Sex,
        covariates = list("AQ", "ASRS", "ICAR", "Age"),
        customPriorSpecification = list(list(components = "AQ", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "ASRS", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "ICAR", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "Sex", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "Age", inclusionProbability = 0.5, scaleFixedEffects = 0.5)),
        effects = TRUE,
        seed = 1234,
        setSeed = TRUE,
        singleModelTerms = ~ ASRS + AQ + Age + ICAR + Sex)

Table 3, Analysis 2, Dependent Variable: DT Fluency

Model Comparison
Models	P(M)	P(M\|data)	BF_M	BF₁₀	error %
ICAR + Age	0.031	0.264	11.095	1.000
ASRS + ICAR + Age	0.031	0.162	6.013	0.616	0.008
Age	0.031	0.112	3.924	0.426	0.009
AQ + ASRS + ICAR + Age	0.031	0.055	1.805	0.209	0.008
AQ + ICAR + Age	0.031	0.054	1.768	0.205	0.009
ASRS + Age	0.031	0.053	1.726	0.200	0.014
Sex + ICAR + Age	0.031	0.052	1.684	0.195	1.430
ICAR	0.031	0.040	1.299	0.153	0.009
Null model	0.031	0.035	1.122	0.133	0.008
Sex + ASRS + ICAR + Age	0.031	0.026	0.821	0.098	1.744

Note. Showing the best 10 out of 32 models.

Analysis of Effects - DT_Fluency
Effects	P(incl)	P(excl)	P(incl\|data)	P(excl\|data)	BF_incl
Sex	0.500	0.500	0.152	0.848	0.180
AQ	0.500	0.500	0.185	0.815	0.227
ASRS	0.500	0.500	0.360	0.640	0.562
ICAR	0.500	0.500	0.708	0.292	2.424
Age	0.500	0.500	0.864	0.136	6.372

Bayesian ANCOVA

jaspAnova::AncovaBayesian(
        version = "0.17.2",
        formula = DT_Flexibility ~ Age + ICAR + ASRS + AQ + Sex,
        covariates = list("AQ", "ASRS", "ICAR", "Age"),
        customPriorSpecification = list(list(components = "AQ", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "ASRS", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "ICAR", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "Sex", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "Age", inclusionProbability = 0.5, scaleFixedEffects = 0.5)),
        effects = TRUE,
        seed = 1234,
        setSeed = TRUE,
        singleModelTerms = ~ ASRS + AQ + Age + ICAR + Sex)

Table 3, Analysis 2, Dependent Variable: DT Flexibility

Model Comparison
Models	P(M)	P(M\|data)	BF_M	BF₁₀	error %
ICAR + Age	0.031	0.425	22.919	1.000
ASRS + ICAR + Age	0.031	0.246	10.123	0.579	8.827×10^-4
AQ + ICAR + Age	0.031	0.122	4.302	0.287	0.011
Sex + ICAR + Age	0.031	0.071	2.365	0.167	1.256
AQ + ASRS + ICAR + Age	0.031	0.054	1.765	0.127	7.660×10^-4
Sex + ASRS + ICAR + Age	0.031	0.037	1.182	0.086	1.599
Sex + AQ + ICAR + Age	0.031	0.020	0.641	0.048	1.615
Sex + AQ + ASRS + ICAR + Age	0.031	0.008	0.254	0.019	1.999
Age	0.031	0.005	0.156	0.012	0.004
ICAR	0.031	0.003	0.078	0.006	0.005

Note. Showing the best 10 out of 32 models.

Analysis of Effects - DT_Flexibility
Effects	P(incl)	P(excl)	P(incl\|data)	P(excl\|data)	BF_incl
Sex	0.500	0.500	0.138	0.862	0.161
AQ	0.500	0.500	0.209	0.791	0.264
ASRS	0.500	0.500	0.349	0.651	0.537
ICAR	0.500	0.500	0.988	0.012	82.975
Age	0.500	0.500	0.995	0.005	186.181

Bayesian ANCOVA

jaspAnova::AncovaBayesian(
        version = "0.17.2",
        formula = Sqrt_DT_Originality ~ Age + ICAR + ASRS + AQ + Sex,
        covariates = list("AQ", "ASRS", "ICAR", "Age"),
        customPriorSpecification = list(list(components = "AQ", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "ASRS", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "ICAR", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "Sex", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "Age", inclusionProbability = 0.5, scaleFixedEffects = 0.5)),
        effects = TRUE,
        seed = 1234,
        setSeed = TRUE,
        singleModelTerms = ~ ASRS + AQ + Age + ICAR + Sex)

Table 3, Analysis 2, Dependent Variable: DT Originality

Model Comparison
Models	P(M)	P(M\|data)	BF_M	BF₁₀	error %
Null model	0.031	0.317	14.409	1.000
Age	0.031	0.090	3.074	0.284	0.003
ICAR	0.031	0.082	2.772	0.259	0.003
ASRS	0.031	0.063	2.078	0.198	0.003
AQ	0.031	0.052	1.702	0.164	0.003
ICAR + Age	0.031	0.039	1.243	0.121	0.008
Sex	0.031	0.038	1.228	0.120	0.124
AQ + ASRS	0.031	0.033	1.063	0.104	0.009
ASRS + Age	0.031	0.030	0.975	0.096	0.009
AQ + ASRS + ICAR + Age	0.031	0.029	0.913	0.090	0.007

Note. Showing the best 10 out of 32 models.

Analysis of Effects - Sqrt_DT_Originality
Effects	P(incl)	P(excl)	P(incl\|data)	P(excl\|data)	BF_incl
Sex	0.500	0.500	0.106	0.894	0.119
AQ	0.500	0.500	0.257	0.743	0.347
ASRS	0.500	0.500	0.277	0.723	0.384
ICAR	0.500	0.500	0.285	0.715	0.399
Age	0.500	0.500	0.305	0.695	0.439

Bayesian ANCOVA

jaspAnova::AncovaBayesian(
        version = "0.17.2",
        formula = Log_CAQ ~ Age + ICAR + ASRS + AQ + Sex,
        covariates = list("AQ", "ASRS", "ICAR", "Age"),
        customPriorSpecification = list(list(components = "AQ", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "ASRS", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "ICAR", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "Sex", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "Age", inclusionProbability = 0.5, scaleFixedEffects = 0.5)),
        effects = TRUE,
        seed = 1234,
        setSeed = TRUE,
        singleModelTerms = ~ ASRS + AQ + Age + ICAR + Sex)

Table 3, Analysis 2, Dependent Variable: Creative Achievements

Model Comparison
Models	P(M)	P(M\|data)	BF_M	BF₁₀	error %
ASRS + ICAR	0.031	0.288	12.526	1.000
ASRS + ICAR + Age	0.031	0.199	7.691	0.691	0.005
ASRS	0.031	0.117	4.113	0.407	0.006
ASRS + Age	0.031	0.083	2.800	0.288	0.007
AQ + ASRS + ICAR	0.031	0.071	2.368	0.247	0.004
AQ + ASRS + ICAR + Age	0.031	0.049	1.604	0.171	0.004
Sex + ASRS + ICAR	0.031	0.039	1.265	0.136	1.386
Sex + ASRS + ICAR + Age	0.031	0.027	0.856	0.093	1.785
AQ + ASRS	0.031	0.020	0.630	0.069	0.010
AQ + ASRS + Age	0.031	0.017	0.529	0.058	0.005

Note. Showing the best 10 out of 32 models.

Analysis of Effects - Log_CAQ
Effects	P(incl)	P(excl)	P(incl\|data)	P(excl\|data)	BF_incl
Sex	0.500	0.500	0.120	0.880	0.136
AQ	0.500	0.500	0.189	0.811	0.233
ASRS	0.500	0.500	0.957	0.043	22.428
ICAR	0.500	0.500	0.714	0.286	2.498
Age	0.500	0.500	0.415	0.585	0.710

Bayesian ANCOVA

jaspAnova::AncovaBayesian(
        version = "0.17.2",
        formula = BICB ~ Age + ICAR + ASRS + AQ + Sex,
        covariates = list("AQ", "ASRS", "ICAR", "Age"),
        customPriorSpecification = list(list(components = "AQ", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "ASRS", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "ICAR", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "Sex", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "Age", inclusionProbability = 0.5, scaleFixedEffects = 0.5)),
        effects = TRUE,
        seed = 1234,
        setSeed = TRUE,
        singleModelTerms = ~ ASRS + AQ + Age + ICAR + Sex)

Table 3, Analysis 2, Dependent Variable: Creative Behaviors

Model Comparison
Models	P(M)	P(M\|data)	BF_M	BF₁₀	error %
ASRS	0.031	0.488	29.598	1.000
ASRS + Age	0.031	0.129	4.578	0.263	0.001
AQ + ASRS	0.031	0.093	3.178	0.190	0.001
Sex + ASRS	0.031	0.080	2.685	0.163	0.931
ASRS + ICAR	0.031	0.074	2.462	0.151	0.001
AQ + ASRS + Age	0.031	0.028	0.887	0.057	0.012
ASRS + ICAR + Age	0.031	0.024	0.763	0.049	0.011
Sex + ASRS + Age	0.031	0.021	0.664	0.043	1.260
AQ + ASRS + ICAR	0.031	0.018	0.574	0.037	0.011
Sex + AQ + ASRS	0.031	0.015	0.469	0.031	1.267

Note. Showing the best 10 out of 32 models.

Analysis of Effects - BICB
Effects	P(incl)	P(excl)	P(incl\|data)	P(excl\|data)	BF_incl
Sex	0.500	0.500	0.140	0.860	0.163
AQ	0.500	0.500	0.169	0.831	0.203
ASRS	0.500	0.500	1.000	6.562×10^-5	15237.717
ICAR	0.500	0.500	0.142	0.858	0.165
Age	0.500	0.500	0.217	0.783	0.277

Bayesian ANCOVA

jaspAnova::AncovaBayesian(
        version = "0.17.2",
        formula = CPS ~ Age + ICAR + ASRS + AQ + Sex,
        covariates = list("AQ", "ASRS", "ICAR", "Age"),
        customPriorSpecification = list(list(components = "AQ", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "ASRS", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "ICAR", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "Sex", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "Age", inclusionProbability = 0.5, scaleFixedEffects = 0.5)),
        effects = TRUE,
        seed = 1234,
        setSeed = TRUE,
        singleModelTerms = ~ ASRS + AQ + Age + ICAR + Sex)

Table 3, Analysis 2, Dependent Variable: Creative Personality

Model Comparison
Models	P(M)	P(M\|data)	BF_M	BF₁₀	error %
AQ	0.031	0.405	21.094	1.000
Null model	0.031	0.103	3.549	0.254	0.005
Sex + AQ	0.031	0.082	2.762	0.202	1.038
AQ + ASRS	0.031	0.079	2.656	0.195	0.007
AQ + Age	0.031	0.070	2.347	0.174	0.007
AQ + ICAR	0.031	0.069	2.313	0.171	0.007
Sex	0.031	0.020	0.646	0.050	0.080
AQ + ASRS + ICAR	0.031	0.017	0.539	0.042	0.005
AQ + ASRS + Age	0.031	0.017	0.534	0.042	0.005
Sex + AQ + ASRS	0.031	0.017	0.522	0.041	1.278

Note. Showing the best 10 out of 32 models.

Analysis of Effects - CPS
Effects	P(incl)	P(excl)	P(incl\|data)	P(excl\|data)	BF_incl
Sex	0.500	0.500	0.168	0.832	0.202
AQ	0.500	0.500	0.815	0.185	4.397
ASRS	0.500	0.500	0.170	0.830	0.205
ICAR	0.500	0.500	0.150	0.850	0.176
Age	0.500	0.500	0.152	0.848	0.180

Bayesian ANCOVA

jaspAnova::AncovaBayesian(
        version = "0.17.2",
        formula = CSE ~ Age + ICAR + ASRS + AQ + Sex,
        covariates = list("AQ", "ASRS", "ICAR", "Age"),
        customPriorSpecification = list(list(components = "AQ", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "ASRS", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "ICAR", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "Sex", inclusionProbability = 0.5, scaleFixedEffects = 0.5), list(components = "Age", inclusionProbability = 0.5, scaleFixedEffects = 0.5)),
        effects = TRUE,
        seed = 1234,
        setSeed = TRUE,
        singleModelTerms = ~ ASRS + AQ + Age + ICAR + Sex)

Table 3, Analysis 2, Dependent Variable: Creative Self Efficacy

Model Comparison
Models	P(M)	P(M\|data)	BF_M	BF₁₀	error %
Sex + AQ	0.031	0.152	5.575	1.000
Sex	0.031	0.149	5.412	0.975	1.018
Null model	0.031	0.129	4.577	0.844	1.018
AQ	0.031	0.129	4.572	0.843	1.018
Sex + AQ + Age	0.031	0.052	1.717	0.344	1.646
AQ + Age	0.031	0.049	1.612	0.324	1.018
Sex + Age	0.031	0.030	0.965	0.198	1.476
Sex + AQ + ICAR	0.031	0.028	0.896	0.184	1.857
Sex + AQ + ASRS	0.031	0.028	0.886	0.182	1.856
Age	0.031	0.027	0.860	0.177	1.018

Note. Showing the best 10 out of 32 models.

Analysis of Effects - CSE
Effects	P(incl)	P(excl)	P(incl\|data)	P(excl\|data)	BF_incl
Sex	0.500	0.500	0.532	0.468	1.138
AQ	0.500	0.500	0.549	0.451	1.218
ASRS	0.500	0.500	0.164	0.836	0.197
ICAR	0.500	0.500	0.147	0.853	0.173
Age	0.500	0.500	0.239	0.761	0.314

Supplementary Results

The following analyses all have a filter applied (AQ_cutoff_filter = 1) to select only individuals that meet the additional AQ inclusion criteria.

The R syntax for the filter applied is: generatedFilter <- ((AQ_cutoff_filter == 1)

Bayesian Independent Samples T-Test

Supplementary Table 2 and Supplementary Table 3

Bayesian Independent Samples T-Test
	BF₁₀	error %
Age	0.184	0.075
ICAR	0.356	0.044
AQ	2.599×10⁺¹⁰⁶	1.412×10^-108
ASRS	8.883×10⁺¹⁹	1.989×10^-22
DT_Fluency	0.227	0.063
DT_Flexibility	2.352	0.009
Sqrt_DT_Originality	0.133	0.098
Log_CAQ	2.794	0.007
BICB	101.373	2.929×10^-4
CPS	0.331	0.046
CSE	0.176	0.078

Bayesian ANCOVA

Supplementary Table 4, Analysis 1, Dependent Variable: DT Fluency

Model Comparison
Models	P(M)	P(M\|data)	BF_M	BF₁₀	error %
ICAR + Age	0.031	0.189	7.243	1.000
Age	0.031	0.163	6.052	0.862	0.006
ADHD + ICAR + Age	0.031	0.135	4.843	0.713	1.074
ADHD + Age	0.031	0.074	2.475	0.390	0.928
Null model	0.031	0.068	2.272	0.361	0.004
ICAR	0.031	0.040	1.290	0.211	0.006
ASD + ICAR + Age	0.031	0.034	1.088	0.179	1.369
ASD + Age	0.031	0.031	1.007	0.166	1.005
ADHD	0.031	0.031	0.998	0.165	0.017
ICAR + Sex + Age	0.031	0.030	0.967	0.160	1.371

Note. Showing the best 10 out of 32 models.

Analysis of Effects - DT_Fluency
Effects	P(incl)	P(excl)	P(incl\|data)	P(excl\|data)	BF_incl
ASD	0.500	0.500	0.153	0.847	0.181
ADHD	0.500	0.500	0.368	0.632	0.582
ICAR	0.500	0.500	0.539	0.461	1.169
Sex	0.500	0.500	0.137	0.863	0.159
Age	0.500	0.500	0.770	0.230	3.343

Bayesian ANCOVA

Supplementary Table 4, Analysis 1, Dependent Variable: DT Flexibility

Model Comparison
Models	P(M)	P(M\|data)	BF_M	BF₁₀	error %
ADHD + ICAR + Age	0.031	0.473	27.799	1.000
ICAR + Age	0.031	0.141	5.108	0.299	0.990
ASD + ADHD + ICAR + Age	0.031	0.093	3.178	0.197	1.705
ASD + ICAR + Age	0.031	0.077	2.592	0.163	1.528
ADHD + Sex + ICAR + Age	0.031	0.077	2.588	0.163	1.767
Sex + ICAR + Age	0.031	0.022	0.704	0.047	1.573
ADHD + Age	0.031	0.019	0.604	0.040	1.286
Age	0.031	0.018	0.565	0.038	0.990
ASD + ADHD + Sex + ICAR + Age	0.031	0.016	0.490	0.033	2.911
ASD + Age	0.031	0.013	0.413	0.028	1.331

Note. Showing the best 10 out of 32 models.

Analysis of Effects - DT_Flexibility
Effects	P(incl)	P(excl)	P(incl\|data)	P(excl\|data)	BF_incl
ASD	0.500	0.500	0.225	0.775	0.290
ADHD	0.500	0.500	0.703	0.297	2.372
Sex	0.500	0.500	0.138	0.862	0.160
ICAR	0.500	0.500	0.931	0.069	13.539
Age	0.500	0.500	0.975	0.025	38.724

Bayesian ANCOVA

Supplementary Table 4, Analysis 1, Dependent Variable: DT Originality

Model Comparison
Models	P(M)	P(M\|data)	BF_M	BF₁₀	error %
Null model	0.031	0.366	17.899	1.000
Age	0.031	0.150	5.480	0.410	0.004
ADHD	0.031	0.079	2.652	0.215	0.024
ICAR	0.031	0.061	2.007	0.166	0.003
ASD	0.031	0.057	1.874	0.156	0.068
Sex	0.031	0.055	1.810	0.151	0.073
ICAR + Age	0.031	0.039	1.254	0.106	0.007
ADHD + Age	0.031	0.033	1.057	0.090	1.058
ASD + Age	0.031	0.024	0.757	0.065	1.084
Sex + Age	0.031	0.023	0.732	0.063	1.085

Note. Showing the best 10 out of 32 models.

Analysis of Effects - Sqrt_DT_Originality
Effects	P(incl)	P(excl)	P(incl\|data)	P(excl\|data)	BF_incl
ASD	0.500	0.500	0.137	0.863	0.159
ADHD	0.500	0.500	0.180	0.820	0.220
Sex	0.500	0.500	0.131	0.869	0.151
ICAR	0.500	0.500	0.162	0.838	0.193
Age	0.500	0.500	0.307	0.693	0.443

Bayesian ANCOVA

Supplementary Table 4, Analysis 1, Dependent Variable: Creative Achievements

Model Comparison
Models	P(M)	P(M\|data)	BF_M	BF₁₀	error %
ADHD + ICAR	0.031	0.501	31.144	1.000
ADHD + Sex + ICAR	0.031	0.184	6.973	0.366	1.915
ADHD + ICAR + Age	0.031	0.109	3.793	0.218	1.134
ASD + ADHD + ICAR	0.031	0.085	2.895	0.170	1.841
ADHD + Sex + ICAR + Age	0.031	0.040	1.308	0.081	1.625
ASD + ADHD + Sex + ICAR	0.031	0.034	1.079	0.067	3.656
ASD + ADHD + ICAR + Age	0.031	0.019	0.609	0.038	1.625
ASD + ADHD + Sex + ICAR + Age	0.031	0.007	0.227	0.015	3.057
ICAR	0.031	0.006	0.172	0.011	0.666
ASD + ICAR	0.031	0.003	0.104	0.007	1.119

Note. Showing the best 10 out of 32 models.

Analysis of Effects - Log_CAQ
Effects	P(incl)	P(excl)	P(incl\|data)	P(excl\|data)	BF_incl
ASD	0.500	0.500	0.152	0.848	0.179
ADHD	0.500	0.500	0.986	0.014	68.915
Sex	0.500	0.500	0.269	0.731	0.368
ICAR	0.500	0.500	0.993	0.007	149.406
Age	0.500	0.500	0.180	0.820	0.219

Bayesian ANCOVA

Supplementary Table 4, Analysis 1, Dependent Variable: Creative Behaviors

Model Comparison
Models	P(M)	P(M\|data)	BF_M	BF₁₀	error %
ADHD + Sex	0.031	0.208	8.151	1.000
ADHD + Sex + Age	0.031	0.164	6.069	0.786	2.284
ADHD + Sex + ICAR	0.031	0.115	4.017	0.551	2.308
ADHD + Sex + ICAR + Age	0.031	0.111	3.862	0.532	1.926
ADHD	0.031	0.084	2.845	0.404	0.802
ADHD + Age	0.031	0.074	2.485	0.356	1.153
ADHD + ICAR + Age	0.031	0.041	1.329	0.197	1.420
ADHD + ICAR	0.031	0.041	1.328	0.197	1.178
ASD + ADHD + Sex	0.031	0.039	1.248	0.186	2.449
ASD + ADHD + Sex + Age	0.031	0.033	1.066	0.160	5.992

Note. Showing the best 10 out of 32 models.

Analysis of Effects - BICB
Effects	P(incl)	P(excl)	P(incl\|data)	P(excl\|data)	BF_incl
ASD	0.500	0.500	0.162	0.838	0.193
ADHD	0.500	0.500	1.000	2.309×10^-4	4329.290
Sex	0.500	0.500	0.712	0.288	2.470
ICAR	0.500	0.500	0.365	0.635	0.575
Age	0.500	0.500	0.466	0.534	0.874

Bayesian ANCOVA

Supplementary Table 4, Analysis 1, Dependent Variable: Creative Personality

Model Comparison
Models	P(M)	P(M\|data)	BF_M	BF₁₀	error %
Null model	0.031	0.351	16.777	1.000
Sex	0.031	0.110	3.836	0.314	0.042
ASD	0.031	0.083	2.809	0.237	0.050
ADHD	0.031	0.076	2.540	0.216	0.024
ICAR	0.031	0.066	2.198	0.189	0.003
Age	0.031	0.066	2.183	0.187	0.003
ASD + Sex	0.031	0.027	0.848	0.076	1.073
ADHD + Sex	0.031	0.025	0.793	0.071	1.066
Sex + Age	0.031	0.022	0.682	0.061	1.084
ASD + ADHD	0.031	0.021	0.667	0.060	1.056

Note. Showing the best 10 out of 32 models.

Analysis of Effects - CPS
Effects	P(incl)	P(excl)	P(incl\|data)	P(excl\|data)	BF_incl
ASD	0.500	0.500	0.197	0.803	0.246
ADHD	0.500	0.500	0.183	0.817	0.224
Sex	0.500	0.500	0.240	0.760	0.316
ICAR	0.500	0.500	0.169	0.831	0.204
Age	0.500	0.500	0.168	0.832	0.201

Bayesian ANCOVA

Supplementary Table 4, Analysis 1, Dependent Variable: Creative Self Efficacy

Model Comparison
Models	P(M)	P(M\|data)	BF_M	BF₁₀	error %
Null model	0.031	0.149	5.439	1.000
ADHD	0.031	0.149	5.428	0.998	0.012
Sex	0.031	0.102	3.520	0.683	0.023
ASD + ADHD	0.031	0.088	3.000	0.591	0.838
ADHD + Sex	0.031	0.078	2.636	0.525	0.946
Age	0.031	0.045	1.444	0.298	0.003
ADHD + Age	0.031	0.044	1.432	0.296	0.960
ASD + ADHD + Sex	0.031	0.039	1.245	0.259	2.340
Sex + Age	0.031	0.030	0.942	0.198	1.031
ASD	0.031	0.027	0.874	0.184	0.061

Note. Showing the best 10 out of 32 models.

Analysis of Effects - CSE
Effects	P(incl)	P(excl)	P(incl\|data)	P(excl\|data)	BF_incl
ASD	0.500	0.500	0.272	0.728	0.373
ADHD	0.500	0.500	0.549	0.451	1.217
Sex	0.500	0.500	0.361	0.639	0.566
ICAR	0.500	0.500	0.159	0.841	0.190
Age	0.500	0.500	0.237	0.763	0.311

Bayesian ANCOVA

Supplementary Table 4, Analysis 2, Dependent Variable: DT Fluency

Model Comparison
Models	P(M)	P(M\|data)	BF_M	BF₁₀	error %
ICAR + Age	0.031	0.202	7.850	1.000
Age	0.031	0.163	6.028	0.806	0.008
ASRS + ICAR + Age	0.031	0.142	5.122	0.702	0.006
ASRS + Age	0.031	0.087	2.966	0.432	0.011
Null model	0.031	0.052	1.706	0.258	0.006
AQ + ICAR + Age	0.031	0.048	1.554	0.236	0.007
AQ + ASRS + ICAR + Age	0.031	0.045	1.470	0.224	0.007
AQ + Age	0.031	0.036	1.150	0.177	0.014
ICAR + Sex + Age	0.031	0.029	0.933	0.145	1.417
ICAR	0.031	0.029	0.924	0.143	0.007

Note. Showing the best 10 out of 32 models.

Analysis of Effects - DT_Fluency
Effects	P(incl)	P(excl)	P(incl\|data)	P(excl\|data)	BF_incl
AQ	0.500	0.500	0.195	0.805	0.242
ASRS	0.500	0.500	0.377	0.623	0.606
ICAR	0.500	0.500	0.558	0.442	1.263
Sex	0.500	0.500	0.123	0.877	0.140
Age	0.500	0.500	0.848	0.152	5.586

Bayesian ANCOVA

Supplementary Table 4, Analysis 2, Dependent Variable: DT Flexibility

Model Comparison
Models	P(M)	P(M\|data)	BF_M	BF₁₀	error %
ASRS + ICAR + Age	0.031	0.387	19.583	1.000
ICAR + Age	0.031	0.233	9.396	0.601	0.012
AQ + ICAR + Age	0.031	0.109	3.785	0.281	0.016
AQ + ASRS + ICAR + Age	0.031	0.093	3.160	0.239	0.012
Sex + ASRS + ICAR + Age	0.031	0.059	1.955	0.153	1.541
Sex + ICAR + Age	0.031	0.041	1.322	0.106	1.245
Sex + AQ + ICAR + Age	0.031	0.019	0.604	0.049	1.578
Sex + AQ + ASRS + ICAR + Age	0.031	0.014	0.451	0.037	1.911
ASRS + Age	0.031	0.009	0.276	0.023	0.012
Age	0.031	0.008	0.242	0.020	0.012

Note. Showing the best 10 out of 32 models.

Analysis of Effects - DT_Flexibility
Effects	P(incl)	P(excl)	P(incl\|data)	P(excl\|data)	BF_incl
Sex	0.500	0.500	0.140	0.860	0.163
AQ	0.500	0.500	0.250	0.750	0.333
ASRS	0.500	0.500	0.573	0.427	1.343
ICAR	0.500	0.500	0.969	0.031	31.201
Age	0.500	0.500	0.984	0.016	61.744

Bayesian ANCOVA

Supplementary Table 4, Analysis 2, Dependent Variable: DT Originality

Model Comparison
Models	P(M)	P(M\|data)	BF_M	BF₁₀	error %
Null model	0.031	0.360	17.436	1.000
Age	0.031	0.125	4.428	0.347	0.003
ICAR	0.031	0.065	2.172	0.182	0.003
ASRS	0.031	0.060	1.968	0.166	0.003
AQ	0.031	0.055	1.820	0.154	0.003
Sex	0.031	0.048	1.561	0.133	0.098
ICAR + Age	0.031	0.038	1.221	0.105	0.008
ASRS + Age	0.031	0.033	1.044	0.091	0.008
AQ + Age	0.031	0.031	0.989	0.086	0.008
AQ + ASRS	0.031	0.021	0.672	0.059	0.009

Note. Showing the best 10 out of 32 models.

Analysis of Effects - Sqrt_DT_Originality
Effects	P(incl)	P(excl)	P(incl\|data)	P(excl\|data)	BF_incl
Sex	0.500	0.500	0.117	0.883	0.133
AQ	0.500	0.500	0.196	0.804	0.243
ASRS	0.500	0.500	0.203	0.797	0.254
ICAR	0.500	0.500	0.204	0.796	0.256
Age	0.500	0.500	0.316	0.684	0.461

Bayesian ANCOVA

Supplementary Table 4, Analysis 2, Dependent Variable: Creative Achievements

Model Comparison
Models	P(M)	P(M\|data)	BF_M	BF₁₀	error %
ASRS + ICAR	0.031	0.433	23.629	1.000
ASRS + ICAR + Age	0.031	0.145	5.272	0.336	0.009
Sex + ASRS + ICAR	0.031	0.109	3.780	0.251	1.257
AQ + ASRS + ICAR	0.031	0.091	3.118	0.211	0.007
AQ + ASRS + ICAR + Age	0.031	0.035	1.140	0.082	8.979×10^-4
Sex + ASRS + ICAR + Age	0.031	0.035	1.134	0.082	1.625
ICAR	0.031	0.024	0.772	0.056	0.004
Sex + AQ + ASRS + ICAR	0.031	0.023	0.736	0.054	1.644
ASRS	0.031	0.017	0.528	0.039	0.004
AQ + ICAR	0.031	0.015	0.471	0.035	0.004

Note. Showing the best 10 out of 32 models.

Analysis of Effects - Log_CAQ
Effects	P(incl)	P(excl)	P(incl\|data)	P(excl\|data)	BF_incl
Sex	0.500	0.500	0.207	0.793	0.260
AQ	0.500	0.500	0.200	0.800	0.250
ASRS	0.500	0.500	0.917	0.083	11.010
ICAR	0.500	0.500	0.957	0.043	22.301
Age	0.500	0.500	0.259	0.741	0.350

Bayesian ANCOVA

Supplementary Table 4, Analysis 2, Dependent Variable: Creative Behaviors

Model Comparison
Models	P(M)	P(M\|data)	BF_M	BF₁₀	error %
ASRS	0.031	0.258	10.761	1.000
ASRS + Age	0.031	0.202	7.862	0.785	0.004
Sex + ASRS	0.031	0.142	5.151	0.553	0.858
Sex + ASRS + Age	0.031	0.101	3.496	0.393	1.129
ASRS + ICAR	0.031	0.049	1.590	0.189	0.003
ASRS + ICAR + Age	0.031	0.043	1.400	0.168	0.012
AQ + ASRS	0.031	0.042	1.357	0.163	0.003
AQ + ASRS + Age	0.031	0.039	1.264	0.152	0.012
Sex + ASRS + ICAR	0.031	0.027	0.865	0.105	1.149
Sex + AQ + ASRS	0.031	0.023	0.735	0.090	1.152

Note. Showing the best 10 out of 32 models.

Analysis of Effects - BICB
Effects	P(incl)	P(excl)	P(incl\|data)	P(excl\|data)	BF_incl
Sex	0.500	0.500	0.347	0.653	0.531
AQ	0.500	0.500	0.155	0.845	0.184
ASRS	0.500	0.500	1.000	1.486×10^-4	6727.282
ICAR	0.500	0.500	0.172	0.828	0.207
Age	0.500	0.500	0.443	0.557	0.795

Bayesian ANCOVA

Supplementary Table 4, Analysis 2, Dependent Variable: Creative Personality

Model Comparison
Models	P(M)	P(M\|data)	BF_M	BF₁₀	error %
AQ	0.031	0.312	14.037	1.000
Null model	0.031	0.185	7.053	0.595	0.004
AQ + Age	0.031	0.068	2.247	0.217	0.007
AQ + ASRS	0.031	0.064	2.105	0.204	0.007
AQ + ICAR	0.031	0.063	2.083	0.202	0.007
Sex + AQ	0.031	0.049	1.613	0.159	1.048
ASRS	0.031	0.036	1.172	0.117	0.005
Age	0.031	0.031	0.992	0.099	0.005
Sex	0.031	0.029	0.922	0.093	0.087
ICAR	0.031	0.025	0.779	0.079	0.005

Note. Showing the best 10 out of 32 models.

Analysis of Effects - CPS
Effects	P(incl)	P(excl)	P(incl\|data)	P(excl\|data)	BF_incl
Sex	0.500	0.500	0.137	0.863	0.158
AQ	0.500	0.500	0.650	0.350	1.859
ASRS	0.500	0.500	0.182	0.818	0.223
ICAR	0.500	0.500	0.163	0.837	0.195
Age	0.500	0.500	0.180	0.820	0.219

Bayesian ANCOVA

Supplementary Table 4, Analysis 2, Dependent Variable: Creative Self Efficacy

Model Comparison
Models	P(M)	P(M\|data)	BF_M	BF₁₀	error %
Null model	0.031	0.283	12.243	1.000
AQ	0.031	0.160	5.910	0.566	0.004
Sex	0.031	0.071	2.363	0.250	0.059
Age	0.031	0.054	1.768	0.191	0.003
AQ + Age	0.031	0.052	1.685	0.182	0.006
AQ + ASRS	0.031	0.043	1.385	0.151	0.007
Sex + AQ	0.031	0.041	1.324	0.145	1.064
ICAR	0.031	0.040	1.300	0.142	0.002
ASRS	0.031	0.039	1.241	0.136	0.002
AQ + ICAR	0.031	0.038	1.218	0.134	0.007

Note. Showing the best 10 out of 32 models.

Analysis of Effects - CSE
Effects	P(incl)	P(excl)	P(incl\|data)	P(excl\|data)	BF_incl
Sex	0.500	0.500	0.202	0.798	0.254
AQ	0.500	0.500	0.438	0.562	0.779
ASRS	0.500	0.500	0.182	0.818	0.222
ICAR	0.500	0.500	0.173	0.827	0.209
Age	0.500	0.500	0.216	0.784	0.276